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Economic Framework For Power Quality

CIGRE/CIRED

Joint Working Group

C4.107

Economic Framework for Power Quality

JWG CIGRE-CIRED C4.107

Members

Jose Gutierrez Iglesias (ES) - (Convener), Detmar Arlt (DE), Gerhard Bartak (AT), Math Bollen (SE),Dave Byrne (EI), David Chapman (UK), Alice Delahunty (UK), Philippe Eyrolles (FR), Elena Fumagalli (IT), Mats Hager (SE), Zbigniew Hanzelka (PL),

Bill Howe (US), Rafaël Jahn (BE), Alex McEachern (US), Ian McMichael (AU), Jovica V. Milanovic (UK), Patxi Pazos (ES), Roman Targosz (PL), MarioTremblay (CN),

Jasper Van Casteren (NL), Mathieu Van Den Bergh (US), Raghavan Venkatesh (IN), Paola Verde (IT)

Copyright © 2011

“Ownership of a CIGRE publication, whether in paper form or on electronic support only infers right of use for personal purposes. Are prohibited, except if explicitly agreed by CIGRE, total or partial reproduction of the publication for use other than personal and transfer to a third party; hence circulation on any intranet or other company network is forbidden”.

Disclaimer notice

“CIGRE gives no warranty or assurance about the contents of this publication, nor does it accept any responsibility, as to the accuracy or exhaustiveness of the information. All implied warranties and conditions are excluded to the maximum extent permitted by law”.

EXECUTIVE SUMMARY

Electric power quality disturbances can have significant economic consequences for many different types of facilities. Although power quality is widely recognized as an important issue, there is no consensus on its total economic impact. Indeed, there is not even consensus on how to measure the impact.

A wide range of potential solutions, with varying degrees of cost and effectiveness, are available to mitigate the consequences associated with poor power quality. Power quality solutions can be applied at different levels or locations within the global electrical system.

The evaluation of power quality improvement alternatives is an exercise in economics. Facility managers and utility engineers must evaluate the economic impacts of the power quality variations against the costs of improving performance for the different alternatives. The best choice will depend on the costs of the problem and the total operating costs of the various solutions.

In general, the costs of these solutions increase as the power level of the load that must be protected increases. This means that economies usually can be achieved if sensitive equipment or controls can be isolated and protected individually from equipment that does not need protection.

Each solution technology needs to be characterized in terms of cost and effectiveness. In broad terms, the solution cost should include initial procurement and installation expenses, operating and maintenance expenses, and any disposal and/or compensation value considerations.

Improving facility performance during power quality variations can result in significant savings and can be a competitive advantage. Therefore, it is important for customers and suppliers to work together in identifying the best alternative for achieving the required level of performance.

A methodology for performing a comparative economic analysis is featured in this report.

A joint working group, JWG C4.107, has been formed between CIGRE (electric power transmission emphasis) and CIRED (electric power distribution emphasis) to develop a systematic approach to this issue.

The JWG works to develop a framework for analysis of the economics of power quality, and has created a bibliography of existing references. However, gathering specific values and data to assess the economics of power quality is beyond the scope of the Group; the work will be limited to developing a framework. Different technologies are evaluated by estimating the improved performance that can be expected after the technology has been applied. The power quality cost savings are calculated for each technology along with the costs of applying the technology.

JWG C4.107 aimed to produce this report that summarizes available information about cost-benefit analysis of power quality, and to propose a framework for how to assess costs, how to assess the economic impact of mitigation, and how to assess the economic impact of immunity.

TABLE OF CONTENTS

EXECUTIVE SUMMARY ...2

1.

1.1. SCOPE OF THE REPORT ...5

1.1.1. The Economic Importance of Power Quality...5

1.2. ECONOMIC CONSEQUENCES OF POOR PQ FOR END USERS ...6

1.3. ECONOMICS OF POWER QUALITY FOR POWER NETWORKS ...7

1.4. ECONOMICS OF POWER QUALITY FOR SOCIETY ...8

1.5. ROLE OF REGULATION...9

1.6. OVERVIEW OF THE DOCUMENT ...10

2.

