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multi-objective optimal power flow with FACTS devices

ENERGY CONVERSION AND MANAGEMENT 2011年第2期52卷 903-910页

作者： Basu, M. Jadavpur Univ Dept Power Engn Kolkata India

This paper presents multi-objective differential evolution to optimize cost of generation, emission and active power transmission loss of flexible ac transmission systems (FACTS) device-equipped power systems. In the proposed approach, optimal power flow problem is formulated as a multi-objective optimization problem. FAD'S devices considered include thyristor controlled series capacitor (TCSC) and thyristor controlled phase shifter (TCPS). The proposed approach has been examined and tested on the modified IEEE 30-bus and 57-bus test systems. The results obtained from the proposed approach have been compared with those obtained from nondominated sorting genetic algorithm-II, strength pareto evolutionary algorithm 2 and pareto differential evolution. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： multi-objective optimal power flow multi-objective differential evolution Flexible ac transmission systems

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multi-objective optimal power flow using grasshopper optimization algorithm

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optimal CONTROL APPLICATIONS & METHODS 2024年第2期45卷 623-645页

作者： Mandal, Barun Roy, Provas Kumar Kalyani Govt Engn Coll Dept Elect Engn Kalyani India Kalyani Govt Engn Coll Dept Elect Engn Kalyani West Bengal India

This paper introduces grasshopper optimization algorithm to efficiently prove its superiority in the optimal power flow problem. To demonstrate the efficiency of the proposed algorithm, it is implemented on the standard IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus test systems with different objectives that reveal presentation indices of the power system. Twelve different cases, in single and multi-objective optimization space, are considered on different curves of fuel cost, environmental pollution emission, voltage profile, and active power loss. The simulation results obtained from grasshopper optimization algorithm techniques are compared to other new evolutionary optimization methods surfaced in the current state-of-the-art literature. It is revealed that the proposed approach secures better consequence over the other newly originated popular optimization techniques and reflects its improved quality solutions and faster convergence speed. The results obtained in this work demonstrate that the grasshopper optimization algorithm method can successfully be applied to solve the non-linear problems connected to power systems. 1. This paper introduces grasshopper optimization algorithm (GOA) to efficiently prove its superiority in the optimal power flow problem (OPF). 2. GOA is implemented on the standard IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus test systems with different objectives. 3. It is revealed that the proposed GOA secures better consequence over the other newly originated popular optimization techniques and reflects its improved quality solutions and faster convergence ***

关键词： backtracking search optimization algorithm emission grasshopper optimization algorithm multi-fuels multi-objective optimal power flow valve-point effect voltage deviation

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multi-objective optimal power flow solutions using a constraint handling technique of evolutionary algorithms

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SOFT COMPUTING 2020年第4期24卷 2999-3023页

作者： Biswas, Partha P. Suganthan, P. N. Mallipeddi, R. Amaratunga, Gehan A. J. Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Kyungpook Natl Univ Daegu South Korea Univ Cambridge Dept Engn Cambridge England

In power systems, optimal power flow (OPF) is a complex and constrained optimization problem in which quite often multiple and conflicting objectives are required to be optimized. The traditional way of dealing with multi-objective OPF (MOOPF) is the weighted sum method which converts the multi-objective OPF into a single-objective problem and provides a single solution from the set of Pareto solutions. This paper presents MOOPF study applying multi-objective evolutionary algorithm based on decomposition (MOEA/D) where a set of non-dominated solutions (Pareto solutions) can be obtained in a single run of the algorithm. OPF is formulated with two or more objectives among fuel (generation) cost, emission, power loss and voltage deviation. The other important aspect in OPF problem is about satisfying power system constraints. As the search process adopted by evolutionary algorithms is unconstrained, for a constrained optimization problem like OPF, static penalty function approach has been extensively employed to discard infeasible solutions. This approach requires selection of a suitable penalty coefficient, largely done by trial-and-error, and an improper selection may often lead to violation of system constraints. In this paper, an effective constraint handling method, superiority of feasible solutions (SF), is used in conjunction with MOEA/D to handle network constraints in MOOPF study. The algorithm MOEA/D-SF is applied to standard IEEE 30-bus and IEEE 57-bus test systems. Simulation results are analyzed, especially for constraint violation and compared with recently reported results on OPF.

