Distributed scheduling of integrated energy system considering the impacts of heating network and load on source-side components

Integrated energy systems (IES), which leverage multiple energy sources synergistically, enhance efficiency, sustainability and resilience. Scheduling is key to optimizing IES operation, typically focusing on source-side components. However, while efforts have been made to synergistic optimization of sources, networks and loads, the impact of network and load on source-side components is often ignored. This is particularly true for combined heat and power IES (CHP-IES), where backwater parameters significantly affect the thermal efficiency of heat-producing components. Misalignments between scheduling models and actual system behavior can reduce optimization accuracy, leading to a decline in economic performance or even infeasible scheduling results. To address this research gap, an optimal source-network-load combined scheduling method is proposed. Specifically, the effect of backwater temperature on thermal efficiency is quantified, analyzed, fitted and incorporated as constraints in the scheduling model, improving the accuracy in reflecting the system's actual characteristics. However, the resulting scheduling problem is a complex mixed-integer nonlinear programming (MINLP) problem that cannot be solved centrally within an acceptable time. Therefore, the original problem is decomposed into two subproblems: one for the source-side and one for the network/load-side. A convergence-enhanced distributed optimization approach is introduced to enable efficient coordination between subproblems. To verify the effectiveness of the proposed scheduling method, a model predictive control (MPC)-based control system is designed for real-time scheduling instruction tracking. A set of operational simulations are carried out based on the IES dynamic mechanistic model. With comparison to two traditional scheduling methods underestimating the impacts of network and load on source-side components, the proposed scheduling method can reduce 95 % and 75 % load tracking deviation respectively. The results demonstrate the effectiveness of the proposed scheduling method in enhancing the reliability of energy supply.