Prediction-Based Charging of PHEVs from the Smart Grid with Dynamic Pricing

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Prediction-Based Charging of PHEVs from the

Smart Grid with Dynamic Pricing

Melike Erol-Kantarci and Hussein T. Mouftah

School of Information Technology and Engineering University of Ottawa

Emails:{melike.erolkantarci, mouftah}@uottawa.ca

Abstract—Coexistence of Plug-in Hybrid Vehicles (PHEVs)

with the emerging smart grids has been recently an attractive and equally challenging research topic. The existing electricity grids are rapidly evolving into smart grids by utilizing the advances in Information and Communication Technologies (ICT). Meanwhile, advances in Lithium-Ion (Li-ion) battery technologies have made manufacturing of PHEVs cost-wise effective, and PHEVs are expected to be widely adopted in the following years. PHEVs have several beneﬁts over conventional vehicles such as, less fuel dependency, lower operating costs and lower amount of 𝐶𝑂₂ emissions. On the other hand, unless PHEVs are powered by off the grid renewable energy resources, they will be drawing electricity from the grid to charge their batteries and they will increase the load on the grid. In the worst case, when the Time Of Charging (TOC) coincides with the critical peak periods, the grid may experience overall or partial failure. For most of the cases, TOC may be during the peak hours when the price of electricity is high. To avoid endangering grid resilience and to avoid high costs, a charging strategy and communication with the smart grid is essential. In this paper, we propose a prediction-based charging scheme which receives dynamic pricing information by wireless communications, predicts the market prices during the charging period and determines an appropriate TOC with low cost. Our prediction-based charging scheme is based-on a simple, light-weight classiﬁcation technique which is suitable for implementation on a vehicle or a charging station. We show that prediction-based charging provides less operating cost and less

𝐶𝑂2 emissions.

Index Terms—Dynamic pricing, plug-in hybrid vehicle,

predic-tion, smart charging, smart grid;

I. INTRODUCTION

PHEVs are promising alternatives to conventional fuel-based cars as they consume less gasoline and have lower operating costs. By the help of the forthcoming regulations worldwide, PHEVs are going to be used as passenger vehicles on the public roads soon.

PHEVs can be plugged into a household electrical outlet for charging, unlike the Hybrid Electrical Vehicles (HEV) which are commercially available for more than a decade. HEVs carry nickel-metal hydride (NiMH) batteries and they use the kinetic energy of the vehicle to charge these batteries whereas PHEVs carry light weight Lithium-Ion (Li-ion) batteries which can be charged from a household electrical outlet or a charging station. Currently, PHEV-20, PHEV-40 and PHEV-60 type passenger vehicles have been developed where 20, 40 and 60 stands for the electrical range of the vehicle, e.g. a PHEV-20 can drive 20 miles on electrical power stored in its battery.

PHEV batteries can be charged from any power source, theoretically. For instance, off the grid, renewable energy resources, such as independently operated solar PhotoVoltaic (PV) farms or wind farms can be used to charge PHEV batteries. However, in practice, the availability of these kind of resources are limited and the PHEVs will be usually charged from the electricity grid either from a household outlet or a charging station. For drivers who do not have private garages, public charging stations will be available. These public charging stations can be located on curb-sides, road-sides, parking lots of shopping centers, municipal lots or company lots. Public charging stations will be most likely used during the daytime while the stations in private garages will be available also during overnight hours.

Charging the PHEVs from the electricity grid will deﬁnitely impact the load on the grid. Even without the additional load of the PHEVs, the demand for electricity has been rising while the grid infrastructure has been aging [1]. Currently, there is a growing interest to renovate the electricity grid and make it smart by increasing the utilization of ICT. Towards the smart grid, one of the most visible renovations in the grid has been the installation of the smart meters. They have been widely deployed in North America, Europe and China and their full deployment is expected to be completed by 2012. Smart meters allow two-way communication between the utility and the consumer, and they provide detailed consumption information such as Time Of Use (TOU). TOU pricing provides different pricing for peak, mid-peak and off-peak hours and the prices generally change twice a year to reﬂect seasonal demand variations. In TOU pricing, distinction of peak, mid-peak and off-peak hours is based on the load information collected over many years. Demand for electricity follows the natural behavior of the consumers and the environmental factors. For example, load makes a peak during midday in the summer season due to high temperatures. Similarly, load is lower during overnight hours when compared to daytime, due to low consumer activity. Naturally, the market price of electricity varies with load. At peak hours, electricity generators and importers use more expensive resources or start-up their addi-tional power generation facilities which have higher costs than the base load generators. As a result of the market dynamics, the price of electricity increases during peak hours.

