i. Title: Industrial DSM Potential Assessment using Baseline Energy Consumption ii. Authors:

Rhee, Chang Ho (Korea Electrotechnology Research Institute, Korea) Yang, Sung-Chul (Korea Electrotechnology Research Institute, Korea) Coito, Fred J. (KEMA-XENERGY, U.S.A.)

iii. Abstract The objective of this paper is to assess technical potential for electric demand-side

improvements in the industrial sector. This paper focused on energy efficiency measures that could be implemented over the next 5 to 10 years and relied on an analysis of secondary-source data, mainly from U.S. sources. The first step in the analysis is to develop a baseline understanding of energy consumption in the industrial sector. Next is to conduct an initial, high-level assessment of energy-efficiency potential for each key industry-process/end-use combination

For developing baseline data, industrial load shape data would be utilized to develop summer peak kW estimates by industrial category. The analysis of baseline data combined information on monthly industrial energy sales with load shape information for the month of August. This paper developed a relationship between annual energy sales and summer peak demand by first looking at industrial energy use that occurred in August (August Sales) and then looking at the fraction of August energy usage, for the load data sample, that occurred in the August peak hour (peak hour fraction, which equals August Peak Hour use divided by Total August Use).

The analysis was conducted for each industrial category and for other energy-consuming sectors. The product of the August Sales and the peak hour fraction provides an estimate of the fraction of annual usage that occurs during the summer peak hour (for industry i). The estimated peak load across all industrial categories and other sectors was then compared to actual peak load. Estimated peak demand was found to be lower than actual peak demand.

Finally, energy sales and peak demand in each industrial category were allocated to end uses and/or processes utilizing industrial survey data. Initial energy-efficiency potential estimates were developed, based on results of a California industrial market assessment. Each industry/end-use segment is identified in the screening study to have the most likely achievable energy-efficiency potential and secondary source data available for which to develop more detailed potential estimates.

This analysis would provide information on different energy-efficiency measures that are applicable to the studied market segments, typical measure costs, measure savings potential, and estimates of avoided-cost savings available from penetration of the energy-efficiency measures. This study presents the methods and possible results of the initial energy-efficiency potential estimates by industrial category.

iv. Contact details for lead author 1. Name:

Rhee, Chang Ho 2. Title:

Manager of Electricity Industry Policy Research Group 3. Organization:

Korea Electrotechnology Research Institute 4. Address:

665 Naseson 2-dong, Uiwang-si, Gyunggi-do, 437-808, KOREA

5. Phone/Fax/E-mail:

Tel: 82-31-420-6120

Fax: 82-31-420-6129

Email: chrhee@keri.re.kr

1. Introduction This paper is conducting a research to assess technical potential for electric demand-side

improvements in the industrial sector. Industrial sector is difficult to measure energy efficiency potential because DSM measures vary. This paper used U.S. sources to assess the potential for electricity DSM in the Korean industrial sectors because Korean industrial sector does not have sufficient information and data on energy efficiency. Therefore the accuracy of this study will depend on the similarities in end-use load shape between similar Korean and U.S. industries.

Energy efficiency measures in industrial sector vary and difficult to get information. Actual measurement would require higher cost and longer time. Estimation or sampling methods were usually used for industrial sector. In this paper, technical steps to estimating potential of DSM measures of industrial sector would be suggested. Furthermore it would show the way how to manage insufficient information for the full analysis.

Nevertheless we found that data availability was a limiting factor for developing reasonable results, finding the alternative way to conduct analysis was a good reward. When we were conducting a research project, we could face the difficulties to get information sufficiently. In this case we should look for the available data or information and use it for the analysis.

2. Methodology The methodology consists of two steps. First step includes development of baseline data,

consisting of KWh and KW energy usage estimates by industry and process/end-use. Second step includes an initial, high-level assessment of energy-efficiency potential for each key industry-process/end-use combination. Figure 2-1 outlines the methodology.

The first work to develop a baseline understanding of energy consumption in the industrial sector is to collecting industrial electricity sales data by appropriate industrial category. The basic Korean industry categories are shown in Table 2-1. These categories are similar to those utilized in the U.S., which facilitated the use of existing U.S. energy data and research studies.

