In a significant stride towards optimizing the integration of electric vehicles (EVs) into the power grid, researchers have developed a novel strategy that could reshape how energy is managed in distribution networks. The study, led by Haoyang Tang from the Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network at Wuhan University, introduces an advanced economic model predictive control (EMPC) algorithm designed to enhance the efficiency of vehicle-to-grid (V2G) systems.
The research, published in the International Journal of Electrical Power & Energy Systems, addresses a critical challenge in the energy sector: the real-time aggregation and control of diverse electric vehicles participating in V2G systems. As lead author Haoyang Tang explains, “The electric vehicle aggregator (EVA) includes various types of EVs, and managing their differing attributes is essential for mitigating uncertainty in distribution networks caused by distributed energy resources.”
The proposed EMPC-based V2G scheduling strategy establishes a multi-attribute EVs aggregation model using Markov chains, incorporating batteries from swapping stations. This model aims to minimize the total cost of EVA, which includes operating and charging costs. To tackle potential optimal control issues, the researchers developed an auxiliary function to determine the optimal control sequence.
The effectiveness of the proposed method was verified using real distribution network data. Comparisons were made with dual-layer MPC, real-time pricing, and disordered charging strategies. The results were impressive, showing a 4–47.4% reduction in charging costs and a significant improvement in load curve management, reducing peak valley differences and load variance.
This research holds substantial commercial implications for the energy sector. By optimizing the integration of EVs into the grid, the EMPC algorithm can enhance the stability and efficiency of distribution networks, ultimately leading to cost savings and improved energy management. As the adoption of EVs continues to grow, such advancements will be crucial in supporting the infrastructure needed to accommodate this shift.
The study also evaluated the optimization effect under different levels of user participation, highlighting the flexibility and adaptability of the proposed strategy. This adaptability is key to its potential widespread application, as it can be tailored to various scenarios and user behaviors.
The findings of this research could shape future developments in the field by providing a robust framework for managing the complexities of EV integration. As Tang notes, “Our method not only reduces costs but also improves the overall performance of the distribution network, making it a valuable tool for energy providers and grid operators.”
In conclusion, the EMPC-based V2G scheduling strategy represents a significant step forward in the quest for more efficient and sustainable energy management. By addressing the challenges of EV aggregation and control, this research paves the way for a more resilient and cost-effective energy future.