Demand Response Program Evaluation for Plugin Hybrid Electric Vehicles Purchase Encouragement M. Hossein Moayyed Jahromi
Behzad Asaei
School of ECE Faculty of Engineering University of Tehran, Iran
[email protected]
School of ECE Faculty of Engineering University of Tehran, Iran
[email protected]
Abstract— The rate of using plugin hybrid electric vehicles (PHEV) will be increased in near future in Iran. In the present article, a comparison between existing electricity pricing method (BR) and time of use (TOU) demand response program was made in order to study whether consumers would be motivated purchasing PHEVs or not. The total monthly electricity consumption and the monthly electricity bill utilizing both methods are calculated. Aimed at economic viability evaluation, rate of return (ROR) and net present value (NPV) are calculated in different cases. A motivating loan is entered to cost and benefit analysis and further economic analysis is made. Keywords-component; PHEV; demand response; economic viability; TOU
I.
INTRODUCTION
The plugin hybrid electric vehicles (PHEV) will be increased in near future around the world. PHEVs are alternatives to conventional vehicles (CV) as they consume less gasoline and have lower operating costs. Demand for PHEVs will begin to grow rapidly after 2015 and reach a combined 7 million per year by 2020, and 100 million by 2050, including more than half of all cars sold in that year in the world [1]. PHEVs have larger battery than hybrid electric vehicles (HEV), so PHEVs consume a large amount of electricity and might have undesirable impact on distribution grid [2]. Present electrical infrastructures are designed for maximum expected demand, which happens only a few hundred hours per year [3]. The grid is generally underutilized; therefore it has enough potential for charging PHEVs. But these vehicles may lead to undesirable peak; so the electricity grid should be renovated and make it smart by using information and communication technology like smart meters [4]. This smart grid infrastructure could be used to make incentive for customers to charge their vehicles at off-peak time. Daily electricity costs for driving are reduced from $0.43 to $0.2 based on data of the California independent system operator by smart charge [5]. Some traditional methods have been proposed in [6] to motivate customers for utilizing potential of grid in off-peak times. Recently, Iran has started new demand response program. On the other hand, Iran plans to produce and use PHEVs in recent future. Consequently, financial analysis for purchasing PHEVs under this demand response program is required [7].
Navid Haghdadi School of ECE Faculty of Engineering University of Tehran, Iran
[email protected]
In new demand response model, electricity consumption tariff for residential consumers is calculated considering the total usage of electricity per month. Furthermore, an incentive for amount of energy consumed in off-peak time and a penalty for the peak time has been taken in to account. Being constant for these incentives and penalties make these regulations useless for PHEV owners with high power demand and the aforementioned method cannot encourage these consumers. So the sufficient incentive for using PHEVs is not meet in this demand response program. Another demand response is necessary to reduce the monthly electricity price bill for PHEVs owners without sacrificing the electricity distribution companies benefit. There are some methods which have time-sensitive pricing scheme such as time-of-use, critical peak pricing, and real-time pricing. Time-of-use pricing divides the day into various periods. Each period has specific rate. Typically, this pricing strategy has an on-peak and an off-peak price for energy. Time-of-use rate structure might encourage residential PHEV owners in order to plan their energy consumption optimally. In this article, a comparison between these two demand response programs has been performed. The effectiveness of TOU program is evaluated for encouraging conventional vehicle owners to replace their cars with electric one and motivating PHEV owners to charge their vehicles in the offpeak time. The electricity consumption price for 16 regional electrical companies customers are calculated with both pricing schemes for charging PHEVs in off-peak and on-peak for 3 prevalent types of vehicles. The difference between these values and the electricity price for CV users and the premium cost of replacing the vehicle with PHEV are considered as cost and the reduced gasoline price is considered as benefit of the investment. Finally, the net present value (NPV) of these costs and benefits are calculated using the inflation ratio and a comparison between different plans is made. Considering two scenarios, the price of electricity and gasoline are forecasted and the analysis is performed for both scenarios. Rate of return (ROR) is computed and economic viability is evaluated. A way to make PHEVs economically viable and to motivate customers to purchase PHEVs, is encouraging policies which is developed by government. Some policies are developed to support industry such as several supports to manufacturing industry and infrastructure while other policies are to encourage increased demand for example subsidizing the purchase, minimizing tax or decreasing in custom [8].
