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Negative correlation peak shaving control in a parking garage in Uppsala, Sweden

Author

Listed:
  • Wallberg, Alexander
  • Castellucci, Valeria
  • Flygare, Carl
  • Lind, Emil
  • Schultz, Egil
  • Mattos, Marina Martins
  • Waters, Rafael

Abstract

As the global transition away from fossil fuels accelerates, energy systems across the globe face a significant challenge. Given the high energy consumption of electric vehicle chargers, effective control is imperative to prevent local grid overload and congestion. In Uppsala, Sweden, a newly built parking garage includes 30 electric vehicle chargers, 62kW solar energy production, and a 60kW/137kWh battery energy storage system. This paper presents a control algorithm that uses a negative correlation scheme, adjusted to the local grid load, to effectively manage the battery energy storage. To improve the performance of the algorithm, a genetic optimization method is applied to find the best feasible daily load profile for the parking garage. The results indicate that peak load and energy consumption during grid high-load hours can be significantly reduced. This also results in an 9.5−12.8% reduction in electricity distribution fees at current prices as well as a peak load reduction of up to 50%. Increasing the battery capacity and charging/discharging power in the scenarios analysed within the study will improve the algorithm’s ability to achieve a satisfactory negative correlation between the load demand of the facility and the local grid. The proposed control algorithm lowers the facility’s impact on the local grid during high-load peak hours by utilizing the battery energy storage system at the parking garage. Moreover, it decreases the distribution fees of the facility by lowering the load peaks and shifting the electricity consumption to the morning and night.

Suggested Citation

  • Wallberg, Alexander & Castellucci, Valeria & Flygare, Carl & Lind, Emil & Schultz, Egil & Mattos, Marina Martins & Waters, Rafael, 2024. "Negative correlation peak shaving control in a parking garage in Uppsala, Sweden," Applied Energy, Elsevier, vol. 375(C).
    • Handle: RePEc:eee:appene:v:375:y:2024:i:c:s030626192401465x
    DOI: 10.1016/j.apenergy.2024.124082
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    References listed on IDEAS

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    1. Di Bella, Gabriel & Flanagan, Mark & Foda, Karim & Maslova, Svitlana & Pienkowski, Alex & Stuermer, Martin & Toscani, Frederik, 2024. "Natural gas in Europe: The potential impact of disruptions to supply," Energy Economics, Elsevier, vol. 138(C).
    2. Chua, Kein Huat & Lim, Yun Seng & Morris, Stella, 2017. "A novel fuzzy control algorithm for reducing the peak demands using energy storage system," Energy, Elsevier, vol. 122(C), pages 265-273.
    3. Hou, Qingchun & Yu, Yanghao & Du, Ershun & He, Hongjie & Zhang, Ning & Kang, Chongqing & Liu, Guojing & Zhu, Huan, 2020. "Embedding scrapping criterion and degradation model in optimal operation of peak-shaving lithium-ion battery energy storage," Applied Energy, Elsevier, vol. 278(C).
    4. Zhou, Bin & Xu, Da & Chan, Ka Wing & Li, Canbing & Cao, Yijia & Bu, Siqi, 2017. "A two-stage framework for multiobjective energy management in distribution networks with a high penetration of wind energy," Energy, Elsevier, vol. 135(C), pages 754-766.
    5. Uddin, Moslem & Romlie, Mohd Fakhizan & Abdullah, Mohd Faris & Abd Halim, Syahirah & Abu Bakar, Ab Halim & Chia Kwang, Tan, 2018. "A review on peak load shaving strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3323-3332.
    6. Chapaloglou, Spyridon & Nesiadis, Athanasios & Iliadis, Petros & Atsonios, Konstantinos & Nikolopoulos, Nikos & Grammelis, Panagiotis & Yiakopoulos, Christos & Antoniadis, Ioannis & Kakaras, Emmanuel, 2019. "Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island’s power system," Applied Energy, Elsevier, vol. 238(C), pages 627-642.
    7. Ghafoori, Mahdi & Abdallah, Moatassem & Kim, Serena, 2023. "Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system," Applied Energy, Elsevier, vol. 340(C).
    8. Md Masud Rana & Mohamed Atef & Md Rasel Sarkar & Moslem Uddin & GM Shafiullah, 2022. "A Review on Peak Load Shaving in Microgrid—Potential Benefits, Challenges, and Future Trend," Energies, MDPI, vol. 15(6), pages 1-17, March.
    9. Nieta, Agustín A. Sánchez de la & Ilieva, Iliana & Gibescu, Madeleine & Bremdal, Bernt & Simonsen, Stig & Gramme, Eivind, 2021. "Optimal midterm peak shaving cost in an electricity management system using behind customers’ smart meter configuration," Applied Energy, Elsevier, vol. 283(C).
    10. Flygare, Carl & Wallberg, Alexander & Jonasson, Erik & Castellucci, Valeria & Waters, Rafael, 2024. "Correlation as a method to assess electricity users’ contributions to grid peak loads: A case study," Energy, Elsevier, vol. 288(C).
    11. Uddin, Moslem & Romlie, M.F. & Abdullah, M.F. & Tan, ChiaKwang & Shafiullah, GM & Bakar, A.H.A., 2020. "A novel peak shaving algorithm for islanded microgrid using battery energy storage system," Energy, Elsevier, vol. 196(C).
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