Outage cost modeling for realiability based network ... - CiteSeerX

METHODS FOR ADVANCED COMPILATION OF INTERRUPTION STATISTICS TO CATER THE RENEWING REGULATION Kimmo Kivikko, Antti Mäkinen, Pertti Järventausta Tampere University of Technology, Institute of Power Engineering P.O.BOX 692, FIN-33101 Tampere, FINLAND Jukka Lassila, Satu Viljainen, Jarmo Partanen Lappeenranta University of Technology P.O.BOX 20, FIN-53851 Lappeenranta, FINLAND ABSTRACT Deregulation of electricity market in the middle 90’s changed power distribution in Finland to more business oriented direction although the network business remained as regulated natural monopoly. According to the Electricity Market Act, the distribution companies have general obligations, e.g., to develop their electricity distribution networks and to have moderate distribution pricing. When assessing the reasonableness of the pricing Finnish Energy Market Authority (the market regulator) takes into account also the efficiency of network companies. For some time now the efficiency has been evaluated with DEA-model, in which power quality in the form of interruption time is at present one of the input parameters. In the future, power quality will be determined more accurately and diversified based on more accurate interruption statistics and interruption cost parameters. Tampere University of Technology (TUT) and Lappeenranta University of Technology (LUT) have had joint projects for developing methods to compose interruption statistics with a reliable way to cater the needs of network planning and operation, and also the needs of the regulation and efficiency determination of network companies. The research has been carried out partly also with the Finnish Energy Market Authority. The use of interruption statistics and interruption cost parameters was studied also with reliability calculation tool to determine the structure of interruption costs (i.e. what percentage of interruption costs is caused by certain customer group and certain interruption type) and average interruption cost parameters using the data of two Finnish distribution companies. To collect diversified interruption figures distribution companies have to know, e.g., the number of different kind of interruptions and the interruption durations, and also the number of customers and their annual energy consumption for each secondary substation. This settles new needs for the compilation of interruption statistics and the data systems, because interruptions have to be saved at the secondary substation level, when previously it was enough to only count the number of secondary substations attached to the interruption. While most progressive companies already at present have modern data systems, which handle automatically a great deal of the work associated with interruption statistics, some companies still handle this with primitive Excel-applications or even with pencil and paper. In the future, the distribution companies have to assure that their data systems meet the requirements of the renewing regulation and interruption statistics. Depending on the automation level and the data systems of the distribution company, it will have some alternative methods for the compilation of interruption statistics.

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INTRODUCTION The number and duration of supply interruptions characterize continuity of supply. It is widely accepted that it is neither technically nor economically feasible for a power system to ensure that electricity is continuously available on demand. Instead, the basic function of a power system is to supply power that satisfies the system load and energy requirement economically and also at acceptable levels of continuity and quality. Voltage quality is usually measured in terms of acceptable values of voltage (i.e. voltage level, harmonics, voltage dips etc.), while continuity of supply refers to uninterrupted electricity supply service. Power quality includes both voltage quality and continuity of supply. Reliability refers to the ability of a power system to provide an adequate and secure supply of electrical energy at any point in time. Supply interruptions, regardless of their cause, mean a reduction in reliability. The four main features of continuity of supply can be summarised as follows [Cou03]: •

• •

•

The type of interruption: planned or unplanned interruptions. Planned interruptions are scheduled, for instance, to carry out necessary maintenance of the network. Planned interruptions, which are not notified to customers, should be recorded as unplanned interruptions. The duration of each interruption: short or long interruptions. In accordance with European technical standard EN 50160, interruptions that last more than 3 minutes are defined as long interruptions, and others as short interruptions. The voltage levels of faults and other causes of interruptions: The interruption of supply to final customers can originate at any voltage level, low/medium/high voltage, in the system. At high voltage level not all faults cause interruptions to final customers, because of the network design. The type of continuity indicators: number or duration of interruptions. The number of interruptions per customer in a year, termed customer interruptions (CI) or System Average Interruption Frequency Index (SAIFI), indicates how many times in a year energy is not supplied. The cumulative yearly duration of interruptions per customer, generally referred to as Customer Minutes Lost (CML) or System Average Interruption Duration Index (SAIDI), indicates how long in a given year energy is not supplied (average per customer). These indices of frequency and duration provide useful information to regulatory authorities on the performance of the network in terms of security and availability, respectively.

