Semidefinite programing as a tool for economic-environmental operation of a microgrid including compressed air energy storage and electric vehicle

This paper proposes a mathematical optimization approach based on semidefinite programing (SDP) for optimal operation of a microgrid (MG) equipped with renewable energy resources (RERs) as well as energy storage systems (ESSs) including compressed air energy storage (CAES) and electric vehicles (EVs). The SDP converts nonlinear and non-convex models proposed for day-ahead optimal operation into a convex approximation, which is easily implemented by commercial software. An optimization problem is formulated such that it minimizes the costs related to operation and environmental effects limited to several technical limitations. It is clear that there is an intrinsic deviation between predicted and actual uncertainty variables in MG. This paper presents a stochastic optimal operation model based on Information Gap Decision Theory (IGDT) with risk averse strategy to overcome this information gap and to help Microgrid Operator (MGO). As demand response programs (DRPs), the shiftable load and time of use (ToU) are considered for enhancing the flexibility of MG. Employing the presented model in the 21-bus test system shows that CAES and EVs, as novel ESSs, have good capability to reduce the costs. The total cost, including operation cost and emission cost can be reduced by 3.8% compared to without considering these ESSs in the day-ahead optimal operation.

Automated summary

This paper introduces a mathematical optimization approach based on semidefinite programming for optimal operation of a microgrid equipped with renewable energy resources and energy storage systems. It formulates an optimization problem to minimize costs related to operation and environmental effects, while overcoming the deviation between predicted and actual uncertainty variables. By incorporating risk averse strategy, the presented model shows a 3.8% reduction in total costs compared to not considering energy storage systems in the day-ahead optimal operation.