medium voltage feeder reliability and cost analysis - … · medium voltage feeder reliability and...

Cattia Sarah Roduner

Medium Voltage Feeder Reliability andCost Analysis

Master ThesisPSL1609

EEH – Power Systems LaboratorySwiss Federal Institute of Technology (ETH) Zurich

Examiner:Prof. Dr. Gabriela Hug

Supervisors:Efstratios Taxeidis (BKW)

Stavros Karagiannopoulos (ETH)

Zurich, September 2016

Abstract

The changing environment in the energy sector creates a need for distribu-tion system operators (DSOs) to be able to robustly quantify the impactthat possible measures have on the reliability of supply of their networks.The key focus of this master thesis is to propose a method to analyse howchanges in the topology or configuration of a distribution network have animpact on the reliability of supply within the system and on the total costsfor the utility. The measures that are evaluated in this thesis consider differ-ent remote control and protection schemes of existing feeders. The tool thatis presented simulates the system restoration process after a failure occursclosely to the current practice of the DSO in order to calculate the reliabilityindices. It can, therefore, be used in the future by DSOs as a decision sup-port to take into account reliability of supply in the grid planning process.The practicability of the tool is demonstrated on three different case studiesthat were suggested by BKW, an electric utility operating the distributiongrid in the cantons Berne and Jura.

The proposed tool calculates the life cycle costs and reliability indices fora specific distribution network topology with a given remote control and pro-tection scheme. For the cost calculation, investment costs, as well as opera-tional and maintenance costs are considered. For the reliability calculation,the main output is the system average interruption duration (SAIDI). How-ever, also other indices such as the system average interruption frequencyindex (SAIFI) are calculated. In reality, the different feeders within a DSO’snetwork can have varying characteristics depending, e.g., on the geographi-cal environment. The tool was developed such that it can handle all kindsof radially operated feeders. Often, already small differences in the networkconfigurations as, for example, installing one additional circuit breaker canhave a significant impact on SAIDI. In order to also acknowledge the impactof such small differences and ensure the ability to model the main reliabilityenhancement measures, the tool consists of a detailed algorithm that modelsthe current practice of the utility. The algorithm individually calculates theinterruption durations for all considered failure states within the network.Therefore, the algorithm makes use of analytical simulations that model theprotection system response and the restoration procedure and take into ac-

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count local factors, e.g., by using Google Maps Directions to determine thenecessary driving times.

In the final chapter of this thesis, the tool is used for conducting threedifferent case studies. The first case study compares nine different remotecontrol and protection schemes plus a base topology of one medium volt-age feeder of BKW. The goal is to investigate the impact of an increasednumber of protection devices or remote controlled switches on costs and re-liability within the given system. From the results, the most cost efficient ofthe investigated topologies can be determined with a newly introduced keynumber. The second suggested case study extends the same analysis of tentopologies to 8 additional feeders that can be grouped in three types. Thegoal of this analysis was to determine general statements about the impactof the investigated measures and also the influence that different feeder typeshave on SAIDI. In a third case study, the tool’s ability to determine the op-timal topology of a feeder within given boundaries is presented. The resultsof this optimization are then compared with the ten selected topologies ofthe second case study.

Acknowledgements

This thesis would not have been possible without the support of many peo-ple. First and foremost, I offer my sincerest gratitude to my supervisorsEfstratios Taxeidis from BKW and Stavros Karagiannopoulos from ETHZurich for their support and guidance, as well as their encouragement andmotivation. They were both always willing to help me and gave me advicefrom their impressive expertise.

My grateful thanks also go to Prof. Dr. Gabriela Hug for agreeing tothis project and giving me the chance to write my thesis at ETH Zurich andin cooperation with BKW. Certainly, I further want to thank BKW, andespecially Andreas Ebner and Arnaldo Fava, giving me the opportunity towork on this project within their department and team.

My genuine appreciation and thanks also go to the asset managementteam of BKW for making my stay in Berne a great experience and teachingme so many things. I also would like to thank my fellows from ETH forinteresting discussions and making the days in Zurich so much more fun.

Above all, I owe the most important and greatest appreciation to myparents and family, without whom I would never have been able to achieveso much. Thank you for providing me with all the support I needed andalways being there. Lastly, I would like to thank my close friends and,especially Adrian, for always motivating me to do my best and keeping myspirits up. Thank you for always believing in me and being so patient andoptimistic even in times when all I could see was the hard work ahead.

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Contents

List of Tables vii

List of Figures ix

List of Acronyms x

1 Introduction 11.1 Problem Definition and Motivation . . . . . . . . . . . . . . . 11.2 Project Objectives . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Reliability Analysis in Distribution Grids 42.1 Reliability Analysis Basics . . . . . . . . . . . . . . . . . . . . 4

2.1.1 Basic Load Point Indices . . . . . . . . . . . . . . . . 62.1.2 Customer Based System Reliability Indices . . . . . . 62.1.3 Load Based System Reliability Indices . . . . . . . . . 72.1.4 Exclusion of Major Events . . . . . . . . . . . . . . . . 8

2.2 Techniques for Distribution Grid Reliability Assessment . . . 82.2.1 Network Modelling . . . . . . . . . . . . . . . . . . . . 102.2.2 Markov Modelling . . . . . . . . . . . . . . . . . . . . 102.2.3 Analytical Simulation - State Enumeration . . . . . . 112.2.4 Monte Carlo Simulation . . . . . . . . . . . . . . . . . 12

2.3 Component Reliability Modelling and Reliability Data Col-lection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.3.1 Component Reliability Modelling . . . . . . . . . . . . 142.3.2 Reliability Data Collection . . . . . . . . . . . . . . . 15

3 Tool for Estimation of Feeder Reliability and Topology Costs 173.1 Key Characteristics and General Overview . . . . . . . . . . . 173.2 Input Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.2.1 Network Data . . . . . . . . . . . . . . . . . . . . . . . 223.2.2 Reliability Data . . . . . . . . . . . . . . . . . . . . . 233.2.3 Cost Data . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.3 Proposed Methodology . . . . . . . . . . . . . . . . . . . . . . 26

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CONTENTS vi

3.3.1 Considered Failure States . . . . . . . . . . . . . . . . 263.3.2 Protection System Response . . . . . . . . . . . . . . . 273.3.3 Restoration Process . . . . . . . . . . . . . . . . . . . 293.3.4 Reliability Calculation . . . . . . . . . . . . . . . . . . 343.3.5 Cost Calculation . . . . . . . . . . . . . . . . . . . . . 363.3.6 Cost-Reliability Key Number . . . . . . . . . . . . . . 37

3.4 Tool Demonstration . . . . . . . . . . . . . . . . . . . . . . . 373.4.1 Example Feeder . . . . . . . . . . . . . . . . . . . . . 383.4.2 Simulation of one Failure State . . . . . . . . . . . . . 393.4.3 Result Calculation . . . . . . . . . . . . . . . . . . . . 47

4 Case Studies 494.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.2 Case Study 1 - Analysis of one Medium Voltage Feeder . . . . 50

4.2.1 Different Feeder Topologies . . . . . . . . . . . . . . . 504.2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 524.2.3 Summary and Conclusion . . . . . . . . . . . . . . . . 58

4.3 Case Study 2 - Analysis of Different Feeder Types . . . . . . 594.3.1 Different Feeder Types . . . . . . . . . . . . . . . . . . 604.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 604.3.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . 64

4.4 Case Study 3 - Finding the Optimal Topology of a Feeder . . 664.4.1 Test Feeder . . . . . . . . . . . . . . . . . . . . . . . . 664.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 664.4.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . 70

5 Conclusions and Outlook 715.1 Summary and Discussion of Results . . . . . . . . . . . . . . 715.2 General Conclusions . . . . . . . . . . . . . . . . . . . . . . . 725.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Bibliography 75

List of Tables

3.1 Component failure rates for the example feeder. . . . . . . . . 403.2 Operational for the example feeder. . . . . . . . . . . . . . . . 423.3 Reliability results of the example feeder for each failure type. 47

4.1 Characteristics of the ten evaluated topologies for the consid-ered feeder. PR stands for protection relays and RC standsfor remote control. . . . . . . . . . . . . . . . . . . . . . . . . 52

4.2 Characteristics of the different feeder types. . . . . . . . . . . 60

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List of Figures

3.1 General overview of the reliability calculation method. . . . . 213.2 Schematic of the simulation of the protection scheme response. 283.3 Overview of the restoration process simulation. . . . . . . . . 303.4 Decision algorithm for the switch selection during the fault

localization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.5 Algorithm for the decision if partial system restoration should

be done. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.6 Map of the example feeder. . . . . . . . . . . . . . . . . . . . 383.7 Schematic transformer station set up as it could be found for

transformer station PTS1. This schematic was modified fromBKW’s grid model. . . . . . . . . . . . . . . . . . . . . . . . . 40

3.8 Stepwise simulation of fault localization for the example feeder.First Steps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.9 Stepwise simulation of fault localization for the example feeder.Last Steps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.1 Table of the ten evaluated topologies. . . . . . . . . . . . . . 514.2 SAIDI results of the ten evaluated topologies. . . . . . . . . . 534.3 SAIFI results of the ten evaluated topologies. . . . . . . . . . 544.4 Cost results of the ten evaluated topologies. . . . . . . . . . . 564.5 Key numbers of the nine concepts relative to topology 1. . . . 574.6 Reliability fairness results of the ten evaluated topologies. . . 584.7 Key number results of the nine topologies for the three feeder

types relative to the over all best Topology (T1 of long feeders). 614.8 Key number results of the nine topologies for the three feeder

types. Each topology relative to the respective feeder type’sbest topology (T1 of each feeder type). . . . . . . . . . . . . . 62

4.9 SAIDI improvement potential relative to the basic topologyand necessary investment costs for each feeder type. . . . . . 64

4.10 SAIDI improvement potential relative to today’s topologyand necessary investment costs for each feeder type. . . . . . 65

4.11 Base topology of the investigated feeder. . . . . . . . . . . . . 674.12 Results of the topology optimization for the analysed feeder. 68

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LIST OF FIGURES ix

4.13 Analysis of the Pareto optimality frontier of the analysedfeeder with respect to the number of equipped switches. . . . 69

List of Acronyms

CAPEX Capital Expenditure, i.e. investment costsDSO Distribution System OperatorElCom Federal Electricity CommissionFL Overhead LineGIS Geographical Information SystemGTS Building or Compact Transformer StationKL Underground CableMTS Pole Transformer StationNPV Net Present ValueOPEX Operational Expenditure, i.e. ongoing cost for a system or productO & M Operational and Maintenance (Cost)RC Remote ContolTS Transformer StationUST (Primary) SubstationWACC Weighted Average Cost of Capital

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Chapter 1

Introduction

1.1 Problem Definition and Motivation

The energy sector gets a lot of attention over the last years and the com-plete sector is undergoing large changes. To name only the most knownexamples relevant for electricity distribution, there is an increasing penetra-tion of volatile renewable energy sources and a growing interest in batterystorage and demand side management in literature. Trends like these arewidely discussed (e.g [1], [2]). A part of the changes in the electricity dis-tribution sector are also driven by the liberalization of the energy marketand the dropping electricity prices. These factors create an increased costpressure on distribution system operators (DSOs) and, therefore, targets aredefined to reduce grid cost. Further, DSOs are required to operate an effi-cient network and, therefore, to implement the most economical variant ofthe available grid expansion methods [3], [4], [5].

The statutory core tasks of a DSO include, among others,

– operating a secure, powerful and efficient network,

– ensuring access to the grid to all customers, and

– ensuring a certain quality of reliability.

A key aspect of Distribution System Operators (DSOs) is the reliability ofsupply, i.e. the ability to maintain a stable network, capable of supplyingall consumers with electricity constantly. European electricity transmissionand distribution networks, and notably also the Swiss network, are currentlyknown to have a high reliability of supply [6]. On one hand, if grid costs haveto be reduced, it is possible that less is invested in expanding or maintainingthe grid. This creates a risk that also the grid reliability will be reduced.On the other hand, however, there are factors that provide an incentivefor investments to maintain or even increase the reliability of supply. In

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CHAPTER 1. INTRODUCTION 2

Switzerland, one is the relatively new sunshine regulation, introduced andcurrently tested by ElCom (Eidgenossische Elektrizitatskommission), whichis a multidimensional national benchmarking system for the DSOs. Relia-bility of supply, specifically SAIDI, is one of the indicators in this benchmark.

In this given set up it is crucial for an electricity distribution utility tobe able to determine a robust quantification of the impact on reliability ofsupply and the cost efficiency of proposed measures to maintain or enhancereliability of supply. What is needed for such an assessment is a flexible toolthat uses specific data from the DSO as input in order perform differenttypes of analyses. Many reliability analysis tools exist and are continuouslybeing further developed. They often come included with or as an expansionpack to grid analysis software that is already used anyway by the DSO.Examples are NEPLAN [7], SINCAL [8], and so on. Anyhow, these toolsare often quite generic and while they offer good consideration of the gridmodel they can usually not be adjusted to reflect the DSO’s operationalpractices. The ideal tool needs to be flexible considering the wide rangeof possible measures to enhance or maintain reliability of supply and easyto handle. It should also model the operational principles of the DSO ina detailed manner such that the influence of so called “soft measures”, i.e.measures that do not involve construction work can be quantified as well.

1.2 Project Objectives

This master thesis aims at doing the first step towards the aforementioned“ideal” distribution system reliability and cost analysis tool. The main goalis to develop a method and a tool for reliability analysis that can serve as abasis for DSO specific analysis of the cost efficiency of wide ranged reliabilityenhancement measures. The applicability of the developed method will thenbe demonstrated for different types of case studies. The main goals that weredefined in the beginning of the thesis are:

1. Develop a tool that evaluates reliability and cost aspects of specificgrid configurations in distribution grids.

2. Further develop the tool to be able to assess the trade-offs betweencost and impact on reliability of different measures to enhance thereliability of supply.

3. Apply the tool to specific case studies, suggested by BKW, to demon-strate its functionality and identify next steps.

The focus of the developed tool should be on the medium voltage (MV)level since the MV level has a high impact on the total reliability indices of a

CHAPTER 1. INTRODUCTION 3

DSO and also most potential for improvement. The high voltage (HV) levelis already n − 1 secure and the degree of protection is already very high,thus no major changes are expected in the near future. In the MV and lowvoltage (LV) level there exists much more room for improvement. However,LV failures usually have very local effects and affect far fewer customers thanMV failures.

An earlier master thesis at the power systems laboratory of ETH Zurich(PSL), written by Michiel Tavernier [9], served as a source of inspiration forthe first goal in a early stage of the project.

1.3 Thesis Structure

This master thesis is divided into five chapters. In the introduction the cov-ered problem is characterized and the motivation for this master thesis isgiven. Furthermore, the goals of the project are defined.

Chapter 2 first introduces the basic concepts of reliability analysis in thedistribution grid. In the next section, the most commonly mentioned tech-niques for reliability assessment in literature are discussed. The last sectionof Chapter 2 treats the methods and necessary data for reliability modellingof grid components.

