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

A day-ahead and real-time two-stage risk economic optimal model of integrated energy system (IES) is estab-lished. 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 inte-grated 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 enforce-ability after considering economics and risk. Shapely Value can fairly and reasonably determines the benefit distribution scheme of different subjects in IES.

Automated summary

In this study, a two-stage economic optimal model of integrated energy system (IES) in day-ahead and real-time contexts is established. The model takes into account the coupling characteristics of electricity and heating in combined heat and power systems and expands the traditional demand response model to include integrated demand response. The model aims to minimize the operation cost in the day-ahead stage and the expected cost in the real-time stage, considering the output power of renewable energy, startup-shutdown plans, reserve capacity, integrated demand response, and the use of energy storage. Risk costs are also quantified using conditional value at risk. Simulation results show that the proposed model can optimize energy supply at different time scales while considering economics and risk.