Dynamic Modelling, Analysis and Design of Smart Hybrid Energy Storage System for Off-grid Photovoltaic Power Systems by Wenlong JING A dissertation submitted to the Swinburne University of Technology in support of an application for the degree of Doctor of Philosophy in Engineering Faculty of Engineering, Computing and Science Swinburne University of Technology Sarawak Campus January 2019
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Dynamic Modelling, Analysis and Design of Smart
Hybrid Energy Storage System for Off-grid
Photovoltaic Power Systems
by
Wenlong JING
A dissertation submitted to the Swinburne University of Technology in support of an
application for the degree of Doctor of Philosophy in Engineering
Faculty of Engineering, Computing and Science
Swinburne University of Technology
Sarawak Campus
January 2019
Dedicated to…
my beloved Parents, Teachers, Partner, Family & Friends
Abstract
Battery technology has been widely utilized in different energy storage
applications. However, limitations and challenges such as heat dissipation, low power
density, unsatisfactory lifetime characteristics, environmental impacts, and high cost
hinder its development in many key areas, for instance, the residential energy storage
applications. To address the issue of the short service life of the battery, hybrid energy
storage system (HESS) of various designs have been reported in the literature. However
the limited focus has been put on the case of the stand-alone residential energy system,
especially for remote rural electrification. This thesis aims at proposing suitable battery-
supercapacitor HESS designs and control strategies that can effectively extend the
battery service lifetime via mitigating its operation stress, thereby realizing the cost
reduction on the installing construction and operating costs of the stand-alone
photovoltaic (PV) power system. For such systems that is planned to be installed in
rural areas, potential suitable battery-supercapacitor HESSs are designed and discussed.
To leverage on existing infrastructure in installed standalone PV-battery power system,
novel smart supercapacitor/Li-ion HESS plug-in module (SHESS) is proposed to relieve
the main battery operation stress. Theoretical analysis and numerical simulations for the
designed HESSs and the SHESS are conducted, and their effectiveness in mitigating
battery stress are investigated and compared via pulse load testing and case studies with
actual data of solar irradiance and load profile from a remote community in Sarawak,
Malaysia. A battery health cost model is formulated to qualitatively evaluate the impact
of battery current on battery health, thus enabling the estimation of service life
improvement as well as the assessment on the economic impact of the remote stand-
alone microgrid. A down-scaled prototype of the proposed HESS is designed and
developed to verify the theoretical analysis and analytical findings. Experiments have
been carried out to test the feasibility and performance of the proposed system in terms
of power-sharing capability in stand-alone PV power system operations. The
experimental results demonstrate the feasibility of the proposed HESSs in retrofitting
existing installed PV power systems and support the theoretical analysis and simulation
outcomes.
Acknowledgments
The journey to achieve the Ph.D. is full of obstacles, challenges, and happiness, which
will always remind me of the importance of humility, exploration and lifelong learning.
The completion of the dissertation marks the end of such an eventful journey and many
people I am willing to offer my sincerest thanks. Firstly, I would like to express my
sincere gratitude to my principal supervisor, Dr. Chean Hung Lai, for his invaluable
inspiration, continuous guidance, constructive criticism and support throughout this
research. This dissertation would not have been possible without his help, support,
resolute dedication, and patience. His expert advice and unsurpassed knowledge of the
various fields of electrical energy systems has always provided me an endless supply of
idea to move to the next step and complete this dissertation.
I also feel privileged that my Ph.D. study was conducted under the supervision of co-
supervisors, Prof. Wallace Wong Shung Hui and Prof. M. L. Dennis Wong. Their
constant encouragement, illuminating guidance, and research suggestions were indeed
essential for the completion of this study and dissertation. Meanwhile, I would like to
thank all colleagues at the Research Laboratory of Swinburne University of Technology
Sarawak Campus for their various forms of help and support so that I can achieve my
research goals. To my best friends and research partners in Malaysia, Dr. Pan, Ms. Jong
Bih Fei, Dr. Liew Lin Shen, Mr. Tay Jia Jun, Abdul Kadir Muhammad Lawan, Mr.
Derrick Ling Kuo Xiong, Mr. Chang Zhi Hao, Dr. Wong Wei Jing and Dr. Chong
Nguan Soon, the memorable times, laughs, countless discussions, sharing, etc that we
have will be the lifelong treasures and I would never forget.
To my dear life partner, Yun Wang, appreciate and value her everlasting love and
understanding so that I could fully focus on my study. I also extend my utmost gratitude
to my dearest teachers, family, and friends, for their encouragement and assistance all
these years. Last but the most important, I would like to give special thanks to my
mother and father for their unconditional support both financially and morally, the grace
of raising me up and deep love, which is the most precious thing in my life.
Declaration
I hereby declare that, to the best of my knowledge, this thesis contains no material
which has been accepted for the award to the candidate of any other degree or
qualification in this, or any other University and contains no material previously
published or written by another person except where due reference is made in the text of
this thesis. Furthermore, any idea, technique, quotation, or any other material from other
people’s work included in this thesis, published or otherwise, are fully acknowledged in
accordance with the standard referencing practices.
----------------------------------
Wenlong JING
January 2019
Publications arising from this study
Journal Papers
Wenlong Jing, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, "A comprehensive study of Battery-SC hybrid ESS for stand-alone PV power system in rural electrification." Applied Energy, Volume 224, May 2018, Pages 340-356.
Wenlong Jing, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, “Dynamic Power Allocation of Battery-SC Hybrid Energy Storage for Stand-alone PV microgrid Applications”, Sustainable Energy Technologies and Assessments, Volume 22, August 2017, Pages 55-64, ISSN 2213-1388.
Wenlong Jing, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, “Battery-SC Hybrid ESS in Stand-alone DC Microgrids: A Review”, in IET Renewable Power Generation, vol. 11, no. 4, pp. 461-469, 2017.
Wenlong Jing, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, “A Comprehensive Review of Energy Storage Technologies and Case Study for PV Residential Energy Storage Applications”, (submitted to Journal of Energy Storage on Sept 2018)
Wenlong Jing, Chean Hung Lai, Derrick K.X. Ling, Wallace SH Wong, and ML Dennis Wong, “Battery Lifetime Enhancement via Smart Hybrid Energy Storage Plug-in Module in Stand-alone Photovoltaic Power System”, Journal of Energy Storage, Volume 21, February 2019, Pages 586-598.
Conference Papers
Wenlong Jing, Derrick K.X. Ling, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, “Hybrid Energy Storage Retrofit for Stand-alone Photovoltaic-Battery Residential Energy System”, The 7th Innovative Smart-grid Technologies (ISGT Asia 2017), Auckland, New Zealand, December 4 – 7, 2017.
Wenlong Jing, Chean Hung Lai, Wallace Wong Shung Hui, M.L. Dennis Wong, “Cost Analysis of Battery-SC Hybrid ESS for Stand-alone PV Systems”, 4th IET International Conference on Clean Energy and Technology, November 2016.
Wenlong Jing, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, “Theoretical Analysis and Software Modeling of Composite Energy Storage Based on Battery and SC in Microgrid Photovoltaic Power System”, (ICISCA-2015) International Conference on Information, System and Convergence Applications, Kuala Lumpur, 2015.
Wenlong Jing, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, “Smart Hybrid Energy Storage for Stand-alone PV Microgrid: Optimization of battery lifespan through dynamic power allocation”, IEEE PES Asia-Pacific Power and Energy Engineering Conference, pp. 1-5, Brisbane, 2015.
Wenlong Jing, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, “The Comparison between Two Types of Bidirectional Dual Active Bridge DC/DC Converter for Photovoltaic Application”, (In progress)
I
Table of Contents
List of Figures .............................................................................................................................. V
List of Tables ................................................................................................................................ X
Nomenclature .............................................................................................................................. XI
Unlike the traditional power system, smart-grid allows seamless integration of
microgrids that contain distributed generations and loads. In general, sustainable energy
technologies such as solar PV, wind turbine and small hydro are often being integrated
into microgrids. Microgrid works as an independent small-scale, localized power system
that includes transmission, distribution, and EMS and energy storage [62]. Because of
the intermittency, variation and instability of renewable energy power generation, the
installation of ESS in the system will be essential to ensure power stability with
acceptable power balance, power quality, and reliability between power generations and
user demands.
Table 2.1 summarizes the applications of ESS in modern power system including
generation, transmission, power distribution, and demand side. In general, the
integration of ESS in the power system can effectively improve the electric grid
flexibility, resilience, technical efficiency and economic performance [42], [63]–[69].
Table 2.1 Applications of energy storage in the power system
Generation (Centralised and Distributed)
Transmission and Distribution
Load demand side
Chapter 2 Review of Energy Storage Technologies
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Renewable energy integration Grid fluctuation suppression Capacity firming Ramping and Load Following Frequency regulation Seasonal energy storage Contingency reserve Black-start Spinning reserve Load Levelling / Peak Shaving
Voltage regulation Voltage-ampere reactive Transmission curtailment Transmission deferral Distribution deferral Transient stability Power quality Outage mitigation
Energy management Time shifting Demand side management Energy arbitrage Uninterrupted power supply Vehicle-to-grid Microgrid
The detailed components of the modern power system are summarized in Fig. 2.3 [70]–
[73]. The power system can be grid-connected or stand-alone or interchangeably with
advanced control strategy and switching. The power system contains energy sources,
interface components, power conversion system, control, and monitoring system, ESS
and loads. The power conversion system is the interconnection between systems of
different electrical characteristics, for example, the conversion from AC to DC or vice
versa. The loads include the end-use customer in different sectors which can be either
AC or DC.
Energy Source• Utility Grid• RES Microgrid• Hybrid Grid
Interface
Power Conversion System• Interconnection of AC/DC• AC/DC switchgear • Rectifier/inverter/converter• Control unit• Protection devices
(overvoltage, cooling, etc)
Loads• DC/AC• Industry• Residential
ESSs
Central Control System• Source/ESS/Interface/PCS/Load• Voltage/Frequency/VAR• Thermal management• Power conversion• Power dispatching• Protection devices • Subsystems control
Interface• Transmission Networks• HVDC/FACTS• Voltage transformers• Distribution Networks • Protection devices
2.4.3 The selection of energy storage technologies
In power system, the application of ESS can be divided into five categories: large-scale
energy services, transmission, and distribution infrastructure services, auxiliary services,
Chapter 2 Review of Energy Storage Technologies
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and residential energy storage solutions. The auxiliary service contains emergency
power back-up, peak shaving, time shifting and etc. Since each type of energy storage
technology has its unique characteristics, it is essential to select the optimal energy
storage technology for specific applications [137]–[139]. In general, the characteristics
that can be used to assess the applicability of energy storage technology for specific
applications include:
Energy rating and power rating; Energy density and power density; Response time; Self-discharge rate; Cycle efficiency; Rated voltage and current; Charge/discharge duration and ampere-hour; Operating temperature; Service life; Environmental impact; Cost, weight, and size.
The above characteristics can be sorted into three main categories: technical, economic
performance and environmental standards. In most cases, the rated power and duration
of discharge are the critical characteristics in selecting appropriate energy storage
technology for various applicationsm; this is mainly because that the functionality is a
prerequisite and to meet the technical requirements of the targeted system is the priority.
At the same time, ESS economic performance and environmental standards are also
important factors that influence the final decision. A typical method of analyzing the
economic performance of an ESS is the Levelized Cost Of Energy (LCOE), sometimes
referred to as Levelized Cost Of Storage (LCOS) [140]–[142].
The environmental standards mainly include the impact on the environment and the life
of the ESS itself. The Life Cycle Impact Assessment (LCIA) is a systematic and
commonly used method to assess the environmental impact of a product or process
system throughout its life cycle [143], [144]. With the various characteristics, no single
energy storage technology can fulfill all desirable requirements in different energy
storage applications. For example, the Lithium-ion battery is one of the best options for
small-scale portable devices because of its high power density, high energy density,
Chapter 2 Review of Energy Storage Technologies
- 40 -
high efficiency, and reasonable cycle life. However, the higher initial cost and thermal
instability hinder its usage in residential energy storage applications. As an alternative,
the Lead-acid battery is a mature technology with relatively low cost and superior
electrical stability. It is a practical option for residential energy storage solution,
especially in stand-alone power systems. At the same time, however, in large-scale
situations (several kWh), the Lead-acid battery is not suitable because of its high M&O
cost and inefficiency.
Fig. 2.25 The framework of the energy storage technology selection
With the growth in ESS applications and options of energy storage technologies, it is
efficient for decision-makers to provide a framework that can facilitate the selection of
suitable ESSs. Fig. 2.25 shows the typical framework of the energy storage selection.
Technical, economic performance and environmental standards are evaluated using sub-
items that need to be balanced by decision-makers. The selection aims at finding the
most suitable energy storage technology that can not only meet the technical constraints
imposed by the target applications but also has the best overall performance in the main
criteria, for example, the one with high technology maturity, low total cost, and small
environmental impact.
Target ESS
Technical
Required installation
Duration
Maturity
Complexity
Economic performance
Initial cost (Budget)
Maintenance
Environmental standard
Lifecycles
Waste treatment
Environmental friendliness
Main-criteria Sub-criteria
Chapter 2 Review of Energy Storage Technologies
- 41 -
The main criteria in the three domains for selection normally include qualitative and
quantitative features. The quantitative considerations include efficiency, lifetime, cost,
power and energy densities, electrical response time, etc. A comprehensive quantitative
assessment can eliminate the effects of human factors and make the selection more
realistic. The qualitative considerations include the evaluation of energy storage
technologies attributes such as device properties, technology maturity, reliability and
safety, environmental impact, etc. Qualitative analysis will mainly base on the user
features, expert experience, and government policies to meet the environmental
requirements, cost requirements, and stability requirements for specific projects. Once
the appropriate energy storage product has been determined, the design of ESS will
follow the procedures:
1. Determination of the required size of the energy storage, such as voltage and
storage duration requirements (large, medium or small);
2. Determination of the energy storage structure that is required to meet the total
ampere-hour capacity (single, parallel or series);
3. Design the control system;
4. Design of construction facility at the installed site, including protection system,
monitoring system, cabling and etc.
The above procedures are the main steps in most ESS design process, and the detailed
design and installation steps will be sequentially expanded according to the different
application scenarios. As a case study to illustrate how to select and design an ESS in a
specific application, a stand-alone PV power system with energy storage will be
discussed in the next chapter.
2.5 General discussion on energy storage technology and future development
The design of energy storage technology normally involves multiple forms of energy,
multiple devices, multiple substances, and multiple processes. Over the past few
decades, researchers and industrial practitioners are putting great efforts in developing
advanced energy storage technologies to reduce economic costs while ensuring
longevity, good load regulation performance, high efficiency, and long-term reliability.
Chapter 2 Review of Energy Storage Technologies
- 42 -
The ESS plays a crucial role in many aspects, including electric power transmission,
large-scale renewable energy based power plant, large-scale grid flexible
interconnection, residential RES based microgrid, and one of the necessary support
technologies for the smart-grid. The main applications of various types of energy
storage technologies are as follows:
1. Large-scale, long-term energy storage facilities, such as PHS and CAES, can be
used for large-scale power grid peaking shaving and seasonal storage. The flow
battery and thermal storage with the second large energy storage capacity, a large
number of cycles, and long service life can be used as load levelling devices in the
grid. While Hydrogen storage can be used to store surplus wind and solar energy
to power fuel cell vehicles.
2. The SC, SMES, flywheel energy storage, sodium-sulfur batteries, and other high
power density ESS are mainly operated in combination with large-scale RESs.
They can quickly respond to surge power generation and stabilize the fluctuations
from the RESs.
3. Lithium-based batteries, Lead-acid batteries, metal-air batteries, and other battery
ESSs are less suitable for large-scale power plants and are mainly used for
distributed residential energy storage applications and electric vehicles.
With the continuous innovation and development of new energy storage materials, it is
expected to make significant breakthroughs in extending service life, increasing energy
density, fast charging time, and reducing costs. Meanwhile, the deployment of ESS in
large-scale power grids and/or microgrids is still facing significant challenges, and they
shape the direction of research and development roadmap of energy storage technology,
which contains three key areas:
1. To enhance energy storage performance and reliability, effective supply chain and
fabrication processes to enable mass production, thus overall cost reduction;
2. Energy storage technology selection and performance optimization complementary
technologies for specific applications;
3. Formalization of a systematic approach in evaluating energy storage technologies
for different applications.
Chapter 2 Review of Energy Storage Technologies
- 43 -
For traditional large-scale energy storage technologies, PHS, CAES, and TES, their
main challenges are low roundtrip efficiency and higher implementation costs. The
refinements of the PHS system are mainly focusing on upscaling the hydroelectric
turbo-machinery, optimizing the operation efficiency via installing enhanced monitoring
system and advanced intelligent energy management system. In addition to
improvements in existing PHS technology, similar ideas are being actively studied such
as seawater usage as a reservoir, tidal barrages, and Gravity Power Module Energy
Storage (GPMES) [145]. The GPMES uses two different sized water shafts to store
energy. The electricity is consumed to pump water in the smaller shaft and raises the
position of a heavyweight piston of the more massive shaft. When the electricity is
needed, the piston is set free, and the water flow rotates the turbine to generator power.
Instead of large-scale CAES, researchers have developed mini-CAES and aboveground
CAES to increase their usability in many applications and to overcome the dependency
of the large-scale CAES on the geographical site selection [146]. Rapid development
has been found in small-scale ground CAES as an alternative to electrochemical energy
storage for the applications such as uninterrupted power supply or backup power supply.
For flywheel energy storage, the core development area should aim to investigate novel
materials for the rotor, high performance and low-loss bearings, a robust control system
and the cost of precision manufacturing processes. Currently, the strength composite
fiber materials are applied in rotor fabrication, which may have significant
improvements in rotation speed, storage capacity, power density, and reliability. The
latest bearing technology is using the high-temperature superconductor, which
significantly increases the efficiency. However, the high production cost, operational
safety issues, and high self-discharge rate still limit its usage in long-term storage
applications.
The research and development in SC mainly focus on enhancing the chemical capacitive
materials to improve the lifespan and storage capability. The latest in SC development
includes the use of carbon graphene-based electrodes and novel nanostructured
materials that can significantly improve the SC surface area and therefore storage
capacity [147], [148]. For SMES, the cost reduction of superconducting coils, the
development of novel low cryogenically sensitive coil materials, and EMS optimization
Chapter 2 Review of Energy Storage Technologies
- 44 -
should be the primary issues to be addressed in order to penetrate the market share in
energy storage industry.
Electrochemical energy storages or battery technologies as one of the mainstream
energy storages for stationary energy storage applications require technology
breakthrough in enhancing the reliability and lifespan, electrode materials, fast charging
and energy management system. Among the battery energy storage technologies, Lead-
acid battery are still one of the most widely used energy storage technologies. For
research and development of new-generation Lead-acid batteries, it should mainly focus
on electrode materials for performance improvement, extending cycling times,
enhancing the deep discharge capability as well as the recycling processes of electrode
materials. At present, one of the advanced Lead-acid battery is developed by adding up
to 40% of activated carbon to the negative electrode composition, wherein the cycle life
is significantly increased by up to 2,000 times [149]–[151].
For lithium-based battery, many research works have been initiated on the innovation of
electrode and electrolyte materials to increase their power capability, energy density and
lifetime. The usage of graphene in replacing the conventional electrolyte materials has
claimed to revolutionary improves the storage capacity and charging time. The
graphene-based lithium battery is still on the early stage of research and development,
but the superior characteristics of graphene have attracted attentions from researchers
and industry [152].
NaS battery is a relatively mature technology and is often used for large-scale energy
storage projects. However, the high temperature operating requirements and eliminating
the corresponding limitations still need to be addressed [153]. The main limitations of
flow battery are the capital cost associated with electrolyte sources, battery stack
manufacture and high maintenance costs. The next generation of flow batteries could be
the structure with hybrid redox fuel cells. Shortly, it is possible to apply in electric
vehicles which makes it easier for electric vehicles to recharge energy by refueling
[118]. The hydrogen energy storage technology is still in its early stages. It requires
extensive testing and validation before it can be fully commercialized. The
improvements in system power conversion efficiency, safe operation, low-cost
Chapter 2 Review of Energy Storage Technologies
- 45 -
hydrogen production, storage, and transportation processes will become the leading
research and development directions for future development [154].
