Robust optimization configuration of energy storage in electric-thermal system considering CVaR and dynamic characteristics of heat networks

Abstract

A quasi-dynamic model of heat networks is established for optimal energy storage configuration in an electric-thermal system. Based on this model， the “virtual energy storage” characteristics of heat networks can be used as a schedulable resource in energy storage capacity planning. Then， a two-stage robust optimization algorithm is used to deal with the uncertainty of renewable energy. The nested C&CG （column-and-constraint generation） algorithm is used to solve the state variable 0-1 of energy storage， and the global optimal solution is thus obtained. Considering the increase in constraints and variables brought about by the quasi-dynamic model of heat networks and the low efficiency of the algorithm， this paper takes the heat source temperature as an intermediate variable to decouple the power system and the thermal system. Then， it conducts robust optimization only for the power system， which significantly reduces the complexity of the model. This paper also introduces conditional value-at-risk （CVaR） to optimize the uncertain interval of new energy output to strike a balance between economy and robustness. An example analysis results show that the proposed method is superior in reducing physical energy storage configuration capacity and system operation cost.

Keywords

• electric-thermal system
• virtual energy storage
• two-stage robust optimization
• risk
• nested-c&cg