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IEEE International Conference on Power and Energy (PECon)

作者： Jumaat, Siti Amely Musirin, Ismail Othman, Muhammad Murtadha Mokhlis, Hazlie Univ Tun Hussein Onn Malaysia UTHM Fac Elect & Elect Engn Batu Pahat 86400 Johor Malaysia Univ Teknologi MARA Malaysia UiTM Fac Elect Engn Shah Alam 40450 Malaysia Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur Malaysia

ISBN: (纸本)9781467350198;9781467350174

This paper presents evolutionary particle swarm optimization (EPSO) based technique for multiple SVCs optimization, in order to minimize the transmission loss in power system. In this study, static var compensator (SVC) is chosen as the FACTS device for the optimal installation for compensation purposes due to reported promising performances of the devices. Introduction of hybrid form of evolutionary Programming (EP) and particle swarm optimization (PSO) termed as EPSO has resulted promising findings in terms of accuracy and computation time reduction.

关键词： component transmission loss static var compensator particle swarm optimization evolutionary particle swarm optimization cost of installation

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Multi-objective evolutionary particle swarm optimization in the assessment of the impact of distributed generation

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ELECTRIC POWER SYSTEMS RESEARCH 2012年 89卷 100-108页

作者： Maciel, Renan S. Rosa, Mauro Miranda, Vladimiro Padilha-Feltrin, Antonio Sao Paulo State Univ UNESP Dept Elect Engn BR-15385000 Ilha Solteira SP Brazil INESCPorto USE Power Syst Unit P-4200465 Oporto Portugal Univ Porto FEUP Fac Engn P-4200465 Oporto Portugal

This paper proposes a multi-objective approach to a distribution network planning process that deals with the challenges derived from the integration of Distributed Generation (DG). The proposal consists of a multi-objective version of the well-known evolutionary particle swarm optimization method (MEPSO). A broad performance comparison is made between the MEPSO and other multi-objective optimization meta-heuristics, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a Multi-objective Tabu Search (MOTS), using two distribution networks in a given DG penetration scenario. Although the three methods proved to be applicable in distribution system planning, the MEPSO algorithm has presented promising attributes and a constant and high level performance when compared to other methods. (C) 2012 Elsevier BM. All rights reserved.

关键词： Distributed generation planning Multi-objective optimization evolutionary particle swarm optimization Genetic Algorithm Tabu Search

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optimization of autonomous combined heat and power system including PVT, WT, storages, and electric heat utilizing novel evolutionary particle swarm optimization algorithm

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RENEWABLE ENERGY 2018年第Apr.期119卷 490-503页

作者： Lorestani, A. Ardehali, M. M. Amirkabir Univ Technol Tehran Polytech Ctr Excellence Power Syst Dept Elect EngnEnergy Syst Lab Tehran Iran

Renewable energy (RE) sources can be incorporated in design of combined heat and power (CHP) systems, so that the advantages of zero environmental emissions as well as higher energy efficiencies are realized simultaneously. Further, due to inability to dispatch renewable energy sources, the integration of thermal and electricity storages is necessary to enhance the performance of RE-CHP systems in terms of overall cost and reliability to meet thermal and electrical loads. In addition, the utilization of excess electrical energy for conversion to heat could be critical to Meeting thermal load and, hence, maintaining the autonomous operation of RE-CHP systems. The goal of this study is to develop a simulation model for optimization of an autonomous RE-CHP system, where thermal and electrical loads are met utilizing photovoltaic (PV)-thermal (PVT) panel, wind turbines (WTs), thermal energy storage, electrical energy storage, and electric heater (EH). For optimization, a newly developed evolutionary particle swarm optimization (E-PSO) algorithm is introduced and validated. It is shown that, as an autonomous RE-CHP system, the combination of PVT, WT, storages, and EH can effectively meet thermal and electrical loads with an acceptable reliability. Moreover, the results confirm the superiority of the proposed E-PSO algorithm among other methods. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Renewable energy Combined heat and power Solar Wind evolutionary particle swarm optimization

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Optimal integration of renewable energy sources for autonomous tri-generation combined cooling, heating and power system based on evolutionary particle swarm optimization algorithm

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ENERGY 2018年 145卷 839-855页

作者： Lorestani, A. Ardehali, M. M. Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Ctr Excellence Power SystEnergy Syst Lab Tehran Iran