In the conventional power grid, peak load can be reduced by 1st IEEE Workshop on Smart Grid Networking Infrastructure SGNI 2010, Denver, Colarado

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“demand response” which is basically reducing the load of the large consumers such as industrial facilities and commercial buildings upon the request of the utility. Generally, these consumers are rewarded with credits when they collaborate. On the other hand, it is also possible to charge higher rates for peak periods which means price is implicitly used for demand response. In this case, consumers are expected to follow the price signals of the utilities and reduce their consumption when the price of electricity exceeds a threshold. In the smart grid, device communications with the smart meter can provide real-time price of the electricity.

Dynamic pricing, Critical Peak Pricing (CPP) and Peak Time Rebate (PTR) can be used for controlling the load on the smart grid. Dynamic pricing reﬂects the actual price of the electricity in the market to the consumer bills. The market price of electricity is generally determined by the regional independent system operator. The independent system operator arranges a settlement for the electricity prices of the next-day or next-hour, based on the load forecasts, supplier bids and importer bids. CPP is applied on several days of a year, e.g. very hot days, when the load exceeds a certain threshold. CPP aims to reduce the load of large industrial or commercial consumers on several days in order to prevent grid failure. Some utilities reward customers with credits for their corporation on critical peak days which is called as Peak Time Rebate (PTR). Previous work has shown that dynamic pricing can reduce peak load [2] therefore, a smart grid employing dynamic pricing provides advantages for utilities and consumers. In the future smart grid, smart outlets or smart appliances (or devices) may determine their time of use based on price signals from the utility or by following the electricity market prices using web services. Similarly, PHEV charging can use electricity market prices to select an optimum TOC. Charging stations may even follow the gasoline prices and choose an optimum mix of fuel in terms of less operating costs and emissions. These new services will be available when device to smart meter communications is established.

It is generally assumed that PHEV charging takes place overnight when the vehicle is parked in a garage or a driveway. However, for residents of apartments or townhouses, public charging options, which will possibly be available during daytime, may be necessary. Moreover, public charging stations can be used for topping up the electrical range when the PHEV is parked in a lot, rather than waiting for charging at home. Public charging stations can be placed in parking lots of ofﬁces or shopping malls which means charging could take place during the day. In this case, it is important that PHEVs avoid charging during peak hours.

In this paper, we propose a charging scheme which is based on communications with the smart grid and price prediction. We assume the smart grid employs dynamic pricing and con-sumers are billed at real-time market prices. Charging station is assumed to receive updates from the smart meter by wireless communications. Our scheme uses the electricity market prices of the previous hours and the k-Nearest Neighbor (kNN) classiﬁcation algorithm to predict the price of electricity during

charging. We show that prediction-based charging reduces the cost of electricity consumption of PHEVs. Moreover, time of charging also affects the 𝐶𝑂₂ emissions. In most of the grids, electricity is supplied from a mix of sources by the base plants while during peak hours, peaker plants supply additional power to accommodate the peak load. Peaker plants generally have higher emission rates. Therefore, avoiding peak hours can reduce the𝐶𝑂₂emissions of PHEVs, as well. We also show the impact of price threshold and the number of training days on the performance of the prediction-based charging scheme. The rest of the paper is organized as follows. We give the related work in Section II. In Section III, we describe our technique and in Section IV, we present the simulation results. Finally, Section V concludes our paper by discussing future directions.

