Optimal Allocation of Shared Energy Storage in Low-Carbon Parks Taking into Account the Uncertainty of Photovoltaic Output and Electric Vehicle Charging

Shang Jiang, Jiacheng Li (), Wenlong Shen, Lu Liang and Jinfeng Wu
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Shang Jiang: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Jiacheng Li: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Wenlong Shen: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Lu Liang: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Jinfeng Wu: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China

Energies, 2025, vol. 18, issue 13, 1-26

Abstract: The growing integration of renewable energy and electric vehicle loads in parks has intensified the intermittency of photovoltaic (PV) output and demand-side uncertainty, complicating energy storage system design and operation. Meanwhile, under carbon neutrality goals, the energy system must balance economic efficiency with emission reductions, raising the bar for storage planning. To address these challenges, this study proposes a two-stage robust optimization method for shared energy storage configuration in a park-level integrated PV–storage–charging system (PV-SESS-CS). The method considers the uncertainties of PV and electric vehicle (EV) loads and incorporates carbon emission reduction benefits. First, a configuration model for shared energy storage that accounts for carbon emission reduction is established. Then, a two-stage robust optimization model is developed to characterize the uncertainties of PV output and EV charging demand. Typical PV output scenarios are generated using Latin Hypercube Sampling, and representative PV profiles are extracted via K-means clustering. For EV charging loads, uncertainty scenarios are generated using Monte Carlo Sampling. Finally, simulations are conducted based on real-world industrial park data. The results demonstrate that the proposed method can effectively mitigate the negative impact of source-load fluctuations, significantly reduce operating costs, and enhance carbon emission reductions. This study provides strong methodological support for optimal energy storage planning and low-carbon operation in park-level PV-SESS-CS.

Keywords: park photovoltaic storage and charging; shared energy storage; uncertainty; robust optimization; two-stage robustness (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: 2025
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