Improved hybrid inexact optimal scheduling of virtual powerplant (VPP) for zero-carbon multi-energy system (ZCMES) incorporating Electric Vehicle (EV) multi-flexible approach

Abstract

This study presents a novel day-ahead optimal scheduling for a Virtual Power Plant (VPP) to enable optimal dispatch of Zero-Carbon Multi-Energy System (ZCMES) and EV multi-flexible potentials and the influence of uncertainties under various scenarios. The Latin Hypercube Sampling (LHS) method is used to quantify the uncertainties, while the multi-energy demand randomness is further analyzed using a robust approach, and a robust-stochastic optimization approach is developed to solve the mathematical problem. The obtained simulation results indicate that for a specified EV flexibility (e.g vehicle-to-electricity use V2EU, vehicle-to-heat use V2HU, or vehicle-to-cooling use V2CU), the VPP optimally dispatches the EV flexibility to offset part of the specified energy demand. With the proposed approach, the EV flexibility is scheduled to be used for electricity and cooling purpose without grid interaction while the charging-discharging behavior of the EVs in the clusters is optimally scheduled. Also, the preferred charging station standard is either IEC61851 Mode 2 or IEC62196 Mode 3 with 45A current rating for full EV multi-flexible usage. Moreover, the multi-flexible approach provides both economic and technical benefits by reducing the overall cost by 8.5%, high EV flexibility ratio (0.54), and reduced the technical stress on energy storage by smoothing the state of charge (SOC) level through discharge behavior regulation. Finally, it was observed that the multi-flexible performance is influenced by the magnitude of multi-energy demand and generation, charging station standards, EV parameters, and robust control parameters. Thus, such a great feasibility analysis of EV multi-flexible potential provides valuable policy and planning reference tools for the energy stakeholders towards energy carbon neutrality in urban areas.