II. RELATEDWORK

It is expected that widespread adoption of PHEVs will be by 2050 [3] and appropriate charging policies are required before they become common. In this section, we ﬁrst give the related work on smart charging research, then we present the previous efforts related with prediction of load and electricity prices. A. Smart Charging

PHEVs have several charging options depending on the electrical infrastructure of the house or the charging sta-tion. General household outlets can provide 1.8kW maximum power for 120 volt and 15 Amperes (A) circuit. When a 240 volt 32 A circuit is used, which is also used for heavy appliances such as clothes dryer, then the maximum power is 7.7 kW [4]. Moreover, for fast charging, several car manufac-turers are offering charging stations with 240 volt and 70 A circuit which can provide 16.8 kW. The maximum power level determines the duration required to fully charge the batteries. For instance, the battery of a Tesla Roadster can be charged in less than 4 hours with maximum power of 16.8 kW [5].

The impact of charging the PHEVs depending on the time of day has been studied in [6], by simulations. [6] compares four different charging proﬁles which are uncontrolled charging, delayed charging, off-peak charging and continuous charging. In the uncontrolled charging case, charging starts as the vehicle is plugged-in at a home outlet and it stops when the battery is fully charged. This generally corresponds to the time when drivers return home after work. In the delayed charging, PHEVs start charging after 10pm, i.e. the beginning of off-peak hours. In the case of off-off-peak charging, charging occurs during overnight similar to delayed charging. The difference is that, the time to start charging is determined by the utility. Utility controls the charging process with an objective of valley ﬁlling where the valleys are the hours with minimum load. The last case is continuous charging, where daytime charging is possible and the vehicle is charged whenever its parked. The study shows that continuous and uncontrolled charging increase the load during peak hours while off-peak charging provides the most promising results. An interesting result of [6] is that, off-peak charging may provide additional cost

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savings for the utilities since it balances the overnight load and the daytime load, leading to less power plant start-up costs.

Charging PHEVs at homes can include coordinating the charging process with the other activities at home so that the overall consumption of the house would not exceed a certain level. In this case, the PHEV can be considered as a heavy appliance and it can be a part of the Home Area Network (HAN). The charging process may use the utility price signals, besides it may take the in-home load into consideration and adapt its charging cycle. The PHEV or the smart charger can use one of the HAN communication techniques described be-low to communicate with the home gateway and coordinate its TOC. The state-of-the-art HAN communication technologies can be grouped as wireline and wireless standards. Recent wireline standards are IEEE P1901, Lonworks, and ITU-T G.hn. IEEE P1901 and Lonworks standards utilize power lines whereas ITU-T G.hn standard utilizes phone lines, power lines, coax and cat-5 cabling present at home. The most dominant wireless technologies in HANs are Zigbee, Wi-Fi and Z-wave. By using wireline or wireless communications, the TOC of a PHEV can be coordinated similar to the appliance coordination employed in [7], [8]. For public charging stations, again, wireless communications can be used.

A recent study [9], proposes an optimization method to minimize the cost and the emissions of PHEVs. The authors show that the cost and emissions are minimized when PHEVs are charged from renewable sources. However in practice, renewable energy generation may not be adequate for pow-ering the PHEVs. The availability and the efﬁciency of the renewable generation depends on the geographical location of the grid.

Besides the fact that charging the PHEVs from the grid is challenging, their ability to discharge, and electricity ﬂow from the vehicle to the grid (V2G) may provide advantages for grid operation. Fleets of PHEVs can be used to support the grid or to sell power to the grid [10], [11]. Power attained from PHEV batteries can be used for ancillary services or for peak load accommodation rather than base load. Ancillary services include spinning reserves and regulation services. Spinning reserves are the idle sources that can dispatch power to the grid within 10 minutes of a request. These are the services which are generally paid to be immediately available. They do not generate power unless requested. Regulation services are generally under real-time control of the system operator and they are used for keeping the system frequency as close to 60Hz as possible. The frequency needs to be regulated because the variable load needs to be balanced with the output of the generation. A ﬂeet of PHEVs can be contracted to provide ancillary services [11].