In general, energy storage technology provides more flexibility and optimizes the power
system operations. In the macro view of future energy storage deployment, the trend
that forcing energy storage technologies to become a central element of the future
power system will mainly depend on the penetration level of RES, the emergence of
smart-grid development and the advancement in low-cost environment-friendly
materials [155]–[157]. Thus its future development could mainly focus on the following
areas.
Firstly, energy storage technology needs to achieve more grid integration to improve its
safety, stability, high efficiency, and reliability. With ESS integration, power electronics,
and information technologies, the electric grid is anticipated to transforming into the
decentralized structure and achieving two-way interaction in every node in the power
system including generation, transmission, distribution, and consumers’ end. This will
eventually realize the upgrade to smart-grid and form the powerful internet-style global
energy grid in the future [158], [159]. Secondly, the longevity and cost of ESS must be
improved, while having both high power density and high energy density, to provide the
auxiliary services such as backup energy, power smoothing and peak shaving for the
renewable power systems that have intermittent and unpredictable output power. Multi-
variate hybrid ESS with complementary characteristics and its optimized supporting
control system will become the enabler in achieving both high power and energy
density requirements. Finally, energy storage will be more widely used in electric
powered transportation systems such as electric vehicles, hybrid vehicles, electric-
driven trains, drones, and aerospace applications, etc. [160]–[162]. This is the highest
technical requirement for ESSs, and it usually requires them to have higher security,
longer duration, shorter charging time, faster response, lighter and cheaper.
2.6 Conclusion
The potential applications of energy storage in modern power system and residential
ESS are presented in this chapter. The desired characteristics of energy storage at a
Chapter 2 Review of Energy Storage Technologies
- 46 -
different part of the power systems are discussed to ease the selection of energy storage
for optimal performance and sustainability. The energy storage technologies in various
forms were comprehensively reviewed and compared, including the electrical
characteristics, storage capability, efficiency, and product features. The introduced
energy storage technologies include CAES, FES, PHS SC, SMES, cell battery, flow
battery, TES, and chemical energy storage. The comparative analysis of available
energy storage technologies was carried out based on storage and electrical properties,
strengths and limitations, technology maturity as well as the potential for future
development. It can be concluded that the wide spectrum of technical characteristics of
current ESS technologies can meet the requirements of different power system
operations, with a suitable combination of different ESS technologies. Finally, this
chapter also summarizes the limitations that exist in various types of energy storage
technologies and the possible future development trends. The integration of ESS
technologies in modern large-scale power system and residential applications are critical
for providing flexibility and ancillary services in smart-grid to handle the increasing
supply-demand challenges.
- 47 -
Chapter 3
Solar PV Technology and Energy Storage
in Stand-alone Power System
3.1 Introduction
The global climate change caused by greenhouse effect has motivated the development
of renewable energy based power system to replace the traditional fossil fuel energy
[163]–[165]. Among various RES technologies, solar PV technology has become one of
the most prominent and widely used technologies due to its modularity, ease of
installation, matured technology, and low operating costs [166]. The modularity of solar
panel allows large-scale centralized PV farm (up to 50MW) to be set up, and at the
same time enables decentralized PV power system such as residential building and
remote area electrification. The main disadvantages of PV power generation are: (1) the
intermittency of output power due to clouds, and (2) variability due to the day-night
cycles as well as seasonal variation. Therefore, the PV power systems, especially for the
stand-alone systems, are generally equipped with ESS to ensure stable and reliable
electricity. Over the past decades, ESSs have been widely used and indispensable in
stand-alone solar PV power generation systems.
This chapter presents an overview of PV technology and its application in power
systems, including the considerations of PV system design, solar irradiance
measurement, load profile estimation and the selection of energy storage technology. A
detailed discussion and analysis of the stand-alone PV-battery power systems will also
be provided that contains an introduction to the system topology, the design of its ESS,
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 48 -
system modelling, numerical simulation and experimental demonstration based on a
lab-scale prototype.
3.2 Overview of solar PV technology
3.2.1 Background of solar cell
Solar cell, also known as PV cell, is a solid state device that converts sunlight directly
into electricity. The name of PV comes from the process of converting light (photons)
into electricity (voltage), which is the so-called PV effect. The PV effect was discovered
by French physicist, Becquerel, in 1839 and was first applied in silicon cell by Bell
Laboratories until 1954. The silicon cell is soon gained applications in U.S space
programs as the power system of earth-orbiting satellites owing to its high power-
generating capacity per unit weight. The space applications inspired the solar cell
development and continued to spread into varies applications ranging from powering
rural villages to feeding national grids globally. Fig. 3.1 shows the classification of solar
cell based on the material property [167]. The family of solar cells in different primary
active materials are divided into silicon solar cell, multi-compound thin film solar cell,
polymer solar cell, modified electrode layers, nano-crystalline solar cell, organic solar
cell and other solar cells with novel materials.
Fig. 3.1 Solar cell classification
Table 3.1 summarizes the efficiency of relatively mature and dominant solar cells in the
market. Mono-crystalline silicon solar cells have been widely used in the large-scale PV
Solar Cell Technologies
Wafer-based Cells
Mono-crystalline
Cells
Poly-crystalline
CellsGaInP,
GaAs, etc.
Thin Film Cells
Amorphous Silicon (a-Si)
Cells
CdTe, CZTS, CIGS, CIS, Condenser
Organic-Materials, Dye Sensitized, etc.
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 49 -
power system. It possesses the highest conversion efficiency and performs significantly
better compared to its counterparts under medium temperature zones such as the
subtropical area [168]. Polycrystalline silicon PV cells have similar properties to
monocrystalline silicon PV cells but suffer from higher temperature sensitivity, which
reduces conversion efficiency. Although amorphous silicon PV cells are a cheaper
option, conversion efficiency is lower, in the range of 5-7%.
Table 3.1 Three main types of solar cells in the market
Solar cells Efficiency Features
Mono-crystalline Silicon 14-17% Higher efficiency but higher price
Poly-crystalline Silicon 11-14% Cheaper than mono-crystalline but less efficient
Amorphous Silicon (a-Si) 5-7% Flexible shape, cheaper, but low efficiency
3.2.2 Solar cell model
In solid-state physic, solar cell works as a classical diode with P-N junction. The
complex PV process in solar cell can be simplified as an equivalent electrical circuit
model as shown in Fig. 3.2. The summation of the output current I, the diode current ID
and the shunt-leakage current 𝐼𝑆𝐻 is equal to the photo-current 𝐼𝐿 . The series resistor
𝑅𝑆 represents the internal resistance which depends on P-N junction characteristics such
as depth, impurities and contact resistance. Due to the material imperfections of solar
cell, there is leakage current at the edge of each solar cell and it is characterized by the
shunt resistor 𝑅𝑆𝐻 and leakage current 𝐼𝑆𝐻. For an ideal solar cell, there is no series loss
(𝑅𝑆 = 0) and no leakage current (𝑅𝑆𝐻 = 𝐼𝑛𝑓𝑖𝑛𝑖𝑡𝑦 ). The efficiency of solar cell is
sensitive to 𝑅𝑆 variation and therefore, minimizing the resistance from P-N junction is
one of the research directions in solar cell to improve the conversion efficiency.
Fig. 3.2 The equivalent circuit of solar cell
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 50 -
The output current of solar cell is given by the following equation:
𝐼 = 𝐼𝐿 − 𝐼𝑑 − 𝐼𝑆𝐻 (3.1)
The diode current 𝐼𝑑 can be calculated by the classical diode current expression:
𝐼𝑑 = 𝐼𝐷[𝑒𝑄𝑉𝑜𝑐𝐴𝑘𝑇 − 1] (3.2)
Where
𝐼𝐷 is the diode saturation current
Q is the electron charge which equals to 1.6 ∗ 10−19𝐶
A is the curve-fitting constant
k is the Boltzmann constant which equals to 1.38 ∗ 10−23J/°K
T is the temperature in °K
The shunt leakage current 𝐼𝑆𝐻 is calculated by open-circuit voltage 𝑉𝑜𝑐 and shut
resistance,
ISH =Voc
RSH
(3.3)
Where 𝑉𝑜𝑐 is obtained when there is zero load. Thus the load current is given by the
expression:
I = IL − ID[eQVocAkT − 1] −
Voc
RSH
(3.4)
The electrical characteristics of solar cell can be graphically represented by the current
versus voltage (I-V) curve and power versus voltage (P-V) curve, as shown in Fig. 3.3.
On the vertical axis, the highest current in I-V curve occurs at zero voltage when the
output terminals are connected. This is called the short-circuit current 𝐼𝑠𝑐 . On the
horizontal axis, the highest terminal voltage occurs when no load is connected, which is
called the open-circuit voltage 𝑉𝑜𝑐. These two parameters are usually given on the solar
cell datasheet. The Maximum Power Point (MPP) occurs at the knee point on the I-V
curve (marked in red circle). The I-V curve in one solar cell will vary based on the sun
irradiation.
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 51 -
Fig. 3.3 The I-V and P-V characteristics of the solar cell
Fig. 3.4 illustrates the different I-V curves of typical solar panel. It can be observed that
the knee points of the I-V curves are shifting to the right and the corresponding MPP on
P-V curves increases as the sun irradiation increases. Therefore, keeping the solar cell to
generate electricity at MPP continually can significantly increase the overall power
efficiency. There are many MPP tracking algorithms available nowadays to maximize
the efficiency of solar PV power systems.
Fig. 3.4 V-I and P-V curves in different intensity
*
P
(W)
0 20 40 60 80 100 120 1400
5
10
1 kW/m2
Cur
rent
(A)
Voltage (V)
Array type: SunPower SPR-305-WHT; 2 series modules; 2 parallel strings
0.75 kW/m2
0.5 kW/m2
0.25 kW/m2
0 20 40 60 80 100 120 1400
500
10001 kW/m2
Pow
er (W
)
Voltage (V)
0.75 kW/m2
0.5 kW/m2
0.25 kW/m2
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 52 -
Fig. 3.5 Solar cells, PV modules, PV panels and PV arrays
A single solar cell typically produces 1W of power with approximately 0.5 volts. A
number of them are interconnected in series and/or parallel to generate the appropriate
voltage and current levels that can be integrated to the power systems. The PV module
is formed by solar cells that are typically sealed in a protective laminate as depicted in
Fig. 3.5. The PV modules are then assembled as a PV panel to achieve the desired
voltage and power. In a large-scale PV power system, PV panels as the fundamental
unit are connected to form PV array in order to generate the desired power generation
capacity. The flexibility and modularity of solar PV allows different size of PV power
systems to be implemented, ranging from large-scale solar farm down to residential PV
power system.
3.2.3 Overview of the PV power system
The typical PV power system consists of core components such as PV array, charge
controller, ESS, power inverter and loads. Modern PV power system also includes smart
meters, AC/DC isolator, sensors, and monitoring systems to ensure system reliability
and performance optimization. In order to achieve specific goals from powering basic
electrical appliances to feeding electricity to the national grid, the PV power system can
be designed in many different topologies. PV power system can be configured either as
a grid-connected PV system or stand-alone PV power system, with and without ESS
and/or other distributed generation sources, as categorized in Fig. 3.6. Grid-connected
PV power systems operate as distributed generation networks that are interconnected
with the utility grid. It uses power electronic inverters, or Power Conditioning Unit
Solar Cell PV Module PV Panel PV Array
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 53 -
(PCU), to convert the DC power produced by the solar cells into AC power that are
synchronized with grid voltage and frequency. With ESS integrated, grid-connected PV
power systems can operate in either grid-connected mode or islanded mode based on the
real-time generation-demand conditions.
Fig. 3.6 The classification of PV power system
Conversely, stand-alone PV power system, or off-grid PV power system, is designed to
operate autonomously and independently that are generally used to supply electricity in
isolated or remote areas. The simplest and primitive form of the off-grid PV power
system is the direct-coupled system, where the PV panel is directly connected to a DC
load, and it usually is in small power capacity. Since there is no energy storage in the
system, it only operates when sunlight is available and commonly applied in ventilation
fans, water pumping system for agriculture, etc. A complete stand-alone PV power
system contains PV panels, charge controller, power inverter, ESS and loads. Stand-
alone PV power systems are widely used for rural electrification, especially in remote
rural areas where grid connection is not economically viable. Hybrid PV power system
is one of the solutions to compensate for the variability of PV generation, where other
distributed generations such as diesel generator, wind power generator, and mini-hydro
generator are integrated.
PV Power System
Grid-connected
Without ESS
Directly connected to Grid
With ESS
Bimodal PV System
Stand-alone
Without ESS
Direct-Couple System
With ESS
DC SystemSelf-Regulating
AC System
Hybrid PV System
With other RES based system
With Diesel Generation
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 54 -
3.3 Stand-alone PV power system
Reliable and affordable electricity is of crucial importance to ensure the quality of life.
However, according to the International Energy Agency (IEA), in 2015, nearly 1.3
billion people are not connected to the utility grid, and over 95% of them live in the
developing countries or remote rural areas [169]. Off-grid rural communities are
generally geographical dispersal, decentralized, low population density and
geographically isolated from the national grid. Due to the resource and financial
constraints, it is difficult to extend the national grid to these areas through the
conventional power transmission and distribution approach. Thus, a typical solution to
provide electricity to the remote and inaccessible area is achieved by using petrol/diesel
generators which have low initial costs but high running costs, and most importantly,
not environmental friendly. Therefore, using renewable energy based power system in
these areas is one of the practical solutions that will bring significant benefits to the
communities, especially in reducing dependence on the expensive petrol/diesel fuel and
the difficulty in transporting them. Solar PV is one of the preferred choices of RES for
remote rural electrification due to its excellent properties such as modular, easy to
install, eco-friendly, and low operating cost [170].
3.3.1 Components and system structure
Fig. 3.7 A typical stand-alone PV-battery power system
A typical configuration of stand-alone PV power system is illustrated in Fig. 3.7. It
consists of PV arrays, charge controller, energy storage, power inverter and loads. The
charge controller with Maximum Power Point Tracking (MPPT) or the one without will
Energy Storage
Charge Controller
Inverter AC Load
DC LoadDC/DC(MPPT)
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 55 -
regulate and control the charging process from the PV arrays to energy storage [171]. In
most cases, a DC/AC power inverter is needed to convert the DC power of ESS to AC
electricity to power up basic electrical appliances such as lighting, television, washing
machines, freezers, etc. In addition, energy storage devices are needed because of the
intermittent nature of the PV output and changeable load demand. It acts as an
intermediate energy buffer compensating the generation-demand mismatch.
To design an effective stand-alone PV power system, engineers need to carry out the
on-site assessment. For example, solar irradiance measurement, user behaviour,
electricity consumption estimation, topographical and meteorological constraints.
Alongside these aspects, the social acceptance, maintenance requirement, and
environmental impacts also need to be taken into consideration before installing PV
power systems.
3.3.2 Solar power generation
Solar irradiance refers to the electromagnetic radiation that reaches the earth surface
after being absorbed, scattered, and reflected by the atmosphere in power per unit area.
The daily solar irradiance profile (sun hours) is the key parameter that determines the
total output power of the PV panels, and it is used to calculate the photo-current
generated from PV panels, as shown in Eq. (3.5) [172]:
𝐼𝐿 = 𝐼𝑠𝑐𝑅𝐺
𝐺𝑅
[1 + 𝛼𝑇(𝑇𝑐 − 𝑇𝑐𝑅)] (3.5)
where 𝐺𝑅 and 𝐺 are the reference solar irradiation and real-time solar irradiation; 𝑇𝑐𝑅
and 𝑇𝑐 is the reference solar cell temperature and real-time solar cell temperature; 𝐼𝑠𝑐𝑅 is
the solar cell short circuit current at 𝐺𝑅 and 𝑇𝑐𝑅; 𝛼𝑇 is temperature coefficient of photo
current. In the case where the ambient temperature variation is relatively small, the real-
time output current of the PV panel can be approximated from the solar radiation.
In order to evaluate and design a sustainable PV power system, it is necessary to
understand the availability and quality of solar irradiance at the targeted site. Sarawak is
the largest state in Malaysia with the total land area of 124,450 km2, with a daily
average of ambient temperature of 26.14 ⁰C and 12 hours average sunlight throughout
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 56 -
the years [173], [174]. The tropical climate with mostly cloudy weather condition
provides average daily sun hours of about 4 to 5 hours. In most cases, the solar input
fluctuates due to the shelter of clouds. Fig. 3.8 illustrates a 7-day solar radiation profile
collected in Kuching, Sarawak, Malaysia.
Fig. 3.8 The 7-day solar irradiance in Kuching, Sarawak, Malaysia
3.3.3 Load demand estimation
In order to design a cost-effective off-grid residential PV power system, an accurate
estimation of the load profile is vital to ensure that the system can generate sufficient
energy and to prevent short battery cycling. Fig. 3.9 depicts a simple load profile
estimation method used in this work. This method is based on the demographics of local
households, the quantity, and characteristics of household appliances and users’
behaviour on daily energy consumption.
Firstly, a site survey was conducted to collect basic information about the population in
the community, living area, public space, daily work routines and behaviours, and the
types of electrical appliances. These appliances include lighting, television, refrigerator,
electric fan, and consumer electronic devices. The characteristics and usage frequency
of each electrical appliance are recorded during the site assessments. Secondly,
aggregate the collected information into a checklist, and assign values to each item
using the appropriate data. The estimated load curve will then be formed by the sum of
12.08 12.09 12.10 12.11 12.12 12.13 12.14 12.150
400
800
1200
1600
2000
2400
Days
Sola
r Rad
iatio
n (W
/m2)
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 57 -
the values of both items. Finally, add a degree of redundancy to the results to form the
final load profile estimation.
Fig. 3.9 Assessment of load profile for a non-electrified rural community
Fig. 3.10 illustrates an example of the estimated load profile for a rural community,
Batang-Ai (1°14’20.5”N, 112°02’10.7”E), in the inner part of Sarawak using the
abovementioned method. The target site has 6 households. It is noticed that peak energy
demand occurs during noon time (between 11 AM to 2 PM) and night time (between 7
PM to 10 PM). This is because most of the power-demanding activities such as cooking
and entertainment are scheduled during these times. The estimated total load demand
per day is around 15kWh.
0 6 12 18 00
0.25
0.5
0.75
Hours of Day
Pow
er (k
W)
Additional Appliances
Community Hall
Total ConsumptionCommon Lighting
Household Use
Fig. 3.10 Estimated load profiles of the target rural site in Sarawak, Malaysia
Load profile
Households
Population Public area
Appliances
Daily activity profile
Standby consumption
Use frequency Types
Appliances list
Load curve for the appliancesTotal load curve
Step 1: Survey
Step 2: Estimation
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 58 -
A 2kW stand-alone PV-battery-genset hybrid power system was designed and installed
at the targeted site during this research work (Appendix 1). The actual load profiles
were measured as shown in Fig. 3.11, and it demonstrates very similar electricity usage
pattern as the one estimated using the abovementioned method.
(a) 2016/10/21 (b) 2016/10/22
Fig. 3.11 Actual load profiles of the target rural site in Sarawak, Malaysia
3.4 Energy storage in stand-alone PV power system
ESS is the key component in stand-alone PV power system to balance the generation-
demand mismatch [175]. It absorbs excess energy generated by solar panels during the
off-peak period and supplies energy when PV generation is insufficient during peak
time or night time. The selection and design of ESS in stand-alone PV power system is
essential as it is one of the major cost components in a typical installation.
