Robust Peak-Shaving for a Neighborhood with Electric Vehicles

Marco Gerards,Johann Hurink

doi:10.3390/en9080594

Abstract

Demand Side Management (DSM) is a popular approach for grid-aware peak-shaving. The most commonly used DSM methods either have no look ahead feature and risk deploying flexibility too early, or they plan ahead using predictions, which are in general not very reliable. To counter this, a DSM approach is presented that does not rely on detailed power predictions, but only uses a few easy to predict characteristics. By using these characteristics alone, near optimal results can be achieved for electric vehicle (EV) charging, and a bound on the maximal relative deviation is given. This result is extended to an algorithm that controls a group of EVs such that a transformer peak is avoided, while simultaneously keeping the individual house profiles as flat as possible to avoid cable overloading and for improved power quality. This approach is evaluated using different data sets to compare the results with the state-of-the-art research. The evaluation shows that the presented approach is capable of peak-shaving at the transformer level, while keeping the voltages well within legal bounds, keeping the cable load low and obtaining low losses. Further advantages of the methodology are a low communication overhead, low computational requirements and ease of implementation.

Highlights

In the future, we expect an increasing penetration of electric vehicles, photovoltaic panels, heat pumps and wind turbines
We compare to no control (NC), where electric vehicle (EV) charge at full power at arrival, and with a grid unaware peak-shaving (P EAK S), which is a simple approach that iteratively selects EVs and decreases their charging as much as possible until the desired load at the transformer is accomplished
To plan the appliances within a house, we propose using online planning

Summary

Introduction

We expect an increasing penetration of electric vehicles, photovoltaic panels, heat pumps and wind turbines. A central entity (e.g., the network operator) requests customers, via a steering signal, to adapt their production or consumption in order to shape the load profile of a certain subgroup of the grid (e.g., a group of houses). These customers can either adapt their behavior manually, or install a device that is referred to as a Home Energy Management System (HEMS), which makes such decisions on their behalf. Energy storage (e.g., batteries) can be used to mitigate a surplus of renewable energy by charging, and a peak in the consumption by discharging

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