A hybrid optimization-based scheduling strategy for combined cooling, heating, and power system with thermal energy storage

Abstract

Energy storage can address the mismatch of the ratio of heat to electricity between a combined cooling, heating, and power (CCHP) system and its users, and thus, it can significantly improve energy efficiency. However, energy storage also increases the complexity of the operation optimization of the system. Existing heuristic optimization algorithms such as genetic algorithm (GA) and particle swarm optimization can hardly obtain the optimal scheduling scheme. In this paper, a hybrid optimization method that combines the GA and dynamic programming (DP) is proposed. The GA is the main optimization framework and is used to optimize the hourly set points of the power generation unit in a day. In the optimization process, the GA generates a feasible solution set, and calls the DP to calculate the optimal energy storage set points for each solution. The DP defines an hour as a decision step, and enumerates all energy storage states in each decision step. This process loops until the optimal solution is obtained. To reduce the computing time, the DP is implemented as a vectorized code. Case studies are conducted to verify the effectiveness of the proposed method. The results demonstrate that the overall performance using the proposed method increases by 1.92% in summer and by 1.91% in winter compared with that using the traditional GA method. Furthermore, the computing time is acceptable for the scheduling of the energy system. The proposed method can also be applied to the operation optimization of the CCHP system considering the demand side response.