In the second section of article, a case study introduced for this financial analysis. Afterwards, the result of this analysis will be shown and finally a conclusion is made.
and a penalty for consuming in on-peak. The prices of each step are obtained from [10]. TABLE I. ENERGY REQUIREMENT AND PREMIUM COST OF PHEVS Required
Iran power grid consists of 16 regional electrical companies. In recent years, a new block rate pricing method is used in Iran to encourage consumers using electricity less than before. More electricity use eventuates to more Rials 1 per kilowatt hour (kWh). As the NPV of different type of vehicles in two demand response programs are dependent to the rate of electricity price and gasoline cost, two scenarios are considered for predicting the price of electricity and gasoline cost. In the first scenario, the fixed growth rate of 5% is considered for both electricity and gasoline price and in the next one the growth rate of electricity price and gasoline are considered 5% and 10 % respectively. The reason of assuming second scenario is crisis of crude oil and great increase in use of renewable energy sources. The grid is simulated in two different courses, first when the vehicles are charged in off-peak and second when they are charged in on-peak. A. Vehicles Taking the different costs of fuels, batteries, vehicle performance and driving habits in to account, a broad spectrum of PHEVs are existed [9]. Three types of vehicle were chosen to show the result of various consumption and circumstances. There are many other types of vehicles, but the majority of vehicles in Iran are in these three types. Table 1 shows the energy related characteristics of each three types [3]. The following assumptions are made for the selected vehicles: 1.
A 30-km all-electric average daily trip (PHEV30)
2.
A 70% maximum allowable depth of discharge. Due to Life-cycle considerations.
3.
An 85% charging efficiency.
B. Demand Response programs Household loads in 16 regions of Iran from 20th of March 2008 to 19th March 2009 are considered, the monthly price of electrical power is calculated for these regions by means of BR and TOU. 1.
BR
The currently used demand response program in Iran, called BR, is mainly dependent on the customers’ total energy consumption per month. It means that the prices grow in the step by step incremental regime. Depending on the climate of the regions, the prices and widths of steps are altered radically. In Fig. 1 steps and their prices for specific regions are depicted. As illustrated in Fig. 1 the electricity rate versus monthly consumption of users is plotted. In order to motivate customers to utilize electricity at off-peak time, some encouraging policies are accompanied with BR such as discount in bill for using electricity in off-peak and mid peak 1
Currency unit of Iran
Fuel Total Energy Premium Consumption Cost [Million Demand
[kWh]
[kWh]
[lit/100km]
Rials]
Compact Sedan
6.86
5.65
6.72
35
Mid-size Sedan
8.14
6.71
9.40
42
Mid-size SUV
10.29
8.47
11.761
50
Electricity Price Factor
CASE STUDY
8
6
4 2.5 1.25 1 100
200
300
400
Total Energy Consumption per Month
500
600
Figure 1. Block rate pricing Time Of Use Demand Response Program Price Rate
II.
Type of PHEV Battery Size
On Peak Time Mid Peak Time
2 Off Peak Time 1 0.25 0
1
2
4
6
7
8
10
12 14 Time (Hour)
16
18 19 20
22
24
Figure 2. TOU pricing
2. TOU A day is divided into three intervals and the prices are calculated relay on the interval in which the electrical power is used. In Fig. 2 the electricity rate versus hour of day is plotted. Electricity rates for off-peak, mid-peak and on-peak are considered 0.25, 1 and 2 respectively [11]. A constant value (CTOU) for each region is selected in order to comply with the price obtained for present average of electrical consumption. C. Household Electricity Consumption Household load profile for a typical house with five residents is obtained for 16 regions and the average value of summer and winter is computed. Considering the pattern of household consumption and these average values, daily load profile for summer and winter for 16 regions are attained. D. Economic Analysis Two main problems are challenging: at first it should be investigated to realize that using which demand response program economic aspects of purchasing PHEV in different regions under various scenarios is more motivating for customers. The next problem is examining to see whether utilizing TOU encourage customers to charge their cars in offpeak.
