Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (5): 37-43.doi: 10.6040/j.issn.1672-3961.0.2019.082

• Engineering—Special Topic on Artificial Intelligence Application • Previous Articles     Next Articles

Bi-level optimal configuration of energy storage system in an active distribution network

Liyan WANG1(),Fei WANG2,Yongji CAO3,*(),Tao ZHANG1,Yaxin ZHANG1,Yi LU3,Zihan LIU3   

  1. 1. Liaocheng Power Supply Company, State Grid Shandong Electric Power Company, Liaocheng 252200, Shandong, China
    2. State Grid Shandong Electric Power Company, Jinan 250001, Shandong, China
    3. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University), Jinan 250061, Shandong, China
  • Received:2019-02-26 Online:2019-10-20 Published:2019-10-18
  • Contact: Yongji CAO E-mail:fazhancehuabu@126.com;caoyongji1991@163.com
  • Supported by:
    国网山东省电力公司科技资助项目(2018A-021)

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Abstract:

In order to optimize the configuration of energy storage system in an active distribution network, a bi-level optimization method was proposed, considering the impact of operation strategy on planning scheme. In the short-scale inner optimization, the high-frequency components of integration power was extracted by the low-pass filtering algorithm. And a multi-objective optimization model was constructed to minimize the variation coefficient of extracted high-frequency component and the rate of the loss of renewable energy, which was simplified into a scalar optimization problem and solved by the particle swarm optimization algorithm. In the long-scale outer optimization, a multi-objective optimization model was established to minimize the investment cost and the rate of the loss of renewable energy, of which the Pareto optimal solutions were searched by the NSGA-Ⅱ. The location and capacity of energy storage system and the rate of the loss of renewable energy were taken as coupling variables, based on which the inner and outer models with different time scales were solved in a united optimization frame. The case study validated the effectiveness of the proposed model and corresponding solving methods, of which the results indicate that the optimal configuration of energy storage system in an active distribution network could enhance the accommodation ability of renewable energy.

Key words: active distribution network, energy storage system, optimal configuration, bi-level optimization

CLC Number: 

  • TM61

Fig.1

Schematic diagram of the interactive relationshipbetween the inner and outer optimization models"

Fig.2

Flow chart of bi-level optimization solving methodbased on PSO and NSGA-Ⅱ"

Table 1

Parameters of the wind generator"

额定功率/kW 切入风速/(m·s-1) 切出风速/(m·s-1) 额定风速/(m·s-1) 发电效率/%
850 3.0 13.5 25.0 95.00

Table 2

Parameters of the photovoltaic panel"

长×宽/(mm×mm) 发电效率/%
1650×992 12.48

Table 3

Installed power-generating capacity"

系统编号 风电场A 风电场B 风电场C 风电场D 光伏电站E
装机容量/MW 49.30 99.45 49.30 99.45 50.00

Table 4

Parameters of the ESS unit"

额定容量/(kW·h) 放电深度/% 最大充电功率/kW 最大放电功率/kW 充电效率/% 放电效率/%
120 40 10 10 80 90

Fig.3

NASA meteorological data of typical day"

Table 5

Optimization results of ESS location"

Pareto最优解编号 风电场A 风电场B 风电场C 风电场D 光伏电站E
1 1 0 1 0 1
2 0 1 1 0 1
3 1 0 1 0 1
4 1 0 1 0 1
5 1 0 1 0 1

Table 6

Optimization results of ESS capacity"

Pareto最优解编号 ESS 1配置单元数量 ESS 2配置单元数量 ESS 3配置单元数量
1 256 336 205
2 100 101 100
3 171 193 143
4 223 252 167
5 164 179 132

Table 7

Optimization objective results of ESS configuration"

Pareto最优解编号 CE/万元 Rce/% Chg/%
1 3 192 17.092 54.17
2 1 200 19.900 82.35
3 2 020 17.266 68.02
4 2 560 17.125 61.82
5 1 896 17.532 73.59

Table 8

Operation performance indices of AND under the scenario of Pareto optimal solution 3"

发电系统编号 Sg/(kW·h) Sc/(kW·h) Chg/%
风电场A 3 123 509 90 058 24.21
风电场B 7 990 714 402 831 10.43
风电场C 2 954 325 79 606 37.33
风电场D 2 712 544 81 172 119.64
光伏电站E 316 920 18 231 148.51

Table 9

Operation performance indices ofAND without ESS"

发电系统编号 Sg/(kW·h) Sc/(kW·h) Chg/%
风电场A 3 067 721 145 813 26.15
风电场B 7 990 714 402 831 10.43
风电场C 2 870 582 157 453 37.46
风电场D 2 712 544 81 172 119.64
光伏电站E 304 593 22 287 156.48
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