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A Stochastic-CVaR Optimization Model for CCHP Micro-Grid Operation with Consideration of Electricity Market, Wind Power Accommodation and Multiple Demand Response Programs

Author

Listed:
  • Yuwei Wang

    (Department of Economic Management, North China Electric Power University, Baoding 071003, China)

  • Yuanjuan Yang

    (Department of Economic Management, North China Electric Power University, Baoding 071003, China)

  • Liu Tang

    (Department of Economic Management, North China Electric Power University, Baoding 071003, China)

  • Wei Sun

    (Department of Economic Management, North China Electric Power University, Baoding 071003, China)

  • Huiru Zhao

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

Abstract

Combined cooling, heating and power (CCHP) micro-grids have the advantage of high energy efficiency, and can be integrated with renewable energies and demand response programs (DRPs). With the deepening of electricity market (EM) reforms, how to carry out operation optimization under EM circumstances will become a key problem for CCHP micro-grid development. This paper proposed a stochastic-CVaR (conditional value at risk) optimization model for CCHP micro-grid operation with consideration of EM participation, wind power accommodation and multiple DRPs. Specifically, based on the stochastic scenarios for EM clearing prices and wind power outputs uncertainties, the stochastic optimization method was applied to ensure the realization of operational cost minimization and wind power accommodation; the CVaR method was implemented to control the potential risk of operational cost increase. Moreover, by introducing multiple DRPs, the electrical, thermal and cooling loads can be transformed as flexible sources for CCHP micro-grid operation. Simulations were performed to show the following outcomes: (1) by applying the proposed stochastic-CVaR approach and considering multiple DRPs, CCHP micro-grid operation can reach better performance in terms of cost minimization, risk control and wind power accommodation etc.; (2) higher energy utilization efficiency can be achieved by coordinately optimizing EM power biddings; etc.

Suggested Citation

  • Yuwei Wang & Yuanjuan Yang & Liu Tang & Wei Sun & Huiru Zhao, 2019. "A Stochastic-CVaR Optimization Model for CCHP Micro-Grid Operation with Consideration of Electricity Market, Wind Power Accommodation and Multiple Demand Response Programs," Energies, MDPI, vol. 12(20), pages 1-33, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3983-:d:278276
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    References listed on IDEAS

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    Cited by:

    1. Juan Carlos Oviedo Cepeda & German Osma-Pinto & Robin Roche & Cesar Duarte & Javier Solano & Daniel Hissel, 2020. "Design of a Methodology to Evaluate the Impact of Demand-Side Management in the Planning of Isolated/Islanded Microgrids," Energies, MDPI, vol. 13(13), pages 1-24, July.
    2. Li, Yanbin & Zhang, Feng & Li, Yun & Wang, Yuwei, 2021. "An improved two-stage robust optimization model for CCHP-P2G microgrid system considering multi-energy operation under wind power outputs uncertainties," Energy, Elsevier, vol. 223(C).
    3. Li, Bingkang & Zhao, Huiru & Wang, Xuejie & Zhao, Yihang & Zhang, Yuanyuan & Lu, Hao & Wang, Yuwei, 2022. "Distributionally robust offering strategy of the aggregator integrating renewable energy generator and energy storage considering uncertainty and connections between the mid-to-long-term and spot elec," Renewable Energy, Elsevier, vol. 201(P1), pages 400-417.
    4. Ethem Çanakoğlu & Esra Adıyeke, 2020. "Comparison of Electricity Spot Price Modelling and Risk Management Applications," Energies, MDPI, vol. 13(18), pages 1-22, September.
    5. Yanbin Li & Yanting Sun & Junjie Zhang & Feng Zhang, 2022. "Optimal Microgrid System Operating Strategy Considering Variable Wind Power Outputs and the Cooperative Game among Subsystem Operators," Energies, MDPI, vol. 15(18), pages 1-20, September.
    6. Yan Xiong & Jiakun Fang, 2022. "Co-Operative Optimization Framework for Energy Management Considering CVaR Assessment and Game Theory," Energies, MDPI, vol. 15(24), pages 1-17, December.

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