So as to analyze economic aspects of using PHEVs, it is necessary to consider the cost of additional electrical power, which used in order to charge batteries, premium cost and cost of gasoline which is not consumed any more. NPV is obtained by following equations. n is the number of years, Bn is annually cost of gasoline in n-th year, which considered as benefit of purchasing PHEVs. Two terms play roles in calculating cost, Cp, which is the cost of replacing CV with PHEVs and Cn, which is additional electrical power consumption as a result of charging PHEVs. e is rate of annual increment in price of electrical power, g is rate of annual increment in rate of gasoline and i is average inflation rate over these 10 years. ∑
∑
(1)
Three types of PHEVs are considered to be charged in the off peak and on peak time. NPV and ROR are calculated for all regions in each scenario, but because of large amount of data we neglect to take the results of some regions with similar circumstances of weather in the table. Cost of this vehicle in some regions are more than benefit of them or benefit of this vehicle is just a little bit more than the costs; therefore ROR in these regions are more than 20 years but the average lifetime of PHEVs is about 10 years[12], so purchasing PHEVs in these regions is not economic. In Khuzestan and Fars region, the prices of electricity are very cheap so NPV in these regions are positive and have an acceptable ROR. TABLE II. ENERGY INFORMATION AND PRICE FOR 6 REGIONS IN IRAN FOR A TYPICAL HOUSEHOLD LOAD Monthly Electricity Use
For both scenarios and each pricing method, the above equation must be calculated in order to analyze the PHEVs economic viability. ROR could be obtained from (1).
III.
Region
On peak or Off peak Charging
Azarbayejan
Tehran
Khorasan
Khuzetan
Fars
Kerman
NPV (Million Rials) ROR (Year) NPV (Million Rials) ROR (Year) NPV (Million Rials) ROR (Year) NPV (Million Rials) ROR (Year) NPV (Million Rials) ROR (Year) NPV (Million Rials) ROR (Year)
CTOU (Rial)
Azarbayejan
216
229
86,773
98,768
360
375
Tehran
330
423
195,670 332,106 195,400 330,905
530
681
Khorasan
211
263
82,575 126,200 82,324 125,904
350
416
Khuzetan
762
995
87,873 116,935 87,689 116,736
103
102
Fars
287
487
80,639 138,831 80,579 138,928
251
248
Kerman
197
331
57,076 138,798 57,113 139,145
260
365
In table II the total monthly electricity consumption and the monthly electricity bill utilizing both methods are calculated. Based on present average consumption of electricity in all regions of Iran without replacing PHEVs, CTOU has been found in order to remain the benefit of distribution companies with BR and TOU pricing models unchanged.
98,795
86,797
NPV AND ROR FOR DIFFERENT PHEVS S1
Region
TOU (Rial)
Winter Summer Winter Summer Winter Summer Winter Summer
RESULT
TABLE III.
BR (Rial)
(kWh)
CSD
S2
MSD
MSV
CSD
MSD
MSV
On
Off
On
Off
On
Off
On
Off
On
Off
On
Off
-9.004
-1.75
-9.7
-10.1
-15
-4.05
4.09
11.34
6.7
15.35
4.6
15.6
15
10.67
14.25
10.25
16.5
11.17
9
7.67
8.75
7.42
9.25
7.75
-18
-10
-20
-11
-28
-17
-4.6
2.6
-3.8
4.9
-8.3
2.63
> 20
16.42
> 20
15.5
> 20
18.08
11.17
9.33
10.75
9
11.5
9.5
-10
-2.89
-11
-2.1
-17
-5.6
2.9
10.2
5.6
14.26
3.05
14.01
16.08
11.17
15
10.67
17.75
11.67
9.25
7.83
8.92
7.58
9.5
7.92
9.92
12.3
14.3
17.2
17.33
21
23.01
25.4
30.7
33.59
37
40.63
7.25
6.83
6.92
6.5
6.83
6.42
6
5.75
5.75
5.5
5.75
5.5
5.97
10.8
9.6
15.4
11.36
18.7
19.07
23.9
25.9
31.75
31
38.3
8.17
7.08
7.66
6.75
7.67
6.75
6.5
5.92
6.25
5.67
6.17
5.67
-3.22
1.62
-2.4
3.3
-6.14
1.2
9.9
14.7
13.88
19.67
13.5
20.8
11.33
9.42
10.83
9
11.83
9.67
7.92
7.08
7.67
6.92
8
7.17
20 CSD
Year
15
MSD
15
15
10
10
5
5
5
1
2
3
4
5
0
6
20
1
2
20 CSD
15
3
4
Scenario 2
5
0
6 MSD
15
10
5
5
5
2
3
4
5
0
6
1
2
3
4
2
3
4
5
5
MSV
0
6
MSV
6
15
10
1
1
20
10
0
BR TOU
20
10
0
Year
Scenario 1
20
1
2
3
4
5
6
Region
Figure 3. ROR using BR and TOU methods at off-peak
shown in Fig. 5 in the case of charging PHEVs at off-peak time, using BR pricing method and giving loan for purchasing PHEVs, ROR is reduced dramatically in some regions. A 30 million Rials loan with an interest ratio of 4% and payback time of 3 years was considered to be given to owner of PHEVs.
