my project

SMART MICROGRID:

ENERGY PROFILING AND DATA ACQUISITION

ALASIRI, OLUWATOSIN GRACE

EEG/2009/037

Progress Report submitted to the

Department of Electronics and Electrical Engineering

Obafemi Awolowo University, Ile-Ife, Osun State

In partial fulfillment of the requirements for the degree of

Bachelor of Science

In

Electronics and Electrical Engineering

November, 2014

SUPERVISOR’S CERTIFICATION

This is to certify that this study on SMART MICROGRID: ENERGY PROFILING AND

DATA ACQUISITION was carried out by ALASIRI, OLUWATOSIN GRACE, of the

Department of Electronics And Electrical Engineering, Faculty of Technology, Obafemi

Awolowo University, Ile-Ife, Osun State, Nigeria under my supervision.

……………………………….

…………………………….....

Mr. O.S. Babalola,

Supervisor

LIST OF FIGURESFig 1:

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ABSTRACT

Emerging new technologies like distributed generation, distributed storage, and demand-

side load management will change the way we consume and produce energy. These

techniques enable the possibility to improve grid stability by optimizing energy streams.

By smartly applying future energy production, consumption, and storage techniques, a

more energy efficient electricity supply chain can be achieved. As shown in my work,

using good predictions based on advance information and planning, a better matching of

demand and supply can be reached.

The microgrid will contain all the information (conglomeration of micro sources) as

regards the generation, transmission, distribution and management of electrical energy.

The energy profile of this system is a bank of information about the generation capacity

of our micro sources which include solar and wind, time of generation and efficiency.

Energy estimation will also be carried out based on the season and weather condition. The

data acquisition has a lot to do with monitoring (interactive maintenance), statistical data

(preventive maintenance) and alarms (emergency maintenance).

Keywords: Microgrid, demand side management, energy streams, energy profile, data

acquisition.

CHAPTER ONE

Introduction1.1. Background to the Project

It has been discovered that a more efficient, secure and resilient power system than we

have now is achievable if a more distributed energy generation is practiced. We want to

be able to conserve the limited energy available and distribute it without having any

power outages such as is common in our immediate environment. To ensure this, we

would want to harness whatever energy we have been able to harvest and simultaneously

avoid energy wastage by shuffling energy supply between loads in the most efficient way.

The beauty of this system is that all that work will be carried out by intelligent Multi

Agent Systems (also known as MAS) with the use of smart sockets that work in

conjunction with the smart microgrid.

1.2. Significance of the project

Many previous works have proposed multi-agent system architectures that deal with

buying and selling of energy within a microgrid and algorithms for auction systems. The

others proposed frameworks for multi-agent systems that could be further developed for

real life control of microgrid systems. However, most proposed methods ignore the

process of sharing energy resources among multiple distinct sets of prioritized loads. It is

important to study a scenario that emphasizes on supporting critical loads during outages

based on the user’s preferences and limited capacity. The situation becomes further

appealing when an excess DER capacity after supplying critical loads is allocated to

support non-critical loads that belong to multiple users. The previous works also ignore

the study of dynamic interactions between the agents and the physical systems. It is

important to study the interaction and time delay when an agent issues a control signal to

control a physical device in a microgrid and when the command is executed. Agents must

be able to respond to the information sensed from the external environment quickly

enough to manage the microgrid in a timely fashion. The ability of agents to disconnect

the microgrid during emergencies should also be studied. These issues are identified as

knowledge gaps that are of focus in this thesis.

1.3. Aim and Objectives of the project

The objective of this research is to design, develop and implement a multi-agent system

that enables real-time management of a microgrid. These include securing critical loads

and supporting non-critical loads belonging to various owners with the distributed energy

resource that has limited capacity during outages.

Major objectives:

Maximize the customer’s power availability (e.g. meet consumer’s instantaneous

load demand).

Minimize economic factors (e.g. fuel costs, operation and maintenance costs,

start-up/shut-down costs, etc.).

