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Game-theoretic genetic-priced optimization of multiple microgrids under uncertainties

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  • Sun, Lu
  • Xu, Qingshan
  • Song, Yun

Abstract

To address an economic dispatch issue of multiple microgrids (MGs) in a game-theoretic framework under source-load uncertainties, a robust optimization scheme with genetic-priced mechanism is proposed. This scheme is a nested iterative algorithm with an outer loop calculating the pricing model in a game and an inner loop solving the robust optimization problem. Specifically, in the outer loop, all stakeholders in a grid-connected microgrid cluster (MGC), i.e. one energy trading center (ETC) and several MGs, are in a game where a genetic-priced model is developed. By utilizing this evolutionary pricing model, the ETC can fix electricity prices to maximize its profits and these prices are passed to the inner loop where a two-stage robust optimization approach is introduced to mitigate adverse effects of uncertainties in MGs induced by renewable energy resources (RERs) and loads. All optimization problems of MGs are solved and optimal values of electricity exchanged between ETC and MGs are passed to the outer loop. This optimization scheme can help address the robust economic dispatch problem in a game-theoretic framework. A grid-connected MGC is used as a case to illustrate the effectiveness of the proposed optimization scheme.

Suggested Citation

  • Sun, Lu & Xu, Qingshan & Song, Yun, 2022. "Game-theoretic genetic-priced optimization of multiple microgrids under uncertainties," Applied Mathematics and Computation, Elsevier, vol. 426(C).
    • Handle: RePEc:eee:apmaco:v:426:y:2022:i:c:s0096300322001291
    DOI: 10.1016/j.amc.2022.127043
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    References listed on IDEAS

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