It’s obvious that charging at on-peak time lead to have negative NPV and very big ROR. ROR is calculated in the case of using TOU and charging PHEVs at off-peak. In Fig. 3 a camparision is made in order to evaluate ROR using BR and TOU methods. ROR is decreased in all regions but its decrement in regions 4 and 5 is less than the others because of inexpensive electricity in these regions. The low price electricity is provided in these regions because of their long hot arid climate. Fig. 4 depicts the results of ROR for charging at off peak times and on peak times for both scenarios with TOU pricing model. ROR for charging in off peak are improved in comparison with on peak charging. Thus it is a good incentive for owner of PHEVs to charge their vehicles at off peak time. This motivation eventuates to peak shaving and made demand profile pattern to be flat. A good alternative to make PHEVs economically viable is giving loan with little interest ratio. Some other policies such as rebating the capital cost of this vehicle by minimizing tax or decreasing in custom. As it is 20 CSD
Year
MSD
15
15
10
10
5
5
5
20
1
2
3
4
5
6 CSD
15
0 20
1
2
3
4
Scenario 2
5
6
0 20
MSD
15 10
10
5
5
5
1
2
3
4
5
6
0
1
2
3
4
5
6
1
2
3
4
5
0
Figure 4. ROR of Off-peak and On-peak charging with TOU method
MSV
6 MSV
15
10
0
On-peak Off-peak
20
10
0
Year
Scenario 1
20
15
As it stated before, Iran’s current pricing model is BR and government did not develop any encourging policies to support PHEVs. Comparision between current circumstance of Iran and the case of using TOU method and giving loans for purchasing PHEVs, shows that the later seems to be necessary. As it is illustrated in Fig. 6, ROR is altered radically. ROR and NPV show better values for the second scenario. This is attributed to higher growth rate of gasoline price compared to electricity price. Therefore, in the case of second scenario vehicles would be more economically viable for customers. According to fuel payment saving, payback period for premium cost of these vehicles becomes shorter.
1
2
3 Region
4
5
6
20 CSD
Year
15
MSD
15
10
5
5
5
2
3
4
5
6
20
0
1
2
20 CSD
15
3
4
5
0
6
Scenario 2
MSD
15
10
5
5
5
2
3
4
5
6
0
1
2
3
4
2
3
4
5
6
5
0
6
MSV
15
10
1
1
20
10
0
MSV
15
10
1
BR(no loan) BR(with loan)
20
10
0
Year
Scenario1
20
1
2
3
4
5
6
Region
Figure 5. ROR for BR with and without governmental financial support for off-peak charging.
20 CSD
Year
15
MSD
15
15
10
10
5
5
5
1
2
3
4
5
6
20
0
1
2
20 CSD
15
3
4
Scenario 2
5
6
0
10
10
5
5
5
1
2
3
4
5
6
0
1
2
3
4
2
3
4
5
6
5
6
MSV
15
10
0
1
20 MSD
15
BR(no loan) TOU(with loan) MSV
20
10
0
Year
Scenario1
20
0
1
2
3
4
5
6
Region
Figure 6. ROR for current circumstance of Iran and the case of using TOU method and giving loans in off-peak charging
IV.