Minimize environmental impact from operating microgrid generators (e.g.

emissions, noise, hazardous waste, etc.).

Maximize the dispatch of shedable loads (e.g. loads capable of reacting to demand

response signals).

Maximize revenue derived from service delivery to the utility grid (including

ancillary services, reserves, etc.).

Minimize energy purchased from outside microgrid.

Maximize the total efficiency of the microgrid (e.g. kWhrs generated versus kJ

fuel consumed).

Maximize capitalized energy sources (e.g. operational efficiency of kWhrs

available versus kWhrs generated).

Minimize the frequency of power reversals across the PCC interconnection.

Minimize transient periods during stabilization in the event of a casualty or

interruption.

Minor objectives:

Maximize load factor (e.g. smooth out the peaks and valleys of load and

subsequently required generation).

Minimize the need for storage assets.

Maximize the microgrid capability to reduce strain on distribution and

transmission assets.

Maximize VAR support to the greater power system.

Maximize the reduction in line losses.

Allow the stable, seamless, and adaptable integration of assets onto the microgrid

(also known as “plug-and-play”).

Primary constraints:

• Availability of renewable resources (e.g. solar insulation, wind energy, etc.).

• Bus voltage, frequency, and stability requirements.

• Physical and electrical characteristics of the microgrid.

• Status of interconnection.

1.4. Scope of the project

The system under study consists of physical (microgrid) and cyber elements (Multi-Agent

System also known as MAS). I will focus more on the MAS. A microgrid is an eco-

friendly power system because renewable sources such as solar and wind power are used

as the main power sources. For this reason, much research, development, and

demonstration projects have recently taken place in many countries. Operation is one of

the important research topics for microgrids. For efficient and economical microgrid

operation, a human operator is required as in other power systems, but it is difficult

because there are some restrictions related to operation costs and privacy issues. To

overcome the restriction, autonomous operation for microgrids is required. Recently, an

intelligent Multi Agent System for autonomous microgrid operation has been studied as a

potential solution. Such systems have been proposed to provide intelligent energy control

and management systems in microgrids due to their inherent benefits of flexibility,

extensibility, autonomy, reduced maintenance and more. The implementation of a control

network based on multi-agent systems that is capable of making intelligent decisions on

behalf of the user has become one of the most important parts of this project.

1.5. Summary of methodology

The first step of course, is to get renewable resources such as the solar and wind. A

challenge facing microgrid MAS design is how to develop capable agents given immense

diversity and uncertainty regarding components the customer may choose to connect to a

microgrid. Therefore, to simplify the agent framework and encourage better

interoperability, all possible microgrid components are segregated into one of four

classifications. By classifying microgrid components, the appropriate agent or

combination of agents may be assigned to it. For split AC/DC bus microgrid MAS design,

the microgrid component classes are:

Generation: capable of sourcing real and/or reactive power.

Load: consumes real power; leading or lagging power factor.

Storage: can source or consume real power.

Node: connection point with measurable electrical quantities.

The microgrid MAS utilizes three basic agent types: the producer, consumer, and

observer agents. For each microgrid component, an agent of appropriate type is assigned

to it based on class, e.g. producer agent with a generation asset. In the case of a storage

asset, both a producer agent and consumer agent are assigned. This is due to the nature of

storage assets that appear to the microgrid as a load when charging or a generator when

discharging. In either case, the assigned agents negotiate to determine the best operating

point for the asset based on objectives. More generally, it would be unwieldy to design a

specific agent for each unique microgrid component, and the agent types are kept as

general as possible. Subsequently, in terms of agent design, specific agents are not

designed for a specific machine or unique load.

The Producer Agent has the following responsibilities:

• Monitor available real/reactive power from component.

• Monitor actual real/reactive power supplied by component.

• Determine relative per unit cost of power supplied by component.

• Determine an instantaneous performance measure indicating how well the component is

achieving optimal operation.

