estimation of power supply cost reduction by introduction of demand ...
ESTIMATION OF POWER SUPPLY COST REDUCTION BY INTRODUCTION OF DEMAND RESPONSE PROGRAM IN JAPAN
Masatoshi TAKAYAMA, Masahito TAKAHASHI, Nobuyuki YAMAGUCHI, and Rikiya KATO Central Research Institute of Electric Power Industry Socio-economic Research Center 1-6-1, Ohtemachi, Chiyoda-ku, Tokyo 100-8126, Japan E-mail: [email protected] Phone: +81 3-3201-6601
Abstract: Demand Response (DR) is a tariff or program established to motivate changes in electricity use by end-use customers, in response to changes in the price of electricity over time or incentive payments designed to make electricity use lower at times of high market prices or when grid reliability is jeopardized. Since demand grows very slowly in Japan, the country is not expected to experience stringent power supply and demand. However, there is still a need to improve the annual load factor. Reducing fossil fuel expenses is necessary as well. Although the potential of DR in Japan has been studied from the customer side through pilot projects using advanced metering infrastructures (AMI) and customer surveys, the cost-benefit analysis of DR introduction has not yet been performed. Using Central Research Institute of Electric Power Industry (CRIEPI)’s long-term power generation system expansion model, we estimated the impact of a peak-cutting DR program on the power generation cost of the Japanese power system until 2040, as part of the cost-benefit analysis. We assumed that the peak-cutting DR program was established as follows. The DR program period was set for three hours between 13h and 16h on the three highest demand days in the summer. During the program period, it is assumed that we can reduce the demand in LT(peak)× (1–α/100) by the DR program, where LT (peak) is the peak demand in fiscal year (FY) T and α (%) is the peak cutting rate. According to the results, the averaged unit generation cost during the estimation period was between FY2010 and FY2040; this is the total generation cost divided by the total generated energy during the period. The averaged unit generation cost decreases as the peak cutting rate of DR increases. When the peak demand is cut by 1.0%, the averaged annual road factor increases by 0.6% and the averaged unit generation cost decreases by 0.19% compared to the reference case without DR. The avoided cost of the DR program, or the power generation cost saved by reducing 1kW of peak demand, is about 8500 Yen/kW on average. This avoided cost is approximately equal to the annualized unit construction cost of an LNG power plant.
1. Introduction In the U.S., electricity demand is expected to grow steadily in the future owing to further progress of information technology (IT) and an increasing population, although electricity demand has recently dropped temporarily because of the Lehman shock. The construction of new power plants is necessary to respond to this growth; however, this also raises issues of enormous new construction costs and environmental impacts. Therefore, state regulators intend to expand Demand Response (DR) programs to reduce or shift peak demand in the U.S. According to the Department of Energy (DOE), DR is a tariff or program established to motivate changes in electric use by end-use customers in response to changes in the price of electricity over time or incentive payments designed to make electricity use lower at times of high market prices or when grid reliability is jeopardized [DOE, 2006]. Japan, on the other hand, undergoes very slow demand growths; therefore, the country is not
1
expected to experience stringent power supply and demand like the U.S. However, there is still a need to improve the annual load factor. Reducing fossil fuel expenses is necessary as well. In addition, high penetration of photovoltaic power generation (PV) and electric vehicles (EV) might change electricity usage patterns substantially in the future, and demand-side management (DSM) might be a useful tool to match the supply and demand of electricity if such a change occurs. Although the potential of DR in Japan has been studied from the customer side through pilot projects, including smart communities using advanced metering infrastructures (AMI) and customer surveys, the cost-benefit analysis of DR introduction including the benefit on the power supply side has not yet been performed. The purpose of this paper is to estimate the impact of a DR program on the power supply cost of the Japanese power system. The remainder of this paper is organized as follows. Section 2 provides a brief overview of the recent developments surrounding DR and approach for the cost-benefit analysis of DR used in the U.S. Section 3 describes the peak-cutting DR program of our assumption. Section 4 elucidates the estimation method and illustrates the results, and Section 5 presents the conclusion and future issues for research.
2. Recent DR Developments in the U.S. and the Cost-Benefit Analysis Approach 2.1. Expectations of the DR Program in the U.S. According to the Energy Independence and Security Act 2007 [EISA, 2007], DR has been promoted in the U.S. through the following three steps. Step 1: The Federal Energy Regulatory Commission (FERC) conducts a National Assessment of DR within 18 months. Step 2: The FERC further develops a National Action Plan on DR within 1 year after the completion of the National Assessment. Step 3: The FERC and DOE submit a proposal to Congress to implement the Action Plan. According to the National Assessment of DR that was reported [FERC, 2009] in Step 1, DR potential in five- and ten-year periods differs according to the availability of dynamic pricing and AMI, the use of enabling technologies, and the varying responses of different customer classes. The report also describes the assumed DR potential for four scenarios. For the scenario with the highest DR penetration, the peak load could be reduced by as much as 150GW by 2019 compared to the base scenario in which it is assumed that existing and currently planned DR programs continue without change over the next ten years. The reduction would be equivalent to the power output of about 2,000 typical peaking power plants.
2.2. Estimated Costs and Benefits of the DR Program On the basis of the expectations for the DR program, the number of power utilities that encourage the expansion of AMI for DR introduction is growing in the U.S. Southern California Edison (SCE), which is a power utility in California, anticipated and reported the estimation of the costs and benefits of AMI introduction to judge the availability [SCE, 2007]. It compared the estimated costs and benefits that would be incurred by AMI introduction between 2007 and 2032, as shown in Figure 2.1. The cost and benefit items of AMI in the SCE report are shown in Table 2.1. DR is one of the benefit items of AMI, because (1) avoiding the energy procurement costs and construction costs of the power plant and (2) avoiding transmission and distribution costs are possible with AMI introduction and the utilization of direct load control and time of use (TOU). The reduced energy procurement costs and construction costs of the power plant are quantitatively estimated through a comparison with the construction and operation costs of combustion turbines (CT).
2
Table 2.1: Cost-Benefit Items of AMI by SCE B/C
Source: [SCE, 2007] Figure 2.1: Estimated Costs and Benefits of AMI by SCE
Items -Operational Benefits Benefits -DR and Energy Conservation Benefits -Pre-deployment Cost -Acquisition and Installation of Meters and Communication Network Equipment -Customer Tariffs, Programs, and Costs Services -Implementation and Operation of New Back Office System -Contingency Source: [SCE, 2007]
2.3. The Cost-benefit Analysis Approach in the U.S. The California Public Utilities Commission (CPUC) and the California Energy Commission (CEC) established the California Standard Practice Manual in 2002 [CPUC, 2002] in order to analyze the economics of demand-side programs (DSP) and projects. According to the report, four tests are needed to analyze the economics of DSP and projects, namely the Participant Test, Ratepayer Impact Measure Test, Total Resource Cost Test, and Program Administrator Test, which are described in Table 2.2. Table 2.2 Tests for the Economic Valuation of Demand-Side Programs Test Definition Participant Test This test is a measure of the quantifiable benefits and costs to the customer from participation in a program. Ratepayer Impact This test measures the effect on customer bills or rates of changes in Measure Test utility revenues and operating costs caused by the program. Total Resource Cost This test measures the net costs of a DSM program as a resource option Test on the basis of the total costs of the program, including both the participants’ and utility’s costs. Program This test measures the net costs of a DSM program as a resource option Administrator Test on the basis of the costs incurred by the program administrator (including incentive costs) excluding any net costs incurred by the participants. Source: [CPUC, 2002] These tests are based on the idea that the economic analysis of the program should be performed from the viewpoints of a variety of stakeholders such as the program participants, customers including nonparticipants, the power utility, and society. In particular, considering that the Total Resource Cost Test is necessary from the viewpoint of society, it should show that the net present value (NPVTRC) of the program is positive if it is economically justified, where the NPVTRC is a discounted value of the net benefits, as shown in Equation 2.1. NPVTRC = BTRC – CTRC1 – CTRC2 Where, BTRC CTRC1 CTRC2
(2.1)
: Benefits of total resource cost test : Utility costs of total resource cost test : Customer costs of total resource cost test
3
Figure 2.2 shows the BTRC, CTRC1, and CTRC2 plotted on the framework of the DR cost-benefit analysis, where the framework was described in the reference [Asano, 1985] in the context of load management. The nodes in the figure represent stakeholders such as the participants, nonparticipants, the power utility, and society. The arrows between the nodes represent the costs and benefits for stakeholders. An arrow pointing to a node indicates the benefit of the node for stakeholders. An arrow coming from a node indicates the cost of the node for stakeholders. In the total resource cost test, only the program costs of the utility and customer are taken into consideration, and other cost and benefit items such as saved electric bill payments and incentives are not considered. Although it is necessary to calculate the BTRC, CTRC1, and CTRC2 in Equation 2.1 to judge the availability of DR, this study focuses on only the benefit (BTRC) and analyzes the reduction in power generation cost resulting from DR introduction, not including the transmission and distribution costs. Introduction of DR Program
Reduction of Power Supply Cost
Program Cost for the Power Utility
BTRC
Program Cost for Participants
CTRC1
Society
Saving Electric Bill Payment
Power Utility
CTRC2 Customers
Incentive
Participants Nonparticipants
: Stakeholders
Source: [Asano, 1985] Figure 2.2: Framework of DR Cost-Benefit Analysis
3. Assumed Peak-Cutting DR Program Peak-cutting DR programs, which are the most widely used type in the U.S., might not be entirely appropriate for Japan’s present needs in load management. However, we assumed a peak-cutting DR program as an example before searching for other applicable DR programs. The description of the assumed DR program is as follows.
