Two-stage scheduling of electric vehicle flexible load regulation and generation-grid–load–storage resources in new-type power systems

New-Type Power Systems (NTPS) are emerging as a response to deep decarbonization goals, driven by high shares of renewable energy, extensive electrification, and advanced digital control. These systems are designed to be clean, flexible, intelligent, and interactive. However, integrating large numbers of Renewable Energy Sources (RESs) and Electric Vehicles (EVs), especially under EV flexible load regulation operation, introduces significant challenges related to uncertainty, system coordination, and scheduling complexity. Therefore, this paper presents a novel two-stage sequential scheduling framework that jointly schedules coordinates Generation-Grid–Load–Storage (GGLS) resources within an NTPS context. The first stage establishes an economic dispatch model that simultaneously optimizes the operation of Distributed Generators (DGs), grid electricity purchases, and Interruptible Loads (ILs), while considering market signals, forecasted prices, and operating constraints. The second stage formulates a reactive power optimization model to reduce network losses and enhance voltage stability by adjusting DGs and reactive power devices, using the first-stage schedules as fixed inputs (i.e., without iterative feedback to update first-stage decisions). To solve the mixed-variable scheduling problem efficiently, a modified Feedback Wolf Pack Algorithm (FWPA) is developed. The algorithm introduces dynamic feedback control, allowing for real–integer variable decoupling, search space dimensionality reduction, and improved global convergence. Simulation results on the IEEE 33-bus system validate the proposed method. For the modified IEEE 33-bus active distribution network, the proposed framework limits peak-load active power losses to about 42.4 kW at a 5.02 MW demand and converges in approximately 72 s, yielding around 50 % and 75 % shorter computation times than Wolf Pack Algorithm (WPA) and Particle Swarm Optimization (PSO), respectively. Compared with conventional algorithms, the framework achieves faster convergence and better economic performance, demonstrating strong applicability for two-stage sequential scheduling in future NTPS environments.