In Finland, Data Envelopment Analysis (DEA-model) is applied to define the efficiency score for each network company. The basic idea of the DEA-model is that the operational efficiency of a decision-making unit is determined by comparing it to other similar units. To be able to calculate the efficiency scores by using the DEA, the input and output parameters used in the efficiency measurements must be chosen. The Energy Market Authority has chosen five factors to be used in the DEA-calculations. These are operational costs, power quality (continuity of supply, i.e. total interruption time of customers), distributed energy, the length of the network and the number of customers. Formerly in the DEA-model the continuity of supply index used to be weighted by the number of secondary substations. The total interruption time for each distribution network company was calculated by multiplying the total number of customers with the ratio of secondary substation hours and the total number of secondary substations (i.e. average interruption time for secondary substations). This method did not take into account that customers are not uniformly divided along the network. All secondary substations were equal

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regardless of the number of customers in the transformer district. That is why the sparsely populated areas had too strong weighting in the continuity of supply indices. Based on the research done by TUT and LUT, Finnish Energy Market Authority has adopted a new approach to model the continuity of supply in the future and the regulator has decided to collect new interruption numbers, which are in the first phase weighted with the annual energy consumption in each secondary substation [Jär03]: 1. Customer’s average annual interruption time that is caused by unexpected interruptions and weighted with annual energy of the secondary substation. 2. Customer’s average annual number of interruptions that are caused by unexpected interruptions and weighted with annual energy of the secondary substation. 3. Customer’s average annual interruption time that is caused by planned interruptions and weighted with annual energy of the secondary substation. 4. Customer’s average annual number of interruptions that are caused by planned interruptions and weighted with annual energy of the secondary substation. 5. Customer’s average annual number of interruptions that are caused by delayed autoreclosings and weighted with annual energy of the secondary substation. 6. Customer’s average annual number of interruptions that are caused by high-speed auto-reclosings and weighted with annual energy of the secondary substation. 7. Annual number of unexpected interruptions in low voltage network. 8. Annual number of unexpected interruptions in medium voltage network. Another, further developed, approach proposed by the researchers was to model the quality of supply as interruption costs in the calculation of the efficiency of distribution companies with DEA-model. If nationwide interruption cost parameters are used, the interruption costs can be calculated with the first six numbers in the list above. Otherwise, if company- and customer group-specific parameters are used, then the interruption costs have to be added as a new parameter to the numbers that are collected from the utilities. The interruption cost model for regulation purposes is discussed in detail e.g. in [Kiv04]. During the research, a lot of work was done to model power quality in the regulation and to define the objective nationwide interruption cost parameters, and a lot of work has still to be done to assure the righteousness of the model and parameters. In following chapters models for the compilation of interruption statistics for distribution network companies with different level of automation are discussed. Also results from interruption cost modelling, parameters and calculations are presented. MODELS FOR THE COMPILATION OF INTERRUPTION STATISTICS The compilation of interruption statistics can be handled in several ways. Some distribution companies handle it automatically with modern data systems (e.g. distribution management systems, DMS) while others still use primitive Excel –applications or even pencil and paper. In this context, two models for the compilation of interruption statistics are presented, i.e. comprehensive model for those utilities that have advanced computer systems and a remarkable amount of distribution automation in use, and simplified model for those utilities that have a lower level of automation and computer systems. Either way, reliable and accurate interruption data can be used for many different purposes. Interruption data can be divided to detailed fault statistics and summaries of interruption statistics. Different interest groups in electricity market include customers, distribution companies, Energy Market Authority and Finnish Electricity Association and whether they

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need detailed or summarised interruption data depends on the party under consideration. Interruption data can be used as background data for reliability calculations and network planning. So the investments can be focused to those areas that cause the largest interruption costs. Different kinds of statistics and reports are made of interruption data for regulation purposes and as well for the companies own needs. Nowadays there are also tools with which customer-specific interruption data can be presented to customers [Kiv03]. Different interest groups that use interruption data are illustrated in figure 1. Detailed fault statistics

Customer

Distribution company

Summaries of interruption statistics

Energy Market Authority

Finnish Electricity Association

Figure 1: Different interest groups and their needs in interruption statistics. Comprehensive model for interruption statistics Comprehensive model for interruption statistics is suitable for those distribution companies that have at least SCADA-system and Distribution Management System. For those companies that are implementing comprehensive model the renewing compilation of interruption statistics does not bring many changes. All the information that is needed for the new statistics is collected and saved into database also at present, if customer data can be linked to corresponding network data. This may be the most problematic issue in some companies where, probably after several system updates, the network and customer databases are not synchronised and individual customers can not be linked to certain secondary substation. If the linking between the databases is correct, the only modifications come to reporting and data processing, because data systems do not at present have functions for the compilation of the new statistics. A key element in the compilation of interruption statistics is the interruption database (see figure 2). Part of the data is collected automatically with other data systems (e.g. SCADA, DMS) or databases (e.g. network database) and the control centre personnel collect some of the data manually. The interruption database in this context is somehow virtual database, because usually it is part of other data systems (e.g. DMS) database.