3.1. DR Program Period The DR program period was set for three hours between 13h and 16h on the three highest demand days in the summer. This implies that the peak-time DR period consists of nine hours per year (3 hours × 3 days). During the peak-time period, it is assumed that we can reduce the demand in LT (peak) × (1–α/100) through the DR program, where LT (peak) is the peak demand in fiscal year (FY) T and α (%) is the peak cutting rate. Figure 3.1 shows a sample of peak demand cut by assumed DR in FY2040. The figure indicates that the demand in the DR case (α = 1.5%) is smaller than that in the non-DR during the peak-time period (13h–16h). Although it is difficult to cut the peak demand of only the three days owing to the uncertainty of demand forecasting, we assumed that the forecasting had 100% accuracy for the simplification.
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3.2. Peak Cutting Rate of the DR Program The planning period for the cost-benefit analysis through Central Research Institute of Electric Power Industry (CRIEPI)’s long-term power generation system expansion model (OPTIGEN) described in the following section is set from FY2000 to FY2040. We assumed that we could reduce the peak demand by a peak cutting rate of α% during the program introduction period (FY2013–FY2040). Table 3.1 shows seven DR cases that differ in peak cutting rates between 0% and 1.5% in 0.25 increments, where α = 0% is the non-DR case, that is, the reference case without DR. The reason we fixed 1.5% for the upper limit of the peak cutting rate is as follows. Figure 3.2 shows the relation between maximum demand and peak cutting rates between 0% and 2.5% in 0.25 increments. Because the DR program was not introduced between FY2000 and FY2010, the maximum demands in the seven cases are constant, independent of the peak cutting rate. Meanwhile, after FY2015, the maximum demand decreases as the peak cutting rate of DR increases from 0% to 1.5%. However, in the case of an over-1.5% peak cutting rate, the maximum demand decreases a little as the peak cutting rate increases. In particular, the maximum demand of the α = 2.5% case is not very different from that of the α = 2.0% case. This is because a secondary peak appears around 17 o’clock outside of the assumed peak-time period (13h–16h) as shown in Figure 3.3. Because the appearance of the secondary peak depends on the assumptions on the hourly load curve and peak-time period (13h–16h), this result doesn’t imply that the peak-cutting DR program is generally limited. The maximum demand does not decrease enough owing to the appearance of the secondary peak. Therefore, the capacity of the power plants does not change very much. As a result, the effect of the DR program is smaller owing to the appearance of the secondary peak. Therefore, we configured the range of the peak cutting rate between 0% and 1.5%, as shown in Table 3.1. According to the DR program mail-in survey [Yamaguchi, 2011], in which the DR targets are commercial and industrial customers, the reduction potential of maximum demand is about 4.7% of the total demand of commercial and industrial customers, although this number is the result of a sample survey targeting only the Kanto area. This number implies that the rate of peak cutting potential is 2.35% (= 4.7%/2) if the number can be extended and interpreted on a nation-wide scale to include residential customers. The upper limit (1.5%) is lower than this potential rate (2.35%) and could be achieved by implementing the DR program in the electricity market.
3.3. Assumed Targets of the DR Program We made the following two assumptions: The DR program is offered to commercial and industrial customers, excluding residential customers. These customers can reduce electricity demand in the peak-time period by changing the operation of the production process, air conditioning, and so on [Yamaguchi, 2011]. Although these assumptions do not affect the benefits of DR programs analyzed in this study, these assumptions are needed for estimating the program costs of the DR program. In addition, the assumptions about types of DR programs (price based, incentive based, etc.) and notification methods might also be necessary to estimate the program costs.
Figure 3.2: Reduced Maximum Demand from DR Introduction
Figure 3.1: Peak Demand Cut by Assumed DR in FY2040
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Figure 3.3: Secondary Peak Incurred by DR Introduction in FY2040
Table 3.1: Assumed Seven Cases of DR with Different Peak Cutting Rates
1
Peak Cutting Rate α (%) 0.0
2
0.25
0
0
0
0.25
0.25
0.25
0.25
0.25
0.25
3
0.50
0
0
0
0.50
0.50
0.50
0.50
0.50
0.50
4
0.75
0
0
0
0.75
0.75
0.75
0.75
0.75
0.75
5
1.00
0
0
0
1.00
1.00
1.00
1.00
1.00
1.00
6
1.25
0
0
0
1.25
1.25
1.25
1.25
1.25
1.25
7
1.50
0
0
0
1.50
1.50
1.50
1.50
1.50
1.50
No.
2000
2005
2010
2015
2020
2025
2030
2035
2040
0
0
0
0
0
0
0
0
0
4. Estimation of Generation Cost Using OPTIGEN This section describes the estimation method and presents the results. Sections 4.1 and 4.2 describe the outline and major data of OPTIGEN, respectively. Sections 4.3 and 4.4 present the results of the analysis.
4.1. Outline of OPTIGEN The input data and model output of OPTIGEN are shown in Figure 4.1. This model minimizes the long-term power generation cost under given external conditions on electricity demand, fuel price, power capacity constraints, and carbon emission constraints on the basis of a linear programming technique, and finally derives the future power generation mix of Japan [Takahashi, 1996], where the long-term power generation cost is composed of construction, operation and management, and fuel costs. The objective function is the present value of the total generation cost during the planning period (T0–TN) as shown in Equation 4.1, where RRT is the present value factor calculated in Equation 4.2.1 We considered six kinds of power generation technologies as optimized targets in the model: nuclear power (NU), coal-fired thermal power (COAL), conventional LNG-fired thermal power (LNG), an LNG combined cycle (LNGCC), oil-fired thermal power (OIL), and pumping storage hydropower (PUMP). Meanwhile, general hydropower and geothermal power are excluded from the optimized targets, and these power outputs are given externally. Aged nuclear and oil power plants, after being in operation for 40 years, are made to retire, although the life extensions of these plants are possible as optimized targets. 1
Because several results in a representative FY are treated as averaged values of three or five years, the present value factor in a representative FY is expressed as the total value of three or five years.
6
Figure 4.1: Input Data and Model Output of OPTIGEN
【Objective Function】 TN
TC RRT (CFT,G CVT,G ) T 0 T
RR T (1 R ) 5T
Where, T RRT CFT,G CVT,G R
(4.1)
G
1 (1 R ) 1 (1 R ) 2 (T T 0 ) (1 R ) 2 (1 R ) 1 (1 R ) 1 (1 R ) 2 (T 1 T TN - 1） 1 (1 R ) (1 R ) 2 （ T TN)
(4.2)
: Representative year between T0 and TN : Present value factor in the year : Fixed cost of generator G in the year : Variable cost of generator G in the year : Discount rate (3%)
In this study, we set nine representative fiscal years between FY2000 and FY2040 in five-year increments so that the representative years are FY2000, FY2005, FY2010, FY2015, FY2020, FY2025, FY2030, FY2035, and FY 2040. We divided the 365 days in a fiscal year into eight representative days, that is, the three highest demand days, seven secondary highest demand days, and weekdays/weekends for three seasons (summer, winter, and spring and autumn), where summer, winter, and spring and autumn are defined as June–September, December–March, and the remaining months, respectively. Each representative day has an hourly electricity load curve reflecting seasonal demand characteristics. The major constraint conditions are as follows. This study does not impose a carbon emission constraint on the generation mix.
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a. Upper and Lower Limits of the Capacity of Power Plants The upper and lower limits of the capacity of power plants are set on the basis of a power supply plan and past trends, as shown in Equation 4.3. KLWRT ,G K T ,G KUPRT ,G
(4.3)
CAPLWRT ,G CAPT ,G CAPUPRT ,G
Where, KLWRT,G KUPRT,G CAPLWRT,G CAPUPRT,G
：Lower limit of capacity of new power plant G in the year ：Upper limit of capacity of new power plant G in the year ：Lower limit of capacity of total power plant G in the year ：Upper limit of capacity of total power plant G in the year
b. Balancing Power Supply and Demand Hourly generated energy should be equal to the hourly demand, as shown in Equation 4.4.
X
T , DAY , HR ,G
S T , DAY , HR HYDT , DAY , HR OTHRS T , DAY , HR LT , DAY , HR
(4.4)
G
Where, XT,DAY,HR,G
: Generated energy of power plants G at the time HR of representative day DAY in the year (sending end) : Pumped-up Energy (sending end) : Generated energy of general hydropower plants : Generated energy of the other power plants : Demand at the time HR of representative day DAY in the year (sending end）
ST,DAY,HR HYDT,DAY,HR OTHRST,DAY,HR LT,DAY,HR
c. Generation Reserve Margin The total capacity of power plants should be more than the maximum demand plus the generation reserve margin, as shown in Equation 4.5.
FACUPR
G , DAY
(1 PLTUSE
G
) CAP T, G ＋ HYD
T, DAY, HR
＋ O THRS T , DAY , HR （1 DELTA ） L T , DAY , HR (4.5)
G
Where, FACUPRG,DAY PLTUSEG DELTA CAPT,G
: Upper limit of the capacity factor of power plants G in representative day DAY : Internal consumption rate of power plants G : Generation reserve margin (10%) : Capacity of power plant G in the year
d. Operation of Pumped Storage Hydropower Generation Equation 4.6 is the definition of the pumping efficiency of pumped storage hydropower (PMEF).
