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Optimal intra-day operations of behind-the-meter battery storage for primary frequency regulation provision: A hybrid lookahead method

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  • Wen, Kerui
  • Li, Weidong
  • Yu, Samson Shenglong
  • Li, Ping
  • Shi, Peng

Abstract

Battery energy storage systems (BESSs) are being widely installed behind-the-meter to reduce electricity bill. By providing grid ancillary services, behind-the-meter BESSs can increase potential revenue streams. This study targets the simultaneous electricity bill reduction and primary frequency regulation (PFR) provision. With the expansion of the application spectrum, the intra-day operations become more and more complicated. In this paper, a hybrid lookahead method with value function approximation strategy is proposed for intra-day operations, wherein the concept of “offline calculation—online application” is devised and implemented. The approximate value function is trained offline to represent the expected long-term benefit. A two-stage robust approximate dynamic programming (ADP) model is formulated for one day operation which is optimized to adjust the power baseline with a forward rolling horizon. Furthermore, multi-dimensional indicators are introduced to evaluate the proposed strategy. Simulations and benchmarking comparisons are performed for a 0.5 MW/1.0 MWh BESS to verify the superior performance of the proposed strategy. The results show that the approximate value function can be obtained offline with 99.07% convergence precision. Moreover, the proposed strategy can ensure the economic benefit and PFR provision within a short online computing time. The resulting intra-day economic benefit can reach 95.55% of the theoretical optimum, and the online optimization consumes only 4.65s for a prediction horizon of 5 min, which ensures the feasibility of real-time predictive optimization.

Suggested Citation

  • Wen, Kerui & Li, Weidong & Yu, Samson Shenglong & Li, Ping & Shi, Peng, 2022. "Optimal intra-day operations of behind-the-meter battery storage for primary frequency regulation provision: A hybrid lookahead method," Energy, Elsevier, vol. 247(C).
  • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003851
    DOI: 10.1016/j.energy.2022.123482
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    2. Soheil Mohseni & Jay Rutovitz & Heather Smith & Scott Dwyer & Farzan Tahir, 2023. "Economic Viability Assessment of Neighbourhood versus Residential Batteries: Insights from an Australian Case Study," Sustainability, MDPI, vol. 15(23), pages 1-27, November.
    3. Zhang, Mingze & Li, Weidong & Yu, Samson Shenglong & Wen, Kerui & Muyeen, S.M., 2023. "Day-ahead optimization dispatch strategy for large-scale battery energy storage considering multiple regulation and prediction failures," Energy, Elsevier, vol. 270(C).
    4. Wei Chen & Na Sun & Zhicheng Ma & Wenfei Liu & Haiying Dong, 2023. "A Two-Layer Optimization Strategy for Battery Energy Storage Systems to Achieve Primary Frequency Regulation of Power Grid," Energies, MDPI, vol. 16(6), pages 1-18, March.

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