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Towards long-period operational reliability of independent microgrid: A risk-aware energy scheduling and stochastic optimization method

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  • Liu, Yixin
  • Shi, Haoqi
  • Guo, Li
  • Xu, Tao
  • Zhao, Bo
  • Wang, Chengshan

Abstract

Independent microgrids (MGs) consisting of diesel generator (DG), photovoltaic (PV), and energy storage system (ESS) are becoming a cost effective solution for the power supply in remote areas. However, besides PV intermittence, limited reserve and time-consuming replenishment of diesel fuel in remote areas make it challenging to guarantee long-period reliable power supply. In this paper, a risk-aware energy scheduling and stochastic optimization method is proposed to enhance long-period operational reliability of independent MGs. The possible extreme scenarios in the future are considered in an energy scheduling optimization model (ESOM). Based on energy forecast results of PV and loads for the next 7 days, ESOM maximizes the reliable power supply probability by optimizing energy scheduling strategies and reserve requirement for future operational risk simultaneously. Subsequently, a day-ahead stochastic optimization model is established to determine optimal power scheduling strategies of DG, PV, ESS, and flexible loads. The conditional value at risk (CVaR) is used to address the operation risk caused by uncertainties of PV and loads. Compared with traditional day-ahead optimization methods by numerous simulations, the proposed method has less expected load losses and PV curtailment, as well as less total supply-demand deviation. The resistance for future operational risks of independent MGs is therefore significantly enhanced.

Suggested Citation

  • Liu, Yixin & Shi, Haoqi & Guo, Li & Xu, Tao & Zhao, Bo & Wang, Chengshan, 2022. "Towards long-period operational reliability of independent microgrid: A risk-aware energy scheduling and stochastic optimization method," Energy, Elsevier, vol. 254(PB).
    • Handle: RePEc:eee:energy:v:254:y:2022:i:pb:s036054422201194x
    DOI: 10.1016/j.energy.2022.124291
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    3. Alrobaian, Abdulrahman A. & Alsagri, Ali Sulaiman, 2023. "Multi-agent-based energy management for a fully electrified residential consumption," Energy, Elsevier, vol. 282(C).

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