Abstract
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.
Highlights
With the continuous development of the economy, many countries are facing severe challenges in preserving the environment and reducing energy consumption so that a sustainable development of the society can be ensured
An effective Hammerstein model identification method for SISO system has been proposed in [24], which we extend to multi-input-single-output (MISO) systems for modeling the micro gas turbine (MGT)-combined cooling and power (CCP)
This section verifies the effectiveness of the multivariate Hammerstein model identification strategy and the coordinated predictive controller designed for the MGT-based combined cooling and power system (MGT-CCP) system
Summary
With the continuous development of the economy, many countries are facing severe challenges in preserving the environment and reducing energy consumption so that a sustainable development of the society can be ensured. In [23], a supervisory MPC controller is proposed to improve the economic efficiency These MPC controllers designed for the MGT-CCHP system do not consider the nonlinearity of the system. The HGPC method was proposed to improve the performance of the predictive control for the nonlinear CCP system. The coordinated predictive control strategy is designed with the hybrid of GPC and HGPC for the MGT-CCP system to meet the simultaneous cooling and power load-tracking requirements. The rest of the article is organized as follows: Section 2 analyzes the dynamics and of MGT-CCP system; Section 3 introduces the identification of multivariate Hammerstein ARMAX nonlinearity of MGT-CCP system; Section 3 introduces the identification of multivariate models; The Hammerstein-GPC controller and the coordinated control strategy for the MGT-CCP.
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