• Give commands to the component regarding startup, shutdown, and quantity of real

and/or reactive power to produce.

• Communicate information to other agents, including the available capacity, the carrying

capacity.

The Consumer Agent has the following responsibilities:

• Monitor real/reactive power consumed by the component or bank.

• If attached component is controllable (e.g. dump loads) or differentiated vital and non-

vital for demand response measures, determine the real/reactive power margin (load pick

up/shed).

• Give startup, shutdown, or configurational orders to component.

• Communicate information to other agents, including the carrying consumption,

consumption margin available, and requests to bring on or shut off consumption.

The Observer Agent has the following responsibilities:

• Monitor specific parameters within the microgrid network (e.g. voltage/frequency

levels, breaker positions, fuel tank levels, etc.).

• Communicate information to other agents regarding the status of the node, e.g. within

specification or not.

CHAPTER TWO

Literature Review2.1. Introduction

A multi-agent system has become increasingly powerful tool in developing a complex

system. A multi-agent system is a combination of several agents working in collaboration

in pursuit of accomplishing their assigned tasks resulting in the achievement of overall

goal of the system. A software program can be declared as an agent if it exhibits the

following characteristics:

1) Interacting with its environment (which may include other agents) (Sociality)

2) Learning from its environment (Autonomy)

3) Reacting to its environment in a timely manner (Reactivity and Pro-activity)

4) Taking initiatives to achieve its goals, and (Autonomy and Pro-activity)

5) Accomplishing tasks on behalf of its user (Sociality and Reactivity)

These properties signify the importance of multi-agent systems in developing complex

systems that enjoy agent’s properties of autonomy, sociality, reactivity and pro-activity.

Multi-agent systems are being applied in many systems today. Applications of agent-

based systems can be divided into the following two categories:

- Single-agent systems

These applications include situations where a human may require assistance while using a

computer software e.g. meeting scheduler software, information retrieval and filtering

software, mail management engine, news filtering engine, search engine, etc.

- Multi-agent systems

This is where multiple agents work together to achieve a particular goal. These can either

be physical systems or simulations of physical systems. Examples include traffic

monitoring, decision support systems, manufacturing systems, telecommunications and

network management, aircraft maintenance, military logistics planning, simulation of real

world, power systems etc.

Multi-agent systems have been proposed for many applications in military systems

information retrieval systems, decision support systems, supply chain, transportation,

communication systems and many more.

2.2 Toolkits/Frameworks for Multi-Agent Systems

Development

It is possible to develop multi-agent systems from scratch. This, however, is not necessary

for this research as many agent development toolkits are available that aid in rapid

development of complex agent systems in a simplified fashion.

2.3 Smart Grid

The U.S. Department of Energy (DOE)’s Modern Grid Initiative signifies the importance

of smart grid in facing the future challenges. It defines a smart grid as a grid that

integrates advanced sensing technologies, control methods and integrated

communications into current electricity grid – both at transmission and distribution levels.

The smart grid is expected to exhibit the following key characteristics:

• self-healing

• consumer friendly

• attack resistant

• provides power quality for 21st century needs

• able to accommodate all generation and storage options

• enables markets and

• optimizes assets and operates efficiently.

Our work discusses the design and implementation of a multi-agent system in the context

of a distributed smart grid or a smart grid located at a distribution level. In particular, we

will focus on implementing the concept of agents in an Intelligent Distributed

Autonomous Power System (IDAPS) environment. IDAPS is a distributed smart grid

concept proposed by Advanced Research Institute of Virginia Tech. Having a built-in

multi-agent functionality, IDAPS can be perceived as a microgrid that is intelligent.

According to the U.S. DOE, a microgrid is defined as an integrated energy system

consisting of interconnected loads and distributed energy resources, which as an

integrated system can operate in parallel with the grid or in an intentional island mode.

