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Operating Strategy for Local-Area Energy Systems Integration Considering Uncertainty of Supply-Side and Demand-Side under Conditional Value-At-Risk Assessment

Author

Listed:
  • Jiaqi Shi

    (State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Yingrui Wang

    (China Energy Engineering Group Tianjin Electric Power Design Institute Co., Ltd., Tianjin 300400, China)

  • Ruibin Fu

    (Inner Mongolia Power (Group) Co., Ltd., Huhehaote 010020, China)

  • Jianhua Zhang

    (State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

Abstract

To alleviate environmental pollution and improve the energy usage efficiency of terminals, energy systems integration (ESI) has become an important paradigm in the energy structure evolution. Power, gas and heat systems are becoming tightly interlinked with each other in ESI. The dispatching strategy of local-area ESI has significant impact on its operation. In this paper, a local-area ESI operational scheduling model based on conditional value-at-risk (CVaR) is proposed to minimize expected operational cost, which considers the uncertainty of energy supply-side and demand-side as well as multi-energy network constraints, including electrical network, thermal network and gas network. The risk cost is analyzed comprehensively under the condition of under- or overestimated cost. On this basis, a hybrid method combining particle swarm optimization with interior point algorithm is executed to compute the optimal solutions of two-stage multi-period mixed-integer convex model. Finally, a case study is performed on ESI to demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Jiaqi Shi & Yingrui Wang & Ruibin Fu & Jianhua Zhang, 2017. "Operating Strategy for Local-Area Energy Systems Integration Considering Uncertainty of Supply-Side and Demand-Side under Conditional Value-At-Risk Assessment," Sustainability, MDPI, vol. 9(9), pages 1-22, September.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:9:p:1655-:d:112346
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    References listed on IDEAS

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    1. Jiaqi Shi & Ling Wang & Yingrui Wang & Jianhua Zhang, 2017. "Generalized Energy Flow Analysis Considering Electricity Gas and Heat Subsystems in Local-Area Energy Systems Integration," Energies, MDPI, vol. 10(4), pages 1-17, April.
    2. Sivasakthivel, T. & Murugesan, K. & Sahoo, P.K., 2014. "Optimization of ground heat exchanger parameters of ground source heat pump system for space heating applications," Energy, Elsevier, vol. 78(C), pages 573-586.
    3. Bai, Linquan & Li, Fangxing & Cui, Hantao & Jiang, Tao & Sun, Hongbin & Zhu, Jinxiang, 2016. "Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty," Applied Energy, Elsevier, vol. 167(C), pages 270-279.
    4. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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    Cited by:

    1. Pavlos S. Georgilakis, 2020. "Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Researc," Energies, MDPI, vol. 13(1), pages 1-37, January.
    2. Guangxiao Hu & Xiaoming Ma & Junping Ji, 2017. "A Stochastic Optimization Model for Carbon Mitigation Path under Demand Uncertainty of the Power Sector in Shenzhen, China," Sustainability, MDPI, vol. 9(11), pages 1-12, October.
    3. Yonggu Kim & Eul-Bum Lee, 2018. "A Probabilistic Alternative Approach to Optimal Project Profitability Based on the Value-at-Risk," Sustainability, MDPI, vol. 10(3), pages 1-24, March.
    4. Liang Tian & Yunlei Xie & Bo Hu & Xinping Liu & Tuoyu Deng & Huanhuan Luo & Fengqiang Li, 2019. "A Deep Peak Regulation Auxiliary Service Bidding Strategy for CHP Units Based on a Risk-Averse Model and District Heating Network Energy Storage," Energies, MDPI, vol. 12(17), pages 1-27, August.

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