Inverse problem method for two-stage co-optimization of solar hybrid combined cooling, heating and power systems with energy storage through improved particle swarm algorithm

For solar hybrid systems, how to co-optimize their design and operation based on comprehensive objectives to maximize their investment benefits is an important but unresolved issue. Moreover, traditional forward-problem-based method can merely conduct local optimum through single-stage solution and limited case comparison. Based on inverse problem method, a two-stage co-optimization method is developed for the design and operation of solar hybrid combined cooling, heating and power (CCHP) systems with energy storage. Taking Qingdao airport as an example, the optimal scheme and guidance method of proposed systems are obtained to maximize the annual incremental investment return rate (IIR) through improved particle swarm algorithm. The results indicate that: (1) The combination of PV driven CCHP systems and MAX-IIR mode is the optimal solution and its annual IIR reaches 9.48. (2) Compared to solar schemes obtained through traditional optimization methods, this scheme can generate economic benefits of up to 17.4 million yuan and reduce cost differences by up to 15.1 million yuan. (3) MAX-IIR mode is developed and adopts a three-level control strategy for the optimal system output configurations. The research shows the advantages of inverse problem method, which provides crucial guidance for the design optimization and economic evaluation of solar hybrid systems.