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Fault repairing team

SCADA

Mobile reporting

Interruption Database

Operator

Statistics and reports

DMS Customer id Annual energy

Customer Database

Substation id Component data History data

Web server

Customers

AM/FM/GIS Network Database Network planning Reliability calculations

Figure 2: Data sources and users in comprehensive model for interruption statistics. If the distribution company has the distribution management system, a great amount of interruption data is gathered automatically to interruption database. With the SCADA –system the operation times of each circuit breaker and remote-controlled disconnector are saved to the database. The switching operations of manually controlled disconnectors are also updated to the DMS for providing the real-time switching status of the whole network. So, together with the network model, those parts of the network that was left without supply can be discovered. The control centre operator or the fault repairing team can supplement the automatically gathered data with manually saved data, e.g. type of interruption (planned, unexpected) and the cause of fault. The fault repairing team may also have mobile reporting devices that make it possible to save, for example, data about the faulted component to the database straight from the field... Figure 3 illustrates an advanced solution of interruption database. In this case the interruption database is a part of larger DMS system database. The detailed customer information is got from the customer database. The secondary substation id links each customer to those interruption sectors that he/she has experienced, and the annual energy information is needed, for example, to calculate the interruption numbers of the Energy Market Authority. Each customer is attached to only one secondary substation, and each customer (secondary substation) can be attached to zero or many interruption sectors. Each interruption sector is depicted with one fault report, but many interruption sectors can be depicted with one fault report, respectively. The fault report includes more detailed data about the interruption (like 5

fault location and the cause of fault) and this data can be used, for example, to discover the causes of component failures. Figure 3 applies to fault interruptions, but same kind of logic applies also to planned interruptions, low voltage network interruptions and auto-reclosing occurrences, although there are also some differences. Customer •ID •Name •Tariff •LoadCurve •DayEnergy •NightEnergy •Phases •Fuse •SecSubstation •Heating

*

SecSubstationInterruptions •ID •InterruptionSector

1 *

1

InterruptionSectors •ID •Number •Type •Feeder •Started •Ended •Duration •SecSubstations •SecSubstationHours •Customers •CustomerHours •ENS

1 *

FaultReport •Number •Time •Utility •Substation •Feeder •CauseOfInterruption •CauseOfFault •FaultLocation •Address •FaultType •Ik •Wind •Humidity •Started •Ended •Duration •SecSubstations •SecSubstationHours •Customers •CustomerHours •ENS

Figure 3: One solution for advanced interruption database. Simplified model for interruption statistics It is clear, that the smallest distribution companies do not have resources to invest in latest technology. The changes and new challenges in the field of compilation of interruption statistics is settling new needs to data systems and these new obligations apply to smallest companies, too. In a simplified form the compilation of interruption statistics includes only a part of the components in figure 2. In this case, the data entry is on the responsibility of control centre personnel, and there are no automatic functions. Because the data is not as detailed as in comprehensive model, there is not as much purpose of use as in the detailed model. Mainly its purpose is to fill the obligation to compile certain interruption statistics, but in a small extent it can be used, for example, in network planning. Interfaces to network and customer databases are needed for the secondary substation and annual energy consumption data, which can be joined to be a part of interruption database, too (figure 4). In the case of simplified model the interruption database is also more concrete, because the application of interruption statistics is a stand-alone system and the interruption database cannot be part of some other database.

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Operator Interruption Database Statistics and reports

Annual energy

Substation id

Customer Database

Network Database

Figure 4: Simplified model for the compilation of interruption statistics. One alternative to implement simplified model for interruption statistics is to use a simple and easy-to-use application that the working group of Finnish Electricity Association on interruption statistics has developed for the smallest distribution companies. Interruption Manager is an application with which a small distribution company can compile the interruption statistics so that the expectations of Energy Market Authority and Finnish Electricity Association can be fulfilled. Interruption Manager makes it possible to save interruption data on the basis of secondary substation-based interruption sectors, make the fault reports and print some standard reports including the interruption numbers for Energy Market Authority and Finnish Electricity Association. In simplified model the distribution network can be depicted with simple three-level model. The method of network representation is pretty similar with the Windows Explorer directory tree. Each secondary substation is attached to the primary substation and feeder, which is feeding it (see figure 5). The situation can be assumed to be constant, i.e. the secondary substation can be linked to one feeder for the whole year based on the normal switching state.

