Research on day-ahead optimal dispatching of virtual power plants considering the coordinated operation of diverse flexible loads and new energy

Large-scale new energy access to the power grid poses significant challenges to its stable operation. Differentiated user-side power consumption patterns further widen peak-valley differences in power demand. This paper focuses on operation scheduling problems of virtual power plants with coordinated optimization of diverse flexible loads and new energy, through efficient aggregation and optimization control of new energy and demand-side resources, improving power supply and demand mismatch. Firstly, Long Short-Term Memory and Latin Hypercube Sampling were employed for predicting the day-ahead output of wind and photovoltaic power. Secondly, wind and photovoltaic power, batteries and a pumped storage plant were aggregated into a virtual power plant, and the day-ahead optimization scheduling model was constructed considering system operation costs, energy curtailment costs and demand response costs. Finally, a simulation analysis was conducted. The results show that when large-scale new energy accesses to the power grid, traditional “Generation varies with Load” regulation modes will cause massive energy waste, while the “Generation-Load Interaction” regulation mode can achieve the linkage optimization between the generation side and the demand side, enhancing the system acceptance of new energy. Demand response can optimize users’ power consumption behaviors, reducing the charging costs by 52.13 % and heating costs by 0.84 %.