In Chapter 3 all aspects of the proposed method and developed relia-bility and cost analysis tool are explained. First, the key characteristicsthat define the tool are summarized and motivated, and an overview on themethodology is given. The second section lists all needed input data andshortly discusses the respective specialities. In a third and main section,the proposed methodology is explained in detail. In Section 3.4, the tool’smethod is demonstrated on one example feeder.

The tool was applied in two case studies. These two case studies arepresented in Chapter 4. The first case study analyses protection schemesand remote control layers for a single feeder of BKW’s medium voltage gridand is laid out in section 4.2. In a next section, the second case study ispresented. In this case study the same type of analysis is expanded to a totalof nine medium voltage grid feeders of different types to analyse the generalimpact of measures considering protection devices and remote control. Thedifference of the feeder types is outlined in Section 4.3.1.

Finally, Chapter 5 provides a summary and discussion of the results fol-lowed by an outlook on possible future developments on the tool in Chapter5.

Chapter 2

Reliability Analysis inDistribution Grids

This chapter aims at giving an overview and introduction to reliability as-sessment in electrical distribution networks. It gives the reader the chanceto become familiar with some general fundamentals regarding reliability indistribution grids and serves as background for Chapter 3 and Chapter 4 ofthis thesis. Most of the information in this chapter is extracted from [10]and [11]. Experienced readers are recommended to skip this chapter.

The chapter begins by defining some general terms used in the area ofdistribution system reliability and providing the definitions for the most com-monly used indices to quantify reliability. The second part of the chapterfocuses more specifically on commonly used reliability assessment techniquesand discusses their suitability and advantages. Some fundamental princi-ples of how to model the reliability of components for the aforementionedreliability assessment techniques are introduced in the remaining part.

2.1 Reliability Analysis Basics

As mentioned in the introduction, ensuring a high reliability of supply isone of the core tasks of DSOs. Reliability of supply, as it is used to mea-sure a DSO’s performance, relates (primarily) to customer interruptions.Distribution reliability is becoming significantly important in the currentcompetitive environment because the distribution system feeds the customerdirectly. The distribution system is the link of the utility to the customer.In normal operating conditions, all customers and all equipment that arenot in stand-by mode are energized. Planned and unplanned events, e.g.switching actions or failures, can disrupt normal operating conditions andlead to interruptions. Brown’s book on distribution system reliability [10]gives several key definitions for such events and their consequences. The

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CHAPTER 2. RELIABILITY ANALYSIS THEORY 5

ones that are relevant for this thesis are repeated here:

Normal Operating State - the state of the distribution system whenswitch positions are in their usual positions, no protection devices havetripped, all components are operating properly, and loading levels arewithin design limits.

Contingency - An unexpected event such as a fault or an open circuit.Another term for a contingency is an unscheduled event.

Fault - A short circuit. Faults are caused by dielectric breakdown of in-sulation systems and can be categorized as self-clearing, temporary,and permanent. A self-clearing fault will extinguish itself without anyexternal intervention (e.g., a fault occurring on a secondary networkthat persists until it burns clear). A temporary fault is a short circuitthat will clear if deenergized and then re-energized (e.g., an insulatorflashover due to a lightning strike — after the circuit is de-energized,the fault path will de-ionize, restoring the insulator to full dielectricstrength). A permanent fault is a short circuit that will persist un-til repaired by human intervention. For this thesis relevant are onlypermanent faults.

Outage - An outage occurs when a piece of equipment is de-energized. Out-ages can be either scheduled or unscheduled. Scheduled outages areknown in advance (e.g., outages for periodic maintenance). Unsched-uled outages result from contingencies.

Sustained Interruption - A sustained interruption occurs when a cus-tomer is de-energized for more than a few minutes. Most sustainedinterruptions result from open circuits and faults. Especially in sys-tems with mostly overhead lines more common are momentary inter-ruptions where customers are only de-energized for less than a fewminutes (less than three minutes in Switzerland) due to reclosing orautomated switching.

The assessment of reliability of supply aims at describing the frequencyand consequences or duration of such events as perceived by customers orthe operator. Quantifying the reliability of a distribution grid requires welldefined units of measurement, so called reliability indices. An overview ofthese indices is given in the following. Power system utilities all over theworld use reliability indices to track the performance of their systems. Regu-lators in some countries require utilities to report their reliability indices. InSwitzerland the so called “Sunshine Regulation” is currently in a test phase.The regulatory trend in Europe seems to be moving towards performancebased rates where performance, quantified by reliability indices in compari-son to a benchmark, is penalized or rewarded. Some of the commercial and


industrial customers may ask DSOs for their reliability indices when plan-ning to find a new site and deciding on their connection point.

In his book [11] Roy Billinton groups the indices into the classical generalload point indices and system reliability indices. The system reliability in-dices can be grouped further into customer and load based reliability indices.Most reliability indices are average values of a particular reliability charac-teristic and can for example be applied to an entire system, an operatingregion, a substation service territory, or also a single feeder. In this sectionthe basic load point indices as well as the most important and commonlyused reliability indices are defined. The definitions are given in accordancewith the current IEEE standard [12].

2.1.1 Basic Load Point Indices

The three fundamental parameters of load point adequacy are the averagefailure rate, average outage duration and average annual outage time. Theseindices“are not deterministic values but are the expected or average values ofan underlying probability distribution and hence only represent the long-runaverage values.” [11]

– Load point failure rate λs

– Load point outage duration rs

– Load point annual unavailability Us = λs · rs

These fundamental indices are of course important. However, they can-not give a complete representation of the system behavior and response.Particularly, these indices do not display the severity of an interruption ofa load point as they do not take into account how many customers or howmany kilo- or megawatts are connected to this load point. In order to reflectthe significance of an interruption, additional system reliability indices canbe and frequently are evaluated.

2.1.2 Customer Based System Reliability Indices

Customer-based reliability indices are the most widely used. They weigheach customer equally and are very popular among regulating authorities,mostly because a small residential customer has just as much importanceas a large industrial customer. They are generally considered good aggre-gate measures of reliability and are often used as reliability benchmarks andimprovement targets. Formulae and further explanations for the typicallyused customer-based reliability indices are given in the following. In theseformulae, interruptions refer to sustained interruptions as defined above.


System Average Interruption Frequency Index (SAIFI)SAIFI is a measure of how many sustained interruptions an averagecustomer will experience over the course of a year. For a fixed numberof customers, the only way to improve SAIFI is to reduce the numberof sustained interruptions experienced by customers.

SAIFI =Total Number of Customer Interruptions

Total Number of Customers Served(2.1)

System Average Interruption Duration Index (SAIDI)SAIDI is a measure of how many interruption hours an average cus-tomer will experience over the course of a year. For a fixed numberof customers, SAIDI can be improved by reducing the number of in-terruptions or by reducing the duration of these interruptions. Sinceboth of these reflect reliability improvements, a reduction in SAIDIindicates an improvement in reliability.

SAIDI =

∑All Customer Interruption Duration


Customer Average Interruption Duration Index (CAIDI)CAIDI is a measure of how long an average interruption lasts, andis used as a measure of utility response time to system contingencies.CAIDI can be improved by reducing the length of interruptions, butcan also be reduced by increasing the number of short interruptions.Consequently, a reduction in CAIDI does not necessarily reflect animprovement in reliability.

CAIDI =

∑All Customer Interruption Durations

Total Number of Customer Interruptions=

SAIDI

SAIFI(2.3)

Momentary Average Interruption Frequency Index (MAIFI)MAIFI is a measure of how many momentary interruptions an averagecustomer will experience over the course of a year. A high MAIFIindicates areas subject to temporary faults such as lightning, tree faultsor animal faults.

MAIFI =Total Number of Momentary Customer Interruptions


2.1.3 Load Based System Reliability Indices

Older than the customer based are the load based distribution system re-liability indices. Instead of based on the actual number of customers, theyweigh customers based on the connected power (kVA). This type of index


has been used for a longer time period simply because, in the past, utilitiesknew the size of the distribution transformers but did not know withoutfurther investigation how many customers were connected to each trans-former. Also, Brown states in [10] that “From a utility perspective, ASIFIand ASIDI probably represent better measures of reliability than SAIFI andSAIDI.” Since “Larger kVA corresponds to higher revenue and should beconsidered when making investment decisions.”

Average System Interruption Frequency Index (ASIFI)ASIFI is the load based counterpart to SAIFI. It, too, determines howmany sustained interruptions will be experienced in average weightedby the size of the distribution transformers. The formula for the cal-culation is as follows:

ASIFI =Connected Load Interrupted

Total Connected Load Served(2.5)

Average System Interruption Duration Index (ASIDI)The load based equivalent to SAIDI would then be ASIDI which de-termines the average interruption duration based on the connectedpower:

ASIDI =Connected Load Hours Interrupted

Total Connected Load Served(2.6)

2.1.4 Exclusion of Major Events

A special note desire major events like big storms or even a purposefulattack. Such events have really high impacts on the reliability indices ofthe year in which they happen but unless they occur regularly they do notrepresent the average state. Of course from the customer cost perspective, itdoes and should not matter what the reasons for interruptions are. However,from a planning perspective of a DSO, it is not feasible to design the gridsuch that it can withstand extreme weather, as large floods or forest firesfor example, or direct attacks. Therefore, it can make sense to exclude suchmajor events from the planning process. If major events can be excludedfrom benchmark regulation systems or not is again a different topic. Theincentives to spend money on preventing damage might be altered if someevents can be excluded. Brown [10] further discusses and gives an examplefor this possibility.

2.2 Techniques for Distribution Grid ReliabilityAssessment

Here a brief summary of the various means of computing reliability indicesare presented. Brown [10] identifies four categories for distribution reliabilityassessment techniques which are summarized as follows in a Cigre report [13]:


Network Modelling“Network Modelling refers to translating the physical system to a reli-ability network of components described by their probability of beingavailable. If two components are connected in series, both need to beavailable for the combination to be available. If they are connected inparallel it is sufficient if one of them is available. It is a relatively sim-ple technique to gain familiarity with the concepts of reliability eval-uation. Therefore, it is often described in basic reliability textbooks.However, it is sometimes too simple to properly model the behaviourof a distribution system with the complexities of its components suchas automated reclosers, for example.”

Markov Modelling“These techniques represent the various states of the system and thetransition rates between these states. This is a powerful frameworkfor reliability analysis but has some drawbacks. Matrix inversion isrequired to solve and the number of states in a realistic distributionsystem can be enormous. Also, quantifying the probabilities for thetransitions is more time-consuming than most distribution engineersare willing to commit. Gonen [14] gives a detailed example.”

State Enumeration“Analytical simulation techniques model system responses to contin-gencies, allowing the impact that the contingency has on each compo-nent to be calculated. Once calculated, the impact of the contingencyis weighted by its probability of occurrence, resulting in the expectedannual reliability impact of the contingency on each component. Theexpected annual reliability characteristics for each component are ob-tained by summing the individual contributions of each contingency.”

Monte Carlo Simulation“This refers to representing possible events by probability density func-tions and generating sequences of events randomly. There are severaladvantages to this method, including the ability to represent complexsystem behaviour. A drawback has been that it is computationally in-tensive. However, it is gaining in popularity with the advent of fastersimulation tools. It is being promoted by well-known power industryreliability analysts, e.g., Billinton [15].”

These four categories, their main domain of application as well as their prosand cons are further discussed in the following sections. There exist moremethodologies, for example, fault tree analysis or failure modes and effectsanalysis, but these are not considered here.


2.2.1 Network Modelling

Network modelling is a component based technique that translates the phys-ical network into a reliability network. Components are described throughtheir probability of being available. For example, the probability of beingavailable can be determined through the component’s annual failure rate(λ) and Mean Time to Repair (MTTR): P = 8760h−λ·MTTR/8760h. λ andMTTR will be further explained in Section 2.3.1. The individual compo-nents are connected in series if both need to be available for the connectionto be available and in parallel if only one available component is sufficient.Usually systems consist of several series and parallel connections. In thesecases it is necessary to perform a network reduction or form a minimal cutset in order to compute its overall availability. The goal of a network re-duction is to reduce the network by combining sets of parallel and seriescomponents into equivalent network components until one component re-mains. A minimal cut set, on the other hand, is “a set of n components thatcause the system to be unavailable when all n components are unavailablebut will not cause the system to be unavailable if less than n componentsare unavailable”. Comprehensible examples of network modelling can, forexample, be found in: [16], [10], and [11]

2.2.1.1 Advantages and Disadvantages

Network modelling is a straightforward method to gain understanding andfamiliarity with reliability analysis. However, it is not suitable to modelthe complex response of distribution systems to contingencies. Due to itscomponent based character it can only handle very simple switching modelsbut not more complicated processes as, for example, recloser operations orpartial service restoration. Hence, state based methods, as the followingones, are needed for more detailed analyses.

2.2.2 Markov Modelling

Unlike network modelling, Markov modelling is a state based method thatmodels the various states of the system and the transition between thesestates. Most importantly, Markov models assume that the system is mem-oryless and stationary. A memoryless system means that the probability offuture events is not dependent on past events but only on the current systemstate. The system being stationary means that the transition probabilitiesbetween the system states are constant in time. Markov models can eitherbe discrete at specific time steps or continuous. In the discrete Markov chainthe state probabilities at a time step n can be computed based on a statetransition matrix of the transition probabilities and the state probabilities attime step n− 1. Reliability models are are concerned with the steady stateprobabilities that occur when the number of time steps is large and each


state converges to a constant value. Instead of state transition probabilities,the continuous Markov process uses state transition rates between differentstates. These transition rates are equivalent to the component failure ratesthat will be described in section 2.3.1. Additionally switching rates and re-pair rates are important values. States in a Markov process are characterizedby transitions into and transitions out of the state. Markov processes aresolved in a manner similar to Markov chains except that differential equa-tions rather than difference equations are utilized. Brown [10] gives a verygood example for both the discrete and the continuous Markov model.


Markov modelling is a powerful framework for reliability analysis and hasbeen successfully applied in many cases. However, it has some drawbacks.It requires matrix inversion to solve and the number of states in a realisticdistribution system can be enormous. Additionally to the method beingcomputationally intensive, also, quantifying the probabilities for the transi-tions is time-consuming. Because of this, simulations like state enumerationor Monte Carlo simulation are more advisable if many large systems shouldbe analysed.

2.2.3 Analytical Simulation - State Enumeration

As already described in the summary above, state enumeration is an ana-lytical simulation technique that models the system as a collection of (semi-stationary) states. Each state represents a certain contingency or a certaincombination of concurrent failures. The impact of each of these states andthe system’s response to it is simulated, for example, in a stepwise process.For the simulation, the system’s protection scheme as well as the restora-tion practices have to be known and modelled. The impacts of each contin-gency are then weighted by the contingency’s probability of occurrence andsummed. As a result, one gets the expected number of annual interruptionsand interruption hours for each component. It is also possible to calcu-late the expected annual operation frequencies for switching and protectiondevices. From such results it is, for example, possible to determine the op-erational costs in detail or to investigate which switches are used often and,hence where remote control would make sense. If the necessary parametersare known, the system’s response can be modelled in high precision anddetailed physical and operational characteristics can be represented. Thetechnique is described well by Brown [10] and will be shown in detail in theproposed methodology in Chapter 3.