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Figure 5: The representation of simplified network model and interruption data. Figure 6 illustrates the database structure of Interruption Manager. It is quite similar as in figure 3, but interruptions cannot be connected to individual customers, they are modelled only in the secondary substation level where also the number of customers and annual energy for each customer group are presented. With this same logic also the interruptions in low voltage network and planned interruptions are represented, but auto-reclosing occurrences are modelled only in the feeder level. SecSubstation •ID •Residential •ResidentialEnergy •Farming •FarmingEnergy •Industry •IndustryEnergy •Public •PublicEnergy •Commercial •CommercialEnergy

SecSubstationInterruption •ID •Interruption

Interruption •ID •Report •Started •Ended •Duration •SecSubstations •SecSubstationHours •Customers •CustomerHours •ENS

FaultReport •Number •Substation •Feeder •InterruptionType •CauseOfInterruption •FaultLocation •Address •FaultType •Wind •Humidity •Started •Ended •Duration •SecSubstations •SecSubstationHours •Customers •CustomerHours •ENS

Figure 6: Interruption database structure of Interruption Manager. INTERRUPTION COST MODELLING Especially in the case of comprehensive model for interruption statistics, modern network information systems offer a very useful tool to assessment of interruption cost structure and 8

analysis of network investments using reliability based network analysis. For any reliability calculations it is important to evaluate first the relevant parameters, e.g. failure rates for network components, interruption time modelling, and interruption cost parameters for different customer groups and interruption types. Issues of failure rates are discussed e.g. in [Ver03] and the definition of interruption cost parameters is described below. Defining the interruption cost parameters In this research, customers were divided into five groups, i.e. residential, agricultural, industry, public and commercial sector, which is a common partition in Finland. The latest researches about interruption costs are about 10 years old [Lem94], so the unit prices for interruption costs have not been updated for a while. Also the method that was used to determine the harm that customers feel in the case of interruption affects to the results. Usually the harm that customers feel is much more than the money they are willing to pay to avoid the interruption. In fact, there is now a new project together with technical universities for updating the interruption cost parameters. Table 1 shows the input data for the interruption cost definition. This data is collected from reference [Lem94], which refers also to older Finnish researches (STYV 1978, R10) about interruption cost parameters. In the table the unit of interruption cost parameters is [€/kW]. WTA (Willingness To Accept) means the sum of money that customer would want to get for accepting a 1 hour interruption. WTP is associated with willingness to pay to avoid an interruption. Any corrections depending on inflation etc. have not been applied. The parameters in table 1 are associated with a time when the use of electricity is rather remarkable and are thus scaled by the relevant load factors of the customer groups. Because at other times the harms are usually less critical, in calculations e.g. average powers instead of peak demand can be used if no other more accurate information is available. Table1: Interruption cost parameters, unexpected interruption. Residential STYV 1978 R10 vtt vtt, WTP Agriculture STYV 1978 R10 vtt vtt, WTA Industry STYV 1978 R10 vtt Commercial STYV 1978 R10 vtt Public STYV 1978 R10 vtt

1s 1s 0,04 1s 0,47 2,10 1s 0,70 1,91 1s 0,47 0,46

2 min 0,45 0,03 2 min 1,11 0,06 2 min 2,70 0,72 2,90 2 min 2,67 0,78 2,11 2 min 2,97 0,65 0,73

15 min 0,93 0,07 15 min 1,77 0,14 15 min 4,44 2,08 5,59 15 min 5,77 2,04 4,84 15 min 6,21 1,44 1,80

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1h

2h 1,90 0,22 0,43 2,23 0,68 1h 2h 3,63 0,40 0,83 11,92 5,44 1h 2h 10,20 6,77 10,04 11,15 1h 2h 12,87 4,68 10,57 12,60 1h 2h 12,14 4,21 3,98 -

4h

8h 4,88 0,86 1,36 9,60 34,42 4h 8h 11,09 1,68 4,31 146,16 58,69 4h 8h 35,90 16,58 22,24 32,25 58,75 4h 8h 53,24 22,38 28,31 33,33 68,71 4h 8h 42,15 10,36 15,89 10,66 23,72