2.4 Multi-Agent Systems Implementation in Microgrids

In the context of power systems, multi-agent technologies can be applied in a variety of

applications, such as to perform power system disturbance diagnosis, power system

restoration, power system secondary voltage control and power system visualization.

Some Authors gave an excellent insight into the strategies, requirements, technical

problems and benefits of multi-agent systems application in power systems. The authors

discussed the work done in the field and potential applications of multi-agent systems in

power systems. The design strategies that could be incorporated for multi-agent systems

design and implementation in power systems were discussed in. Mainly two strategies

have been considered for the control and communication within microgrids: Hierarchical

or centralized control and decentralized or distributed control. Hierarchical control

requires a central controller that manages the entire system. The concept is based on the

same approach used for SCADA systems in the past. Decentralized or distributed control

approach is implemented using the multi-agent systems technology. In previous work,

authors devised control and communication strategies using multi-agent systems for

optimal operation of microgrids. Some authors have provided a demonstration of the use

of agent-based technology to increase security and reliability of distributed power

resources. The authors defined a military camp setting consisting of HQ, barracks, mess

and hospital, each having a generator, one critical and one non-critical load (operating

independently), a communal bus connecting the four generators and the agent system

consisting of four agents to operate the microgrid. Necessary protocols and policies are

defined to manage 1) the interaction among agents and 2) the situations that arise in the

microgrid. The work provided a framework that can be extended for the management of

real-world power systems.

Multi-agent system implementation controlling a small microgrid (PV generator,

converters and inverters, batteries and controllable loads) has been presented in previous

work. The work was performed in the microgrid’s grid-connected mode. The system was

implemented using JADE framework. The objective of this work was to minimize the

operational cost of microgrid. The issue of buying and selling of energy in a microgrid

market was expressed as symmetrical assignment problem in which persons are matched

with objects in order to maximize the benefit. Previously, a multi-agent system was

proposed that attempts to restore a distribution system network after a fault. It proposed a

hierarchical multi-agent system architecture in which lower layer agents sense the

absence of energy and inform the higher layer agents, while the higher layer agents try to

restore energy by negotiating with their peers. Some particular authors mentioned multi-

agent system architecture for micro-grid management. The goal of the system is to

perform tasks such as measurement data acquisition, DSM (Demand Side Management)

functions for load shifting, load curtailment and generation scheduling. The architecture

includes several agent entities capable of retrieving generation scheduling patterns from

external database. The authors mentioned two case studies performed in islanding mode

on a test bed consisting of two batteries, a generator and loads each controlled by a set of

agents. The authors’ multi-agent system architecture consists of Micro-grid Central

Controller (MCC, connected to external database for generation scheduling patterns

retrieval), Micro-grid Source Controller (MSC, connected to DERs) and Load Controller

(LC, connected to loads). MCC, MSC and LC further consist of several agents

performing their specific tasks. The multi-agent system provides various functionalities

among which great emphasis has been given to the secondary regulation system. The

secondary regulation system starts operating when a change in load is detected. The

change in load causes the batteries to modify their power level and secondary regulation

system kicks in. Secondary regulation system then assigns the power requirement

difference to generator which fulfills the power requirements. The multiagent system was

implemented using JADE. However, proprietary protocols were used for the interaction

of agents to their environment i.e. loads and DERs. Additionally, a strategy was presented

for buying energy from the distributed energy resources to meet the demand at lowest

possible price. A multi-agent system was presented as a control framework for

microgrids, and a scalable multi-agent system for microgrids was discussed.

2.5 Knowledge Gap

Some authors proposed the multi-agent framework for control strategies of microgrids.

The work mainly dealt with energy trading within a microgrid that was done between an

agent operating the loads and an agent controlling the DER resource. A system restoration

scheme was proposed that incorporated multi-agent systems. Also, authors had developed

a multi-agent system capable of performing tasks of data acquisition, demand side

management and generation scheduling.

