ELECTRICITY OUTAGE COST STUDY

Energy Research Institute
Chulalongkorn University

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Executive Summary

1. Introduction

One of the main tasks of each utility is to provide and supply reliable electricity to customers at reasonable prices. The prices of electricity normally vary in accordance with the level of utility’s reliability standards. The more reliable, the higher price it normally is. However, if the system reliability is low, outages tend to occur more often and will eventually give more damages to business sectors. However, if each utility utilizes its components in the system closed to their limit or rated capacity, the electricity price may probably be lower. This kind of operation can be done in exchange of lower reliability and security of the system. The present electricity supply industry under the three utilities has considerable reserve capacity and generally utilizes their networks far below its rated capacity. Therefore the system has rather or too high reliability. The balance between economical and technical considerations is therefore necessary for utility’s operation regardless of working under competitive environment or not.

Generally, there is no obligation in choosing reliability levels in power system planning and operation. It mostly depends on the past criteria and work experiences. Under the present vertically integrated structure, utilities can fix the minimum requirement of capacity reserve as a percentage of the peak demand or apply a maximum Loss of Load Probability (LOLP) as its planning criteria. In addition distribution utilities may allow the maximum power flows in any particular feeders not higher than eighty percents of its rated capacity. However, production expansion or additional construction to enhance better capacity and services at a lower price is inevitable for present and future system operation. One of the problems to be faced by the utilities or the power pool in the future is how to fix their own appropriate reliability and security level. However, such the level can be solved theoretically by comparing the cost of supply and distribution with customers’ benefits at different reliability levels. The optimum reliability level will be at the balanced point between the total cost of supply and the benefits from the customers. As a result, we need to estimate cost of electricity services at different reliability levels separately from the estimation of reliability value.

This economical study can be used as the basic way to fix appropriate reliability level, which will be at the balanced point between the cost of service and the customers’ benefits as shown in figure 1.


Figure 1 Costs and reliability

Though the estimation of customers’ reliability worth is fairly difficult, each utility has for a long time estimated its investment cost in order to obtain the required reliability level. The reliability worth evaluation is usually done through the evaluation of reliability indices, which indirectly reflects the reliability worth. The worth is generally known as the outage costs and can be evaluated using operating statistics of the components installed in the system to obtain the Interrupted Energy Rate (IER) or the Value of Lost Load (VOLL). Therefore, to evaluate the outage cost, it requires a good understanding about customer’s damages when an outage is occurred. The outage cost can also be used as an unreliability index and represented as reliability worth to be analyzed and compared for future plans and operations.

The outage can cause both direct and indirect damages. Loss of production and raw materials, inconvenience and damages to life and assets are its direct result. While other damages such as crimes, move of factories or offices as well as the cancellation of goods orders as a result of late deliveries can be indirectly caused. Impacts and outage cost should be estimated in monetary value, which however is quite impossible in practice. Estimating the impacts on raw materials damaged during an outage is possible whereas estimating the impacts on life is somehow not easy, for example. This is so because the perspective of each consumer on the impacts of outage differs accordingly to his or her objective of power usage. Consumer categories, power quantity, interrupted activities, duration and period of outages should thus be the criteria of cost estimation.

The Energy Research Institute, Chulalongkorn University, in cooperation with the Electricity Generating Authority of Thailand (EGAT), MEA and PEA had studied the outage cost for the first time in 1995-1996. Since then, however, each utility has improved its system as shown in the MEA’s and PEA’s System Average Interruption Frequency Index (SAIFI) and the System Average Interruption Duration Index (SAIDI) in Table 1 . The average outage duration and frequency have been gradually improved. Therefore it is necessary to conduct the outage cost study and should be revised to update information reflecting power condition, consumption quantity as well as locations of the consumers. The study can further be useful for the operation improvement and an appropriate price calculation.

Table 1 Distribution utilities’ SAIFI and SAIDI

Years MEA PEA
SAIFI
(f/year)
SAIDI
(min/year)
SAIFI
(f/year)
SAIDI
(min/year)
1998 3.30 98.35 19.37 1549.99
1999 3.09 71.84 17.71 1298.18
2000 3.12 65.99 18.11 1188.13

Source: National Energy Policy Office

2. Objective

This study aims to estimate outage costs in different areas throughout the country. The outage costs will be evaluated for customers in the whole country and five other distribution zones as follows:

  1. MEA areas,
  2. North zone of PEA,
  3. Northeast zone of PEA,
  4. Central zone of PEA, and
  5. South zone of PEA.

3. Scope of study

The process of study is divided into 2 main stages:

  1. Outage cost model development
  2. Outage cost estimation

Details of each stage can be resumed as follows:

3.1 Outage cost model development

The outage cost model is developed according to electricity tariff which classifies customers into seven categories, i.e.