2.1. METHODOLOGY FOR QUANTIFYING THE ECONOMIC IMPACT OF VOLTAGE SAGS AND SHORT INTERRUPTIONS...12

2.1.1. Introduction...12

2.1.2. Overview of Existing Methodologies ...12

2.1.3. IEEE Guidelines...14

2.1.4. Analytical Economic Analysis...14

2.1.5. Indirect Economic Analysis ...17

2.1.6. Reported PQ-Related Losses from Around the World...19

2.2. METHODOLOGY FOR QUANTIFYING THE ECONOMIC IMPACT OF HARMONICS ...23

2.2.1. Introduction...23

2.2.2. Overview of Existing Methodologies ...24

2.3. METHODOLOGY FOR QUANTIFYING THE ECONOMIC IMPACT OF OTHER PQ PHENOMENA...29

2.3.1. Voltage and Current Unbalance...29

2.3.2. Surges and Transients ...34

2.3.3. Flicker ...35

2.4. CONCLUSIONS ...36

3.

3.1. INTRODUCTION...37

3.2. REVIEW OF LITERATURE AND DOCUMENTED METHODOLOGIES...38

3.3. COSTS ASSOCIATED WITH PQ ...38

3.3.1. Costs Incurred by the Utility to Mitigate PQ Issues...39

3.3.2. Costs Associated with Improving Reliability but not PQ ...45

3.3.3. Costs for Responding to PQ Issues ...46

3.4. SUMMARY ...47

3.5. CONCLUSIONS ...47

4.

4.1. INTRODUCTION...49

4.2. IMPORTANCE AND MOTIVATION ...49

4.3. END-USER PERSPECTIVE ...50

4.3.1. Technical Data ...50

4.3.2. Economic Data...52

4.4. DNO PERSPECTIVE: DATA COLLECTION ...60

4.5. CONCLUSIONS ...62

5.

5.1. INTRODUCTION...63

5.2. ECONOMIC ANALYSIS OF THE COSTS OF PQ...63

5.2.1. Economic Impact of Power Quality Variations ...63

5.3. END-USE PQ SOLUTIONS...71

5.4. CHOOSING THE OPTIMAL PQ SOLUTION ...79

5.5. CONCLUSION ...80

A COMMON PQ PHENOMENA...81

A.1 Categories of Power Quality Variations ...81

B RESPONSE OF SENSITIVE EQUIPMENT TO PQ EVENTS...87

B.1 Data Processing and Communications Equipment ...87

B.2 Variable-Speed Drives...88

B.3 Lighting ...88

B.4 Solenoid-Operated Contactors...89

C ADDITIONAL LOSSES CAUSED BY POOR PQ ...89

C.1 Cables ...89

C.2 Transformers...90

C.3 Motors ...90

APPENDIX 2 ...91

A OVERVIEW OF INTERRUPTION COST CALCULATION...91

B PROBABILISTIC VOLTAGE DIP COSTS CALCULATION...92

C OVERVIEW OF EQUIPMENT SENSITIVITY...92

D UNCERTAINTY INVOLVED WITH EQUIPMENT SENSITIVITY...93

E COUNTING OF PROCESS TRIPS ...94

F COST ASSESSMENT...96

G NUMERICAL EXAMPLE...96

I TYPICAL LOSS VALUES...102

J TYPICAL FINANCIAL LOSS VALUES - SUMMARY...108

K FORMULAE FOR COMPUTING HARMONIC LOSSES FOR THE MAIN ELECTRICAL COMPONENTS...113

L METHODS FOR PROBABILISTIC EVALUATIONS...116

APPENDIX 3 ...123

A COST ASPECTS...123

B HYDRO-QUEBEC-IREQ REPORT FOR ECONOMICAL ASPECT OF HARMONICS ON DISTRIBUTION AND TRANSMISSION SYSTEM ...128

B.1 Harmonics Power Losses Evaluation ...128

B.2 Harmonics Losses Evaluation ...128

B.3 Harmonic Losses Cost Evaluation...129

B.4 Conclusion...129

APPENDIX 4 ...131

A STRUCTURING THE DATA COLLECTION PROCESS ...131

B EXECUTING DATA COLLECTION PROCESS – END USER PERSPECTIVE ...133

C CONCLUSIONS ...134

APPENDIX 5 ...136

A ILLUSTRATIVE CASE STUDY ...136

A.1 Base Case: Facility Data and Base Case Calculations...136

A.2 Case 1: Redundancy in the Utility Supply...137

A.3 Case 2: Applying a Battery UPS ...138

A.4 Case 4: Using Distributed Energy Resources (DER) ...140

B CASE COMPARISON AND SENSITIVITY ...141

REFERENCES ...143

1.

