Stochastic Planning of Integrated Energy System via Frank-Copula Function and Scenario Reduction

Uncertainty introduces both significant complexity and the high risk of suboptimal investment decisions into power system planning. Considering the multiple uncertainties of wind and solar power output, load demands, and energy prices as well as pollutant emission factors during the planning period, a multiscenario stochastic programming model of an integrated energy system (IES) is constructed in this paper. Scenarios of wind and solar power output are generated based on non-parametric kernel density estimation and the Frank-Copula function; scenarios of load demands are generated through DeST software, and energy prices and pollutant emission factors are generated corresponding to a uniform distribution. Then the generated scenario results of wind and solar power output and load demands are reduced by k-means clustering; the generated scenarios of energy prices and pollutant emission factors are reduced by discrete approximation of continuous distribution based on Gaussian quadrature. An illustrative example with 8 cases is performed to analyze the influences of each uncertain parameter on the optimal configuration and economy of the IES.