Renewable energy (RE) sources can be integrated to serve autonomous tri-generation combined cooling, heating and power (CCHP) systems, so that the advantages of zero environmental emissions as well as higher energy efficiencies in generation and consumption are realized simultaneously. However, to override the inherent intermittent availability of RE sources and to enhance the performance of RE-CCHP systems, it is necessary to include thermal and electrical storage mechanisms. The objective of this study is to develop a simulation model for optimization of different configuration alternatives of autonomous RE-CCHP system for meeting cooling, heating and electrical loads, based on photovoltaic-thermal (PVT) panel, wind turbine (WT), thermal energy storage (TES), electrical energy storage (EES), absorption chiller (CRABS), electric chiller (CHELEC) and electric heater (EH). For operation of autonomous RE-CCHP system, two operational strategies, namely, following electric load (FEL) and following thermal load (FTL), are used. For optimization, a newly developed evolutionary particle swarm optimization (E-PSO) algorithm is examined and validated. It is demonstrated that the most cost effective configuration alternative of the autonomous RE-CCHP system is PVT+WT+EES+TES+CHABS+EH operating based on FTL operational strategy, where utilization of CHELEC is not needed. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Renewable energy Combined cooling Heating and power Energy storage evolutionary particle swarm optimization

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Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information

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RENEWABLE ENERGY 2015年 75卷 301-307页

作者： Osorio, G. J. Matias, J. C. O. Catalao, J. P. S. Univ Beira Interior R Fonte Lameiro P-6201001 Covilha Portugal INESC ID P-1000029 Lisbon Portugal Univ Lisbon IST P-1049001 Lisbon Portugal

The non-stationary and stochastic nature of wind power reveals itself a difficult task to forecast and manage. In this context, with the continuous increment of wind farms and their capacity production in Portugal, there is an increasing need to develop new forecasting tools with enhanced capabilities. On the one hand, it is crucial to achieve higher accuracy and less uncertainty in the predictions. On the other hand, the computational burden should be kept low to enable fast operational decisions. Hence, this paper proposes a new hybrid evolutionary-adaptive methodology for wind power forecasting in the short-term, successfully combining mutual information, wavelet transform, evolutionary particle swarm optimization, and the adaptive neuro-fuzzy inference system. The strength of this paper is the integration of already existing models and algorithms, which jointly show an advancement over present state of the art. The results obtained show a significant improvement over previously reported methodologies. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Forecasting Wind power evolutionary particle swarm optimization Neuro-fuzzy system Mutual information Wavelet transform

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Differential evolutionary particle swarm optimization (DEEPSO): a successful hybrid

Differential Evolutionary Particle Swarm Optimization (DEEPS...

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1st BRICS Countries Congress on Computational Intelligence / 11th Brazilian Congress on Computational Intelligence (BRICS-CCI and CBIC)

作者： Miranda, Vladimiro Alves, Rui Univ Porto INESC TEC INESC Technol & Sci Rua Campo Alegre 823 P-4100 Oporto Portugal Univ Porto FEUP Fac Engn P-4100 Oporto Portugal

ISBN: (纸本)9781479931941

This paper explores, with numerical case studies, the performance of an optimization algorithm that is a variant of EPSO, the evolutionary particle swarm optimization method. EPSO is already a hybrid approach that may be seen as a PSO with self-adaptive weights or an evolutionary Programming approach with a self-adaptive recombination operator. The new hybrid DEEPSO retains the self-adaptive properties of EPSO but borrows the concept of rough gradient from Differential Evolution algorithms. The performance of DEEPSO is compared to a well-performing EPSO algorithm in the optimization of problems of the fixed cost type, showing consistently better results in the cases presented.

关键词： evolutionary particle swarm optimization Differential Evolution fuzzy clustering unit commitment PAR location

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Profit Based Unit Commitment Using evolutionary particle swarm optimization

Profit Based Unit Commitment Using Evolutionary Particle Swa...

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IEEE AFRICON Conference - Science, Technology and Innovation for Africa

作者： Bikeri, Adline Kihato, Peter Maina, Christopher Jomo Kenyatta Univ Agr & Technol Sch Elect Elect & Informat Engn Juja Kenya Tech Univ Kenya Dept Elect & Power Engn Nairobi Kenya

ISBN: (纸本)9781538627754

The profit based unit commitment (PBUC) problem determines an optimal unit commitment schedule for a generation company (GENCO) participating in a deregulated environment with the aim of maximizing its profit. This is done using predicted prices of energy and other ancillary services including supply of reserve power. Several techniques have been proposed in literature to solve the optimization problem and this paper applies the evolutionary particle swarm optimization (EPSO) algorithm. Simulation results carried out in MATLAB software for a test GENCO with 10 thermal units shows that the EPSO algorithm provides better solutions and has better convergence characteristics than the classic PSO algorithm.

关键词： Profit Based Unit Commitment evolutionary particle swarm optimization GENCO Lagrangian Relaxation

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Fuzzy Modeling and Similarity based Short Term Load Forecasting using evolutionary particle swarm optimization

Fuzzy Modeling and Similarity based Short Term Load Forecast...

