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The reform of the electricity market is an inevitable trend of our country’s development and the requirements of the times. Electricity supply and demand will maintain a balance of resource utilization through real-time transactions, so as to fulfill the global strategic goal of energy conservation and emission reduction. In this context, high-precision short-term load forecasting can formulate efficient and economical power generation plans for power dispatch management departments, rationally arrange unit output, and ensure the safety and stability of the power system(Kong et al., 2017; Lekshmi et al., 2019). At the same time, various facilities such as pumped storage, electric vehicles, and energy storage power stations have been connected to the grid one after another, making the range and amplitude of the load-side response continue to increase, and the range of preferred users for demand-side response has gradually expanded(Hao et al., 2019 ;Mohamed et al., 2018).
The demand-side is an important part of power market planning. By analyzing the characteristics of the demand-side and integrating the supply and use methods of the electric energy system, it can assist the stable operation of the system and improve the market pricing mechanism.Adenso et al. (2002) comprehensively summarized the main problems encountered by OECD countries implementing demand side response projects, introduced the implementation experience of various countries, and clearly pointed out the important role of two demand side response mechanisms in power grid operation. In order to effectively implement demand-side projects,Hopper et al. (2006) conducted a study on the success factors of real-time electricity price project operation, emphasizing convenience, fairness, and information transparency in the implementation of electricity price projects. Based on the implementation of demand response projects under the smart power grid, Fell et al. (2014) considered factors such as time-of-use electricity prices, subsidy policies, accounting and energy storage technology and distributed power generation technology to construct the power distribution income-expense model of demand-side response projects. Under the premise of wind power uncertainty, Qadrdan et al. (2017) established a two-tier planning model for wind power system dispatch with day-ahead hourly electricity price optimization and incentive demand-side response. This model promotes power users to cut peaks and fill valleys, effectively guides the adoption of wind power, reduces the cost of thermal power generation, and improves the benefits of power users.