Electric Vehicle Fast Charging: A Congestion-Dependent Stochastic Model Predictive Control under Uncertain Reference

Di Giorgio, Alessandro; De Santis, Emanuele; Frettoni, Lucia; Felli, Stefano; Liberati, Francesco · 2023 · DOAJ

DOI: 10.3390/en16031348

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the operational challenges of managing a fast-charging service area for plug-in electric vehicles (PEVs) equipped with renewable energy sources (RES) and a stationary Energy Storage System (ESS). The primary motivation is the high cost associated with peak power flows at the Point of Connection (POC) with the distribution grid, which can render such service areas economically unfeasible. The study aims to develop a control strategy that minimizes these power peaks while ensuring minimal charging times for drivers and maintaining the stability of the ESS state-of-charge. The problem is complicated by uncertainties in PEV arrival patterns, charging demands, and renewable energy generation. To address these challenges, the authors propose a Stochastic Model Predictive Control (MPC) algorithm. Unlike deterministic approaches that require precise future demand knowledge, this method utilizes the expected values of power demand and generation, which can be estimated from historical data. The control framework jointly manages the ESS charging/discharging power and the actual power delivered to PEVs, allowing the system to reduce charging power only during periods of extreme congestion to alleviate grid stress. The study evaluates two distinct hardware configurations: a BUS scheme, where charging stations, RES, and ESS connect directly to the grid, and a UPS scheme, where the ESS acts as an energy buffer between the grid and the other components. The MPC objective function incorporates congestion-dependent and state-dependent weights to dynamically prioritize peak reduction and ESS stability based on the current operational state. The research validates the proposed strategy through simulations focused on tracking uncertain power references, mitigating congestion, and ensuring ESS stability over time. The results demonstrate the effectiveness of the stochastic MPC in reducing power flow at the POC compared to uncontrolled or deterministic scenarios. By adjusting the charging power provided to vehicles during high-congestion periods, the controller successfully lowers operational costs without significantly compromising the user experience. The comparison between the BUS and UPS configurations highlights the specific advantages and disadvantages of each setup regarding conversion losses and uncertainty handling. The significance of this work lies in its practical approach to the economic viability of fast-charging infrastructure. By providing a robust control method that handles uncertainty and dynamically balances grid constraints with user needs, the study offers a solution that avoids the need for oversizing ESS capacity. The proposed stochastic MPC formulation advances the field by integrating flexible load control with energy storage management, offering a scalable and cost-effective strategy for service area operators aiming to integrate electromobility infrastructure into the existing electrical grid.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• teleoperation remote driving