A robust microgrid formation method for risk-resistant service restoration considering subsequent contingency
•Propose a proactive robust MG formation scheme under subsequent contingency.•Develop improved uncertainty sets of wind power and subsequent contingency.•Introduce auxiliary variables indicating the updated fictitious flow and node energization status.•Establish a robust SR model solved by column-an...
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Veröffentlicht in: | International journal of electrical power & energy systems 2024-08, Vol.159, p.109994, Article 109994 |
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Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •Propose a proactive robust MG formation scheme under subsequent contingency.•Develop improved uncertainty sets of wind power and subsequent contingency.•Introduce auxiliary variables indicating the updated fictitious flow and node energization status.•Establish a robust SR model solved by column-and-constraint generation algorithm.
Service restoration (SR) is essential for resilience enhancement of distribution systems (DSs) when natural disasters occur. However, a restored DS may face potential outage risks caused by subsequent contingency during the prolonged event. To improve the risk-resistance of SR strategies, this paper proposes a microgrid-based SR method to continuously supply critical loads when considering subsequent contingency. The microgrids are proactively formed to mitigate the influence of subsequent contingencies, which can effectively reduce the load shedding and wind power curtailment during the SR process. To deal with uncertainties of subsequent contingency and wind power, a two-stage robust model is established. Moreover, an improved uncertainty set is established to further reduce the conservatism of robust optimization. Specially, the line interruption probability is considered when constructing the Shannon entropy-based uncertainty set of subsequent contingencies, and the spatial–temporal correlations of wind power is considered when formulating the budget-constrained uncertainty set. Then, the robust model is solved by column-and-constraint generation algorithm efficiently. Numerical results demonstrate that the proposed SR method can effectively enhance the risk-resistance of SR strategy and reduce the conservatism while ensuring the robustness of solutions. |
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ISSN: | 0142-0615 |
DOI: | 10.1016/j.ijepes.2024.109994 |