3.4.1 Discussion about the selection of ESS
As discussed in Chapter 2, no single energy storage technology can have all the desired
features to fulfill the criteria in any specific application scenarios. The determination for
selecting an appropriate energy storage product normally requires a series of trade-offs
and considerations. Thus the selection of suitable energy storage device for stand-alone
PV power system in remote areas normally needs to consider the following criteria:
capacity, efficiency, availability and cost, stability, and lifespan. Ideally, in remote
applications, energy storage device with small size, high efficiency, fast response,
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 59 -
mature technology, good stability, longevity, clean and low maintenance cost will be the
preferred choice. Based on the analysis in Table 2.3, SMES and SC have relatively high
efficiency, fast response, clean, and longevity, but their energy density is low and with
high self-discharge rate. In addition, SMES is a relatively new technology and
extremely expensive due to superconductive wiring coil usage.
On the other hand, the voltage and capacity of a single SC are minimal, and it usually
requires a lot of them to connect in series and in parallel to reach the rated voltage and
rated capacity. Therefore, the entire system requires power electronic converters as the
controller to maintain the voltage balance and manage the power flow, which increases
the complexity. Thus, neither SMES nor SC is suitable as the primary energy storage in
this application. Similarly, in the hydrogen and flywheel energy storage, the issues of
relatively high cost, immature technology, high self-discharge rate (Flywheel), and the
difficulty in transporting the hydrogen to remote areas make them unsuitable for remote
off-grid applications.
Among all ESS technologies discussed above, electrochemical energy storage turns out
to be the most appropriate ESS for stand-alone PV power system. Firstly, the fluctuating
and unpredictable nature of the PV output cannot allow the nickel-based battery to
operate effectively without losing its capacity due to memory effects. Thus Nickel-
based battery also is not suitable in PV power system. While NaS battery and flow
battery provide robust lifespan and relatively low cost, but NaS battery requires high
maintenance and complicated system to ensure to the high-temperature operating
environment. Flow battery and metal-air battery are still under research and
development stage.
Till now, the Lead-acid battery and Lithium-ion battery are the two remaining ESS
options. They are both suitable for the remote applications, but Lithium-ion battery is
thermally less stable than the Lead-acid battery (safety reason) and higher initial cost,
which needs reliable SoC monitoring and control to avoid overcharging. Conversely,
the Lead-acid battery is much stable (electrically and thermally) and robust. Thus, the
Lead-acid battery is chosen as the most suitable energy storage technology for stand-
alone PV energy system as its ESS.
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 60 -
3.4.2 Lead-acid battery and its stress factors
In a stand-alone PV-battery power system, the battery bank is often one of the most
expensive components regarding initial cost and operating cost [176]–[178]. This is
because the aging mechanism of Lead-acid batteries results from various stress factors
caused by the intrinsic characteristics of charging and discharging processes. The life-
limiting factors in the Lead-acid battery are listed below [95], [179]–[181]:
1. Prolonged low SoC;
2. Overcharging;
3. High operating temperature;
4. Frequent deep or full discharge, high depth of discharge;
5. Charging/discharging with a high C-rate;
6. Frequent charge-discharge transitions;
7. Partial cycling in low SoC.
Most of these life-limiting factors are closely related to SoC, which is the amount of
remaining available energy in the battery and expressed as a percentage of rated energy
[182], [183].
Fig. 3.12 Electrochemical reaction of the Lead-acid battery
Fig. 3.12 shows the electrochemical reaction in a typical Lead-acid battery [180].
During the discharging process, sponge lead (𝑃𝑏) is converted to lead sulfate (𝑃𝑏𝑆𝑂4)
on the negative plate and generate electrons simultaneously. The lead dioxide (𝑃𝑏𝑂2) is
converted into 𝑃𝑏𝑆𝑂4 on the positive plate and sulfuric acid (𝐻2𝑆𝑂4) is consumed in the
electrolyte. The 𝑃𝑏𝑆𝑂4 in the process of becoming the 𝑃𝑏𝑆𝑂4 will cause an increase of
the battery volume. While during the charging process, the 𝑃𝑏𝑆𝑂4 is converted back to
Porous LeadSulfuric
AcidPorous lead
DioxideLead sulfate Water Lead sulfate
Active material of
negativeplate
Electrolyte
Active material of
positive plate
Active material of
negative plate
Electrolyte
Active material of
positiveplate
Discharging
Charging
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 61 -
𝑃𝑏𝑂2 again, and the battery volume will shrink. When the battery undergoes repeatedly
shrinkage and expansion, the inter-bonding between the 𝑃𝑏𝑂2 particles will be
gradually loosened, and eventually leading to shortened battery life. If the DoD can be
reduced, the degree of such shrinkage and expansion will be significantly reduced, and
the binding force between the lead dioxide particles will continue to be maintained, thus
increasing the lifetime of the battery [184].
Another stress factor that accelerates the performance deterioration of the Lead-acid
battery is the Charge and Discharge Rate (C-rate). High C-rate will cause the removal of
active materials on the plate and reduces the battery rated capacity and lifetime [185]. In
Fig. 3.13, the relation of the C-rate, DoD, battery rated capacity and lifecycles are
presented [186], [187]. The graphs show that the performance and cycle life of Lead-
acid battery be improved by carefully controlling the DoD and C-rate. Therefore,
mitigating the stress factors can effectively prolong the service life of the Lead-acid
battery.
(a) LA battery lifecycles vs. DoD (b) LA battery rated capacity vs. Discharge time
34
s
Fig. 3.13 The curves of the Lead-acid battery healthy and stress factors
3.5 Case study of stand-alone PV-Battery power system
A Matlab Simulink model of a 5 kW stand-alone PV-battery power system is developed
as shown in Fig. 3.14. Actual solar irradiance data recorded on typical sunny days and
partially cloudy days were used to simulate PV power generation. While the estimated
load profile (as shown in Fig. 3.10) is considered in the simulation and analysis.
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 62 -
Fig. 3.14 Matlab Simulink model of stand-alone PV-Lead-acid battery system
0 4 8 12 16 20 0-100
-60
-20
20
60
100
Hours of Day
Cur
rent
(A)
(a) PV-Load
(b) LA Battery
Fig. 3.15 Simulation current of stand-alone PV-battery power system (sunny)
0 4 8 12 16 20 0
-60
-20
20
60
100
Hours of Day
Cur
rent
(A)
(a) PV-Load
(b) LA Battery
Fig. 3.16 Simulation current of stand-alone PV-battery power system (cloudy)
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 63 -
The net power flow to and from the battery bank is illustrated in Fig. 3.15 (for sunny
day condition) and Fig. 3.16 (for partly cloudy day condition). The Lead-acid battery
bank (4000Ah) absorbs or supplies the net difference between the PV output and the
load demand. When its current is negative, the Lead-acid battery will be charged and
being discharged when net current is positive. as can be observed from the battery
current profiles, severe fluctuations occur during day time where PV generation is
available, even during sunny day condition.
3.5.1 Scaled prototype of stand-alone PV power system
A down-scaled prototype of stand-alone PV-battery power system was constructed to
demonstrate the operating characteristics of the Lead-acid battery experimentally. The
voltage on the DC bus is 12V, and the capacity of the Lead-acid battery is 30Ah. A
typical commercial charge controller is used to regulate the charging process from PV
to the Lead-acid battery. The experimental results in Fig. 3.17 demonstrate the battery
current profiles measured during partly cloudy weather at Swinburne University
Sarawak Campus, Kuching, Malaysia. The PV output current changes abruptly and
fluctuates randomly. The Lead-acid battery starts to charge at around 7:00 a.m. and
discharge at around 4:00 PM.
0 4 8 12 16 20 0-1.5
-1
-0.5
0
0.5
1
1.5
Hours of Day
Cur
rent
(A)
(a) LA Battery (b) PV-Load
Fig. 3.17 Experimental current of stand-alone PV-battery power system (one day)
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 64 -
In Fig. 3.18, the 2 hours operating current can indicate that the battery closely follows
the net difference in PV and load currents. There are rapid charge/ discharge conditions,
from -1.5A to 1.5A, as well as high current charging or discharging requirements, which
has a significant adverse effect on battery life.
8
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Hours of Day
Cur
rent
(A)
(a) LA Battery (b) PV-Load
Fig. 3.18 Experimental testing of stand-alone PV-battery power system (2 hours)
3.5.2 The concept of SC/Lead-acid battery HESS
In stand-alone PV power system, the Lead-acid battery has to continuously charge and
discharge based on the fluctuating power requirement as a result of solar intermittency
and load variability. This highly dynamic charge/discharge processes are putting heavy
operation stress on the battery bank, thus accelerating performance deterioration and
aging process. Over the past decades, Battery-SC hybrid ESSs has been proposed by
many researchers for mitigating charge-discharge stress on the Lead-acid [188]–[190].
SC as an energy storage device with excellent power density, fast response time and
most importantly long cycle life, has turned out to be a great complementary energy
storage device to compensate the limitations of the electrochemical battery bank. The
primary idea of Battery-SC hybrid ESS is to allow the SC to absorb/supply the dynamic
power exchange while letting the primary battery bank in responding to the average
Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System
- 65 -
power demand. Although the concept of HESS sounds promising, special consideration
must be taken into accounts such as topology design, control system, and energy
management strategy. The next chapter will introduce the Battery-SC HESS in details.
3.6 Conclusion
This chapter introduced solar cell technology and PV power generation systems,
including literature review, working principles, classification, and typical system
architecture. In rural electrification, stand-alone PV power systems have been widely
installed and applied. This chapter provided an overview and discussion of system
design, solar irradiance measurement, load estimation methods, system modelling, and
analysis. Based on the energy storage technology discussed in Chapter 2, the selection
of energy storage technology in the stand-alone PV power system is critically discussed
and concluded that the Lead-acid battery is still one of the most suitable ESS for remote
applications.
- 66 -
Chapter 4
Study on Battery-SC HESS: Topology,
Control Strategy, and Application
4.1 Introduction
Lead-acid battery as one of the mainstream energy storage devices used in stand-alone
PV power system suffers from short service life, despite the excellent electrical
characteristics and lower initial cost. The ESS acts as an intermediate buffer to absorb
excess energy and supply the stored energy when the power deficit. In addition, it also
plays a vital role in regulating instantaneous power variations, power quality, and
system reliability. Especially for a stand-alone PV power system, it relies heavily on
energy storage to balance the unmatched generation and power consumption profiles
[191]–[193]. For example, surge demand power to start motors in appliances can be 7 to
10 times larger than normal operation current [194], and the PV output fluctuates
dynamically due to cloud shelter on PV arrays. The fluctuating and variable power flow
could potentially accelerate the aging process of the Lead-acid battery. Thus, without
proper control, the Lead-acid battery normally can only last 3-5 years which leads to
higher operating cost [195]. Therefore, effectively prolonging the service life of Lead-
acid battery bank will have a significant economic impact on the market of the off-grid
PV power system.
Hybridization of different ESS technologies turns out to be one of the promising ways
to mitigate the battery charge-discharge stress by directing the short term power
fluctuation to another form of ESS such as the SC [196]–[198]. The SC stores electricity
Chapter 4 Study on Battery-SC HESS: Topology, Control Strategy, and Application
- 67 -
via electrons in the static electric field and possesses high power density, has short
charging-discharging time, and nearly unlimited cycle life. The battery and SC
combination has been considered in most HESS developments because of their
availability, similarity in working principle, relatively low cost and most importantly.
They complement each other’s limitations very effectively. Recently, the development
of Battery-SC HESS for residential energy storage applications is beginning to generate
positive outcomes, and it typically is connected to the power system via AC or DC
coupling [199]–[204]. Power electronic converters are used to control the power flow
among different ESS elements [205]–[207]. Depending on the complexity of the control
strategies, the use of power converters and microcontrollers can be costly [208]. Hence,
the trade-off between economic feasibility and technical advantages exist, and it is
crucial in determining the financial and technical sustainability of the system.
Various Battery-SC HESS topologies had been proposed in microgrid applications
aiming to optimally utilize the benefits of different ESS elements [209], [210]. Besides
having correct HESS topology and appropriate sizing, energy management and control
strategy of HESS is another key to improve system efficiency, maximize energy
throughput and prolong the lifetime of HESS [211]–[213]. This chapter presents a
comprehensive review and discussion of Battery-SC HESS, including their system
topologies design, electrical characteristics, energy management system, control
algorithms and applications in stand-alone PV power system.
4.2 Battery-SC HESS topologies
In Battery-SC HESS, the two ESS elements can be coupled to either a common DC or
AC bus. For stand-alone microgrid, common DC bus is the preferred choice due to
various reasons [214], [215]. First, most ESS elements and RES based generators
operate in DC voltage. Therefore, maintaining a DC bus minimizes the needs of power
converter [216]. Second, DC bus does not require synchronization which greatly
reduces the complexity of the overall system [217], [218]. As a result, DC coupling is
more efficient and lower cost than equivalent AC bus systems [219]–[222]. In general,
the topologies of the Battery-SC HESS can be classified based on the interfacing
approach, as shown in Fig. 4.1. For passive connection, the terminals of energy storage
Chapter 4 Study on Battery-SC HESS: Topology, Control Strategy, and Application
- 68 -
are directly connected to the DC bus for which the power-sharing mechanism and
response is purely determined by the electrical characteristic of the energy storage
devices. On the other hand, active HESS topologies employ active components such as
bi-directional DC/DC power converter to interface the energy storage elements from
DC bus and to actively control their power flow.
Battery-SC HESS
Passive HESS Semi-Active HESS
Full Active HESS
SC Semi-Active
Battery Semi-Active
Parallel
Cascaded
Multi-LevelHESS
Fig. 4.1 Classification of the battery-SC HESS topologies
4.2.1 Passive Battery-SC HESS
The passive connection of battery and SC modules to DC bus offers the simplest and
cheapest form of HESS [223]. It has been reported to effectively suppress transient
current under pulse load conditions, increase the peak power and reduce internal losses
[224]–[226].
Battery SC
DC Bus
Fig. 4.2 Passive HESS topology
As shown in Fig. 4.2, the battery and SC are connected to the DC bus directly, thus
sharing the same terminal voltage. The DC bus voltage is purely depending on the
battery SoC, and therefore it is highly influenced by the charge-discharge characteristic
Chapter 4 Study on Battery-SC HESS: Topology, Control Strategy, and Application
- 69 -
of the battery. In some rural microgrid applications, the battery capacity is sized up to
five days as a reserve without any external source of energy [227]. Consequently, most
of the time the battery will be cycled with relatively low DoD and charged/discharged in
a relatively low C-rate. As a result, the fluctuation in DC bus voltage will be minimal,
ensuring a relatively stable system voltage.
However, the system current will be drawn from or feed into the battery and SC based
on their respective internal resistances. Therefore, the transient power handling
capability of the SC is not optimally utilized. In addition, as the voltage variation of the
battery terminal is small, the SC will not be operating at its full SoC range which results
in poor volumetric efficiency [228].
4.2.2 Semi-Active Battery-SC HESS
To make better use of the ESS elements in Passive HESS, power electronic converters
are included between the ESS elements and DC bus. This allows the power flow to be
actively controlled [229]. In Semi-Active HESS topology, only one of the two energy
storage elements is actively controlled as illustrated in Fig. 4.3.
#Note 1 – Initial cost of Lead-acid battery ($256/kWh) and Lithium-ion battery ($290/kWh) are considered.[269] #Note 2 – Typical life cycle / Cost of battery utilization; (typical life cycle for Lead-acid – 500 cycles, Lithium-ion – 4000 cycles and SC >100,000 cycles)[269] #Note 3 – Estimated to perform 50% of the expected lifecycles of the Lithium-ion battery when Lead-acid battery is replaced #Note 4 – Percentage cost reduction is calculated based on battery-only system.
Since the SC lifetime is nearly infinite, it is not considered in the annual operating cost
of the ESS in stand-alone PV power system. The Passive HESS reduces the battery cost
by 6.3% and 10.8% respectively for sunny and cloudy days, while both SC Semi-Active
HESS and Multi-level HESS demonstrate significant ESS cost reduction of about 43%
on a sunny day and 60% on a cloudy day. Despite the higher upfront battery cost (Lead-
acid and Lithium-ion) in Multi-level HESS, it only requires 20% of the SC capacity
compared to other HESSs.
Chapter 5 Battery-SC HESS for Stand-alone PV Power System in Rural Electrification
- 119 -
5.5 Experiment verification
5.5.1 Testbed setup
To demonstrate the feasibility of the selected HESS topologies and to verify the
simulation analysis presented in the previous section, scale-down prototypes of stand-
alone PV-battery power system with the selected HESSs were designed and developed
as illustrated in Fig. 5.41. The test-bed is tested in campus of Swinburne University.
MPPT
Lead-acid Battery
Solar Irradiance
ipv
vpv
Charge Controller
Dpv
SC
SC
Ls
Cbus
Sw1 Sw2
ISC(ref)
EMS(Fig.5.11)
Sw1 Sw2
SC
Ls
Cbus
Sw1 Sw2
ISC(ref)
IBus
Li-ion
Ls
Cbus
Sw3 Sw4
EMS(Fig.5.16)
ILi-ion(ref)
Sw1 Sw2 Sw4Sw3
Pulse Load
IBus
IBus
DC/DC
InstalledPV Panel
EMS + HESS
SC Semi-active HESS
Multi-level HESS Passive HESS
Estimated load
0 6 12 18 00
0.5
1
1.5
2
2.5
3
Hours of Day
Pow
er (k
W)
Community HallTotal ConsumptionHousehold UseCommon Lighting
BK8500
Fig. 5.41 Experiment setup of selected HESS topologies
Inside the system, the Lead-acid battery is connected to the DC Bus through a charge
controller. The three HESS modules, marked with red dashed lines, can be individually
connected to the DC bus and tested. A programmable DC electronic load (BK Precision
BK8500) is used to emulate the pulsed load as well as the estimated load profiles. A
15W solar panel is used to generate the PV power. The current flows in energy storage
devices are measured by current sensors (ACS712) and logged by using the NI USB-
6008 data acquisition device. The power allocation algorithm is controlled by Arduino
(ATMEGA328P). The design and implementation of bidirectional buck-boost DC/DC
converter and its control system are presented in Appendix 2.1. The parameters of the
experiment testbed are summarized in Table 5.3.
Chapter 5 Battery-SC HESS for Stand-alone PV Power System in Rural Electrification
- 120 -
Table 5.3 Experimental testbed parameters
System Parameters Passive HESS SC Semi-Active HESS Multi-level HESS
PV panel peak power 15W
Daily load energy consumption 0.4 kWh
Lead-acid battery nominal voltage 12V
Lead-acid battery capacity 30Ah
SC Capacitance - 50F 25F
SC Equivalent Series Resistance - 0.001Ω 0.001Ω
Lithium-ion Battery nominal voltage - - 12V
Lithium-ion Battery capacity - - 6Ah
5.5.2 Pulse load response
A repetitive pulsed current profile with amplitude of 1 Ampere, period of 120s, and 50
percent in duty ratio is generated by using BK8500. Figs. 5.42-5.44 show the responses
of battery and SC currents in each selected HESSs under pulse load test.
300 350 400 450
-0.5
0
0.5
1
1.5
Time (s)
Curr
ent (
A)
(a) LA Battery(b) SC(c) Pulse Load
Fig. 5.42 Experimental responses to pulsed load of Passive HESS
In Passive HESS (Fig. 5.42), the SC responded instantaneously to the step change in
current, while the battery picked up slowly with a time constant of about 1.5s. In SC
Semi-Active HESS, an Arduino controlled bi-directional buck/boost DC/DC converter
is used to control the current flow in SC module with a simple digital LPF to allocation
Chapter 5 Battery-SC HESS for Stand-alone PV Power System in Rural Electrification
- 121 -
current among SC and battery modules. As can be seen from Fig. 5.43, the SC
responded quickly to the step change in current and allowed the battery to gently
supply/absorb the current change.