  1. Residential,
  2. Small business,
  3. Medium business,
  4. Large business,
  5. Specific business,
  6. Government and non-profit organizations, and
  7. Agricultural pumping.

For the customers of types 2,3 and 4 are further divided into two sectors, i.e. industry and business & services. While the industrial sector, the country’s most power consumer is sub-divided into nine classifications according to the Thai Standard Industrial Classification (TSIC). They are composed of

  1. Food beverage & tobacco,
  2. Textile and leather products,
  3. Wood and wood products,
  4. Pulp and paper,
  5. Chemical and rubber,
  6. Non-metallic mineral,
  7. Basic material,
  8. Fabricated metal,
  9. Other manufacturing.

3.2 Outage cost estimation

The outage cost is evaluated for the following customers.

  1. Customers in the whole country
  2. Customers in distribution utilities,
    • MEA areas,
    • North zone of PEA,
    • Northeast zone of PEA,
    • Central zone of PEA, and - South zone of PEA.

4. Questionnaires development

Data used in the outage cost study this time had been gathered from interviews with power consumers, concerned persons in different agencies and households. There was also a survey via internet for those who really want to express their opinion directly. Documents had to be installed on the internet for this purpose as well as for gathering concerned data at specified time. The study team had developed questionnaires so that they would suit the three main categories of consumers, which are consumers in industrial sector, business and service sector and household sector. Questionnaires for direct interview and for the internet are alike in order to get similar kinds of data.

5. Survey results

The customer direct survey began in April 2000, whereas the survey via internet started by March 2000. The number of all usable responses was first estimated at least 1100 customers, comprising 300 factories, 400 business & services and government organizations, and 400 residential customers. In addition, the Thai Standard Industrial Classification (TSIC) and the electricity tariff were employed to appropriately distribute among targeted respondents as shown in table 2. However, the number of responses obtained from both types of the survey is better than expected as will be described in the following sections.

Table 2 Number of responses

TSIC Type Target Responses
31 Food, beverage & tobacco 46 159
32 Textiles & leather products 30 38
33 Wood and wood products 25 32
34 Paper and paper products 25 62
35 Chemical and rubber 35 88
36 Non metallic mineral 25 42
37 Basic material 15 19
38 Fabricated metals 40 142
39 Other manufacturing 10 5
61 Whole sales 37 7
62 Retail sales 120 300
63 Restaurant and Hotel 65 12
71 Warehouse and transportation 8 37
81 Bank and financial institute 16 20
82 Assurance 9 6
83 Real estate 15 12
91 Civil servant, administration & defence 16 11
93 Community and social services 60 48
94 Leisure services 15 71
95 Individual services 30 164
00 Residential 400 963
Grand Total 1,042 2238

The number of responses according to electricity tariff in MEA and PEA areas is summarized in tables 3 and 4 respectively.

Table 3 Responses in MEA’s Area

Type of customers Responses
Residential 299
Small business 193
Medium business 60
Large business 29
Specific business 10
Government organization 11
Total 602

Table 4 Responses in PEA’s Area

Type of customers Responses
Residential 664
Small business 610
Medium business 287
Large business 99
Specific business 10
Government organization 9
Total 1679

 

6. Customer damage model development

The development of the customer damage model will be presented in this chapter. The models can be divided into 2 types, i.e. the Average customer damage model using only the average value to represent the damage cost, and the Fuzzy customer damage model which represents the damage cost by fuzzy data including the impact of the vagueness and uncertainty. The fuzzy model is more appropriated than the average model in case that there are limited numbers of data in each interested category, there is high deviation among the obtained data. The obtained fuzzy data is an index with membership value, which represents its possible level. With the application of such a model, the outage cost will have higher flexibility to be implemented in the future competitive environment.

The details and examples of the customer damage model development will be presented in the following sections.

6.1 Damage Cost Factors

In developing the customer damage model as mentioned above, we have to calculate total damage cost which comprises several types of damages. For industrial and business & services customers, the damage cost of each customer comprise six types of damages, i.e.

  1. Salary or work payment,
  2. Cost of loss of profit opportunity,
  3. Overtime payment,
  4. Cost of loss of raw material,
  5. Cost of re-starting the process, and
  6. Cost of damaged equipment.

We can see that these damage costs may vary with interruption duration until they reach a certain figure last longer than a specific duration, then these damage cost could be constant. For example, the cost of loss of profit opportunity varies with the interruption duration only if the outage does not last longer than 8 - 24 hours. It means, if the working time of this customer is 10 hours per day, this damage cost is proportion to the interruption duration until 10 hours, after that it remains constant and equals to those of 10 hours. The damage costs of types 3–6 are normally varied with interruption duration, normally about 8 hours, then the customer will be able to manage his or her business to alleviate the damage cost. Therefore, the damage cost of the interruption, which last longer than 8 hours will be constant and equals to those of 8 hours. However, the first damage cost, salary, depend strongly on the damage cost for all outage duration.

