Research on Two-Stage Energy Storage Optimization Configurations of Rural Distributed Photovoltaic Clusters Considering the Local Consumption of New Energy

Yang Liu, Dawei Liu, Keyi Kang, Guanqing Wang, Yanzhao Rong, Weijun Wang and Siyu Liu ()
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Yang Liu: State Grid Inner Mongolia Eastern Electric Power Co., No. 11 Ordos East Street, Saihan District, Hohhot 010020, China
Dawei Liu: State Grid Inner Mongolia Eastern Electric Power Co., No. 11 Ordos East Street, Saihan District, Hohhot 010020, China
Keyi Kang: School of Economics and Management, North China Electric Power University, No. 2 Beinong Road, Beijing 100006, China
Guanqing Wang: State Grid Inner Mongolia Eastern Electric Power Co., No. 11 Ordos East Street, Saihan District, Hohhot 010020, China
Yanzhao Rong: China Railway Construction Engineering Group First Construction Co., Room 1202, 12th Floor, 201, Building 4, 2nd to 13th Floor, No. 1 Yuren South Road, Fengtai District, Beijing 100071, China
Weijun Wang: Department of Economic Management, North China Electric Power University, No. 689, Huadian Road, Baoding 071003, China
Siyu Liu: Department of Economic Management, North China Electric Power University, No. 689, Huadian Road, Baoding 071003, China

Energies, 2024, vol. 17, issue 24, 1-31

Abstract: As photovoltaic technologies are being promoted throughout the country, the widespread installation of distributed photovoltaic systems in rural areas in rural regions compromises the safety and stability of the distribution network. Distributed photovoltaic clusters can be configured with energy storage to increase photovoltaic local consumption and mitigate the impact of grid-connected photovoltaic modes. Against this background, this paper focuses on rural areas, combines typical operation modes of distributed photovoltaic clusters, and constructs the two-stage energy storage optimization configuration model for rural distributed photovoltaic clusters. Taking a Chinese village as an example, the proposed model is optimized with an improved particle swarm optimization algorithm. Given different combinations with and without energy storage and demand response, comparative analyses are conducted on photovoltaic local consumption and the economic benefits of independent operators in various scenarios. Simulations indicate that the photovoltaic local consumption proportion of distributed photovoltaic clusters with energy storage reaches 62.64%, which is 34.02% more than the scenario without energy storage. The results indicate that configuring energy storage for rural distributed photovoltaic clusters significantly improves the photovoltaic local consumption level. Meanwhile, implementing demand response can achieve the same photovoltaic local consumption effect while reducing the energy storage configuration, and the life-cycle economic benefits are appreciable. The simulation results show that participating in demand response can reduce the energy storage system cost by 7.15% at a photovoltaic local consumption proportion of 60%. This research expands application channels of rural distributed photovoltaic clusters and provides references for investment and operation decisions of distributed photovoltaic energy storage systems.

Keywords: distributed photovoltaic cluster; energy storage optimization configuration; demand response; PV local consumption; improved particle swarm optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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