Design optimization and two-stage control strategy on combined cooling, heating and power system

Abstract Combined cooling, heating and power (CCHP) is a promising energy supply technology to improve the overall energy utilization efficiency. It provides an opportunity to achieve low or zero carbon energy supply with tactful control strategies. In this study, the two-stage model predictive control (MPC) strategy with combined rolling optimization and real-time adjustment is demonstrated to be highly effective for CCHP system application. In most previous studies, the MPC strategy was performed under a given design configuration, which was not optimally constructed. In real practice, the system configuration, the installed equipment capacity, and the control strategy altogether affect the CCHP system performance. Both the system design optimization and the two-stage MPC strategy were conducted in our numerical analysis through a hypothetical case study. Our simulation results illustrated that the equipment selection affects the running cost of the CCHP system, and the desirable solution is well linked to the building load profile. The effect of forecasting error on the two-stage MPC was also investigated. The results showed that although the two-stage MPC strategy is able to give better performance than the traditional strategies in most cases, poor performance may still exist when the load forecasting error is more than 8.8%.