Each element of the total damage cost of the customer defined by TSIC 31 (Food, Beverage and Tobacco) and TSIC 62 (Retail sales) in PEA’s area is illustrated, to show the relation between each damage type and interruption duration, in figures 2 and 3 respectively.

Figure 2 Elements of the total damage cost of customers defined by TSIC 31
in PEA’s area

Figure 3 Elements of the total damage cost of customers defined by TSIC 62
in PEA’s area

From the figure 2, we can see that if there is an electric interruption, most of the total damage cost of industrial factory customers of this TSIC is dominated by the damage cost from salary, loss of profit opportunity and overtime charged. For most business & services customers of TSIC 62, as shown in figure 3, loss of profit opportunity has dominated most of other damage costs since their damage values are less compared to this cost.

6.2 Comparison between the average and fuzzy customer damage models

The comparison between the average and fuzzy customer damage models is shown in figure 4. The average model is plotted on the same axis as the fuzzy model, and on the plane of membership value of 1.0.

Figure 4 Comparison between average and fuzzy customer damage models

Figure 4 shows that the average model is covered by the fuzzy model at the membership value of 1.0. Therefore, it can be concluded that the fuzzy model is more general and covers all possible data. In addition, the obtained results from fuzzy model is a range of possible number, thus, it is flexible to be used in any objective. For this reason, the fuzzy model and average model will be used altogether in this study. With the obtained results, there will be some flexibility to allow outage cost to be negotiated and implemented in the future environment.

7. Customer damage models

The Customer damage models as described in chapter 4 can be divided into 2 types, i.e. average customer damage model and fuzzy customer damage model. In this chapter, the obtained data for each customer type as presented in section 5 will be used to develop the Customer damage models, which can be divided according to 4 classifications as follows:

  1. Electricity tariff
  2. Industrial estates
  3. Thai Standard Industrial Classification (TSIC)
  4. Large industrial factories

Each model is classified into 2 areas, covering MEA and PEA distribution zones, which consequently is used in evaluating the customer interruption cost.

In this section, only the average models classified by electricity tariff, which are used in outage cost evaluation, will be shown. The others are already shown in final report.

7.1 Damage cost model by electricity tariff

7.1.1 MEA’s area

Type number Interruption duration
flickering 1 min. 30 min. 1 hr. 2 hr. 4 hr. 8 hr.
Residential 299 0.000 0.487 5.361 11.454 25.364 53.184 114.031
Small business 193 0.000 0.234 10.995 76.918 180.284 409.187 833.323
Medium business 60 8.271 3.903 30.710 97.775 178.019 293.841 505.871
Large business 29 0.299 0.977 10.239 29.549 70.417 100.581 182.753
Specific business 10 0.753 0.000 0.308 3.049 8.470 13.610 26.107
Government organization 11 0.000 0.000 5.479 9.324 15.454 26.916 47.447

7.1.2 PEA’s area

Type number Interruption duration
flickering 1 min. 30 min. 1 hr. 2 hr. 4 hr. 8 hr.
Residential 664 0.000 0.272 4.078 8.694 19.050 39.762 80.716
Small business 610 38.287 46.740 96.447 166.172 288.467 591.748 1054.216
Medium business 287 3.287 7.855 29.482 55.006 92.647 193.661 363.221
Large business 99 6.661 10.824 34.311 50.877 79.913 145.614 251.938
Specific business 10 0.000 0.000 0.529 1.890 4.044 8.248 15.904
Government organization 9 0.277 6.104 11.219 20.025 28.827 40.175 50.941

 

8. Interruption cost

Interrupted Energy Rate (IER), generally known as Outage Cost or Value of Lost Load (VOLL) can be evaluated from the customer damage model, proposed in section 7, which are classified by electricity tariff. These models can be called as Secteral Customer Damage Function (SCDF). In this chapter, the outage cost will be evaluated as follows:

  1. Outage cost of each distribution area of, MEA and PEA, and
  2. Outage cost of all customers in the kingdom.

The outage cost of each distribution area will be grouped into distribution zones according to the criterias of MEA and PEA. The evaluation process starts from defining the areas for both PEA and MEA. Then methodologies of the outage cost evaluation will be proposed. Finally, results of this study are compared with those obtained from the previous one.

8.1 Distribution areas

The areas required for the outage cost evaluation are defined as mentioned above, and can be presented in the following sub-sections.

