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Optimizing reserve-constrained economic dispatch: Cheetah optimizer with constraint handling method in static/dynamic/single/multi-area systems

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ENERGY 2024年 313卷

作者： Zare, Mohsen Farhang, Saman Akbari, Mohammad Amin Azizipanah-Abarghooee, Rasoul Trojovsky, Pavel Jahrom Univ Fac Engn Dept Elect Engn Jahrom Iran Proximus Luxembourg Luxembourg Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran WSP Energy Advisory Dept Manchester England Univ Hradec Kralove Fac Sci Dept Math Hradec Kralove 50003 Czech Republic

Managing the power-generating units on the horizon of one-day scheduling considering all practical equality, inequality, and realistic constraints has always been a significant challenge in power systems. The constraints, such as valve-point effects, prohibited operation zones, transmission losses, and ramp rate limits corresponding to dynamic economic dispatch, change the optimization problem to a complex, nonlinear, non-smooth, high- dimensional, and non-convex one. Therefore, an efficient algorithm and a suitable constraint handling method are needed to solve practical constrained dynamic economic dispatch (DED). This paper proposes a newly developed Cheetah optimizer (CO) that coincides with a backward-forward constraint handling method to tackle the optimum operational cost. The CO algorithm's performance is verified using eight DED and ED test cases from five different systems. The suggested technique is compared with several state-of-theart optimization algorithms regarding the effectiveness of achieved results. Numerical results evaluate the performances of the CO advantages on the benchmarks and the DED cases where the results of 5-,10- and 30-unit systems are enhanced in different cases. To achieve a higher level of realism in modeling the ED and DED problem, adopting a multi-area DED (MADED) approach has emerged as a promising strategy. In this paper, three distinct cases of MAED and MADED problems are investigated to demonstrate the effectiveness of the proposed method. Specifically, incases involving DED-10 and 30 units, two-area 40 units ED, and four-area 40 units DED, significantly improved solutions were obtained compared to previous studies.

关键词： Cheetah optimizer constraint handling method Multi-area dynamic economic dispatch Reserve-constrained dynamic economic dispatch Spinning reserve requirement Valve point effect

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Study on the constraint handling method for high-dimensional optimization of cascade reservoirs

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JOURNAL OF CLEANER PRODUCTION 2024年 449卷

作者： Li, Shuliang He, Zhongzheng Huang, Wei Wei, Bowen Yan, Feng Fu, Jisi Xiong, Bin Nanchang Univ Sch Infrastruct Engn Nanchang 330031 Peoples R China Nanchang Univ Key Lab Poyang Lake Environm & Resource Utilizat Minist Educ Nanchang 330031 Peoples R China

Multicomplex constraints often need to be considered in the optimal operation of cascade reservoirs with highdimensional decision variables, making it difficult for traditional optimization methods and modern intelligent algorithms to solve such problems. Therefore, this study proposes a constraint handling method combining a penalty function nested DPSA-POA and an intelligent algorithm, to determine the optimal flood control operation of cascade reservoirs in the middle reaches of the Ganjiang River, a problem with decision variables of up to 2196 dimensions. The results indicate that the constraint handling method proposed in this paper can solve highdimensional optimization problems in three modes: continuous nesting (Mode 1), optimization with intelligence after obtaining a feasible solution (Mode 2), and optimization with the DPSA-POA after obtaining a feasible solution (Mode 3). Of the three modes, Mode 2 has the highest accuracy, but its calculation time is approximately 10 h. Although the accuracy of Mode 3 is slightly worse, can only achieve 98% - 99% of Mode 2, its calculation time is only approximately 1 - 3 h. The comprehensive performance of Mode 1 is poor, the convergence accuracy can only reach 97% of Mode 2, which corresponds to a calculation time of approximately 4 - 6 h. The existing superiority of feasibility (SF), stochastic ranking (SR), penalty function (PF), epsilon -constraint (EC) and adaptive epsilon -constraint (Adaptive EC) methods cannot converge to a feasible solution stably, and the accuracy of the results of these methods under the condition of obtaining a feasible solution is significantly lower than that of Mode 2 and Mode 3, reaching only 95%.

