Operation planning and decision-making approaches for Volt/Var multi-objective optimization in power distribution systems

被引:18
|
作者
Vitor, Tiago S. [1 ]
Vieira, Jose Carlos M. [2 ]
机构
[1] IFSP, Fed Inst Educ Sci & Technol Sao Paulo, Sao Paulo, SP, Brazil
[2] Univ Sao Paulo, USP, Sao Carlos Sch Engn, Sao Paulo, SP, Brazil
关键词
Conservation voltage reduction; Distributed generation; Distribution systems; Evolutionary multi-objective optimization; Volt/Var control; EVOLUTIONARY ALGORITHMS; CONTROL STRATEGIES; GENETIC ALGORITHM; PENETRATION; REDUCTION; DISPATCH; WIND;
D O I
10.1016/j.epsr.2020.106874
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents a new specialized evolutionary system for Volt/Var optimization (SES-VVO) to efficiently solve the multi-objective optimization problem (MOP) when the number of tap-changing operations of voltage regulating devices is considered together with the conservation voltage reduction (CVR) goals. This problem is a challenging task for evolutionary algorithms (EAs) in terms of meeting voltage limits constraints and reaching near optimal solutions. The proposed mathematical model allowed the development of specialized search mechanisms, which contributed to improve the overall performance of the day-ahead operation planning and the hourly decision-making approaches, giving more realistic and cost saving solutions for distribution systems' operators. Simulations on the IEEE 34-bus system were carried out in the presence of photovoltaic (PV) generation. Comparisons with other methods were conducted to clarify the effectiveness of the proposed method. The SES-VVO corroborated with the development of an advanced strategy that has potential for real-time operation considering the variation of load demand and the intermittence of PV generation, which were modelled by using Monte Carlo simulations. Finally, it was demonstrated that the tap change costs of voltage regulating devices have a decisive impact on the economics when the CVR goals are pursued by the SES-VVO approach proposed in this paper.
引用
收藏
页数:14
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