CONCLUSION
In this article, a comparison between BR and TOU pricing methods was made to investigate whether consumers would encourage purchasing PHEVs or not. With BR and TOU pricing models based on present average consumption of electricity in all regions of Iran without replacing PHEVs, the benefit of distribution companies would remain equal. In case of purchasing PHEVs net present value of such investment is improved with TOU rate structure. This can be an effective encouragement for customer to purchase PHEVs instead of CVs. This method not only reduces payback period but also
motivates the customers to consume at off-peak time which leads to PHEVs participating in peak shaving. Finally current circumstance of Iran and the case of using TOU method and giving loans for purchasing PHEVs is compared and ROR is reduced dramatically. ACKNOWLEDGMENT The authors would like to thank Ms. Ziba Gandomkar and Mr. Nima Haghdadi for their great reviews and helpful comments.
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http://www.iea.org/roadmaps/plug_in_electric_vehicles.asp Clement-Nyns, K.; Haesen, E.; Driesen, J.; , "The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid," Power Systems, IEEE Transactions on , vol.25, no.1, pp.371-380, Feb. 2010 [3] Hajimiragha, A.; Caizares, C.A.; Fowler, M.W.; Elkamel, A.; , "Optimal Transition to Plug-In Hybrid Electric Vehicles in Ontario, Canada, Considering the Electricity-Grid Limitations," Industrial Electronics, IEEE Transactions on , vol.57, no.2, pp.690-701, Feb. 2010 [4] Erol-Kantarci, M.; Mouftah, H.T.; , "Prediction-based charging of PHEVs from the smart grid with dynamic pricing," Local Computer Networks (LCN), 2010 IEEE 35th Conference on , vol., no., pp.10321039, 10-14 Oct. 2010 [5] Rotering, N.; Ilic, M.; , "Optimal Charge Control of Plug-In Hybrid Electric Vehicles in Deregulated Electricity Markets," Power Systems, IEEE Transactions on , vol.PP, no.99, pp.1, 0 [6] “Assessment of Demand Response and Advanced Metering,” Federal Energy Regulatory Commission, Washington D.C., Dec. 2008. [Online]. Available: http://www.ferc.gov/ legal/staff-reports/12-08demand-response.pdf [7] Mallette, M.; Venkataramanan, G.; , "Financial incentives to encourage demand response participation by plug-in hybrid electric vehicle owners," Energy Conversion Congress and Exposition (ECCE), 2010 IEEE , vol., no., pp.4278-4284, 12-16 Sept. 2010 [8] Economic Viability of Electric Vehicles, 4th September 2009, Department of Environment and Climate Change, AECOMAustraliaPtyLtd [9] A. Simpson, "Cost-benefit analysis of plug-in hybrid electric vehicle technology", October 23-28, 2006, http://www.nrel.gov/ vehiclesandfuels/vsa/pdfc/39614.pdf [10] http://www.tbtb.ir/ [11] “Assessment of Demand Response and Advanced Metering,” Federal Energy Regulatory Commission, Washington D.C., Dec. 2008. [Online]. Available: http://www.ferc.gov/ legal/staff-reports/12-08demand-response.pdf [12] M. Duvall, “Advanced batteries for electric-drive vehicles,” Electric Power Research Institute (EPRI), May 2004.
M. Hossein Moayyed Jahromi was born in Jahrom,Fars, Iran, in 1985. He received the B.Sc. degree in electrical engineering from Khaje Nasir Toosi University, Tehran, Iran, in 2009. He is currently with the University of Tehran as M.Sc. student. His research interests include renewable energy systems, grid impacts of plugin hybrid electric vehicles, integrated energy systems, and smart grid.
Dr. Behzad Asaei received his B.Sc and M.Sc degrees from the University of Tehran in 1988 and 1990 respectively, and his Ph.D. degree from the Sydney University in 1995, all in electrical engineering. Since 2006, he has been at University of Tehran, school of Electrical and Computer Engineering, where he is the director of the Energy and Automotive Technology Laboratory . His research is focused in the field of automotive electronics, solar energy, electric and hybrid elecrtic vehicles, power electronics, and motor drives in which several projects including two electric vehicles, hybrid electric locomotive, solar car, hybrid motorcycle, electric bike, and automotive electronics are completed.
Navid Haghdadi was born in Mashhad, Iran in 1986. He received the B.S. degree in electrical engineering from the University of Tehran in 2009, where he is currently pursuing the M.Sc. degree. His research interests include the distributed generation planning and operation, photovoltaic energy and smart grid.