X
T , DAY , HR ," PUMP "
PMEF (1 PLTUSE
" PUMP"
2 ） S T , DAY , HR
(4.6)
HR
HR
e. New Construction and Retirement of Power Plants Aged power plants are retired after a specific number of years, and new power plants are constructed to meet the demand, as shown in Equation 4.7. (4.7)
CAPT ,G CAPT 1,G K T ,G K T TLG .G
Where, CAPT,G, CAPT-1,G KT,G KT-TLG,G
: Capacity of power plants G in the year : Capacity of new power plants G in the year : Capacity of retired power plants G in the year
8
f. Constraints on Load Following Capability The load following capability of power generators is considered, as shown in Equation 4.8. (1 FLWLWR
Where, FLWLWRG FLWUPRG
G
) X T , DAY
, HR , G
X T , DAY
, HR 1 , G
(1 FLWUPR
G
) X T , DAY
, HR , G
(4.8)
：Lower limit of load following capability rate ：Upper limit of load following capability rate
g. Constraints on Capacity Factor of Power Plants The generated energy of power plant G in a representative day DAY is limited under the workable capacity of the power plant as shown in Equation 4.9. This constraint simulates the situation in which power plants cannot work continuously throughout the year owing to periodic inspections. X T , DAY , HR ,G FACUPR
G , DAY
(1 PLTUSE
G
) CAP T ,G
(4.9)
4.2. Major Input Data for OPTIGEN 4.2.1 Load Data The estimated hourly load curves by representative days in FY2000 are shown in Figure 4.2. We calculated the hourly load curves for each representative FY, which correspond to the forecasted maximum demand (kW) and energy (kWh) in each representative FY, on the basis of FY2000 data. The forecasted maximum demand and energy data are constructed by the authors according to the Long-term Prospect of Supply and Demand of Energy [METI, 2008] and the Outline of Power Supply Plan [METI, 2010] reported by the Ministry of Economy, Trade, and Industry in Japan. Figure 4.3 and Table 4.1 show the forecasted maximum demand and energy together. The assumption is that the annual load factor is improved by FY2040 because the energy growth is higher than the maximum demand growth, as shown in Table 4.1.
Note: D1 and D2 are the averaged demand of the three highest days and seven secondary
highest days, respectively. D3, D4, and D5 are the averaged demand of weekdays in summer, winter, and spring and autumn, respectively. D6, D7, and D8 are the averaged demand of holidays in summer, winter, and spring and autumn, respectively. Figure 4.2: Estimated Hourly Load Curve in FY2000
9
Figure 4.3: Maximum Demand and Energy Forecast until FY2040
Table 4.1 Maximum Demand and Energy Forecast until FY2040 FY
Annual growth (%)
Energy (GWh)
Maximum Demand (MW)
Annual Load Factor (%)
2000
877,900
168,540
59.5
2010
916,800
168,220
62.2
2020
1,029,300
182,600
64.4
2030
1,076,100
184,740
66.5
2040
1,125,100
186,910
68.7
2000–2010
0.43%
–0.02%
－
2010–2020
1.17%
0.82%
－
2020–2030
0.45%
0.12%
－
2030–2040
0.45%
0.12%
－
Source: [METI, 2008], [METI, 2010]
4.2.2 Cost Data The unit construction costs of existing and new power plants are as shown in Table 4.2. The unit construction costs of existing power plants before FY2000 are the averaged experienced costs, while that of new power plants after FY2000 are quoted from the Outline of Power Supply Plan in FY2005 [METI, 2005(1)]. We set the unit construction costs of existing power plants to calculate the total power generation cost with accuracy during the estimation period defined in Section 4.4, although the unit construction costs do not affect the power supply planning. Table 4.3 shows the unit fossil fuel costs during the planning period. We applied actual fuel purchase costs averaged over nine power companies to the unit fossil fuel cost between FY2000 and FY2005. Meanwhile, the cost after FY2010 was estimated using data from World Energy Outlook [IEA, 2010]. Figure 4.4 illustrates the changes in unit fossil fuel cost during the planning period.
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Table 4.2: Unit Construction Cost during the Planning Period Unit Construction Cost (1,000 Yen/kW) Power Plant Before FY2000 After FY2000 （Existing Power Plant） （New Power Plant） NU 377 279 COAL 304 272 LNG 214 164 LNGCC 232 164 OIL 206 269 PUMP 196 196 Source: Unit construction costs of existing power plants before FY2000 are the averaged experienced costs. The unit construction costs of new power plants after FY 2000 are quoted from the Outline of Electric Power Development in FY2005 [METI, 2005(1)].
Table 4.3: Unit Fossil Fuel Cost during the Planning Period FY
COAL
LNG
(Yen/kg) （Yen/kg)
CRUDE
HEAVY
(Yen /l)
(Yen/l)
2000
4.94
28.91
25.99
25.59
2005
7.94
38.15
46.65
44.93
2010
10.77
54.80
48.81
46.80
2015
10.81
68.10
68.31
65.50
2020
11.25
74.80
74.80
83.94
2025
11.52
79.27
79.34
89.03
2030
11.68
83.17
83.12
93.27
2035
11.78
85.41
85.38
95.81
2040
11.78
85.41
85.38
95.81
Source: We applied the actual purchase cost averaged
over nine power companies to the unit fossil fuel cost between FY2000 and FY2005. The cost after FY2010 was the cost for FY2005 multiplied by the growth rate forecasted by World Energy Outlook [IEA, 2010].
Figure 4.4: Unit Fossil Fuel Cost during the Planning Period
The data for calculating the generation cost are shown in Table 4.4. We applied three annual expense rates (AER1, AER2, and AER3) to calculate the generation cost, where AER1 is the annual expense rate for averaged capital charges during the depreciation period, AER2 is the annual expense rate for residual values after the depreciation period, and AER3 is the annual expense rate for averaged capital charges during the plant’s years in operation. First, we applied AER3 to derive the generation mix using OPTIGEN. Second, we applied AER1 and AER2 to recalculate the annual expense in the representative year, where AER1 and AER2 are applied during the depreciation period and number of years in operation excluding the depreciation period, respectively.
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Table 4.4: Data for Calculating Generation Cost Number of Years in Operation (years)
Power Plant
Depreciation Period (years)
AER1 (%)
AER2 (%)
AER3 (%)
NU
40
15
8.65
0.59
5.32
COAL
40
15
8.95
0.69
5.31
LNG
40
15
8.92
0.73
5.31
LNGCC
40
15
8.92
0.73
5.31
OIL
40
15
8.92
0.73
5.31
PUMP
50
40
5.60
0.59
4.95
4.2.3 Generation Capability and Operating Restriction Data Figure 4.5 shows the higher heating value quoted from the Standard Heating Values of Each Energy Source [METI, 2005(2)]. Figure 4.6 shows the gross thermal efficiency and internal consumption rate, and Figure 4.7 shows the upper limit of the capacity factor of power plants. Each of these data has been derived by the authors on the basis of the Outline of Power Supply and Demand in 2000 [METI, 2000] reported by the Ministry of Economy, Trade and Industry in Japan. Table 4.6: Gross Thermal Efficiency and Internal Consumption Rate
Table 4.5: Higher Heating Value
Gross Thermal Efficiency (%)
Internal Consumption Rate (%)
Fuel
@
Higher Heating Value (kcal/@)
NU
kWh
860
COAL
kg
6139
NU
100
4.5
LNG
kg
13043
COAL
40.1
6.5
CRUDE
l
9412
LNG
38.2
4.5
HEAVY
l
9842
LNGCC
43.8
2.9
OIL
37.5
6.3
PUMP
100
0.5
Source: [METI, 2005(2)]
Power Plant
Source: [METI, 2000]
Power Plant
Table 4.7: Upper Limit of the Capacity Factor of Power Plants Spring and Three Highest Days Summer Winter and Seven Secondary Autumn Weekdays/Holidays Weekdays/Holidays Highest Days Weekdays/Holidays
NU
86.0 (%)
83.0 (%)
85.0 (%)
85.0 (%)
COAL
96.0 (%)
91.0 (%)
86.0 (%)
76.0 (%)
LNG
94.0 (%)
90.0 (%)
81.0 (%)
79.0 (%)
LNGCC
91.0 (%)
90.0 (%)
91.0 (%)
85.0 (%)
OIL
90.0 (%)
85.0 (%)
82.0 (%)
80.0 (%)
PUMP
96.0 (%)
96.0 (%)
93.0 (%)
93.0 (%)
Source: Generation and maintenance schedule in [METI, 2000]
12
4.3. Model Results in the Reference Case This section describes the simulation results2 of OPTIGEN in the reference case (the non-DR case: α = 0). Figures 4.5, 4.6, and 4.7 show the total capacity, total generated energy, and unit generation cost of power plants during the planning period, respectively. While the maximum demand grows very slowly until FY2040, as shown in Figure 4.3, the total capacity of the power plants decreases from FY2015 to FY2025, as shown in Figure 4.5, because aged and unused power plants such as oil-thermal power plants are retired.
Figure 4.6: Total Generated Energy of Power
Figure 4.5: Total Capacity of Power Plants during the Planning Period
Plants during the Planning Period
Figure 4.7: Unit Generation Cost during the Planning Period in the Non-DR Case
4.4 Estimation of Power Generation Reduction from the DR Program This section describes the estimation of power generation reduction from the DR program. Although the model analysis period is from FY2000 to FY2040, we redefined the 31 years between FY2010 and FY2040 as the estimation period, as shown in Figures 4.9 and 4.10, and analyzed the results during only this estimation period in order to exclude the non-DR period of FY2000–FY2010 from the model analysis period.