In some later research, the focus is on the parts that have not been addressed in the

previous works mentioned above. The following knowledge gaps were identified:

1. Sharing limited capacity of a DER unit among multiple loads.

Previous works have not touched the problem of supporting multiple loads belonging to

different users and prioritized according to user’s will, given that the DER unit has a

limited capacity.

2. Dynamic interaction/agent control microgrid in a timely manner

Previous works have not shown the multi-agent systems’ capability of timely transition

from the grid connected to islanding mode.

3. Load control and prioritization

Previously, the multi-agent systems are only presented as a software entity that receives

signals from its environment and reacts, while their interactive side has altogether been

ignored. An interactive multi-agent system is one that:

a) allows the user to control on/off status of loads,

b) allows the user to prioritize loads as they will,

c) allows the user to specify the time.

4. Integrating additional functionalities

In the proposed multi-agent system, new changes or functionalities can be easily

incorporated in the system by attaching new java classes to the external programming

code.

CHAPTER THREE

DESIGN METHODOLOGY3.1 Design Methodology

Any demand-supply mismatch causes grid instability unless rapidly rectified. In the

absence of computational and analytics support for automated decisions, the human grid

operators are ill equipped to examine and utilize millions of data and control points for

managing the dynamism in energy patterns of sensing data to draw insights into the

system behavior and automate the available controls. This is, interestingly, a “Big Data”

challenge that requires advanced informatics techniques and cyber-infrastructure. Energy

use events streaming from a number of smart meters, sampled at a minimum interval of

15 minutes, need to be collected and correlated with a consumer’s historical profile. Data

mining and pattern matching are necessary for online detection of critical situations and

to correct them with low latency to ensure grid stability. Analytical and computational

models can help predict the power supply and demand within a service area to take

preemptive actions for curtailing demand by notifying and incentivizing consumers.

These efforts are multi-disciplinary, and require power engineers, data analysts,

behavioral psychologists, and microgrid managers to collaboratively share knowledge for

optimal operations, with the active participation of consumers as research has shown.

Demand response optimization (DR) is an approach to reduce customers’ consumption, in

response to a peak energy signal from the utility, by shifting, shaving and shaping

electricity load. It contrasts with energy efficiency by soliciting curtailment on-demand

only during periods of supply-demand mismatch. Peak loads may be caused by a drop in

the supply from renewable generation or an increase in the demand due to, say, a heat-

wave in a region. Current grid technology limits DR to static strategies, such as time-of-

use pricing and day-ahead notification based on historical averages. But Smart Grid

infrastructure offers instantaneous communication capability between the utility and the

customer, and automated controls at residences and buildings that enable dynamic

demand response optimization (D2R) for near real-time detection, notification and

response.

However, besides the hardware infrastructure, the key to successful D2R is intelligent

decision making on when, by how much, and whom to target for reliable and accurate

curtailment, and this requires advanced data analytics. The benefits of D2R are

considerable. It increases the reliability of the grid by using the customers as a virtual

energy source during peak periods (negative demand → positive supply); by lowering the

peak, it avoids the need to build power plants for standby capacity; it limits the

environmental impact since the cleanest energy is to avoid using energy; and it helps

integrate renewables by using demand-side management to address supply fluctuations.

From the point of view of smart micro-grids design and planning, software development

and field test are need to enable their large scale implementation. There are two visions to

design and built smart grids: first, a smart grid can be designed from scratch or, existing

systems should be modified into a smart grid. The second process is being carried out in

rural areas where existing facilities are complemented to convert them into smart micro-

grids. Micro-grid is a complex entity which means that it is difficult to build. That is why

steps towards real microgrid operation needs be planned. The conventional planning

methods are designed based on electricity production in centralized power generators and

delivery through passive distribution networks to end-users. The micro-grid design

methodology offers a systematic approach for planning, large-scale deployment, and

autonomous control of distributed generation rather than dealing with individual

generation sources with diverse technologies. One interesting point in the design of Rural

