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A conditional value-at-risk based planning model for integrated energy system with energy storage and renewables

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  • Xuan, Ang
  • Shen, Xinwei
  • Guo, Qinglai
  • Sun, Hongbin

Abstract

Owing to the potential higher energy supply efficiency and operation flexibility, integrated energy system (IES), which usually includes electric power, gas and heating/cooling systems, is considered as one of the primary forms of energy carrier in the future. However, with the increasing complexity of multiple energy devices and systems integration, IES planning is facing a significant challenge in terms of risk assessment. To this end, an energy hub (EH) planning model considering renewable energy sources (RES) and energy storage system (ESS) integration is proposed in this paper, in which the risk is measured by Conditional Value-at-Risk (CVaR). The proposed IES planning model includes two stages: 1) investment planning on equipment types and capacity (e.g., energy converters, distributed RES and ESS) and 2) optimizing the potential risk loss in operation scenarios along with confidence level and risk preference. The problem solving is accelerated by Benders Decomposition and Improved Backward Scenario Reduction Method. The numerical results illustrate the proposed method’s effectiveness in balancing the potential operation risk and investment cost. Moreover, the effectiveness of reducing potential operation risk by introducing ESS and RES are also verified.

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  • Xuan, Ang & Shen, Xinwei & Guo, Qinglai & Sun, Hongbin, 2021. "A conditional value-at-risk based planning model for integrated energy system with energy storage and renewables," Applied Energy, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:appene:v:294:y:2021:i:c:s0306261921004451
    DOI: 10.1016/j.apenergy.2021.116971
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    23. Tan, Hong & Li, Zhenxing & Wang, Qiujie & Mohamed, Mohamed A., 2023. "A novel forecast scenario-based robust energy management method for integrated rural energy systems with greenhouses," Applied Energy, Elsevier, vol. 330(PB).

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