2
Because we do not impose a constraint on CO2 emissions as described in Section 4.1, the unit CO2 emissions per electricity demand grow rapidly owing to the growth of the ratio of COAL power plants to total power plants, as shown in Figure 4.6.
13
4.4.1 Change in Total and Unit Generation Costs from the DR Program Figure 4.8 shows the reduced maximum demand from DR introduction. The reduced maximum demand is almost the same after FY2020 among seven cases because the forecasted maximum demand is not very different. For example, in the α = 1.0% case, it is assumed that the maximum demand is reduced by 1,800 MW/year as a result of the DR program after FY2020, as shown in Figure 4.9.
Figure 4.9: Reduced Maximum Demand in the α = 1.0% Case
Figure 4.8: Reduced Maximum Demand from DR Introduction
Figure 4.10 shows the FY2010 present value of total generation cost during the estimation period (FY2010–FY2040) in seven cases that differ in peak cutting rates between 0% and 1.5% in 0.25 increments. According to the result, the total generation cost is 140.83 trillion yen in the non-DR case. The total generation cost decreases as the peak cutting rate of DR increases. When the peak demand is cut by 1.0%, the total generation cost is 140.56 trillion yen, which represents a decrease of 0.19% compared to the non-DR case. Figure 4.11 shows the increase and decrease of fixed and variable costs. While the fixed cost decreases, the variable cost modestly increases. The variable cost increases owing to increases in the utilization factor of pumped storage hydropower and fossil fuel generation. However, the fixed cost decreases owing to the reduced construction costs of LNG and COAL power plants, as shown in Figure 4.12. In the α = 1.0% case, the present value of the fixed cost decreases by 330 billion yen from the non-DR case, including the LNG and COAL power plants that reduce by 220 billion yen and 110 billion yen, respectively. Figure 4.13 shows the increase and decrease in the new capacity of LNG and COAL power plants. The figure indicates the following.
New capacity of the LNG power plant decreases as the peak cutting rate of DR increases, as shown in Figure 4.13(a) Reduced new capacity of the COAL power plant in FY2025 is offset by increases in new capacity in FY2030, as shown in Figure 4.13(b). The results indicate that the construction cost reduction of COAL power plants is due to deferment construction during the estimation period.
14
Figure 4.10: Present Value of Total Generation Cost during the Estimation Period
Figure 4.11: Increase and Decrease of Fixed and Variable Costs
Figure 4.12: Breakdown of Present Value of Fixed Cost
(b) COAL (a) LNG Figure 4.13: Increase and Decrease in New Capacity of LNG and COAL Power Plants
15
Figure 4.14 shows the unit generation costs during the estimation period. Although the unit generation cost in the DR case (α = 1.5%) is lower than that in the non-DR case (α = 0%) from FY2000 to FY2035, the relation reverses in FY2040. The results indicate that the same trend is not reflected in all representative FYs, because the objective function of OPTIGEN is the present value of total generation cost during the planning period, as shown in Equation 4.1. Therefore, instead of the annual unit generation cost, we applied the “averaged unit generation cost” as the index of cost impact from DR, as follows. The averaged unit generation cost P during the estimation period is calculated as the total generation cost divided by total generated energy, as shown in Equation 4.10, where RRT is the present value factor in FY T. The averaged annual load factor LF during the estimation period is calculated by Equation 4.11, where, DT is the number of years for representative FY. P (Yen / kWh ) TC /
TN
( RR
T T 2
LF (%)
TN
(LF
T T 2
T
PE T )
(4.10)
TN
T
DT ) / DT
(4.11)
T T 2
Figure 4.15 shows the averaged unit generation cost and averaged annual load factor calculated by Equations 4.10 and 4.11. The averaged unit generation cost and the averaged annul load factor are 6.661 Yen/kWh and 65.4%, respectively, in the non-DR case. The averaged unit generation cost decreases as the peak cutting rate of DR increases, in the same way as the total generation cost. When the peak demand is cut by 1.0%, the averaged unit generation cost is 6.648 Yen/kWh, which represents a decrease of 0.19% compared to the non-DR case, and the averaged annual load factor is 66.0%, which represents an increase of 0.6% compared to the non-DR case.
Figure 4.15: Averaged Unit Generation Cost and Averaged Annual Load Factor during the Estimation Period
Figure 4.14: Unit Generation Cost during the Estimation Period
4.4.2 Avoided Cost per kW from the DR Program This paragraph considers the generation cost reduction from the DR program on the basis of the calculated avoided cost. The avoided cost is defined as the avoidable generation cost from reducing 1kW of maximum demand incrementally. If the program costs for the DR introduction are not lower than the avoided cost, we cannot justify implementing the DR program. In other words, the avoided cost indicates the breakeven cost of the DR program, although the transmission and distribution costs are not included. Figure 4.16 shows the total maximum demand and total generation cost during the estimation period. The total generation cost is approximately proportionate to the total maximum demand. Equation 4.12 is a linear approximation obtained from Figure 4.16.
16
Total generation cost (Yen) ＝ 8500 × total maximum demand (kW) + 10930 × 106
(4.12)
Where, if the reduction of the total generation cost and that of the total maximum demand are defined as ΔTCα、and ΔPDα respectively, the avoided cost per kW is calculated by Equation 4.13. According to the result as shown in Figure 4.17, the avoided cost per kW is 8500 Yen/kW on an average. This value is equal to the slope of Equation 4.12. AvoidedCos t（ （ / PDα 0.25－PDα） ΔTCα /ΔPDα α Yen / kW ) (TCα 0.25－TCα）
(4.13)
Figure 4.17: Avoided Cost per kW from DR Introduction
Figure 4.16: Total Maximum Demand and Total Generation Cost during the Estimation Period
If the unit construction cost of LNG and annual expense rate of averaged capital charge during the years in operation are UCLNG (Yen/kW) and AER3, respectively, the annualized unit construction cost is calculated to be about 8704 Yen/kW, as shown in Equation 4.14. The avoided cost is approximately equal to the annualized unit construction cost of the LNG power plant. Annualized Unit Construction Cost (Yen/kW) = UCLNG × AER3 = 164000 × 0.0531 = 8704 ≒ Avoided Cost (Yen/kW)
(4.14)
5. Conclusions We estimated the impact of a peak-cutting DR program on the power generation cost of the Japanese power system as part of the cost-benefit analysis. We assumed the peak-cutting DR program as follows. The DR program period was set for three hours between 13H and 16H on the three highest demand days in the summer. During the program period, it is assumed that we can reduce the demand in LT(peak) × (1–α/100) by the DR program. Using CRIEPI’s power generation mix model for Japan—OPTIGEN—we estimated the power generation cost reduction during the estimation period (FY2010–FY2040) from the DR program that is introduced after FY2013. According to our result, the averaged unit generation cost decreases as the peak cutting rate of DR increases. When the peak demand is cut by 1.0%, the annual load factor increases by 0.6% and the unit generation cost decreases by 0.19% compared to the non-DR case. The avoided cost of the DR program, which is the power generation cost saved by reducing 1kW of peak demand, is about 8500 Yen/kW on an average. This avoided cost is approximately equal to the annualized unit construction cost of the LNG power plant. In the future, we should estimate not only the impact of the DR program on the generation cost, but also on the transmission, distribution, and program costs to analyze the social cost benefit. We would also like to study about the possible applications of the DR program other than peak-cutting DR programs.
