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A Multi-objective Scheduling Optimization Model for Hybrid Energy System Connected with Wind-Photovoltaic-Conventional Gas Turbines, CHP Considering Heating Storage Mechanism

Author

Listed:
  • Yao Wang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Economic and Electrical Research Institute of Shanxi Electrical Power Company of SGCC, Taiyuan 030002, China)

  • Yan Lu

    (State Grid Jibei Electric Economic Research Institute, Beijing 100095, China)

  • Liwei Ju

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Ting Wang

    (Department of Power Engineering, Shanxi University, Taiyuan 030000, China)

  • Qingkun Tan

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Jiawei Wang

    (Economic and Electrical Research Institute of Shanxi Electrical Power Company of SGCC, Taiyuan 030002, China)

  • Zhongfu Tan

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

In order to meet the user’s electricity demand and make full use of distributed energy, a hybrid energy system (HES) was proposed and designed, including wind turbines (WTs), photovoltaic (PV) power generation, conventional gas turbines (CGTs), incentive-based demand response (IBDR), combined heat and power (CHP) and regenerative electric (RE) boilers. Then, the collaborative operation problem of HES is discussed. First, the paper describes the HES’ basic structure and presents the output model of power sources and heating sources. Next, the maximum operating income and minimum load fluctuation are taken as the objective function, and a multi-objective model of HES scheduling is proposed. Then an algorithm for solving the model is proposed that comprises two steps: processing the objective functions and constraints into linear equations and determining the optimal weight of the objective functions. The selected simulation system is a microgrid located on an eastern island of China to comparatively analyze the influence of RE-heating storage (RE-HS) and price-based demand response (PBDR) on HES operation in relation to four cases. By analyzing the results, the following three conclusions are drawn: (1) HES can comprehensively utilize a variety of distributed energy sources to meet load demand. In particular, RE technology can convert the abandoned energy of WT and PV into heat during the valley load time, to meet the load demand combined with CHP; (2) The proposed multi-objective scheduling model of HES operation not only considers the maximum operating income but also considers the minimum load fluctuation, thus achieving the optimal balancing operation; (3) RE-HS and PBDR have a synergistic optimization effect, and when RE-HS and PBDR are both applied, an HES can achieve optimal operation results. Overall, the proposed decision method is highly effective and applicable, and decision makers could utilize this method to design an optimal HES operation strategy according to their own actual conditions.

Suggested Citation

  • Yao Wang & Yan Lu & Liwei Ju & Ting Wang & Qingkun Tan & Jiawei Wang & Zhongfu Tan, 2019. "A Multi-objective Scheduling Optimization Model for Hybrid Energy System Connected with Wind-Photovoltaic-Conventional Gas Turbines, CHP Considering Heating Storage Mechanism," Energies, MDPI, vol. 12(3), pages 1-28, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:425-:d:201753
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    References listed on IDEAS

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    1. Torkan, Ramin & Ilinca, Adrian & Ghorbanzadeh, Milad, 2022. "A genetic algorithm optimization approach for smart energy management of microgrids," Renewable Energy, Elsevier, vol. 197(C), pages 852-863.

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