8.1.1 MEA areas

MEA function is to distribute electric power in Bangkok, Nonthaburi and samutprakarn. They are responsible for 14 distribution zones, i.e.

  1. Klongtoey
  2. Yannawa
  3. Ratboorana
  4. Samasen
  5. Bangken
  6. Bangkapi
  7. Bangyai
  8. Samutprakarn
  9. Bangplee
  10. Minburi
  11. Nonthaburi
  12. Thonburi
  13. Watliab
  14. Bangkhuntien

8.1.2 PEA areas

A PEA distribution system covers all areas in Thailand except the ones under responsibility of MEA. The outage cost for PEA will be calculated for their 12 areas as follows:

  1. PEA North 1,
  2. PEA North 2,
  3. PEA North 3,
  4. PEA Central 1,
  5. PEA Central 2,
  6. PEA Central 3,
  7. PEA Northeast 1,
  8. PEA Northeast 2,
  9. PEA Northeast 3,
  10. PEA South 1,
  11. PEA South 2, and
  12. PEA South 3

8.1.3 All customers

Since EGAT is responsible for generation and transmission of electrical power to its customers, i.e. MEA, PEA and direct customers all over the country. Interruption in EGAT’s power system certainly gives impacts to all customers. Therefore, the outage cost of EGAT is the outage cost of all customers, and will be evaluated using the information obtained from the customers of both MEA and PEA.

Table 5 The energy consumption of MEA in the year 2000

Customer type Energy Consumption
(kW-hr/Year)
Ratio
(%)
*Load Factor
(%)
Residential 6,669,770,011 21.29 78.78
Small general business 4,333,581,655 13.83 67.17
Medium general business 7,494,029,395 23.92 68.99
Large general business 10,247,235,776 32.71 77.70
Specific business 1,441,700,934 4.60 88.99
Government and non-profit organization 1,141,333,883 3.64 59.90
All MEA 31,327,651,654 100.00 76.15

*From : Load Study NEPO 1997

Table 6 The energy consumption of PEA in the year 2000

Customer type Energy Consumption
(kW-hr/Year)
Ratio
(%)
*Load Factor
(%)
Northern      
Residential 2,862,182,839 40.78 46.97
Small general business 859,674,317 12.25 63.70
Medium general business 1,256,480,055 17.90 62.04
Large general business 1,335,025,273 19.02 71.63
Specific business 197,106,057 2.81 78.44
Government and non-profit organization 507,538,105 7.23 70.58
Central      
Residential 2,990,475,370 43.51 60.63
Small general business 845,327,926 12.30 49.00
Medium general business 1,090,153,381 15.86 73.66
Large general business 1,306,058,837 19.00 90.33
Specific business 133,580,282 1.94 79.07
Government and non-profit organization 506,809,911 7.37 69.52
Northeastern      
Residential 3,649,701,314 12.44 52.52
Small general business 1,532,989,436 5.22 46.36
Medium general business 5,682,472,013 19.36 85.60
Large general business 17,490,373,129 59.60 78.74
Specific business 455,306,269 1.55 73.51
Government and non-profit organization 533,965,140 1.82 61.48
Southern      
Residential 2,687,149,529 31.78 54.89
Small general business 874,362,029 10.34 72.67
Medium general business 1,790,987,770 21.18 76.36
Large general business 2,155,683,456 25.50 90.37
Specific business 524,717,025 6.21 81.53
Government and non-profit organization 421,306,924 4.98 66.44
All PEA      
Residential 12,189,509,052 23.58 55.51
Small general business 4,112,353,708 7.96 67.80
Medium general business 9,820,084,219 19.00 73.91
Large general business 22,287,140,695 43.12 89.98
Specific business 1,310,709,993 2.54 79.71
Government and non-profit organization 1,969,620,080 3.81 67.91
Total 51,689,417,747 100.00 83.31

*From : Load Study NEPO 1997

By weighting the average or fuzzy models of each sector, classified by electric tarif, the CCDF can be obtained. We can see that the CCDF, which is in the unit of Baht/kWavg, is the function of interruption duration, and it can be written as CCDF(t). The CCDF(t) is used in outage cost evaluation later.