B. Load and Price Forecasting

Load forecasting is widely studied in the electricity grid. Load forecasts are essential for dispatchers, who are the com-mercial or governmental bodies responsible for dispatching electricity to the grid. Load forecasting provides tools for operation and planning of a dispatcher where decisions such

as purchasing or generating power, bringing peaker plants online, load switching and infrastructure development can be made [12]. Electricity market regulators and dispatchers rely on forecasting tools that provide short, medium and long-term forecasts.

Short-term load forecasts cover hourly or daily forecasts where medium-term forecasts span a time interval from a week to a year and long-term forecasts cover several years. Forecasting techniques may differ according to this range. In this paper, we use prediction for a short time period, therefore we limit the scope of this section to short-term forecasting techniques. For short-term forecasting, the similar day ap-proach searches the historical database of days to ﬁnd a similar day with properties such as weather, day of the week, etc [13]. Regression is another widely used statistical technique for load forecasting. Regression methods aim to model the relationship of load and environmental factors, e.g. temperature [14]. Time series methods try to ﬁt a model to data. Previous studies have employed a wide variety of time series methods such as Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), Autoregressive Moving Average with eXogenous variables (ARMAX) and Autore-gressive Integrated Moving Average with eXogenous variables (ARIMAX) methods. ARIMAX has been shown to have suc-cessful load forecasting performance since it does not require stationarity and it can incorporate weather and time of use as exogenous variables [15]. On the other hand, using neural networks [16], expert systems [17], support vector machines [18] and fuzzy logic [19] are among the recent forecasting studies. The accuracy of forecasting techniques may differ depending on the markets, utilities and regions because the geographical conditions and the usage patterns in one region are not identical with another [12]. A detailed survey of load forecasting techniques can be found in [12] and [20].

The techniques proposed for load forecasting can be used for price forecasting, as well. Nevertheless, several recent studies focus on price forecasting only. [21] proposes a tech-nique based on weighted nearest neighbors approach for the prediction of the next-day electricity market prices and [22] employs game theory for price forecasting.

Utilities and the system operators have large databases of previous loads and environmental conditions where they can employ the above techniques and achieve accurate forecasts. However, these data may not be available for the consumers. In this case, consumers can employ simpler techniques on limited data sets and achieve less accurate predictions, which are still adequate for making decisions such as determining the TOC. In this paper, we use a limited amount of data to avoid storage cost at the charging station.

III. PREDICTION-BASEDCHARGING

In this paper, we consider a smart grid employing dynamic pricing. The market prices of electricity, in addition to extra fees related with regulation and taxes, etc. are used in billing the consumers. To make dynamic pricing practical, consumers need to be equipped with smart appliances or smart outlets that

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can respond to the instant variations in price or the day-head prices need to be announced. We assume all the consumers use smart outlets that can communicate with the grid and learn the dynamic price of electricity. We consider the use of prediction for charging the batteries of PHEVs, therefore we assume the charging station is able to store limited amount of past price signals of the utility and run a light-weight prediction algorithm. Since the charging process can span several hours, the current price, in addition to several hours-ahead prices are needed to determine the best TOC. For instance, a PHEV may be plugged in at 6:59am which is an off-peak hour, just before the morning peak, i.e., 7am. At this time instant, the price of electricity is low and the PHEV may start charging. However, the charging process will continue for several hours which coincide with the peak hours. To avoid this, we propose a prediction-based charging scheme and consider the future price of the electricity.

Our prediction-based charging scheme adopts a simple, light-weight classification technique, which is the k-nearest neighbors algorithm (k-NN). In k-NN, an unclassified sample point is assigned to the class of the nearest set of the previously classified points. k-NN does not require complex model fitting operations as the time series models do. k-NN considers the training set as the model.

