Research Specifications

With the exponential penetration of renewable energy sources (RES), the need for compatible scheduling of these has increased from economic and environmental points of view. Due to the high-efficiency and fast-response features of combined heat and power (CHP) generation units, these units can immunize the system against RES fluctuations. To address the operational challenges associated with RES, this paper aims to schedule the arbitrage of cryogenic energy storage (CES) not only to maximize its owner but also to minimize RES variability. On the other hand, plug-in electric vehicles (PEV) are applied in the proposed model as responsible loads to smooth the system’s load profile by changing the consumers’ consumption patterns. The proposed problem is modeled as second-order cone programming and solved by the dominated group search optimization algorithm. To verify the applicability and effectiveness of the proposed approach, four different case studies have been executed.