Baht/kWaverage (1)

where i is customer type,
n is a number of each customer type,
ci is energy consumption of customer type i, and
SCDFi is sector customer damage function of customer type i.
LFi is load factor of customer type i

The average CCDF(t) (Baht/kWavg) of MEA, PEA areas and all customers are shown in table 7

Table 7 Average customer damage model
Unit Baht/kWaverage

Interruption Duration flickering 1 min. 30 min. 1 hr. 2 hr. 4 hr. 8 hr.
MEA 3.033 1.944 19.020 65.996 136.717 245.184 458.956
PEA 8.533 13.131 37.661 62.794 105.610 208.010 374.720
PEA -North1 11.003 15.988 42.866 74.089 128.080 257.370 467.600
PEA-North 2 9.882 14.428 38.198 67.943 119.760 242.870 444.600
PEA-North 3 9.067 14.047 40.190 69.033 118.460 236.270 429.870
PEA-Central 1 8.691 13.596 40.324 64.161 104.880 201.200 357.100
PEA-Central 2 9.427 14.387 41.484 67.662 112.110 218.870 391.470
PEA-Central 3 10.382 15.837 44.900 75.370 126.210 250.760 452.120
PEA-Northeast 1 13.232 18.304 45.172 78.545 136.970 276.190 499.610
PEA-Northeast 2 13.010 17.941 43.592 76.669 134.810 272.790 494.540
PEA-Northeast 3 11.570 16.632 43.810 74.623 128.020 255.560 461.400
PEA-South 1 7.792 12.107 35.039 58.716 99.269 195.630 353.300
PEA-South 2 8.435 12.492 33.821 59.558 103.460 209.510 382.540
PEA-South 3 8.506 12.903 35.827 61.735 105.770 211.760 384.550
All customers 6.452 8.905 30.587 63.881 117.097 221.618 405.735

8.2 Outage cost evaluation

The outage cost can be evaluated using the obtained CCDF and the actual interruption statistics. The obtained outage costs can be divided into two types as follows:

  1. Interrupted Energy Rate (IER, Unit : Baht/kWh), and
  2. Interruption Cost Per Event (ICPE, Unit : Baht/event).

These costs are calculated from CCDF shown in table 7 and interruption statistic by using these equations.

(2)

(3)

where CCDF is Composite Customer Damage Function,
tj is Interruption Duration of jth interruption,
Pj is Load loss of jth interruption, and
n is A number of interruption.

8.3 Outage cost of the distribution areas

Based on the concept mentioned in the sections 6.1-6.2, the outage cost of MEA and PEA for each interested area can be calculated. Both IER and ICPE are presented. The average costs are calculated from the average-CCDF. In addition, fuzzy costs are calculated from the fuzzy-CCDF, however, only the value at membership of 0.5 is shown in this section. The detailed results of fuzzy IER and ICPE are shown in the final report.

8.3.1 Outage Cost of MEA

The interruption statistics of 14 distribution zone of MEA from years 1998 – 2000 are used in this calculation. With the assumption that the damage cost of each customer is not time-varying but depends only on the interruption duration, the results of IER and ICPE are shown in tables 7 and 8 respectively.

Table 8 Interruption energy assessment rate of MEA (IER)
Unit:Baht/kWh

Zone Year 1998 Year 1999 Year 2000
MEAN MIN MAX MEAN MIN MAX MEAN MIN MAX
1 55.652 36.457 70.260 54.872 35.562 69.874 55.947 36.647 71.585
2 53.983 35.342 68.359 54.044 35.538 69.708 51.876 34.188 67.590
3 54.052 35.886 69.215 51.348 33.758 66.162 53.099 34.897 68.797
4 56.671 36.839 71.738 55.778 36.982 72.096 52.842 35.248 69.321
5 56.006 37.063 70.974 51.721 34.050 67.132 51.493 33.793 66.923
6 55.160 36.406 70.087 49.947 32.956 64.030 51.778 34.681 68.257
7 58.664 38.770 74.907 55.338 36.248 70.831 56.563 37.727 72.985
8 55.670 36.836 71.089 53.192 34.892 68.721 54.047 35.488 69.639
9 58.377 37.979 73.628 57.297 37.342 72.753 57.180 37.986 73.947
10 55.375 36.370 70.211 52.525 34.608 68.237 46.923 32.524 64.562
11 57.986 39.730 76.468 49.663 33.826 66.753 53.261 35.921 68.938
12 50.859 33.871 65.326 54.046 36.093 70.726 52.062 34.400 67.251
13 56.327 36.762 71.911 57.638 37.625 73.625 51.366 34.216 67.620
14 57.249 37.578 72.275 54.354 36.344 70.479 51.884 34.146 67.025
MEA 55.858 36.685 70.812 54.123 35.552 69.597 53.799 35.559 69.739

*Minimum and Maximum values are calculated from fuzzy model of membership value of 0.5

