Two-Layer Optimal Configuration of Distribution Network Energy Storage with Distributed Photovoltaic Considering Optimal Marginal Benefit

Qi PENG, Sen OUYANG, Yunxiang XIE

South Power Sys Technol ›› 2025, Vol. 19 ›› Issue (11) : 122-131.

PDF(1784 KB)
Home Journals Southern Power System Technology
Southern Power System Technology

Abbreviation (ISO4): South Power Sys Technol      Editor in chief:

About  /  Aim & scope  /  Editorial board  /  Indexed  /  Contact  / 
PDF(1784 KB)
South Power Sys Technol ›› 2025, Vol. 19 ›› Issue (11) : 122-131. DOI: 10.13648/j.cnki.issn1674-0629.2025.11.011
Energy Storage Optimization Configuration

Two-Layer Optimal Configuration of Distribution Network Energy Storage with Distributed Photovoltaic Considering Optimal Marginal Benefit

Author information +
History +

Abstract

Photovoltaic energy storage configuration is the current development trend, but the value of energy storage investment directly affects the interests of energy storage owners and the actual market behavior, it is necessary to consider the interaction between energy storage benefits and costs, and carry out further research on the basis of traditional multi-objective optimization of the grid side or the maximum economic benefit of energy storage. Therefore, a two-layer optimal configuration model of distribution network energy storage with distributed photovoltaic, which adopts multi-objective particle swarm optimization and considers the optimal marginal benefit. Firstly, the concept of marginal benefit of energy storage is put forward, the marginal benefit model of energy storage is established, and the benefit of energy storage is measured from the perspective of energy storage owners. Then, a two-layer optimization model is established. The outer layer optimization takes the maximum marginal benefit and the minimum voltage deviation after energy storage access as the target, and takes the network loss rate as the constraint condition. The inner layer optimization takes the maximum daily energy storage income as the target to optimize the energy storage output. Finally, the IEEE 33-node system is used for energy storage optimization calculation. The results show that on the basis of traditional energy storage capacity optimization with net income over the whole life cycle, based on the optimal marginal benefit of the energy storage owner, the secondary allocation of energy storage capacity with the goal of considering marginal benefit and voltage deviation can improve the net income, line loss rate and voltage deviation after energy storage access.

Key words

multi-target particle swarm / marginal benefit / two-layer optimization / energy storage configuration / distributed photovoltaic

Cite this article

Download Citations
Qi PENG , Sen OUYANG , Yunxiang XIE. Two-Layer Optimal Configuration of Distribution Network Energy Storage with Distributed Photovoltaic Considering Optimal Marginal Benefit[J]. Southern Power System Technology. 2025, 19(11): 122-131 https://doi.org/10.13648/j.cnki.issn1674-0629.2025.11.011