100 200 300 400
-1
-0.5
0
0.5
1
1.5
Time (s)
Cur
rent
(A)
(a) LA Battery(b) SC(c) Pulse Load
Fig. 5.43 Experimental responses to pulsed loads of SC Semi-Active HESS
800 900 1000 1100
-1
-0.5
0
0.5
1
1.5
2
Time (s)
Cur
rent
(A)
(a) LA Battery(b) Li-ion Battery(c) SC(d) Pulse Load
Fig. 5.44 Experimental responses to pulsed loads of Multi-level HESS
The current response of the Multi-level HESS is shown in Fig. 5.44. The structure
enables the primary battery to gently supply/absorb the changes but with notably lower
Chapter 5 Battery-SC HESS for Stand-alone PV Power System in Rural Electrification
- 122 -
peak current because the Lithium-ion battery module shares part of the current demand
that can be determined by setting the scaling factor (set at 0.85 for this experiment). The
experimental results of pulsed load responses agree to the simulation results presented
in section 5.3.
5.5.3 Daily operation in stand-alone PV power system
The daily operational test with 15W stand-alone PV power system was carried out on
HESSs under test separately on four different partly cloudy days in Swinburne
University of Technology Sarawak Campus, Kuching, Malaysia. The net current demand
(𝐼𝑃𝑉 − 𝐼𝐿𝑜𝑎𝑑, black line), primary battery current (red line) and SC current (blue line) are
depicted in Figs. 5.45 – 5.48, respectively for battery-only system, system with Passive
HESS, system with SC Semi-Active HESS and system with Multi-level HESS.
0 4 8 12 16 20 0-1.5
-1
-0.5
0
0.5
1
1.5
Hours of Day
Curr
ent (
A)
(a) LA Battery (b) SC(c) PV-Load
Fig. 5.45 Experimental currents of battery-only in stand-alone PV power system
Minimal mitigation of current fluctuation is demonstrated in Passive HESS (Fig. 5.46),
while SC Semi-Active HESS and Multi-level HESS remove majority of the primary
battery current fluctuation as can be observed from Fig. 5.47 for SC Semi-Active HESS
and Fig. 5.48 for Multi-level HESS. In addition, the Multi-level HESS shares part of the
current demand with Lithium-ion battery module with a pre-determined scaling factor.
The experimental results demonstrate the feasibility of the HESS under test in stand-
alone PV power system and validate the simulation analysis presented in Section 5.4.2.
Chapter 5 Battery-SC HESS for Stand-alone PV Power System in Rural Electrification
- 123 -
0 4 8 12 16 20 0-1.5
-1
-0.5
0
0.5
1
1.5
Hours of Day
Curr
ent (
A)
(a) LA Battery (b) SC(c) PV-Load
Fig. 5.46 Experimental currents of Passive HESS in stand-alone PV power system
0 4 8 12 16 20 0-1.5
-1
-0.5
0
0.5
1
1.5
Hours of Day
Cur
rent
(A)
(a) LA Battery(b) SC(c) PV-Load
Fig. 5.47 Experimental currents of Semi-Active HESS in stand-alone PV power system
Chapter 5 Battery-SC HESS for Stand-alone PV Power System in Rural Electrification
- 124 -
0 4 8 12 16 20 0-1.5
-1
-0.5
0
0.5
1
1.5
Hours of Day
Curr
ent (
A)
(a) LA Battery(b) Li-ion Battery(c) SC(d) PV-Load
Fig. 5.48 Experimental currents of Multi-level HESS in stand-alone PV power system
5.6 Conclusion
Stand-alone PV power system with Lead-acid battery has been one of the preferred
choices in off-grid rural electrification. However, the nature of solar energy is causing
the additional impact on the battery which accelerates the deterioration of battery
performance and cycle life. This chapter presented a comprehensive study of Battery-
SC HESS and their feasibility in stand-alone PV power system. Three potential HESS
topologies and their associated power allocation strategy, and control system had been
discussed in this chapter, followed by numerical simulation and experimental
verification. The Matlab Simulink models of the selected HESSs were developed and
simulated with actual solar irradiance data and estimated load profile to evaluate the
effectiveness in mitigating battery stress. The simulation analysis and results had been
verified by experiments with the developed lab-scale prototype of HESS under
consideration. Simulation results, battery health cost and financial analyses, and
empirical outcomes suggest that the combination of active secondary energy storage
with the passive primary battery could be the optimal setting for stand-alone PV power
system applications.
- 125 -
Chapter 6
Smart HESS Plug-in Module for Stand-
alone PV-Battery Power System
6.1 Introduction
The structure of stand-alone PV power system, illustrated in Fig. 6.1, consists of PV
arrays, charge controller, inverter, the Lead-acid battery, and AC/DC loads. This
conventional stand-alone PV-battery power system has been widely installed in off-grid
rural communities. Chapter 5 discussed and compared three HESS topologies that are
suitable for stand-alone PV power system and the technical analysis and simulation
outcomes showed that the proposed Multi-level HESS could effectively mitigate battery
stress and thus leading to enhanced lifetime characteristic of the Lead-acid battery bank.
However, to achieve the Multi-level Battery-SC HESS as demonstrated in Chapter 5, a
complete redesign of the existing installed ESS structure is required. This may not be
financially viable in most cases, especially in remote applications.
Fig. 6.1 The stand-alone PV power system with Lead-acid battery
Lead-acid Battery
Charge Controller
Inverter AC Load
DC LoadDC/DC(MPPT)
Plug-inModule
Chapter 6 Smart HESS Plug-in Module for Stand-alone PV-Battery Power System
- 126 -
Typical stand-alone PV-battery power system consists of PV arrays, charge controller,
inverter, ESS and loads. Typical solar charge controller features MPPT that maximizes
the power generation, and regulates the battery charging process by monitoring the
battery SoC to prevent overcharging and overly discharged [284]–[286]. However, other
life-limiting factors that accelerate the deterioration of battery performance such as high
C-rate, fluctuating power exchange, frequent charge-discharge transition, deep-
discharging, and other external factors are often not considered in the research of the
charge controllers [287]–[289].
In this chapter, a novel Smart Hybrid Energy Storage System (SHESS) plug-in module is
proposed that is retrofittable on typical stand-alone PV-battery power systems, as shown
in Fig. 6.1. The proposed module is designed as a plug-in that can be adopted directly in
existing installed infrastructure in the installed stand-alone PV-battery power system. By
design, it mitigates the principal Lead-acid battery operation stress from current
fluctuations and surge demand without changing the structure of the original system.
Such a scheme is simple, effective and will have a significant economic impact on the
market of the installed PV system.
The proposed SHESS plug-in module consists of SC and Lithium-ion battery modules
that operate in two different modes based on weather conditions: (1) light mode for
operation under sunny day condition and (2) heavy mode for operation under cloudy day
condition. The performance metric of the three energy storage technologies is tabulated
in Table 6.1 [176], [290], [291].
Table 6.1 Comparison of energy storage technologies in SHESS
Lead-acid Battery SC Lithium-ion Battery
Specific Energy Density 30 -50 Wh/kg 0.1 – 15 Wh/kg 100 - 250 Wh/kg
Specific Power Density 75 - 300 W/kg 500-15,000 W/kg 230– 340 W/kg
Life Cycle 500-1000 100,000+ 1,000-20,000
Charge/Discharge Efficiency 80 – 90 % >90% >90%
Shortest Response Time 1 min 0.1 – 1 s 30 s
Self-Discharge/ Day 0.1-0.3% 20-40% 0.1-0.3%
Unit Cost ($/kWh) 300 2000 500
Chapter 6 Smart HESS Plug-in Module for Stand-alone PV-Battery Power System
- 127 -
In sunny day condition, PV power is relatively stable for which only SC module will be
activated (light mode) to complement the battery operation. In light mode, the SC will be
interfaced to battery terminal by using a bidirectional DC/DC converter that is actively
controlled by the microcontroller. While in cloudy day condition, the PV generates less
power and fluctuates more frequently. Therefore, the heavy mode will be activated
where an additional Lithium-ion battery module is activated to provide the required
capacity in absorbing the current fluctuation. The current exchange will be decomposed
into three frequency components (high, middle and low) and allocated reasonably to the
SHESS plug-in modules of different lifetime characteristics (SC and Lithium-ion) and
the primary Lead-acid battery bank.
Computational model of the proposed SHESS plug-in module is developed and
evaluated in Matlab Simulink. Dynamic modelling and numerical simulations are carried
out to examine the effectiveness of the SHESS plug-in module in mitigating battery
stresses under different operating conditions. A scaled-down prototype of the proposed
SHESS plug-in module is examined experimentally to verify the simulation outcomes
and demonstrate the feasibility of the proposed system. To evaluate the technical and
financial improvements of the stand-alone PV power system with SHESS, a financial
analysis is presented by quantitatively investigating the lifetime improvement with
battery health cost model proposed in Chapter 5.
6.2 SHESS Plug-in module
6.2.1 System structure
The structure of the proposed SHESS plug-in module is illustrated in Fig. 6.2. The
SHESS is connected to the terminal of the Lead-acid battery. A mode controller
interfacing the Lead-acid battery and the SHESS plug-in module is implemented to
control the mode of operation. In light mode, only the SC module is interfaced to the
Lead-acid battery terminal via an actively controlled bidirectional DC/DC converter. The
active connection ensures DC bus stability while allowing the SC to operate in a wide
range of terminal voltages. In this mode, high-frequency current fluctuation will be
directed to the SC module that is controlled by the power allocator.
Chapter 6 Smart HESS Plug-in Module for Stand-alone PV-Battery Power System
- 128 -
Differently, both SC and Lithium-ion modules will be activated in heavy mode, where
both SC and Lithium-ion modules are interfaced with actively controlled bidirectional
DC/DC converters. This configuration will ensure sufficient capacity in handling severe
fluctuation in current without requiring a large amount of costly SC to be installed. In
this setting, the SC module will absorb/supply the high-frequency current fluctuation,
while the Lithium-ion module is controlled to absorb/supply the medium frequency
component of the fluctuating current, and at the same time to supply a portion of the total
power demand. A power-sharing factor W1 determines the proportion of power-sharing
within the power allocator. The power allocator adopts the low pass filtering approach in
decomposing the power demand into multiple frequency components. A power
controller is used to coordinate the operation of mode controller and power allocator
while generating appropriate PWM signals to operate the individual bidirectional DC/DC
converters.
Fig. 6.2 SHESS plug-in module in stand-alone PV-battery power system
Chapter 6 Smart HESS Plug-in Module for Stand-alone PV-Battery Power System
- 129 -
6.2.2 Power allocation and control strategy
The signal of power difference is collected and process via the central control system
that contains Power Allocation Controller (PAC) and Energy Management Unit (EMU).
The EMU is used to process the signals, determine the corresponding operating mode,
and send the signal to the PAC, as well as the DC/DC converters and Mode controller.
According to different working modes, the system will divide the signal into different
frequency parts and send it to the control unit. Fig. 6.3 shows the mode selection process
when the SHESS is operational. The mode controller can be configured as the automatic
mode or manual mode. In automatic mode, the selection of mode will be made
automatically based on real-time or predicted (from solar irradiance data) weather
conditions. While in manual mode, the operating mode will be manually set by the user.
This simple method will be attractive for the applications in remote areas.
Solar Irradiance
Manual setup
WeatherConditions
Sunny
Cloudy
Plug-in/off
Mode Selection
SC Li-ion
Off Mode(LA-Only) Off Off
Light Mode(Sunny) On Off
Heavy Mode(Cloudy) On On
Controller signal
Automatic setup
Fig. 6.3 Mode selection process of the proposed SHESS plug-in module
There are three modes of operation, namely off mode, light mode and heavy mode. In
light mode, SC module will be configured to respond actively to the high-frequency
power exchange while the average power demand (low-frequency components) will be
supplied passively by the primary Lead-acid battery. In the heavy mode, both the
Lithium-ion battery module and SC module will be parallel connected to the Lead-acid
battery. The power demand will be decomposed into three frequency components, (1)
high-frequency, (2) intermediate frequency, and (3) low-frequency. The high-frequency
power exchange will be responded by the SC module, while the Lithium-ion battery
module will cover the intermediate frequency, and lastly, the average power demand
(low-frequency) will be powered by the primary Lead-acid battery passively.
Chapter 6 Smart HESS Plug-in Module for Stand-alone PV-Battery Power System
- 130 -
Heavy Mode
Ppv
Pload
+-
PESS
LPF (Primary)
+ -
+-
PSC (ref)
LPF (Secondary)
W1
PLi-ion (ref)
(a) Power Allocator for SHESS
PSC(ref) 1/VSCISC(ref)
PI PWMD+
-
ISC
Switching Signal(SC Converter)
PLi-ion(ref) 1/VLi-ionILi-ion (ref)
PI PWMD+
-
ILi-ion
Switching Signal (Li-ion Converter)
(b) Linearized Model of the Li-ion and SC Current Control Loop
Mode Control
Light Mode
Fig. 6.4 Power allocation strategy for the SHESS plug-in mode
(1) Light mode
In the light mode, the power allocation strategy is shown in Fig. 6.4(a). High-frequency
component PSC(ref) is extracted from the net demand power PESS (PPV - Pload) using the
secondary LPF. The SC actively handles the high-frequency components, and the battery
responsible for the low-frequency ones in a passively way. The signal of PSC(ref) will be
the input reference signal for the controller to generate appropriate PWM signals that
operates the bidirectional DC/DC converter for the SC module as shown in Fig. 6.4(b).
While the remaining smoothed power demand (low-frequency component) will be
supplied by the passively connected Lead-acid battery.
(2) Heavy mode
In the heavy mode, the SHESS utilizes two LPFs to decompose the power demand into
three different frequency components as shown in Fig. 6.4(a). The highest frequency
component PSC(ref) will be supplied by the SC module. While the middle frequency
component PLithium-ion(ref) will be covered by the Lithium-ion battery module. A weight
factor W1 is implemented to set the load proportion of Lithium-ion battery module with
reference to the total power demand PESS. In this way, the load of the Lithium-ion battery
Chapter 6 Smart HESS Plug-in Module for Stand-alone PV-Battery Power System
- 131 -
module can be flexibly adjusted according to the installed capacity, to achieve optimal
power-sharing among different modules. Moreover, the W1 can be set manually or
modified dynamically by advanced control algorithms to satisfy different operating
conditions. In Fig. 6.4(b), it depicts the control systems for generating appropriate PWM
signals to operate bidirectional DC/DC converters of both SC and Lithium-ion battery
modules. Finally, the original Lead-acid battery passively absorbs/supplies the low-
frequency currents.
In addition to the hybrid integration of the Lead-acid battery and SHESS, it is also
critical to determine the cutoff frequency of LPF in both operation modes. In practice,
the cut-off frequency needs to be carefully considered based on the capacity of the Lead-
acid battery itself and the installed capacity of the SHESS. The cut-off frequencies
determine the smoothness of the fluctuating current in the Lead-acid battery and can be
calculated in real-time using advanced algorithms, or be manually adjusted by preset
optimization values. Researchers have made considerable efforts to optimize cutoff
frequency determination to maximize the benefits of HESS. The main research of this
chapter is to propose the SHESS plug-in module that can extend the lifetime of the main
battery in the installed PV system, and use simulation test and experimental verification
to demonstrate its operating characteristics and verify its feasibility. Therefore, the
algorithms for the mode selection, cut-off frequency determination, and the dynamic
adjustment of weight factor will not be discussed in details in the following subsections.
6.3 Simulation analysis
In this subsection, the Matlab Simulink model and simulation results of the proposed
SHESS plug-in module are presented. The model is tested with standard pulsed load and
actual PV-battery PV system operational.
6.3.1 Pulse load response
The pulsed load used in simulation has a period of 10s and 5A of amplitude with a fixed
50% duty cycle. In light mode, only the SC module is activated to absorb the fluctuating
current. While in heavy mode, both the SC module and Lithium-ion battery module are
Chapter 6 Smart HESS Plug-in Module for Stand-alone PV-Battery Power System
- 132 -
activated to perform power-sharing. Fig. 6.5 shows the SHESS model developed in
Matlab Simulink for pulse load test. Two switch breakers are implemented to simulate
the mode controller. The EMS simulates real-time power allocation and control
algorithms. The capacity of Lead-acid Battery and Li-ion Battery is set at 300Ah (12V) and
15Ah (12V), respectively. While the installed capacitance of SC is 50F.
Fig. 6.5 Matlab Simulink model of SHESS used in pulse load testing
(1) Light mode
Fig. 6.6 shows the simulation result for power-sharing between the Lead-acid battery and
the SC module under pulsed current load. During step change in current (from 0A to 5A),
the SC module is activated and discharges rapidly to fulfill the sudden change in current,
while the Lead-acid battery discharges gradually towards the current demand. In the OFF
state (from 5A to 0A), the SC module absorbs the excess current and gradually reduces
the Lead-acid battery current towards zero. Throughout the process, the battery can be
slowly discharged and charged at its own rate. The sum of the charge and discharge from
the two energy storage elements is equal to the value of the pulse load.
Chapter 6 Smart HESS Plug-in Module for Stand-alone PV-Battery Power System
- 133 -
15 20 25 30 35 40 45 50-6
-4
-2
0
2
4
6
8
10
Time (s)
Cur
rent
(A)
LA BatterySCPulse Load
SC ModuleActivated
Fig. 6.6 Simulated results in pulse load testing in light mode
(2) Heavy mode
On the other hand, both of the SC module and Lithium-ion battery module are parallel
connected (both switch breakers are activated) with Lead-acid battery terminal in heavy
mode. Fig. 6.7 depicts the current profiles for Lead-acid battery, Lithium-ion battery
module, and SC module under pulsed current load.
0 5 10 15 20 25 30 35 40 50-6
-4
-2
0
2
4
6
8
10
Time (s)
Cur
rent
(A)
Li-ion BatteryLA BatteryPulse LoadSC
SC/Li-ion HESSModule Activated
Fig. 6.7 Simulated results in pulse load testing in heavy mode
Chapter 6 Smart HESS Plug-in Module for Stand-alone PV-Battery Power System
- 134 -
The pulsed current was split into three frequency components, where the SC module
responded quickly to step change, while the Lithium-ion battery module took a small
portion of the overall capacity and at the same time responded to the middle frequency
component. As can be observed from the figure, the peak current of the Lead-acid
battery is reduced due to the W1 factor where the Lithium-ion battery module supplies a
small portion of the current demand.
6.3.2 Operation in stand-alone PV-battery power system
Fig. 6.8 illustrates the Matlab Simulink model of the SHESS plug-in module integrated
into a five kilowatts stand-alone PV-battery power system. The system model contains
#Note 1 – Initial cost of Lead-acid battery ($256/kWh) and Lithium-ion battery ($290/kWh) are considered.[269] #Note 2 – Typical life cycle / Cost of battery utilization; (typical life cycle for Lead-acid – 500 cycles, Li-ion – 4000 cycles and SC >100,000 cycles)[269] #Note 3 – Estimated to perform 50% of the expected lifecycles of the Li-ion battery when LA battery is replaced; #Note 4 – Percentage cost reduction is calculated based on battery-only system; #Note 5 – Assume the LA Battery: 4000 Ah with 12 volts; Li-ion Battery: 200Ah with 12 Volts (Weight factor: 0.5); #Note 6 – Cost of 500F SC is around $50 https://item.taobao.com/item.htm?ft=t&spm=a21m2.8958473.0.0.668e3663aIH9be&id=529538036640,
(2) Daily operation in stand-alone PV-battery power system
The SHESS plug-in module is tested while connecting to the DC bus by running 24
hours in both operations, sunny and cloudy, as shown in Figs. 6.23 and 6.24 respectively.
With the integration of the SHESS plug-in module, the Lead-acid battery current
fluctuations are suppressed noticeably in both weather scenarios. In Fig. 6.23, SC
module absorbed most of the high peak currents variations in a range of -0.5 A to 1A. In
Fig. 6.24, the solar irradiance varies much heavy than in sunny mode and leads more
power demand fluctuations with rich surge peak current components. The Lithium-ion
Chapter 6 Smart HESS Plug-in Module for Stand-alone PV-Battery Power System
- 147 -
battery shared a portion of total power demand in a moderate variation frequency, and
the SC still operated in the same range as in sunny mode. Accordingly, it is evident that
the SHESS plug-in module works appropriately and efficiently releases the Lead-acid
battery operate stress all over the day.