In our scheme, the training set is the price signals recorded in the several previous days. The number of days used in the training set is denoted by 𝐷_𝑡. We employ a sliding window over the training set. The size of the sliding window is 𝑊_𝑠. The training set is split into time slots of one hour and 𝑃𝑖 is the hourly average price for time slot𝑖. Each two consecutive time slots (𝑃𝑖𝑃𝑖+1) are used by the classiﬁcation algorithm to determine the corresponding class, 𝑘 for the time slot 𝑃_𝑖₊₂. We deﬁne maximum number of classes 𝑁_𝑚𝑎𝑥 based on the market prices, before running the prediction algorithm. First the training set is grouped under𝑁_𝑚𝑎𝑥 classes, then the class of the 𝑃_𝑖₊₂ price signal is predicted. If the predicted price is greater than the price threshold, 𝑇_𝑝, i.e.𝑃 𝑟_𝑗 > 𝑇_𝑝, charging is delayed for one hour. At the beginning of the next hour the prediction algorithm predicts the new price and if this exceeds the threshold, charging is again delayed. Otherwise, PHEV starts charging. At the beginning of each hour, until the PHEV is unplugged or fully charged, the charging station predicts

𝑃 𝑟𝑗, and decides whether to start charging or to delay it. We assume that PHEVs can be charged during daytime at any time. They are assumed to be plugged-in wherever there is a charging station available however, being plugged in does not necessarily mean that the PHEV starts charging. Charging starts only when the predicted prices are below 𝑇_𝑝 and continues at least for a minimum amount of time𝑡_𝑠𝑡 and until the battery is fully charged or PHEV is unplugged by the driver.

Our scheme is a simple hourly price prediction technique. Since PHEV charging algorithms will be either on-board or they will be embedded on a charging station, their complexity needs to be low. However, this technique may not be con-venient for price or load forecasting in electricity markets.

0 200 400 600 800 1000 1200 5000 6000 7000 8000 9000 10000 11000

Time (5 min intervals)

Demand (MW)

Fig. 1. Electricity demand in NSW on four days in April.

These forecasts can be done by using the various techniques which are summarized in the related work. These techniques use large data sets and employ complex forecasting algorithms. For this reason, they are not preferred in predicting the suitable timeslots for PHEV charging.

IV. SIMULATIONRESULTS

We implement the prediction-based PHEV charging in MATLAB. We use the data sets provided by the Australian Energy Market Operator (AEMO) [23]. AEMO stores the historical database for load and price for ﬁve Australian jurisdictions. The AEMO database is chosen for its high resolution data, i.e raw electricity prices at 5-minute intervals, are available to public. The price of electricity set by the system operators are generally less than the prices billed to the consumers since they do not include taxes, transmission, distribution or other utility related costs. In several electricity markets, e.g. Ontario power market, these prices are regulated and ﬁxed twice a year, for winter and summer [24]. However the raw price of electricity represents the behavior of the dynamic price signals which may be available in the future smart grids. In Figure 1 and Figure 2, we give the demand and price variation for four days in April, starting at 12am, for New South Wales (NSW) jurisdiction. Morning and evening peaks are clearly observed in the plots.

We simulate the prediction-based charging where the charg-ing station communicates with the smart meter, receives real-time prices and stores a limited amount of previous price values for prediction. For the ﬁrst set of simulations, we use 576 samples for the training set, corresponding to price information at 5 minute intervals for two days, i.e. 𝐷_𝑡 = 2. Later, we investigate the impact of the number of training days on the performance of the prediction based charging scheme. The training set moves forward in time, in a sliding window where the size of the sliding window is 𝑊_𝑠 = 12ℎ𝑜𝑢𝑟𝑠. In our simulations, we use the monthly price data set of NSW

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0 200 400 600 800 1000 1200 0 5 10 15 20 25 30 35 40 45 50 55

Time (5 min intervals)

Market price ($/MWh)

Fig. 2. Electricity price in NSW on four days in April.

jurisdiction, collected on July 2009 and January 2010. These two months are selected to represent winter and summer sea-sons. In each data set, on several days, the price of electricity has been unexpectedly high. These outliers are cleared before the data sets are used in the simulations.

