Day-ahead optimal dispatch of integrated energy system considering wind power forecasting and integrated demand response

Due to natural conditions in northern China, heat power is the main energy supply, and there is a large proportion of wind power connected to the grid. During the heating period in winter, combined heat and power (CHP) unit operates in the "determining power generation by heat" mode, which coincides with the time when wind power is abundant, resulting in serious wind abandonment in the system. P2G is adapted to convert excess wind power to natural gas in order to promote wind power consumption and decoupling to "determine power generation by heat". Aiming at the uncertainty of wind power output, this paper uses Autoregressive Integrated Moving Average model (ARIMA) model and Latin hypercube sampling to generate a large number of scenes, and K-means clustering algorithm to form typical scene sets. Considering the structure of the distribution network and the gas network, a multi-energy flow calculation model is established. Based on the energy hub model, a day-ahead optimal dispatch model of the Electricity-Gas-Heat-Cooling Integrated Energy System (EGHC-IES) is established, taking into account the comprehensive demand response on the user side, wind power output forecast and energy storage. The results of the case studies shows that the optimized dispatching solution can promote the consumption of renewable energy and improve energy efficiency.