For developing baseline data, industrial load shape data would be utilized to develop summer peak kW estimates by industrial category. The analysis of baseline data combined information on monthly industrial energy sales with load shape information for the month of August. This paper developed a relationship between annual energy sales and summer peak demand by first looking at industrial energy use that occurred in August (August Sales) and then looking at the fraction of August energy usage, for the load data sample, that occurred in the August peak hour (peak hour fraction, which equals August Peak Hour use divided by Total August Use).

Figure 2-1 Methodology Overview

Table 2-1 Korean Industrial Categories.

Agricultural & Fisheries

Mining

Food & Beverage

Textile, Apparel, Leather& Shoes

Wood & Lumber

Pulp & Paper

Printing & Publishing

Plastic, Chemical & Refinery

Glasses & Cement

Basic metals

Fabricated metal products

Other Machinery

Office machine

Electric appliances

Image, Sound & Communication

Medical treatment & Optic

Automobiles

Other transportation

Furniture & Other

Reclaimed materials

The analysis was conducted for each industrial category and for other energy-consuming

sectors. The product of the August Sales and the peak hour fraction provides an estimate of the fraction of annual usage that occurs during the summer peak hour (for industry i).

si

sii UseAugustTotal

UsePeakAugustSalesAugustKWPeak

,

,

____

__ ×= (1)

KWh sales by industry

Industrial Load

Shape Information

Industry / End-

Use Analysis

Technical

Potential

KWh by Industry

and End Use

KW by Industry and

End Use

Load Shape

Analysis

General DMS

Potential

Potential by Industry/ End Use

KWh and KW

Where August sales and annual sales were from public information and August use was calculated using load shape data provided by KEPCO (Korea Electric Power Corporation), as follows:

ii

i

UseEnergyTotalWeekdayFractionHourPeakWeekdayUsePeakAugust

________

×=

(2)

and

∑ ×=j

jijii UseDailyDaysofNumberUseAugustTotal ,, _____ (3)

Where the number of days for day type j for August 2003 were determined to be: 4

Mondays, 17 weekdays, 5 Saturdays, 4 Sundays, and 1 holiday. The estimated peak load across all industrial categories and other sectors was then

compared to actual peak load. Estimated peak demand was found to be lower than actual peak demand. This result may be due to the load data sample, which may reflect usage of larger customers with flatter load shapes. To adjust for this underestimation, peak demand in each industrial category was adjusted upward by the ratio of total actual peak demand to total estimated peak demand.

∑×=

ii

ii KWPeakKWPeakActualKWPeakKWPeakCalibrated

______ (4)

Energy sales and peak demand in each industrial category were allocated to end uses

and/or processes utilizing industrial survey data. Since the Korean industrial load shapes were found to be relatively flat, we applied the annual peak hour fraction to all end uses within an industrial category.

3. Application to Korean Industrial Sector Initial energy-efficiency potential estimates were developed, based on results of Korean

electricity consumption by end-use in industrial sector and a California industrial market assessment. Each industry/end-use segment is identified in the screening study to have the most likely achievable energy-efficiency potential and secondary source data available for which to develop more detailed potential estimates. This analysis would provide information on different energy-efficiency measures that are applicable to the studied market segments, typical measure costs, measure savings potential, and estimates of avoided-cost savings

available from penetration of the energy-efficiency measures. 3.1 Baseline Consumption The first step in the baseline energy consumption analysis is to collect energy use data.

Monthly data for 2003 of Korean Industry Electricity Sales Data is shown in Table 3-1, along with the fraction of annual energy use consumed in the month of August.

Table 3-1 Korean Industry Electricity Sales Data (GWh) in 2003

2002 2003

Industry Total (GWh) 144,454 150,387

Agricultural & Fisheries 6,156 5,944

Mining 1,155 1,205

Food & Beverage 6,768 7,061

Textile, Apparel, Leather & Shoes 15,543 14,654

Wood & Lumber 1,388 1,445

Pulp & Paper 8,170 8,178

Printing & Publishing 1,159 1,174

Plastic, Chemical & Refinery 29,267 31,223

Glasses & Cement 9,923 10,286

Basic Metals 24,982 25,656

Fabricated metal Products 4,399 4,716

Other Machinery 5,288 5,681

Office machine 2,653 2,511

Electric appliances 2,507 2,576

Image, Sound & Communication 11,604 13,360

Medical treatment & Optic 630 695

Automobiles 8,414 9,244

Other transportation 2,472 2,724

Furniture & Other 1,570 1,610

Reclaimed materials 405 444

Source: Korea Electric Power Corporation. The Annual Report on Major Electric Power Statistics (July

2004)

Next, Korean industrial load shape data is used to develop estimates of summer peak

demand. The peak demand calculation of Top 5 selected industry is summarized in Table 3-2. The initial estimate of peak demand is 17,216 MW in 2003. Results for all market segments were then calibrated to actual peak demand of 24,972 MW by multiplying by the ratio of 24,972 to 17,216.

