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Fairness and usability analysis in renewable power curtailment: A microgrid network study using bankruptcy rules

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  • Alyami, Saeed

Abstract

The integration of renewable energy sources globally has effectively reduced carbon emissions and enhanced community self-sufficiency through local resource use. However, challenges arise with higher renewables penetration, especially at local and distribution levels. Curtailment of renewables becomes necessary during peak generation intervals to ensure power quality and system stability, raising fairness concerns. This study examines the feasibility of using bankruptcy rules to address fairness concerns in renewable power curtailment in microgrid networks. Four bankruptcy rules - proportional, constrained equal awards (CEA), constrained equal losses (CEL), and sequential priority rules - are evaluated using the IEEE 33-bus system transformed into a network of five microgrids with solar and wind generators. Voltage profiles of microgrid networks in Detroit and Chicago under high renewable penetration are assessed, and bankruptcy rules are applied to curtail surplus renewable power. The fairness of each rule is analyzed using Jain's fairness index, while a usability index evaluates power usability across microgrids under different bankruptcy rules. Simulation results show that the proportional and CEA rules exhibit better fairness and equal usability among microgrids, with voltage profiles just below the upper bound. Conversely, the priority rule improves voltage profiles at the expense of fairness and equal usability.

Suggested Citation

  • Alyami, Saeed, 2024. "Fairness and usability analysis in renewable power curtailment: A microgrid network study using bankruptcy rules," Renewable Energy, Elsevier, vol. 227(C).
  • Handle: RePEc:eee:renene:v:227:y:2024:i:c:s0960148124004890
    DOI: 10.1016/j.renene.2024.120424
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    References listed on IDEAS

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    1. Stringer, Naomi & Haghdadi, Navid & Bruce, Anna & MacGill, Iain, 2021. "Fair consumer outcomes in the balance: Data driven analysis of distributed PV curtailment," Renewable Energy, Elsevier, vol. 173(C), pages 972-986.
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    3. Lei, Xingyu & Yang, Zhifang & Zhao, Junbo & Yu, Juan, 2022. "Data-driven assisted chance-constrained energy and reserve scheduling with wind curtailment," Applied Energy, Elsevier, vol. 321(C).
    4. Andoni, Merlinda & Robu, Valentin & Früh, Wolf-Gerrit & Flynn, David, 2017. "Game-theoretic modeling of curtailment rules and network investments with distributed generation," Applied Energy, Elsevier, vol. 201(C), pages 174-187.
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