In the classiﬁcation algorithm we use𝑘= 1for simplicity and we set 𝑁_𝑚𝑎𝑥= 6, empirically. For the ﬁrst set of simu-lations, the price threshold 𝑇_𝑝 is set to 22 $/MWh and the charging period is assumed to be at least 𝑡_𝑠𝑡= 2 hours. Later, we analyze the impact of 𝑇_𝑝 on the delay introduced by the prediction-based scheme by modifying𝑇_𝑝between 20 $/MWh and 24 $/MWh.

The portion of the battery charged in two hours depends on the electrical circuit of the charging station. We use various maximum power levels between 2kW and 16kW which can be available in various circuits. In Figure 3, the percentage of battery charged in two hours at varying maximum power levels are given. We analyze the cost of PHEV operating and

𝐶𝑂2 emissions for one month. We compare the prediction-based scheme with the on-demand charging scenario where the PHEV starts charging as soon as it is plugged in. We assume each day 10 PHEVs are plugged in at random times. We present the average results of 20 runs.

In Figure 4, we compare the cost of the prediction-based charging scheme with the on-demand charging in July 2009. The cumulative cost represents the cost for multiple PHEVs charging at a public station. Prediction-based scheme has almost 25% lower monthly cost than on-demand charging at the maximum charging power. For a standard household outlet, (around 2kW maximum power) the cost of the prediction-based scheme is slightly less than on-demand charging. Sav-ings increase as more electricity is drawn. Note that, we use raw market prices, however regulated prices are expected to be higher and the numeric results may vary according to the ﬁnal price billed to the consumers.

In Figure 5, we compare the cost of the prediction-based

2 4 6 8 10 12 14 16 5 10 15 20 25 30 35 40 45 50

Maximum charging power (kW)

Percentage of battery charged

Fig. 3. Percentage of battery charged in two hours.

2 4 6 8 10 12 14 16 0 50 100 150 200 250 300 350 400

Cummulative cost of charging at a public station($)

On−demand charging Prediction−based charging

Fig. 4. Cost of charging the PHEV batteries in a public charging station in July 2009.

charging scheme with the on-demand charging in January 2010. Prediction-based scheme has almost 15% lower monthly cost than on-demand charging at the maximum charging power. The cost of PHEV charging and the difference between two schemes are less than in July 2009, since peak hours and the prices are different in winter.

In Figure 6, we present the 𝐶𝑂₂ emissions resulting from the electricity consumption on different charging times in July 2009. During peak hours, power plants with higher emission rates are generally utilized [25]. Therefore, we assume the peak prices imply higher emission rates. However, if utilities choose to announce emission rates, as well as price, charging stations can also use this information. Figure 6 shows that, prediction-based charging can reduce the emissions by almost 18%. In the prediction-based scheme, price signals help to avoid from peak usage which consequently reduces the 𝐶𝑂₂

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2 4 6 8 10 12 14 16 0 50 100 150 200 250 300 350

Cummulative cost of charging at a public station($)

Fig. 5. Cost of charging the PHEV batteries in a public charging station in January 2010. 2 4 6 8 10 12 14 16 0 1000 2000 3000 4000 5000 6000 7000 8000 9000

CO

2

emissions due charging from the grid(kg CO

2

)

Fig. 6. 𝐶𝑂₂emission of the PHEVs based on TOC in July 2009.

emissions due to electricity. On-demand charging may occur during peak hours, thus a charging period may overlap with a peak period.

In Figure 7, we present the 𝐶𝑂2 emissions resulting from the prediction-based charging scheme and on-demand charging, in January 2010. Prediction-based charging reduces the emissions by almost 12%. Note that, PHEVs may have additional emissions due to using gasoline and the amount of emissions from electricity is highly dependent on the energy mix used. For example, the emissions of PHEVs could be lower in Ontario than other states because in Ontario’s power generation is based on hydro and nuclear plants which have low emission rates. Moreover, the emissions of PHEVs may vary depending on the battery usage policy of the PHEV, which can adopt several strategies such as charge-sustaining or blended mode. 2 4 6 8 10 12 14 16 0 1000 2000 3000 4000 5000 6000 7000 8000

CO

2

emissions due charging from the grid(kg CO

2

)

Fig. 7. 𝐶𝑂₂emission of the PHEVs based on TOC in January 2010.

