基于改进海洋捕食者算法的配电网储能多目标优化配置

doi:10.19799/j.cnki.2095-4239.2023.0267

摘要/Abstract

摘要：

分布式电源(distributed generation，DG)的大量接入是配电网重要发展趋势之一，而储能系统的合理配置是提升配电网接纳DG能力的重要手段。本工作考虑高比例DG接入配电网造成电能质量下降问题，建立使配电网电压偏移最小、线损率最低、储能规划成本最优三个指标的储能多目标优化配置模型；针对目前侧重改进传统优化算法求解储能配置多目标优化问题性能不足方面，采用一种基于快速非支配排序和边界交叉构造权重设置参考点的方法对海洋捕食者算法改进，进而求解配电网储能多目标优化模型，得出储能在配电网中的最佳并网位置、额定容量和储能电池调度周期内的充放电功率。通过在IEEE-33节点系统上进行算例分析，结果显示：采用改进的多目标海洋捕食者算法能够有效地求解出在最优储能规划成本下使配电网经济、稳定运行的储能配置方案，及储能电池运行周期内最佳的充放电策略；并且通过对比多种智能优化算法，证明了所提改进海洋捕食者算法在求解储能多目标优化配置问题上具有良好的收敛性能、分布性能。

关键词: 改进海洋捕食者算法, 多目标算法优化, 配电网, 分布式电源, 储能配置

Abstract:

The extensive integration of distributed generation (DG) is one of the vital development trends in distribution networks. The reasonable configuration of energy storage systems is an essential means to enhance the ability of distribution networks to accept DG. This article considers the problem of power quality degradation caused by the high proportion of DG connected to the distribution network and establishes a multiobjective optimization configuration model for energy storage that minimizes voltage deviation, line loss rate, and optimal energy storage planning cost in the distribution network. Because of the shortcomings of the traditional optimization algorithms focusing on improving the performance of solving the multiobjective optimization of energy storage configurations, a method based on fast nondominated sorting and boundary crossing construction weight to set reference points is adopted to improve the marine predator algorithm and then solve multiobjective optimization model of energy storage in the distribution network to obtain the optimal grid connection position, rated capacity, and charging and discharging power of energy storage batteries in the distribution network during the scheduling period. By conducting numerical analysis on an IEEE-33 node system, the improved algorithm can effectively solve the energy storage configuration scheme that ensures stable operation of the distribution network at the optimal planning cost. Moreover, by comparing various intelligent optimization algorithms, the proposed improved algorithm has good convergence and distribution performance in solving multiobjective optimization configuration problems for energy storage.

Key words: improved marine predators algorithm, multi-objective algorithm optimization, distribution networks, distributed generation, energy storage configuration

中图分类号:

TM 732

肖小龙, 史明明, 周琦, 魏于凯, 赵波. 基于改进海洋捕食者算法的配电网储能多目标优化配置[J]. 储能科学与技术, 2023, 12(8): 2565-2574.

Xiaolong XIAO, Mingming SHI, Qi ZHOU, Yukai WEI, Bo ZHAO. Multiobjective optimization configuration of energy storage in distribution networks based on improved marine predator algorithm[J]. Energy Storage Science and Technology, 2023, 12(8): 2565-2574.