Table 9 Interruption cost per event of MEA (ICPE)
Unit:Baht/event

Zone Year 1998 Year 1999 Year 2000
MEAN MIN MAX MEAN MIN MAX MEAN MIN MAX
1 336,130 220,190 424,350 193,310 125,280 246,160 195,880 128,310 250,640
2 354,090 231,820 448,390 176,420 116,010 227,550 154,770 102,000 201,660
3 231,120 153,440 295,960 141,450 92,990 182,250 119,190 78,331 154,430
4 178,710 116,170 226,230 169,660 112,490 219,300 125,490 83,707 164,630
5 423,440 280,220 536,610 190,130 125,170 246,780 149,340 98,003 194,090
6 199,060 131,380 252,930 150,370 99,216 192,770 104,470 69,974 137,720
7 183,880 121,520 234,790 144,960 94,951 185,540 153,310 102,260 197,820
8 220,560 145,940 281,650 138,690 90,975 179,180 151,010 99,154 194,570
9 348,560 226,770 439,620 207,040 134,930 262,890 178,380 118,500 230,680
10 199,210 130,840 252,580 109,370 72,061 142,080 66,408 46,030 91,372
11 124,780 85,498 164,560 56,066 38,187 75,360 129,390 87,263 167,470
12 215,120 143,270 276,310 125,320 83,693 164,000 160,610 106,120 207,460
13 130,840 85,391 167,040 137,120 89,509 175,150 103,220 68,758 135,880
14 323,170 212,130 407,990 153,730 102,790 199,330 132,820 87,416 171,590
MEA 283,350 186,090 359,210 165,320 108,600 212,590 147,500 97,493 191,200

*Minimum and Maximum values are calculated from fuzzy model of membership value of 0.5

We can see from the table 8 that IER of each MEA area tends to slightly decrease each year. Mean value of the IER in the year 2000 is 53.799 Baht/kWh, which is decreased from year 1998 and 1999, i.e. 55.858 and 54.123 Baht/kWh respectively. Moreover, the IER of each area are closed to each other. The fuzzy IER at the membership of 0.5 in year 2000 lies between 35.559 - 69.739 Baht/kWh.

Considering table 9, we can clearly see that ICPE decrease each year. The ICPE of MEA in year 2000 is 283,350 Baht/event, whereas for the year 1998 and 1999 are 165,320 and 147,500 Baht/event respectively. The fuzzy ICPE of membership of 0.5 in year 2000 lies between 97,493 - 191,200 Baht/event

8.3.2 Outage cost of PEA

Actual interruption statistics of 12 distribution areas of PEA in the year 2000 are considered in this evaluation. The statistical data are considered with the composite customer damage models of PEA to evaluate the IER and ICPE. The results are shown in tables 10 and 11 respectively.

Table 10 Interruption energy assessment rate of PEA (IER)
Unit:Baht/kWh

Zone MEAN MIN MAX
PEA-North1 73.281 44.692 84.675
PEA-North 2 64.991 39.882 75.295
PEA-North 3 71.450 43.439 82.754
PEA-Central 1 61.396 39.599 75.637
PEA-Central 2 64.256 41.185 78.185
PEA-Central 3 75.498 46.876 89.148
PEA-Northeast 1 76.702 45.046 85.011
PEA-Northeast 2 75.582 43.944 82.907
PEA-Northeast 3 83.690 47.453 90.381
PEA-South 1 55.138 35.452 67.201
PEA-South 2 54.142 34.400 64.617
PEA-South 3 59.450 36.707 69.426
PEA 60.165 38.236 72.634

*Minimum and Maximum values are calculated from fuzzy model of membership value of 0.5

Table 11 Interruption cost per event of PEA (ICPE)
Unit:Baht/event

Zone MEAN MIN MAX
PEA-North1 304,900 185,950 352,300
PEA-North 2 40,648 24,944 47,092
PEA-North 3 43,295 26,322 50,144
PEA-Central 1 71,437 46,076 88,006
PEA-Central 2 220,940 141,610 268,830
PEA-Central 3 31,903 19,808 37,671
PEA-Northeast 1 42,986 25,245 47,643
PEA-Northeast 2 48,374 28,125 53,062
PEA-Northeast 3 15,772 8,943 17,033
PEA-South 1 37,887 24,361 46,177
PEA-South 2 32,472 20,631 38,755
PEA-South 3 27,527 16,997 32,147
PEA 62,723 39,861 75,721

*Minimum and Maximum values are calculated from fuzzy model of membership value of 0.5

In table 10, the mean IER of PEA in the year 2000 is 60.165 Baht/kWh and the fuzzy value at membership value of 0.5 lies between 38.236 - 72.634 Baht/kWh. The maximum average value of IER of PEA is of the northeast area 3, i.e. 83.69 Baht/kWh, whereas the minimum value is 54.14 Baht/kWh which is of the south area 2. Table 11 shows that mean ICPE of PEA is 63,000 Baht/event and the fuzzy value is in the interval of 40,000 - 76,000 Baht/event. Compared to those of MEA, we found that, the IER of PEA is higher, however ICPE is lower. It means the damage cost of each event in MEA is higher than PEA, nevertheless, the damage cost per unit energy of PEA is higher than MEA.

