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Distribution network reconfiguration with distributed generation based on parallel slime mould algorithm

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  • Wang, Hong-Jiang
  • Pan, Jeng-Shyang
  • Nguyen, Trong-The
  • Weng, Shaowei

Abstract

This paper proposes a solution for the distribution network reconfiguration (DNR) problem with distributed generation (DG) based on the parallel slime mould algorithm (PSMA). Firstly, we construct the four optimization objectives of active power loss, voltage stability index, load balance degree, and switching operation times are integrated by the analytic hierarchy process (AHP) and the DNR problem models with different DG types. Next, we propose PSMA based on the grouping communication strategy and inertia weight; we verify the performance of PSMA by testing a CEC2014 test suite. Finally, we apply the PSMA for the six types of DG of the IEEE-33 bus distribution network as the case study; the outcomes are compared with the other four algorithms. Experimental results show that the PSMA can solve the DNR problem with DG more accurately and quickly than the other three algorithms. At the same time, the results show that the type and access location of DG will affect the DNR problem in different degrees.

Suggested Citation

  • Wang, Hong-Jiang & Pan, Jeng-Shyang & Nguyen, Trong-The & Weng, Shaowei, 2022. "Distribution network reconfiguration with distributed generation based on parallel slime mould algorithm," Energy, Elsevier, vol. 244(PB).
    • Handle: RePEc:eee:energy:v:244:y:2022:i:pb:s0360544221032606
    DOI: 10.1016/j.energy.2021.123011
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    Cited by:

    1. Wu, Huayi & Xu, Zhao, 2024. "Multi-energy flow calculation in integrated energy system via topological graph attention convolutional network with transfer learning," Energy, Elsevier, vol. 303(C).
    2. Li, J.Y. & Chen, J.J. & Wang, Y.X. & Chen, W.G., 2024. "Combining multi-step reconfiguration with many-objective reduction as iterative bi-level scheduling for stochastic distribution network," Energy, Elsevier, vol. 290(C).

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