Optimal planning of a hybrid system integrating of combined cooling, heat and power and energy storage resources

Abstract The Energy Storage System (ESS) can address the difference in the ratio of heat to electricity among a combined cooling, heating, power (CCHP) system and its customers, and the ESS can progress energy efficiency. Due to various limitations of this model, the ESS raises the complexity of performance of optimizing system. It is reasonable to employ a powerful tool to solve this problem, thus, this paper proposes an improved hybrid Virus Colony Search (VCS) with the Genetic Algorithm (GA) method. The combination of these two VCS and GA methods has significantly improved the local and global searching process. To evaluate the proposed algorithm and planning problem, a test system is selected with various operating conditions in 24 h. The simulation results shown that the overall performance by using of proposed hybrid algorithm increases by 1.93% in summer and 1.92% in winter as compared to other methods. Also, the numerical results demonstrated that there are acceptable performance in both the computing time and the optimal solution. The successful optimization algorithm for day-ahead and real-time planning problems is integrated the CCHP system with TES having demand-side response.