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

Author

Listed:
  • 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
    Key Laboratory of Control of Power Transmission and Conversion (SITU), Ministry of Education, Shanghai 200240, 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)

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.

Suggested Citation

  • Shang Jiang & Jiacheng Li & Wenlong Shen & Lu Liang & Jinfeng Wu, 2025. "Optimal Allocation of Shared Energy Storage in Low-Carbon Parks Taking into Account the Uncertainty of Photovoltaic Output and Electric Vehicle Charging," Energies, MDPI, vol. 18(13), pages 1-26, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3280-:d:1685518
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    References listed on IDEAS

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