8/18/2019 Serdar ı̇plı̇kçı̇ Optimization Applications in the Renewable Energy Systems http://slidepdf.com/reader/full/serdar-iplikci-optimization-applications-in-the-renewable-energy-systems 1/33 The 4th Renewable Energy Systems Winter School │Prof. Dr. Serdar İPLİKÇİ OPTIMIZATION in RENEWABLE ENERGY SYSTEMS OPTIMIZATION in RENEWABLE ENERGY SYSTEMS 1/33 16/01/2015 Prof. Dr. Serdar İ PL İ K Çİ Pamukkale University, Dept. of Electrical and Electronics Eng., Kınıklı Campus, 20040, Denizli e-mail: [email protected]web: www.pau.edu.tr/iplikci phone: +90 (258) 2963197
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Serdar i̇pli̇kçi̇ Optimization Applications in the Renewable Energy Systems
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8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
Heuristic methods are the methods that produce sufficient (even if not optimum)solutions to the large scale problems very rapidly.
• Meta-heuristics are generalizations of heuristics in the sense that they can be applied
to a wide set of problems.
• Heuristic methods can be categorized as follows:• Trajectory vs population
• memory-based vs memoryless
• nature-inspired vs non-nature-inspired
8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
http://slidepdf.com/reader/full/serdar-iplikci-optimization-applications-in-the-renewable-energy-systems 7/33The 4th Renewable Energy Systems Winter School │ Prof. Dr. Serdar İPLİKÇİ
Some of the meta-heuristics trajectory methods are :
• Iterated Local Search (ILS)
• Pareto Archived Evolution Strategy (PAES)
• Multi-Objective Simulated Annealing (MOSA)
8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
http://slidepdf.com/reader/full/serdar-iplikci-optimization-applications-in-the-renewable-energy-systems 8/33The 4th Renewable Energy Systems Winter School │ Prof. Dr. Serdar İPLİKÇİ
8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
http://slidepdf.com/reader/full/serdar-iplikci-optimization-applications-in-the-renewable-energy-systems 9/33The 4th Renewable Energy Systems Winter School │ Prof. Dr. Serdar İPLİKÇİ
8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
http://slidepdf.com/reader/full/serdar-iplikci-optimization-applications-in-the-renewable-energy-systems 10/33The 4th Renewable Energy Systems Winter School │ Prof. Dr. Serdar İPLİKÇİ
8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
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8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
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8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
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2.3.4) Quasi-Newton Methods and Second-order Approximate Methods
Davidon-Fletcher-Powell (DFP) :
, + ∆ ∆ ∆
, +
Gauss-Newton (GN):
−
Levenberg-Marquardt (LM): − .
Quasi-Newton Methods
Second-order Approximate Methods
Broydon-Fletcher-Goldfarb-Shanno (BFGS) :
− , + ∆ , +
OPTIMIZATION in RENEWABLE ENERGY SYSTEMS
8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
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OPTIMIZATION in RENEWABLE ENERGY SYSTEMS
14/3316/01/2015
3) Optimization App. in the Renewable and Sustainable Energy Systems
PROBLEM ÇÖZÜM
Community-scale renewable energy systems planning is an
important problem consisting of justifying the allocation
patterns of energy resources and services, formulation of localpolicies regarding energy consumption, economic
development and energy structure, and analysis of
interactions among economic cost, system reliability and
energy-supply security.
• Interval Linear Programming (ILP)
• Chance-Constrained Programming
• Mixed Integer-Linear Programming (MILP)
a long-term dynamic multi-objective planning model for
distribution network expansion along with distributed energy
options
Immune Genetic Algorithm (I-GA)
minimum cost expansion of power transmission networks
under carbon emission trading programs
• Mixed-Integer Programming (MIP)
• Genetic Algorithms (GA)
• Simulated Annealing (SA )
• Tabu Search (TS)
annual peak load forecasting in an electrical power system
with the aim of minimizing the error associated with the
estimated model parameters
Particle Swarm Optimization (PSO)
new renewable energy sources penetration
and congestion management so that electricity supply and
demand are always evenly balanced
• Nelder–Mead Simplex (NMS) and PSO
• Honey Bee Mating Optimization (HBMO)
• Ant Colony Optimization (ACO), ANN, GA
Energy demand prediction • Yapay Sinir Ağları (ANN)
• Destek Vektör Makineleri (SVM)
OPTIMIZATION in RENEWABLE ENERGY SYSTEMS
8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
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OPTIMIZATION in RENEWABLE ENERGY SYSTEMS
15/3316/01/2015
3) Optimization App. in the Renewable and Sustainable Energy Systems3.1) Wind Power
3.3.1) Introduction
•
Wind is a periodical phenomenon for large geographical areas like Mexico.• the increasing sizes of turbines and the lower prices per installed production
capacity of electricity.
• Wind energy systems may not be technically viable in all locations because of
low wind speeds and the fact that it is more unpredictable than solar energy
• Areas where winds are stronger and more constant, such as offshore and high
altitude sites, are preferred locations for wind farms.