0 4 8 12 16 20 0-1.5
-1
-0.5
0
0.5
1
1.5
Hours of Day
Cur
rent
(A)
(a) LA Battery(b) SC(c) PV-Load
Fig. 6.23 Experimental one day testing of SHESS plug-in module (light mode)
0 4 8 12 16 20 0-1.5
-1
-0.5
0
0.5
1
1.5
Hours of Day
Cur
rent
(A)
(a) LA Battery(b) Li-ion Battery(c) SC(d) PV-Load
Fig. 6.24 Experimental one day testing of SHESS plug-in module (heavy mode)
Chapter 6 Smart HESS Plug-in Module for Stand-alone PV-Battery Power System
- 148 -
6.5 Conclusion
Typical stand-alone PV power systems with the Lead-acid battery are widely installed in
remote areas. Typical charge controllers lack the considerations the life-limiting factors
of the battery such as C-rate, DoD and current fluctuations, which are commonly
encountered in stand-alone power systems. This chapter proposed a SHESS plug-in
module that is retrofittable to existing installed PV-battery power systems to mitigate
battery stress by absorbing the damaging current profile. Two operation modes are
designed to face the sunny and cloudy weather conditions. Matlab Simulink model of the
SHESS plug-in module has been developed and simulated to investigate the power-
sharing capability. Battery health cost analysis is presented to qualitatively evaluate the
improvement in battery health and reduction in system operating cost. The analysis
shows that after installing the SHESS plug-in module, the annual operating cost can be
reduced up to 53.7%. A lab-scale prototype of the SHESS plug-in HESS was constructed
and tested under the same lab-scaled stand-alone PV-battery power system in Chapter 3,
including the pulse load test, plug in/off test, and one-day test. The experiment results
demonstrated the ease of being integrated into existing installed PV-battery power
system and were in good agreement with the simulation results.
- 149 -
Chapter 7
Conclusion and Future Work
7.1 Summary
Energy storage technology provides a way to increase grid flexibility and enable the
integration of intermittent, non-distributable renewable power generations. In Chapter
2, an overview of the state-of-the-art in energy storage technologies was presented,
including CAES, FES, PHS SC, SMES, cell battery, flow battery, TES, and chemical
energy storage. Their operation principles, technical performance, and economic aspects
and the current research and development status were discussed, classified, compared
and analyzed. Following this, a comprehensive comparison and potential applications
analysis of the reviewed technologies were presented systematically to illustrate the
advantages and limitations of various energy storage technologies in the view of
different perspectives. Selection criteria and consideration that intends to help decision
makers and researchers to select suitable energy storage technologies in specific
applications was presented. The future development trend of different energy storage
technologies was discussed.
The PV-based power system is seen to be a promising renewable energy technology in
the off-grid applications in remote areas. Chapter 3 presents an introduction of solar
cells and the core components in a typical PV power system. The design methodology,
including site evaluation, solar irradiance measurement, load profile estimation, and
system analysis were presented. Based on the collected data of solar irradiance and the
estimated load profile, dynamic modelling and simulation of a stand-alone PV-battery
power system were conducted in Matlab Simulink. The results show the typical
Chapter 7 Conclusion and Future Work
- 150 -
behaviour of the battery charge-discharge process under off-grid operation. With actual
data of solar irradiance and estimated load profiles, the battery current is demonstrated
to behave under severely fluctuating conditions, which tends to accelerate the
performance deterioration and aging process of the Lead-acid battery bank.
Hybridization of battery and supercapacitor has been reported to be one of the effective
ways to mitigate the operational stress on the Lead-acid battery, leading to enhanced
lifetime characteristics. Energy storage elements of different electrical and lifetime
characteristics complement each other up and down during the operation and thus
optimize the operation and longevity. Chapter 4 reviews different HESSs, including the
system topology, control strategies, and the associated energy management system.
Their corresponding characteristics, advantages, disadvantages and possible
applications in stand-alone PV power system were discussed and compared.
A feasibility study is presented in Chapter 5 and HESS topologies, and associated
power allocation strategy that is practically applied to remote microgrid were identified,
considering their system complexity, technical merits, and limitations. Theoretical
analysis and numerical simulation in Matlab Simulink for the selected Battery-SC
HESS were carried out, and their effectiveness in mitigating battery stress was
investigated and compared. The simulation analysis and results have been verified by
experiments with the developed prototype of hybrid energy storage system under
consideration. A battery healthy cost function that quantitatively evaluates the impact of
the damaging factors on the Lead-acid battery was proposed in this work. This allows
the effectiveness of different HESSs in mitigating battery operation stress to be
evaluated and compared. Furthermore, the proposed battery health cost model can be
used to analyze the economic improvement of the stand-alone PV power system by
estimating the extension in battery service life as a result of stress mitigation. Their
performances and effectiveness in stress mitigation were examined with a lab-scaled
prototype of HESS under consideration. The experimental outcomes verify the
theoretical analysis and simulation results done in this work. Simulation results, battery
health cost and financial analyses, and empirical outcomes suggest that the combination
of active secondary energy storage with the passive primary battery could be the
optimal setting for standalone PV power system applications.
Chapter 7 Conclusion and Future Work
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To leverage on existing infrastructure in installed stand-alone PV-battery power system,
a smart HESS plug-in module (SHESS) was proposed in Chapter 6 to complement the
ESS operation. The design and development of the retrofittable SHESS module were
systematically presented, including the control system and power allocation strategy
used. The effectiveness of the proposed SHESS plug-in module in mitigating battery
operation stress was simulated in Matlab Simulink with actual solar irradiance data and
estimated load profile for a rural community in Sarawak Malaysia. Based on the
simulation results, the battery healthy cost analysis suggests that the battery lifetime can
be enhanced through mitigating damaging loading current to the primary battery bank,
hence reduction in overall system operating cost can be achieved. The analysis shows
that after installing the SHESS plug-in module, the annual operating cost can be reduced
up to 53.7%. The prototype of the proposed SHESS was constructed, and the
experimental results demonstrate the feasibility of being integrated into existing
installed PV-battery power system and the effectiveness of the proposed SHESS are in
good agreement with the simulation outcomes.
7.2 Future work
In this study, Battery-SC HESSs were implemented in stand-alone PV power system to
mitigate the primary Lead-acid battery operating stress factors. Throughout the research
and development work, progress was made in discovering promising directions which
could not be achieved because of time and/or scope considerations. Based on the
presented research work in this thesis, the following suggestions are provided for further
explorations:
(1) EMS optimization with SoC and temperature management
In this work, the design of EMS mainly focuses on power allocation strategy and
power-sharing capability without accurately evaluating and managing the real-time SoC
of energy storage devices and considering the effect of battery temperature into the
optimization processes. In EMS, the estimation and management of battery SoC, as well
as temperature monitoring, are also two of the essential aspects to be addressed. These
two parameters should be accurately monitored and controlled for performance
Chapter 7 Conclusion and Future Work
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optimization. SoC estimation can be achieved by complementing sophisticated
approximation algorithms, empirical and statistical data. Therefore, one of the main
focuses of the extension of this work will be to devise an accurate and reliable battery
SoC estimation method that will serve as a useful parameter for the battery performance
optimization process. Moreover, at the same time, take into consideration of battery
temperature into the EMS and performance optimization process.
(2) HESS prototype in actual sized PV-battery power system
The proof-of-concept prototypes of the proposed SHESS plug-in module were
developed and tested in the lab-scaled facility. Research and development work can be
extended to design and develop an actual working prototype of the proposed SHESS
and to be tested in the real PV-battery power system to verify the feasibility of the
proposed system further. In addition, proof-of-concept to commercialization will be one
of the main aims of this research work.
(3) Demand-side management and load prediction
In this study, energy storage in HESS is designed to absorb/supply the power mismatch
between renewable energy sources and load in real-time, without considering demand-
side management such as load forecasting and demand response. If the load forecast can
be included in the energy management scheme, the power allocation strategy among
energy storage elements can be pre-determined (in a more optimal way) rather than
passively responding to it. For HESS design, future works include integration of
demand-side management into the optimization process, and daily/hourly load
prediction algorithm shall be considered in order to improve the efficiency and
sustainability of the overall system further.
(4) Battery healthy cost function extension
The battery health cost function proposed in this study considered five major life-
limiting factors that would have a negative impact on battery life. Exploration of other
internal or external factors that contribute to the aging process of battery should be one
of the major direction of the extension of this work. This would significantly improve
Chapter 7 Conclusion and Future Work
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the model accuracy of the complex lifetime characteristic of battery and thus, providing
a more realistic battery state-of-health assessment.
(5) Battery lifetime characteristic study and evaluation
The Battery-SC HESSs in this study are designed to mitigate the operating pressure of
Lead-acid battery in stand-alone PV power systems and thus extend battery lifetime.
However, the exact extended lifetime of the Lead-acid battery under HESS is not given
in this study. Future work could involve organizing a series of experiments to assess the
Lead-acid battery operation lifetime in different HESS topologies, which provides
reliable evidence on battery lifetime extension and economic improvement with HESS
and thus commercialization can be realized.
- 154 -
Reference
[1] M. Lorena and M. Lochinvar, “A review of the development of Smart Grid technologies,” Renew. Sustain. Energy Rev., vol. 59, pp. 710–725, 2016.
[2] P. Palensky and D. Dietrich, “Demand Side Management : Demand Response , Intelligent Energy Systems , and Smart Loads,” IEEE Trans. Ind. Informatics, vol. 7, no. 3, pp. 381–388, 2011.
[3] S. A. Arefifar, M. Ordonez, and Y. A. I. Mohamed, “Voltage and Current Controllability in Multi- Microgrid Smart Distribution Systems,” vol. 3053, no. c, pp. 1–10, 2016.
[4] Q. Shi, F. Li, Q. Hu, and Z. Wang, “Dynamic demand control for system frequency regulation : Concept review , algorithm comparison , and future vision,” Electr. Power Syst. Res., vol. 154, pp. 75–87, 2018.
[5] T. Strasser et al., “A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems,” IEEE Trans. Ind. Electron., vol. 62, no. 4, pp. 2424–2438, 2015.
[6] Y. Wang, N. Zhang, C. Kang, M. Miao, R. Shi, and Q. Xia, “An Efficient Approach to Power System Uncertainty Analysis with High-Dimensional Dependencies,” IEEE Trans. Power Syst., vol. 8950, no. DDC, pp. 1–11, 2017.
[7] S. Amini, F. Pasqualetti, and Hamed Mohsenian-Rad, “Dynamic Load Altering Attacks Against Power System Stability : Attack Models and Protection Schemes,” IEEE Trans. Smart Grid, vol. 3053, no. c, pp. 1–11, 2016.
[8] Q. Zhang, B. C. Mclellan, T. Tezuka, and K. N. Ishihara, “An integrated model for long-term power generation planning toward future smart electricity systems,” Appl. Energy, vol. 112, pp. 1424–1437, 2013.
[9] O. Palizban and K. Kauhaniemi, “Energy storage systems in modern grids—Matrix of technologies and applications,” J. Energy Storage, vol. 6, pp. 248–259, 2016.
[10] H. Saboori, R. Hemmati, S. Mohammad, S. Ghiasi, and S. Dehghan, “Energy storage planning in electric power distribution networks – A state- of-the-art review,” Renew. Sustain. Energy Rev., vol. 79, no. December 2016, pp. 1108–1121, 2017.
[11] M. Na and A. Khamis, “Microgrid and load shedding scheme during islanded mode : A review,” Renew. Sustain. Energy Rev., vol. 71, no. June 2015, pp. 161–169, 2017.
[12] M. S. Guney and Y. Tepe, “Classification and assessment of energy storage systems,” Renew. Sustain. Energy Rev., vol. 75, no. November 2016, pp. 1187–1197, 2017.
[13] A. Alhamali and G. Bevan, “Review of Energy Storage Systems in Electric Grid and their potential in Distribution Networks,” in Power Systems Conference (MEPCON), 2016 Eighteenth International Middle East, 2016.
Reference
- 155 -
[14] S. Rajanna and R. P. Saini, “Modeling of integrated renewable energy system for electri fi cation of a remote area in India,” Renew. Energy, vol. 90, pp. 175–187, 2016.
[15] REN21, “Renewables 2017: global status report (GSR),” Renew. Energy Policy Netw. 21st century, pp. 1–302, 2017.
[16] (IEA) International Energy Agency, Trends 2016 in Photovoltaic Applications(Survey Report of Selected IEA Countries between 1992 and 2015). 2016.
[17] L. R. Valer, A. R. A. Manito, T. B. S. Ribeiro, R. Zilles, and J. T. Pinho, “Issues in PV systems applied to rural electrification in Brazil,” Renew. Sustain. Energy Rev., vol. 78, no. April, pp. 1033–1043, 2017.
[18] J. Linssen, P. Stenzel, and J. Fleer, “Techno-economic analysis of photovoltaic battery systems and the influence of different consumer load profiles,” Appl. Energy, vol. 185, pp. 2019–2025, 2017.
[19] J. Hoppmann, J. Volland, T. S. Schmidt, and V. H. Hoffmann, “The economic viability of battery storage for residential solar photovoltaic systems - A review and a simulation model,” Renew. Sustain. Energy Rev., vol. 39, pp. 1101–1118, 2014.
[20] R. Dufo-lópez, J. M. Lujano-rojas, and J. L. Bernal-agustín, “Comparison of different lead – acid battery lifetime prediction models for use in simulation of stand-alone photovoltaic systems,” Appl. Energy, vol. 115, pp. 242–253, 2014.
[21] J. Ruan, P. D. Walker, N. Zhang, and J. Wu, “An investigation of hybrid energy storage system in multi-speed electric vehicle,” Energy, vol. 140, pp. 291–306, 2017.
[22] W. Jing, C. H. Lai, W. S. H. Wong, and M. L. D. Wong, “Smart hybrid energy storage for stand-alone PV microgrid: Optimization of battery lifespan through dynamic power allocation,” in Power and Energy Engineering Conference (APPEEC), IEEE PES Asia-Pacific, 2015, vol. 2016–Janua, pp. 3–7.
[23] N. R. Tummuru, M. K. Mishra, and S. Srinivas, “Dynamic Energy Management of Hybrid Energy Storage System With High-Gain PV Converter,” vol. 30, no. 1, pp. 150–160, 2014.
[24] F. Li, K. Xie, and J. Yang, “Optimization and Analysis of a Hybrid Energy Storage System in a Small-Scale Standalone Microgrid for Remote Area Power Supply (RAPS),” Energies, vol. 8, pp. 4802–4826, 2015.
[25] W. Jing, C. H. Lai, W. S. H. Wong, and M. L. D. Wong, “Dynamic power allocation of battery-supercapacitor hybrid energy storage for standalone PV microgrid applications,” Sustain. Energy Technol. Assessments, vol. 22, pp. 55–64, 2017.
[26] A. González, E. Goikolea, J. A. Barrena, and R. Mysyk, “Review on supercapacitors: Technologies and materials,” Renew. Sustain. Energy Rev., vol. 58, pp. 1189–1206, 2016.
[27] X. Wang, D. Yu, S. Le, Z. Zhao, and P. Wilson, “A novel controller of a battery-supercapacitor hybrid energy storage system for domestic applications,” Energy Build., vol. 141, pp. 167–174, 2017.
[28] Q. Xu et al., “A Decentralized Dynamic Power Sharing Strategy for Hybrid Energy Storage System in Autonomous DC Microgrid,” IEEE Trans. Ind. Electron., vol. 64, no. 7, pp. 5930–5941, 2017.
[29] Y. Kim and V. Raghunathan, “Design and Management of Battery-Supercapacitor Hybrid Electrical Energy Storage Systems for Regulation Services,” IEEE Trans. Multi-Scale Comput. Syst., vol. 3, no. 1, pp. 12–24, 2017.
[30] M. Choi, S. Kim, and S. Seo, “Energy Management Optimization in a Battery / Supercapacitor Hybrid Energy Storage System,” IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 463–472, 2012.
Reference
- 156 -
[31] A. Choudar, D. Boukhetala, S. Barkat, and J.-M. Brucker, “A local energy management of a hybrid PV-storage based distributed generation for microgrids,” Energy Convers. Manag., vol. 90, pp. 21–33, 2015.
[32] M. Masih-Tehrani, M. R. Ha’iri-Yazdi, V. Esfahanian, and A. Safaei, “Optimum sizing and optimum energy management of a hybrid energy storage system for lithium battery life improvement,” J. Power Sources, vol. 244, pp. 2–10, 2013.
[33] M. Manas, “Renewable energy management through microgrid central controller design: An approach to integrate solar, wind and biomass with battery,” Energy Reports, vol. 1, pp. 156–163, 2015.
[34] A. Abdelkader, A. Rabeh, D. Mohamed, and J. Mohamed, “Multi-objective genetic algorithm based sizing optimization of a stand-alone wind / PV power supply system with enhanced battery / supercapacitor hybrid energy storage,” Energy, vol. 163, pp. 351–363, 2018.
[35] E. E. Gaona, C. L. Trujillo, and J. A. Guacaneme, “Rural microgrids and its potential application in Colombia,” Renew. Sustain. Energy Rev., vol. 51, pp. 125–137, 2015.
[36] J. Sachs and O. Sawodny, “A Two-Stage Model Predictive Control Strategy for Economic Diesel-PV-Battery Island Microgrid Operation in Rural Areas,” IEEE Trans. Sustain. Energy, vol. 7, no. 3, pp. 1–11, 2016.
[37] T. Urmee, D. Harries, and H.-G. Holtorf, Photovoltaics for Rural Electrification in Developing Countries: A Road Map. Springer, 2016.
[38] L. W. Chong, Y. W. Wong, R. K. Rajkumar, and D. Isa, “An adaptive learning control strategy for standalone PV system with battery- supercapacitor hybrid energy storage system,” J. Power Sources, vol. 394, no. May, pp. 35–49, 2018.
[39] S. K. ; Mishra and M. Kumar, “A Supervisory Power Management System for a Hybrid MicrogridWith HESS,” IEEE Trans. Ind. Electron., vol. 64, no. 5, pp. 3640–3649, 2017.
[40] H. Samani and X. Fernando, “Battery Current’s Fluctuations Removal in Hybrid Energy Storage System Based on Optimized Control of Supercapacitor Voltage,” IEEE Embed. Syst. Lett., vol. 8, no. 3, pp. 53–56, 2016.
[41] D. B. Wickramasinghe Abeywardana, B. Hredzak, and V. G. Agelidis, “A Fixed-Frequency Sliding Mode Controller for a Boost-Inverter-Based Battery-Supercapacitor Hybrid Energy Storage System,” IEEE Trans. Power Electron., vol. 32, no. 1, pp. 668–680, 2017.
[42] T. Kousksou, P. Bruel, A. Jamil, T. El Rhafiki, and Y. Zeraouli, “Energy storage: Applications and challenges,” Sol. Energy Mater. Sol. Cells, vol. 120, no. PART A, pp. 59–80, 2014.
[43] E. Ozdemir, S. Ozdemir, K. Erhan, and A. Aktas, “Energy Storage Technologies Opportunities and Challenges in Smart Grids,” in Smart Grid Workshop and Certificate Program (ISGWCP), International, 2016, pp. 15–20.
[44] M. Mcgrail, S. Hagaman, J. Leung, T. George, and L. Mardira, “Meeting New Distribution Power System Challenges,” in Power and Energy Engineering Conference (APPEEC), 2015 IEEE PES Asia-Pacific, 2015.
[45] C. A. O. Wangzhang, L. I. Bin, Z. Aixia, Q. I. Bing, Y. Zenghui, and S. U. Yun, “The Architecture and Technology of Demand Response within Energy Internet,” 2016 China Int. Conf. Electr. Distrib. (CICED 2016), vol. 3, no. Ciced, pp. 10–13, 2016.
[46] T. Weitzel and C. H. Glock, “Energy management for stationary electric energy storage systems : A systematic literature review,” Eur. J. Oper. Res., vol. 264, no. 2, pp. 582–606, 2018.