17
References [Asano, 1985] Load Management and the Cost Benefit Analysis, Power Economic Research No. 19, 1985. (in Japanese only) [CPUC, 2002] California Standard Practice Manual: Economic Analysis of Demand-Side Programs and Projects, California Public Utilities Commission and California Energy Commission, Jul., 2002. [DOE, 2006] Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them, Department of Energy, 2006. [EISA, 2007] Energy Independence and Security Act, TITLE V Sec. 529, Dec., 2007. [FERC, 2009] A National Assessment of Demand Response Potential, Federal Energy Regulatory Commission, Jun., 2009. [IEA, 2010] World Energy Outlook, International Energy Agency, 2010. [METI, 2000] Outline of Power Supply and Demand in 2000, Ministry of Economy, Trade and Industry, 2000. (in Japanese only) [METI, 2005(1)] Outline of Electric Power Development in 2005, Ministry of Economy, Trade and Industry, 2005. (in Japanese only) [METI, 2005(2)] Standard Heating Values of Each Energy Source, Ministry of Economy, Trade and Industry, 2005. (in Japanese only) [METI, 2008] Long-term Prospects of Supply and Demand of Energy, Ministry of Economy, Trade and Industry, 2008. (in Japanese only) [METI, 2010] Outline of Power Supply Plan, Ministry of Economy, Trade and Industry, 2010. (in Japanese only) [SCE, 2007] Edison Smartconnect Deployment Funding and Cost Recovery, Errata to Exhibit 3: Financial Assessment and Cost Benefit Analysis, Southern California Edison, Dec., 2007. [Takahashi, 1996] Analysis for Optimal Power Development Plan Based on CO2 Emission Limitation, Takahashi, Nagata, and Uchiyama, 15th Workshop of Japanese Society of Energy and Resources, 1996. (in Japanese only) [Yamaguchi, 2011] Demand Reduction Potential of Demand Response in Commercial and Industrial Sectors (Questionnaire Survey in the Kanto Area), CRIEPI Report Y10020, Yamaguchi and Takayama, 2011. (in Japanese only)
18
Masatoshi TAKAYAMA, Masahito TAKAHASHI, Nobuyuki YAMAGUCHI, and Rikiya KATO Central Research Institute of Electric Power Industry Socio-economic Research Center 1-6-1, Ohtemachi, Chiyoda-ku, Tokyo 100-8126, Japan E-mail: [email protected] Phone: +81 3-3201-6601
Abstract: Demand Response (DR) is a tariff or program established to motivate changes in electricity use by end-use customers, in response to changes in the price of electricity over time or incentive payments designed to make electricity use lower at times of high market prices or when grid reliability is jeopardized. Since demand grows very slowly in Japan, the country is not expected to experience stringent power supply and demand. However, there is still a need to improve the annual load factor. Reducing fossil fuel expenses is necessary as well. Although the potential of DR in Japan has been studied from the customer side through pilot projects using advanced metering infrastructures (AMI) and customer surveys, the cost-benefit analysis of DR introduction has not yet been performed. Using Central Research Institute of Electric Power Industry (CRIEPI)’s long-term power generation system expansion model, we estimated the impact of a peak-cutting DR program on the power generation cost of the Japanese power system until 2040, as part of the cost-benefit analysis. We assumed that the peak-cutting DR program was established as follows. The DR program period was set for three hours between 13h and 16h on the three highest demand days in the summer. During the program period, it is assumed that we can reduce the demand in LT(peak)× (1–α/100) by the DR program, where LT (peak) is the peak demand in fiscal year (FY) T and α (%) is the peak cutting rate. According to the results, the averaged unit generation cost during the estimation period was between FY2010 and FY2040; this is the total generation cost divided by the total generated energy during the period. The averaged unit generation cost decreases as the peak cutting rate of DR increases. When the peak demand is cut by 1.0%, the averaged annual road factor increases by 0.6% and the averaged unit generation cost decreases by 0.19% compared to the reference case without DR. The avoided cost of the DR program, or the power generation cost saved by reducing 1kW of peak demand, is about 8500 Yen/kW on average. This avoided cost is approximately equal to the annualized unit construction cost of an LNG power plant.
1. Introduction In the U.S., electricity demand is expected to grow steadily in the future owing to further progress of information technology (IT) and an increasing population, although electricity demand has recently dropped temporarily because of the Lehman shock. The construction of new power plants is necessary to respond to this growth; however, this also raises issues of enormous new construction costs and environmental impacts. Therefore, state regulators intend to expand Demand Response (DR) programs to reduce or shift peak demand in the U.S. According to the Department of Energy (DOE), DR is a tariff or program established to motivate changes in electric use by end-use customers in response to changes in the price of electricity over time or incentive payments designed to make electricity use lower at times of high market prices or when grid reliability is jeopardized [DOE, 2006]. Japan, on the other hand, undergoes very slow demand growths; therefore, the country is not
1
expected to experience stringent power supply and demand like the U.S. However, there is still a need to improve the annual load factor. Reducing fossil fuel expenses is necessary as well. In addition, high penetration of photovoltaic power generation (PV) and electric vehicles (EV) might change electricity usage patterns substantially in the future, and demand-side management (DSM) might be a useful tool to match the supply and demand of electricity if such a change occurs. Although the potential of DR in Japan has been studied from the customer side through pilot projects, including smart communities using advanced metering infrastructures (AMI) and customer surveys, the cost-benefit analysis of DR introduction including the benefit on the power supply side has not yet been performed. The purpose of this paper is to estimate the impact of a DR program on the power supply cost of the Japanese power system. The remainder of this paper is organized as follows. Section 2 provides a brief overview of the recent developments surrounding DR and approach for the cost-benefit analysis of DR used in the U.S. Section 3 describes the peak-cutting DR program of our assumption. Section 4 elucidates the estimation method and illustrates the results, and Section 5 presents the conclusion and future issues for research.
2. Recent DR Developments in the U.S. and the Cost-Benefit Analysis Approach 2.1. Expectations of the DR Program in the U.S. According to the Energy Independence and Security Act 2007 [EISA, 2007], DR has been promoted in the U.S. through the following three steps. Step 1: The Federal Energy Regulatory Commission (FERC) conducts a National Assessment of DR within 18 months. Step 2: The FERC further develops a National Action Plan on DR within 1 year after the completion of the National Assessment. Step 3: The FERC and DOE submit a proposal to Congress to implement the Action Plan. According to the National Assessment of DR that was reported [FERC, 2009] in Step 1, DR potential in five- and ten-year periods differs according to the availability of dynamic pricing and AMI, the use of enabling technologies, and the varying responses of different customer classes. The report also describes the assumed DR potential for four scenarios. For the scenario with the highest DR penetration, the peak load could be reduced by as much as 150GW by 2019 compared to the base scenario in which it is assumed that existing and currently planned DR programs continue without change over the next ten years. The reduction would be equivalent to the power output of about 2,000 typical peaking power plants.
2.2. Estimated Costs and Benefits of the DR Program On the basis of the expectations for the DR program, the number of power utilities that encourage the expansion of AMI for DR introduction is growing in the U.S. Southern California Edison (SCE), which is a power utility in California, anticipated and reported the estimation of the costs and benefits of AMI introduction to judge the availability [SCE, 2007]. It compared the estimated costs and benefits that would be incurred by AMI introduction between 2007 and 2032, as shown in Figure 2.1. The cost and benefit items of AMI in the SCE report are shown in Table 2.1. DR is one of the benefit items of AMI, because (1) avoiding the energy procurement costs and construction costs of the power plant and (2) avoiding transmission and distribution costs are possible with AMI introduction and the utilization of direct load control and time of use (TOU). The reduced energy procurement costs and construction costs of the power plant are quantitatively estimated through a comparison with the construction and operation costs of combustion turbines (CT).
2
Table 2.1: Cost-Benefit Items of AMI by SCE B/C
Source: [SCE, 2007] Figure 2.1: Estimated Costs and Benefits of AMI by SCE
Items -Operational Benefits Benefits -DR and Energy Conservation Benefits -Pre-deployment Cost -Acquisition and Installation of Meters and Communication Network Equipment -Customer Tariffs, Programs, and Costs Services -Implementation and Operation of New Back Office System -Contingency Source: [SCE, 2007]
2.3. The Cost-benefit Analysis Approach in the U.S. The California Public Utilities Commission (CPUC) and the California Energy Commission (CEC) established the California Standard Practice Manual in 2002 [CPUC, 2002] in order to analyze the economics of demand-side programs (DSP) and projects. According to the report, four tests are needed to analyze the economics of DSP and projects, namely the Participant Test, Ratepayer Impact Measure Test, Total Resource Cost Test, and Program Administrator Test, which are described in Table 2.2. Table 2.2 Tests for the Economic Valuation of Demand-Side Programs Test Definition Participant Test This test is a measure of the quantifiable benefits and costs to the customer from participation in a program. Ratepayer Impact This test measures the effect on customer bills or rates of changes in Measure Test utility revenues and operating costs caused by the program. Total Resource Cost This test measures the net costs of a DSM program as a resource option Test on the basis of the total costs of the program, including both the participants’ and utility’s costs. Program This test measures the net costs of a DSM program as a resource option Administrator Test on the basis of the costs incurred by the program administrator (including incentive costs) excluding any net costs incurred by the participants. Source: [CPUC, 2002] These tests are based on the idea that the economic analysis of the program should be performed from the viewpoints of a variety of stakeholders such as the program participants, customers including nonparticipants, the power utility, and society. In particular, considering that the Total Resource Cost Test is necessary from the viewpoint of society, it should show that the net present value (NPVTRC) of the program is positive if it is economically justified, where the NPVTRC is a discounted value of the net benefits, as shown in Equation 2.1. NPVTRC = BTRC – CTRC1 – CTRC2 Where, BTRC CTRC1 CTRC2
(2.1)
: Benefits of total resource cost test : Utility costs of total resource cost test : Customer costs of total resource cost test
3
Figure 2.2 shows the BTRC, CTRC1, and CTRC2 plotted on the framework of the DR cost-benefit analysis, where the framework was described in the reference [Asano, 1985] in the context of load management. The nodes in the figure represent stakeholders such as the participants, nonparticipants, the power utility, and society. The arrows between the nodes represent the costs and benefits for stakeholders. An arrow pointing to a node indicates the benefit of the node for stakeholders. An arrow coming from a node indicates the cost of the node for stakeholders. In the total resource cost test, only the program costs of the utility and customer are taken into consideration, and other cost and benefit items such as saved electric bill payments and incentives are not considered. Although it is necessary to calculate the BTRC, CTRC1, and CTRC2 in Equation 2.1 to judge the availability of DR, this study focuses on only the benefit (BTRC) and analyzes the reduction in power generation cost resulting from DR introduction, not including the transmission and distribution costs. Introduction of DR Program
Reduction of Power Supply Cost
Program Cost for the Power Utility
BTRC
Program Cost for Participants
CTRC1
Society
Saving Electric Bill Payment
Power Utility
CTRC2 Customers
Incentive
Participants Nonparticipants
: Stakeholders
Source: [Asano, 1985] Figure 2.2: Framework of DR Cost-Benefit Analysis
3. Assumed Peak-Cutting DR Program Peak-cutting DR programs, which are the most widely used type in the U.S., might not be entirely appropriate for Japan’s present needs in load management. However, we assumed a peak-cutting DR program as an example before searching for other applicable DR programs. The description of the assumed DR program is as follows.