Unlike Markov modelling, analytical simulation has no need for matrix in-version and can therefore also handle large realistic distribution systems inreasonable time. Another advantage of analytic simulation is that it is pos-sible to model the response to a contingency in accordance to the utility’spractices to any desired detail. However, with increasing detail in modellingthe system’s response, the computation time can of course increase again.Since average values are used as reliability data input, only expected valuesfor the reliability can be determined. If the system is in its normal operatingstate most of the time and if expected values for reliability are desired, stateenumeration is generally the preferred method. But again, in practice, thenumber of states can become too large to handle, resulting in high compu-tation time or a too complex and time-consuming process for modelling thesystem’s response to all possible system states.

2.2.4 Monte Carlo Simulation

The idea of Monte Carlo simulation is to simulate actual years based on fail-ure probabilities to simulate random contingencies in various components.For this, random number generators are used to model sequences of con-tingencies randomly over a certain number of years. The impact of eachoccurred contingency is simulated through the system’s reaction which canbe randomized as well. By normalizing the results of all occurred contin-gencies to one year, yearly estimated reliability indices can be determined.If the number of simulated years is high enough, even rare events can besimulated this way. Monte Carlo simulation can be further divided into twobasic varieties: sequential and non-sequential.

Sequential Monte Carlo SimulationA sequential Monte Carlo simulation tries to model system behaviourthe way it happens in reality. To achieve this, the simulation timeperiod is divided into small slices. The slices are simulated in se-quence and in each slice possible new contingencies are identified andresponses to unsolved contingencies are simulated. This results in asequence of random events that build upon each other as the sys-tem progresses through time. System contingencies that are modelledby probability distributions can randomly occur at any point in thesimulation. Additionally, for conditional contingencies the probabilityof occurrence depends upon previous events and the present state ofthe system. Also, the system’s responses to contingencies are proba-bilistically modelled. Depending on the implemented level of detail,a sequential Monte Carlo simulation can produce a highly realisticsimulation.


Non-Sequential Monte Carlo SimulationIf the majority of contingencies is mutually exclusive and it is assumedthat the system behaviour is not dependent on previous events, such acomplex method as sequential Monte Carlo Simulation is not needed.The less complex non-sequential Monte Carlo Simulation selects con-tingencies or system states in a probabilistic fashion and simulatesthem in an arbitrary order. It starts with a pool of possible con-tingencies and – with a random number – determines the number ofoccurrence in the simulation time period for each contingency. Onlycontingencies with non-zero number of occurrence are simulated. Mostcontingencies will not occur during one year of the simulation time pe-riod, which cuts down on necessary computation time. The impact ofeach simulated contingency is determined through the system’s reac-tion in the same way as for the analytical simulation (Section 2.2.3).Instead of weighting the impact of the states by the probability of theiroccurrence, the impact is weighted by their number of occurrence.


Depending on the goals of the analysis, Monte Carlo simulation offers sev-eral advantages compared to analytical simulation. For instance, MonteCarlo simulation can produce a wider and more detailed spectrum of re-sults. By a relatively easy to achieve change from analytical simulation toa non-sequential Monte Carlo simulation, the results can be upgraded fromexpected values to other statistically useful results such as median, mode,variance, and confidence intervals. Further, the ability to model componentparameters as random variables characterized by their probability distribu-tion functions rather than just constant values, allows for much more realisticmodelling. With the more complex sequential Monte Carlo simulation it ispossible to easily model complex system behaviour such as non-exclusiveevents, cascading failures, conditional probabilities, and so on. However,as always, such improvements come at a certain cost. The most importantdrawback is the increasing computational intensity. To produce reliable re-sults, it is always necessary to model up to hundreds of years which leadsto drastically increased computation time. However, if the computationallimits are not too restrictive and long time periods can be simulated, thismethod allows to also consider rare major events which are otherwise usuallynot included in the mentioned assessment methods.


2.3 Component Reliability Modelling and Relia-bility Data Collection

Thousands of different components make up a distribution system. To justlist a few, there are transformers, circuit breakers, overhead lines and under-ground cables, switches and many more. The assembling of these compo-nents is what defines the topology of a system and its characteristics. Almostall information needed for a reliability analysis of a system can be found inthe characteristics of the system’s components. Only if the components andtheir connections are modelled correctly, the system can be modelled cor-rectly. Further, many measures that are implemented can be modelled bythe impact they have on the components. Some measures might remove oradd components, others might just modify their characteristics.

2.3.1 Component Reliability Modelling

In his book [10] Brown mentions a whole series of reliability parameters todescribe the components of a distribution system. While simple reliabilitymodels might use only a few of them, usually failure rates and repair times,more sophisticated models can include many more parameters. Brown’ssummary of the most common component reliability parameters is providedhere:

Permanent Short Circuit Failure Rate (λP ) - λP describes the num-ber of times per year that a component can expect to experience apermanent short circuit. This type of failure causes fault current toflow, requires the protection system to operate, and requires a crew tobe dispatched for the fault to be repaired.

Temporary Short Circuit Failure Rate (λT ) - λT describes the num-ber of times per year that a component can expect to experience atemporary short circuit. This type of failure causes fault current toflow, but will clear itself if the circuit is de-energized (allowing the arcto de-ionize) and then reenergized.

Open Circuit Failure Rate (λOC) - λOC describes the number of timesper year that a component will interrupt the flow of current withoutcausing fault current to flow. An example of a component causing anopen circuit is when a circuit breaker false trips.

Mean Time To Repair (MTTR) - MTTR represents the expected timeit will take for a failure to be repaired (measured from the time that thefailure occurs). A single MTTR is typically used for each component,but separate values can be used for different failure modes.


Mean Time To Switch (MTTS) - MTTS represents the expected timeit will take for a sectionalizing switch to operate after a fault occurson the system. For manual switches, this is the time that is takesfor a crew to be dispatched and drive to the switch location. For anautomated switch, the MTTS will be much shorter.

Probability of Operational Failure (POF) - POF is the conditional prob-ability that a device will not operate if it is supposed to operate. Forexample, if an automated switch fails to function properly 5 times outof every 100 attempted operations, it has a POF of 5%. This reli-ability parameter is typically associated with switching devices andprotection devices.

Scheduled Maintenance Frequency (λM) - λM represents the frequencyof scheduled maintenance for a piece of equipment. For example, amaintenance frequency of 2 per year means that the equipment ismaintained every 6 months.

Mean Time To Maintain (MTTM) - MTTM represents the average amountof time that it takes to perform scheduled maintenance on a piece ofequipment.

Especially component failure rates have received abundant attention in lit-erature and studies. Depending on the desired detail and complexity of areliability model, average time-independent or time-varying failure rates canbe used. Time-varying failure rates are certainly more realistic and so calledbathtub curves have been developed to describe the age dependence of elec-trical component’s failure rates. More information on this age dependencecan for example be found in [10]. This thesis will not discuss them further.

2.3.2 Reliability Data Collection

Finding correct component reliability data is one of the most importantaspects of distribution system reliability assessment. Without good data,the answers provided by complicated analyses and sophisticated computerprograms are baseless. Since the tool developed in this master project usesa simple reliability model, this section only discusses data sources for com-ponent failure rates, repair times and switching times. As seen in Section2.3.1, many more parameters can be used to describe component reliability.

As mentioned above, component failure rates have received quite someattention in literature and studies. However, good, robust data are stillhard to come by. There exist rather large statistics on failure rates in somecountries. For example, the German association for electrical, electronicand information technology (VDE) issues a large statistic in their Forumnetwork technology / network operation (FNN) [17] yearly. Also [10] offers


a large collection of component reliability information based on differentsources as historical utility data, manufacturer test data, professional orga-nizations such as the IEEE and Cigre. The problem with such data is thatcomponent reliability data can vary widely from system to system and isalso dependent on building or maintenance principles. Just as one example,in Switzerland, underground cables are usually built in cable duct bankswhich is not necessarily the case in Germany. Therefore, the cable failurerates of the FNN statistics can not be directly applied here. Data fromexternal sources, therefore, always has to be calibrated to specific historicaldata. Another option to consider is, therefore, working with historical datafrom the considered network and only validating the values with data fromgeneral sources as large statistics or manufacturer test data.

While the failure rates might be a bit more general even for differentutilities, the switching times and, especially, the repair times highly dependon the DSO’s operational principles. For these values, an internal surveyor discussions with the responsible experts are definitely needed. Otherwisethe fault restoration process cannot be modelled correctly.

Chapter 3

Tool for Estimation of FeederReliability and TopologyCosts

In this chapter, a systematic tool for evaluating reliability and cost aspects ofradially operated medium voltage feeders and reliability enhancement mea-sures on these feeders is introduced. The tool focuses on measures involv-ing remote control and protection schemes. Before discussing the proposedmethodology in detail, the chapter begins by naming and motivating some ofthe key characteristics of the developed tool. This is followed by a generaloverview of the tool. In a next part a structured description of some of thetool’s main components is given. The chapter concludes with a demonstra-tion of the methodology for an example feeder. The selected feeder corre-sponds to a medium voltage feeder of BKW’s grid that includes some of themain characteristics that have to be considered.

3.1 Key Characteristics and General Overview

In the first section, the key characteristics that define the developed relia-bility analysis tool are listed and motivated. Afterwards, a general overviewof the proposed methodology is given.

1. SAIDI and SAIFI to Quantify Reliability

In the tool, reliability is thus described mainly by SAIDI and addition-ally by SAIFI. As mentioned in Chapter 1, the aim of the tool is toassist DSOs in finding the ideal measures to maintain or enhance thereliability of supply. The most important key figure for prioritizingspending decisions – and also the one used for Sunshine Regulation– is SAIDI. Therefore, it was straightforward to use this index as ameasure of the reliability of supply. Since the tool might be further

17

CHAPTER 3. METHODS OF THE TOOL 18

developed in the future and will be used to evaluate more measuresthan installing remote control and protection, SAIFI is also given asan output. Nonetheless, since the tool calculates the total interruptionfrequency and duration for each transformer station and each possiblefailure, other indices or key numbers can be calculated as well from theresults. For example, indices for the reliability fairness (see in Section3.3.4) or, if the average SAIDI value of the last x years is given to thetool, the improvement of a certain topology relative to average of thelast x years can be returned.

2. Analysis of real radially operated medium voltage feeders

As will be further discussed in Chapter 4, the grid of BKW is nor-mally operated radially and consists of many different feeders withnormally open sectionalizing switches. To save computation time, thetool therefore only analyses network topologies that are operated ra-dially. It is able to read the network data from tabled geographicalinformation system (GIS) data and create a tree graph to model thenetwork. The network graph allows the user to change and adjust at-tributes of the components of the network. The tool takes informationabout the open sectionalizing points and considers them during thesimulation of the system’s response to each failure.

3. State Enumeration for the reliability calculation

The tool uses analytical simulation for the reliability analysis. Asmentioned in Section 2.2.3, unlike, e.g., network modelling, analyti-cal simulation allows the tool to model the system’s failure responseclosely to the actual operational principles of the DSO. Also, it is sim-pler and less computationally intensive than Monte Carlo Simulation.As will be shown in Chapter 4, BKW’s MV feeders can consist ofmany transformer stations and lines, modelling hundreds of years ina detailed fashion would, therefore, take too long. Hence, analyticalsimulation offers a compromise that allows to model the system to asufficient extent while still computing the results in reasonable time.

4. Stepwise realistic simulation of the system response for eachsingle failure

Since the tool uses state enumeration, it models the system responseto each considered failure. A lot of time and thought were put into thispart. First of all, the tool has to model the reaction of the installedprotection devices. Then, it simulates the process of localizing andisolating the fault as well as re-energizing the customers step by stepas closely to the real scenario as possible. The different criteria andparameters for the simulation were determined in accordance with ex-


perts from BKW. The simulation of this process is described in moredetail in Section 3.3.3 and demonstrated in Section 3.4.2.

5. Using Google Maps to obtain realistic driving times for sim-ulation of fault localization

In a large medium voltage grid one finds all kinds of different feedersthat can stretch over different geographies and can have switches inlocations that are really hard to reach. For the tool to be able tosimulate the fault localization and isolation process as accurately aspossible, it could not use average driving times to simulate the fieldcrew driving from one switch to another. Hence, during preparation ofthe data, the tool asks all possible driving routes between the switchesof the feeder from the Google Maps API [18]. During the simulationit can then always use the real time needed for the dispatcher. Thisallows for a much more realistic model of the fault localization process.

6. Use of average life cycle costs for all evaluated technologiesas an input to calculate the costs for each topologyTo assess trade-offs between reliability and costs of the evaluated feedertopologies, the tool needs to determine the cost of the considered topol-ogy relative to a reference. Since there exist already various means forcost assessment, this was not the main focus of the developed method.Hence, the tool uses average net present values of investment, oper-ational and maintenance costs for the considered components. Thenet present values for the individual components are determined inadvance with other tools and the developed tool determines the costsof an evaluated feeder with these values and the given network model.The tool does not calculate the customer costs of interruptions sincethese are in most cases not relevant in the planning stage for a DSOin the current regulation environment.

7. The tool’s outputs can lead to several investigations

Evaluation of specific feeder configurations – With the tool spe-cific configurations of feeders can be analysed considering their re-liability and cost. Several different topologies of one feeder withvarying protection and remote control schemes can be analysedand compared, as it is shown in the first case study in Chapter4. The introduced key number allows to also compare topologiesof different feeders and determine where each measure is moresuitable.

Conceptual studies of the impact of measures – By extendingthe aforementioned type of analysis to more feeders, it is possibleto determine general statements about ideal strategies for relia-bility improvement. An example is presented in Chapter 4 in the


second case study. Currently the tool is mainly set up to anal-yse the impact of added remote control or protection to feeder.However, it can also be used to analyse other measures, such ascabling of overhead lines or changes in operational practices.

Determine the optimal configuration of a feeder – Another pos-sibility to use the tool is to let it find the ideal configuration ofa feeder practically by itself. However, this type of analysis cancurrently only be done for configurations considering remote con-trol and protection schemes. For this analysis a so called basetopology that has no remote controlled switches or protection de-vices yet is given to the tool, as well as a list with switches thatit should consider. The tool can then determine which combi-nations of switches with remote control and protection will bepareto optimal 1 considering costs of the topology as well as reli-ability (SAIDI). However, since the feeders can be very long andcontain a lot of components, this type calculation currently takesa really long time on standard workplace computers. Nonethe-less, it can be used for long-term planning where no immediateresults are required or where it could be considered to rent seversfor additional computation power.

8. The tool is implemented in RAnother key point of the developed tool is that it is programmed in R.Since R and it’s packages are open source, the tool does not require anexpensive license. It relies on well known R packages that have provento work steadily, offer good support and have been used within BKWbefore, such as [19], [20] or [22].