关键词： multi-objective optimal power flow Cost Emission MOEA/D algorithm Superiority of feasible solutions (SF)

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multi-objective optimal power flow considering wind power cost functions using enhanced PSO with chaotic mutation and stochastic weights

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ELECTRICAL ENGINEERING 2019年第3期101卷 699-718页

作者： Man-Im, Anongpun Ongsakul, Weerakorn Singh, J. G. Madhu, M. Nimal Asian Inst Technol Sch Environm Resources & Dev Dept Energy Environm & Climate Change Khlong Luang Dist 12120 Pathum Thani Thailand

A multi-objective optimal power flow (OPF) solution using an enhanced NSPSO, incorporating chaotic mutation and stochastic weight trade-off features, is proposed here. The objective functions considered embody the different aspects of the power system, viz. financial, reliability and operational efficiency. The proposed OPF algorithm balances the exploration of global and the utilization of local bests with the stochastic weight and dynamic coefficient trade-off techniques, thus enhancing the searching capability. Also, for countering the premature convergence issue and to improve diversity, the feature of chaotic mutation is incorporated. The pareto-optimal front is provided by the combination of crowding distance approach and non-dominated sorting principle. The best solution can be obtained from a dual-stage process, by selecting from the collective of local best compromises using a fuzzy function. To assess the performance, the proposed method is tested on a standard IEEE 30-bus test system and is compared with popular multi-objective algorithms. IEEE 118-bus system is also used for testing applicability to large systems. Based on the obtained results, the proposed method gives a more reliable operating solutions, better optimal front and hence an improved solution providing a better trade-off.

关键词： Particle swarm optimization multi-objective optimal power flow Wind power Operation fuel cost Active power loss

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multi-objective optimal power flow Using Stochastic Weight Trade-off Chaotic NSPSO

Multi-objective Optimal Power Flow Using Stochastic Weight T...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA 2015)

作者： Man-Im, Anongpun Ongsakul, Weerakorn Singh, J. G. Boonchuay, Chanwit Asian Inst Technol Sch Environm Resources & Develoment Energy Field Study Pathum Thani Thailand Rajamangala Univ Technol Rattanakosin Fac Ind & Technol Dept Elect Engn Technol Prachuap Khiri Khan Thailand

ISBN: (纸本)9781509012374

In this paper, a stochastic weight trade-off chaotic non-dominated sorting particle swarm optimization (SWTC_NSPSO) is proposed for solving multi-objective optimal power flow considering generator fuel cost and active power loss. The SWTC_NSPSO algorithm improves the solution search capability by balancing between global best exploration and local best utilization through the stochastic weight trade-off technique together with dynamistic coefficients trade-off methods. The chaotic mutation applied to the inertia weight factor enhances the search capability by diversifying the search space. Moreover, the non-dominated sorting and crowding distance techniques are used to provide the optimal Pareto front. Simulation results on IEEE 30-bus test system indicate that SWTC_NSPSO can provide a lower and wider Pareto front than non-dominated sorting genetic algorithm II (NSGAII), non-dominated sorting particle swarm optimization (NSPSO), non-dominated sorting chaotic particle swarm optimization (NS_CPSO), and a stochastic weight trade-off non-dominated sorting particle swarm optimization (SWT_NSPSO) leading to a lower generator fuel cost and a higher system reliability trade-off solution.

