A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response

Abstract

A day-ahead and real-time two-stage risk economic optimal model of integrated energy system (IES) is established. First, considering the electricity and heating coupling characteristics of combined heat and power, the feasible region is described by mathematical model, and the integrated demand response model is expanded from the traditional demand response model. Second, the objective functions and constraints of two stages are established respectively. The first stage optimal objective is to minimize the pre-scheduled operation cost of day-ahead, which arranges the output power of renewable energy and the startup-shutdown plan, output power and reserve capacity of adjustable equipment. The second stage optimal objective is to minimize the re-scheduled expected cost of real-time, which will call reserve capacity, curtail renewable energy output, implement integrated demand response, and use energy storage to cope with power deviations. In order to quantify the risk cost of multiple uncertainties of power, load, and price, the real-time stage objective function is further improved to a form of conditional value at risk. Finally, simulations implemented on a green park show that: the proposed model can achieve the optimization of energy supply at different time scales and improve scheduling enforceability after considering economics and risk. Shapely Value can fairly and reasonably determines the benefit distribution scheme of different subjects in IES.