电动汽车实时承载能力评估：一种先预测后优化的序贯方法
Real-time Hosting Capacity Assessment for Electric Vehicles: A Sequential Forecast-then-Optimize Method
AI总结：
针对现有电动汽车承载能力评估方法侧重长期视角、忽略需求随机性导致实时运行误差的问题，提出含概率预测、风险分析、概率优化的三步实时评估方法，采用ASTGCN提升预测精度，实验显示预测误差更低且承载能力较长期评估提升64%。
摘要：
电动汽车（EV）承载能力（HC）评估对于电动汽车的安全并网以及电力系统的可靠运行至关重要。现有方法主要从长期视角（例如系统规划）展开研究，且将电动汽车充电需求视为标量值，由于电动汽车本身具有随机性，这类方法在实时运行中会产生误差。针对这一问题，本文提出一种电动汽车实时HC评估方法，该方法通过三步流程实现，涵盖实时概率预测、风险分析以及概率优化。具体而言，我们通过执行确定性预测并拟合预测误差的分布，开展实时概率预测以捕捉多个充电站的电动汽车充电需求的随机性。确定性预测采用自适应时空图卷积网络（ASTGCN）实现。ASTGCN利用自适应空间特征提取、基于注意力的时间特征提取以及二阶图表示来提升预测性能。随后，基于电动汽车充电需求的概率预测结果，我们利用概率潮流计算开展实时风险分析与运行边界识别，以评估安全运行约束的潜在越限情况。此外，我们提出了考虑随机电动汽车充电需求预期满意度的电动汽车实时HC公式，并构建了用于电动汽车实时HC评估的优化模型。在真实世界数据集上的数值实验表明，所提出的ASTGCN模型优于现有最先进的预测模型，其均方根误差低至0.0442；同时，与长期HC评估相比，实时HC提升了64%。
英文摘要：
Hosting capacity (HC) assessment for electric vehicles (EVs) is crucial for EV secure integration and reliable power system operation. Existing methods primarily focus on a long-term perspective (e.g., system planning), and consider the EV charging demands as scalar values, which introduces inaccuracies in real-time operations due to the inherently stochastic nature of EVs. In this regard, this paper proposes a real-time HC assessment method for EVs through a three-step process, involving real-time probabilistic forecasting, risk analysis and probabilistic optimization. Specifically, we conduct real-time probabilistic forecasting to capture the stochastic nature of EV charging demands across multiple charging stations by performing deterministic forecasting and fitting the distribution of forecasting errors. The deterministic forecasting is conducted using an adaptive spatio-temporal graph convolutional network (ASTGCN). ASTGCN leverages adaptive spatial feature extraction, attention-based temporal feature extraction, and second-order graph representation to improve the forecasting performance. Subsequently, based on the probabilistic forecasting of EV charging demands, we conduct real-time risk analysis and operational boundary identification by utilizing probabilistic power flow calculations to assess potential violations of secure operation constraints. Furthermore, we present the formulation of real-time HC of EVs considering expected satisfaction of stochastic EV charging demands, and propose an optimization model for real-time HC assessment of EVs. Numerical experiments on a real-world dataset demonstrate that the proposed ASTGCN model outperforms state-of-the-art forecasting models by achieving the lowest root mean square error of 0.0442, and the real-time HC is improved by 64% compared to long-term HC assessment.
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