Research on a Day-Ahead Grouping Coordinated Preheating Method for Large-Scale Electrified Heat Systems Based on a Demand Response Model

In recent years, the increasing winter load peak has brought great pressure on the operation of power grids. The demand response on the load side helps to alleviate the expansion of the power grid and promote the consumption of renewable energy. However, the response of large-scale electric heat loads to the same electricity price curve will lead to new load peaks and regulation failure. This paper proposes a grouping coordinated preheating framework based on a demand response model, which realizes the interaction of information between the central controller and each regulation group. The room thermal parameter model and the performance map of the inverter air conditioner/heat pump are integrated into the demand response model. In this framework, the coordination mechanism is adopted to avoid regulation failure, an edge computing structure is applied to consider the users' preferences and plans, the grouping and parallel computing structure is proposed to improve the computing efficiency. Users optimize their heat load curves based on a demand response model, which can consider travel planning and ensure user comfort. The central controller updates the marginal cost curve based on the predicted scenario set to coordinate the regulation groups and suppress the new peaks. The simulation results show that the proposed method can promote the consumption of renewable energy through coordinated preheating and reduce the system energy consumption cost and user bills. The parallel computing structure within the regulation group also ensures the computing efficiency under large-scale loads.

Automated summary

In recent years, the increasing winter load peak has put a lot of pressure on power grid operation. Demand response on the load side can help alleviate power grid expansion and promote renewable energy consumption. However, large-scale electric air conditioning/heat pump loads responding to the same electricity price curve can lead to new peak loads and regulation failures. This paper presents a grouping coordinated preheating framework based on a demand response model, facilitating information interaction between a central controller and each regulation group. By integrating the room thermal parameter model and the performance map of inverter air conditioner/heat pump into the demand response model, this framework adopts coordination mechanisms to prevent regulation failure, applies an edge computing structure to consider user preferences and plans, and proposes a grouping and parallel computing structure to enhance computation efficiency.