关键词： High -dimensional optimization problem constraint handling method DPSA-POA Cascade reservoirs Multiple objectives

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An efficient geometric constraint handling method for surrogate-based aerodynamic shape optimization

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ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2023年第1期17卷 Article: e2153173页

作者： Wang, Kai Han, Zhong-Hua Zhang, Ke-Shi Song, Wen-Ping Natl Key Lab Sci & Technol Aerodynam Design & Res Xian Peoples R China Northwestern Polytech Univ Inst Aerodynam & Multidisciplinary Design Optimiza Sch Aeronaut Xian Peoples R China

handling a large number of geometric constraints brings a big challenge to the surrogate-based aerodynamic shape optimization (ASO) driven by computational fluid dynamics (CFD). It is not feasible to calculate the geometric constraint functions directly during the sub-optimization of a surrogate-based optimization, as the geometric constraint functions are to be evaluated thousands of times for each updating cycle and the total cost of a number of cycles can be prohibitive. This article proposes an efficient method of handling geometric constraints within the framework of a surrogate-based optimization to address this problem. The core idea is to use the Kreisselmeier-Steinhauser (KS) method to aggregate all geometric constraints into one that can be approximated by a cheap surrogate model, in order to avoid the large computational cost associated with tremendous calculation of geometric constraint values. The proposed method is verified by an analytical test case. Then, the proposed method is demonstrated and compared with the methods of building surrogate models of all geometric constraints and calculating all geometric constraints directly during each sub-optimization by drag minimizations of NACA0012 air foil and ONERA M6 wing in transonic flows. To investigate the ability of the proposed method for handling various geometric constraints, drag minimization of CRM wing in viscous transonic flow driven by CFD is performed. Results show that the proposed method can dramatically improve the optimization efficiency of ASO with the number of geometric constraints ranging from 15 to 1429 and the number of types of geometric constraints up to 3, which offers great potential for handling a larger number and more types of geometric constraints.

关键词： Surrogate-based optimization aerodynamic shape optimization constraint handling method constrained optimization

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Hydrothermal coordination by bi-level optimization and composite constraint handling method

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 62卷 476-489页

作者： Hajinassiry, Mojtaba Amjady, Nima Sharifzadeh, Hossein Semnan Univ Dept Elect Engn Semnan Iran

This paper presents a new adaptive bi-level optimization approach to solve short-term hydrothermal coordination (STHTC) problem with AC network constraints. The proposed bi-level approach is composed of an integer coded genetic algorithm (ICGA) to solve unit commitment (UC) in the upper level and a new modified chaotic simulated annealing (MCSA) technique to determine economic dispatch (ED) for each time interval in the lower level. Moreover, a new composite constraint handling method is also presented, which enhances the efficiency of the proposed approach to solve STHTC problem with AC constraints. The proposed methodology is tested on the 9-bus and IEEE 118-bus test systems. The obtained results confirm the validity of the developed approach. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Short-term hydrothermal coordination Bi-level optimization approach ICGA MCSA constraint handling method

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Voting enabled dynamic constraint handling portfolios for evolutionary multi/many-objective optimization

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INFORMATION SCIENCES 2025年 694卷

作者： Zhou, Jiajun Liu, Zhao Li, Yongxiang Lu, Chao Gao, Liang China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Guizhou Univ Engn Sci Sch Mech Engn Bijie 551700 Peoples R China Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

constraint handling technique (CHT) plays a key role in handling constrained multi-objective optimization problems (CMOPs). In general, CHTs are associated with their own pros and cons under different scenarios, giving rise to an inherent risk in the selection of appropriate CHTs. To take advantages of diverse CHTs, we design a novel competitive co-evolution scheme for CHT portfolios in which multiple complementary CHTs are synergized via a single-population competitive ensemble framework. A self-organizing voting strategy is developed to allocate restrictive resources to constituent CHTs adaptively according to the feedbacks of CHTs as the search proceeds. In addition, an archive update strategy is designed to facilitate effective usage of potential solutions and achieve better compromise between convergence and diversity. Then the concrete algorithm, namely, VP-CMOEA, is presented. We have comprehensively evaluated our proposal on diverse benchmark functions and real-world applications. Empirical results demonstrate the better generality of VP-CMOEA compared with state-of-the-art peers.

