Optimal Battery Energy Storage System Sizing in EV Fast Charging Applications

A Multi-objective Framework for Demand Charge Management at Fast Charging Stations using Genetic Algorithms

Master thesis (2020)

Authors

G.A. Koolman Electrical Engineering, Mathematics and Computer Science

Contributors

P. Bauer DC systems, Energy conversion & Storage - EEMCS (supervisor 1)
M. Stecca DC systems, Energy conversion & Storage - EEMCS (coach)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 George Koolman
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Published Date

16-12-2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The need for proper fast charging infrastructures is one of the key challenges for the wide adoption of elec- trical vehicles (EV). The high pulsating demand of fast charging stations (FCS) together with high demand tariffs can cause monthly DSO demand charges to account for a significant fraction of a station’s electric bill. Therefore, weakening the business case for stations located in these high tariff regions. To tackle this issue, demand charge management (DCM) can be applied to suppress peak power demands at FCSs using battery energy storage systems (BESS). This enables the reduction of cost while retaining the station’s fast charging capabilities. However, the implementation of such systems remains a large investment and the proper BESS sizing in fast charging applications is not well studied. This thesis proposes a multi-objective approach for optimal BESS sizing at FCSs considering demand charges and station performance. A BESS assisted FCS model is formulated to analyse the performance of a station’s design based on power flow, charging delays and the expected BESS lifetime. Furthermore, based on a worst-case demand scenario, a multi-objective optimization framework is formulated using the genetic algorithm NSGA-II to obtain the optimal BESS and grid-tie sizing for an existing FCS. Lastly, with demand data measured at four FCSs in the Netherlands, a set of numerical case studies has been conducted in the Mosaik and Pymoo environments to assess the feasibility and the effectiveness of the proposed formulation. These case studies provide new insights on the demand charge reduction and optimal sizing regarding dif- ferent station characteristics, BESS prices, and demand tariffs. These insights show how the FCS utilization rate and installed capacity can effect the optimal BESS sizing and how different demand tariffs or BESS cost can result in different optimal power to energy (P/E) ratios, and thus affecting the performance of a BESS.