Monthly economic scheduling analysis of virtual power plant with electric vehicles: Analyzing from single-objective to tri-objective perspectives

The integration of diverse clean energy sources poses both challenges and opportunities for future energy systems. A critical issue that necessitates attention is the uncertainty related to clean energy production, as well as the complexities involved in optimizing its scheduling. This study employs the FEDformer model to forecast and analyze the real-time output of photovoltaic (PV) and wind power generation, in addition to residential power demand. In terms of mean absolute error and mean square error, the performance of FEDformer is 5 % higher than that of the second best, including LSTM, Transformer, Informer, and Autoformer. Next, an optimal scheduling model for the Virtual Power Plant (VPP) is proposed, which integrates PV power generation, wind turbine power generation, electric vehicle (EV), energy storage systems, and gas turbines. The primary goal of this model is to maximize the economic benefits of the VPP, while also considering environmental sustainability, scheduling flexibility (the ability to adapt scheduling strategies flexibly in response to changing energy demands and supply conditions to maintain stability and efficiency), and customer satisfaction (improving user satisfaction through optimized EV charging strategies and dynamic pricing). Furthermore, the study presents a highly effective methodology for classifying daily operational modes, which significantly reduces the computational complexity associated with scheduling and enhances overall efficiency. The research also explores four scheduling strategies, progressing from single-objective optimization to multi-objective optimization. The single-objective to tri-objective perspectives method is utilized to analyze the interactions between objectives, transitioning from a simplified single-objective analysis to a more complex tri-objective approach. As the number of objectives increases, economic benefits gradually decrease, highlighting the trade-offs between multi-objective optimization and economic efficiency. This methodology offers a comprehensive and intuitive framework for decision-making. The results of the simulation indicate that the proposed VPP effectively meets the established objectives and provides a comprehensive economic benefit analysis from various perspectives. • FEDformer-Based Forecasting : FEDformer forecasts PV, wind, and electricity load, enhancing accuracy and scheduling. • Virtual Power Plant Optimization : Optimized VPP with PV, wind, electric vehicles, energy storage systems, and gas turbines to maximize economic benefits. • Efficiency Enhancement : Introduces a date classification method to identify residential load patterns, reducing complexity. • Multidimensional Scheduling Strategies : Proposes four scheduling strategies, enhancing VPP optimization with multi-objective analysis.