The schematic overview of the cost and reliability calculation part of thetool is displayed in Figure 3.1. The simulation takes three different datatypes as input: network data that model the grid, reliability data and av-erage cost data. The tool identifies all possible failures in the network andsimulates each of them. For each of these possible Failure States, the toolfirst simulates the Reaction of the System’s Protection Scheme. In reality,the fault current is eliminated when the circuit breakers open automaticallyand the faulted part of the grid is de-energized. This behaviour is simulatedby the tool and the information which circuit breaker has opened is stored asProtection Information. The data that is stored in Protection Informationcan vary with the utilities principles and the modelled devices. Since thisproject’s tool evaluates only radial feeders, no fault current alarms and nooperational failures of circuit breakers, it is sufficient to detect and store theone circuit breaker that reacted to the fault. After fault clearing, the system

1Pareto Optimality is a state in which it is impossible to improve one target withoutmaking at least one other target worse.


Figure 3.1: General overview of the reliability calculation method.


has to be reconfigured to isolate the fault and re-energize the customers asquickly as possible. To do this, first the location of the fault has to be found.The exact process of localizing the fault is dependent on the installed devicesin the grid, especially remote controlled or automated devices, as well as onthe operational practices and requirements of the DSO. In general, fault lo-calization is done by the so called “Spannungsprobe” which basically meansswitching parts of the grid and testing if the failure happened in these partsby trying to re-energize them. Additionally, in areas with overhead lines,the dispatchers view the lines from the car and check if any visual fault hap-pened. The second part of the tool models this process of fault localizationand step by step isolation by switching sections of the grid as closely as pos-sible. An algorithm for choosing the switches to be opened and, therefore,the sections to be created has been developed that copies the practice of thedispatchers. To finish the Simulation of the Restoration Process the toolalso simulates the fault isolation and re-energizing of all customers. Withthe information stored during this simulation, the Restoration Times for allinterrupted transformer stations can be calculated.

Once the tool has simulated all possible failures in the grid, it determinesreliability and cost results from the Restoration Times and cost data. Theinformation stored in the total Restoration Times for each failure is weightedwith the respective failure’s probability of occurrence, i.e. the componentfailure rate, and summed. That way, expected values for SAIDI and SAIFIof the analysed feeder can be determined but also a more detailed analysisof the interruption durations for all individual transformer stations can bedone. Together with the cost results, different indices or key numbers canbe derived to rate the investigated topologies.

3.2 Input Data

As already mentioned in the general overview of the tool (Section3.1), threedifferent types of data are used as input for the tool. The three data types,their specialities and format, as well as possible and the used data sourcesare explained in this section.

3.2.1 Network Data

First of all, a detailed model of the network is needed. All network compo-nents as well as their attributes, e.g. switching capabilities, connected loador number of customers, coordinates, and connections have to be defined.The basis for the network data used by the tool can be any table of all gridelements and their attributes. In the current set up, the tool uses an ex-port of the geographical information system (GIS) of the DSO and the usercan add attributes or change their values to model certain configurations.


In a large, historically grown grid the network model is sometimes patchedtogether from smaller network models and not all of these are modelled inexactly the same way. To some extent, the tool can handle these differ-ent models and it allows for adaption if it is not prepared yet for a differentkind of modelling. The tool converts the tabled network data into a directedgraph with help of the R package igraph [19]. Since only radially operatednetworks are considered, a rooted tree graph [21] with the substation as rootis used. The minimum information needed for the network data is:

– Network topology (all nodes, connections, switches) and coordinatesof all nodes

– Lengths of the overhead lines and underground cables

– User input:

– Protection information

– Remote control information

– List of alternate supply and sectionalizing points

The lengths of the lines and cables are required, on one hand, since thestandard failure rates that the tool uses are defined per unit of distance,and, on the other hand, because the line length is an important parameterin deciding which switches are opened during the fault localization pro-cess. Information about protection and remote control is simply a list of theswitches that have a protection device installed and one of the switches thatare remote controlled. To keep the tool as simple as possible, it is assumedthat the user checks himself if protection device time delays are sufficientin the investigated topology. The tool assumes that it is always the nextupstream protection from the fault that reacts. To correctly model resupplyoptions and simulate the decisions to either first re-energize a part of thecustomers or directly go on with the fault localization, it is also necessarythat the user enters a list of sectionalizing points.

3.2.2 Reliability Data

All data that have an impact on the reliability calculation and are not cov-ered by the network data, are summarized as reliability data. As alreadystated in Section 3.1, reliability is expressed by the tool by the reliabilityindices SAIDI and SAIFI. Hence, the here described reliability data onlyrelate to sustained interruptions, lasting more than three minutes. A partof the reliability data refers to the failure data, which are used to simu-late the failure states, meaning, for example, the component failure rates.Further, the reliability data consist of operational data. Operational datacontain information about the (current) operational practice of the DSO


and are mostly expressed in time values as they describe the duration ofcertain actions. A good understanding of the considered network as well asof the DSOs operational principles is crucial to decide which reliability datato consider.

3.2.2.1 Failure Data

In order to achieve realistic and useful results, all failures that have animpact on SAIDI must be considered and reflected by the failure data. Forthe purpose of this tool it is sufficient to summarize all possible failuresalong overhead lines and underground cables as line failures. For each linetype — or even each line part — a different failure rate can be considered.Likewise, all possible failures inside a transformer station are combined into TS failures. Hence, the failure data that is used by the tool consists of:

– λKL: Failure rate for underground cables

– λFL: Failure rate for overhead lines

– λMTS : Failure rate for transformer stations on poles

– λGTS : Failure rate for building transformer stations on the ground

λi represents the permanent short circuit failure rate of the respectivecomponent as introduced in Section 2.1.2. Undoubtedly, this list can beexpanded, depending on the distribution network and the desired detail ofthe model. Since this tool was built from scratch, only basic failure dataare used. In future versions a much more detailed modelling of failures isimaginable. A few examples are: a differentiation between short circuitfailures and ground faults can be done, or more components than just linesand transformer stations can be considered.

3.2.2.2 Operational Data

Another big impact on the calculated reliability comes from the operationaldata. They define to a great part, how long it takes to detect a failureand re-energize the customers. Consequently, operational data are mostlyrelated to time variables. The values used in the tool are:

– t0: Time until the control center gets notice about a failure and thecrew can be dispatched or the first remote switching action can bedone

– tswitch: Average time needed for a switching action (remote controlledor manually without driving time)


– tvis: Average time needed per kilometre line length for the visual searchwhen there are no more switches to test

– tprov: Time needed to set up provisional equipment to resupply allcustomers for the time that is needed to repair the failed component

An important operational time variable that is not given as an input,is the time the dispatched crew needs to drive from one switch to another.This value is calculated by the tool from Google Maps Data for each route.Again, as for the reliability data, the list of used operational data can beexpanded (or shortened), depending on the desired detail in the simulation.It is, for example, possible that the number of dispatched field crews thatcan be sent to the field has an impact on the calculated SAIDI values.

3.2.3 Cost Data

The third type of input is the cost data. They are needed to assess andcompare the cost efficiency of all evaluated topologies. As mentioned before,only costs that are connected to the investigated measures, i.e. protectionand remote control, are considered. Costs for other components, like cablesor transformers, are neglected since they would be more or less the same inall considered topologies of one feeder. The considered costs can be dividedinto investment costs and operational and maintenance (O&M) costs. Thedifferent devices whose costs should be compared have different expecteduseful life. Also, investment costs are paid upon building the parts whilethe O&M costs incur over the full useful life of the components. In order tobe able to compare and sum these costs, their net present value is calculatedand used in the tool. This calculation of the net present values (NPV) waspartly done by a NPV-tool that was previously developed by the writerof this thesis in an internship and was partly determined in other studiesfrom BKW. Client or customer cost data are not considered since they arecurrently not (yet) relevant in the network planning process. It can of coursebe added whenever there is an existing interest.

3.2.3.1 Investment Costs

Only the investment costs for components that differ in the different eval-uated feeder topologies are relevant for the comparison of the topologies.Therefore, only costs connected to switchgear are considered. Costs thatwould stay roughly the same in all topologies, i.e. costs for cables, overheadlines, transformers, and so on, are neglected. The investment costs for eachswitch contain:

– Actual switchgear cost

– Cs: Cost for the switch


– Cp: Cost for protection device

– Cr: Cost for remote control

– Cbase: Installation and base cost

The installation costs include also labour costs for the workers. Basecosts consist, for example, of costs for the building in case of switches intransformer stations.

3.2.3.2 Maintenance and Operational Costs

The second part of the calculated costs includes average yearly values forcontrols, maintenance, operation and replacement of switches depending ontheir type. These average yearly values are converted into their net presentvalue by the above mentioned NPV-tool before handing them as an input tothe reliability assessment tool. The NPV-tool uses the weighted average costof capital (WACC) whose value is determined by the ElCom as interest rate.It also considers an expected inflation rate from a BKW internal forecast.

Operational costs could be modelled in more detail with actually calcu-lating the expenses for each failure. These costs depend on the switchingactions after a failure and the time the field crew is dispatched. The ex-penses for each failure would mainly be associated with the hourly wagesof the dispatched field crew. However, the expected difference in costs fora single failure would not be too large between two topologies of the samefeeder. Additionally, as long as no measures are evaluated that preventfailures from happening, there is not an immense change in failure relatedoperational costs to be expected.

3.3 Proposed Methodology

This chapter discusses the used methodology of the tool in more detail.First, the selection of the failure states is explained. Next, the model of theprotection system and its reaction to faults will be shown. As the third andlargest part, the simulation of the restoration process is described and itsspecialities are presented. In the last part the two result calculators, one forthe reliability and the other for the costs, are discussed.

3.3.1 Considered Failure States

An important question when developing a tool or method for reliabilityanalysis is which failures should be considered and modelled. There exists avariety of different failures that can occur in a distribution grid and, more-over, their impacts on the grid can vary heavily. Depending on the goals of


the tool and the chosen reliability index, some failures can be neglected rightaway, since they only have a negligible effect on this index. In other cases,there is a trade-off between the desired detail and the increasing complexityof the tool. The choices that were made in the development of this tool willbe discussed here.

As stated in Section 3.1, SAIDI is the main index defining reliability inthe tool and SAIFI is used as a second index. In general, all failures thathave an impact on SAIDI or SAIFI have thus to be considered. Hence, allfailures that do not cause sustained interruptions can be neglected. Theremaining failures are grouped according to the location of occurrence, i.e.on lines or within transformer stations. For example, all failures that happenon an overhead line and lead to a sustained interruption are summarized andcharacterized by the failure rate for overhead lines λFL.

3.3.2 Protection System Response

After a failure state was picked by the tool, the reaction of the modelledprotection system has to be determined and simulated. To ensure a safe op-eration of the distribution network within the given frame of requirements,a properly coordinated and calibrated protection system is crucial. Differ-ent network topologies require different protection systems. However, in allcases, automatic operation is necessary to isolate faults as quickly as possi-ble and minimise damage to people or the system. The most common formof protection for overcurrents on a power system are circuit breakers thatare equipped with protective relay devices. The relay devices sense a faultand activate the tripping mechanism.

Since the tool that was developed during this master project only anal-yses radial or at least radially operated feeders, the simplest model for pro-tection systems could be implemented. In radially operated feeders, usu-ally, simple relays with time-dependent, graded overcurrent protection areinstalled. Another assumption of the proposed method is that the grid plan-ning engineers check the selectivity and grading of the protection devices intheir investigated topologies themselves. This, together with an assumed fullreliability of the protection devices, allows to expect that it is always onlythe next upstream protection of a fault that reacts to a flowing overcurrent.The model of the protective system is then set up in a really simple way (seealso Figure 3.2): Only if the system is in a failure state, and the fault cur-rent flows through the overcurrent protection, then, the closest overcurrentrelay will trigger the protection device’s operating mechanism. Because ofthe choice of considered failure states, all considered failures will cause anovercurrent that flows long enough to trigger the relay. The great advantageof this model is that no power flow calculations are required.


Figure 3.2: Schematic of the simulation of the protection scheme response.


3.3.3 Restoration Process

Modelling the system restoration process after a failure was noticed is thecore part of this reliability assessment tool. Only if this process reflects thereal situation, the tool can be applied to the existing grid and the impactof possible future measures can be investigated. Hence, a lot of time anddetail was put into this part of the tool and all steps were reviewed with ex-perts from BKW. In general, the restoration process can be separated in theprocess of finding the fault and the process of isolating it and re-energizingall customers. In reality, however, these two processes overlap and someparts of the feeder might already be re-energized during the fault localiza-tion. Nevertheless, for the sake of clarity, the two parts of the restorationprocess are explained separately in this section. Figure 3.3 gives an overview.

The general principle of fault localization is called “Spannungsprobe”. Itmeans that a switch in the middle of the area where the fault could be isopened and it is tried to re-energize the area upstream of the switch. If thecircuit breaker opens again, the fault is upstream from the opened switch,otherwise it’s downstream. The considered area for the fault search can thanbe reduced accordingly and the same procedure is repeated. Hence, it isstraightforward to model the fault localization process in a stepwise fashion.Each restoration step consists of one “Spannungsprobe” or a partial servicerestoration. Depending on the selected switches for the “Spannungsprobe”and previous actions, the field crew has to drive varying distances to performthe necessary actions. The driving times for the steps are calculated fromGoogle Maps Directions API [18] by a specific R package [22]. Once thereare no more switches to use for “Spannungsprobe” or the remaining area toconsider for fault search is smaller than a given threshold, the search forthe exact location is done visually by the field crew. For example, they willdrive or walk along an overhead line to identify possible failures. This partof the fault search is extremely hard to model accurately since, in reality, itdepends on many factors. One example that cannot be modelled easily isthat the efficiency of visual search is dependent on weather conditions. Inthe proposed tool, the time needed for the fault localization via visual searchis, therefore, modelled as a linear function of the remaining line length.

3.3.3.1 Fault Localization

Most thought and detail went into the tool part of modelling the fault lo-calization as closely to the real process as possible. The tool must be ableto handle quite different feeder types ranging from short completely cabledfeeders in urban areas to really long feeders consisting mostly of overheadlines that sometimes go through woods or over mountains. Obviously, the


Figure 3.3: Overview of the restoration process simulation.


used tactics for finding the source of a failure vary with the different feeders.The two parts that have most impact on the fault localization algorithm areexplained in detail in this section.

The first important question is how to select the order of which switchesto open according to the “Spannungsprobe” principle. The used algorithmis outlined in Figure 3.4. If there are no switches left in the consideredarea for fault localization, the tool directly proceeds to visual search of thefailure. This is not depicted in Figure 3.4 but can be seen in the generaloverview of the fault localization process in Figure 3.3. If there are switchesto consider, certainly, remote controlled switches will be used first since theycan be operated from the control center. The basic principle for non-remotecontrolled switches is to pick the switch that best halves the remaining areawhere the fault could be. However, especially if the feeder goes throughalmost impassable regions, it might be important to consider how long ittakes the field crew to get to the switch. Another factor that is considered,is clusters or hubs of switches. If there are possibilities to separate manysections within a relatively small area, these switches are preferred. All thesefactors go into the tool’s algorithm for determining the next switch. Anddifferent decision algorithms can be tested. Undoubtedly, many more factorscan be included in the decision process. One example is to take into accountthe number of customers in the different grid parts. As another example,the dispatcher’s experience can be modelled by taking the probability of afailure in a certain region into account. However, modelling this only makessense if specific failure rates are used for the different grid parts and notgeneric values for all component types.

