Multimodal multi-objective hierarchical distributed consensus method for multimodal multi-objective economic dispatch of hierarchical distributed power systems

Linfei Yin and Zhenjian Cai

Energy, 2024, vol. 295, issue C

Abstract: As the scale of power systems increases, the multi-layer distributed multi-objective consensus algorithm (MLDMOCA) can speed up computation and enhance privacy. However, the MLDMOCA needs to face: (1) the problems of error enlargement of hierarchy bringing deteriorating results; (2) over-dependence of certain weights on the choice of power balance adjustment factors. Moreover, multimodal multi-objective economic dispatch has not been performed for either small or large systems, resulting in poor diversity of economic scheduling solutions. Therefore, this study proposes a multimodal multi-objective hierarchical distributed consensus approach. The multimodal multi-objective hierarchical distributed consensus approach contains multimodal increments, and the proposed multimodal search method can obtain multimodal results. By changing the multimodal factor and multimodal critical threshold, the multimodal results and multimodal decision solutions of the proposed method are adjusted. In this study, the multimodal multi-objective hierarchical distributed consensus approach is simulated in IEEE 118-bus and IEEE 3059-bus cases. The simulation results show that the multimodal multi-objective hierarchical distributed consensus approach can achieve multiple multimodal solutions quickly and can adapt the power balance adjustment factor to almost all weights. Moreover, multiple linear-weighted multimodal objective values of the proposed approach are smaller than the objective value of the existing hierarchical distributed method.

Keywords: Multimodal multi-objective hierarchical distributed consensus approach; Multimodal increment; Multimodal factor; Multimodal multi-objective economic dispatch (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:295:y:2024:i:c:s0360544224007680

DOI: 10.1016/j.energy.2024.130996

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