Optimal security-constrained scheduling of a renewable energy-based multi-microgrid with compressed air energy storage and demand response

The operation of a multi-microgrid (MG) is exposed to random failures as well as high uncertainties from renewable energy sources (RESs) and load. The stochastic method encompasses multiple uncertainties, including wind speed, electricity price, solar irradiation, and load demand. Moreover, a scenario reduction approach is employed to mitigate computational complexity in stochastic optimization by selecting a representative subset of scenarios. CAES presents a flexible solution for managing transmission congestion and consuming renewable energy. Compressed air energy storage system (CAES) integration and multi-MG operation of security-constrained unit commitment (SCUC) are becoming increasingly important in these networks due to the extensive use of renewable energy. This paper proposes a stochastic SCUC multi-MG dispatch model to coordinate CAES with a demand response (DR) program, ensuring system flexibility under uncertain scenarios while maintaining operational cost-effectiveness. The DR program might flatten the load curve and move energy use from peak to off-peak hours. This integration aims to reduce total costs, minimize renewable curtailment, and enhance voltage profile and stability. The model employs a mixed-integer quadratically constrained programming (MIQCP) approach and a Bender decomposition technique to achieve a global optimum solution with minimal computational time. The benefits of the proposed model are examined through various case studies implemented on the IEEE 30-bus and IEEE 118-bus systems. The findings show that the total cost is reduced by 6.981 %, voltage stability improves by 5.32 %, and voltage deviations decrease by 30.22 % in the presence of renewable power and CAESs, considering the DR program compared to the base case.