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IEEE Power and Energy Society General Meeting

作者： Amit Jain M. Babita Jain IEEE

ISBN: (纸本)9781467327275

There are a lot of uncertainties in planning and operation of electric power system, which is a complex, nonlinear, and non-stationary system. Advanced computational methods are required for planning and optimization, fast control, processing of field data, and coordination across the power system for it to achieve the goal to operate as an intelligent smart power grid and maintain its operation under steady state condition without significant deviations. One of the important aspects to operate power system in such manner is accurate and consistent short term load forecasting (STLF). This paper presents a methodology for the STLF using the similar day concept combined with fuzzy logic approach and evolutionary particle swarm optimization (EPSO) technique. A Euclidean distance norm with weight factors considering the weather variables and day type is used for finding the similar days. Fuzzy logic is used to modify the load curves of the selected similar days of the forecast by generating the correction factors for them. The input parameters for the fuzzy system are the average load, average temperature and average humidity differences of the forecasted previous days and their similar days. These correction factors are applied to the similar days of the forecast day. The tuning of the fuzzy input parameters is done using the EPSO technique on the training data set of the considered data and tested. The results of load forecasting shows that the proposed EPSO tunes fuzzy system provides better results than the fuzzy stand alone system (without EPSO).

关键词： Euclidean norm evolutionary particle swarm optimization Fuzzy logic approach particle swarm optimization Short term load forecasting Similar day method load forecasting power systems European Personnel Selection Office Fuzzy systems fuzzy modeling particle swarm optimization CORRECTION FACTOR input parameter WEIGHTING FACTORS STEADY-STATE CONDITIONS Fuzzy logic

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evolutionary Canonical particle swarm Optimizer - A Proposal of Meta-optimization in Model Selection

Evolutionary Canonical Particle Swarm Optimizer - A Proposal...

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18th International Conference on Artificial Neural Networks (ICANN 2008)

作者： Zhang, Hong Ishikawa, Masumi Kyushu Inst Technol Dept Brain Sci & Engn Kitakyushu Fukuoka 8080196 Japan

ISBN: (纸本)9783540875352

We proposed evolutionary particle swarm optimization (EPSO) which provides a new paradigm of meta-optimization for model selection in swarm intelligence. In this paper, we extend the technique of online evolutionary computation of EPSO to Canonical particle swarm Optimizer (CPSO), and propose evolutionary Canonical particle swarm Optimizer (ECPSO) for optimizing CPSO. In order to effectually evalute the performance of CPSO, a temporally cumulative fitness function of the best particle is adoped in ECPSO as the behavioral representative for entire swarm. Applications of the proposed method to a suite of 5-dimensional benchmark problems well demonstrate the effectiveness. Our experimental results clearly indicate that (1) the proper parameter sets in CPSO for solving various optimization problems are not unique;(2) the values of parameters in them are quite different from that of the original CPSO;(3) the search performance of the optimized CPSO is superior to that of the original CPSO, and to that of RGA/E except for the result of the Rastrigin's benchmark problem.

关键词： canonical particle swarm optimization evolutionary particle swarm optimization temporally cumulative fitness real-coded genetic algorithm model selection

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Electricity prices forecasting by a hybrid evolutionary-adaptive methodology

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ENERGY CONVERSION AND MANAGEMENT 2014年第Apr.期80卷 363-373页

作者： Osorio, G. J. Matias, J. C. O. Catalao, J. P. S. Univ Beira Interior P-6201001 Covilha Portugal INESC ID P-1000029 Lisbon Portugal Univ Lisbon IST P-1049001 Lisbon Portugal

With the restructuring of the electricity sector in recent years, and the increased variability and uncertainty associated with electricity market prices, it has become necessary to develop forecasting tools with enhanced capabilities to support the decisions of market players in a competitive environment. Hence, this paper proposes a new hybrid evolutionary-adaptive methodology for electricity prices forecasting in the short-term, i.e., between 24 and 168 h ahead, successfully combining mutual information, wavelet transform, evolutionary particle swarm optimization, and the adaptive neuro-fuzzy inference system. In order to determine the accuracy, competence and proficiency of the proposed methodology, results from real-world case studies using real data are presented, together with a thorough comparison considering the results obtained with previously reported forecasting tools. Not only is the accuracy an important factor, but also the computational burden is relevant in a comparative study. The results show that it is possible to reduce the uncertainty associated with electricity market prices prediction without using any exogenous data, just the historical values, thus requiring just a few seconds of computation time. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Forecasting Market prices Mutual information Wavelet transform evolutionary particle swarm optimization Neuro-fuzzy

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