Work

Multi-timescale stochastic optimization for enhanced dispatching and operational efficiency of electric vehicle photovoltaic charging stations

Year: 2025

Type: article

Abstract: Addressing the integration of global day-ahead dispatching and the necessity for real-time dispatch precision, this study proposes a novel multi-timescale stochastic dispatch strategy for photovoltaic (PV) charging stations equipped with energy storage systems. Initially, the dispatch center optimizes the energy storage system’s charging status using reduced scenario forecast data to minimize operational costs, considering uncertainties in PV power generation and charging demand. As the day progresses, this strategy dynamically updates forecasts for PV power and charging loads based on real-time data, enabling ongoing optimization of the storage system to reduce operational costs. The method strategically schedules charging and discharging activities, effectively diminishing daily operational expenses. Simulation results show that the proposed method reduces forecast errors, lowers operational costs, enhances resilience, and reliably meets electric vehicle charging demand, presenting a robust solution for future energy dispatch challenges.

Source: International Journal of Electrical Power & Energy Systems

DOI: 10.1016/j.ijepes.2025.111096

Authors: Qinglin Meng, Sheharyar Hussain, Ying He, Jinghang Lu, Josep M. Guerrero

Institutions: Tianjin Renai College, Aalborg University, Universitat Politècnica de Catalunya

Language: English

Topic: Electric Vehicles and Infrastructure

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $3,920