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A CVaR-based risk assessment method for park-level integrated energy system considering the uncertainties and correlation of energy prices

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  • Mu, Yunfei
  • Wang, Congshan
  • Cao, Yan
  • Jia, Hongjie
  • Zhang, Qingzhu
  • Yu, Xiaodan

Abstract

The park-level integrated energy system (PIES), a typical user-side energy supply mode, has developed rapidly in recent years due to its economic and environmentally friendly advantages. Natural gas and part of the electricity of PIES are usually purchased from external gas or power systems and then converted to other forms of energy, such as heating and cooling. However, the uncertainties and correlation of the natural gas price (NGP) and electricity price (EP) bring high risk to PIES planning and may lead to economic deterioration of PIES at the operation stage. Therefore, a risk assessment method for PIES planning is proposed. The marginal distributions of NGP and EP are established by the kernel density estimation method. Then, the copula function is adopted to construct the joint distribution of NGP and EP considering their correlation, according to which the Monte Carlo method is utilized to generate joint scenarios of NGP and EP. On this basis, a risk assessment model considering the uncertainties of energy prices for PIES planning is established, where the conditional value at risk (CVaR) is adopted as the risk measure since it can quantify the impact of extreme uncertainties of energy prices on PIES planning. Case studies show that deepening the degree of multi-energy coupling and the utilization of distributed renewable energy generation can enhance the risk-resistance abilities of the PIES. The proposed method provides an effective tool for decision making related to PIES planning, which can help avoid the risk caused by the uncertainties and correlation of energy prices.

Suggested Citation

  • Mu, Yunfei & Wang, Congshan & Cao, Yan & Jia, Hongjie & Zhang, Qingzhu & Yu, Xiaodan, 2022. "A CVaR-based risk assessment method for park-level integrated energy system considering the uncertainties and correlation of energy prices," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222004522
    DOI: 10.1016/j.energy.2022.123549
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