According to the formula (1) in the methodology section, summer peak demand was calculated and the results show in table 3-2 (C). It used the August sales data and average load by day type. Sales Estimation was calculated first using formula (3) in the methodology section and then the weekday’s load fraction was derived from the estimation. It’s process is showed in table 3-2 (B). Then, estimated August peak use (MW) was calculated using formula (2) in the methodology section.

Table 3-2 Summer Peak Demand Calculation

(A) Average Load in August (MW)

Top 5 Industry August Sales

(GWh) Monday Weekday Saturday Holiday Sunday

Textile, Apparel,

Leather & Shoes 1205 5,233 5,437 5,345 5,476 4,963

Plastic, Chemical &

Refinery 2579 24,828 25,172 24,481 31,171 25,379

Glasses & Cement 833 6,886 6,860 6,471 7,820 7,625

Basic Metals 1935 6,892 7,069 6,933 7,080 6,780

Image, Sound &

Communication 1200 6,670 6,689 6,529 6,547 6,249

Source: MOCIE. Analysis of End-User’s Electric Power Consumption Trend Using Load Curve

(December 2002).

(B) Sales Estimation by Average Load in August (KWh)

Top 5 Industry Monday Weekday Saturday Holiday Sunday Estimated

Sales

Weekday

Fraction

(%)

Textile, Apparel,

Leather & Shoes 125,592 130,476 128,280 131,424 119,112 3,969,732 3.29

Plastic, Chemical

& Refinery 595,882 604,126 587,549 748,114 609,089 18,775,874 3.22

Glasses & Cement 165,259 164,645 155,306 187,680 183,010 5,156,249 3.19

Basic Metals 165,396 169,651 166,399 169,925 162,722 5,198,465 3.26

Image, Sound &

Communication 160,085 160,534 156,696 157,126 149,978 4,909,930 3.27

Medical treatment

& Optic 33,997 34,612 33,382 33,445 32,278 1,053,857 3.28

(C) Summer Peak Demand

Top 5 Industry Weekday Load

Factor

Estimated

Peak Load

(MW)

Calibrated

Peak Load

(MW)

Peak Load

Fraction

Peak Load

(MW)

Textile, Apparel, Leather & Shoes 1.0161 1,677 1,678 9.74% 2,351

Plastic, Chemical & Refinery 1.0215 3,532 3,534 20.53% 4,951

Glasses & Cement 0.7798 864 865 5.02% 1,212

Basic Metals 0.888 2,336 2,338 13.58% 3,275

Image, Sound & Communication 1.0295 1,683 1,684 9.78% 2,359

Source: MOCIE. Analysis of End-User’s Electric Power Consumption Trend Using Load Curve

(December 2002).

Finally, end-use fractions were developed from U.S. data obtained through the Manufacturing Energy Consumption Survey (or any available source). The end-use shares are presented in Table 3-4. The end-use fractions is applied to both Korean annual energy use and peak demand to provide estimates of end-use consumption. Table 3-5 provides the end-use energy estimates, and Table 3-6 provides the end-use peak demand estimates.

For example, the basic metals industry in Korea approximately 25,656 GWh per year and contributes about 3,275 MW to summer peak demand in 2003. August sales of basic metals industry is 1,935 GWh and its weekday fraction using estimation by average load is 3.26%. The estimated weekday average load is 2,631 MW and its estimated peak load is 2,336 MW because weekday load factor of basic metals industry is 0.888. Then we calibrated peak load by the ratio of actual load and estimated. As a result, we calculated peak load fraction of 13.58% from total estimated load. The fractions are used to provide peak load results from the actual system peak result.

