Rolling optimization of transmission network recovery and load restoration considering hybrid wind-storage system and cold load pickup

Lei Sun, Weijia Liu, C. Y. Chung, Ming Ding, Jiang Ding

    Research output: Journal article publicationJournal articleAcademic researchpeer-review

    9 Citations (Scopus)

    Abstract

    A common solution to deal with the stochasticity introduced by fast-ramping wind power integration is to equip wind farms (WFs) with energy storage systems (ESSs) to formulate hybrid WF-ESSs. In addition to leveling off wind power fluctuations during normal operations, a hybrid WF-ESS can be a flexible power source to accumulate the power system restoration. In this paper, we propose a rolling optimization model for transmission network recovery and load restoration considering the contributions of WF-ESSs. The proposed model is formulated as a mixed integer linear programming problem that simultaneously optimizes the amount and location of restorable load blocks as well as the restoration lines. The cold load pickup features of interrupted loads considering the outage duration are modeled in detail. A chance-constrained method is employed to deal with the uncertainty of wind power, and a rolling horizon-based framework is adopted to reduce the influence of forecast error. Case studies are conducted on both New England 39-bus system and part of a provincial power system in China. The results show that the load restoration process can be significantly accelerated by employing the proposed method and contributions of hybrid WF-ESSs to power system restoration are validated.

    Original languageEnglish
    Article number108168
    JournalInternational Journal of Electrical Power and Energy Systems
    Volume141
    DOIs
    Publication statusPublished - Oct 2022

    Keywords

    • Chance-constrained optimization
    • Energy storage system
    • Load restoration
    • Rolling horizon optimization
    • Transmission network restoration
    • Wind power

    ASJC Scopus subject areas

    • Energy Engineering and Power Technology
    • Electrical and Electronic Engineering

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