An optimal load distribution and real-time control strategy for integrated energy system based on nonlinear model predictive control

Efficient operational scheduling and control are vital for the operation process of the Integrated Energy System (IES), especially under varying operational conditions. An optimal load distribution and real-time control strategy for IES based on Nonlinear Model Predictive Control has been developed to synergistically improve equipment output performance and reduce operating costs. In the proposed strategy, the IES model that integrates linear parameter-varying models for devices and dynamic models for pipe networks, has been constructed via system identification methodology under varying operational conditions. Thus, a hierarchical operation and control framework is established using the model predictive control strategy, including real-time prediction objective, optimal load distribution objective, and optimization algorithms. Consequently, a case study is performed to investigate the supply-demand balance and economic benefit of the proposed strategy. From the aspect of supply-demand balance, the average energy supply-demand discrepancy by using the proposed strategy (for cooling and heating) is approximately 25 %∼33 % that under the steady-state strategy. By means of the proposed strategy, an outstanding equipment output performance will be achieved and supply-demand discrepancy can be dramatically reduced. From the perspective of economic performance, the proposed strategy, which fully considers the nonlinearity of devices, exhibits a 16 % decrease in the operational cost compared to that under the linear dynamic strategy. Through the proposed strategy, efficiency losses are expected to degrade with an excellent economic benefit. Therefore, the suggested strategy is promising and suitable for varying operational condition scenarios.