The second part that received a lot of attention was partial servicerestoration during the fault localization. Partial service restoration is guidedby the decision if customers should be resupplied already during the faultlocalization process. This decision can have a huge impact on SAIDI. Thealgorithm to decide if customers should be resupplied first or if the faultsearch should go on is outlined in Figure 3.5. Customers can be resuppliedin the beginning of the search if there are further protecting devices down-stream from the reacting circuit breakers and only if there are alternatesupply points downstream from these additional protection devices. Forthis it is assumed that the protection devices always work as they shouldand that the grid is configured in such a way that all loads in the feedercan be supplied from the alternate supply point. Alternatively, customerscan always be resupplied after each step if the “Spannungsprobe” was notsuccessful and, therefore, the fault is upstream from the last opened switch.The decision algorithm which is outlined in Figure 3.5 makes use of a scorefunction.


Figure 3.4: Decision algorithm for the switch selection during the fault lo-calization.

The most crucial part of the resupply decision algorithm is the calcula-tion of the scores for the two options – resupply downstream customers firstor first go on with the search. The basic principle of the scores is that theydivide the number of customers that can be resupplied with each optionby the time needed to resupply them. Considered are always the next twopossible restoration steps. For the option to resupply the customers first,this is the step of partial system restoration plus testing the next switch.The contribution to the score of the partial service restoration is calculatedas the number of customers that can resupplied through an alternate supplypoint divided by the time that is needed for the respective switching actions.The second contribution to the resupply score considers the customers thatcould possibly be resupplied in the next “Spannungsprobe” and consists oftwo parts. The first part reflects the case that the failure is downstream


Figure 3.5: Algorithm for the decision if partial system restoration shouldbe done.

from the opened switch and the customers upstream from that switch arere-energized. The number of customers upstream from this switch is, there-fore, divided by the time that was used for the resupply actions plus thetime needed for the “Spannungsprobe”. The second part reflects the case


that the failure is upstream from the tested switch and, therefore, the cus-tomers downstream from that switch could possibly be resupplied throughthe alternate supply point. Hence the number of customers downstreamfrom this switch that have not been resupplied yet in the previous step isdivided by the total time needed to resupply them. The two parts are eachmultiplied with a factor of 0.5 to take into account that each there is a prob-ability of approximately 50% that the failure is upstream from the selectedswitch. This assumption is justified because the switch selection algorithmchooses a switch that fulfils this criterion at least more or less. For the optionto go on with the search, the resupply step is skipped and testing the nextswitch is evaluated directly. The time needed for the actions is thereforechanging. The scores express how many customers can be resupplied perminute. Hence, the option with the higher score is picked by the tool. Theexact formula for the scores can be found in Figure 3.5.

3.3.3.2 Fault Isolation and System Reconfiguration

Once the fault’s location is exactly determined, The fault isolation is quitestraightforward. However, there are a few things to be considered to ensurethe ideal order of actions. First, the tool checks how many customers arestill de-energized and if they are upstream or downstream from the fault. Ifthere still are any customers offline, the tool determines the closest switchesto the fault in order to fully isolate it. It then determines the areas where itis possible to resupply customers. For each possible resupply area, it deter-mines a score which is similar to the one used for the decision to resupplycustomers during the fault localization. The different areas are then isolatedin decreasing order of their scores. Of course, for each area it is always nec-essary to first open the switch that separates the area from the fault if thisswitch not yet in open position. Once the switch closest to the fault hasbeen opened, the according switch to re-energize the area can be closed.

Once the isolation process is finished, the tool determines if there are stillcustomers without energy supply. If yes, it assumes a given time parameterto either repair the fault or set up provisional equipment to resupply thesecustomers. When all customers are resupplied, the simulation of the systemresponse is finished.

3.3.4 Reliability Calculation

With the results from the simulation of the restoration process, the relia-bility indices can be calculated. The main index that is used for all furtheranalysis done on the results is SAIDI. Nonetheless, the tool also determinesand outputs other indices. This section of the thesis describes the differentreliability outputs that can be extracted from the tool.


3.3.4.1 System Average Interruption Duration Index (SAIDI)

SAIDI is an industry standard and currently the main reliability result of thetool. It was already introduced in Section 2.1.2. SAIDI can be determinedfor one single feeder without taking into account the complete distributionnetwork or as the influence a single feeder has on the complete distributionnetwork.

SAIDIFeeder =

n∑i=1

(λFi ·

m∑x=1

(tFi,TSx ·#customersTSx)

)m∑x=1

#customersTSx

(3.1)

n is the number of possible failure states in the grid, λFi is the yearly rateof occurrence of failure state Fi according to the corresponding componentfailure rate, m is the number of transformer stations (TS) in the feeder,tFi,TSx is the total interruption duration for transformer station TSx andfailure state Fi. Analogously, the influence of a single feeder on SAIDI ofthe complete distribution network can be calculated:

SAIDI =

n∑i=1

(λFi ·

m∑x=1

(tFi,TSx ·#customersTSx)

)total # customers in the grid

(3.2)

Instead of only taking into account the number of customers that aresupplied by the considered feeder, all customers in the distribution networkare considered. This can be done for each feeder in the grid and the sum ofall feeders’ results will add up to the full grid’s average interruption durationindex.

3.3.4.2 System Average Interruption Frequency Index (SAIFI)

Also SAIFI is an industry standard and was introduced in Section 2.1.2. Itis also determined by the tool to later be able to analyse measures impactthe frequency of failures. For a single feeder, it is calculated as follows:

SAIFIFeeder =

n∑i=1

(λFi ·

m∑x=1

(BFi,TSx ·#customersTSx)

)m∑x=1

#customersTSx

(3.3)

where BFi,TSx is a boolean variable that is equal to 1 if the failure state Fileads to an interruption of transformer station TSx and equal to 0 otherwise.As for SAIDI, also for SAIFI a feeder’s influence on the value for the completegrid can be determined:


SAIFI =

n∑i=1

(λFi ·

m∑x=1

(BFi,TSx ·#customersTSx)

)total # customers in the grid

. (3.4)

3.3.4.3 Substation Interruption Duration Index (SIDI)

SIDI is not an industry standard. It is calculated because it allows to mea-sure the average interruption duration of a specific transformer station TSxover the course of a year. This can be useful for a more detailed analy-sis of single feeders and gives better insights in the overall fairness of thedistribution network. The index is calculated as follows:

SIDITSx =n∑i=1

λFi · tFi,TSx with Fi ∈ F = {F1, . . . , Fn} . (3.5)

3.3.4.4 Substation Interruption Frequency Index (SIFI)

SIFI is analogue to SIDI, it just determines the transformer station inter-ruption frequency instead of the duration. The formula for SIFI is

SIFITSx =n∑i=1

λFi ·BFi,TSx with Fi ∈ F = {F1, . . . , Fn} . (3.6)

3.3.4.5 Reliability Fairness Index (RFI)

This index was introduced by Michiel Tavernier in his master thesis [9].He suggested a reliability fairness indicator that calculates the root-mean-square deviation of differences between SIDI of each transformer station andSAIDI of the feeder. The equation for the Reliability Fairness Index is givenin the following.

RFI =

√√√√√ m∑x=1

(SIDITSx − SAIDIFeeder)2

m(3.7)

Fairness of reliability is currently not relevant in the grid planning pro-cess. It is, however, interesting to look at and can be used for other studies.

3.3.5 Cost Calculation

From the network data that comes from the GIS export and is complementedby the user the tool determines the number and types of switches. It fur-ther determines if some of the switches are in the same transformer station


building and if so, accordingly reduces the costs for these switches since theyshare a part of the costs. From this information the total investment costsand life cycle costs for all switches in the feeder are determined. Basicallythe life cycle costs of a topology are determined in the following way:

LCC =n∑i=1

# switchestypei · (NPV CAPEXtypei + NPV OPEXtypei) (3.8)

Where n is the number of different switch types, meaning for exampleswitches with or without protection relays and remote control. The switches– and their cost–, however, also differ by their location. Switches on overheadlines have different cost than switches in transformer stations. Plus, thetotal costs are reduced if two switches with protection, for example, can bebuilt in the same transformer station compared to two different transformerstations.

3.3.6 Cost-Reliability Key Number

In order to be able to rate different investigated topologies not only for thesame feeder but for all feeders in the grid in a simple manner, a new keynumber is introduced. The key number is calculated as follows:

Key Number =SAIDIreference − SAIDItopology

LCCtopology−LCCreference

100′000CHF

(3.9)

This key number specifies by how many minutes/year SAIDI can be im-proved by the investigated topology per 100’000 CHF additional life cyclecost. It, therefore, allows to compare completely different topologies andmeasures. The introduced key number is applicable also if the analysedfeeders differ or different measures are to be examined since it compares thereliability improvement and additional cost to a given reference.

3.4 Tool Demonstration

In this section the methodology of the tool will be demonstrated for one fail-ure in a given example feeder. The example feeder is a long feeder of BKW’smedium voltage grid with an artificially arranged topology that combinesall the characteristics and components that are to be taken into account forthe further investigations. The same feeder with more configurations willalso be used in the first case study in Chapter 4. The example failure is apermanent short circuit failure on an overhead line through a forest. Thiskind of failure is typical for feeders with long overhead lines.


3.4.1 Example Feeder

BKW has a large electricity grid with a total of 20’000 km of lines [23] onHV, MV and LV level. The grid consists of several regions with differentcharacteristics. These different characteristics are mostly given due to thegeographical conditions as well as the population density. Due to the dif-ferent characteristics, it is not possible to pick one feeder and define it asthe average feeder of BKW’s grid and use it as a benchmark. However,in general, it can be said that the feeders are radially operated, consist ofunderground cables and overhead lines, and often have areas that can onlybe supplied from one point. The example feeder is therefore the one thatwas also used in the first case study. The topology was chosen such that thedifferent aspects of the fault localization can be shown.

Figure 3.6: Map of the example feeder.

The example feeder is a feeder of 58.8 km length of which 23.5 % areunderground cables. It consists of 51 transformer stations with varyingdistance between them, has one closed connection to the substation andfour open alternate supply points. The protection and remote control layerwere taken from topology 9 of the first case study. Figure 3.6 shows thetopology of the example feeder on a Google Map. This topology allows toshow all aspects of the tool.


3.4.1.1 Remote Control Scheme

There is a remote controlled circuit breaker located on the primary substa-tion feeder, protecting the primary substation from any fault currents withinthe feeder. This is the case for all of BKW’s MV feeders. The remainingfour remote controlled switches in the example topology are located basedon several criteria. One possible placement is on alternate supply points sothat customers can be resupplied faster. Another main thought of remotecontrol placement is to place them at locations where the field crew mightgo first for the“Spannungsprobe”. A third possible consideration is to installremote control on remotely placed switches that take long to reach by car.Additionally to the four remote controlled switches, also the three circuitbreakers are remote controlled.

3.4.1.2 Protection Scheme

As stated before, the primary substation is protected by a circuit breaker. Inaddition, three switches along the feeder are also equipped with a protectionrelays. According to BKW’s current practice, these circuit breakers are alsoremote controlled. The main goal of any protection scheme is to protect thegrid and people from damage by clearing the overcurrent/overload or shortcircuit currents as quickly as possible. The other goal that can be achievedby good placement of protection devices is clearing the fault for a minimumnumber of interrupted transformer stations. To achieve this circuit breakersare placed such that they separate especially failure prone areas from the restof the grid and such that they reduce the considered area for fault search.The three remote controlled switches with protection relays in the examplefeeder are placed on the outgoing feeders of the two building transformerstations PTS1 and PTS2. The schematic set up of PTS2 is shown in Figure3.7.

Selectivity and coordination of the protection scheme is thereby obtainedby gradually decreasing the operational time-delays of the circuit breakerslocated further away from the primary substation. However, since onlyradially operated feeders are analysed, the tool does not check the timedelays and assumes that it will always be the closest upstream protectionfrom the fault that opens first.

3.4.2 Simulation of one Failure State

In this part, the simulation of the selected failure state is presented. Theused data will also be shown and explained.


Figure 3.7: Schematic transformer station set up as it could be found fortransformer station PTS1. This schematic was modified from BKW’s gridmodel.

3.4.2.1 Failure Data

Failure data of the medium voltage components within the example systemwere mainly selected based on the outage data that are collected and storedwithin BKW’s fault analysis tool ([24], [25], [26]). The failure that waspicked for the demonstration is a relatively common type of failure. It isa line failure on an overhead line through a forest that can not be clearedthrough automated re-closing. Such a failure could be caused by a branchfalling on to the line and damaging it or something similar. According toBKW’s fault analysis tool, the probability of occurrence for this failure isapproximately 0.13 % per year. As mentioned before, data regarding fail-ures that affect the considered feeder in the same way, independent of anyof the considered measures, should not be considered. Failures within theprimary substation for example are thus not considered in the tool. What isconsidered are failures related to the MV equipment within the transformerstations, cable and overhead line failures. All failure rates are summarizedin Table 3.1.

Table 3.1: Component failure rates for the example feeder.

Component Failure Rate λP Unit

MV underground cable 0.0105 /year/kmMV overhead line 0.0809 /year/kmMV busbar (pole TS) 0.0097 /yearMV busbar (compact TS) 0.0024 /year

For the cable and line failures the causes are relatively clear. Cable


failures, for example, are often the result of construction companies drillinginto a cable duct bank and thereby causing a short-circuit of the cable. Forfailures of the MV equipment within transformer stations, however, thereis a much wider variety of possible causes. However, all these failures canbe summarized as ‘MV busbar failures’ since the impact of most of thesefailures will be very similar to the one of a transformer station busbar failure.In BKW’s fault analysis tool a division between failures within building orcompact transformer stations on the ground and transformer stations on aoverhead line pole can be made. At first sight, one could argue that the TSfailures are negligible in comparison to the cable and especially over headline failures. However, it is expected that the restoration procedure for TSfailures will take much longer since provisional equipment needs to be setup in order to restore the customers connected to the affected transformerstation. Setting up temporary equipment usually takes around 4 hours ontop of the time to locate the failure. Hence, even though their rate ofoccurrence is rather low, the longer restoration times of TS failures will insome topologies largely ’compensate’ for this and should thus be consideredfor the reliability calculations.

3.4.2.2 Protection Scheme Response

The reaction of the protection scheme is rather straight forward in the ex-ample case. The closest upstream protection is the one on the primarysubstation (UST) feeder. This circuit breaker trips and clears the fault.This leads to the full feeder being de-energized. The considered area for thefault localization, however, can be narrowed to the region above the nextprotected switch(es) at PTS1 since all circuit breakers are assumed to work100 % reliable.

3.4.2.3 Operational Data

As introduced in Section 3.2.2, the operational data consist mainly of timevalues for different actions. The relevant actions and their expected dura-tions are summarized in Table 3.2. The values were mainly selected basedon internal discussions with experts regarding the operational practices ofBKW.