In Figure 8, we present the impact of varying𝑇_𝑝 values on the cost of charging and the delay experienced for charging. We plot the delay in charging per day in hours in the y-axis on the left. The cost of charging is given by the y-axis on the right. In these set of simulations, the maximum power level is set to 6kWh. As the price threshold increases, the cost of charging increases, however the delay for waiting for a convenient timeslot decreases. When the threshold is set to 24 $/MWh, delay is slightly more than two hours and the cost of charging exceeds $130. For 𝑇_𝑝= 20$/MWh, the delay is more than 17 hours and the cost is slightly more than $60. In this case, the vehicles wait for the off-peak hours and charge when the price of electricity is the lowest. Although this is convenient when the charging station is reserved for the use of a single vehicle, and the vehicle is parked for a long time, for daytime charging from a public station scenario, waiting for several hours may not be possible.

In Figure 9, we show the effect of the number of training days used in the prediction algorithm on the percentage of savings. We set the maximum power level to 6kWh, once more. When the training data set covers the previous two days, the savings introduced by prediction-based scheme is %25 more than the on-demand charging. As the number of training days increase the savings attained decrease. This shows that using more recent data for training, gives better performance which may be correlated with a large number of factors. We believe that the weather conditions being similar for consecutive days increases the chances of accurate estimations. As larger number of previous days are used for training, the classiﬁcation based on price variations yield to lower savings.

V. CONCLUSION

PHEVs are expected to penetrate to the passenger vehicle market widely, in the next decade or so. Their widespread adoption will deﬁnitely impact the load on the grid. When they are charged appropriately they can reduce the utility

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20 22 24 2 4 6 8 10 12 14 16 18 Delay (hours) Price threshold ($\MWh) 60 70 80 90 100 110 120 130 140 Cost of charging($)

Fig. 8. Tradeoff between delay and cost of charging for varying for varying

𝑇𝑝values in July 2009. 2 3 4 5 0 10 20 30 40 Percentage of savings (%)

Training days (days)

Fig. 9. The impact of training days on the percentage of savings introduced by prediction-based charging in July 2009.

costs of starting up power plants, or they can discharge to balance the intermittent renewable power, or they can beneﬁt from lower prices for the sake of their owners. All of these beneﬁts become available with mitigation from on-demand charging to price-aware or demand-aware charging. These charging strategies are possible with communications between the stations and the smart grid.

In this paper, we proposed a prediction-based charging scheme. Our scheme uses a training set obtained from previous days, it employs a simple k-NN classiﬁcation technique and it predicts the price of the electricity for the charging period. If the predicted price is above a certain threshold, our scheme delays the charging until it determines a convenient time of charging for the PHEV. We have shown that our prediction-based charging scheme reduces the operating cost of PHEVs

and the𝐶𝑂2 emissions. We analyzed the impact of the price threshold and the size of the training set on the performance of our scheme.

As a future work, prediction-based charging can be com-pared with the other available prediction schemes, such as the time-series methods, neural networks or fuzzy logic. Moreover, the impact of driving and charging patterns such as increased amount of charging demand at the end of business hours or individual driving habits can be also incorporated to the prediction-based charging scheme. In this case, learning mechanisms can be adopted from artificial intelligence fields to increase the potential benefits of these schemes. We also plan to integrate the PHEV to the HAN and study its charging characteristic when the charging process is coordinated with the electricity market prices and the household load. Further-more, we plan to analyze the use of public charging stations which are able to provide a mix of gas and electricity where the stations follow the fuel prices, electricity prices and emission rates from the web, and minimize the cost and emissions by choosing the appropriate charging strategy and fuel type.

ACKNOWLEDGMENT

This work was partially supported by an ORF-RE grant from the ministry of Research and Innovation of the Gov-ernment of Ontario.

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