Peak Shaving for Electric Vehicle Charging Infrastructure—A Case Study in a Parking Garage in Uppsala, Sweden

Abstract

The need for a more flexible usage of power is increasing due to the electrification of new sectors in society combined with larger amounts of integrated intermittent electricity production in the power system. Among other cities, Uppsala in Sweden is undergoing an accelerated transition of its vehicle fleet from fossil combustion engines to electrical vehicles. To meet the requirements of the transforming mobility infrastructure, Uppsala municipality has, in collaboration with Uppsala University, built a full-scale commercial electrical vehicle parking garage equipped with a battery storage and photovoltaic system. This paper presents the current hardware topology of the parking garage, a neural network for day-ahead predictions of the parking garage’s load profile, and a simulation model in MATLAB using rule-based peak shaving control. The created neural network was trained on data from 2021 and its performance was evaluated using data from 2022. The performance of the rule-based peak shaving control was evaluated using the predicted load demand and photovoltaic data collected for the parking garage. The aim of this paper is to test a prediction model and peak shaving strategy that could be implemented in practice on-site at the parking garage. The created neural network has a linear regression index of 0.61, which proved to yield a satisfying result when used in the rule-based peak shaving control with the parking garage’s 60 kW/137 kWh battery system. The peak shaving model was able to reduce the highest load demand peak of 117 kW by 38.6% using the forecast of a neural network.

An Analysis of Vehicle-to-Grid in Sweden Using MATLAB/Simulink

“…If the load has a high load factor originally, it could be reduced by implementing controlled charging and V2G. This is illustrated and analyzed in [27,28]. Secondly, EVs could also be used strategically in smaller energy systems consisting of one or a few loads together with local generation from, e.g., solar PV, potentially operating in island mode using the car batteries to balance the system.…”

A System for the Efficient Charging of EV Fleets

“…For example, in [9], a possible design of charging infrastructure for company locations is presented while considering charging preferences and trip data of a bakery in Germany. A threeday experiment in a so-called "mobility house" (containing student housing, a grocery store, and a parking garage) showed that rule-based peak shaving and load demand forecasting can reduce load demand peaks by 25.4-38.5% while ensuring a minimum SoC of 50% [10]. The applicability of smart charging approaches that were designed specifically for charge-at-work scenarios, such as [11,12], and various related scheduling strategies, e.g., refs.…”