An Improved NSGA-III Algorithm for Optimal Power Dispatch with Power Flow Constraints

被引：1
作者：
Tian, Yazhang [1 ]
Zhang, Huifeng [1 ]
Ma, Haining [1 ]
机构：
[1] Nanjing Univ Posts & Telecommun, Inst Adv Technol Carbon Neutral, Nanjing, Peoples R China
来源：
2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC | 2023年
关键词：
NSGA-III; Multi-objective optimization; Differential evolution; CULTURAL DIFFERENTIAL EVOLUTION; ECONOMIC EMISSION;
D O I：
10.1109/CCDC58219.2023.10326616
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Optimal power dispatching for power systems is a non-linear, non-convex and non-smooth optimization problem with multiple competing objective functions and non-linear constraints. In this paper, an improved NSGA-III algorithm is proposed to solve the power optimisation scheduling problem for power systems. The algorithm utilizes a differential evolutionary selection variation strategy to enhance convergence and diversity. In addition, a hybrid multi-constraint processing mechanism is proposed to enhance the exploration capability. The results of the method on an IEEE 30-bus system are compared with other operators. The simulation results show the superiority and feasibility of the improved NSGA-III algorithm in solving the power optimization scheduling problem for power systems.
引用
收藏
页码：1026 / 1031
页数：6
相关论文
共 22 条
[1]   An Adapted Nondominated Sorting Genetic Algorithm III (NSGA-III) With Repair-Based Operator for Solving Controller Placement Problem in Software-Defined Wide Area Networks [J].
Adekoya, Oladipupo ;
Aneiba, Adel .
IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, 2022, 3 :888-901
[2]  
[Anonymous], 2014, ENERGY CONVERSION MA, DOI DOI 10.1016/J.ENCONMAN.2013.09.028
[3]  
Lafeta TFD, 2019, IEEE SYS MAN CYBERN, P3673, DOI 10.1109/SMC.2019.8914158
[4]   Controller of Fatigue Testing Machine for Aerospace Thermal Connections based on Improved NSGA-III Algorithm [J].
Duan, Jianguo ;
Shao, Fan ;
Zhou, Ying ;
Zhang, Qinglei .
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2022, 47 (02) :1873-1883
[5]  
Fu Y., 2014, IMPROVED NSGAII SOLV
[6]   Solving interval many-objective optimization problems by combination of NSGA-III and a local fruit fly optimization algorithm [J].
Ge, Fawei ;
Li, Kun ;
Han, Ying .
APPLIED SOFT COMPUTING, 2022, 114
[7]   An improved NSGA-III algorithm based on distance dominance relation for many-objective optimization [J].
Gu, Qinghua ;
Xu, Qingsong ;
Li, Xuexian .
EXPERT SYSTEMS WITH APPLICATIONS, 2022, 207
[8]   Multi-objective planning for voltage sag compensation of sparse distribution networks with unified power quality conditioner using improved NSGA-III optimization [J].
Guo Yubo ;
Zhu Xiaoguang ;
Deng Junbo ;
Li Shurong ;
Li Han .
ENERGY REPORTS, 2022, 8 :8-17
[9]   Fuzzy Inference NSGA-III Algorithm-Based Multi-Objective Optimization for Switched Reluctance Generator [J].
Li, Jie ;
Li, Yihui ;
Wang, Yanbo .
IEEE TRANSACTIONS ON ENERGY CONVERSION, 2021, 36 (04) :3578-3581
[10]   A chaos-coupled multi-objective scheduling decision method for liner shipping based on the NSGA-III algorithm [J].
Ma, Weihao ;
Zhang, Jinfeng ;
Han, Yueyi ;
Zheng, Huarong ;
Ma, Dongfang ;
Chen, Mingzhang .
COMPUTERS & INDUSTRIAL ENGINEERING, 2022, 174
123