关键词： Algorithm configuration Multi-objective optimization Evolutionary algorithm constraint handling method Dynamic resource allocation

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Unit Commitment Strategy Based on Multi Energy Power System 6

Unit Commitment Strategy Based on Multi Energy Power System

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6th International Conference on Renewable Energy and Power Engineering (REPE)

作者： Du, Jianwei Yao, Yubin Yang, Fan Liu, Mingyu Dalian Maritime Univ Sch Marine Elect Engn Dalian Peoples R China

ISBN: (纸本)9798350306194;9798350306187

In order to promote the absorption of wind and photovoltaic power generation, this paper proposes a two-stage constraint handling method for the multi-energy unit commitment problem. In the first stage, constraints such as minimum startup and shutdown times, unit output, and power balance are processed. In the second stage, handling equality constraints such as energy storage capacity and total shiftable load. The coupling of these two stages enables quick and effective seek the optimal startup and shutdown solution, minimizing the operating cost of the system. A case involving 10 thermal power units, wind farms, and a photovoltaic plant was used to test the performance of the proposed method. The results show that the proposed method can efficiently obtain a better unit commitment solution and improve the system economy.

关键词： unit commitment constraint handling method shiftable load intelligent algorithm

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A fuzzy logic constrained particle swarm optimization algorithm for industrial design problems

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Sun, Bo Peng, Peixi Tan, Guang Pan, Mingjun Li, Luntong Tian, Yonghong Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Peoples R China Pengcheng Lab Shenzhen Peoples R China Peking Univ Shenzhen Grad Sch Sch Elect & Comp Engn Shenzhen Peoples R China Peking Univ Natl Engn Res Ctr Visual Technol Sch Comp Sci Beijing Peoples R China

Most of the industrial design problems have non-linear constraints, high computational cost, non-convex, complicated, and large number of solution spaces. This poses a challenge for algorithms to effectively handle constraints and improve solution accuracy. To address these challenges, a fuzzy logic particle swarm optimization algorithm incorporating a correlation-based constraint handling method (FILPSO-SCA") is proposed. In FILPSO-SCA", an adaptive " constraint handling method with correlation analysis is introduced to dynamically adjust the utilization of constraints and the objective function information. The particle swarm optimization algorithm is employed as the searcher, and to augment its search capability, a set of fuzzy logic rules integrating individual feasibility is designed. These rules dynamically generate parameters in learning strategies by considering fitness and the distance between individuals. To mitigate premature convergence problems, we introduce an individual learning mechanism utilizing stagnation detection. 28 constrained optimization problems and 2 industrial design problems are utilized for comparison with 16 well-known constrained evolutionary algorithms. The proposed algorithm ranks first among the 16 comparative algorithms, with a success rate of 100% in solving industrial design problems.

关键词： Constrained optimization evolutionary algorithms Particle swarm optimization Spearman correlation analysis Fuzzy logic constraint handling method

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Integration of the wind and solar power for the dynamic economic emission dispatch with the charging and discharging of plug-in electric vehicles

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 138卷

作者： Zou, Dexuan Ma, Lejie Li, Can Ouyang, Haibin Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Jiangsu Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Guangdong Peoples R China

