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山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (5): 37-43, 51.doi: 10.6040/j.issn.1672-3961.0.2019.082

• 电气工程———人工智能应用专题 • 上一篇    下一篇

基于两层优化的主动配电网储能优化配置

王李龑1(),王飞2,曹永吉3,*(),张涛1,张亚新1,卢奕3,刘子菡3   

  1. 1. 国网山东省电力公司聊城供电公司, 山东 聊城 252200
    2. 国网山东省电力公司, 山东 济南 250001
    3. 电网智能化调度与控制教育部重点实验室(山东大学), 山东 济南 250061
  • 收稿日期:2019-02-26 出版日期:2019-10-20 发布日期:2019-10-18
  • 通讯作者: 曹永吉 E-mail:fazhancehuabu@126.com;caoyongji1991@163.com
  • 作者简介:王李龑(1975—)，男，山东聊城人，高级工程师，主要研究方向为电网规划和新能源发电技术.E-mail:fazhancehuabu@126.com
  • 基金资助:
    国网山东省电力公司科技资助项目(2018A-021)

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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摘要：

针对主动配电网储能系统优化配置问题,考虑运行控制策略对规划方案的影响,提出一种基于两层优化的配置方法。在短时间尺度的内层优化中,利用低通滤波算法提取上网功率高频分量,以高频分量变异系数和可再生能源浪费率最小为目标,基于标量化方法和粒子群算法优化主动配电网控制策略。在长时间尺度的外层优化中,构建多目标优化模型,最小化投资成本和可再生能源浪费率,采用NSGA-Ⅱ算法求取储能系统配置的Pareto最优解。由于运行控制和规划配置间存在相互影响,将不同时间尺度的内外层优化置于统一的框架内,以可再生能源浪费率、储能系统配置位置和容量为耦合变量交替迭代求解。结合算例,对所提模型及其求解方法进行了验证。算例分析表明:主动配电网中储能系统优化配置能够有效提高电网对可再生能源的消纳能力。

关键词: 主动配电网, 储能系统, 优化配置, 两层优化

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

中图分类号: 

  • TM61

图1

内外层优化模型交互关系示意图"

图2

基于PSO和NSGA-Ⅱ的两层优化求解方法"

表1

风电机组参数"

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

表2

光伏模块参数"

长×宽/(mm×mm) 发电效率/%
1650×992 12.48

表3

发电装机容量"

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

表4

ESS单元参数"

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

图3

NASA典型日气象数据"

表5

ESS配置位置优化结果"

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

表6

ESS配置容量优化结果"

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

表7

ESS配置优化目标计算结果"

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

表8

Pareto最优解3场景下AND运行性能指标"

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

表9

未配置ESS场景下AND运行性能指标"

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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