Abstract

This paper presents a plug-in electric vehicle (PEV) charging control algorithm, Adjustable Real-Time Valley Filling (ARVF), to improve PEV charging and minimize adverse effects from uncontrolled PEV charging on the grid. ARVF operates in real time, adjusts to sudden deviations between forecasted and actual baseloads, and uses fuzzy logic to deliver variable charging rates between 1.9 and 7.2 kW. Fuzzy logic is selected for this application because it can optimize nonlinear systems, operate in real time, scale efficiently, and be computationally fast, making ARVF a robust algorithm for real-world applications. In addition, this study proves that when the forecasted and actual baseload vary by more than 20%, its real-time capability is more advantageous than algorithms that use optimization techniques on predicted baseload data.

Highlights

  • IntroductionAs cities expand and populations increase, the amount of available fossil fuels decreases and air quality laws are made stricter [1]

  • Charging via Adjustable Real-TimeAs cities expand and populations increase, the amount of available fossil fuels decreases and air quality laws are made stricter [1]

  • A controller is assumed to be attached to distribution transformers and strategically vary the charging rate supplied to each vehicle

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Summary

Introduction

As cities expand and populations increase, the amount of available fossil fuels decreases and air quality laws are made stricter [1]. California has initiated measures to encourage the adoption of ZEVs, to have at least 5 million ZEVs on the road by 2030 to reduce emissions from transportation sources [5]. Battery electric vehicles (BEVs), fuel cell electric vehicles (FCEVs), and plug-in electric vehicles (PEVs) are the three types of ZEVs [6]. Since 2011, almost two million PEVs have been sold, with. Increased PEV adoption will lead to disturbances in the electric grid because of power demands exceeding the grid’s initial design conditions [10,11,12,13,14]

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