For further analysis these parameters were processed to parameters A [€/kW] and B [€/kWh] by using linear regression using the values corresponding to 1 s … 1 h. The purpose of linear regression was to remove the discreteness of the questionnaires that were used to gather the data. For residential and agricultural customers linear regression was not used because for these groups were not enough data available about the shortest interruptions. Figure 1 shows the linear regression results and the parameters from the research for industrial customers. The slope of the line depicts the parameter B and the intersection of y-axis depicts the parameter A (see figure 7). Industry, unexpected outage

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Value [€/kW]

10 8 Linear regression

6

Parameters from research

4 2 0 0

10

20

30

40

50

60

70

Duration [min]

Figure 7: Results from linear regression. For planned interruptions linear regression was also used similar way to define the interruption cost parameters. Also with this interruption type residential and agricultural customer groups make an exception, and linear regression was not used. For residential and agricultural customer groups the harm of planned interruption was assumed to be 50 % of the harm of one-hour interruption for residential sector, and 34 % for agricultural sector, respectively [Lem94]. The price for auto-reclosing occurrences was taken from the VTTresearch (see table 1) for industry, commercial and public customers. For residential and agriculture customers the price was conservative assumed to be 5 % of one-hour interruption price for high-speed auto-reclosing and 13 % for delayed auto-reclosing. Table 2 describes the interruption cost parameters of different customer groups and interruption types based on the analysis and conclusions in [Jär03] for further studies. Table 2: Customer groups’ interruption cost parameters.

€/kW

€/kWh

€/kW

€/kWh

High speed AR €/kW

0,068 0,54 2,6 0,65 1,9

0,61 4,9 8,7 3,4 11

0,034 0,18 0,8 0,23 0,8

0,3 1,6 3,8 1,5 7,2

0,034 0,25 1,1 0,23 0,95

Unexpected outage

Residential Agriculture Industry Public Commercial

Planned outage

10

Delayed AR €/kW 0,088 0,7 2,9 0,73 2,1

Example calculation results In the interruption cost analysis there is two methods to choose. The first method is to calculate the interruption cost parameters and interruption costs at the secondary distribution substation level. This method takes also into account the customer group structure of the distribution substation. The other method is to use average interruption cost parameters at the national level without considering the customer groups more precisely. In these calculations the first method was applied to assess the structure of interruption costs. Usually major part (even over 80%) of customers’ interruption times is caused by interruptions in medium voltage networks and thus only these are now considered. A reliability calculation software [Ver03] was used to calculate the structure of interruption costs for two medium-size rural Finnish network companies, which customer structures differ a little from each other. Table 3 shows the results for company 1 and the results were rather similar for company 2, too (see table 4). In the calculations the interruptions were divided into four categories, i.e. unexpected and planned interruptions, and high-speed and delayed autoreclosing occurrences. In the table the column percentage means e.g. that 38,4 % of residential customers interruption costs are caused by unexpected interruptions. The row percentage means that e.g. 9,5 % of unexpected interruption costs is caused by residential customers’ interruptions. Table3: Structure of interruption costs for company 1. Residential Unexpected (col %) Unexpected (row %) Planned (col %) Planned (row %) Delayed AR (col %) Delayed AR (row %) High speed AR (col %) High speed AR (row %) Sum of column % Group's percentage

38,4 % 9,5 % 15,2 % 10,0 % 28,3 % 7,5 % 18,1 % 7,6 % 100,0 % 8,6 %

Agriculture Industry 41,8 % 22,3 % 10,8 % 15,2 % 28,6 % 16,4 % 18,8 % 17,0 % 100,0 % 18,4 %

30,3 % 30,7 % 11,3 % 30,5 % 36,5 % 39,7 % 21,9 % 37,5 % 100,0 % 35,0 %

Public 35,2 % 8,9 % 12,5 % 8,4 % 34,7 % 9,5 % 17,7 % 7,6 % 100,0 % 8,7 %

Commercial 33,5 % 28,5 % 15,9 % 35,9 % 29,5 % 26,9 % 21,1 % 30,3 % 100,0 % 29,3 %

Sum of outage type 34,5 % 100,0 % 13,0 % 100,0 % 32,1 % 100,0 % 20,4 % 100,0 % 100,0 % 100,0 %

Table 4: Structure of interruption costs for company 2. Residential Unexpected (col %) Unexpected (row %) Planned (col %) Planned (row %) Delayed AR (col %) Delayed AR (row %) High speed AR (col %) High speed AR (row %) Sum of column % Group's percentage

