Distributed Power Generation Planning Method Based on Small World Theory and Improved Dung Beetle Algorithm

Yu ZHANG; Dehui WANG; Wenyang CAI; Shirong ZOU; Jiayan WANG; Haidong TAN

doi:10.13648/j.cnki.issn1674-0629.2026.07.009

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South Power Sys Technol ›› 2026, Vol. 20 ›› Issue (7) : 90-100. DOI: 10.13648/j.cnki.issn1674-0629.2026.07.009

New Energy & Microgrid

Distributed Power Generation Planning Method Based on Small World Theory and Improved Dung Beetle Algorithm

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Abstract

Aiming at the problem of high-dimensional solving and coexisting discrete and continuous variables in the site selection and capacity planning of distributed power generation, a method is proposed to first determine candidate locations and then a heuristic algorithm is used to optimize the capacity and location. Firstly, the nodes are screened according to the three indicators: active loss improvement rate, betweenness centrality and closeness centrality, and adjacent nodes are merged according to the node power load conditions to form a set of candidate nodes. Subsequently, an optimization model with the goal of minimizing total active loss, maximizing voltage stability, and minimizing total capacity of distributed power generation is constructed. In order to effectively solve the model, dung beetle algorithm is improved based on chaotic mapping, adaptive weights and Levy flight strategy to optimize the locations and capacities of distributed generation in the candidate nodes. Finally, simulation verifications on IEEE-33 and IEEE-69 node systems show that the proposed method performs well in reducing total active power loss and node voltage deviation.

Key words

distributed power generation / site selection and capacity planning / improved dung beetle algorithm / multi-objective optimization

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Yu ZHANG , Dehui WANG , Wenyang CAI , et al . Distributed Power Generation Planning Method Based on Small World Theory and Improved Dung Beetle Algorithm[J]. Southern Power System Technology. 2026, 20(7): 90-100 https://doi.org/10.13648/j.cnki.issn1674-0629.2026.07.009

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