The low energy efficiency, high heat loss, and insufficient dynamic load matching of district heating systems (DHS) restrict its low‐carbon transition. This paper reviews the research progress of source‐network‐load modeling and optimization in DHS system, focusing on the key technologies of heat source modeling, pipe network modeling, load forecasting, and system optimization. The research covers thermoelectric decoupling techniques in heat source modeling and multi‐energy flow coupling energy hubs (EHs), mechanism‐based and data‐driven modeling methods of pipe network, as well as long‐ and short‐term applications of load forecasting algorithms. As for system optimization, the effectiveness of MILP, PSO, NSGA‐II algorithms in multi‐objective scheduling and planning is compared and analyzed. Finally, some future directions are proposed for developing DHS into a fully autonomous intelligent, zero‐carbon, and digital twin ecological heating system in the future.
Ye et al. (Thu,) studied this question.
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