Scheduling optimization model for integrated energy systems considering conditional value-at-risk

The integrated energy system (IES) promotes the integration of renewable energy and improves energy efficiency, but it also brings uncertainty to the system. The uncertainty of the output of renewable energy may lead to imbalance of supply and demand, which brings the risk of cost increase to the economic dispatch. This paper mainly studies IES with energy hub as the research object, and studies the IES modeling and the optimal scheduling problem considering Conditional Value-at-Risk (CVaR). Firstly, the mathematical model of energy hub is established. Next, the network model of power system and natural gas system is established and the energy hub is reasonably connected to them. Then, for analyzing the uncertainty of wind power output, this paper develops scenarios using Latin hypercube sampling method and uses K-medoids clustering algorithm to reduce the scenarios. After defining the risk cost under uncertainty, CVaR is introduced into the objective function of IES optimal dispatch to describe the risk which operating personnel faces. Based on CVaR, the IES scheduling model is established and linearized into a mixed integer linear programming problem to be solved. Finally, numerical case studies verify that the IES scheduling model can reduce the risks with least costs and determine whether the system needs to avoid risks.