Introduction to the Economics of Power Quality

1.1. Scope of the Report

Various independent studies have been undertaken by power companies, consultants, regulators, and research organizations to estimate the cost of power quality problems to the power companies and their customers. A good understanding of the basis for determining these costs is important in assessing appropriate interventions (either by the distribution network operator or by the customers themselves). A joint working group has been launched with CIRED, where the question has been a subject of a 2001 round-table discussion. This is supported by the convener of CIRED S2 (EMC & Power Quality). The scope of the proposed JWG was to:

• Review and document the economic implications of the power quality parameters: voltage dips, short interruptions, and voltage waveform quality. Long interruptions were not considered.

• Review and document methods of assessing these costs that have been used to date, including aspects such as:

I. Direct and indirect costs to customers (e.g. production losses and plant damage). II. Energy losses associated with poor power quality.

III. Cost of energy not supplied.

IV. Methods of collecting customer costs.

V. Actual customer costs collected to date for various industry sectors.

• Propose a standardized method of collecting the above information, based on the experience of various international studies.

• Recommend a methodology of using this data to cost and motivate power quality interventions on the power system or within the customer plant.

• Provide indicative costs for specific industry sectors, where possible.

Many professionals, including industry regulators, consultants, system and installation designers, maintenance managers, production managers, and financial mangers, are concerned about the impact of the costs of poor power quality on businesses and how these costs can be managed. Techniques for avoiding or reducing the impact of power quality issues are well known and the cost of their deployment relatively easily determined. However, assessing the potential cost impact of power quality (PQ) issues is difficult because, for example, the incidence of problems, the response of equipment, and the effect on process continuity are statistical in nature and are difficult to quantify. Although there have been

numerous case studies, there has been, so far, no consensus on how the calculation or assessment of these costs should be approached.

This report provides a methodology for examining the economic framework for PQ. It will enable all interested parties to establish costs and benefits of PQ improvement and mitigation measures in a consistent and open manner.

1.1.1. The Economic Importance of Power Quality

“Power quality” is the term generically used to describe the extent to which the electrical power available at the point of use is compatible with the needs of the load equipment connected at that point. The effects of a lack of compatibility are termed PQ problems or PQ issues. Compatibility is a two-sided equation because both the characteristics of the electrical power supply AND the sensitivity of the load equipment are important variables.

There are many parameters for which compatibility is necessary, including supply voltage level, voltage stability, waveform distortion due to harmonics and interharmonics, voltage unbalance between the phases, and long- and short-term availability of the supply.

When there is a lack of compatibility, end-user equipment may cease to function, may operate erratically or incorrectly, or may operate outside its normal envelope at reduced efficiency or in such a way that its operating life is reduced. The situation is further complicated by the fact that many PQ issues are caused by the operation (or mis-operation) of end-use equipment that is connected to the network.

Electrical and electronic equipment rarely operate in isolation. Even the simplest of commercial operations requires the interoperation of several items of equipment—the use of a personal computer usually requires the aid of some communications equipment, a network, and a printer, for example. In other words, the failure of one piece of equipment usually results in the failure of a process that may or may not affect other processes. Regardless, however, when process equipment ceases operation, the result is a financial loss. Depending on factors such as the nature of the business, the organization of the work flow (whether continuous processing or batch production) and the value of the product, this loss may range from the trivially small to the extremely large.