3.1. DR Program Period The DR program period was set for three hours between 13h and 16h on the three highest demand days in the summer. This implies that the peak-time DR period consists of nine hours per year (3 hours × 3 days). During the peak-time period, it is assumed that we can reduce the demand in LT (peak) × (1–α/100) through the DR program, where LT (peak) is the peak demand in fiscal year (FY) T and α (%) is the peak cutting rate. Figure 3.1 shows a sample of peak demand cut by assumed DR in FY2040. The figure indicates that the demand in the DR case (α = 1.5%) is smaller than that in the non-DR during the peak-time period (13h–16h). Although it is difficult to cut the peak demand of only the three days owing to the uncertainty of demand forecasting, we assumed that the forecasting had 100% accuracy for the simplification.
4
3.2. Peak Cutting Rate of the DR Program The planning period for the cost-benefit analysis through Central Research Institute of Electric Power Industry (CRIEPI)’s long-term power generation system expansion model (OPTIGEN) described in the following section is set from FY2000 to FY2040. We assumed that we could reduce the peak demand by a peak cutting rate of α% during the program introduction period (FY2013–FY2040). Table 3.1 shows seven DR cases that differ in peak cutting rates between 0% and 1.5% in 0.25 increments, where α = 0% is the non-DR case, that is, the reference case without DR. The reason we fixed 1.5% for the upper limit of the peak cutting rate is as follows. Figure 3.2 shows the relation between maximum demand and peak cutting rates between 0% and 2.5% in 0.25 increments. Because the DR program was not introduced between FY2000 and FY2010, the maximum demands in the seven cases are constant, independent of the peak cutting rate. Meanwhile, after FY2015, the maximum demand decreases as the peak cutting rate of DR increases from 0% to 1.5%. However, in the case of an over-1.5% peak cutting rate, the maximum demand decreases a little as the peak cutting rate increases. In particular, the maximum demand of the α = 2.5% case is not very different from that of the α = 2.0% case. This is because a secondary peak appears around 17 o’clock outside of the assumed peak-time period (13h–16h) as shown in Figure 3.3. Because the appearance of the secondary peak depends on the assumptions on the hourly load curve and peak-time period (13h–16h), this result doesn’t imply that the peak-cutting DR program is generally limited. The maximum demand does not decrease enough owing to the appearance of the secondary peak. Therefore, the capacity of the power plants does not change very much. As a result, the effect of the DR program is smaller owing to the appearance of the secondary peak. Therefore, we configured the range of the peak cutting rate between 0% and 1.5%, as shown in Table 3.1. According to the DR program mail-in survey [Yamaguchi, 2011], in which the DR targets are commercial and industrial customers, the reduction potential of maximum demand is about 4.7% of the total demand of commercial and industrial customers, although this number is the result of a sample survey targeting only the Kanto area. This number implies that the rate of peak cutting potential is 2.35% (= 4.7%/2) if the number can be extended and interpreted on a nation-wide scale to include residential customers. The upper limit (1.5%) is lower than this potential rate (2.35%) and could be achieved by implementing the DR program in the electricity market.
3.3. Assumed Targets of the DR Program We made the following two assumptions: The DR program is offered to commercial and industrial customers, excluding residential customers. These customers can reduce electricity demand in the peak-time period by changing the operation of the production process, air conditioning, and so on [Yamaguchi, 2011]. Although these assumptions do not affect the benefits of DR programs analyzed in this study, these assumptions are needed for estimating the program costs of the DR program. In addition, the assumptions about types of DR programs (price based, incentive based, etc.) and notification methods might also be necessary to estimate the program costs.
Figure 3.2: Reduced Maximum Demand from DR Introduction
Figure 3.1: Peak Demand Cut by Assumed DR in FY2040
5
Figure 3.3: Secondary Peak Incurred by DR Introduction in FY2040
Table 3.1: Assumed Seven Cases of DR with Different Peak Cutting Rates
1
Peak Cutting Rate α (%) 0.0
2
0.25
0
0
0
0.25
0.25
0.25
0.25
0.25
0.25
3
0.50
0
0
0
0.50
0.50
0.50
0.50
0.50
0.50
4
0.75
0
0
0
0.75
0.75
0.75
0.75
0.75
0.75
5
1.00
0
0
0
1.00
1.00
1.00
1.00
1.00
1.00
6
1.25
0
0
0
1.25
1.25
1.25
1.25
1.25
1.25
7
1.50
0
0
0
1.50
1.50
1.50
1.50
1.50
1.50
No.
2000
2005
2010
2015
2020
2025
2030
2035
2040
0
0
0
0
0
0
0
0
0
4. Estimation of Generation Cost Using OPTIGEN This section describes the estimation method and presents the results. Sections 4.1 and 4.2 describe the outline and major data of OPTIGEN, respectively. Sections 4.3 and 4.4 present the results of the analysis.
4.1. Outline of OPTIGEN The input data and model output of OPTIGEN are shown in Figure 4.1. This model minimizes the long-term power generation cost under given external conditions on electricity demand, fuel price, power capacity constraints, and carbon emission constraints on the basis of a linear programming technique, and finally derives the future power generation mix of Japan [Takahashi, 1996], where the long-term power generation cost is composed of construction, operation and management, and fuel costs. The objective function is the present value of the total generation cost during the planning period (T0–TN) as shown in Equation 4.1, where RRT is the present value factor calculated in Equation 4.2.1 We considered six kinds of power generation technologies as optimized targets in the model: nuclear power (NU), coal-fired thermal power (COAL), conventional LNG-fired thermal power (LNG), an LNG combined cycle (LNGCC), oil-fired thermal power (OIL), and pumping storage hydropower (PUMP). Meanwhile, general hydropower and geothermal power are excluded from the optimized targets, and these power outputs are given externally. Aged nuclear and oil power plants, after being in operation for 40 years, are made to retire, although the life extensions of these plants are possible as optimized targets. 1
Because several results in a representative FY are treated as averaged values of three or five years, the present value factor in a representative FY is expressed as the total value of three or five years.
6
Figure 4.1: Input Data and Model Output of OPTIGEN
【Objective Function】 TN
TC RRT (CFT,G CVT,G ) T 0 T
RR T (1 R ) 5T
Where, T RRT CFT,G CVT,G R
(4.1)
G
1 (1 R ) 1 (1 R ) 2 (T T 0 ) (1 R ) 2 (1 R ) 1 (1 R ) 1 (1 R ) 2 (T 1 T TN - 1） 1 (1 R ) (1 R ) 2 （ T TN)
(4.2)
: Representative year between T0 and TN : Present value factor in the year : Fixed cost of generator G in the year : Variable cost of generator G in the year : Discount rate (3%)
In this study, we set nine representative fiscal years between FY2000 and FY2040 in five-year increments so that the representative years are FY2000, FY2005, FY2010, FY2015, FY2020, FY2025, FY2030, FY2035, and FY 2040. We divided the 365 days in a fiscal year into eight representative days, that is, the three highest demand days, seven secondary highest demand days, and weekdays/weekends for three seasons (summer, winter, and spring and autumn), where summer, winter, and spring and autumn are defined as June–September, December–March, and the remaining months, respectively. Each representative day has an hourly electricity load curve reflecting seasonal demand characteristics. The major constraint conditions are as follows. This study does not impose a carbon emission constraint on the generation mix.
7
a. Upper and Lower Limits of the Capacity of Power Plants The upper and lower limits of the capacity of power plants are set on the basis of a power supply plan and past trends, as shown in Equation 4.3. KLWRT ,G K T ,G KUPRT ,G
(4.3)
CAPLWRT ,G CAPT ,G CAPUPRT ,G
Where, KLWRT,G KUPRT,G CAPLWRT,G CAPUPRT,G
：Lower limit of capacity of new power plant G in the year ：Upper limit of capacity of new power plant G in the year ：Lower limit of capacity of total power plant G in the year ：Upper limit of capacity of total power plant G in the year
b. Balancing Power Supply and Demand Hourly generated energy should be equal to the hourly demand, as shown in Equation 4.4.
X
T , DAY , HR ,G
S T , DAY , HR HYDT , DAY , HR OTHRS T , DAY , HR LT , DAY , HR
(4.4)
G
Where, XT,DAY,HR,G
: Generated energy of power plants G at the time HR of representative day DAY in the year (sending end) : Pumped-up Energy (sending end) : Generated energy of general hydropower plants : Generated energy of the other power plants : Demand at the time HR of representative day DAY in the year (sending end）
ST,DAY,HR HYDT,DAY,HR OTHRST,DAY,HR LT,DAY,HR
c. Generation Reserve Margin The total capacity of power plants should be more than the maximum demand plus the generation reserve margin, as shown in Equation 4.5.
FACUPR
G , DAY
(1 PLTUSE
G
) CAP T, G ＋ HYD
T, DAY, HR
＋ O THRS T , DAY , HR （1 DELTA ） L T , DAY , HR (4.5)
G
Where, FACUPRG,DAY PLTUSEG DELTA CAPT,G
: Upper limit of the capacity factor of power plants G in representative day DAY : Internal consumption rate of power plants G : Generation reserve margin (10%) : Capacity of power plant G in the year
d. Operation of Pumped Storage Hydropower Generation Equation 4.6 is the definition of the pumping efficiency of pumped storage hydropower (PMEF).
