A cost-effective two-stage optimization model for microgrid planning and scheduling with compressed air energy storage and preventive maintenance

This paper proposes a cost-effective two-stage optimization model for microgrid (MG) planning and scheduling with compressed air energy storage (CAES) and preventive maintenance (PM). In the first stage, we develop a two-objective planning model, which consists of power loss and voltage deviation, to determine the optimal location and size of MG. Then, a stochastic scheduling model is presented in the second stage to balance outputs of distributed generations (DGs), charging and discharging power of CAES, power exchange costs of MG and PM costs of DGs. Whilst we derive a credibility assessment-based risk aversion model, named conditional value-at credibility (CVaC), to hedge against uncertain wind power. The proposed model has been evaluated on the IEEE testing system and numerical results demonstrate the effectiveness of the model by providing the optimal tradeoff solution in terms of the economy and security.