There are two obvious approaches to ensuring 100% compatibility between electric power supply and end-use loads: Either design and construct a perfect electric power delivery grid, or make all end-use devices perfectly tolerant of common PQ issues. Unfortunately, for a number of reasons, neither of these approaches represents the economic optimum. Firstly, some loads are relatively insensitive to many PQ phenomena while being rather sensitive to others. Incandescent lighting is insensitive to harmonic distortion but overly sensitive—in combination with the human response—to flicker. On the other hand, electronic equipment is not disturbed by the scale of voltage instability that causes flicker; however, it is very sensitive to voltage dips and to higher levels of harmonic voltage distortion. Making every supply suitable for every load would be expensive and is unnecessary. Secondly, although the cost of designing and manufacturing any individual piece of equipment to be “immune” is not large, that cost is multiplied by the total number of pieces of equipment in use and represents a very large economic burden on

consumers. Thirdly, the option of building a very robust, very clean power system would be extremely high and it would be very difficult, if not impossible, to guarantee a minimum performance level at all points of common coupling. Increased penetration of distributed generation will make this even more difficult as generation is added at medium and low voltage levels. Lastly, many PQ issues arise within the

consumer’s premises, due to the characteristics of the installed equipment, sub-optimal installation of equipment and cabling, poor electromagnetic compatibility (EMC) performance of earthing systems, etc., so perfection at the point of common coupling is no guarantee of adequacy at the point of use.

1.2. Economic Consequences of Poor PQ for End Users

From the descriptions of equipment responses, it is apparent that the economic consequences of poor PQ fall into three broad categories:

• Complete or partial loss of one or more processes (e.g. loss of control following a dip)

• Poor long-term productivity or product quality (e.g. as a result of operator fatigue due to flicker)

• Increased costs due to reduction of equipment lifetime resulting in premature failure (e.g. overheating of transformers due to harmonics)

These consequences take effect over very different time scales.

A process failure, triggered by a PQ event such as a dip, has immediate consequences followed by a period of recovery, during which further costs may be incurred. It is relatively easy to identify the costs that result from a single event or to predict what the costs might be.

Continuous or prolonged conditions, such as flicker, can reduce long-term productivity. If the problem is prolonged and widespread, the business may become uncompetitive and may require additional

borrowing to sustain it.

Premature failure of equipment will usually result in process failure with similar consequences to those caused by single PQ events. The difference is that the causes are in the past and were unrecognized, suggesting that predicting these costs is difficult unless a survey procedure is put in place.

Depending on the type of operation in question, the economic consequences may range from trivial to catastrophic. The user can take several approaches:

• Do nothing, and suffer the consequences.

• Take responsibility by adding mitigation equipment or hardening measures within the installation. • Work with a supplier to improve the level of PQ by local measures specifically for, but external to,

the installation.

• Negotiate with a supplier for a guarantee of a defined level of PQ, along the lines of an insurance policy.

“Doing nothing”—business as usual—is viable only for those enterprises that use batch processing for manufacturing and data handling. Process interruptions are limited in their impact and are relatively easily mitigated by, for example, reorganizing work schedules. The economic consequences are not zero but are acceptable to the business.

In every other case, the first steps in analysis of the economic impact of PQ on a particular organization or part of an organization include:

• Obtaining a thorough and continuing measurements of relevant PQ parameters • Logging of process failures and their costs and relating their occurrence to PQ events

• Assessing the likely failure modes and failure rates of processes and items of equipment, bearing in mind the different time scales involved

• Considering options for redesigning processes to reduce interdependence and reduce the risk of cascading failures

• Investigate options for hardening process equipment against PQ events and conditions

1.3. Economics of Power Quality for Power Networks

Network operators are usually required to maintain a certain quality of service to end-users by local legislation or regulation. Quality of service may be defined by a number of parameters, such as availability and voltage stability. Achieving the required level requires that the operator invest in, for example:

a. A monitoring program to identify potential failures (e.g. in transformers) so that repair or maintenance work can be planned, and unplanned downtime can be avoided

b. Careful planning of maintenance to avoid excess unavailability

c. Maintenance work to avoid damage to lines, such as tree cutting programs

Network operators need to ensure that consumers are connected appropriately (e.g. at a suitable voltage level) to avoid negative impacts on other local consumers from excessive harmonic currents or voltage disturbances. This usually involves offering consumers pre-connection support so that such issues can be avoided.

Some of the measures taken by utilities and consumers to reduce the impact of power quality issues require the installation of additional equipment. Apart from the obvious cost issue, this equipment has environmental consequences; electrical energy efficiency is reduced. and the additional equipment consumes materials and energy for its manufacture. The so-called externalities need to be taken into account.

