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Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch

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  • Qian Zhang
  • Jinjin Ding
  • Weixiang Shen
  • Jinhui Ma
  • Guoli Li

Abstract

Multiobjective optimization (MOO) dispatch for microgrids (MGs) can achieve many benefits, such as minimized operation cost, greenhouse gas emission reduction, and enhanced reliability of service. In this paper, a MG with the PV-battery-diesel system is introduced to establish its characteristic and economic models. Based on the models and three objectives, the constrained MOO problem is formulated. Then, an advanced multiobjective particle swarm optimization (MOPSO) algorithm is proposed to obtain Pareto optimization dispatch for MGs. The combination of archive maintenance and Pareto selection enables the MOPSO algorithm to maintain enough nondominated solutions and seek Pareto frontiers. The final trade-off solutions are decided based on the fuzzy set. The benchmark function tests and simulation results demonstrate that the proposed MOPSO algorithm has better searching ability than nondominated sorting genetic algorithm-II (NSGA-II), which is widely used in generation dispatch for MGs. The proposed method can efficiently offer more Pareto solutions and find a trade-off one to simultaneously achieve three benefits: minimized operation cost, reduced environmental cost, and maximized reliability of service.

Suggested Citation

  • Qian Zhang & Jinjin Ding & Weixiang Shen & Jinhui Ma & Guoli Li, 2020. "Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, March.
    • Handle: RePEc:hin:jnlmpe:5695917
    DOI: 10.1155/2020/5695917
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    Cited by:

    1. Liya Yue & Pei Hu & Shu-Chuan Chu & Jeng-Shyang Pan, 2023. "Multi-Objective Gray Wolf Optimizer with Cost-Sensitive Feature Selection for Predicting Students’ Academic Performance in College English," Mathematics, MDPI, vol. 11(15), pages 1-16, August.
    2. Ramakrishna S. S. Nuvvula & Devaraj Elangovan & Kishore Srinivasa Teegala & Rajvikram Madurai Elavarasan & Md. Rabiul Islam & Ravikiran Inapakurthi, 2021. "Optimal Sizing of Battery-Integrated Hybrid Renewable Energy Sources with Ramp Rate Limitations on a Grid Using ALA-QPSO," Energies, MDPI, vol. 14(17), pages 1-23, August.

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