References

[1]
谢仕炜, 胡志坚, 王珏莹, 等. 基于不确定随机网络理论的主动配电网多目标规划模型及其求解方法[J]. 电工技术学报201934 (5): 1038 - 1054.
XIE Shiwei HU Zhijian WANG Jueying, et al. A multi-objective planning model of active distribution network based on uncertain random network theory and its solution algorithm[J]. Transactions of China Electrotechnical Society201934 (5): 1038 - 1054.
[2]
焦乾致. 基于改进多目标粒子群算法的混合储能容量优化配置研究[D]. 林芝:西藏农牧学院, 2023.
[3]
黄立滨, 蔡海青, 顾浩瀚, 等. 计及分布式光伏和储能主动支撑的配电网日前日内协调优化运行策略[J]. 南方电网技术202418(8):51 - 59.
HUANG Libin CAI Haiqing GU Haohan, et al. Coordinated optimal strategy of day-ahead and intra-day operation of distribution network considering the active support of distributed photovoltaic and energy storage system[J]. Southern Power System Technology202418(8):51 - 59.
[4]
陈峰, 赵鹏.基于可逆抽水储能的新能源配电网协调优化方法[J]. 中国电力202558(8):103 - 108.
CHEN Feng ZHAO Peng. Coordination optimization method for new energy distribution network based on reversible pumped hydro-storage[J]. Electric Power202558(8):103 - 108.
[5]
李响,武海潮,王文雪,等.考虑大规模新能源接入的电网性能评价指标体系[J].电力系统保护与控制202452(15):178 - 187.
LI Xiang WU Haichao WANG Wenxue, et al. Performance evaluation index system of a power grid considering large-scale new energy[J].Power System Protection and Control202452(15):178 - 187.
[6]
陈建国, 郑拓, 郝俊毅, 等. 新能源发电接入下储能系统双层优化模型[J]. 中南民族大学学报(自然科学版)202443 (2): 245 - 251.
CHEN Jianguo ZHENG Tuo HAO Junyi, et al. Bi-level optimization model of energy storage systems under new energy generation integration[J]. Journal of South-central Minzu University(Natural Science Edition)202443 (2): 245 - 251.
[7]
曾志辉, 刘云鹏, 韦延方, 等. 基于改进蝙蝠算法的混合储能系统容量优化配置[J]. 河南理工大学学报(自然科学版)202342 (5): 130 - 136.
ZENG Zhihui LIU Yunpeng Yanfang WEl, et al. Optimal capacity allocation of hybrid energy storage system based on improved bat algorithm[J]. Journal of Henan Polytechnic University(Natural Science)202342 (5): 130 - 136.
[8]
陈寒阳, 刘洋, 许立雄, 等. 考虑容量削减的居民用户共享储能优化配置[J]. 电力建设202344 (9): 43 - 57.
CHEN Hanyang LIU Yang XU Lixiong, et al. Optimal configuration of shared energy storage on residential user side considering capacity reduction[J]. Electric Power Construction202344 (9): 43 - 57.
[9]
孟贤, 郭启蒙, 李颖欢, 等. 考虑双向需求的混合储能容量多目标优化配置[J]. 太阳能学报202344 (8): 45 - 53.
MENG Xian GUO Qimeng LI Yinghuan, et al. Multi-objective optimal configuration of hybrid energy storage capacity considering two-way demand[J]. Acta Energiae Solaris Sinica202344 (8): 45 - 53.
[10]
王欣, 谭永怡, 秦斌. 基于改进MOGOA的风储容量优化配置研究[J]. 电力科学与技术学报202439(2):159 - 169.
WANG Xin TAN Yongyi Bin QlN. Improved multi-objective grasshopper algorithm and its application in optimal configuration of wind farm energy storage capacity[J]. Journal of Electric Power Science and Technology202439(2):159 - 169.
[11]
丁明, 吴杰, 张晶晶. 面向风电平抑的混合储能系统容量配置方法[J]. 太阳能学报201940 (3): 593 - 599.
DING Ming WU Jie ZHANG Jingjing. Capacity optimization method of hybrid energy storage systemfor wind power smoothing[J]. Acta Energiae Solaris Sinica201940 (3): 593 - 599.
[12]
夏新茂, 关洪浩, 丁鹏飞, 等. 基于改进型量子遗传算法的储能系统容量配置与优化策略[J]. 储能科学与技术20198 (3): 551 - 558.
XIA Xinmao GUAN Honghao DING Pengfei, et al. Capacity allocation and optimization strategy of an energy storage system based on an improved quantum genetic algorithm[J]. Energy Storage Science and Technology20198 (3): 551 - 558.
[13]
汤杰, 李欣然, 黄际元, 等. 以净效益最大为目标的储能电池参与二次调频的容量配置方法[J]. 电工技术学报201934 (5): 963 - 972.
TANG Jie LI Xinran HUANG Jiyuan, et al. Capacity allocation of BESS in secondary frequency regulation with the goal of maximum net benefit[J]. Transactions of China Electrotechnical Society201934 (5): 963 - 972.
[14]
禹海峰, 黄婧杰, 蒋诗谣, 等. 计及储能使用年寿命的风电场整体性储能配置[J]. 电力科学与技术学报202237 (4): 152 - 160.
YU Haifeng HUANG Jingjie Shiyao JlANG, et al. The overall energy storage configuration of wind farms considering these rvice life of electric energy storage[J]. Journal of Electric Power Science and Technology202237 (4): 152 - 160.