X
T , DAY , HR ," PUMP "
PMEF (1 PLTUSE
" PUMP"
2 ） S T , DAY , HR
(4.6)
HR
HR
e. New Construction and Retirement of Power Plants Aged power plants are retired after a specific number of years, and new power plants are constructed to meet the demand, as shown in Equation 4.7. (4.7)
CAPT ,G CAPT 1,G K T ,G K T TLG .G
Where, CAPT,G, CAPT-1,G KT,G KT-TLG,G
: Capacity of power plants G in the year : Capacity of new power plants G in the year : Capacity of retired power plants G in the year
8
f. Constraints on Load Following Capability The load following capability of power generators is considered, as shown in Equation 4.8. (1 FLWLWR
Where, FLWLWRG FLWUPRG
G
) X T , DAY
, HR , G
X T , DAY
, HR 1 , G
(1 FLWUPR
G
) X T , DAY
, HR , G
(4.8)
：Lower limit of load following capability rate ：Upper limit of load following capability rate
g. Constraints on Capacity Factor of Power Plants The generated energy of power plant G in a representative day DAY is limited under the workable capacity of the power plant as shown in Equation 4.9. This constraint simulates the situation in which power plants cannot work continuously throughout the year owing to periodic inspections. X T , DAY , HR ,G FACUPR
G , DAY
(1 PLTUSE
G
) CAP T ,G
(4.9)
4.2. Major Input Data for OPTIGEN 4.2.1 Load Data The estimated hourly load curves by representative days in FY2000 are shown in Figure 4.2. We calculated the hourly load curves for each representative FY, which correspond to the forecasted maximum demand (kW) and energy (kWh) in each representative FY, on the basis of FY2000 data. The forecasted maximum demand and energy data are constructed by the authors according to the Long-term Prospect of Supply and Demand of Energy [METI, 2008] and the Outline of Power Supply Plan [METI, 2010] reported by the Ministry of Economy, Trade, and Industry in Japan. Figure 4.3 and Table 4.1 show the forecasted maximum demand and energy together. The assumption is that the annual load factor is improved by FY2040 because the energy growth is higher than the maximum demand growth, as shown in Table 4.1.
Note: D1 and D2 are the averaged demand of the three highest days and seven secondary
highest days, respectively. D3, D4, and D5 are the averaged demand of weekdays in summer, winter, and spring and autumn, respectively. D6, D7, and D8 are the averaged demand of holidays in summer, winter, and spring and autumn, respectively. Figure 4.2: Estimated Hourly Load Curve in FY2000
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Figure 4.3: Maximum Demand and Energy Forecast until FY2040
Table 4.1 Maximum Demand and Energy Forecast until FY2040 FY
Annual growth (%)
Energy (GWh)
Maximum Demand (MW)
Annual Load Factor (%)
2000
877,900
168,540
59.5
2010
916,800
168,220
62.2
2020
1,029,300
182,600
64.4
2030
1,076,100
184,740
66.5
2040
1,125,100
186,910
68.7
2000–2010
0.43%
–0.02%
－
2010–2020
1.17%
0.82%
－
2020–2030
0.45%
0.12%
－
2030–2040
0.45%
0.12%
－
Source: [METI, 2008], [METI, 2010]
4.2.2 Cost Data The unit construction costs of existing and new power plants are as shown in Table 4.2. The unit construction costs of existing power plants before FY2000 are the averaged experienced costs, while that of new power plants after FY2000 are quoted from the Outline of Power Supply Plan in FY2005 [METI, 2005(1)]. We set the unit construction costs of existing power plants to calculate the total power generation cost with accuracy during the estimation period defined in Section 4.4, although the unit construction costs do not affect the power supply planning. Table 4.3 shows the unit fossil fuel costs during the planning period. We applied actual fuel purchase costs averaged over nine power companies to the unit fossil fuel cost between FY2000 and FY2005. Meanwhile, the cost after FY2010 was estimated using data from World Energy Outlook [IEA, 2010]. Figure 4.4 illustrates the changes in unit fossil fuel cost during the planning period.
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Table 4.2: Unit Construction Cost during the Planning Period Unit Construction Cost (1,000 Yen/kW) Power Plant Before FY2000 After FY2000 （Existing Power Plant） （New Power Plant） NU 377 279 COAL 304 272 LNG 214 164 LNGCC 232 164 OIL 206 269 PUMP 196 196 Source: Unit construction costs of existing power plants before FY2000 are the averaged experienced costs. The unit construction costs of new power plants after FY 2000 are quoted from the Outline of Electric Power Development in FY2005 [METI, 2005(1)].
Table 4.3: Unit Fossil Fuel Cost during the Planning Period FY
COAL
LNG
(Yen/kg) （Yen/kg)
CRUDE
HEAVY
(Yen /l)
(Yen/l)
2000
4.94
28.91
25.99
25.59
2005
7.94
38.15
46.65
44.93
2010
10.77
54.80
48.81
46.80
2015
10.81
68.10
68.31
65.50
2020
11.25
74.80
74.80
83.94
2025
11.52
79.27
79.34
89.03
2030
11.68
83.17
83.12
93.27
2035
11.78
85.41
85.38
95.81
2040
11.78
85.41
85.38
95.81
Source: We applied the actual purchase cost averaged
over nine power companies to the unit fossil fuel cost between FY2000 and FY2005. The cost after FY2010 was the cost for FY2005 multiplied by the growth rate forecasted by World Energy Outlook [IEA, 2010].
Figure 4.4: Unit Fossil Fuel Cost during the Planning Period
The data for calculating the generation cost are shown in Table 4.4. We applied three annual expense rates (AER1, AER2, and AER3) to calculate the generation cost, where AER1 is the annual expense rate for averaged capital charges during the depreciation period, AER2 is the annual expense rate for residual values after the depreciation period, and AER3 is the annual expense rate for averaged capital charges during the plant’s years in operation. First, we applied AER3 to derive the generation mix using OPTIGEN. Second, we applied AER1 and AER2 to recalculate the annual expense in the representative year, where AER1 and AER2 are applied during the depreciation period and number of years in operation excluding the depreciation period, respectively.
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Table 4.4: Data for Calculating Generation Cost Number of Years in Operation (years)
Power Plant
Depreciation Period (years)
AER1 (%)
AER2 (%)
AER3 (%)
NU
40
15
8.65
0.59
5.32
COAL
40
15
8.95
0.69
5.31
LNG
40
15
8.92
0.73
5.31
LNGCC
40
15
8.92
0.73
5.31
OIL
40
15
8.92
0.73
5.31
PUMP
50
40
5.60
0.59
4.95
4.2.3 Generation Capability and Operating Restriction Data Figure 4.5 shows the higher heating value quoted from the Standard Heating Values of Each Energy Source [METI, 2005(2)]. Figure 4.6 shows the gross thermal efficiency and internal consumption rate, and Figure 4.7 shows the upper limit of the capacity factor of power plants. Each of these data has been derived by the authors on the basis of the Outline of Power Supply and Demand in 2000 [METI, 2000] reported by the Ministry of Economy, Trade and Industry in Japan. Table 4.6: Gross Thermal Efficiency and Internal Consumption Rate
Table 4.5: Higher Heating Value
Gross Thermal Efficiency (%)
Internal Consumption Rate (%)
Fuel
@
Higher Heating Value (kcal/@)
NU
kWh
860
COAL
kg
6139
NU
100
4.5
LNG
kg
13043
COAL
40.1
6.5
CRUDE
l
9412
LNG
38.2
4.5
HEAVY
l
9842
LNGCC
43.8
2.9
OIL
37.5
6.3
PUMP
100
0.5
Source: [METI, 2005(2)]
Power Plant
Source: [METI, 2000]
Power Plant
Table 4.7: Upper Limit of the Capacity Factor of Power Plants Spring and Three Highest Days Summer Winter and Seven Secondary Autumn Weekdays/Holidays Weekdays/Holidays Highest Days Weekdays/Holidays
NU
86.0 (%)
83.0 (%)
85.0 (%)
85.0 (%)
COAL
96.0 (%)
91.0 (%)
86.0 (%)
76.0 (%)
LNG
94.0 (%)
90.0 (%)
81.0 (%)
79.0 (%)
LNGCC
91.0 (%)
90.0 (%)
91.0 (%)
85.0 (%)
OIL
90.0 (%)
85.0 (%)
82.0 (%)
80.0 (%)
PUMP
96.0 (%)
96.0 (%)
93.0 (%)
93.0 (%)
Source: Generation and maintenance schedule in [METI, 2000]
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4.3. Model Results in the Reference Case This section describes the simulation results2 of OPTIGEN in the reference case (the non-DR case: α = 0). Figures 4.5, 4.6, and 4.7 show the total capacity, total generated energy, and unit generation cost of power plants during the planning period, respectively. While the maximum demand grows very slowly until FY2040, as shown in Figure 4.3, the total capacity of the power plants decreases from FY2015 to FY2025, as shown in Figure 4.5, because aged and unused power plants such as oil-thermal power plants are retired.
Figure 4.6: Total Generated Energy of Power
Figure 4.5: Total Capacity of Power Plants during the Planning Period
Plants during the Planning Period
Figure 4.7: Unit Generation Cost during the Planning Period in the Non-DR Case
4.4 Estimation of Power Generation Reduction from the DR Program This section describes the estimation of power generation reduction from the DR program. Although the model analysis period is from FY2000 to FY2040, we redefined the 31 years between FY2010 and FY2040 as the estimation period, as shown in Figures 4.9 and 4.10, and analyzed the results during only this estimation period in order to exclude the non-DR period of FY2000–FY2010 from the model analysis period.
