The growth in recent years of electric vehicle (EV) fleets represents a valuable resource for enhancing grid flexibility. Indeed, Vehicle-to-Grid (V2G) technology can provide peak load-shaving services and reduce variability in renewable power generation. Despite V2G being commercially available, its adoption is still lower than expected due to factors such as users' willingness to share their EV batteries and the lack of robust V2G market platforms. Moreover, existing research has primarily focused on maximizing aggregator benefits without adequately considering the economic conditions that trigger the V2G services in the market. Therefore, this work aims to address these overlooked aspects through sensitivity analysis based on real-world data, where considered economic conditions involve the needs of all stakeholders, including aggregators, the grid, and EV users. Additionally, aspects such as the impact of load forecasting errors on aggregator income have not received sufficient attention, and they are investigated in the paper. Particularly, overestimates in the amplitude of the EV loading curve lead to an 18% reduction in net gain compared with the optimal case. Time shifting error of the curve can lead to reductions of 3.5%.
Economic Challenges in V2G Implementation: An Italian Market Analysis
De Caro F.
;Graber G.;Calderaro V.;Ippolito L.;Galdi V.
2024
Abstract
The growth in recent years of electric vehicle (EV) fleets represents a valuable resource for enhancing grid flexibility. Indeed, Vehicle-to-Grid (V2G) technology can provide peak load-shaving services and reduce variability in renewable power generation. Despite V2G being commercially available, its adoption is still lower than expected due to factors such as users' willingness to share their EV batteries and the lack of robust V2G market platforms. Moreover, existing research has primarily focused on maximizing aggregator benefits without adequately considering the economic conditions that trigger the V2G services in the market. Therefore, this work aims to address these overlooked aspects through sensitivity analysis based on real-world data, where considered economic conditions involve the needs of all stakeholders, including aggregators, the grid, and EV users. Additionally, aspects such as the impact of load forecasting errors on aggregator income have not received sufficient attention, and they are investigated in the paper. Particularly, overestimates in the amplitude of the EV loading curve lead to an 18% reduction in net gain compared with the optimal case. Time shifting error of the curve can lead to reductions of 3.5%.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