1.4. Economics of Power Quality for Society

While the economic impact of power quality for users and for utilities is readily identifiable, the impact on society in general is less tangible.

In the recent years, the thrust on sustainable development, clean development mechanisms, and various other global (green) initiatives have necessitated extrapolating the local and short-term aspects to the globe and long-term impacts to identify the true holistic impact of industrial activities.

Specific to energy, the focus is shifting from local short-term to global long-term and while computing the impact of energy conservation it makes sense to consider the energy leverage.

Electrical energy is only an intermediate form of energy used only for bulk power transmission. Due to this, the role of electrical energy is very critical, and due to the various transformations taking place right from environment to end use, the impact of energy conservation at end use assumes a very high

significance.

Considering a simple case of power quality improvement (such as power factor improvement or harmonic mitigation), the benefits to various players at different levels are as follows:

• End user – Reduction in utility bill, direct economic benefits

• Utility – Reduction in T&D loss, better asset management, higher operational efficiencies • Power generator – Better asset management, higher operating efficiency

• Society – Lower carbon foot print, reduced global warming, sustainable development

The investment to improve the power quality could have been made at any level, by any player, but if the holistic benefit is considered, the investment decision is expected to appear better, which is more realistic considering the societal benefits.

The need is to drive decision making based on long-term global impacts rather than local short-term benefits. This is expected to influence decision making, and in most cases, the benefit of PQ improvement is expected to be more than what is being considered at present. This is expected to help in selection of an optimal/appropriate solution for a specific PQ issue and in general to make power quality improvements more attractive.

While methodologies can be developed to factor the long-term societal impact of power quality and deployment of power conditioning solutions, it is also important to develop a framework that will ensure proper application of the methodology.

Players in the arena: • Utility

• Users

• Manufacturers of equipment • Regulators

The roles are:

• Good voltage quality at the customer bus is the utility’s responsibility.

• Good quality for load current drawn from the bus in the customer’s responsibility.

• Developing and supplying cost-effective power conditioning devices and equipment with adequate tolerance to power quality with appropriate technology are the manufacturers’ responsibility. • Ensuring an efficient balance of responsibilities is the role of the regulator.

In short, the responsibility of considering long-term societal impact has to be with the regulator, and this framework can be used to:

• Drive investment decisions considering long-term societal impact rather than short-term local benefits. This is mainly applicable for what a utility spends on improving power quality. • Drive government policies and investment decisions.

• Formulate tariff guidelines as to capture the true cost of power quality, and this indirectly influences power quality improvement initiatives.

• Formulate standards and guidelines, policies, and power quality norms and ensure compliance based on global impact of power quality.

• Influence equipment standards as to enhance the compatibility and performance of equipment.

1.5. Role of Regulation

Because of the sensitivity of end-user equipment, voltage quality is of primary concern to industrial users, service providers, and especially for parameters such as supply voltage variations, even household consumers. In particular, productivity and competitiveness of the manufacturing and service sectors depend highly on the quality of the electricity supply. Indeed, even after the liberalization of the

electricity market, the quality of electricity mainly relies upon transmission and distribution networks, i.e. upon investments and operation practices of regulated firms. As a consequence, voltage disturbances are a crucial issue not only for distribution network operators, transmission system operators, manufacturers of electric appliances, and designers of electrical installations, but also for energy regulators.

For example, after years of work devoted primarily to commercial quality and continuity of supply, European energy regulators are becoming increasingly involved with the regulation of voltage quality. However, regulation in this area encounters a main difficulty. Because power quality results from the interaction between the

network and the customers’ equipment, a tradeoff exists between investments in the network and higher immunity levels for end-user equipment. From a regulatory perspective, performance standards on equipment immunity are to be defined in close relationship with voltage quality requirements for power networks.

In this sense, the proposed introduction of the concept of “responsibility sharing” in technical standards (and in particular in the EN 50160) is fundamental to enable energy regulators to define enforceable requirements for all stakeholders [1]. This idea is easily explained with an example: in South Africa, according to the National Standard NRS 048-2:2003, customer installations are expected to tolerate voltage dips with residual voltage over 70% with duration up to 150 ms, over 80% up to 600 ms, and over 85% for longer durations. For all other dips, the allowed number of events is limited by the National Standards [1,2].

