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Data-driven distributionally robust joint chance-constrained energy management for multi-energy microgrid

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  • Zhai, Junyi
  • Wang, Sheng
  • Guo, Lei
  • Jiang, Yuning
  • Kang, Zhongjian
  • Jones, Colin N.

Abstract

Multi-energy microgrid (MEMG) has the potential to improve the energy utilization efficiency. However, the uncertainty caused by distributed renewable energy resources brings an urgent need for multi-energy co-optimization to ensure secure operation. This paper focuses on the distributionally robust energy management problem for MEMG. Various flexible resources in different energy sectors are utilized for uncertainty mitigation, then, a data-driven Wasserstein distance-based distributionally robust joint chance-constrained (DRJCC) energy management model is proposed. To make the DRJCC model tractable, an optimized conditional value-at-risk (CVaR) approximation (OCA) formulation is proposed to transfer the joint chance-constrained model into a tractable form. Then, an iterative sequential convex optimization algorithm is tailored to reduce the solution conservatism by tuning OCA. Numerical result illustrates the effectiveness of the proposed model.

Suggested Citation

  • Zhai, Junyi & Wang, Sheng & Guo, Lei & Jiang, Yuning & Kang, Zhongjian & Jones, Colin N., 2022. "Data-driven distributionally robust joint chance-constrained energy management for multi-energy microgrid," Applied Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922011965
    DOI: 10.1016/j.apenergy.2022.119939
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    References listed on IDEAS

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