Reference
- 157 -
[47] S. X. Chen, H. B. Gooi, and M. Q. Wang, “Sizing of energy storage for microgrids,” IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 142–151, 2012.
[48] M. Kloess and K. Zach, “Electrical Power and Energy Systems Bulk electricity storage technologies for load-leveling operation – An economic assessment for the Austrian and German power market,” Int. J. Electr. Power Energy Syst., vol. 59, pp. 111–122, 2014.
[49] V. Knap, S. K. Chaudhary, D. Stroe, M. Swierczynski, B. Craciun, and R. Teodorescu, “Sizing of an Energy Storage System for Grid Inertial Response and Primary Frequency Reserve,” IEEE Trans. Power Syst., vol. 31, no. 5, pp. 3447–3456, 2016.
[50] R. Shah, N. Mithulananthan, R. C. Bansal, and V. K. Ramachandaramurthy, “A review of key power system stability challenges for large-scale PV integration,” Renew. Sustain. Energy Rev., vol. 41, pp. 1423–1436, 2015.
[51] H. K. Chappa and S. C. Srivastava, “Reactive Power Loss Based Voltage Instability Detection using Synchrophasor Technology,” in Power and Energy Engineering Conference (APPEEC), 2015 IEEE PES Asia-Pacific, 2015.
[52] L. Wu and D. In, “Power system frequency management challenges – a new approach to assessing the potential of wind capacity to aid system frequency stability,” IET Renew. Power Gener., no. July, pp. 733–739, 2014.
[53] Y. Liu, R. Fan, and V. Terzija, “Power system restoration: a literature review from 2006 to 2016,” J. Mod. Power Syst. Clean Energy, vol. 4, no. 3, pp. 332–341, 2016.
[54] J. Barr and R. Majumder, “Integration of Distributed Generation in the Volt / VAR Management System for Active Distribution Networks,” IEEE Trans. Smart Grid, vol. 6, no. 2, pp. 576–586, 2015.
[55] R. Kabiri, D. G. Holmes, B. P. Mcgrath, and L. G. Meegahapola, “LV Grid Voltage Regulation Using Transformer Electronic Tap Changing , With PV Inverter Reactive Power Injection,” IEEE J. Emerg. Sel. Top. Power Electron., vol. 3, no. 4, pp. 1182–1192, 2015.
[56] M. Hossain, K. Tushar, and C. Assi, “Volt-VAR Control through Joint Optimization of Capacitor Bank Switching , Renewable Energy , and Home Appliances,” IEEE Trans. Smart Grid, vol. 3053, no. c, pp. 1–10, 2017.
[57] X. Xu, M. Bishop, E. Camm, and M. J. S. Edmonds, “Transmission Voltage Support Using Distributed Static Compensation,” in PES General Meeting | Conference & Exposition, 2014 IEEE, 2014, pp. 3–7.
[58] D. A. Sbordone et al., “Reactive power control for an energy storage system : A real implementation in a Micro-Grid,” J. Netw. Comput. Appl., vol. 59, pp. 250–263, 2016.
[59] S. Sabihuddin, A. Kiprakis, and M. Mueller, “A Numerical and Graphical Review of Energy Storage Technologies,” Energies, vol. 8, no. 1, pp. 172–216, 2014.
[60] A. M. Vega, F. Santamaria, and E. Rivas, “Modeling for home electric energy management : A review,” Renew. Sustain. Energy Rev., vol. 52, pp. 948–959, 2015.
[61] Y. Yoldaş, A. Önen, S. M. Muyeen, A. V. Vasilakos, and İ. Alan, “Enhancing smart grid with microgrids: Challenges and opportunities,” Renew. Sustain. Energy Rev., vol. 72, no. October 2016, pp. 205–214, 2017.
[62] E. Hossain, E. Kabalci, R. Bayindir, and R. Perez, “Microgrid testbeds around the world: State of art,” Energy Convers. Manag., vol. 86, pp. 132–153, 2014.
[63] A. Chatzivasileiadi, E. Ampatzi, and I. Knight, “Characteristics of electrical energy storage technologies and their applications in buildings,” Renew. Sustain. Energy Rev., vol. 25, pp. 814–830, 2013.
Reference
- 158 -
[64] K. Zhou, S. Yang, and Z. Shao, “Energy Internet: The business perspective,” Appl. Energy, vol. 178, pp. 212–222, 2016.
[65] X. Luo, J. Wang, M. Dooner, and J. Clarke, “Overview of current development in electrical energy storage technologies and the application potential in power system operation,” Appl. Energy, vol. 137, pp. 511–536, 2015.
[66] H. Kondziella and T. Bruckner, “Flexibility requirements of renewable energy based electricity systems – a review of research results and methodologies,” Renew. Sustain. Energy Rev., vol. 53, pp. 10–22, 2016.
[67] C. Gouveia, J. Moreira, C. L. Moreira, and J. A. Pecas Lopes, “Coordinating storage and demand response for microgrid emergency operation,” IEEE Trans. Smart Grid, vol. 4, no. 4, pp. 1898–1908, 2013.
[68] H. Blanco and A. Faaij, “A review at the role of storage in energy systems with a focus on Power to Gas and long-term storage,” Renew. Sustain. Energy Rev., vol. 81, no. May 2017, pp. 1049–1086, 2018.
[69] M. Faisal, M. A. Hannan, P. J. Ker, A. Hussain, M. Mansur, and F. Blaabjerg, “Review of Energy Storage System Technologies in Microgrid Applications : Issues and Challenges,” IEEE Access, vol. 6, pp. 35143–35164, 2018.
[70] E. Planas, J. Andreu, J. I. Gárate, I. M. De Alegría, and E. Ibarra, “AC and DC technology in microgrids : A review,” Renew. Sustain. Energy Rev., vol. 43, pp. 726–749, 2015.
[71] S. Upadhyay and M. P. Sharma, “A review on configurations, control and sizing methodologies of hybrid energy systems,” Renew. Sustain. Energy Rev., vol. 38, pp. 47–63, 2014.
[72] D. Wu, F. Tang, T. Dragicevic, J. C. Vasquez, and J. M. Guerrero, “Autonomous active power control for islanded AC microgrids with photovoltaic generation and energy storage system,” IEEE Trans. Energy Convers., vol. 29, no. 4, pp. 882–892, 2014.
[73] D. P. Zafirakis, Overview of energy storage technologies for renewable energy systems. Woodhead Publishing Limited, 2010.
[74] L. M. Al-hadhrami and M. Alam, “Pumped hydro energy storage system : A technological review,” Renew. Sustain. Energy Rev., vol. 44, pp. 586–598, 2015.
[75] T. M. I. Mahlia, T. J. Saktisahdan, A. Jannifar, M. H. Hasan, and H. S. C. Matseelar, “A review of available methods and development on energy storage ; technology update,” Renew. Sustain. Energy Rev., vol. 33, pp. 532–545, 2014.
[76] L. Wagner, “Overview of Energy Storage Methods,” no. December, pp. 0–15, 2007. [77] U. S. D. of Energy, “DOE Global Energy Storage Database,” Off. Electr. Deliv. energy
Reliab. <http//energystorageexchange.org/projects/>, 2017. [78] E. Barbour, I. A. G. Wilson, J. Radcliffe, Y. Ding, and Y. Li, “A review of pumped
hydro energy storage development in signi fi cant international electricity markets,” Renew. Sustain. Energy Rev., vol. 61, pp. 421–432, 2016.
[79] A. J. Pimm, S. D. Garvey, and M. De Jong, “Design and testing of Energy Bags for underwater compressed air energy storage,” Energy, vol. 66, pp. 496–508, 2014.
[80] L. Xiang, M. Na, P. Pan, F. Xiang, P. Hu, and D. Xiang, “A novel isobaric adiabatic compressed air energy storage (IA-CAES) system on the base of volatile fluid,” Appl. Energy, vol. 210, no. June 2017, pp. 198–210, 2018.
[81] X. Zhang et al., “A near-isothermal expander for isothermal compressed air energy storage system,” Appl. Energy, vol. 225, no. December 2017, pp. 955–964, 2018.
Reference
- 159 -
[82] H. Lund and G. Salgi, “The role of compressed air energy storage (CAES) in future sustainable energy systems,” Energy Convers. Manag., vol. 50, no. 5, pp. 1172–1179, May 2009.
[83] Y. Yan, C. Zhang, K. Li, and Z. Wang, “An integrated design for hybrid combined cooling , heating and power system with compressed air energy storage,” Appl. Energy, vol. 210, pp. 1151–1166, 2018.
[84] M. Budt, D. Wolf, R. Span, and J. Yan, “A review on compressed air energy storage :
Basic principles , past milestones and recent developments,” Appl. Energy, vol. 170, pp. 250–268, 2016.
[85] P. Zhao, J. Wang, and Y. Dai, “Capacity allocation of a hybrid energy storage system for power system peak shaving at high wind power penetration level,” Renew. Energy, vol. 75, pp. 541–549, 2015.
[86] Y. M. Kim, J. H. Lee, S. J. Kim, and D. Favrat, “Potential and evolution of compressed air energy storage: Energy and exergy analyses,” Entropy, vol. 14, no. 8, pp. 1501–1521, 2012.
[87] C. Guo, L. Pan, K. Zhang, C. M. Oldenburg, C. Li, and Y. Li, “Comparison of compressed air energy storage process in aquifers and caverns based on the Huntorf CAES plant,” Appl. Energy, vol. 181, pp. 342–356, 2016.
[88] H. Zhu, X. Chen, Y. Cai, J. Chen, and Z. Wang, “The Fracture Influence on the Energy Loss of Compressed Air Energy Storage in Hard Rock,” Mathemeatical Probl. Eng., 2015.
[89] A. A. K. Arani, H. Karami, G. B. Gharehpetian, and M. S. A. Hejazi, “Review of Flywheel Energy Storage Systems structures and applications in power systems and microgrids,” Renew. Sustain. Energy Rev., vol. 69, no. November 2016, pp. 9–18, 2017.
[90] S. M. G. Mousavi, F. Faraji, A. Majazi, and K. Al-haddad, “A comprehensive review of Flywheel Energy Storage System technology,” Renew. Sustain. Energy Rev., vol. 67, pp. 477–490, 2017.
[91] A. Rupp, H. Baier, P. Mertiny, and M. Secanell, “Analysis of a fl ywheel energy storage system for light rail transit,” Energy, vol. 107, pp. 625–638, 2016.
[92] Jochen Fricke and W. L. Borst, Essentials of Energy Technology: Sources, Transport, Storage, Conservation. John Wiley & Sons, 2013.
[93] P. Tixador, “Superconducting Magnetic Energy Storage : Status and Perspective,” in IEEE/CSC & ESAS European Superconductivity News Forum (ESNF), 2008, no. 3, pp. 1–14.
[94] M. H. Ali, B. Wu, and R. A. Dougal, “An Overview of SMES Applications in Power and Energy Systems,” IEEE Trans. Sustain. Energy, vol. 1, no. 1, pp. 38–47, 2010.
[95] J. Jung, L. Zhang, and J. Zhang, “Lead-Acid Batteries: Fundamentals, Technologies, and Applications,” in Lead-Acid Battery Technologies: Fundamentals, Materials, and Applications, CRC Press, 2015, p. 365.
[96] G. De Oliveira and P. Hendrick, “Lead – acid batteries coupled with photovoltaics for increased electricity self-sufficiency in households,” Appl. Energy, vol. 178, pp. 856–867, 2016.
[97] M. Greenleaf, O. Dalchand, H. Li, and J. P. Zheng, “A Temperature-Dependent Study of Sealed Lead-Acid Batteries Using Physical Equivalent Circuit Modeling With Impedance Spectra Derived High Current / Power Correction,” IEEE Trans. Sustain. Energy, vol. 6, no. 2, pp. 380–387, 2015.
Reference
- 160 -
[98] S. Sundararagavan and Erin Baker, “Energy storage technologies for wind power integration,” Sol. Energy, vol. 86, no. March, pp. 2707–2717, Sep. 2010.
[99] L. Ahmadi, M. Fowler, S. B. Young, R. A. Fraser, B. Gaffney, and S. B. Walker, “Energy efficiency of Li-ion battery packs re-used in stationary power applications,” Sustain. ENERGY Technol. ASSESSMENTS, vol. 8, pp. 9–17, 2014.
[100] M. Astaneh, R. Roshandel, R. Dufo-lópez, and J. L. Bernal-agustín, “A novel framework for optimization of size and control strategy of lithium- ion battery based off -grid renewable energy systems,” Energy Convers. Manag., vol. 175, no. September, pp. 99–111, 2018.
[101] A. Purvins and M. Sumner, “Optimal management of stationary lithium-ion battery system in electricity distribution grids,” J. Power Sources, vol. 242, pp. 742–755, 2013.
[102] M. Kamibayashi, D. K. Nichols, and T. Oshima, “Development Update of the NAS Battery,” in Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES, 2002, pp. 1664–1668.
[103] S. Tewari and N. Mohan, “Value of NAS Energy Storage Toward Integrating Wind : Results From the Wind to Battery Project,” IEEE Trans. Power Syst., vol. 28, no. 1, pp. 532–541, 2013.
[104] T. Hatta, “Applications of Sodium-Sulfur Batteries,” in Transmission and Distribution Conference and Exposition (T&D), 2012 IEEE PES, 2012, pp. 5–7.
[105] L. Pietrelli, B. Bellomo, D. Fontana, and M. Montereali, “Characterization and leaching of NiCd and NiMH spent batteries for the recovery of metals,” Waste Manag., vol. 25, pp. 221–226, 2005.
[106] H. Chen, T. N. Cong, W. Yang, C. Tan, Y. Li, and Y. Ding, “Progress in electrical energy storage system: A critical review,” Prog. Nat. Sci., vol. 19, no. 3, pp. 291–312, 2009.
[107] S. Chang and K. Young, “Reviews of European Patents on Nickel/Metal Hydride Batteries,” MDPI-Batteries, vol. 3, p. 25, 2017.
[108] W. H. Zhu, Y. Zhu, Z. Davis, and B. J. Tatarchuk, “Energy efficiency and capacity retention of Ni – MH batteries for storage applications,” Appl. Energy, vol. 106, pp. 307–313, 2013.
[109] T. Danner, S. Eswara, V. P. Schulz, and A. Latz, “Characterization of gas diffusion electrodes for metal-air batteries,” J. Power Sources, vol. 324, pp. 646–656, 2016.
[110] T. Peng, K. Wei, S. Zongping, M. Liu, and N. Meng, “Advances in modeling and simulation of Li-air batteries,” Prog. Energy Combust. Sci., vol. 62, pp. 155–189, 2017.
[111] A. R. Mainar et al., “Alkaline aqueous electrolytes for secondary zinc – air batteries : an overview,” Int. J. ENERGY Res., no. 40, pp. 1032–1049, 2016.
[112] X. Zhang, X. Wang, Z. Xie, and Z. Zhou, “Recent progress in rechargeable alkali metal e air batteries,” Green Energy Environ., vol. 1, no. 1, pp. 4–17, 2016.
[113] D. Sharon, D. Hirshberg, M. Afri, A. A. Frimer, M. Noked, and D. Aurbach, “Aprotic metal-oxygen batteries : recent findings and insights,” J. Solid State Electrochem., vol. 21, no. 7, pp. 1861–1878, 2017.
[114] A. L. Zhu, D. P. Wilkinson, X. Zhang, Y. Xing, A. G. Rozhin, and S. A. Kulinich, “Zinc regeneration in rechargeable zinc-air fuel cells — A review,” J. Energy Storage, vol. 8, pp. 35–50, 2016.
Reference
- 161 -
[115] M. Skyllas-Kazacos, M. H. Chakrabarti, S. A. Hajimolana, F. S. Mjalli, and M. Saleem, “Progress in Flow Battery Research and Development,” J. Electrochem. Soc., vol. 158, no. 8, pp. 7–10, 2011.
[116] F. Scuiller, T. Tang, Z. Zhou, M. Benbouzid, and J. Fre, “A review of energy storage technologies for marine current energy systems,” Renew. Sustain. Energy Rev., vol. 18, pp. 390–400, 2013.
[117] A. Esmaili, B. Novakovic, A. Nasiri, and O. Abdel-Baqi, “A hybrid system of li-ion capacitors and flow battery for dynamic wind energy support,” IEEE Trans. Ind. Appl., vol. 49, no. 4, pp. 1649–1657, 2013.
[118] A. Z. Weber, M. M. Mench, J. P. Meyers, P. N. Ross, J. T. Gostick, and Q. Liu, “Redox flow batteries : a review,” J. Appl. Electrochem., vol. 41, pp. 1137–1164, 2011.
[119] Y. Parvini, A. Vahidi, and S. A. Fayazi, “Heuristic Versus Optimal Charging of Supercapacitors, Lithium-Ion, and Lead-Acid Batteries: An Efficiency Point of View,” IEEE Trans. Control Syst. Technol., no. 99, pp. 1–14, 2017.
[120] C. Spanos, D. E. Turney, and V. Fthenakis, “Life-cycle analysis of fl ow-assisted nickel zinc- , manganese dioxide- , and valve-regulated lead-acid batteries designed for demand-charge reduction,” Renew. Sustain. Energy Rev., vol. 43, pp. 478–494, 2015.
[121] G. Li and X. Zheng, “Thermal energy storage system integration forms for a sustainable future,” Renew. Sustain. Energy Rev., vol. 62, pp. 736–757, 2016.
[122] M. Liu et al., “Review on concentrating solar power plants and new developments in high temperature thermal energy storage technologies,” Renew. Sustain. Energy Rev., vol. 53, pp. 1411–1432, 2016.
[123] H. Zhang, J. Baeyens, G. Cáceres, J. Degrève, and Y. Lv, “Thermal energy storage : Recent developments and practical aspects,” Prog. Energy Combust. Sci., vol. 53, pp. 1–40, 2016.
[124] S. Niaz, T. Manzoor, and A. Hussain, “Hydrogen storage : Materials , methods and perspectives,” Renew. Sustain. Energy Rev., vol. 50, pp. 457–469, 2015.
[125] V. Das, S. Padmanaban, and K. Venkitusamy, “Recent advances and challenges of fuel cell based power system architectures and control – A review,” Renew. Sustain. Energy Rev., vol. 73, no. November 2016, pp. 10–18, 2017.
[126] S. Satyapal, “Hydrogen and Fuel Cell Overview,” U . S . Dep. Energy <https//www.energy.gov/sites/prod/files/2016/03/f30/fcto_fc_expo_2016_satyapal.pdf>, 2016.
[127] D. Feroldi, E. Roig, M. Serra, and J. Riera, “Energy management strategies for fuel cell-hybrid vehicles,” Iber. Symp. Hydrog. Fuel Cells Adv. Batter., pp. 1–26, 2008.
[128] J. I. Levene, M. K. Mann, R. M. Margolis, and A. Milbrandt, “An analysis of hydrogen production from renewable electricity sources,” Sol. Energy, vol. 81, pp. 773–780, 2007.
[129] P. Nikolaidis and A. Poullikkas, “A comparative overview of hydrogen production processes,” Renew. Sustain. Energy Rev., vol. 67, pp. 597–611, 2017.
[130] A. H. Abedin and M. A. Rosen, “A Critical Review of Thermochemical Energy Storage Systems,” open Renew. energy, vol. 4, pp. 42–46, 2011.
[131] International Energy Agency, “Energy Storage Technology Roadmap,” IEA Flagsh. Publ., vol. March, 2014.
[132] K. Bradbury, L. Pratson, and D. Patiño-echeverri, “Economic viability of energy storage systems based on price arbitrage potential in real-time U . S . electricity markets,” Appl. Energy, vol. 114, pp. 512–519, 2014.
Reference
- 162 -
[133] R. Madlener and J. Latz, “Economics of centralized and decentralized compressed air energy storage for enhanced grid integration of wind power,” Appl. Energy, vol. 101, pp. 299–309, 2013.