图/表 13

图1

图2

图3

表1

图4

图5

图6

图7

表2

图A1

图A2

图A3

表A1

参考文献 19

1	李红霞, 李建林, 米阳. 新能源侧储能优化配置技术研究进展[J]. 储能科学与技术, 2022, 11(10): 3257-3267.
	LI H X, LI J L, MI Y. Summary of research on new energy side energy storage optimization configuration technology[J]. Energy Storage Science and Technology, 2022, 11(10): 3257-3267.
2	古宸嘉, 王建学, 李清涛, 等. 新能源集中并网下大规模集中式储能规划研究述评[J]. 中国电力, 2022, 55(1): 2-12, 83.
	GU C J, WANG J X, LI Q T, et al. Review on large-scale centralized energy storage planning under centralized grid integration of renewable energy[J]. Electric Power, 2022, 55(1): 2-12, 83.
3	徐国栋, 程浩忠, 马紫峰, 等. 用于缓解电网调峰压力的储能系统规划方法综述[J]. 电力自动化设备, 2017, 37(8): 3-11.
	XU G D, CHENG H Z, MA Z F, et al. Overview of ESS planning methods for alleviating peak-shaving pressure of grid[J]. Electric Power Automation Equipment, 2017, 37(8): 3-11.
4	王成山, 王瑞, 于浩, 等. 配电网形态演变下的协调规划问题与挑战[J]. 中国电机工程学报, 2020, 40(8): 2385-2395.
	WANG C S, WANG R, YU H, et al. Challenges on coordinated planning of smart distribution networks driven by source-network-load evolution[J]. Proceedings of the CSEE, 2020, 40(8): 2385-2395.
5	YANG Y Q, BREMNER S, MENICTAS C, et al. Battery energy storage system size determination in renewable energy systems: A review[J]. Renewable and Sustainable Energy Reviews, 2018, 91: 109-125.
6	闫群民, 穆佳豪, 马永翔, 等. 分布式储能应用模式及优化配置综述[J]. 电力工程技术, 2022, 41(2): 67-74.
	YAN Q M, MU J H, MA Y X, et al. Review of distributed energy storage application mode and optimal configuration[J]. Jiangsu Electrical Engineering, 2022, 41(2): 67-74.
7	GU C J, WANG J X, YANG Q, et al. Assessing operational benefits of large-scale energy storage in power system: Comprehensive framework, quantitative analysis, and decoupling method[J]. International Journal of Energy Research, 2021, 45(7): 10191-10207.
8	闫群民, 董新洲, 穆佳豪, 等. 基于改进多目标粒子群算法的有源配电网储能优化配置[J]. 电力系统保护与控制, 2022, 50(10): 11-19.
	YAN Q M, DONG X Z, MU J H, et al. Optimal configuration of energy storage in an active distribution network based on improved multi-objective particle swarm optimization[J]. Power System Protection and Control, 2022, 50(10): 11-19.
9	招景明, 苏洁莹, 潘峰, 等. 考虑光伏波动的有源配电网分布式储能双目标优化规划[J]. 可再生能源, 2022, 40(11): 1546-1553.
	ZHAO J M, SU J Y, PAN F, et al. Dual objective optimization planning of distributed energy storage for active distribution network considering photovoltaic fluctuations[J]. Renewable Energy Resources, 2022, 40(11): 1546-1553.
10	王泽爽, 陈嘉俊, 朱建全, 等. 计及循环寿命的储能优化配置与运营策略[J]. 电力自动化设备, 2021, 41(10): 75-81.
	WANG Z S, CHEN J J, ZHU J Q, et al. Optimal configuration and operation strategy of energy storage considering cycle life[J]. Electric Power Automation Equipment, 2021, 41(10): 75-81.
11	AL AHMAD A K, SIRJANI R. Optimal allocation of energy storage system in transmission system considering wind power[C]//2020 7th International Conference on Electrical and Electronics Engineering (ICEEE). April 14-16, 2020, Antalya, Turkey. IEEE, 2020: 181-187.
12	李建林, 谭宇良, 王含, 等. 配网及光储微网储能系统配置优化策略[J]. 高电压技术, 2022, 48(5): 1893-1902.
	LI J L, TAN Y L, WANG H, et al. Research on configuration optimization of energy storage system in distribution network and optical storage microgrid[J]. High Voltage Engineering, 2022, 48(5): 1893-1902.
13	郭久亿, 刘洋, 郭焱林, 等. 不同典型用户侧储能配置评估与运行优化模型[J]. 电网技术, 2020, 44(11): 4245-4253.
	GUO J Y, LIU Y, GUO Y L, et al. Configuration evaluation and operation optimization model of energy storage in different typical user-side[J]. Power System Technology, 2020, 44(11): 4245-4253.
14	GUPTA R, SOSSAN F. Optimal sizing and siting of energy storage systems considering curtailable photovoltaic generation in power distribution networks[J]. Applied Energy, 2023, 339: doi: 10.1016/j.apenergy.2023.120955.
15	AL AHMAD A, SIRJANI R, DANESHVAR S. New hybrid probabilistic optimisation algorithm for optimal allocation of energy storage systems considering correlated wind farms[J]. Journal of Energy Storage, 2020, 29: doi: 10.1016/j.est.2020.101335.
16	FARAMARZI A, HEIDARINEJAD M, MIRJALILI S, et al. Marine predators algorithm: A nature-inspired metaheuristic[J]. Expert Systems With Applications, 2020, 152: doi: 10.1016/j.eswa.2020. 113377.
17	DEB K, JAIN H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4): 577-601.
18	TIAN Y, CHENG R, ZHANG X Y, et al. PlatEMO: A MATLAB platform for evolutionary multi-objective optimization[educational forum[J]. IEEE Computational Intelligence Magazine, 2017, 12(4): 73-87.
19	TAN Q L, MEI S F, YE Q, et al. Optimization model of a combined wind-PV-thermal dispatching system under carbon emissions trading in China[J]. Journal of Cleaner Production, 2019, 225: 391-404.