Table 3-3 Industrial End-Uses Machine Drive (Motor)

Lighting HVAC Fan Pump Comp. Air Misc.

Process

Heat

Process

Cool

Process

Electro-

Chem.

Misc.

Table 3-4 End-Use Fractions (U. S. A.)

Industry Lighting HVAC BoilersMachine

Drive

Process

Heat

Process

Cool

Process

Electro-

Chemical

Process

Other

Miscella-

neous

Food 0.0710 0.0802 0.0165 0.5204 0.0282 0.2623 0.0011 0.0011 0.0192

Beverages 0.0963 0.1446 0.0066 0.4276 0.0102 0.2850 0.0000 0.0064 0.0232

Tobacco 0.0963 0.1446 0.0066 0.4276 0.0102 0.2850 0.0000 0.0064 0.0232

Textiles 0.0892 0.1380 0.0075 0.5853 0.0483 0.0876 0.0010 0.0046 0.0385

Apparel 0.1484 0.2529 0.0089 0.4884 0.0226 0.0285 0.0000 0.0000 0.0503

Leather&Shoes 0.1237 0.1671 0.0000 0.5092 0.0618 0.0395 0.0000 0.0066 0.0921

Wood&Lumber 0.0555 0.0528 0.0201 0.7604 0.0445 0.0046 0.0000 0.0029 0.0593

Pulp&Paper 0.0400 0.0446 0.0246 0.8247 0.0190 0.0160 0.0056 0.0034 0.0220

Printing &

Publishing 0.1240 0.1815 0.0032 0.4981 0.0324 0.0542 0.0042 0.0054 0.0970

Refinery 0.0271 0.0331 0.0077 0.8689 0.0075 0.0469 0.0001 0.0000 0.0087

Chemicals 0.0386 0.0539 0.0063 0.5938 0.0238 0.0749 0.1921 0.0007 0.0159

Plastic 0.0773 0.0912 0.0047 0.5232 0.1576 0.0826 0.0012 0.0021 0.0601

Glass 0.0541 0.0618 0.0011 0.3950 0.3714 0.0593 0.0095 0.0145 0.0333

Cement 0.0258 0.0252 0.0001 0.8181 0.1041 0.0138 0.0000 0.0084 0.0045

Basic metal 0.0297 0.0308 0.0027 0.2819 0.2935 0.0072 0.3349 0.0034 0.0160

Fabricated

Metal product 0.1070 0.0939 0.0034 0.5051 0.1510 0.0192 0.0438 0.0039 0.0728

Other Machinery 0.1526 0.1853 0.0060 0.4405 0.0898 0.0323 0.0057 0.0061 0.0817

Office Machine 0.1323 0.2606 0.0040 0.2286 0.1350 0.0962 0.0300 0.0118 0.1015

Electric

appliance 0.1189 0.1388 0.0036 0.3895 0.1787 0.0417 0.0242 0.0152 0.0895

Image, Sound &

Communication. 0.1323 0.2606 0.0040 0.2286 0.1350 0.0962 0.0300 0.0118 0.1015

Medical

Treatment &

optics

0.1281 0.3103 0.0138 0.3015 0.0914 0.0547 0.0039 0.0000 0.0962

Automobile 0.1415 0.1563 0.0049 0.4736 0.0940 0.0429 0.0085 0.0096 0.0688

Other transport. 0.1415 0.1563 0.0049 0.4736 0.0940 0.0429 0.0085 0.0096 0.0688

Furniture & other 0.1425 0.1374 0.0028 0.5178 0.0460 0.0178 0.0000 0.0079 0.1280

Miscellaneous 0.1281 0.3103 0.0138 0.3015 0.0914 0.0547 0.0039 0.0000 0.0962

Source: KEMA-Xenergy. Industrial DSM Potential Assessment- Final Report for KERI (August 2004).

As shown in Table 3-5, machine drive (motor) systems account for about half of overall electricity use. HVAC and process heat are also the significant end-use of energy consumption. But, in basic metals industry, process electro-chemical is the largest end-use and machine drive is the second. Therefore the characteristics of industry could be informed from the energy consumption results.

Table 3-5 End-use Energy Consumption Estimation of Top 5 Industry (GWh per Year)

Machine Drive (Motor)

Top 5 Industry Ligh-

ting

HV

AC Fan Pump Comp.

Air Misc.

