Stochastic optimization of integrated energy system considering network dynamic characteristics and psychological preference

Abstract

To improve the flexibility of integrated energy system (IES) and promote the clean energy accommodation, an IES stochastic optimization model considering the network dynamic characteristics and psychological preference is proposed. Firstly, the uncertainty and spatial-temporal correlation between multiple wind farms are dealt by the auto-regressive and moving average (ARMA) time series and Cholesky decomposition, and then typical scenarios are constructed by the scenario method. Secondly, considering the dynamic characteristics of heat and gas networks such as heat network delay, thermal inertia of buildings and gas linepack, the IES model which coordinates the supply side, transmission side and demand side is proposed. Finally, based on the mean-variance Markowitz theory and endowment effect in behavioral psychology, the influence of human psychological preference (such as risk concern and loss aversion) on the IES is reflected. A 52-node electricity-gas-heat IES is used to verify the proposed model. The simulation results reveal that the network dynamic characteristics and demand response (DR) can effectively improve the IES flexibility and wind power accommodation. Meanwhile, the psychological preference and wind power spatial-temporal correlation also have significant influence on the IES optimization.