2
Because we do not impose a constraint on CO2 emissions as described in Section 4.1, the unit CO2 emissions per electricity demand grow rapidly owing to the growth of the ratio of COAL power plants to total power plants, as shown in Figure 4.6.
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4.4.1 Change in Total and Unit Generation Costs from the DR Program Figure 4.8 shows the reduced maximum demand from DR introduction. The reduced maximum demand is almost the same after FY2020 among seven cases because the forecasted maximum demand is not very different. For example, in the α = 1.0% case, it is assumed that the maximum demand is reduced by 1,800 MW/year as a result of the DR program after FY2020, as shown in Figure 4.9.
Figure 4.9: Reduced Maximum Demand in the α = 1.0% Case
Figure 4.8: Reduced Maximum Demand from DR Introduction
Figure 4.10 shows the FY2010 present value of total generation cost during the estimation period (FY2010–FY2040) in seven cases that differ in peak cutting rates between 0% and 1.5% in 0.25 increments. According to the result, the total generation cost is 140.83 trillion yen in the non-DR case. The total generation cost decreases as the peak cutting rate of DR increases. When the peak demand is cut by 1.0%, the total generation cost is 140.56 trillion yen, which represents a decrease of 0.19% compared to the non-DR case. Figure 4.11 shows the increase and decrease of fixed and variable costs. While the fixed cost decreases, the variable cost modestly increases. The variable cost increases owing to increases in the utilization factor of pumped storage hydropower and fossil fuel generation. However, the fixed cost decreases owing to the reduced construction costs of LNG and COAL power plants, as shown in Figure 4.12. In the α = 1.0% case, the present value of the fixed cost decreases by 330 billion yen from the non-DR case, including the LNG and COAL power plants that reduce by 220 billion yen and 110 billion yen, respectively. Figure 4.13 shows the increase and decrease in the new capacity of LNG and COAL power plants. The figure indicates the following.
New capacity of the LNG power plant decreases as the peak cutting rate of DR increases, as shown in Figure 4.13(a) Reduced new capacity of the COAL power plant in FY2025 is offset by increases in new capacity in FY2030, as shown in Figure 4.13(b). The results indicate that the construction cost reduction of COAL power plants is due to deferment construction during the estimation period.
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Figure 4.10: Present Value of Total Generation Cost during the Estimation Period
Figure 4.11: Increase and Decrease of Fixed and Variable Costs
Figure 4.12: Breakdown of Present Value of Fixed Cost
(b) COAL (a) LNG Figure 4.13: Increase and Decrease in New Capacity of LNG and COAL Power Plants
15
Figure 4.14 shows the unit generation costs during the estimation period. Although the unit generation cost in the DR case (α = 1.5%) is lower than that in the non-DR case (α = 0%) from FY2000 to FY2035, the relation reverses in FY2040. The results indicate that the same trend is not reflected in all representative FYs, because the objective function of OPTIGEN is the present value of total generation cost during the planning period, as shown in Equation 4.1. Therefore, instead of the annual unit generation cost, we applied the “averaged unit generation cost” as the index of cost impact from DR, as follows. The averaged unit generation cost P during the estimation period is calculated as the total generation cost divided by total generated energy, as shown in Equation 4.10, where RRT is the present value factor in FY T. The averaged annual load factor LF during the estimation period is calculated by Equation 4.11, where, DT is the number of years for representative FY. P (Yen / kWh ) TC /
TN
( RR
T T 2
LF (%)
TN
(LF
T T 2
T
PE T )
(4.10)
TN
T
DT ) / DT
(4.11)
T T 2
Figure 4.15 shows the averaged unit generation cost and averaged annual load factor calculated by Equations 4.10 and 4.11. The averaged unit generation cost and the averaged annul load factor are 6.661 Yen/kWh and 65.4%, respectively, in the non-DR case. The averaged unit generation cost decreases as the peak cutting rate of DR increases, in the same way as the total generation cost. When the peak demand is cut by 1.0%, the averaged unit generation cost is 6.648 Yen/kWh, which represents a decrease of 0.19% compared to the non-DR case, and the averaged annual load factor is 66.0%, which represents an increase of 0.6% compared to the non-DR case.
Figure 4.15: Averaged Unit Generation Cost and Averaged Annual Load Factor during the Estimation Period
Figure 4.14: Unit Generation Cost during the Estimation Period
4.4.2 Avoided Cost per kW from the DR Program This paragraph considers the generation cost reduction from the DR program on the basis of the calculated avoided cost. The avoided cost is defined as the avoidable generation cost from reducing 1kW of maximum demand incrementally. If the program costs for the DR introduction are not lower than the avoided cost, we cannot justify implementing the DR program. In other words, the avoided cost indicates the breakeven cost of the DR program, although the transmission and distribution costs are not included. Figure 4.16 shows the total maximum demand and total generation cost during the estimation period. The total generation cost is approximately proportionate to the total maximum demand. Equation 4.12 is a linear approximation obtained from Figure 4.16.
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Total generation cost (Yen) ＝ 8500 × total maximum demand (kW) + 10930 × 106
(4.12)
Where, if the reduction of the total generation cost and that of the total maximum demand are defined as ΔTCα、and ΔPDα respectively, the avoided cost per kW is calculated by Equation 4.13. According to the result as shown in Figure 4.17, the avoided cost per kW is 8500 Yen/kW on an average. This value is equal to the slope of Equation 4.12. AvoidedCos t（ （ / PDα 0.25－PDα） ΔTCα /ΔPDα α Yen / kW ) (TCα 0.25－TCα）
(4.13)
Figure 4.17: Avoided Cost per kW from DR Introduction
Figure 4.16: Total Maximum Demand and Total Generation Cost during the Estimation Period
If the unit construction cost of LNG and annual expense rate of averaged capital charge during the years in operation are UCLNG (Yen/kW) and AER3, respectively, the annualized unit construction cost is calculated to be about 8704 Yen/kW, as shown in Equation 4.14. The avoided cost is approximately equal to the annualized unit construction cost of the LNG power plant. Annualized Unit Construction Cost (Yen/kW) = UCLNG × AER3 = 164000 × 0.0531 = 8704 ≒ Avoided Cost (Yen/kW)
(4.14)
5. Conclusions We estimated the impact of a peak-cutting DR program on the power generation cost of the Japanese power system as part of the cost-benefit analysis. We assumed the peak-cutting DR program as follows. The DR program period was set for three hours between 13H and 16H on the three highest demand days in the summer. During the program period, it is assumed that we can reduce the demand in LT(peak) × (1–α/100) by the DR program. Using CRIEPI’s power generation mix model for Japan—OPTIGEN—we estimated the power generation cost reduction during the estimation period (FY2010–FY2040) from the DR program that is introduced after FY2013. According to our result, the averaged unit generation cost decreases as the peak cutting rate of DR increases. When the peak demand is cut by 1.0%, the annual load factor increases by 0.6% and the unit generation cost decreases by 0.19% compared to the non-DR case. The avoided cost of the DR program, which is the power generation cost saved by reducing 1kW of peak demand, is about 8500 Yen/kW on an average. This avoided cost is approximately equal to the annualized unit construction cost of the LNG power plant. In the future, we should estimate not only the impact of the DR program on the generation cost, but also on the transmission, distribution, and program costs to analyze the social cost benefit. We would also like to study about the possible applications of the DR program other than peak-cutting DR programs.
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References [Asano, 1985] Load Management and the Cost Benefit Analysis, Power Economic Research No. 19, 1985. (in Japanese only) [CPUC, 2002] California Standard Practice Manual: Economic Analysis of Demand-Side Programs and Projects, California Public Utilities Commission and California Energy Commission, Jul., 2002. [DOE, 2006] Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them, Department of Energy, 2006. [EISA, 2007] Energy Independence and Security Act, TITLE V Sec. 529, Dec., 2007. [FERC, 2009] A National Assessment of Demand Response Potential, Federal Energy Regulatory Commission, Jun., 2009. [IEA, 2010] World Energy Outlook, International Energy Agency, 2010. [METI, 2000] Outline of Power Supply and Demand in 2000, Ministry of Economy, Trade and Industry, 2000. (in Japanese only) [METI, 2005(1)] Outline of Electric Power Development in 2005, Ministry of Economy, Trade and Industry, 2005. (in Japanese only) [METI, 2005(2)] Standard Heating Values of Each Energy Source, Ministry of Economy, Trade and Industry, 2005. (in Japanese only) [METI, 2008] Long-term Prospects of Supply and Demand of Energy, Ministry of Economy, Trade and Industry, 2008. (in Japanese only) [METI, 2010] Outline of Power Supply Plan, Ministry of Economy, Trade and Industry, 2010. (in Japanese only) [SCE, 2007] Edison Smartconnect Deployment Funding and Cost Recovery, Errata to Exhibit 3: Financial Assessment and Cost Benefit Analysis, Southern California Edison, Dec., 2007. [Takahashi, 1996] Analysis for Optimal Power Development Plan Based on CO2 Emission Limitation, Takahashi, Nagata, and Uchiyama, 15th Workshop of Japanese Society of Energy and Resources, 1996. (in Japanese only) [Yamaguchi, 2011] Demand Reduction Potential of Demand Response in Commercial and Industrial Sectors (Questionnaire Survey in the Kanto Area), CRIEPI Report Y10020, Yamaguchi and Takayama, 2011. (in Japanese only)
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