Process

Heat

Process

Cool

Process

Electro

-Chem.

Misc.

Textile, Apparel,

Leather &Shoes 1765 2726 3018 509 1228 2978 728 760 5 938

Plastic, Chemical

& Refinery 1488 1855 1897 8315 3800 6656 2160 2127 2013 910

Glasses & Cement 411 448 1600 250 1582 2807 2452 376 49 312

Basic Metals 762 791 1104 629 1037 4462 7599 184 8592 497

Image, Sound &

Communication 1767 3481 77 1783 694 500 1857 1285 401 1514

Table 3-6 End-use Peak Demand Estimation of Top 5 Industry (MW)

Machine Drive (Motor)

Top 5 Industry Ligh-

ting

HV

AC Fan Pump Comp.

Air Misc.

Process

Heat

Process

Cool

Process

Electro

-Chem.

Misc.

Textile, Apparel,

Leather& Shoes 293 453 501 85 204 494 121 126 1 156

Plastic, Chemical

& Refinery 244 305 311 1365 624 1093 355 349 330 149

Glasses & Cement 50 55 195 31 193 342 299 46 6 38

Basic Metals 101 105 146 83 137 590 1004 24 1135 66

Image, Sound &

Communication 323 636 14 326 127 91 340 235 73 277

On the other hand, the one of largest peak demand sub category in machine drive end-use

is compressed air (if excepted miscellaneous use) among top 5 industry. Compressed air systems account for about 9% of Korean industrial electricity use 12, 143 GWh per year and 2,007 MW of summer peak demand.

3.2 Industrial DSM Potential Based on previous estimates, we developed potential estimates of energy-efficiency

measures for Korean industry. The percent-savings estimates for key end-uses from the U.S. data are applied to the energy-use estimates shown in Table 3-5. Savings percents of Korean industry which used the U.S. data are shown in Table 3-7. It was developed by KEMA-Xenergy Co. and we would utilize it as a proxy.

Table 3-7 End-use Energy Savings Fractions from California Industrial Study

Machine Drive (Motor) Industry Lighting HVAC

Fan Pump Comp. Air Others

Agricultural & Fisheries 19% 14% 12% 26% 23% 8%

Mining 19% 14% 12% 26% 23% 8%

Food & Beverage 19% 14% 12% 26% 23% 8%

Textile,Apparel,Leather & Shoes 19% 11% 13% 27% 24% 9%

Wood & Lumber 22% 22% 10% 24% 21% 6%

Pulp & Paper 18% 19% 10% 25% 22% 7%

Printing & Publishing 19% 22% 14% 29% 25% 10%

Plastic, Chemical & Refinery 25% 8% 11% 25% 22% 7%

Glasses & Cement 29% 20% 13% 28% 25% 10%

Basic Metals 20% 16% 11% 26% 23% 8%

Fabricated metal Products 20% 22% 13% 27% 24% 9%

Other Machinery 20% 9% 14% 28% 25% 10%

Office machine 19% 24% 12% 22% 9% 9%

Electric appliances 18% 12% 12% 27% 24% 9%

Image, Sound & Communication 19% 6% 13% 27% 24% 9%

Medical treatment & Optic 19% 24% 12% 22% 9% 9%

Automobiles 20% 24% 11% 26% 23% 8%

Other transportation 20% 24% 11% 26% 23% 8%

Furniture & Other 20% 23% 13% 25% 24% 9%

Reclaimed materials 20% 22% 13% 27% 24% 9%

Source: KEMA-Xenergy. California Industrial Energy Efficiency Market Characterization Study

(December 2001).

Energy savings potential estimates for the Korean industrial sector are shown in Table 3-8

and Table 3-9. These estimates are based on an analysis of measures with simple payback periods that are generally 3 years or less.