In defining a responsibility-sharing curve, duties and rights of all parties should be taken into account. In other words, the choice of a responsibility-sharing curve should be the result of an agreement between network operators, final customers, equipment manufacturers, and energy regulators. As a result, performance requirements given by regulators will not be in conflict with other technical standards, for instance product, emission, and immunity standards. Moreover, these requirements will not impose unsustainable costs on any stakeholders.

European energy regulators have begun to work in this direction, under the aegis of the European Regulatory Group for Electricity and Gas (ERGEG). In fact, ERGEG has already suggested to introduce several revisions to the EN 50160, among which is the introduction of a responsibility-sharing curve [3]. Although the existence of international standards is important, to design a workable regulatory

framework, regulators need information on both consumer costs for voltage disturbances as well as the level of voltage quality provided on distribution networks and the cost of providing that quality. Indeed, regulatory standards, for instance on the number of events, should be developed at the national level and allow for differentiations according to network structures, protection schemes, and characteristics of withdrawal. Several European regulators are already engaged in monitoring power quality levels and assessing customer cost.

Others, such as the energy regulators of Norway, Hungary, and France, already enforce voltage quality standards, which, in a number of cases, are more demanding than the values given in the EN 50160. The work of European energy regulators on voltage quality is thoroughly described in CEER (Council of European Energy Regulators) Benchmarking Reports on Electricity Supply [4,5,6,7]. Altogether, significant developments are expected in this area of regulation in the incoming years.

EMC COORDINATION

EMISSION

IMMUNITY

1.6. Overview of the Document

Chapter 2 provides a general overview of established and new methodologies used to assess the financial

losses incurred to industries by power quality (PQ) disturbances, in particular voltage dips, short interruptions, and harmonics. The first part focuses on quantifying the economical damage suffered by industrial customers due to nuisance process trips induced by voltage dips and short interruptions. For this purpose, guidelines provided by IEEE standards are discussed and critically assessed to reveal their major strengths and weaknesses. Next, methodologies recently proposed by researchers for financial loss assessment of voltage dips and short interruptions are gathered and discussed. It is generally found that accurate assessment involves careful consideration of three major factors: voltage dip profile at the busbars involved, customer load susceptibility, and calculation of the losses induced by process interruption. Finally, representative studies conducted in Europe, U.S., and Asia are investigated, with their findings and reported losses presented, to demonstrate the scale of the losses.

The second part of the chapter deals with methods and techniques used to economically quantify the effects of harmonics on electrical systems. The economic evaluation includes the increased losses, the premature ageing and the malfunction of the equipment present in the system. The economical value of the losses and premature aging versus the harmonic pollution level can give indications of the amount of costs for equipment to be met/saved for a given increase/decrease of harmonics. Regarding the

malfunction, its economical value requires computing the effects of the malfunction on the process in which the equipment is inserted. The analysis in most cases can be conducted adopting the methods valid in the same case as of other disturbances, like micro-interruptions or voltage dips.

Finally, the economic consequences of unbalanced voltages and flicker are discussed.

Chapter 3 examines the economics of PQ from the network operator’s point of view. It identifies the

costs involved in three categories: providing a response to customer issues; mitigation of PQ issues by network design, asset management, and maintenance; and the measures to ensure reliability. Indicative costs of measures are given where appropriate and existing methods of collecting data are reviewed. There is not a great experience from PQ-projects, methods and experiences with the assessment of financial losses due inadequate quality of electricity supply.

Faults within an industry supply, or other installation, will cause voltage dips in the local supply system, but in most cases these will not propagate upstream and affect other customers supplied from the same HV- system. When immunity requirements and equipment immunity are discussed for a specific installation and/or process, both sources of dips (faults in the HV supply and “internal” faults within a specific industry) must be considered and equipment performance must be chosen with respect to the actual electrical environment. It is quite obvious that the reduction of faults within the customer’s own

plant is the owner’s responsibility, and so is the interest to minimize economical losses due to consequences of all faults.