8.4 Outage of all customers

The principle of evaluation of outage cost of all the customers is similar to those of MEA and PEA. However, it needs to consider the composite customer model of all area representing damage cost of Thailand and interruption statistics of all areas, aggregating from those of MEA and PEA. The IER and ICPE in year 2000 are shown in table 12

Table 12 Indices of all customers

  MEAN MIN MAX
IER 60.348 38.524 73.637
ICPE 64,991 41,489 79,303

*Minimum and Maximum values are calculated from fuzzy model of membership value of 0.5

From table 12, the mean value of IER is 60.348 Baht/kWh. In addition, it ranges from 38.524 to 73.637 Baht/kWh. The ICPE of all customer is 64,991 Baht/event and lies between 41,000 - 79,000 Baht/event. From these results, we can conclude that the IER and ICPE of all customers are closed to those of PEA. Since the number of interruption events of PEA is 119,515 events, which is far more than those of MEA, i.e. 2,472 events.

8.5 Results comparison

The above results show that the outage cost of MEA and PEA in year 2000 have average IER of 53.799 and 60.165 Baht/kWh, and average ICPE of 147,500 and 62,723 Baht/event respectively. In addition, the average values of all customers are 60.348 Baht/kWh and 64,991 Baht/event. If we compare to those of the previous study conducted in year 1995-1996, we can see that the IER of MEA, PEA and all customers are 62.02, 83.40 and 68.06 Baht/kWh respectively. Furthermore, ICPE are 246,800.58, 177,317.37 and 217,314.60 Baht/event respectively. We can see that IER in year 2000 is clearly decreased within the last 5 years. The main cause of this result comes from two factors, as stated in the section 8.2, i.e.

  1. The change in customer damage models, and
  2. The improvement in service quality of the utilities.

The average models obtained from this study are compared to those of the previous one. It can be seen that the average value of the damage cost in year 2000 is lower than those of the years 1995-1996 for the interruption duration lower than 1 hour, and it is higher when the interruption duration is higher than 1 hour. This results in the decrease of IER since most interruption events of MEA lasted shorter than 1 hour.

For PEA, we found that both models are quite closed. Therefore, the main factor of the decrement of IER probably comes from the better service quality of this utility. We can see from table 1 that SAIFI and SAIDI of MEA and PEA from year 1998-2000 are continuously decrease, resulting in the improvement of quality.

The study teams did not have the information about the SAIFI and SAIDI in 1995 – 1996. However, we can see the trend of the improvement from years 1998-2000. Therefore, we can conclude that the SAIFI and SAIDI of PEA in years 1995 - 1996 are probably higher than those of the year 1998. That results in the decrease of IER and ICPE of PEA.

9. The future VOLL and its applications

The outage cost evaluation as described in the previous chapter presents the Interrupted Energy Rtae (IER) and the Interrupted Cost per Event (ICPE). Both costs require two main types of information, i.e. customer damage models, and outage statistics. The IER, widely known in the present as the Value of Lost Load (VOLL), is calculated based on the past actual outage statistics. The figures are therefore appropriated for the past. However, it needs to be adjusted for future implementation.

This chapter will address the approach for evaluating and adjusting the VOLL which the utilities or concerned organizations can conduct in the future themselves. In addition future application of the VOLL will be presented.

9.1 Future VOLL evaluation

The previous chapter presents the VOLL evaluation methodology which require two main types of information, i.e. customer damage models, and outage statistics. Therefore if we can predict these information, the VOLL adjustment for future application will be possible. Details of the evaluation process are addressed below.

9.1.1 Customer damage models

The development of the customer damage models presented in the previous chapters is based on direct customer survey. It concerns the customers’ perception on the cost of damages to be occurred if an outage with different duration, seconds to 8 hours, does occur. The results show that the damage models obtained from this study is fairly closed to the ones obtained from the previous study which was conducted five years ago. This result is similar to that has been obtained in other countries [4,7], which demonstrated that the customer damage models obtained from the survey conducted twice for the period of eleven years difference were closed to each other. Therefore if we need to readjust the VOLL in the future, we may carry out the study according to following suggestions.

  1. Short-term (< 5 years): The present models can be implemented without conducting a new survey.
  2. Medium-term (6-10 years): Some present information can still be implemented, however new survey with a smaller samples size for particular customers may be required and used together with the present information to develop the models. In addition since the outage duration in the MEA mostly lasts not longer than 2 hours, therefore the new survey may focus more on the damage of this short duration.
  3. Long-term (> 10 years): The new survey should be conducted.

