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Game Optimization Theory and Application in Distribution System Expansion Planning, Including Distributed Generation

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  • Ran Li

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Huizhuo Ma

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Feifei Wang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Yihe Wang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Yang Liu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Zenghui Li

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

Abstract

Based on Game Theory and Multi-objective optimization problems (MOP), Game Optimization Theory (GOT) is discussed in this paper. Optimization Stability Analysis (OSA), Distance Entropy Multi-Objective Particle Swarm Optimization (DEMPSO) and Fuzzy Multi-weights Decision-making Method (FMW) are proposed as well. Game Optimization Theory, which is a comprehensive system, could not only handle multi-objective optimization problems effectively, but also could offset the disadvantages of traditional optimization theories, such as lack of framework and the insufficient consideration of relevant elements. In this paper GOT is used for the first time in solving the distribution systems planning (DSP) issue by implementing distributed generation. The proposed model integrates costs, losses, and voltage index to achieve optimal size and site of distributed generation. The model allows minimizing total system costs, system power losses and maximizing voltage improvement. A detailed DSP example is used for verifying the effectiveness and reasonableness of GOT in this context.

Suggested Citation

  • Ran Li & Huizhuo Ma & Feifei Wang & Yihe Wang & Yang Liu & Zenghui Li, 2013. "Game Optimization Theory and Application in Distribution System Expansion Planning, Including Distributed Generation," Energies, MDPI, vol. 6(2), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:2:p:1101-1124:d:23710
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    References listed on IDEAS

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    4. Bayod-Rújula, Angel A., 2009. "Future development of the electricity systems with distributed generation," Energy, Elsevier, vol. 34(3), pages 377-383.
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    Cited by:

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    5. Xiangang Peng & Lixiang Lin & Weiqin Zheng & Yi Liu, 2015. "Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem," Energies, MDPI, vol. 8(12), pages 1-19, December.
    6. Suman Bhullar & Smarajit Ghosh, 2018. "Optimal Integration of Multi Distributed Generation Sources in Radial Distribution Networks Using a Hybrid Algorithm," Energies, MDPI, vol. 11(3), pages 1-15, March.
    7. Liaqat Ali & S. M. Muyeen & Hamed Bizhani & Arindam Ghosh, 2019. "Comparative Study on Game-Theoretic Optimum Sizing and Economical Analysis of a Networked Microgrid," Energies, MDPI, vol. 12(20), pages 1-14, October.
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