情景	综合评价指标值	储能规划方案			配电网运行结果
情景	综合评价指标值	安装节点	容量/kWh	C_pro/万元	电压偏移指标	线损率
1	—	—	—	—	12.15	6.28%
2	5.846	18	2000	0.360	11.16	6.00%
3	5.711	18	2311	0.413	10.80	5.90%
4	5.700	18	2774	0.494	10.71	5.87%

对比算法	IMOMPA	NSGA-Ⅱ	MOPSO
f 1结果范围	0.355～0.584	0.340～0.442	0.360～0.658
f 2结果范围	10.7～12.1	10.8～17.3	14.1～17.9
f 3结果范围	5.87～6.01	5.92～6.27	5.88～6.07
IGD值	0.15	0.28	3.55
迭代优化时间	42.9 s	35.9 s	23.2 s

参数	数值
充电效率	90%
放电效率	90%
能量倍率	0.25 C
功率成本系数	0.16
容量成本系数	0.25
辅助设备成本系数	0.28
电池折旧成本系数	0.1
电池等效寿命/年	15
折现率	6.5%
荷电状态范围	[0.1, 0.9]
初始SOC	45%

[1]	张伟, 罗世刚, 滕婕, 白永利. 考虑温控负荷聚合调控的新能源-储能联合规划[J]. 储能科学与技术, 2023, 12(6): 1901-1912.
[2]	郭霄宇, 于浩, 郑新, 刘雨佳, 左元杰, 张苗苗. 光伏系统液流电池储能优化配置[J]. 储能科学与技术, 2023, 12(4): 1158-1167.
[3]	侯美倩, 牛启帆, 邢洁, 单英浩. 计及可靠性的含源配电网储能系统的优化配置[J]. 储能科学与技术, 2023, 12(2): 504-514.
[4]	曾伟, 熊俊杰, 李建林, 马速良, 武亦文. 基于权重自适应鲸鱼优化算法的多能系统储能电站最优配置[J]. 储能科学与技术, 2022, 11(7): 2241-2249.
[5]	李秀慧, 崔炎. 考虑调峰调频需求的新能源电网储能优化配置[J]. 储能科学与技术, 2022, 11(11): 3594-3602.
[6]	陈彪, 俞伟, 方旭峰, 李豪鑫, 曹磊, 潘俊. 一种基于区内电池共享模式的主动配电网-孤岛微网协同经济调度策略[J]. 储能科学与技术, 2021, 10(6): 2181-2190.
[7]	寇凌峰, 熊雄, 侯小刚, 牛耕, 屈小云, 陈凡. 面向低压配变台区的微电网技术[J]. 储能科学与技术, 2019, 8(4): 665-670.
[8]	易斌, 赵伟, 张科杰, 李爱魁, 梁崇淦, 罗敏. 锂电池储能在配电网低压台区的应用[J]. 储能科学与技术, 2019, 8(4): 671-677.
[9]	王守相1，王凯1，赵歌2. 平抑有源配电网功率波动的储能配置与控制方法研究综述[J]. 储能科学与技术, 2017, 6(6): 1188-.
[10]	朱张涛1，陈豪杰2，戴俊杰1，李卫彬1，李雪2. 基于空间变换的含风电场和电动汽车配电网概率潮流计算[J]. 储能科学与技术, 2017, 6(1): 127-134.