[134] C. Heymans, S. B. Walker, S. B. Young, and M. Fowler, “Economic analysis of second use electric vehicle batteries for residential energy storage and load-levelling,” Energy Policy, vol. 71, pp. 22–30, 2014.
[135] B. Zakeri and S. Syri, “Electrical energy storage systems: A comparative life cycle cost analysis,” Renew. Sustain. Energy Rev., vol. 42, pp. 569–596, 2015.
[136] D. O. Akinyele and R. K. Rayudu, “Review of energy storage technologies for sustainable power networks,” Sustain. Energy Technol. Assessments, vol. 8, pp. 74–91, 2014.
[137] J. Ren and X. Ren, “Sustainability ranking of energy storage technologies under uncertainties,” J. Clean. Prod., vol. 170, pp. 1387–1398, 2018.
[138] A. Barin et al., “Multiple Criteria Analysis for Energy Storage Selection,” Energy Power Eng., vol. 2011, no. September, pp. 557–564, 2011.
[139] Z. Zhao, “The Review of Energy Storage Technologies Selection,” J. Electr. Electron. Syst., vol. 5, no. 1, pp. 1–4, 2016.
[140] C. S. Lai and M. D. Mcculloch, “Levelized cost of electricity for solar photovoltaic and electrical energy storage,” Appl. Energy, vol. 190, pp. 191–203, 2017.
[141] A. Belderbos, E. Delarue, K. Kessels, and D. William, “Levelized cost of storage — Introducing novel metrics,” Energy Econ., vol. 67, pp. 287–299, 2017.
[142] M. Obi, S. M. Jensen, J. B. Ferris, and R. B. Bass, “Calculation of levelized costs of electricity for various electrical energy storage systems,” Renew. Sustain. Energy Rev., vol. 67, pp. 908–920, 2017.
[143] M. Zackrisson, K. Fransson, J. Hildenbrand, G. Lampic, and C. O. Dwyer, “Life cycle assessment of lithium-air battery cells,” J. Clean. Prod., vol. 135, pp. 299–311, 2016.
[144] Q. Wang et al., “Environmental impact analysis and process optimization of batteries based on life cycle assessment,” J. Clean. Prod., vol. 174, pp. 1262–1273, 2018.
[145] E. M. G. Rodrigues, R. Godina, S. F. Santos, A. W. Bizuayehu, and J. Contreras, “Energy storage systems supporting increased penetration of renewables in islanded systems,” Energy, vol. 75, pp. 265–280, 2014.
[146] G. Venkataramani, P. Parankusam, V. Ramalingam, and J. Wang, “A review on compressed air energy storage – A pathway for smart grid and polygeneration,” Renew. Sustain. Energy Rev., vol. 62, pp. 895–907, 2016.
[147] F. Markoulidis, C. Lei, and C. Lekakou, “Fabrication of high-performance supercapacitors based on transversely oriented carbon nanotubes,” Appl. Phys. A, vol. 111, no. 1, pp. 227–236, 2013.
[148] B. Zhang, F. Kang, J. Tarascon, and J. Kim, “Progress in Materials Science Recent advances in electrospun carbon nanofibers and their application in electrochemical energy storage,” Prog. Mater. Sci., vol. 76, pp. 319–380, 2016.
[149] B. B. McKeon, J. Furukawa, and S. Fenstermacher, “Advanced Lead – Acid Batteries and the Development of Grid-Scale Energy Storage Systems,” Proc. IEEE, vol. 102, no. 6, pp. 951–963, 2014.
[150] M. S. Rahmanifar, “Electrochimica Acta Enhancing the cycle life of Lead-Acid batteries by modifying negative grid surface,” Electrochim. Acta, vol. 235, pp. 10–18, 2017.
Reference
- 163 -
[151] F. Wang, C. Hu, M. Zhou, K. Wang, J. Lian, and J. Yan, “Research progresses of cathodic hydrogen evolution in advanced lead – acid batteries,” Sci. Bull., 2016.
[152] W. Lv, Z. Li, Y. Deng, Q. Yang, and F. Kang, “Graphene-based materials for electrochemical energy storage devices : Opportunities and challenges,” Energy Storage Mater., vol. 2, pp. 107–138, 2016.
[153] V. A. Online, K. B. Hueso, and M. Armand, “High temperature sodium batteries: status, challenges and future trends,” Energy Environ. Sci., pp. 734–749, 2013.
[154] P. Preuster, A. Alekseev, and P. Wasserscheid, “Hydrogen Storage Technologies for Future Energy Systems,” Annu. Rev. Chem. Biomol. Eng., vol. 8, pp. 445–471, 2017.
[155] X. Tan, Q. Li, and H. Wang, “Advances and trends of energy storage technology in Microgrid,” Int. J. Electr. Power Energy Syst., vol. 44, no. 1, pp. 179–191, 2013.
[156] J. X. Jin, “Emerging SMES Technology into Energy Storage Systems and Smart Grid Applications,” Large Scale Renew. Power Gener. Springer Singapore, vol. 7, pp. 77–125, 2014.
[157] D. Larcher and J-M.Tarascon, “Towards greener and more sustainable batteries for electrical energy storage,” Nat. Chem. | Rev., vol. 7, no. January, 2015.
[158] K. Wang et al., “A Survey on Energy Internet : Architecture , Approach , and Emerging Technologies,” IEEE Syst. J., no. 99, pp. 1–14, 2017.
[159] H. Zhang, Y. Li, D. W. Gao, and J. Zhou, “Distributed Optimal Energy Management for Energy Internet,” IEEE Trans. Ind. Informatics, vol. XX, no. 99, pp. 1–15, 2017.
[160] S. Vazquez, S. M. Lukic, E. Galvan, L. G. Franquelo, and J. M. Carrasco, “Energy Storage Systems for Transport and Grid Applications,” IEEE Trans. Ind. Electron., vol. 57, no. 12, pp. 3881–3895, 2010.
[161] A. Ostadi, M. Kazerani, and S. K. Chen, “Hybrid Energy Storage System (HESS) in vehicular applications: A review on interfacing battery and ultra-capacitor units,” 2013 IEEE Transp. Electrif. Conf. Expo Components, Syst. Power Electron. - From Technol. to Bus. Public Policy, ITEC 2013, 2013.
[162] M. A. Hannan, M. M. Hoque, A. Mohamed, and A. Ayob, “Review of energy storage systems for electric vehicle applications : Issues and challenges,” Renew. Sustain. Energy Rev., vol. 69, no. November 2016, pp. 771–789, 2017.
[163] C. L. Benson and C. L. Magee, “On improvement rates for renewable energy technologies: Solar PV, wind turbines, capacitors, and batteries,” Renew. Energy, vol. 68, pp. 745–751, 2014.
[164] J. Xue, “Photovoltaic agriculture - New opportunity for photovoltaic applications in China,” Renew. Sustain. Energy Rev., vol. 73, no. December 2015, pp. 1–9, 2017.
[165] A. Foley and A. G. Olabi, “Renewable energy technology developments , trends and policy implications that can underpin the drive for global climate change,” Renew. Sustain. Energy Rev., vol. 68, pp. 1112–1114, 2017.
[166] P. Mohanty, T. Muneer, and E. J. Gago, “Solar Radiation Fundamentals and PV System Components,” Sol. Photovolt. Syst. Appl., p. pp 7-47, 2015.
[167] S. Adhikari, Z. Lei, W. Peng, and Y. Tang, “A Battery / Supercapacitor Hybrid Energy Storage System for DC Microgrids,” ECCE Asia, pp. 8–14, 2016.
[168] C. Kagiri and X. Xia, “Optimal control of a hybrid Battery / Supercapacitor storage for Neighborhood Electric Vehicles,” Energy Procedia, vol. 105, pp. 2145–2150, 2017.
[169] International Energy Agency, “World Energy Outlook 2017,” IEA, vol. Chapter 1, 2017.
Reference
- 164 -
[170] N. Idris, A. M. Omar, and S. Shaari, “Stand-Alone Photovoltaic Power System Applications in Malaysia,” in The 4th International Power Engineering and Optimization Conference (PEOCO2010), 2010, no. June, pp. 474–479.
[171] M. Bortolini, M. Gamberi, and A. Graziani, “Technical and economic design of photovoltaic and battery energy storage system,” Energy Convers. Manag., vol. 86, pp. 81–92, 2014.
[172] S. Koohi-Kamali, N. A. Rahim, and H. Mokhlis, “Smart power management algorithm in microgrid consisting of photovoltaic, diesel, and battery storage plants considering variations in sunlight, temperature, and load,” Energy Convers. Manag., vol. 84, pp. 562–582, 2014.
[173] J. K. S. Yeo, S. Chen, W. X. Shen, and H. S. Chua, “Energy evaluation and smart microgrid for rural Sarawak,” 2014 IEEE Innov. Smart Grid Technol. - Asia, ISGT ASIA 2014, pp. 459–464, 2014.
[174] N. Dalilah and H. Abdul, “A novel optimization method for designing stand alone photovoltaic system,” Renew. Energy, vol. 89, pp. 706–715, 2016.
[175] H. Akbari et al., “Efficient energy storage technologies for photovoltaic systems,” Sol. Energy, no. March, pp. 1–25, 2018.
[176] M. E. Glavin, P. K. W. Chan, S. Armstrong, and W.G Hurley, “A Stand-alone Photovoltaic Supercapacitor Battery Hybrid Energy Storage System,” in 13th International Power Electronics and Motion Control Conference, 2008, pp. 1688–1695.
[177] R. Kaiser, “Optimized battery-management system to improve storage lifetime in renewable energy systems,” J. Power Sources, vol. 168, pp. 58–65, 2007.
[178] A. S. Jacob, R. Banerjee, and P. C. Ghosh, “Sizing of hybrid energy storage system for a PV based microgrid through design space approach,” Appl. Energy, vol. 212, no. December 2017, pp. 640–653, 2018.
[179] S. Chalise, J. Sternhagen, T. M. Hansen, and R. Tonkoski, “Energy management of remote microgrids considering battery lifetime,” Electr. J., vol. 29, no. 6, pp. 1–10, 2016.
[180] H. A. Catherino, F. F. Feres, and F. Trinidad, “Sulfation in lead – acid batteries,” J. Power Sources, vol. 129, pp. 113–120, 2004.
[181] H. Wenzl et al., “Life prediction of batteries for selecting the technically most suitable and cost effective battery,” J. Power Sources, vol. 144, pp. 373–384, 2005.
[182] D. J. Deepti and V. Ramanarayanan, “State of Charge of Lead Acid Battery,” in India International Conference on Power Electronics, 2006, pp. 89–93.
[183] M. Coleman, C. K. Lee, C. Zhu, and W. G. Hurley, “State-of-Charge Determination From EMF Voltage Estimation : Using Impedance , Terminal Voltage , and Current for Lead-Acid and Lithium-Ion Batteries,” IEEE Trans. Ind. Electron., vol. 54, no. 5, pp. 2550–2557, 2007.
[184] S. Drouilhet and B. L. Johnson, “A battery life prediction method for hybrid power applications,” in AIAA, Aerospace Sciences Meeting & Exhibit, 35th, Reno, NV, 1997, no. January.
[185] P. Ruetschi, “Aging mechanisms and service life of lead-acid batteries,” Power Sources, vol. 127, pp. 33–44, 2004.
[186] J. Schiffer, D. Uwe, H. Bindner, T. Cronin, P. Lundsager, and R. Kaiser, “Model prediction for ranking lead-acid batteries according to expected lifetime in renewable energy systems and autonomous power-supply systems,” J. Power Sources, vol. 168, pp. 66–78, 2007.
Reference
- 165 -
[187] G. J. May, A. Davidson, and B. Monahov, “Lead batteries for utility energy storage : A review,” J. Energy Storage, vol. 15, pp. 145–157, 2018.
[188] X. Wang, D. Yu, S. Le Blond, Z. Zhao, and P. Wilson, “A novel controller of a battery-supercapacitor hybrid energy storage system for domestic applications,” Energy Build., vol. 141, pp. 167–174, 2017.
[189] A. M. Gee and R. W. Dunn, “Novel battery / supercapacitor hybrid energy storage control strategy for battery life extension in isolated wind energy conversion systems,” 2010.
[190] D. Shin, Y. Kim, J. Seo, N. Chang, Y. Wang, and M. Pedram, “Battery-supercapacitor hybrid system for high-rate pulsed load applications,” 2011 Des. Autom. Test Eur., pp. 1–4, 2011.
[191] T. Ma, H. Yang, and L. Lu, “A feasibility study of a stand-alone hybrid solar-wind-battery system for a remote island,” Appl. Energy, vol. 121, pp. 149–158, 2014.
[192] L. W. Chong, Y. W. Wong, R. K. Rajkumar, and D. Isa, “An optimal control strategy for standalone PV system with Battery-Supercapacitor Hybrid Energy Storage System,” J. Power Sources, vol. 331, pp. 553–565, 2016.
[193] P. Sanjeev, N. P. Padhy, and P. Agarwal, “A Novel Configuration for PV-Battery-DG Integrated Standalone DC Microgrid,” 2018 Int. Conf. Power, Instrumentation, Control Comput., pp. 1–6, 2018.
[194] S. K. Kollimalla, M. K. Mishra, and N. L. Narasamma, “Design and analysis of novel control strategy for battery and supercapacitor storage system,” IEEE Trans. Sustain. Energy, vol. 5, no. 4, pp. 1137–1144, 2014.
[195] J. Shen and A. Khaligh, “Design and Real-Time Controller Implementation for a Battery-Ultracapacitor,” IEEE Trans. Ind. INFORMATICS, vol. 12, no. 5, pp. 1910–1918, 2016.
[196] J. Xiao, P. Wang, and Leonardy Setyawan, “Multilevel Energy Management System for Hybridization of Energy Storages in DC Microgrids,” IEEE Trans. Smart Grid, vol. 7, no. 2, pp. 847–856, 2016.
[197] L. Kouchachvili, W. Yaïci, and E. Entchev, “Hybrid battery / supercapacitor energy storage system for the electric vehicles,” J. Power Sources, vol. 374, no. October 2017, pp. 237–248, 2018.
[198] L. W. Chong, Y. W. Wong, R. K. Rajkumar, and D. Isa, “Modelling and Simulation of Standalone PV Systems with Battery- supercapacitor Hybrid Energy Storage System for a Rural Household,” Energy Procedia, vol. 107, no. September 2016, pp. 232–236, 2017.
[199] L. Gao, R. A. Dougal, and S. Liu, “Power Enhancement of an Actively Controlled Battery/Ultracapacitor Hybrid,” Power, vol. 20, no. 1, pp. 236–243, 2005.
[200] N. R. Tummuru, M. K. Mishra, and S. Srinivas, “Dynamic Energy Management of Renewable Grid Integrated Hybrid Energy Storage System,” IEEE Trans. Ind. Electron., vol. 62, no. 12, pp. 7728–7737, 2015.
[201] H. Yin, C. Zhao, M. Li, and C. Ma, “Utility Function-Based Real-Time Control of A Battery Ultracapacitor Hybrid Energy System,” IEEE Trans. Ind. Informatics, vol. 11, no. 1, pp. 220–231, 2015.
[202] P. C. Loh, D. Li, Y. K. Chai, and F. Blaabjerg, “Autonomous control of interlinking converter with energy storage in hybrid AC-DC microgrid,” IEEE Trans. Ind. Appl., vol. 49, no. 3, pp. 1374–1382, 2013.
[203] X. Liu, P. Wang, and P. C. Loh, “A hybrid AC/DC microgrid and its coordination control,” IEEE Trans. Smart Grid, vol. 2, no. 2, pp. 278–286, 2011.
Reference
- 166 -
[204] Y. Zhan, Y. Guo, J. Zhu, and L. Li, “Power and energy management of grid-PEMFC-battery-supercapacitor hybrid power sources for UPS applications,” Electr. Power Energy Syst., vol. 67, pp. 598–612, 2015.
[205] M. G. Simões, T. D. C. Busarello, A. S. Bubshait, F. Harirchi, J. A. Pomilio, and F. Blaabjerg, “Interactive smart battery storage for a PV and wind hybrid energy management control based on conservative power theory,” Int. J. Control, vol. 7179, no. October, pp. 1–21, 2015.
[206] S. Dusmez, A. Hasanzadeh, and A. Khaligh, “Comparative Analysis of Bidirectional Three-Level DC-DC Converter for Automotive Applications,” IEEE Trans. Ind. Electron., vol. 62, no. 5, pp. 3305–3315, 2015.
[207] M. A. Abdullah, A. H. M. Yatim, C. W. Tan, and A. S. Samosir, “Control of a bidirectional converter to interface ultracapacitor with renewable energy sources,” Proc. IEEE Int. Conf. Ind. Technol., pp. 673–678, 2013.
[208] X. Yu, A. M. Khambadkone, H. Wang, and S. T. S. Terence, “Control of parallel-connected power converters for low-voltage microgrid - Part I: A hybrid control architecture,” IEEE Trans. Power Electron., vol. 25, no. 12, pp. 2962–2970, 2010.
[209] Z. Guoju, “Research on Battery Supercapacitor Hybrid Storage and its application in MicroGrid,” Asia-Pacific Power Energy Eng. Conf., pp. 5–8, 2010.
[210] A. Lahyani, P. Venet, A. Guermazi, and A. Troudi, “Battery/Supercapacitors Combination in Uninterruptible Power Supply (UPS),” IEEE Trans. Power Electron., vol. 28, no. 4, pp. 1509–1522, 2013.
[211] I. Chotia and S. Chowdhury, “Battery Storage and Hybrid Battery Supercapacitor Storage Systems : A Comparative Critical Review,” in IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), 2015.
[212] J. Xiao, P. Wang, and Leonardy Setyawan, “Hierarchical Control of Hybrid Energy Storage System in DC Microgrids,” IEEE Trans. Ind. Electron., vol. 62, no. 8, pp. 4915–4924, 2015.
[213] U. Supatti and S. Sungtum, “Bidirectional hybrid batteries/ultra-capacitors energy storage system for vehicular applications,” IEEE Ind. Appl. Soc. - 51st Annu. Meet. IAS 2015, Conf. Rec., pp. 6–11, 2015.
[214] L. Xu and D. Chen, “Control and operation of a DC microgrid with variable generation and energy storage,” IEEE Trans. Power Deliv., vol. 26, no. 4, pp. 2513–2522, 2011.
[215] A. Etxeberria, I. Vechiu, H. Camblong, J. M. Vinassa, and H. Camblong, “Hybrid Energy Storage Systems for renewable Energy Sources Integration in microgrids: A review,” 2010 Conf. Proc. IPEC, pp. 532–537, 2010.
[216] Y. Gu, X. Xiang, W. Li, and X. He, “Mode-Adaptive Decentralized Control for Renewable DC Microgrid With Enhanced Reliability and Flexibility,” IEEE Trans. Power Electron., vol. 29, no. 9, pp. 5072–5080, 2014.
[217] J. J. Justo, F. Mwasilu, J. Lee, and J. W. Jung, “AC-microgrids versus DC-microgrids with distributed energy resources: A review,” Renew. Sustain. Energy Rev., vol. 24, pp. 387–405, 2013.
[218] S. Bahrami, V. W. S. Wong, and J. Jatskevich, “Optimal power flow for AC-DC networks,” 2014 IEEE Int. Conf. Smart Grid Commun., pp. 49–54, 2015.
[219] L. Che and M. Shahidehpour, “DC microgrids: Economic operation and enhancement of resilience by hierarchical control,” IEEE Trans. Smart Grid, vol. 5, no. 5, pp. 2517–2526, 2014.
Reference
- 167 -
[220] P. A. Madduri, J. Rosa, S. R. Sanders, E. A. Brewer, and M. Podolsky, “Design and verification of smart and scalable DC microgrids for emerging regions,” 2013 IEEE Energy Convers. Congr. Expo. ECCE 2013, pp. 73–79, 2013.
[221] J. S. Hwang et al., “Validity analysis on the positioning of superconducting fault current limiter in neighboring AC and DC microgrid,” IEEE Trans. Appl. Supercond., vol. 23, no. 3, pp. 3–6, 2013.
