Optimized Energy Storage System Configuration for Voltage Regulation of Distribution Network With PV Access

被引:10
|
作者
Li, Qiang [1 ]
Zhou, Feijie [2 ]
Guo, Fuyin [2 ]
Fan, Fulin [3 ]
Huang, Zhengyong [1 ]
机构
[1] Chongqing Univ, Sch Elect Engn, Chongqing, Peoples R China
[2] Northeast Elect Power Univ, Key Lab Modern Power Syst Simulat & Control & Ren, Minist Educ, Jilin, Jilin, Peoples R China
[3] Univ Strathclyde, Dept Elect & Elect Engn, Glasgow, Lanark, Scotland
来源
基金
加拿大自然科学与工程研究理事会;
关键词
distribution network; energy storage system; particle swarm optimization; photovoltaic energy; voltage regulation; REACTIVE POWER-CONTROL; OVERVOLTAGE PREVENTION; COORDINATED CONTROL; RISE MITIGATION; HIGH PENETRATION; SMART GRIDS; CURTAILMENT; WIND; INVERTERS; IMPACT;
D O I
10.3389/fenrg.2021.641518
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
With the large-scale integration of renewable energy such as wind power and PV, it is necessary to maintain the voltage stability of power systems while increasing the use of intermittent renewable energy sources. The rapid development of energy storage technologies permits the deployment of energy storage systems (ESS) for voltage regulation support. This paper develops an ESS optimization method to estimate the optimal capacity and locations of distributed ESS supporting the voltage regulation of a distribution network. The electrical elements of the network integrated with PV and ESS are first modelled to simulate the voltage profile of the network. Then an improved multi-objective particle swarm optimization (PSO) algorithm is employed to minimise a weighted sum of the overall nodal voltage deviation from the nominal level across the network and across the time horizon and the energy capacity of ESS reflecting the associated investment. The improved PSO algorithm adaptively adjusts the inertia weight associated with each particle based on its distance from the best known particle of the population and introduces the cross-mutation operation for a small distance to avoid falling into local optimal solutions. Then the dynamic dense distance arrangement is taken to update the non-inferior solution set and indicate potential global optimal solutions so as to keep the scale and uniformity of the optimal Pareto solution set. To mitigate the impact of decision makers' preference, the information entropy based technique for order of preference by similarity to ideal solution is used to select the optimal combination of the ESS access scheme and capacity from the Pareto solution set. The proposed ESS optimization method is tested based on the IEEE 24-bus system with additional imports from high-voltage power supply. The voltage profile of the network simulated without the ESS or with the random or optimized ESS placement is compared to illustrate the effectiveness of the optimized ESS in performing voltage regulation under normal operation and supporting emergency power supply during high-voltage transmission failures.
引用
收藏
页数:14
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