Smart Grids is the dimensioning. On one hand, micro-grid loads must be instantaneously

fed by intermittent generators, which require the implementation of storage devices to

provide active power. On the other hand, simulations made show that sensitivity for

voltage fluctuations gets higher in weaker grids and the control system needs to be more

accurate and stabile. Thus, one of the challenges of the control in weak grids is the need

of reactive power to control the voltage but if reactive power is transferred in the grid, it

causes losses. All these reasons must be kept on mind when dimensioning micro-grid

components. In this line, Phrakonkham et al. proposed a combination of simulation,

design and optimization tools within open architecture software such as Matlab/Simulink

to cope with the multi-objective optimization and the micro-grid configuration of a

standalone renewable hybrid system. For this project, Matlab will be made use of.

3.2 Smart Control Strategies

The control system of a micro-grid is designed to safely operate the system when it is

connected to the grid or in stand-alone mode. This system may be based on a central

controller or embedded as autonomous parts of each distributed generator.

Micro-grid controllers must ensure that:

Micro-sources work properly at predefined operating point or slightly different

from the predefined operating point but still satisfy the operating limits;

Active and reactive powers are transferred according to necessity of the micro-

grids and/or the distribution system;

Disconnection and reconnection processes are conducted seamlessly;

In case of general failure, the micro-grid is able to operate through black-start; and

Energy storage systems can support the micro-grid.

Control of the micro-grid is totally different to what distributed generation units are used

to do. When the micro-grid is disconnected from the grid, control system must control the

local voltage and frequency and provide the instantaneous active and reactive power

differences between generators and loads.

When the micro-grid is in stand-alone mode, frequency control is the most important

problem. The frequency response of conventional systems is based on rotating masses

and these are considered as essential for the stability of the systems. In contrast, micro-

grids are inherently converter-dominated grids with very little directly connected rotating

masse. Thus, the electronic converter control systems must be adapted to provide the

response previously obtained from directly connected rotating masses. The frequency

control strategy of microgrids should coordinate the capabilities of the micro sources to

change their active power (through frequency control droops), the response of the storage

systems, and load shedding.

Voltage regulation is considered the second main objective of networks to guarantee their

local reliability and stability. It has been observed that systems with high penetration of

distributed generation experience voltage and/or reactive power changes and oscillations

in the connection point. Local voltage control must be designed to avoid these changes

and the circulation of reactive currents between sources. The capability of modern power

electronic interfaces offers solutions to the provision of reactive power locally by the

adoption of a voltage versus reactive current droop controller, similar to the droop

controller for frequency control. One of the most important problems of rural smart grid

control is the nature of load characteristics, like load diversity and time variability. To

cope with this problem, the control system must have a hierarchical structure. The control

level of hierarchical systems can be classified on:

*Local controllers consisting of Micro-source Controllers (MCs) and Load Controllers

(LCs);

* Micro-grid Central Controllers (MGCCs); and

* Distribution Management System (DMS).

Local controls are the basis of micro-grid controls and their main purpose is to control

micro-sources. These controllers are intended to control operating points of the micro-

sources as well as their power-electronic interfaces, based on local voltages and currents

measured data.

CHAPTER FOUR

Progress made so far

So far, this is has been a very interesting and challenging work. It happens to be a group

project and the effort of each and every six of us integrated by that of our wonderful

supervisor has brought about sustainable progress so far.

Our intention concerning this smart microgrid is to use a laboratory, PY102 lecture

theatre and our supervisor’s office as our case study. Having succeeded in ordering for

two solar panels and installing one, we have the wind turbine to deal with as regards

renewable energy sources.

Historical data concerning the solar and wind energies available on OAU campus have

also been sourced leaving me with that of the water course through the earth. These data

will be imported into the programme being used which happens to be Matlab and will

form a backbone for the operation of the intelligent Multi Agent System. Initially,

SCADA (Supervisory Control And Data Acquisition) was to be used as a means of

control and data acquisition but a better alternative was found due to the propriety of

SCADA applications and implementations.

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