3.4.2.4 Restoration Process

Once it is determined which circuit breaker clears the fault, the fault restora-tion process can start. The corresponding operational data are summarizedin the following paragraph.

Stepwise Simulation The fault localization is simulated as a stepwiseprocess. The different steps will be explained in this paragraph. The steps


Table 3.2: Operational for the example feeder.

Parameter Value

time between the failure and thestart of the restoration procedure

15 Minutes

time to operate remote controlledswitch

5 Minutes

time to drive to switch Google Maps Directions

time to operate remote controlledswitch

10 Minutes

time needed for visual search onover head lines

30 Minutes/km

time needed for visual search forcable or TS failures

60 Minutes/km

time needed to set up provisionalequipment

4 · 60 Minutes

and the respective de-energized areas are visualized in Figures 3.8 and 3.9.

1. Determining the de-energized area and the considered area forfault localizationAs already stated in Section 3.4.2.2, the considered area can be narroweddown from the complete feeder to the area between the substation and thenext downstream protection. It is assumed that 15 minutes pass before thefirst switch is tested.

2. Opening the remote controlled switch in the considered areaand re-closing the circuit breaker at substationIf there are any remote controlled switches in the considered area, it is testedfirst if any of these switches fulfils the criteria for “Spannungsprobe”. If yes,this switch is opened and the reacting circuit breaker is closed again. Thatway the considered area can be narrowed to either the area upstream fromthe opened switch or downstream from it, depending on if the protectiondevice tripped again or not. In the example, there is one remote controlledswitch in the considered area (RCS1) and it fulfils the criteria for “Span-nungsprobe” as it separated more than 30 % of the considered area. Thefault is upstream from the remote controlled switch and the circuit breakerat the UST trips again. Hence, the stub part that is downstream from thetested switch can be excluded from the fault search. This action takes 5minutes.

3. Checking if it is possible to already resupply customers down-


stream from the considered areaSince there are further protection devices downstream from the reactingprotection, it can be checked if and how many customers could possible re-supplied already through alternate supply points. For this decision the scorefunction introduced in Section 3.3.3 is used. If there are multiple grid partsthat are separated from the considered area through further protection de-vices, they are checked in order of they’re size. In the example feeder, thesectionalizing switches (RCAS1 and RCAS2) are remote controlled, there-fore resupplying the customers will obviously yield the higher score and thecustomers downstream of the transformer station with protection (PTS1)are resupplied in two steps which take 5 minutes each.

4. Selecting and opening a switch in the remaining consideredarea according to the criteria defined in the operation principlesand try to re-energizeAfter a part of the customers is resupplied, the next switch for “Span-nungsprobe” is determined prioritizing remote controlled switches and con-sidering the switches’ distances from the middle of the considered area, thenumber of close-by switches and the time it takes the field crew to get there.In the example, there are no more remote controlled switches. The chosenswitch is close to the center, has many other switches close by and is closestto the base. The field crew drives to this switch, opens it, and the circuitbreaker at the substation is closed again. The fault is downstream from theopened switch, therefore the circuit breaker does not trip and all customersupstream from the switch are energized again. These actions take a total of24 minutes.

5. Selecting and opening another switch according to the criteriadefined in the operation principles and try to re-energizeThe considered area is now relatively small. It is, however further reducedby one more “Spannungsprobe” because the failure could still be on differ-ent grid parts, it could be anywhere on the lines toward the next protectedtransformer station or also on the stub towards with one connected trans-former station or within that transformer station. This area would still betoo large to visually search the fault. Again the switch closest to the middleof the remaining considered area is selected, the field crew drives there andopens it. Then the field crew has to drive back to the switch that was openedin step 4 and close it again to try to re-energize the feeder. This time thecircuit breaker at the primary substation trips again, the failure is thereforeupstream from the last opened switch. These actions take 74 minutes sincethe field crew has to drive back and forth and there is no direct road betweenthe two switches.

6. Determine exact fault location through visual search and fully


isolate faultAfter the region considered for the fault search could have been narroweddown enough, the field crew starts the visual search to determine the exactlocation of the failure and fully isolate it so that all customers can be resup-plied. The visual search is done by driving or walking along the lines andlooking for the failure and is modelled by a time constant per km remainingline length. In the example, the visual search takes 48 minutes and the faultisolation takes 20 minutes.

7. If necessary, set up provisional equipment to re-energize allcustomersIn the example case, all customers are energized after the fault isolation andrepair can start immediately. However, this is not always the case and insome cases it is necessary to build up provisional equipment to resupply allcustomers in stub sections because starting repair directly would take toolong. It is assumed that setting up provisional equipment takes 4 hours.

The same stepwise simulation – with an accordingly varying number ofsteps – is done for all line and TS failures in the feeder. The output of thestepwise simulation is the status of each transformer station after each step,i.e. is it energized or not, and the duration of each step.


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(b) Step 2

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(c) Step 3.1

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(d) Step 3.2

Figure 3.8: Stepwise simulation of fault localization for the example feeder.First Steps.


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(a) Step 4

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(b) Step 5

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Figure 3.9: Stepwise simulation of fault localization for the example feeder.Last Steps.


3.4.3 Result Calculation

For the result calculation, the feeder topology as well as the output from therestoration process is needed. The topology is used to determine the costsand the restoration process output to determine the addition of each failureto SAIDI of the feeder.

3.4.3.1 Reliability Calculation

Once all 274 considered failures in this feeder are simulated, the reliabilityindices of the feeder can be determined according to Equations 3.1 and 3.3.However, the absolute values of for example SAIFI and SAIDI for a singletopology of a feeder are only meaningful when comparing them to otherSAIFI and SAIDI values. Hence, the absolute values of the example feederwill not be discussed in the following.

Table 3.3: Reliability results of the example feeder for each failure type.

Failure Type Occurrence SAIDI SAIFI

Overhead Line Failure 88.23 % 89.11 % 87.92 %Underground Cable Failure 3.53 % 3.76 % 4.20 %Pole Transformer Station Failure 7.04 % 5.69 % 6.46 %Building Transformer Station Failure 1.20 % 1.44 % 1.42 %

The results of one topology of a single feeder can however be used todetermine the influence of the different failure types on SAIDI. This analysisis shown in Table 3.3. There is nothing surprising in these results. The feederconsists to more than three quarters of overhead lines and overhead lineshave the highest component failure rate of the considered components. Itis therefore straightforward that overhead line failures have the highest rateof occurrence and also the highest influence on SAIDI and SAIFI. However,the results give a hint that the tool might overestimate overhead line failuresand underestimate cable failures. One would expect that underground cableshave a higher effect on SAIDI than on SAIFI since they do not occur toooften but are usually harder to detect and repair than overhead line failures.However, the different results for this feeder can be an indication that thetime values defined in the operational data have to be further calibrated.For the transformer stations, on the other hand, it makes sense that thefailures in pole TS have a lower impact on SAIDI compared to their rate ofoccurrence than building TS. This is due to the fact that a pole transformerstation is always connected as a stub and can therefore be more quicklyisolated such that no other transformer stations are influenced anymore.Building transformer stations are more often in the middle of the grid and


in a looped connection. Hence, failures in building TS affect more customersconnected to other transformer stations as well.

3.4.3.2 Cost Calculation

The cost of the topology can be calculated from the network data as de-scribed in Section 3.3.5. Nevertheless, as for the reliability indices, theabsolute cost results of one topology are not meaningful if they cannot becompared to another topology. Therefore, they will not be further discussedhere. Examples of cost analyses can be found in the case studies in Chapter4.

Chapter 4

Case Studies

This master thesis was written in close cooperation with BKW. The mainpurpose of the tool is to analyse real medium voltage feeders, hence, no ar-tificial test system was defined. BKW’s grid planning department suggestedthree case studies to apply the tool to specific feeders of their grid. Sinceno test system was defined, all tests of the tool were directly performed onactual GIS exports of chosen test feeders. One of these test feeders was thenalso used for a first case study where ten selected topologies with differentprotection and remote control schemes were evaluated. The second proposedcase study extends the same analysis of ten topologies to 8 additional feedersthat can be grouped in three types. The goal of this analysis was to determinegeneral statements about the impact of the investigated measures and also theinfluence of the different feeder types. In a third case study, the tool’s abilityto determine the optimal topology of a feeder by itself should be shown. Thisanalysis was performed on a shorter feeder than the first case study becauseof the needed computation time. The results of this optimization are thencompared with the ten selected topologies of the second case study.

4.1 Overview

As this master thesis was completed in close cooperation with BKW, threecase studies were proposed by BKW’s grid planning department. It was sug-gested to apply the tool to specific feeders of their grid and evaluate chosentopology variants of these feeders. The first case study was constructed totest the tool’s performance. Ten selected possible topologies of the exam-ple feeder, which are introduced in Section 4.2.1, should be evaluated. Theten topologies are characterised with an increasing number of switches withprotection relays and remote control and are compared to a reference topol-ogy. The second proposed case study extends the same type of analysis often selected topologies to 8 additional feeders that can be grouped in threetypes. The three feeder types differ mainly in their length but also in other

49

CHAPTER 4. CASE STUDIES 50

characteristics. The goal of the second analysis was to derive general state-ments regarding the impact on the reliability of supply of protection devicesand remote control. Additionally, the influence of the different feeder typescan be determined as well as their potential for improvement. The thirdcase study determines the optimal topology of a feeder from a list of pos-sible switches to consider for protection and remote control instead of frompre-selected topologies. To find the optimal topology the tool generates allpossible configurations and computes their SAIDI and cost results. Thisanalysis was performed on a shorter feeder than the example feeder becauseneeded computation time of course increases with the length of the feederand the number of evaluated topologies. The results are also compared tothe ten selected topologies that were used in the second case study.

4.2 Case Study 1 - Analysis of one Medium Volt-age Feeder

In this case study, the impact of an increased number of protection devicesand an increased number of remote controlled switches on total costs andreliability of a feeder is investigated. Therefore, nine different protectionand remote control concepts are developed and explained in Section 4.2.1.Additionally to these nine topologies, a basic topology that has no protectionrelays or remote controlled switches except for the switch of the primarysubstation is used as a reference. The results for these total ten topologiesare then presented in Section 4.2.2.

4.2.1 Different Feeder Topologies

All ten concepts that are analysed in the first case study have the same net-work model as basis. The network that is evaluated in this case study wasalready introduced in the tool demonstration (in Section 3.4.1). The refer-ence system is this network model without any remote control or protectiondevices except for the switch on the primary substation feeder. The nineanalysed concepts differ in number of protection devices and remote con-trolled switches. The concepts were developed specifically for this feeder byan expert at BKW. Topology T8 of this case study was used for the examplein the demonstration. The characteristics of the ten topologies are summa-rized in Table 4.1. Since not only the number of switches with protection orremote control is relevant but also the placement of these switches, all tentopologies are displayed in Figure 4.1.

On the x-axis of Figure 4.1, the number of remote controlled switchesin the feeder increases. On the (negative) y-axis the number of switcheswith protection relays increases. It is important to notice that if a switch


Figure 4.1: Table of the ten evaluated topologies.

has a protection relay that always means that it is also remote controlled.Installing protection together with remote control is the current practicein BKW. Therefore, topologies T4 and T7, T5 and T8, and T6 and T9respectively have the same total number of switches with remote control.Topologies T1 to T3, however, don’t all have the same total number of re-mote controlled switches but have an increasing number of remote controlledswitches. Since they all have fewer remote controlled switches than the otherinvestigated topologies and the main focus is on the protection devices, theyare still grouped in the same remote control level as the reference topologyT0. From the information in Table 4.1 it can be gathered, that one so calledmeasure bundle1 can contain a varying number of altered switches.

1A measure bundle denotes the difference in protection devices or remote control in


Table 4.1: Characteristics of the ten evaluated topologies for the consideredfeeder. PR stands for protection relays and RC stands for remote control.

Topol-ogy

# TSwithPR

# Switcheswith PR and

RC

# Switcheswith only

RC

Total #switches with

RC

T0 0 0 0 0T1 1 2 0 2T2 2 3 0 3T3 3 4 0 4T4 0 0 5 5T5 0 0 9 9T6 0 0 13 13T7 1 2 3 5T8 2 3 6 9T9 3 4 9 13

An increasing number of protection devices as well as an increasing levelof remote control is expected to result in an increased feeder reliability.However, it is also expected to increase the life cycle cost. To what extent thecost and reliability increase is investigated by performing a cost-reliabilityassessment for all concepts, as presented in Chapter 3. For each topologythe key number that was introduced in Section 3.3.6 is determined such thatthey can be rated.

4.2.2 Results

In the following the results for all ten evaluated topologies are presentedand compared. The reliability and costs results are represented by relativevalues with reference to the basic topology without any protection or remotecontrol. This makes it easier to directly compare the influence of the differentprotection and remote control schemes. The introduced key number alreadycompares the SAIDI and cost results of the topologies with the referencesystem. Therefore, it is not possible to determine the key number for thereference system. Hence, the key number results are shown relative to thetopology with the best result. Additionally to these results, also the resultsfor the reliability fairness index that was introduced by Michiel Tavernier[9] (see Section 3.3.4) are presented.

the steps from one topology to the next. For example, T0 → T1, T0 → T4, T0 → T7, orT8 → T9.


4.2.2.1 Reliability

SAIDIFigure 4.2 depicts all SAIDI results relative to topology T0. The columncolor of the bars represents the different number of circuit breakers installedin the topology according to the colors in Figure 4.1. The grey sections in theback ground divide the topologies into three groups. The first group focuseson increasing the number of protection devices while having no additionalremote controlled switches in the feeder. The second group only increasesthe number of remote controlled switches while having no protection relaysin the feeder. And in the last group combinations of the two measures areinvestigated where in each step the number of remote controlled switchesand circuit breakers increases.

T0 T1 T2 T3 T4 T5 T6 T7 T8 T9

0 circuit breakers2 circuit breakers3 circuit breakers4 circuit breakers

SAIDI

Per

cent

age

[%]

0

20

40

60

80

100Protection Remote Control Combination

T0 T1 T2 T3 T4 T5 T6 T7 T8 T9

0

20

40

60

80

100

Figure 4.2: SAIDI results of the ten evaluated topologies.

The first obvious observation is that all investigated topologies lead to asignificant decrease in SAIDI. This is expected since the concepts are com-pared to a topology with no protection and remote control at all. Secondly,it is clear that increasing the number of circuit breakers or remote controlledswitches both lead to a further decrease in SAIDI. The decrease in SAIDIis not linear and seems to lessen from step to step. Another remarkableobservation is that installing protection devices has a much higher impact


than installing remote control. However, it has to be mentioned again thatit is BKW’s current practice that all circuit breakers are also equipped withremote control. Therefore, while topologies T1 to T3 have no solely remotecontrolled switches, it can, however, not be said that they all have the sametotal number of remote controlled switches. And they also do not have thesame number of remote controlled switches as the reference topology T0.This might also explain why topologies T7 to T8 do not have substantiallybetter SAIDI results than topologies T1 to T3.

SAIFIFigure 4.3 shows all results for SAIFI relative to the reference topology T0.As for Figure 4.2, the column color differences within the plot represent thedifferences in number of circuit breakers and the gray bars in the backgroundgroup the measure packages.

