Due to the liberalization of energy markets

energy prices are volatile and plagued with strong uncertainties. In this context

park-level integrated energy system (PIES) planning is confronted with potential cost variation risks caused by uncertain energy prices and does not know the strength of planning results to resist risks. This paper proposes a risk-averse planning and evaluation method for PIESs considering uncertainties in natural gas price (NGP) and electricity price (EP). Based on the seasonal variation characteristics and correlation between NGP and EP

the dynamic conditional correlation (DCC) model and copula theory are used to formulate a seasonal joint probability distribution of NGPs and EPs for price scenario generation. Then

conditional value-at-risk (CVaR) is introduced to quantify the risk of cost variation

and a CVaR-based planning model that considers uncertainties of energy prices is developed. Under the generated joint price scenarios

the planning schemes at different confidence levels are determined by minimizing the CVaR of the total cost (including investment

fuel

and maintenance costs). By introducing a margin of safety ratio (MSR) index

the risk-averse ability to uncertain energy prices of the planning scheme is evaluated to support decision-making. Finally

a case study is conducted to validate the effectiveness of the proposed planning method.