Multi-timescale optimization method for virtual power plant based on SOCR-ACOPF

The rapid development of renewable energy and the widespread adoption of electric vehicles (EVs) have brought new opportunities and challenges to the power system. This paper proposes a multi-timescale optimization method for virtual power plants involving various distributed resources such as electric vehicles, renewable energy, and energy storage systems. The method is based on the Second-order Cone Relaxation Alternating Current Optimal Power Flow Model (SOCR-ACOPF). By considering the coordination and cooperation among various components on both daily and intra-day timescales, energy optimization and management for virtual power plants are achieved. The application of second-order cone relaxation techniques to solve mixed-integer nonlinear programming problems effectively enhances the efficiency of optimization solutions. Finally, the proposed model's effectiveness and practicality are demonstrated through simulation experiments conducted on an IEEE 33-node distribution system.