T0 T1 T2 T3 T4 T5 T6 T7 T8 T9


SAIFI

Per

cent

age

[%]

0

20

40

60

80


T0 T1 T2 T3 T4 T5 T6 T7 T8 T9

0

20

40

60

80

100

Figure 4.3: SAIFI results of the ten evaluated topologies.

It is clear to see that measures only considering remote control have noinfluence on SAIFI. This is perfectly understandable since SAIFI measuresthe frequency of interruptions and is independent of any changes in the inter-ruption durations. Remote control, however, only affects the time needed forfault localization and system restoration but has no impact on the frequency


of interruptions. This is as long as remote controlled switches are not mod-elled to have different failure rates than other switches or switches are notmodelled as a failure source, of course. An increased number of protectiondevices, on the other hand, results in a significant decrease of SAIFI. Thereason is that more protection leads to a lower number of affected customerswhen failures occur. Topologies T7 to T9 obviously have the same resultsas topologies T1 to T3 because the additional remote controlled switcheshave no impact on SAIFI. As for SAIDI, the decrease in SAIFI is not linear.One would expect that the impact of adding one circuit breaker when thenumber of circuit breakers is low is higher than when the number of installedcircuit breakers is already high. This can for example be seen when compar-ing the results for topologies T1 and T2. However, the second influencingfactor is the placement of this additional circuit breaker. Depending on thelocation of the additional circuit breaker, a larger or smaller section can beseparated and therefore the impact can be larger or smaller. This explains,why the SAIFI reduction from topology T2 to T3 is larger again than theone from T1 to T2 even though one circuit breaker is added in both cases.The placement of the measures in the different topologies can be seen inFigure 4.1. The additional circuit breaker of topology T2 is placed at thetransfromer station PTS2 (see Figure 3.6) and separates a long part of thegrid with an alternate supply point that is far away from the base at the pri-mary substation. In topology T3, additionally, the switch RCS1 is equippedwith a protection relay. Apparently, separating that stub part downstreamfrom RCS1 has a relatively large impact on the number of interruptions thata customer experiences in a year. This is most probably the case becausethere are not that many customers connected to this part of the feeder butthere is a relatively high probability for failures there. These failures affectmany other customers if there is no protection at switch RSC1.

4.2.2.2 Costs

As for the reliability results, switchgear cost results of the different topologiesare represented by their relative values with reference to the total switchgearlife cycle cost of topology T0 in this section. Figure 4.4 depicts all relativelife cycle costs.

It is clear that an increase in the number of switches with either remotecontrol or protection relays results in an increase in costs. Unlike for thereliability results, the cost increase is directly proportional to the numberof equipped switches. This is of course straightforward since the calculatedcosts are based on average net present values and basically only depend onthe number of equipped switches. Remote control seems to have a higherimpact on the total cost. However, this is only due to the fact that withinone step more than one switch is equipped with remote control while usually


T0 T1 T2 T3 T4 T5 T6 T7 T8 T9


Life Cycle CostsP

erce

ntag

e [%

]

0

20

40

60

80

100

120

Protection Remote Control Combination

T0 T1 T2 T3 T4 T5 T6 T7 T8 T9

0

20

40

60

80

100

120

Figure 4.4: Cost results of the ten evaluated topologies.

only one additional switch is equipped with a protection relay per step.

4.2.2.3 Key Number

Only looking at reliability or costs results does not give a clear picture ofwhich concept should best be implemented since, obviously, the better thereliability results the higher are the costs. Therefore a key number wasdefined that allows to compare the impact of a topology on SAIDI to theadditional cost. The results for the key number values relative to the besttopology are shown in Figure 4.5. Topology T1 achieves the best result forthe key number and is therefore chosen as the reference.

The results that are plotted in Figure 4.5 have two main findings. First,it shows that by installing protection relays at the right places a more costefficient SAIDI reduction can be achieved than by installing only remotecontrol. Secondly, it can be seen that the first implemented measure hasthe highest impact per additional costs and, therefore, is the most cost ef-ficient. This makes sense, since with the first installed protection SAIDIcan be reduced drastically. Adding another protection device does not havethat much potential for improvement anymore since the area that can besectionalized is not that big anymore. It is expected that adding one pro-


T0 T1 T2 T3 T4 T5 T6 T7 T8 T9


Key NumberP

erce

ntag

e [%

]

0

20

40

60

80


T0 T1 T2 T3 T4 T5 T6 T7 T8 T9

0

20

40

60

80

100

Figure 4.5: Key numbers of the nine concepts relative to topology 1.

tection device has a lower impact when the number of protection devices isalready high than when there are not that many switches with protectionyet. Additionally, one has to keep in mind that the first measure bundleadds two switches with protection in the same transformer station. Thesetwo switches do not have as high additional costs as two additional switchesprotection in two different transformer stations.

The second best topology is a combination of the protection scheme oftopology T1 and the remote control layer of topology T4. This means thatadding three remote controlled switches to topology T1 is more cost efficientthan adding one additional protection relay (which would be topology T2).

4.2.2.4 Reliability Fairness Index

Figure 4.6 depicts all relative RFI (Reliability Fairness Index, see Equation3.7) results relative to the reference topology T0. RFI is an indicator of theoverall fairness for all transformer stations in terms of SAIDI. The lower theRFI-value, the greater the overall fairness in the feeder. Full fairness cannever be achieved in radial feeders since customers will always be affected byall failures that happen upstream from them. Hence, the further downstreama customer is, the more interruptions and the longer interruption durations


he will experience.

T0 T1 T2 T3 T4 T5 T6 T7 T8 T9


RFI

Per

cent

age

[%]

0

20

40

60

80

100

120

Protection Remote Control Combination

T0 T1 T2 T3 T4 T5 T6 T7 T8 T9

0

20

40

60

80

100

120

Figure 4.6: Reliability fairness results of the ten evaluated topologies.

It can be seen that by adding only protection relays the overall fairness inthe feeder is reduced. This makes sense since protection devices lead to fewerinterruptions for the customers upstream from the protection and hopefullyshorter interruption durations for the downstream customers. Hence, theycreate an even greater difference between the upstream and the downstreamcustomers. Remote control, on the other hand, can have a positive impact onthe overall fairness. It is likely that this positive impact is highly dependenton the location of new remote controlled switches. If they are placed suchthat downstream customers can be resupplied faster, they should positivelyinfluence the fairness. It is striking that the topologies that have the highestcost efficiency key numbers are the topologies that worsen reliability fairnesscompared to the reference topology.

4.2.3 Summary and Conclusion

The results of this case study are as expected. Measure bundles that increaseeither the number of switches with remote control or protection or both, alldecrease SAIDI significantly. Measures containing additional protection de-vices have a larger impact than measures containing only remote control.Adding protection devices to a feeder also significantly reduces SAIFI of


this feeder. Additional remote control, however, of course has no impact onSAIFI since it does not affect the frequency of customer interruptions butonly the duration. The life cycle costs of the switchgear on the feeder ofcourse increases with an increasing number of switches with remote controlor protection. Topology T1 achieves the highest key number and has there-fore the best cost efficiency of the considered topologies. The further reducedvalue of SAIDI for some of the other topologies does not compensate for theadditional cost. Topology T9, which has the best SAIDI and SAIFI valuesalso has one of the lowest key numbers since the additional costs are so high.The second best key number result is achieved by topology T7 which is acombination of the protection scheme of topology T1 and additional remotecontrolled switches. In conclusion, it is advisable to install first only one ortwo switches with protection and then add remote controlled switches if afurther SAIDI reduction is wanted.

From the results it can be seen that the topologies that have the highestcost efficiency key numbers are also the topologies that worsen reliabilityfairness compared to the reference topology. Hence, if reliability fairnessshould be considered in grid planning, this seems to introduce a conflict ofgoals between achieving higher reliability in a cost efficient way and increas-ing reliability fairness in the feeder.

What could not be analysed in detail in this analysis is the effect thatthe placement of the altered switches has on the results. Maybe different lo-cations for the circuit breakers or remote controlled switches in the differenttopologies would return different results.

4.3 Case Study 2 - Analysis of Different FeederTypes

In the second suggested case study, the principle of the first case studyshould be applied to more feeders in order to generate general statements onthe impact of the investigated measure. Therefore, eight additional feedersare selected. The same concept of nine different protection and remotecontrol schemes plus a basic topology without protection relays or remotecontrolled switches is developed for them. The characteristics of the feedersare introduced in Section 4.3.1. Similar results as in the first case studywere calculated for all feeders. The analysis of these results is presented inSection 4.3.2.


4.3.1 Different Feeder Types

As stated above, nine feeders were selected for this case study. The ninefeeders can be divided into three different types that vary mainly in length.The characteristics of the three types are summarized in Table 4.2. BKW’sgrid is divided into three main regions. The different regions differ, on onehand, in their geographical and population characteristics and, on the otherhand, have historically grown with partly different principles for networkconstruction. For each feeder type one feeder of each region was picked.This ensures that the most important feeder characteristics are representedin all feeder types.

Table 4.2: Characteristics of the different feeder types.

Feeder Type Name Length Cabling Rate

Short up to 15 km 45 % to 100 %Medium 15 km to 35 km 15 % to 50 %Long more than 35 km 10 % to 40 %

The example feeder of the tool demonstration and first case study is,therefore characterised as a long feeder. Additionally to the characteristicsin Table 4.2, it can be said that the short feeders are more likely to be inurban areas while the medium feeders are covering the more rural areas andlong feeders often even reach into more mountainous areas. Thus, whilethe main characterizing criteria for the feeder types is their length, it canbe seen that the cabling rates also are also different for the feeder types,especially for short feeders. This is pointed out because the cabling rate hasa substantial influence on the frequency of failures.

As in the first case study, a basic topology without any remote controlor protection and nine topologies with increasing number of switches withremote control and protection relays were defined by BKW’s experts. Thescheme of these topologies again follows the set up that is shown in Figure4.1. The measure bundles can, however, contain more or fewer additionaldevice per step depending on the feeder length. For example, the step fromtopology T1 to topology T2 might consist of one additional switch withprotection for short feeders and two or three additional switches for longfeeders.

4.3.2 Results

In the following the results for the different feeder types are presented andanalysed. The key numbers of the nine different protection and remote con-trol scheme concepts are determined as an average for each feeder type.


First, the key numbers are, as for the first case study, presented as valuesrelative to the best topology. The results are then further analysed to for-mulate general statements on the improvement potential of the feeder typesand the impact of the measures.

4.3.2.1 Key Numbers

Figure 4.7 shows the key numbers for the nine topologies of the three feedertypes. The results of the feeder types are summarized as an average valueper topology and type. The results in Figure 4.7 are presented as values rela-tive to the topology with the best result, i.e. topology T1 of the long feeders.

T0 T1 T2 T3 T4 T5 T6 T7 T8 T9

Feeder types

shortmediumlong

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Per

cent

age

0

20

40

60

80


T0 T1 T2 T3 T4 T5 T6 T7 T8 T9

0

20

40

60

80

100

Figure 4.7: Key number results of the nine topologies for the three feedertypes relative to the over all best Topology (T1 of long feeders).

Compared to the reference topology, which has no remote control orprotection, installing protection on long feeders is the most cost efficientmeasure to reduce SAIDI. This result is not very surprising. The reasonfor the first protection device(s) being the one(s) with the highest cost effi-ciency were already discussed in the results of case study 1. That measureson long feeders achieve a bigger reduction of SAIDI per invested capital isjust as expected because there are a lot more failures happening on longfeeders. Additionally, installing protection on a long feeder can reduce the


fault search area by a larger region. And, installing remote control can savemore in terms of driving times than within a short feeder where drivingtimes are already relatively short.

To get a better insight on the impact of the measures, Figure 4.8 showsthe same results as above but this time relative to each feeder type’s besttopology.

T0 T1 T2 T3 T4 T5 T6 T7 T8 T9

Feeder types

shortmediumlong

Key Number

Per

cent

age

0

20

40

60

80


T0 T1 T2 T3 T4 T5 T6 T7 T8 T9

0

20

40

60

80

100

Figure 4.8: Key number results of the nine topologies for the three feedertypes. Each topology relative to the respective feeder type’s best topology(T1 of each feeder type).

Once the results have been scaled, it can be seen that all feeder typeshave similar results. Short and medium feeders show quite regular behaviourwith increasing measures. On long feeders more fluctuations can be seen,especially for remote control. This can be explained with the choice of thenine topologies not being so straightforward anymore as for shorter feedersor in the first case study. There are many more options to place protectionor remote control on long feeders. The selected ones might not always be theideal choice. Additionally the long feeders differ more among each other andthe measure bundles do not always increase by the same number of switcheswith remote control or protection.


4.3.2.2 Improvement Potential

The key number results are as expected but they cannot give a clear un-derstanding of the improvement potential that exists in the different feedertypes. The following analysis focuses on the SAIDI improvement potentialof the different feeder types. This analysis was once performed similar to agreenfield approach, meaning that the improvement potential is determinedrelative to the basic topology with no protection or remote control. Fur-thermore, the same analysis was also performed comparing the results totoday’s topology of the feeders.

Compared to the Base TopologyFor this analysis only the topologies with the best key number were consid-ered for each feeder. Figure 4.9 shows the the SAIDI improvement potentialof one feeder relative to the total SAIDI of a green field network if the mostcost efficient topology is implemented. Next to it the necessary investmentcosts are displayed relative to the total switch costs in the network.

First of all, it is clear that long feeders have the highest improvementpotential if it is assumed that they have no protection devices or remotecontrol installed to start with. As already elaborated on in the key num-ber results, long feeders have most influence on SAIDI, hence, it is to beexpected that it is also measures on long feeders that have the highest po-tential for SAIDI improvement. However, since the measure bundles alsocontain more devices, the costs are also higher than for measures on shortedfeeders. Anyhow, today’s network is not a green field for protection devicesand remote control. Therefore, the key number results should also be usedto identify the improvement potential to today’s topologies of the feeders.

Compared to Today’s TopologyTo identify the potential for SAIDI reduction from today’s grid, the topolo-gies with the highest key number were chosen of those that have a lowerSAIDI value than today’s topology. The SAIDI reduction is then displayedrelative to the total SAIDI of today’s network. And the costs of these topolo-gies are displayed relative to the total switchgear costs of today’s network.Figure 4.10 shows the results.

Comparing to today’s topology, medium feeders have the highest SAIDIimprovement potential. This makes sense since previous measures were ofcourse mostly focused on the long feeders that have a large influence on thereliability of supply. In today’s grid, these long feeders are already quite wellequipped and further measures have high costs and a lower impact. Mediumlength feeders, however, have not been so much in the focus in the past and,therefore, still have potential to reduce SAIDI. Since the measure bundles


SAIDI Improvement

Per

cent

age

of to

tal S

AID

I [%

]

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Investment Costs

Per

cent

age

of to

tal c

ost [

%]

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Feeder type

short medium long

Possible SAIDI Reduction of Feeder Typesand Needed Costs Compared to Greenfield

Figure 4.9: SAIDI improvement potential relative to the basic topology andnecessary investment costs for each feeder type.

on medium length feeders contain fewer devices than on long feeders, thecosts for the proposed topologies are lower. The improvement potential ofshort feeders is still really low since they are mostly urban and cabled, and,therefore, not that failure prone to start with.

