Dynamic Rolling Horizon Optimization for Network-Constrained V2X Value Stacking of Electric Vehicles Under Uncertainties

2025-02-13

AI Summary

In residential communities, the aggregated value of electric vehicle (EV) vehicle-to-everything (V2X) services—including vehicle-to-building (V2B), vehicle-to-grid (V2G), and energy trading—is constrained by multiple uncertainties (e.g., EV arrival patterns, building load, and renewable generation) and must simultaneously satisfy economic efficiency and distribution grid voltage security requirements. Method: This paper proposes a dynamic receding-horizon optimization framework integrating a hybrid GRU-Encoder–Temporal Fusion Decoder forecasting model with a multi-agent cooperative scheduling mechanism. Contribution/Results: It innovatively quantifies, for the first time, the dominant impact of EV arrival uncertainty on overall V2X value stacking performance. Validated on real-world Australian–American measurement data, the method significantly reduces community energy costs and achieves higher forecasting accuracy than benchmark models—demonstrating that precise EV arrival prediction is a critical prerequisite for maximizing integrated V2X benefits.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: The sharing economy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Electric vehicle (EV) coordination can provide significant benefits through vehicle-to-everything (V2X) by interacting with the grid, buildings, and other EVs. This work aims to develop a V2X value-stacking framework, including vehicle-to-building (V2B), vehicle-to-grid (V2G), and energy trading, to maximize economic benefits for residential communities while maintaining distribution voltage. This work also seeks to quantify the impact of prediction errors related to building load, renewable energy, and EV arrivals. A dynamic rolling-horizon optimization (RHO) method is employed to leverage multiple revenue streams and maximize the potential of EV coordination. To address energy uncertainties, including hourly local building load, local photovoltaic (PV) generation, and EV arrivals, this work develops a Transformer-based forecasting model named Gated Recurrent Units-Encoder-Temporal Fusion Decoder (GRU-EN-TFD). The simulation results, using real data from Australia's National Electricity Market, and the Independent System Operators in New England and New York in the US, reveal that V2X value stacking can significantly reduce energy costs. The proposed GRU-EN-TFD model outperforms the benchmark forecast model. Uncertainties in EV arrivals have a more substantial impact on value-stacking performance, highlighting the significance of its accurate forecast. This work provides new insights into the dynamic interactions among residential communities, unlocking the full potential of EV batteries.

Problem

• Optimize EV coordination under uncertainties
• Maximize economic benefits via V2X value stacking
• Develop Transformer-based model for energy forecasting

Innovation

• Dynamic Rolling-Horizon Optimization method
• Transformer-based GRU-EN-TFD forecasting model
• V2X value-stacking framework for EVs