With the above suggestions, the appropriated Sectoral Customer Damage Function (SCDF) will be obtained to evaluate the IER.

9.1.2 Outage statistics

In practice we will not know in advance the actual number and outage duration of each event to calculate the VOLL. However, the utilities should gather the operating records of main components in their system, i.e. failure rate, repair rate, and repair time, etc. These records should be compiled and used to evaluate the future indices, i.e. SAIFI, SAIDI, load point frequency index, etc. In addition to the evaluation of the past and future reliability indices, the operational statistics can also be used together with the SCDF to obtain the future VOLL.

9.2 VOLL applications

Two applications of the VOLL in the power system planning are presented as follows:

  1. power system operation, design, and planning
  2. power pool.

The detailed applications are described below.

9.2.1 Power system operation, design, and planning

In general if there is a high load growth and the extension of the transmission and distribution networks is required, the utilities will firstly pay attention on the investment and find the way to sufficiently serve the demand. The quality and reliability problems are normally received attention in the second priority. However, for a system with well developed networks and low load growth, the system and operation improvement is normally required to meet its standard. In this regards, the VOLL can be used to support the system operation, planning, and design, together with the reliability evaluation as illustrated in figure 7.1.

Figure 5 System costs and reliability

Figure 5 shows that total system cost comprise supply cost and demand cost which results from electricity outage. We can express it as the following equation.

Total Cost = Supply Cost + Customer Outage Cost (4)

It can be proved that the minimum total cost occurs as follows:

(5)

Equation 5 means that the minimum total cost occurs at the point of which the change in system reliability (D R ) results in the increase of the investment cost (D C )equals to the decrease of the customer damage cost (D B), which can be calculated from VOLL*EENS (Expected Energy Not Supplied).

The evaluation of reliability indices including EENS requires operating statistics of system equipment. Therefore, if the utilities don’t have such kind of information in hand, the future VOLL evaluation will be impossible, and consequently the optimum point as shown in figure 5 cannot be found.

9.2.2 Power pool application

As defined in the future Thailand Electric Supply Industry (ESI), the VOLL may be used as the market Price Cap. In addition it may be used as a capacity adder to attract new entrants. Such the application has been done in the power pools of Argentina, Columbia, Spain, and U.K. However, this concept needs careful consideration and further clarification before applying in the future ESI.

9.2.3 VOLL uncertainties

The VOLL evaluation methodology as presented in the previous chapters demonstrates high uncertainties in the SCDF. Therefore the VOLL should be flexible to be implemented and should not be limited only to the average value. Therefore the applied figure of the VOLL depends on the policy and strategy to support, e.g. a high VOLL would result in better system investment on the new generation, transmission, and distribution than a low VOLL. In case of the present high reserve capacity in Thailand, if the VOLL will be implemented, a rather low VOLL would be more appropriated in the beginning. With this reason, the results from the fuzzy models as presented in chapters provides more flexibility and suitable

10. Conclusions

The direct customer survey has been conducted to develop the customer damage models, which are consequently used in conjunction with the actual outage statistics of the years 1998-2000, and 2000 to evaluate the outage costs for MEA and PEA respectively.

There are more than 2,200 responses obtained from the survey which has been conducted in the field and via the internet. The responses cover all types of customers classified by electricity tariff excluding the agricultural pumping which has fairly low ratio of the electricity consumption.

Results of the IER and the ICPE have been summarized in table 13.

Table 13 Outage cost results

  IER (Bath/kWh) ICPE (Bath/outage)
MEA 53.799 147,500
PEA 60.165 62,723
All customer 60.348 64,911

In addition to the results provided in table 13, the fuzzy outage costs are also presented in table 14.

Table 14 Fuzzy outage costs

  IER (Baht/kWh) ICPE (Bath/outage)
minimum Maximum minimum maximum
MEA 35.559 69.739 97,493 119,190
PEA 38.236 72.634 39,861 75,721
All customers 38.524 73.637 41,489 79,303

The results of fuzzy outage cost shown above provide more flexibility for VOLL to be implemented and should be suitable for the Thailand future electric supply industry. The flexible VOLL will suit the business for which the negotiation can be made.

The study team has also suggested the approach to revise the future VOLL concerning two main issues.

1) Revision of the sectoral damage models should be considered according to the future lead time which is divided into three periods, i.e.

2) Outage statistics: The transmission and distribution utilities should collect operating statistics of all major equipment, then use them to evaluate future system reliability rather than using only the past performance records as having been done for a long time. With the component operating statistics, we will be able to evaluate reliability indices and the VOLL. In addition, the utility planning and operation in the future will be more appropriated.

 

 

Posted 21 December 2001