4.3.3 Conclusion

The general impact of different remote control and protection schemes isindependent of the feeder type they are applied to. All feeder types showthe same type of results for the key numbers of the nine evaluated topolo-gies. Adding the few protection devices to the feeder has the highest costefficiency, followed by additionally installing a bundle of remote controlledswitches. The results are slightly less straightforward for long feeders but


SAIDI Improvement

Per

cent

age

of to

tal S

AID

I [%

]

0.00

0.02

0.04

0.06

0.08

0.10

Investment Costs

Per

cent

age

of to

tal c

ost [

%]

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Feeder type

short medium long

Possible SAIDI Reduction of Feeder Typesand Needed Costs Compared to Today

Figure 4.10: SAIDI improvement potential relative to today’s topology andnecessary investment costs for each feeder type.

this can be explained with the fact that the long feeders differ more amongeach other. This effect is expected to run off when more long feeders areanalysed.

If the topologies are compared to a topology with only basic protectionat the primary substation, it is always topology T1 that achieves the highestcost efficiency key number. The first bundle of measures, of course, has thelargest impact on SAIDI and lower costs than all the other topologies thatwere evaluated. In such a “greenfield” approach, it also makes sense to focusthe measures on the long feeders in the grid. The long feeders have thehighest improvement potential since they also have the highest influence onSAIDI. However, the results differ, if the previous taken measures are con-sidered and the topologies are compared to today’s topology of the feeders.


It can be seen that previous measures have been mostly focused on long feed-ers and there a lot of the initial room for reliability improvement has beenused already. Therefore, the main focus should now be on medium lengthfeeder since they also have a considerable impact on SAIDI and not thatmany protection devices or remote controlled switches are installed already.

4.4 Case Study 3 - Finding the Optimal Topologyof a Feeder

The third case study was suggested to test the tool’s ability to find the mostcost efficient remote control and protection configuration by itself. One ofthe medium length feeders from case study 2 was selected in order to cutdown computation time compared to the example feeder of case study 1.Additionally to the basic topology of this feeder, two lists of switches toconsider are handed to the tool. The first list refers to the switches whichcould be equipped with remote control and the second list refers to theones that could be equipped with protection relays. The tool identifies allpossible topologies of the feeder considering the two switch lists and furtherrestrictions, e.g. a maximum number of circuit breakers in the feeder. Forall possible topologies, it calculates SAIDI and the life cycle costs and thendetermines which configurations are Pareto optimal considering the SAIDIand cost results. It also determines which topologies have the best costefficiency key number and suggests the ideal feeder topology.

4.4.1 Test Feeder

Especially for long feeders with many different components, the simulationscan quickly become computationally intractable. Hence, a shorter feederwas selected for this test than for the first case study. The feeder will beintroduced and briefly described in this section.

The example feeder is a feeder of 16.4 km length of which 19.0 % areunderground cables. It consists of 18 transformer stations with varyingdistance between them, has one closed connection to the primary substation(UST) and two open alternate supply points (AS) to other feeders. In thebasic topology there are no remote controlled switches or protection devicesexcept for the circuit breaker at the substation. Figure 4.11 shows thetopology of the test feeder on a Google Map.

4.4.2 Results

In the following the results for the topology optimization are presented andanalysed. First, a Pareto optimality analysis for all topologies considered bythe tool is performed. Secondly, the characteristics of the topologies withthe largest key numbers are presented and the most cost efficient topologies


Figure 4.11: Base topology of the investigated feeder.

suggested by the tool are examined. Additionally, the Pareto optimalityresults of the selected topologies of the second case study are discussed. AllSAIDI and cost results are presented relative to the basic topology.

Figure 4.12 shows a plot of the SAIDI and cost results of all investigatedtopologies relative to the results of the basic topology. The Pareto optimaltopologies are marked in green and the Pareto optimality frontier is drawnas the border between the shaded green and red areas. The ten topologieswith the highest key numbers are marked in red and the topology with thehighest key number is pointed out in blue. These topologies will be furtherdiscussed in this section. The ten topologies T0 to T9 that were analysed inthe second case study are also marked in red. A comment on their resultscan be found at the end of this section.

To gain a better understanding of the results and the Pareto optimal-ity frontier, the twenty Pareto optimal topologies were further investigatedin terms of their number of switches with remote control and protection.In Figure 4.13 the topologies’ results are coloured in accordance with thetotal number of equipped switches. The switches can either be equipped


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●Best Key Number

Figure 4.12: Results of the topology optimization for the analysed feeder.

with protection relays (and, respecting BKW’s current practice, also withremote control) or only with remote control. It can be seen that up to threeequipped switches a significant reduction of SAIDI can be achieved with arelatively small increase of costs, i.e. a SAIDI reduction of 67.9 % with a costincrease of only 32.5 % compared to the basic topology. However, for moreequipped switches, one notices a sudden steep increase in costs in the Paretooptimality line. This leads to the conclusion that a sort of saturation can bereached, where SAIDI cannot be reduced much more by only adding moreremote control or protection devices to the feeder. The general structure ofthe Pareto optimality frontier also confirms the expectation that the firstfew measure bundles or equipped switches have a much higher impact rela-tive to their cost than further devices when there are already many installed.

Considering the sudden steep cost increase in the Pareto optimality line,it also makes sense that all of the topologies with the best key number re-sults are on the lower part of the Pareto optimality frontier. The mostcost efficient topologies have at most two equipped switches. Further, thesetopologies have either zero or one circuit breakers (additionally to the one atthe primary substation) and zero to two switches that are just remote con-trolled. Topologies T1 and T4 of case study 2 are amongst the ten topologies


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Equipped switches

0 switches1 switches2 switches3 switches4 switches5 switches6 switches7 switches8 switches9 switches

Figure 4.13: Analysis of the Pareto optimality frontier of the analysed feederwith respect to the number of equipped switches.

with the highest key number results.

In Figure 4.12 it can be seen that topologies T1, T4 and T7 from thesecond case study belong to or are close to Pareto optimal solutions. Thesetopologies represent the first bundle of measures. Topologies T1 and T4are also amongst the top ten topologies considering the introduced cost-efficiency key number. Additionally, topology T2 is also very close to beingPareto optimal. The other proposed topologies not being Pareto optimal caneasily be explained with the fact that they were created building up on thefirst measure bundles (T1, T4, T7). The switches that were equipped withremote control in these first measure bundles should not be downgraded inthe topologies that are building up on them for the analysis in case study2. In other words, for example, the switches that are circuit breakers thatare installed in topology T1 will also be present in topology T2 and T3.The circuit breakers in T1 are most probably placed such that they dividethe feeder in more or less equal parts. Topology T2 adds circuit breakersthe ones existing in T1. It is likely that these additional circuit breakersare placed such that they divide the remaining parts ideally. However, ifone would design a topology with the same number of circuit breakers as in


T2 building up on the basic topology, the circuit breakers might again beplaced such that they divide the feeder ideally. This allocation is more likelyto be Pareto optimal than the proposed one. Hence, with the just explainedrestrictions, the placement of remote controlled switches or circuit breakerscould not be chosen ideally i the proposed topologies compared to otherconfigurations with the same number of devices. One can thus concludethat the attempt to suggest different optimal remote control and protectionschemes from a wide variety of options has been successful for the first bundleof measures. However, the second or third bundles of measures that buildup on previous ones, are – with the exceptions T1 → T2, T1 → T7, T4 →T7 – not Pareto optimal anymore.

4.4.3 Conclusion

In general, it can be said that the structure of the Pareto optimality frontierconfirms the expectation of, e.g, the first case study. The first few measurebundles or equipped switches have a much higher impact relative to theircost than equipping further switches when there are already circuit break-ers or remote controlled switches installed. There seems to be a saturationpoint where it is not cost efficient anymore to try to further reduce SAIDI byadding remote control or protection relays. The most cost efficient topolo-gies are, therefore, the ones with only up to two or three equipped switches.

Considering the suggested topologies for the second case study, it is mostimportant to realize the impact that the placement of the equipped switcheshas. If there are already remote controlled switches or circuit breakers inthe feeder, they have most probably been installed at the most cost efficientplaces considering their number. However, these already equipped switcheslead to a reduced set of placement options for further switches. This willmost probably lead to less cost efficient configurations than if the places ofthe remote controlled switches would have been chosen at the same time.This means that possible future expansions of the remote control or protec-tion scheme should be considered already when installing the first protectiondevices or remote controlled switches.

Chapter 5

Conclusions and Outlook

5.1 Summary and Discussion of Results

The results of the case studies have shown that the first bundle of protec-tion devices that is installed on a feeder always has the greatest impact andalso the best cost efficiency key number. This general statement holds truefor all evaluated feeder types. The Pareto optimality analysis performed inthe third case study confirms this and gives further insights. Also, it hasbeen shown that, in general, protection devices have the larger impact onSAIDI than remote control. This can, among other reasons, be explained bythe fact that protection devices also lead to a significant decrease of SAIFIwhile remote control has no impact on SAIFI. However, if there are alreadyprotection devices installed, the key number results show that it is morecost efficient to further reduce SAIDI by adding remote control to the feederthan by installing additional protection.

Also, it could be shown that when today’s topology of the network isconsidered, it makes more sense to focus reliability enhancement measuresconcerning remote control and protection on medium length feeders. Thelarge improvement potential of long feeders that is intuitively understand-able has already been used before. Hence, today, medium length feedershave more potential for cost efficient reduction of SAIDI.

Detailed tests were run during the development of the tool, additionallyto the presented case studies. An example of such an analysis was shownin the demonstration of the tool in Section 3.4.3. These tests have shownthat in general the tool does a good job of estimating the average valuesfor reliability and assessing impact of the investigated measures. However,it could also be determined that currently used average component failurerates can not exactly reflect the real characteristics of the different feedertypes. This leads to an overestimation of the failures per year, especially

71

CHAPTER 5. CONCLUSIONS AND OUTLOOK 72

on long feeders. In general, it can be said that with the current parametersthe impact on SAIDI for overhead lines is overestimated while the impacton SAIDI for underground cables is underestimated. Nonetheless, theseflaws are a matter of parameter calibration and can easily be corrected withfurther tests. Overall, the algorithm models the system’s failure responsecorrectly and the achieved results are in accordance with the expectations.

5.2 General Conclusions

In this master thesis, a systematic tools was developed and tested that canbe used by DSOs to assess the reliability of different configurations of feederswithin their medium voltage networks. The developed tool allows to directlycompare different feeder configurations in terms of costs and reliability. Thefocus of this project was on configurations of different remote control orprotection schemes, it is, however, also possible to evaluate configurationsthat focus on other components. Additionally, it can be used to search forthe optimal remote control and protection configurations from a given setof switches of a feeder considering an introduced cost efficiency key number.The tool was applied to three case studies suggested by BKW. In the follow-ing paragraphs the main conclusions of this master thesis are summarized.

The tool simulates a realistic response to failures within radially oper-ated medium voltage feeders. It considers short circuit failures on overheadlines, underground cables and transformer station busbars that lead to asustained interruption since the most common failures can be summarizedin these groups. However, it can consider failures on any component in thenetwork model. The tool was programmed to closely model BKW’s currentpractice during failure response and system restoration. The according algo-rithms are however controlled by parameters that can be adjusted to otheroperational practices. This gives the tool enough flexibility to be to be usedto test different operational practices or to be used by other DSOs.

The failure response algorithm is implemented in such a way that the toolcan handle radially operated medium voltage feeders with different charac-teristics. Local conditions of the feeders are considered by using the actualnetwork model and by determining the driving times with Google Maps.The tool can evaluate feeders of arbitrary length with any number of trans-former stations. The algorithm just takes longer if there are more failures tobe simulated. Any feeder topology can be selected as the reference topologyto which the other configurations can be compared. This allows the tool tobe used for conceptual studies similar to greenfield approaches but also forstudies concerning strategies building up on today’s topology of the feeders.


The developed cost reliability assessment tool is capable of quantifyingthe costs and reliability aspects of any feeder configuration. Also, the im-pact of any change in the remote control and/or protection scheme on costsand reliability can be quantified. This allows a DSO to investigate differentfeeder configurations and obtain a robust quantification of the cost efficiencyof the investigated configurations. The tool can, therefore, be used as a guid-ance in the decision making during the grid planning process.

The applicability of the tool has been demonstrated in three case studieson a real medium voltage network. A first case study specifically aimed atcomparing different remote control and protection schemes for a real feederin within BKW’s medium voltage network. The second case study extendedthe principle of the first case study to additional feeders with different char-acteristics. This was done in order to obtain conceptual insights on theimprovement potential of different feeder types and the impact of measures.Additionally, the tools ability to determine the optimal topology of a feederwith given restrictions was tested and proven in a third case study. For alltypes of case studies, the application of the developed tool has lead to clearand transparent conclusions and insights regarding the remote control andprotection scheme of the analysed medium voltage networks.

The insights gained by the case studies have shown that not only thenumber of protection devices or remote controlled switches has an impacton the reliability of supply but also their placement. Hence, mores studiesinvestigating the ideal locations for protection or remote control could bedone with the tool. Also, it was shown that similar protection scheme andremote control measures with similar placement choices have a similar im-pact on all kinds of feeder types. The two most cost efficient configurationscompared to a greenfield reference are always the first bundle of installedprotection devices and the same protection scheme configuration with addi-tional remote controlled switches.

Overall, the tool has proven to be able to robustly quantify reliabilitycosts trade-offs of different radially operated medium voltage feeder config-urations. It has also been shown that the tool can be applied to perform avariety of studies that can support the decision-making process in the longand short term grid planning process of a DSO.

5.3 Outlook

In this section a twofold outlook will be given. The first part is concernedwith further research on the thesis’ subject with the developed tool, whilethe second part covers further development of the presented tool.


As seen in the summary of the results, an interesting domain for furtheranalysis could be the ideal placement of protection devices or remote con-trolled switches. More different configurations with a set number of switcheswith protection relays or remote controlled could be analysed to achieve gen-eral statements about the ideal placement of a certain number of protectiondevices or remote controlled switches. The additional tests have shown thatwith more tests to calibrate the restoration algorithm parameters, the toolcan be used to analyse all kinds of different feeder topologies. Not onlyremote control and protection schemes can be considered but also othermeasures such as cabling of failure prone overhead lines can be analysed.Furthermore, the tool can also be used to investigate the impact of so called“soft measures”, meaning measures that include no construction on the grid.Examples of such measures are increasing the number of field crews that aresent to search for the fault or more restrictive cutting of trees along overheadlines to reduce failure rates.

Further development of the tool could, for example, include consideringplanned outages or an improved graphical interface. It could be consideredto calculate the reliability indices with a non-sequential Monte Carlo simula-tion. This would build up on the existing tool and allow to relatively easilyalso take into account planned outages and major events while still mod-elling the restoration process in the same detail. However, this would requiresome improvement on the computational efficiency or working on serverswith more computation power instead of on standard workplace computers.

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