Abstract

<p>This paper proposes a demand response method that aims to reduce the long-term charging cost of a plug-in electric vehicle (PEV) while overcoming obstacles such as the stochastic nature of the user’s driving be- haviour, traffic condition, energy usage, and energy price. The problem is formulated as a Markov Decision Process (MDP) with unknown transition probabilities and solved using deep reinforcement learning (RL) techniques. Existing methods using machine learning either requires initial user behaviour data, or converges far too slowly. This method does not require any initial data on the PEV owner’s driving behaviour and shows improvement on learning speed. A combination of both model-based and model-free learning called Dyna-Q algorithm is utilized. Every time a real experience is obtained, the model is updated and the RL agent will learn from both real data set and “imagined” experience from the model. Due to the vast amount of state space, a table-look up method is impractical and a value approximation method using deep neural networks is employed for estimating the long-term expected reward of all state-action pairs. An average of historical price is used to predict future price. Three different user behaviour without any initial PEV owner behaviour data are simulated. A purely model-free DQN method is shown to run out of battery during trips very often, and is impractical for real life charging scenarios. Simulation results demonstrate the effectiveness of the proposed approach and its ability to reach an optimal policy quicker while avoiding state of charge (SOC) depleting during trips when compared to existing PEV charging schemes for all three different users profiles.</p>

Highlights

  • 1.1 Background and MotivationElectric vehicles (EV) use electric motors powered by a battery for propulsion instead of burning gasoline

  • It was found that all the electric cars on average produce less than half of the global warming emissions when compared with traditional gasoline vehicles, even when taken into consideration the higher emissions caused by electric car manufacturing [2]

  • Low state of charge (SOC) and Cheap Scheme: The Plug-in electric vehicle (PEV) will always charge when it has less than 20% battery SOC, and will charge/discharge when the electricity price is lower/higher than the historical average price otherwise

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Summary

Introduction

Electric vehicles (EV) use electric motors powered by a battery for propulsion instead of burning gasoline. Plug-in electric vehicle (PEV) is a sub-class of EVs that can be plugged in at home, in public, or private charging stations. Since PEVs are able to recharge their batteries from renewable energy sources such as wind, solar, hydroelectric, or nuclear power sources, their greenhouse gas emission is effectively zero when charged with renewable energy [1]. Many governments are giving incentives to purchasing PEVs as they are environmentally friendly. Transport Canada, a Canadian government agency, is offering a purchase incentive program for electric vehicles. Any Canadian who purchase a PEV are eligible for an amount of $2500 to $5000. These types of programs are very common today for many countries

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