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An Improved Bare Bone Multi-Objective Particle Swarm Optimization Algorithm for Solar Thermal Power Plants

Author

Listed:
  • Qun Niu

    (School of Mechanical Engineering and Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai 200444, China)

  • Han Wang

    (School of Mechanical Engineering and Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai 200444, China)

  • Ziyuan Sun

    (School of Mechanical Engineering and Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai 200444, China)

  • Zhile Yang

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

Abstract

Solar energy has many advantages, such as being abundant, clean and environmentally friendly. Solar power generation has been widely deployed worldwide as an important form of renewable energy. The solar thermal power generation is one of a few popular forms to utilize solar energy, yet its modelling is a complicated problem. In this paper, an improved bare bone multi-objective particle swarm optimization algorithm (IBBMOPSO) is proposed based on the bare bone multi-objective particle swarm optimization algorithm (BBMOPSO). The algorithm is first tested on a set of benchmark problems, confirming its efficacy and the convergency speed. Then, it is applied to optimize two typical solar power generation systems including the solar Stirling power generation and the solar Brayton power generation; the results show that the proposed algorithm outperforms other algorithms for multi-objective optimization problems.

Suggested Citation

  • Qun Niu & Han Wang & Ziyuan Sun & Zhile Yang, 2019. "An Improved Bare Bone Multi-Objective Particle Swarm Optimization Algorithm for Solar Thermal Power Plants," Energies, MDPI, vol. 12(23), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4480-:d:290577
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    References listed on IDEAS

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    1. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    2. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
    3. Alexandra Vrînceanu & Ines Grigorescu & Monica Dumitrașcu & Irena Mocanu & Cristina Dumitrică & Dana Micu & Gheorghe Kucsicsa & Bianca Mitrică, 2019. "Impacts of Photovoltaic Farms on the Environment in the Romanian Plain," Energies, MDPI, vol. 12(13), pages 1-18, July.
    4. Yang Li & Binyu Xiong & Yixin Su & Jinrui Tang & Zhiwen Leng, 2019. "Particle Swarm Optimization-Based Power and Temperature Control Scheme for Grid-Connected DFIG-Based Dish-Stirling Solar-Thermal System," Energies, MDPI, vol. 12(7), pages 1-23, April.
    5. P. L. Yu, 1973. "A Class of Solutions for Group Decision Problems," Management Science, INFORMS, vol. 19(8), pages 936-946, April.
    6. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    7. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    8. Li, Yuqiang & Liu, Gang & Liu, Xianping & Liao, Shengming, 2016. "Thermodynamic multi-objective optimization of a solar-dish Brayton system based on maximum power output, thermal efficiency and ecological performance," Renewable Energy, Elsevier, vol. 95(C), pages 465-473.
    9. Carrillo Caballero, Gaylord Enrique & Mendoza, Luis Sebastian & Martinez, Arnaldo Martin & Silva, Electo Eduardo & Melian, Vladimir Rafael & Venturini, Osvaldo José & del Olmo, Oscar Almazán, 2017. "Optimization of a Dish Stirling system working with DIR-type receiver using multi-objective techniques," Applied Energy, Elsevier, vol. 204(C), pages 271-286.
    10. V. Srinivasan & Allan Shocker, 1973. "Linear programming techniques for multidimensional analysis of preferences," Psychometrika, Springer;The Psychometric Society, vol. 38(3), pages 337-369, September.
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