关键词： Particle Swarm Optimization multi-objective optimal power flow operation flue cost active power loss

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multi-objective optimal power flow with Transient Stability Constraints

Multi-objective Optimal Power Flow with Transient Stability ...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Lu, Jin-ling Zhang, Qiang North China Elect Power Univ Dept Elect Engn Baoding 071003 Hebei Peoples R China

ISBN: (纸本)9783038351153

In this paper, the deficiency of the general transient stability constrained OPF model was analyzed, on the basis of which, a multi-objective transient stability constrained OPF model was proposed. The transient stability constraints was to be as a objective function instead of the general constraints, which and power generation cost function were taken weighted sum to be converted into a single-objective optimization model. multiple optimal solutions under different weights were given based on the original dual interior point method, which can offer a variety of options for operating personnel according to actual needs. In terms of improving the efficiency of the algorithm, The two groups of differentiated rotor equations were eliminated to be one set of equations. And the transient differential equations were dispersed to inequality constraints instead of equality constraints. the dimensions of the correction equation and the scale of the problem solved were significantly reduced. Numerical simulations on the New England 10-generator 39-bus power system, demonstrated the effectiveness and feasibility of the proposed method.

关键词： Transient stability constrained multi-objective optimal power flow Primal-dual interior point method Time-domain simulation power system

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multi-objective optimal power flow model for power system operation dispatching

Multi-objective optimal power flow model for power system op...

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IEEE PES Asia-Pacific power and Energy Engineering Conference (APPEEC)

作者： Tan, Shuwen Lin, Shunjiang Yang, Liuqing Zhang, Anqi Shi, Weiwei Feng, Hanzhong S China Univ Technol Sch Elect Power Engn Guangzhou 510641 Guangdong Peoples R China

ISBN: (纸本)9781479925223

For the basic requirements of power system operation of security, high quality, economy, environmental protection, a multi-objective optimal power flow(MOPF) model is established to minimize three objective functions of load buses voltage deviations, network active power loss, pollution gas emissions and meanwhile to satisfy the security constraints of power transmission limits in lines. The normal boundary intersection method (NBI) is adopted to transform three-objective optimal power flow model into a series of single-objective optimization model, and then the interior point method is used to obtain the evenly distributed Pareto frontier in objective functions space. According to fuzzy membership and entropy weight of various targets, the comprehensive compromise optimal solution can be identified from the Pareto frontier surface, which is employed as the operation dispatching scheme of the system. By the multi-objective optimization calculation of the IEEE 9-buses system and the IEEE 39-buses system, the results validate the effectiveness of the proposed model and algorithm, and indicate that the comprehensive compromised optimal solution can be used as an optimal dispatching scheme of power system operation.

关键词： multi-objective optimal power flow Pareto frontier Normal Boundary Intersection method optimal dispatching power system

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multi-objective optimal power flow of thermal-wind-solar power system using an adaptive geometry estimation based multi-objective differential evolution

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APPLIED SOFT COMPUTING 2023年第PartA期149卷

作者： Huy, Truong Hoang Bao Doan, Hien Thanh Vo, Dieu Ngoc Lee, Kyu-haeng Kim, Daehee Soonchunhyang Univ Dept Future Convergence Technol Asan 31538 Chuncheongnam D South Korea Sogang Univ Dept Elect Engn Seoul South Korea Ho Chi Minh City Univ Technol HCMUT Dept Power Syst 268 Ly Thuong Kiet StDist 10 Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Ho Chi Minh City Vietnam Dankook Univ Dept Mobile Syst Engn Yongin 16890 Gyeonggi Do South Korea

Sustainable energy is a key component of sustainable development. The current grid can be supplied by fossil fuel generators and renewable energy sources (RESs)-based generators, such as solar photovoltaic (PV) and wind power generators. In an electrical network, power generation from several sources must be optimally coordi-nated to ensure efficient and economical operation. However, the intermittent and uncertain nature of RESs complicate the operation of power systems. In this study, an adaptive geometry estimation-based multi-objective differential evolution (AGE-MODE) method is proposed for multi-objective optimal power flow in a hybrid power system of thermal, wind, and solar energy sources (MOOPF-TWS). In the proposed approach, wind and solar PV power outputs are predicted based on Weibull and lognormal probability distribution functions, respectively. Therefore, the generation costs for solar and wind power can be divided into direct costs, penalty costs for underestimation, and reserve costs for overestimation. Furthermore, the emissions, voltage deviation, and real power loss are considered in particular cases. AGE-MODE is applied to modified IEEE 30-bus and 57-bus systems, where different case studies are simulated with combinations of two-, three-, and four-objective optimizations in MOOPF-TWS problems. Comparisons between AGE-MODE and other recently developed multi-objective methods demonstrate its effectiveness in resolving MOOPF-TWS problems, particularly for cases with more than two objectives.

