通过动态滚动优化，让电动车在多场景中协同调度，降低社区用电成本。
Dynamic Rolling Horizon Optimization for Network-Constrained V2X Value Stacking of Electric Vehicles Under Uncertainties
- 采用动态滚动时域优化，整合车网、车楼及能源交易多重收益
- 真实数据验证：可显著降低用电成本，电动车到站预测误差影响最大
- 基于Transformer的预报模型优于基准，适合智能电网与社区微网应用
电动汽车（EV）协同可通过车对万物（V2X）与电网、建筑及其他电动车互动带来显著效益。本文构建了一个包含车对建筑（V2B）、车对电网（V2G）及能源交易的V2X价值叠加框架，旨在最大化住宅社区经济收益的同时维持配电电压稳定。研究量化了建筑负荷、可再生能源和电动车到站预测误差的影响。采用动态滚动时域优化（RHO）方法，以利用多条收入来源并最大化电动车协同潜力。为应对小时级建筑负荷、本地光伏发电及电动车到站等能源不确定性，提出一种基于Transformer的预报模型GRU-EN-TFD。使用澳大利亚国家电力市场以及美国新英格兰和纽约独立系统运营商的真实数据进行仿真，结果表明V2X价值叠加可显著降低能源成本。所提GRU-EN-TFD模型性能优于基准模型。电动车到站不确定性对价值叠加表现影响最为显著，凸显精准预测的重要性。本工作为住宅社区间的动态交互提供了新见解，充分释放电动车电池的潜力。
原文摘要 · Abstract (English)
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.
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