[222] A. Kuperman and I. Aharon, “Battery-ultracapacitor hybrids for pulsed current loads: A review,” Renew. Sustain. Energy Rev., vol. 15, no. 2, pp. 981–992, 2011.
[223] D. Shin, Y. Kim, Y. Wang, N. Chang, and M. Pedram, “Constant-current regulator-based battery-supercapacitor hybrid architecture for high-rate pulsed load applications,” J. Power Sources, vol. 205, pp. 516–524, 2012.
[224] J. P. Zheng, T. R. Jow, and M. S. Ding, “Hybrid power sources for pulsed current applications,” IEEE Trans. Aerosp. Electron. Syst., vol. 37, no. 1, pp. 288–292, 2001.
[225] T. a. Smith, J. P. Mars, and G. a. Turner, “Using supercapacitors to improve battery performance,” 2002 IEEE 33rd Annu. IEEE Power Electron. Spec. Conf. Proc. (Cat. No.02CH37289), vol. 1, pp. 124–128, 2002.
[226] S. Pay and Y. Baghzouz, “Effectiveness of battery-supercapacitor combination in electric vehicles,” 2003 IEEE Bol. PowerTech - Conf. Proc., vol. 3, pp. 728–733, 2003.
[227] S. K. Singal, Varun, and R. P. Singh, “Rural electrification of a remote island by renewable energy sources,” Renew. Energy, vol. 32, no. 15, pp. 2491–2501, 2007.
[228] R. A. Dougal, S. Liu, and R. E. White, “Power and Life Extension of Battery – Ultracapacitor Hybrids,” IEEE Trans. Components Packag. Technol., vol. 25, no. 1, pp. 120–131, 2002.
[229] Z. Song, H. Hofmann, J. Li, X. Han, X. Zhang, and M. Ouyang, “A comparison study of different semi-active hybrid energy storage system topologies for electric vehicles,” J. Power Sources, vol. 274, pp. 400–411, 2015.
[230] Y. Wang, W. Wang, Y. Zhao, L. Yang, and W. Chen, “A Fuzzy-Logic Power Management Strategy Based on Markov Random Prediction for Hybrid Energy Storage Systems,” Energies, vol. 9, no. 1, p. 25, 2016.
[231] W. Li and Géza Joós, “A power electronic interface for a battery supercapacitor hybrid energy storage system for wind applications,” PESC Rec. - IEEE Annu. Power Electron. Spec. Conf., pp. 1762–1768, 2008.
[232] A. Etxeberria, I. Vechiu, H. Camblong, and J. M. Vinassa, “Comparison of three topologies and controls of a hybrid energy storage system for microgrids,” Energy Convers. Manag., vol. 54, no. 1, pp. 113–121, 2012.
[233] I. J. Cohen, C. S. Westenhover, D. A. Wetz, J. M. Heinzel, and Q. Dong, “Evaluation of an actively contolled battery-capacitor hybrid energy storage module (HESM) for use in driving pulsed power applications,” Dig. Tech. Pap. Int. Pulsed Power Conf., vol. 2015–Octob, 2015.
[234] A. Khaligh and Z. Li, “Battery, ultracapacitor, fuel cell, and hybrid energy storage systems for electric, hybrid electric, fuel cell, and plug-in hybrid electric vehicles: State of the art,” IEEE Trans. Veh. Technol., vol. 59, no. 6, pp. 2806–2814, 2010.
[235] A. Kuperman, I. Aharon, S. Malki, and A. Kara, “Design of a semiactive battery-ultracapacitor hybrid energy source,” IEEE Trans. Power Electron., vol. 28, no. 2, pp. 806–815, 2013.
Reference
- 168 -
[236] B. Hredzak, V. G. Agelidis, and G. D. Demetriades, “A low complexity control system for a hybrid DC power source based on ultracapacitor-lead-acid battery configuration,” IEEE Trans. Power Electron., vol. 29, no. 6, pp. 2882–2891, 2014.
[237] Z. Song, J. Hou, H. Hofmann, J. Li, and M. Ouyang, “Sliding-mode and Lyapunov function-based control for battery / supercapacitor hybrid energy storage system used in electric vehicles,” Energy, vol. 122, pp. 601–612, 2017.
[238] Z. Cabrane, M. Ouassaid, and M. Maaroufi, “Battery and supercapacitor for photovoltaic energy storage : a fuzzy logic management,” IET Renew. Power Gener., vol. 11, no. 4, pp. 1157–1165, 2017.
[239] E. Jamshidpour, P. Poure, and S. Saadate, “Energy Management and Control of a Stand-Alone Photovoltaic/Ultra Capacitor/Battery Microgrid,” 2015 IEEE Jordan Conf. Appl. Electr. Eng. Comput. Technol., vol. 2, no. 1, pp. 1–12, 2016.
[240] S. M. Lukic, S. G. Wirasingha, F. Rodriguez, J. Cao, and A. Emadi, “Power management of an ultracapacitor/battery hybrid energy storage system in an HEV,” 2006 IEEE Veh. Power Propuls. Conf., 2006.
[241] J. Cao and E. Ali, “A New Battery/UltraCapacitor Hybrid Energy Storage System for Electric, Hybrid, and Plug-In Hybrid Electric Vehicles,” IEEE Trans. Power Electron., vol. 27, no. 1, pp. 122–132, 2012.
[242] W. Jing, C. H. Lai, W. S. H. Wong, and M. L. D. Wong, “A comprehensive study of battery-supercapacitor hybrid energy storage system for standalone PV power system in rural electrification,” Appl. Energy, vol. 224, no. April, pp. 340–356, 2018.
[243] H. Zhou, T. Bhattacharya, D. Tran, T. S. T. Siew, and A. M. Khambadkone, “Composite Energy Storage System Involving Battery and Ultracapacitor With Dynamic Energy Management in Microgrid Applications,” IEEE Trans. Power Electron., vol. 26, no. 3, pp. 923–930, 2011.
[244] X. Feng, H. B. Gooi, and S. X. Chen, “Hybrid Energy Storage With Multimode Fuzzy Power Allocator for PV Systems,” IEEE Trans. Sustain. Energy, vol. 5, no. 2, pp. 389–397, 2014.
[245] T. Li, H. Liu, and D. Ding, “Predictive energy management of fuel cell supercapacitor hybrid construction equipment,” Energy, vol. 149, pp. 718–729, 2018.
[246] N. Bigdeli, “Optimal management of hybrid PV/fuel cell/battery power system: A comparison of optimal hybrid approaches,” Renew. Sustain. Energy Rev., vol. 42, pp. 377–393, 2015.
[247] O. Erdinc and M. Uzunoglu, “Optimum design of hybrid renewable energy systems: Overview of different approaches,” Renew. Sustain. Energy Rev., vol. 16, no. 3, pp. 1412–1425, 2012.
[248] P. Bajpai and V. Dash, “Hybrid renewable energy systems for power generation in stand-alone applications: A review,” Renew. Sustain. Energy Rev., vol. 16, no. 5, pp. 2926–2939, 2012.
[249] Y. Zhang, Z. Jiang, and X. Yu, “Control Strategies for Battery/Supercapacitor Hybrid Energy Storage Systems,” 2008 IEEE Energy 2030 Conf., pp. 5–10, 2008.
[250] T. Bocklisch, “Hybrid energy storage systems for renewable energy applications,” Energy Procedia, vol. 73, pp. 103–111, 2015.
[251] S. Teleke, M. E. Baran, S. Bhattacharya, and A. Q. Huang, “Rule-Based Control of Battery Energy Storage for Dispatching Intermittent Renewable Sources,” IEEE Trans. Sustain. ENERGY, vol. 1, no. 3, pp. 117–124, 2010.
Reference
- 169 -
[252] J. Shen and A. Khaligh, “A Supervisory EnergyManagement Control Strategy in a Battery/Ultracapacitor Hybrid Energy Storage System,” IEEE Trans. Transp. Electrif., vol. 1, no. 3, pp. 223–231, 2015.
[253] J. Gao, F. Sun, H. He, G. G. Zhu, and E. G. Strangas, “A Comparative Study of Supervisory Control Strategies for a Series Hybrid Electric Vehicle,” 2009 Asia-Pacific Power Energy Eng. Conf., pp. 1–7, 2009.
[254] A. H. Badi, H. Yousef, O. Alaamri, M. Alabdusalam, Y. Alshidi, and Nasser AlHarthy, “Performance of a Stand-Alone Renewable Energy System Based on Hydrogen Energy Storage,” Commun. Control Signal Process. (ISCCSP), 6th Int. Symp. on. IEEE, pp. 2321–2324, 2014.
[255] R. Bessa, C. Moreira, B. Silva, and M. Matos, “Handling renewable energy variability and uncertainty in power systems operation,” Wiley Interdiscip. Rev. Energy Environ., vol. 3, no. 2, pp. 156–178, 2014.
[256] H. Kanchev, D. Lu, F. Colas, V. Lazarov, and B. Francois, “Energy Management and Operational Planning of a Microgrid With a PV-Based Active Generator for Smart Grid Applications,” IEEE Trans. Ind. Electron., vol. 58, no. 10, pp. 4583–4592, 2011.
[257] I. L. Sarioǧlu, O. P. Klein, H. Schröder, and F. Küçükay, “Energy management for fuel-cell hybrid vehicles based on specific fuel consumption due to load shifting,” IEEE Trans. Intell. Transp. Syst., vol. 13, no. 4, pp. 1772–1781, 2012.
[258] A. Mohamed, V. Salehi, and O. Mohammed, “Real-Time Energy Management Algorithm for Mitigation of Pulse Loads in Hybrid Microgrids,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1911–1922, Dec. 2012.
[259] H. ZHAO, Q. WU, C. WANG, L. CHENG, and C. N. RASMUSSEN, “Fuzzy logic based coordinated control of battery energy storage system and dispatchable distributed generation for microgrid,” J. Mod. Power Syst. Clean Energy, vol. 3, no. 3, pp. 422–428, 2015.
[260] B. Robyns, A. Davigny, and C. Saudemont, “Methodologies for supervision of Hybrid Energy Sources based on Storage Systems - A survey,” Math. Comput. Simul., vol. 91, pp. 52–71, 2013.
[261] H. Zhang, A. Davigny, F. Colas, Y. Poste, and B. Robyns, “Fuzzy logic based energy management strategy for commercial buildings integrating photovoltaic and storage systems,” Energy Build., vol. 54, pp. 196–206, 2012.
[262] A. Chauhan and R. P. Saini, “A review on Integrated Renewable Energy System based power generation for stand-alone applications: Configurations, storage options, sizing methodologies and control,” Renew. Sustain. Energy Rev., vol. 38, pp. 99–120, 2014.
[263] L. Suganthi, S. Iniyan, and A. A. Samuel, “Applications of fuzzy logic in renewable energy systems - A review,” Renew. Sustain. Energy Rev., vol. 48, pp. 585–607, 2015.
[264] B. Hredzak, V. G. Agelidis, and M. Jang, “A model predictive control system for a hybrid battery-ultracapacitor power source,” IEEE Trans. Power Electron., vol. 29, no. 3, pp. 1469–1479, 2014.
[265] D. Kothari, “Power system optimization,” Comput. Intell. Signal Process. (CISP), 2nd Natl. Conf. on. IEEE, pp. 18–21, 2012.
[266] A. Arabali, M. Ghofrani, M. Etezadi-Amoli, M. S. Fadali, and Y. Baghzouz, “Genetic-Algorithm-Based Optimization Approach for Energy Management,” IEEE Trans. Power Deliv., vol. 28, no. 1, pp. 162–170, 2013.
Reference
- 170 -
[267] F. Nejabatkhah and Y. Li, “Overview of PowerManagement Strategies of Hybrid AC/DC Microgrid,” IEEE Trans. POWER Electron., vol. 30, no. 12, pp. 7072–7089, 2015.
[268] M. Fadaee and M. A. M. Radzi, “Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review,” Renew. Sustain. Energy Rev., vol. 16, no. 5, pp. 3364–3369, 2012.
[269] V. I. Herrera, H. Gaztanaga, A. Milo, A. Saez-de-Ibarra, I. Etxeberria-Otadui, and T. Nieva, “Optimal Energy Management and Sizing of a Battery – Supercapacitor-Based Light Rail Vehicle With a Multiobjective Approach,” IEEE Trans. Ind. Appl., vol. 52, no. 4, pp. 3367–3377, 2016.
[270] Z. Song et al., “Multi-objective optimization of a semi-active battery / supercapacitor energy storage system for electric vehicles,” Appl. Energy, vol. 135, pp. 212–224, 2014.
[271] Z. Zhang, Y. Cai, Y. Zhang, D.-J. Gu, and Y.-F. Liu, “A Distributed Architecture Based on Micro-Bank Modules with Self-Reconfiguration Control to Improve the Energy Efficiency in the Battery Energy Storage System,” IEEE Trans. Power Electron., vol. 8993, no. 1, pp. 304–317, 2015.
[272] Q. Xie, Y. Wang, Y. Kim, M. Pedram, and N. Chang, “Charge Allocation for Hybrid Electrical Energy Storage Systems,” 2011 IEEE/ACM/IFIP Int. Conf. Hardware/Software Codesign Syst. Synth., vol. 32, no. 7, pp. 1003–1016, 2013.
[273] A. Abuaish and M. Kazerani, “Single-phase bidirectional integrated onboard battery charger for EVs featuring a battery-supercapacitor hybrid energy storage system,” Proc. IEEE Int. Conf. Ind. Technol., pp. 543–548, 2017.
[274] A. T. Elsayed, A. A. Mohamed, and O. A. Mohammed, “DC microgrids and distribution systems: An overview,” Electr. Power Syst. Res., vol. 119, pp. 407–417, 2015.
[275] N. J. Williams, P. Jaramillo, J. Taneja, and T. S. Ustun, “Enabling private sector investment in microgrid-based rural electrification in developing countries: A review,” Renew. Sustain. Energy Rev., vol. 52, pp. 1268–1281, 2015.
[276] J. P. Torreglosa, P. García-Triviño, L. M. Fernández-Ramirez, and F. Jurado, “Control strategies for DC networks: A systematic literature review,” Renew. Sustain. Energy Rev., vol. 58, pp. 319–330, 2016.
[277] P. A. Madduri, J. Poon, J. Rosa, M. Podolsky, E. Brewer, and S. Sanders, “A scalable DC microgrid architecture for rural electrification in emerging regions,” IEEE Appl. Power Electron. Conf. Expo., pp. 703–708, 2015.
[278] S. Konar and A. Ghosh, “Interconnection of islanded DC microgrids,” 2015 IEEE PES Asia-Pacific Power Energy Eng. Conf., pp. 1–5, 2015.
[279] N. Eghtedarpour and E. Farjah, “Distributed charge/discharge control of energy storages in a renewable-energy-based DC micro-grid,” Renew. Power Gener. IET, vol. 8, no. April, pp. 45–57, 2014.
[280] G. E. M. Ruiz, N. Munoz, and J. B. Cano, “Design methodologies and programmable devices used in power electronic converters — A survey,” 2015 IEEE Work. Power Electron. Power Qual. Appl., pp. 1–6, 2015.
[281] A. López-González, B. Domenech, D. Gómez-Hernández, and L. Ferrer-Martí, “Renewable microgrid projects for autonomous small-scale electrification in Andean countries,” Renew. Sustain. Energy Rev., vol. 79, no. September 2016, pp. 1255–1265, 2017.
Reference
- 171 -
[282] Mashood Nasir, H. A. Khan, A. Hussain, Laeeq Mateen, and N. A. Zaffar, “Solar PV Based Scalable DC Microgrid for Rural Electrification in Developing Regions,” IEEE Trans. Sustain. Energy, vol. 3029, no. c, pp. 1–9, 2016.
[283] T. Ma, H. Yang, and L. Lu, “Development of hybrid battery-supercapacitor energy storage for remote area renewable energy systems,” Appl. Energy, vol. 153, pp. 56–62, 2015.
[284] M. Müller, R. Bründlinger, O. Arz, W. Miller, and G. Lauss, “PV-off-grid hybrid systems and MPPT charge controllers , a state of the art analyses,” Energy Procedia, vol. 57, pp. 1421–1430, 2014.
[285] M. LokeshReddya, P. J. R. P. Kumara, S. A. M. Chandraa, T. S. Babu, and N. Rajasekara, “Comparative study on charge controller techniques for solar PV system,” Energy Procedia, vol. 117, pp. 1070–1077, 2017.
[286] Y. E., A. Eldahab, N. H. Saad, and A. Zekry, “Enhancing the design of battery charging controllers for photovoltaic systems,” Renew. Sustain. Energy Rev., vol. 58, pp. 646–655, 2016.
[287] C. Vimalarani, N. Kamaraj, and B. C. Babu, “Improved method of maximum power point tracking of photovoltaic ( PV ) array using hybrid intelligent controller,” Opt. - Int. J. Light Electron Opt., vol. 168, pp. 403–415, 2018.
[288] A. Mirzaei, M. Forooghi, A. A. Ghadimi, A. H. Abolmasoumia, and M. R. Riahi, “Design and construction of a charge controller for stand-alone PV / battery hybrid system by using a new control strategy and power management,” Sol. Energy, vol. 149, pp. 132–144, 2017.
[289] U. Yilmaz, A. Kircay, and S. Borekci, “PV system fuzzy logic MPPT method and PI control as a charge controller,” Renew. Sustain. Energy Rev., vol. 81, no. August 2017, pp. 994–1001, 2018.
[290] F. Rahman, S. Rehman, and M. A. Abdul-majeed, “Overview of energy storage systems for storing electricity from renewable energy sources in Saudi Arabia,” Renew. Sustain. Energy Rev., vol. 16, no. 1, pp. 274–283, 2012.
[291] C. Lupangu and R. C. Bansal, “A review of technical issues on the development of solar photovoltaic systems,” Renew. Sustain. Energy Rev., vol. 73, no. November 2016, pp. 950–965, 2017.
[292] M. C. Argyrou, P. Christodoulides, and S. A. Kalogirou, “Energy storage for electricity generation and related processes : Technologies appraisal and grid scale applications,” Renew. Sustain. Energy Rev., vol. 94, pp. 804–821, 2018.
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Appendix
A.1. Case study
Stand-alone PV-battery Power System for Batang Ai National Park Headquarter
(BANP)
A.1.1 Existing power generation and distribution system in BANP
Two major buildings are supplied with electricity namely the main office and Barrack A
(staff quarter) as shown in Fig. A.1.
Barrack A(User 1 - 6)
Main
Office
Main Office Distribution box
Barrack A Distribution Box
N
Genset Room
Main Distribution box
Fig. A.1 Layout of BANP and current electricity supply system
Barrack A is a longhouse-liked terrace that houses 6 families in total. Currently, a
5.6KVA petrol generator is used to supply electricity in BANP. In normal day, the
generator will be operated on average 3 hours in between 9am-1pm to carry out routine
Appendix
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work on PC, and 6pm-11pm for basic electricity needs for the residents in Barrack A.
Their primary source of power is diesel generators and can only be started at specific
times. Based on the power consumption data collected during site visit on 28 March
2016, the power consumption pattern of the 2 main buildings is shown in Fig. A.2. The
barrack A consumes on average 1.4KW at night while the office consumes an additional
300W-500W on top of the Barrack A. A summary of electrical appliances that are
currently in use are tabulated in Table A.1.
Table A.1 Electrical appliances (currently in-use) of main office and barrack A
User Lighting
(20W) Fan Entertainment
Portable
Device
Refrigerator
/ Freezer
PC
workstation
Office 5 1 - - - 3
1 5 1 1 Radio 5 2-door -
2 5 1 1 TV + Astro 5 Freezer -
3 4 - - - - -
4 5 1 1 TV + Astro 3 - -
5 4 - - 1 - -
6 4 1 - 1 1-door -
As can be seen from the graph in Fig. A.2, the average load consumption is
approximately half the rated power of the petrol generator.