关键词： Adaptive geometry estimation Differential evolution multi-objective optimal power flow Solar power Wind power

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A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm

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ELECTRICAL ENGINEERING 2015年第1期97卷 1-12页

作者： Kahourzade, Solmaz Mahmoudi, Amin Bin Mokhlis, Hazlie Univ Malaya UMPEDAC Kuala Lumpur Malaysia Univ Malaya Dept Elect Engn Kuala Lumpur Malaysia

This paper compares the performance of three population-based algorithms including particle swarm optimization (PSO), evolutionary programming (EP), and genetic algorithm (GA) to solve the multi-objective optimal power flow (OPF) problem. The unattractive characteristics of the cost-based OPF including loss, voltage profile, and emission justifies the necessity of multi-objective OPF study. This study presents the programming results of the nine essential single-objective and multi-objective functions of OPF problem. The considered objective functions include cost, active power loss, voltage stability index, and emission. The multi-objective optimizations include cost and active power loss, cost and voltage stability index, active power loss and voltage stability index, cost and emission, and finally cost, active power loss, and voltage stability index. To solve the multi-objective OPF problem, Pareto optimal method is used to form the Pareto optimal set. A fuzzy decision-based mechanism is applied to select the best comprised solution. In this work, to decrease the running time of load flow calculation, a new approach including combined Newton-Raphson and Fast-Decouple is conducted. The proposed methods are tested on IEEE 30-bus test system and the best method for each objective is determined based on the total cost and the convergence values of the considered objectives. The programming results indicate that based on the inter-related nature of the objective functions, a control system cannot be recommended based on individual optimizations and the secondary criteria should also be considered.

关键词： Evolutionary programming Genetic algorithm multi-objective optimal power flow Particle swarm Pareto optimal method

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Enhanced Genetic Algorithm based computation technique for multi-objective optimal power flow solution

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INTERNATIONAL JOURNAL OF ELECTRICAL power & ENERGY SYSTEMS 2010年第6期32卷 736-742页

作者： Kumari, M. Sailaja Maheswarapu, Sydulu Natl Inst Technol Dept Elect Engn Warangal Andhra Pradesh India

optimal power flow (OPF) is used for developing corrective strategies and to perform least cost dispatches. In order to guide the decision making of power system operators a more robust and faster OPF algorithm is needed. OPF can be solved for minimum generation cost, that satisfies the power balance equations and system constraints. But, cost based OPF solutions usually result in unattractive system losses and voltage profiles. In the present paper the OPF problem is formulated as a multi-objective optimization problem, where optimal control settings for simultaneous minimization of fuel cost and loss, loss and voltage stability index, fuel cost and voltage stability index and finally fuel cost, loss and voltage stability index are obtained. The present paper combines a new Decoupled Quadratic Load flow (DQLF) solution with Enhanced Genetic Algorithm (EGA) to solve the OPF problem. A Strength Pareto Evolutionary Algorithm (SPEA) based approach with strongly dominated set of solutions is used to form the pareto-optimal set. A hierarchical clustering technique is employed to limit the set of trade-off solutions. Finally a fuzzy based approach is used to obtain the optimal solution from the tradeoff curve. The proposed multi-objective evolutionary algorithm with EGA-DQLF model for OPF solution determines diverse pareto optimal front in just 50 generations. IEEE 30 bus system is used to demonstrate the behavior of the proposed approach. The obtained final optimal solution is compared with that obtained using Particle Swarm Optimization (PSO) and Fuzzy satisfaction maximization approach. The results using EGA-DQLF with SPEA approach show their superiority over PSO-Fuzzy approach. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Enhanced Genetic Algorithms Particle Swarm Optimization multi-objective optimal power flow Decoupled Quadratic load flow Strength Pareto Evolutionary Algorithm

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