• EV charging scheduling framework for demand-side management. • Australian case study using real-time operational data. • EV Scheduling Vector for coordinated charging control. • G2V and V2G strategies for grid support and flexibility. This paper proposes two approaches to reduce load fluctuation in three EV-dominated states within Australia i.e., New South Wales (NSW), Victoria, and Queensland. The first method utilizes the demand side management (DSM) strategy using the Genetic Algorithm (GA), where the EV Scheduling Vector (ESV) is formed using the grid-to-vehicle (G2V) technology. In the second method, DSM uses GA by utilizing the G2V and Vehicle to Grid (V2G) technologies. The results indicate a substantial reduction in load fluctuations. With the help of method 1, the load fluctuations are reduced by 2406.6 MW, 2239.7 MW, and 2679.5 MW for NSW, Victoria, and Queensland, respectively. With the implementation of method 2, the load fluctuations are further reduced by 123 MW, 142 MW, and 126 MW for NSW, Victoria, and Queensland, respectively. Utilizing the V2G approach, method 2 considers EVs as dynamic energy storage that can utilize the additional generated power. The effect of different levels of EV penetration using Method-2 is studied to further validate the practical applicability of the approach. Compared to uncontrolled EV charging, the proposed unidirectional and bidirectional scheduling strategies reduce demand fluctuations by up to 90% and 95%, respectively, while maintaining strong performance even under reduced EV penetration levels. Additionally, the effectiveness of the proposed charging scheduling method in enhancing ancillary services is demonstrated. Both approaches help to enhance grid stability parameters, such as frequency deviation, utilizing sustainable energy management techniques.
Ahmed et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: