A Two-Layer Planning Method for Distributed Energy Storage with Multi-point Layout in High Photovoltaic Penetration Distribution Network

被引:2
作者
Wei, Yukai [1 ]
Zhao, Bo [1 ]
Hu, Juan [2 ]
Xiao, Xiaolong [3 ]
Shi, Mingming [3 ]
Zhou, Qi [3 ]
机构
[1] Beijing Informat Sci & Technol Univ, Beijing, Peoples R China
[2] China Elect Power Res Inst, Beijing, Peoples R China
[3] State Grid Jiangsu Elect Power Co Ltd Res Inst, Nanjing, Peoples R China
关键词
Distribution network; Distributed energy storage; Multi-point layout; Operation strategy; Site selection and capacity determination; SYSTEMS;
D O I
10.1007/s42835-024-01945-1
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In the planning of energy storage system (ESS) in distribution network with high photovoltaic penetration, in order to fully tap the regulation ability of distributed energy storage and achieve economic and stable operation of the distribution network, a two-layer planning method of distributed energy storage multi-point layout is proposed. Combining with the operation characteristic model of energy storage battery (ESB), a multi-point energy storage collaborative operation strategy considering the service life of ESB is proposed. A planning-operation two-layer model is constructed, in which the outer layer considers the total cost of ESS planning to determine the layout point number and capacity of ESS, and the inner layer focuses on the utilization rate of ESB and the operation stability of distribution network. The hybrid particle swarm optimization and non-dominated sorting genetic algorithm is used to solve the planning and operation results of distributed energy storage multi-point layout. Example analysis shows that after configuring a multi-point layout ESS, the total planning cost decreases by 20.25%, the utilization rate of ESB increases to over 50%, and the voltage fluctuation and loss rate of the distribution network decrease by 19.09% and 11.76%, respectively.
引用
收藏
页码:1 / 12
页数:12
相关论文
共 29 条
[1]   New hybrid probabilistic optimisation algorithm for optimal allocation of energy storage systems considering correlated wind farms [J].
Al Ahmad, Ahmad ;
Sirjani, Reza ;
Daneshvar, Sahand .
JOURNAL OF ENERGY STORAGE, 2020, 29
[2]  
Bin N., 2020, POWER SYST TECHNOL, V48, P84
[3]   Defining a standard for particle swarm optimization [J].
Bratton, Daniel ;
Kennedy, James .
2007 IEEE SWARM INTELLIGENCE SYMPOSIUM, 2007, :120-+
[4]   Optimal Energy Management Strategy for an Islanded Microgrid with Hybrid Energy Storage [J].
Chen, Haipeng ;
Gao, Lin ;
Zhang, Zhong ;
Li, He .
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, 2021, 16 (03) :1313-1325
[5]  
[程瑜 Cheng Yu], 2022, [电力系统自动化, Automation of Electric Power Systems], V46, P84
[6]   A Methodology for Optimal Distributed Storage Planning in Smart Distribution Grids [J].
Damavandi, Mohammad Ghasemi ;
Marti, Jose R. ;
Krishnamurthy, Vikram .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2018, 9 (02) :729-740
[7]   An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints [J].
Deb, Kalyanmoy ;
Jain, Himanshu .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2014, 18 (04) :577-601
[8]   Study of energy storage systems and environmental challenges of batteries [J].
Dehghani-Sanij, A. R. ;
Tharumalingam, E. ;
Dusseault, M. B. ;
Fraser, R. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2019, 104 :192-208
[9]   Optimal Economic and Environmental Indices for Hybrid PV/Wind-Based Battery Storage System [J].
Elnozahy, Ahmed ;
Yousef, Ali M. ;
Ghoneim, Sherif S. M. ;
Abdelwahab, Saad A. Mohamed ;
Mohamed, Moayed ;
Abo-Elyousr, Farag K. .
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, 2021, 16 (06) :2847-2862
[10]  
[郭斌 Guo Bin], 2022, [储能科学与技术, Energy Storage Science and Technology], V11, P615