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A Hybrid Constraints Handling Strategy for Multiconstrained Multiobjective Optimization Problem of Microgrid Economical/Environmental Dispatch

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  • Xin Li
  • Jingang Lai
  • Ruoli Tang

Abstract

Microgrid (MG) economical/environmental dispatch (MGEED) problem is a complex multiobjective optimization topic. Since the generators are diversified and the operation mode changes frequently, the MGEEED problem always has different types of constraints, such as the load balance constraints and the ramp rates constraints, which make it a nonlinear, nonconvex optimization problem. In this paper, the mathematical model of a typical MG system applied in northwest China is established. Then, a hybrid constraints handling strategy (HCHS) based on nondominated sorting genetic algorithm II (NSGAII) is proposed to deal with the typical constraints, by which the constraints violations can be removed in several steps during the evolutionary process. A dimensionality reduction method is introduced to simplify the optimization model. And an individual repair approach is designed for the violations of ramp rates constraints. In order to balance the weights of various types of constraints, the process of constraints handling in standard NSGAII is revised. Thereafter, HCHS-NSGAII is applied to some typical MGEED problems, considering all kinds of typical constraints. The results show that HCHS-NSGAII can obtain feasible Pareto sets with satisfactory convergence and distribution, which is efficient in handling complex practical industrial MGEED problems with the change of constraints combinations.

Suggested Citation

  • Xin Li & Jingang Lai & Ruoli Tang, 2017. "A Hybrid Constraints Handling Strategy for Multiconstrained Multiobjective Optimization Problem of Microgrid Economical/Environmental Dispatch," Complexity, Hindawi, vol. 2017, pages 1-12, December.
    • Handle: RePEc:hin:complx:6249432
    DOI: 10.1155/2017/6249432
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    References listed on IDEAS

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    Cited by:

    1. Fei Han & Yu-Wen-Tian Sun & Qing-Hua Ling, 2018. "An Improved Multiobjective Quantum-Behaved Particle Swarm Optimization Based on Double Search Strategy and Circular Transposon Mechanism," Complexity, Hindawi, vol. 2018, pages 1-22, November.
    2. Bo Wang & Yanjing Li & Fei Yang & Xiaohua Xia, 2019. "A Competitive Swarm Optimizer-Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV-Battery Hybrid Systems," Complexity, Hindawi, vol. 2019, pages 1-15, October.
    3. Yuchao Su & Qiuzhen Lin & Jia Wang & Jianqiang Li & Jianyong Chen & Zhong Ming, 2019. "A Constrained Solution Update Strategy for Multiobjective Evolutionary Algorithm Based on Decomposition," Complexity, Hindawi, vol. 2019, pages 1-11, May.
    4. Yadong Yu & Haiping Ma & Mei Yu & Sengang Ye & Xiaolei Chen, 2018. "Multipopulation Management in Evolutionary Algorithms and Application to Complex Warehouse Scheduling Problems," Complexity, Hindawi, vol. 2018, pages 1-14, April.

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