Chapter 3 discusses how to find the total cost contribution of dips due to faults in the distribution and transmission systems affecting several customers. If such “total socioeconomic cost” can be provided (or at least a method of how to calculate it), identifying the most cost-effective mitigation can include actions in the distribution and transmission systems, and perhaps open a wider perspective to the “economics” of voltage-dip immunity and emission.

Chapter 4 discusses the methodology for the collection of cost data in four parts: the cost of process

interruptions resulting from discrete PQ events, the cost of operation and maintenance of mitigation equipment and of reduced energy efficiency of equipment, the cost of reduced equipment lifetimes, and the capital and installation cost of mitigation equipment.

To perform a PQ events cost analysis, information is needed regarding improving/mitigating cost at: • Customer (equipment/installation) level

• Utilities (network) level

Measurement of power quality can be focused on:

• The total number of disturbances in networks, for benchmarking purposes. Thus not all disturbances affect equipment.

• Only on the number of disturbances affecting equipment/processes.

It is necessary to limit the scope for PQ measurements to perform. Are costs accurate and well founded? Are costs based mainly on broad assumptions and only backed up by sparse data?

It is also important to understand to whom data information will be presented. Readers could be: customers, utilities, regulators, manufacturers, and also standardization bodies and government institutions.

Chapter 5 provides a methodology for the economic assessment of PQ solutions. It proposes calculation

of the net present value of PQ investments, which is calculated using a nominal ten-year lifetime. A case study is described that illustrates practical application on the method.

The costs to industrial and commercial electric power end users from unmitigated PQ and reliability phenomena are significant and have been well documented by detailed studies These studies have focused principally on quantifying the actual or reported cost to businesses of PQ and reliability phenomena that result in unplanned businesses losses brought about by such factors as process

interruptions, equipment damage, extra labor costs, and increased scrap. Although many of these studies also inquire about mitigation equipment employed by end users to try to minimize the business impact of PQ and reliability phenomena, in general, the numbers given for the “cost of PQ and reliability” focus only on the impact of unmitigated phenomena and exclude the cost of preventing unplanned business losses. As such, an unprotected facility might be said to suffer significant PQ and reliability costs, while a facility protected with, say, a double-redundant uninterruptible power supply (UPS) and N+1 backup generation might be said to suffer no PQ or reliability costs whatsoever—a circumstance that does not reflect true business decision-making wherein the costs of outages are balanced against the costs of mitigation. Because of this, a comprehensive strategy to evaluate optimization of overall PQ-related cost is needed, including:

• Costs to industry and electric power providers based on unmitigated PQ phenomena

• Costs to industry and electric power providers based on prevention and mitigation of the impacts of PQ phenomena

The key challenge is to balance both of these broad cost categories. Although any number of economic analysis approaches may be employed to arrive at an optimum, this chapter emphasizes a simple 10-year net present value (NPV) approach whereby all costs and benefits may be combined to determine the mitigation scenario that optimizes today’s economic performance.

2.

Overview of Methodologies for Assessment of Economic

Impact – End User Perspective

2.1. Methodology for Quantifying the Economic Impact of Voltage Sags

and Short Interruptions

2.1.1. Introduction

Voltage dips and short interruptions are major contributors to economic losses incurred by end users in terms of power quality (PQ)-related costs. Although they are not as detrimental as long-duration outages, voltage dips and short interruptions (up to 3 minutes) occur much more frequently. Generally, economic losses are incurred when the supply voltage disturbances cause nuisance trips or malfunction of sensitive equipment, which in turns affects (or completely interrupts) the production processes or service. In case of large industrial and commercial customers, the cost of process disruption can be very high.

Reliable information regarding the economic losses incurred because of voltage dips and short interruptions is essential to both customers and the utility. It provides the very basis for cost-benefit analysis for all potential investments in mitigating solutions. The actual incurred economic losses, however, are customer-specific and depend on many factors including customer category (industrial, commercial, etc.), type and nature of activities interrupted, the extent of the interruption (both duration and number of activities interrupted or affected), etc. Therefore, estimating the economic impact of voltage dips and short interruptions is a daunting task that requires careful consideration of many technical and non-technical aspects and usually requires significant deployment of personnel and resources.

