Real-time Hosting Capacity Assessment for Electric Vehicles: A Sequential Forecast-then-Optimize Method

TL;DR

This paper introduces a real-time hosting capacity assessment method for electric vehicles that combines probabilistic forecasting with risk analysis and optimization, enhancing accuracy and operational efficiency in power systems.

Contribution

It proposes a novel three-step real-time HC assessment framework using an adaptive spatio-temporal graph convolutional network for EV demand forecasting, improving accuracy over existing methods.

Findings

ASTGCN outperforms state-of-the-art forecasting models with RMSE of 0.0442.

Real-time HC assessment improves capacity utilization by 64%.

The method effectively captures EV demand stochasticity for reliable power system operation.

Abstract

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.