Table 3-8 End-use Savings Potential Estimates (GWh) Machine Drive (Motor)

Industry Lighting HVAC Fan Pump Comp. Air Others

Agricultural & Fisheries 62.66 43.92 65.86 61.91 86.13 268.63

Mining 9.15 7.34 22.49 7.63 42.63 26.30

Food & Beverage 117.92 121.74 29.28 138.48 57.10 177.03

Textile, Apparel, Leather & Shoes 335.31 299.85 392.29 137.43 294.75 267.98

Wood & Lumber 17.64 16.78 13.34 13.89 19.12 48.98

Pulp & Paper 58.91 69.28 133.28 530.07 67.54 208.92

Printing & Publishing 27.66 46.89 13.09 2.38 10.72 44.02

Plastic, Chemical & Refinery 372.01 148.43 208.72 2078.66 836.10 465.94

Glasses & Cement 119.17 89.52 207.97 70.12 395.57 280.66

Basic Metals 152.49 126.52 121.44 163.49 238.52 356.98

Fabricated metal Products 100.88 97.42 25.93 79.58 139.21 117.67

Other Machinery 173.33 94.75 24.48 91.96 99.41 160.15

Office machine 63.11 157.05 1.74 73.73 11.73 8.46

Electric appliances 55.11 42.90 3.04 158.14 54.69 14.78

Image, Sound & Communication 335.74 208.88 10.04 481.42 166.47 45.00

Medical treatment & Optic 16.92 51.79 3.44 11.44 2.99 8.62

Automobiles 261.62 346.83 104.51 212.54 188.10 143.45

Other transportation 77.08 102.18 30.79 62.62 55.42 42.26

Furniture & Other 45.89 50.87 46.30 1.54 24.93 33.07

Reclaimed materials 11.37 30.30 2.38 8.96 5.08 5.50

Total 2413.96 2153.23 1460.43 4385.99 2796.20 2724.42

Chemical industry is the biggest consumer of electricity and it has the largest electricity

savings potential also. In lighting end-use, an industry which has the second largest savings potential is communication industry, but it has the largest savings potential in peak load savings. For example, savings potential for compressed air system is estimated to be 2,796 GWh and 460.51 MW per year. Pump system has the largest savings potential in a whole industry.

In this paper, calculation by measures was not conducted and the savings potential by measures would be needed for acquiring exact amount of energy efficiency impact in the future.

Table 3-9 End-use Saving Potential Estimates (MW) Machine Drive (Motor)

Industry Lighting HVAC Fan Pump Comp. Air Others

Agricultural & Fisheries 11.67 8.18 12.27 11.53 16.05 50.04

Mining 1.57 1.26 3.87 1.31 7.34 4.53

Food & Beverage 23.98 24.76 5.96 28.17 11.61 36.01

Textile, Apparel, Leather & Shoes 55.68 49.79 65.14 22.82 48.94 44.50

Wood & Lumber 3.01 2.86 2.27 2.37 3.26 8.35

Pulp & Paper 8.50 10.00 19.23 76.49 9.75 30.15

Printing & Publishing 4.22 7.15 2.00 0.36 1.63 6.71

Plastic, Chemical & Refinery 61.07 24.37 34.26 341.23 137.25 76.49

Glasses & Cement 14.53 10.91 25.36 8.55 48.23 34.22

Basic Metals 20.15 16.72 16.05 21.61 31.52 47.18

Fabricated metal Products 20.28 19.59 5.21 16.00 27.99 23.66

Other Machinery 37.33 20.41 5.27 19.81 21.41 34.49

Office machine 13.96 34.73 0.39 16.30 2.59 1.87

Electric appliances 9.77 7.60 0.54 28.02 9.69 2.62

Image, Sound & Communication 61.38 38.19 1.83 88.01 30.43 8.23

Medical treatment & Optic 2.98 9.14 0.61 2.02 0.53 1.52

Automobiles 50.79 67.33 20.29 41.26 36.52 27.85

Other transportation 15.18 20.13 6.07 12.33 10.92 8.32

Furniture & Other 7.37 8.16 7.43 0.25 4.00 5.31

Reclaimed materials 1.90 5.06 0.40 1.50 0.85 0.92

Total 425.32 386.33 234.44 739.93 460.51 452.96

4. Conclusion If Korea or other countries which have insufficient energy efficiency data could analyze

their energy efficiency impact using available data from foreign countries as a proxy. This paper shows the way to analyze and provide the results. This is a goal of the study and a significant achievement. Effects of demand-side management in the industrial sector would be measured and verified objectively through this study.

Especially, data for individual energy efficiency measures is highly variable from application to application and was not available. Therefore the availability of data is the most significant factor for the detailed analysis of the future. This study presents the methods and possible results of the initial energy-efficiency potential estimates by industrial category.

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