Wind and solar power are incorporated into the dynamic economic emission dispatch with the plug-in electric vehicles (DEED-PEV) in this paper, seeking to reduce the generation cost and pollutant emission significantly. A Laplacian non-dominated sorting genetic algorithm-II (LNSGA-II) and a constraint handling method (CHM) are proposed for the DEED-PEV. The LNSGA-II applies the Laplace distribution to both the crossover and mutation. The Laplace-based crossover makes each individual carry out unidirectional search towards its partner and enhances the information exchange between each pair of individuals. The Laplace-based mutation makes each individual carry out bi-directional search surrounding itself and adjust the searching range adaptively. Therefore, the LNSGA-II is able to explore and exploit the decision space of the DEED-PEV sufficiently, contributing to the reductions of the generation cost and pollutant emission in the objective space. For the variables of every individual, the CHM limits them within the feasible operating zones. The CHM also limits them within the dynamic boundaries. It further adjusts them according to the power balances. The power generation constraints and up/down ramp rate constraints can be always satisfied while the elimination of the total violation of the other constraints can be expedited. Therefore, the CHM can transform infeasible individuals to feasible ones rapidly. Four strategies are investigated for six PEV charging/discharging scenarios. The first three strategies consider no more than one kind of renewable energy generation while the fourth strategy involves both the wind power and solar power. Experimental results suggest that the fourth strategy is more economical and environment-friendly than the other three strategies. Furthermore, the LNSGA-II outperforms the other three competitor algorithms for six DEED-PEV problems in the light of hypervolume, coverage rate and spacing.

关键词： Dynamic economic emission dispatch Plug-in electric vehicles Wind power Solar power Laplacian non-dominated sorting genetic algorithm-II constraint handling method constraint handling method

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DC-SHADE-IF: An infeasible-feasible regions constrained optimization approach with diversity controller

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期224卷

作者： Li, Wei Sun, Bo Sun, Yafeng Huang, Ying Cheung, Yiu-ming Gu, Fangqing Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Peoples R China Gannan Normal Univ Sch Math & Comp Sci Ganzhou 341000 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Guangdong Univ Technol Sch Appl Math Guangzhou Peoples R China

To address constrained optimization problems (COPs), there are two crucial trade-offs in preeminent con-strained evolutionary algorithms: (1) the trade-off between exploration and exploitation, and (2) the one between the constraints and the objectives. Moreover, the problem of inaccurate diversity regulation in constrained optimization evolutionary algorithms has yet to be completely solved. To achieve the first trade -off, this paper designs a diversity controller (DC) based on a small-world network. Then, the fitness distance correlation information is applied to adjust the reconnection probability of the small-world network to dynamically control the diversity. To achieve the second trade-off, we propose an infeasible-feasible regions constraint handling method (IF). There are two stages in the IF method. The first stage is to search the boundary between the infeasible and feasible regions, while the second stage adopts the self-adaptive Epsilon constraint handling method. Furthermore, combining the DC and IF constraint handing methods into the success-history-based parameter adaptive differential evolution (SHADE) algorithm, a DC-SHADE-IF algorithm is proposed. Twenty-eight constrained optimization problems provided in the CEC2017 competition on constrained real parameter optimization are employed to compare the performance between the proposed DC-SHADE-IF and seven state-of-the-art algorithms. Besides that, two real-world constrained optimization problems are employed to examine the performance of the proposed DC-SHADE-IF in real-world problems. Experimental results show the superior performance of the proposed algorithm in terms of accuracy and convergence.

关键词： Constrained optimization evolutionary algorithms Diversity Small-world network Fitness distance correlation constraint handling method

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Differential evolution algorithm with improved crossover operation for combined heat and power economic dynamic dispatch problem with wind power

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第4期10卷 1821-1837页

作者： Li, Mengdi Zou, Dexuan Ouyang, Haibin Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Jiangsu Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Guangdong Peoples R China

This paper proposes a differential evolution algorithm with improved crossover operation (ICRDE) to deal with combined heat and power dynamic economic dispatch (CHPDED) problems with wind power. First, the improved crossover operation is used to maintain the population diversity by using original individuals, first mutated individuals, and second mutated individuals. Second, the scaling factor and weighted factor are incorporated into the mutation operation to improve the convergence efficiency of the algorithm. Third, adaptive control parameters are introduced to balance local exploitation and global exploration. Moreover, after being updated by the mutation and crossover operation of ICRDE at each generation, the solutions of ICRDE will be further amended using a constraint handling method, which improves the chance of acquiring feasible solutions. Experimental results demonstrate that ICRDE has strong global optimization ability and surpasses the compared algorithms for the CEC2017 benchmark functions, the combined heat and power economic dispatch problems, and the CHPDED problem with and without wind power.

关键词： combined heat and power dynamic economic dispatch wind power differential evolution improved crossover operation weighted factor adaptive control parameters constraint handling method

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