8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
http://slidepdf.com/reader/full/serdar-iplikci-optimization-applications-in-the-renewable-energy-systems 16/33The 4th Renewable Energy Systems Winter School │ Prof. Dr. Serdar İPLİKÇİ
OPTIMIZATION in RENEWABLE ENERGY SYSTEMS
16/3316/01/2015
3) Optimization App. in the Renewable and Sustainable Energy Systems3.1) Wind Power
3.3.1) Optimization in Wind Turbine Design
• In particular, two important problems are often considered: wind turbine and wind farm layout
The power output of a turbine is a function of the:• density of the air
• area swept out by the turbine blades
• cube of the wind speed
• Numerous metrics are used to measure the power quality of a wind turbine, such as
• the power factor, reactive power,
• Harmonic distortion
the optimization of the geometrical parameters of the rotor
configuration of stall-regulated horizontal-axis wind turbines with
the aim of achieving the best trade-off performance between the
total energy production per square meter of wind park and cost
Multi-Objective Evolutionary Algorithm
(MOEA)
the optimization of the ranges of gearbox ratios and power ratings
of multihybrid permanent-magnet wind generator systems
Genetic Algorithms (GA)
determining the optimum capacity taking into account uncertainties
arising from wind speed distribution and power–speed
characteristics
Mixed-Integer Nonlinear Programming
(MINLP)
prediction of wind speed at a selected location based on the data
collected at the neighbouring locations
Fuzzy Logic Modelling
OPTIMIZATION in RENEWABLE ENERGY SYSTEMS
8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
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OPTIMIZATION in RENEWABLE ENERGY SYSTEMS
17/3316/01/2015
3) Optimization App. in the Renewable and Sustainable Energy Systems3.1) Wind Power
3.3.2) Optimization in Wind Farm Layout
• Wind farm layout consists of determining the optimum positions of wind turbines
within the farm in order to maximize energy production.
optimal placement of wind turbines for maximum production capacity while
limiting the number of turbines installed and the acreage of land occupied by
each wind farm
Genetic Algorithms (GA)
optimum wind farm configuration problem which is driven by an integralwind farm cost model based on the cumulative net cash flow value
throughout the wind farm’s lifespan
Evolutionary Algorithms (EA)
wind turbine placement based on wind distribution with the aim of both
maximizing the wind energy capture and minimizing an index that
determines constraint violations
Multi-Objective Evolutionary
Algorithm (MOEA)
determining the optimal type, number and placement of wind turbines
considering the given wind conditions and wind park area
Mixed-Integer Nonlinear
Programming (MINLP)
OPTIMIZATION in RENEWABLE ENERGY SYSTEMS
8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
The 4th Renewable Energy Systems Winter School │ Prof. Dr. Serdar İPLİKÇİ
OPTIMIZATION in RENEWABLE ENERGY SYSTEMS
21/3316/01/2015
3) Optimization App. in the Renewable and Sustainable Energy Systems3.4) Geothermal Energy
3.4.1) Introduction
• Geothermal energy is the energy contained as heat inside the Earth. Geothermal heat
pumps are a highly efficient, renewable energy technology for heating and cooling.
• This technology relies on the fact that, at depth, the Earth has a relatively constant
temperature, warmer than the air in winter and cooler than the air in summer.
• The main advantage of using geothermal energy is that this renewable energy source
can provide power 24 h a day due to it is constant, without intermittence problems
compared to other renewable resources such as wind or solar energy.
• It is expensive to build a power station but operating costs are low, resulting in low
energy costs for suitable sites.
• Geothermal power plants now exist in 19 countries, and new plants are commissioned
annually. However, only a small fraction of the geothermal potential has beendeveloped so far, and there is ample space for an accelerated use of geothermal energy
both for electricity generation and direct applications.
optimization of the exploitation system of a low enthalpy geothermal aquifer, with
the aim of determining the annual pumping cost of the required flow and the
amortization cost of the pipe network, which carries the hot water from the wells
to a central water tank, situated on the border of the geothermal field
Genetic Algorithms (GA)
OPTIMIZATION in RENEWABLE ENERGY SYSTEMS
8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
The 4th Renewable Energy Systems Winter School │ Prof. Dr. Serdar İPLİKÇİ 23/3316/01/2015
4) Modeling and Prediction4.1) Introduction
4.1.1) The Concept of Modeling
• Modeling is a method that is used when a real-world «process» cannot be described
analytically or mathematically. Here, the «process» can be all sorts of biological,physical, chemical, electrical, mechanical, meteorological, social and financial
dynamical systems.
• There are many such systems in the real world. For instance, dynamics of weather
conditions is so complex that it cannot be described mathematically, and thus it should
be modeled.similarly, stock market is a very complex system that incorporates manyvariables and parameters. There some problems in the energy systems that need
modeling:
• Electrical load prediction
• Energy demand prediction
• Therefore, when needed, we have to collect sufficient data from such energy systems
and then try to obtain a reliable model.
OPTIMIZATION in RENEWABLE ENERGY SYSTEMS
8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems
The 4th Renewable Energy Systems Winter School │ Prof. Dr. Serdar İPLİKÇİ 32/3316/01/2015
5) Conclusions
• The optimization methods that have beenn used for solving optimization problems inrenewable energy systems have been developed day-by-day. They are especially used
in hybrid systems.
• Some of these methods are based on the traditional methods like mixed-integer,interval linear-programming, Lagrangian relaxation, quadratic programming, andNelder–Mead Simplex, while others are based on some heuristics methods such as GAand PSO.
•
On the other hand, the multi-objective function problems in the energy systems havebeen solved by Pareto-optimization techniques.
• The problems in the renewable energy systems that can be solved by optimizationtechniques are• Planning• Management of supply-demand balance• Optimization of design parameters
• Prediction of power curve• Configuration• Optimization of economical load distribution• Wind-photovoltaic capacity coordination• Modeling and prediction
OPTIMIZATION in RENEWABLE ENERGY SYSTEMS
8/18/2019 Serdar ıplıkçı Optimization Applications in the Renewable Energy Systems