43,2 % 10,1 % 14,5 % 9,4 % 26,3 % 7,3 % 16,0 % 7,5 % 100,0 % 8,7 %

Agriculture Industry 47,0 % 2,6 % 9,6 % 1,5 % 27,4 % 1,8 % 16,0 % 1,8 % 100,0 % 2,1 %

34,7 % 51,9 % 12,2 % 50,1 % 33,6 % 59,8 % 19,6 % 58,2 % 100,0 % 55,3 %

Public 39,3 % 8,8 % 13,1 % 8,1 % 32,0 % 8,6 % 15,6 % 7,0 % 100,0 % 8,3 %

Commercial 38,1 % 26,5 % 16,2 % 30,9 % 27,2 % 22,5 % 18,5 % 25,5 % 100,0 % 25,6 %

Sum of outage type 37,0 % 100,0 % 13,4 % 100,0 % 31,0 % 100,0 % 18,6 % 100,0 % 100,0 % 100,0 %

As we can see from the tables above the percentage of auto-reclosings is in both of them surprisingly high, although the results with these two companies are very similar when compared to each other. If we take a look at table 2 where the interruption cost parameters 11

were defined we can see that costs for high-speed auto-reclosings are 5 – 10 % and for delayed auto-reclosings 10 – 20 % of the cost for one hour interruption. But still the percentage of costs of auto-reclosings is much more than was expected. If we think of the directing effects of this kind of cost structure, it may direct even rather strongly to decrease the number of auto-reclosings, e.g. by using arc suppression-coils. Thus in the future still more precise studies on relevant cost parameters for short interruptions are very well motivated. In Finland there is an ongoing project together with technical universities on updating the interruption cost parameters for all customer groups and interruption types. CONCLUSIONS Renewing interruption statistics settle new needs also for the data systems of the distribution companies. What it comes to filling the future expectations in interruption statistics, distribution companies are in different situation when compared to each other. Some companies are able to meet the new requirements immediately while others still have some work to do with their data systems. Anyway, further research has to be done to assure the righteousness of the regulation model, and to update the interruption cost parameters for each customer group. A project together with technical universities on updating the interruption cost parameters is ongoing at present. Regulation should also lead in progress in the business models and data systems of distribution network companies, not only to encourage the companies in the similar way of thinking than decades ago. In the paper the use of continuity of supply in the regulation of distribution network companies is discussed. A lot of work has been done to improve the directing effects of the regulation model of Finnish network companies. It is certain that in the future power quality in larger form will be a part of the regulation model. It seems that by adding interruptions as costs to operational costs in efficiency determination has better directing effects than the model that is in use at present. Also some example calculations are presented to illustrate the structure of interruption costs for two Finnish distribution companies. This was done to get a better knowledge of the righteousness of the proposed interruption cost parameters. It was seen that the cost of auto-reclosing occurrences was relatively high even the parameters in question were only a small portion of the equivalent parameter for one-hour interruption. REFERENCES [Cou03]

Council of European Energy Regulators, Working Group on Quality of Electricity Supply. Second Benchmarking Report on Quality of Electricity Supply. September 2003. 98 pages.

[Jär03]

Järventausta P., Mäkinen A., Nikander A., Kivikko K., Partanen J., Lassila J., Viljainen S., Honkapuro S. Sähkön laatu jakeluverkkotoiminnan arvioinnissa. Publications of the Finnish Energy Market Authority 1/2003. 171 p. (in Finnish)

[Kiv03]

Kivikko K., Antila S., Mäkinen A., Järventausta P. Web-based customer interruption monitoring using DMS system database. Proceedings of the AUPEC 2003 conference. Christchurch, New Zealand, September 2003. 6 p.

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[Kiv04]

Kivikko, K., Mäkinen, A., Verho, P., Järventausta, P., Lassila, J., Viljainen, S., Honkapuro, S., Partanen, J. Outage cost modelling for reliability based network planning and regulation of distribution companies. Proceedings of the DPSP 2004 Conference, Amsterdam, Netherlands, April 2004. 4 p.

[Lem94]

Lemström, B. and Lehtonen, M. 1994. Kostnader för elavbrott. Nordiska ministerrådets serie TemaNord, 1994:627, 165 p. (in Swedish).

[Ver03]

Verho P., Järventausta P., Mäkinen A., Nousiainen K., Juuti P., Pouttu M. 2003. Information system solution for reliability based analysis and development of distribution networks. CIRED 2003, International Conference on Electricity Distribution. Barcelona, Spain 2003.

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