[15]
刘红. 基于改进粒子群算法的储能调峰容量优化配置研究[J]. 广东电力202336 (1): 68 - 76.
LIU Hong. Research on optimal configuration of energy storage peak shaving capacity based on improved particle swarm optimization algorithm[J]. Guangdong Electric Power202336 (1): 68 - 76.
[16]
尤毅, 刘东, 钟清, 等. 主动配电网储能系统的多目标优化配置[J]. 电力系统自动化201438 (18): 46 - 52.
YOU Yi LIU Dong ZHONG Qing, et al. Multi-objective optimal placement of energy storage systems in an active distribution network[J]. Automation of Electric Power Systems201438 (18): 46 - 52.
[17]
麻常辉, 冯江霞, 张磊, 等. 基于PSO算法的风电场储能容量优化计算[J]. 山东科学201326 (6): 35 - 39.
MA Changhui FENG Jiangxia ZHANG Lei, et al. PSO based model for the optimization calculation of wind farm energy storage capacity[J]. Shandong Science201326 (6): 35 - 39.
[18]
徐奇锋, 王激华, 乔松博, 等. 考虑源-荷匹配的区域电网风光储容量规划-运行联合优化方法[J]. 电力建设202445(8): 85 - 86.
XU Qifeng WANG Jihua QIAO Songbo, et al. Planning and operation joint optimization method for wind, solar and energy storage capacity in regional power grids considering source load matching[J]. Electric Power Construction202445(8): 85 - 86.
[19]
段树勋. 面向分布式风电光伏的储能容量优化配置方法研究[J]. 自动化应用202364 (19): 68 - 70.
DUAN Shuxun. Research on optimization configuration method of energy storage capacity for distributed wind power photovoltaics[J]. Automation Application202364 (19): 68 - 70.
[20]
曼昆. Principles of Economics[M]. 北京: 北京大学出版社, 2009: 56 - 58.
[21]
盛泉源. 基于边际效益系数排序的梯级水电站发电调度方法及应用[D]. 武汉:华中科技大学, 2021.
[22]
王旭阳, 杨卫红, 崔文婷, 等. 基于规划角度的中压配电网可靠性提升边际效益分析[J]. 供用电201734 (4): 52 - 58.
WANG Xuyang YANG Weihong Wenting CUl, et al. Marginal benefit analysis of reliability of medium voltage distribution network from plan point[J]. Distribution & Utilization201734 (4): 52 - 58.
[23]
林森, 文书礼, 朱淼, 等. 考虑碳交易机制的海港综合能源系统电-热混合储能优化配置[J]. 上海交通大学学报202458(9): 1344 - 1365.
LIN Sen WEN Shuli ZHU Miao, et al. Optimal allocation of electric-thermal hybrid energy storage for seaport integrated energy system considering carbon trading mechanism[J]. Journal of Shanghai Jiaotong University202458(9): 1344 - 1365.
[24]
王骞, 易传卓, 张学广, 等. 兼顾捕碳强度与可再生能源消纳的储能容量配置优化方法[J]. 中国电机工程学报202343 (21): 8295 - 8309.
WANG Qian YI Chuanzhuo ZHANG Xueguang, et al. Optimization of energy storage capacity sizing considering carbon capture intensity and renewable energy consumption[J]. Proceedings of the CSEE202343 (21): 8295 - 8309.
[25]
王凤学, 欧阳森, 辛曦. 计及保供电交易模式和容量市场的用户侧储能优化配置[J]. 高电压技术202349 (7): 2785 - 2795.
WANG Fengxue OUYANG Sen XIN Xi. Optimal configuration of user-side energy storage considering power supply transaction mode and capacity market[J]. High Voltage Engineering202349 (7): 2785 - 2795.
[26]
刘旭民, 张彦, 刘晓波. 基于ICEEMDAN的微电网混合储能容量配置[J]. 南方电网技术202519(1): 140 - 149.
LIU Xumin ZHANG Yan LlU Xiaobo. Hybrid energy storage capacity configuration of microgrid based on ICEEMDAN[J]. Southern Power System Technology202519(1): 140 - 149.
[27]
李子健, 郭佩乾, 马宁宁, 等. 融合双重策略粒子群算法的分布式电源配网无功优化[J]. 南方电网技术202216 (6): 14 - 22,81.
Zijian Ll GUO Peiqian MA Ningning, et al. Reactive power optimization for distribution system with DG by particle swarm optimization algorithm integrating dual strategies[J]. Southern Power System Technology202216 (6): 14 - 22,81.
[28]
王盼宝, 徐殿国, 谭岭玲, 等. 基于改进MOPSO的多能互补型微电网多元优化运行策略[J]. 南方电网技术202216 (10): 130 - 140.
WANG Panbao XU Dianguo TAN Lingling, et al. Multivariate optimal operation strategy of multi-energy complementary microgrid based on improved MOPSO[J]. Southern Power System Technology202216 (10): 130 - 140.
[29]
曾伟哲, 曾启林, 黎恒, 等. 基于HPSOGA的多目标电动汽车充电优化[J]. 南方电网技术202317 (1): 94 - 102,135.
ZENG Weizhe ZENG Qilin LI Heng, et al. Multi-objective electric vehicle charging optimization based on HPSOGA[J]. Southern Power System Technology202317 (1): 94 - 102,135.

Funding

the National Natural Science Foundation of China(52177085)
PDF(1784 KB)

Accesses

Citation

Detail

Sections
Recommended

/