Improving Simultaneous Cooling and Power Load-Following Capability for MGT-CCP Using Coordinated Predictive Controls

The distributed energy system is an energy supply method built around the end users, which can achieve energy sustainability and reduce emissions compared to traditional centralized energy systems. The micro gas turbine (MGT)-based combined cooling and power (CCP) system has received renewed attention as an important distributed energy system technology due to its substantial energy savings and reduced emission levels. The task of the MGT-CCP system is to quickly adapt to changes in various renewable energy sources to maintain the balance in energy supply and demand in a distributed energy system. Therefore, it is imperative to improve the load tracking capability of the MGT-CCP system with advanced control technologies toward achieving this goal. However, the difficulty of controlling the MGT-CCP system is that the MGT responds very fast while CCP responds very slowly. To this end, the dynamic characteristics and nonlinear distribution of the MGT and CCP processes are analyzed, and a coordinated predictive control strategy is proposed by utilizing the generalized predictive control for the MGT system and the Hammerstein generalized predictive control for the CCP system. The coordinated predictive control of generalized predictive control and Hammerstein generalized predictive control was implemented in an 80 kW MGT-CCP simulator to verify the effectiveness of the proposed method. The simulation results show that compared with PID and MPC, the proposed control method not only can greatly improve simultaneous cooling and power load-following capability, but also has the best control effect when accessing with renewable energy.

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