Over the years, numerous studies have been performed to address the problem of economic losses incurred by end users by supply voltage disturbances. This chapter presents a general overview of existing methodologies to assess the economic losses caused by voltage dips and short interruptions. It also presents a summary of the results of cost estimates from various projects carried out around the world.

2.1.2. Overview of Existing Methodologies

Assessing the cost of voltage dips and interruptions is a cumbersome task. Over the years, a number of projects attempted to establish the value of economic consequences of voltage dips. Some proposed methods to obtain network level [8, 9] losses due to voltage dips, while others concentrated on customer plant-level losses [10-14]. Regardless of the level (network or individual plant) that the studies were focused on, precise information about the cost of a single process failure/malfunction is essential for the overall accurate assessment of economic losses.

Generally, to accurately assess the economic losses due to these disturbances, a thorough understanding of customer plant and processes involved is essential. Therefore, a good methodology has to take into account all the aspects involved, from in the supply voltage profile at the point of connection of an industrial plant, to equipment and process sensitivity to voltage dips and to all direct and indirect costs associated with process disruption.

A voltage-dip profile at the customer busbar provides the information regarding the frequency and characteristics of voltage dips and short interruptions. Normally, this information is obtained from historical data or from site monitoring. However, when there are no records available, or when the monitoring period is too short to draw sufficient conclusions, a voltage-dip profile has to be predicted. The fault positions method [8, 9, 15, 16] is a common method used to determine the expected

characteristics of voltage dips resulting from for the short-circuit faults in the network. Statistical processing of existing (limited-duration) monitoring records [12], e.g. extrapolation of data, can also be used to predict future dip performance.

The sensitivity/resilience of equipment used in industrial processes to voltage dips and short interruptions directly influences the response of the industrial process to incoming voltage dips and interruptions, and therefore has direct impact on the resulting financial losses. The sensitivity of equipment is normally

expressed in terms of the magnitude and duration of the voltage dip. The voltage-tolerance curve for an individual device (equipment) can be obtained either from the equipment manufacturer, from laboratory or field measurements, or if none of the previous is available, a voltage-tolerance curve from existing standards could be used (at least as a guidance). The commonly used standards for characterizing

equipment sensitivity are the Computer Business Equipment Manufacturers Association (CBEMA) curve, Information Technology Industry Council (ITIC) curve, and the “semiconductor processing” (SEMIF47) curve [17].

Because different types of equipment exhibit different sensitivities to voltage dips and short interruptions, equipment-specific voltage-tolerance curves [15, 18-20] have been and are being developed from

laboratory tests.

According to the IEEE Standard 1346-1998 [19], there is a range of uncertainty in the magnitude– duration plane associated with voltage-tolerance curves. To account for this uncertainty, various methods have been developed in the past and used in assessments of equipment sensitivity to voltage dips, including probabilistic methods [15, 16, 21], fuzzy logic [22], and voltage-dip severity indices [23]. On a higher level, process sensitivity depends on many factors, including but not limited to equipment interconnections, composition ratio of equipment, function and significance of each equipment type, and the relationship between equipment failure modes and process operation. To address these factors, various approaches have been attempted by researchers around the world. The approaches include probabilistic methods [15, 16, 20, 21], fault tree analysis [24, 25], fuzzy logic [25], loss of voltage during dip [26], loss of energy during dip [26], and one-parameter characterization method [26].

Once the information about the voltage-dip profile and customer process sensitivity is available, the number of process failures or malfunctions can be determined. Following the estimation of expected number of process failures, the next step is to determine the economic losses associated with each of them and to add up losses associated with individual events in order to come up with the annual plant exposure. Detailed methodologies for calculation of the costs associated with voltage dips have been proposed in [27, 28]. Cost calculation involves careful investigation of all direct and indirect costs caused by voltage dips. Theoretical and mathematical formulae are derived to represent various causes of losses. The cost functions of equipment, sub-processes, and processes are then incorporated into the technical states of the processes to determine the costs of each process and the plant.

Determining the cost of voltage dips and interruptions based on previously described calculations would generate a very accurate cost estimation for every dip providing that reliable input data is available. The drawback is that one would require a significant amount of information regarding all direct and indirect costs for every individual sub-process in the plant. These cost figures, however, are very difficult to obtain without a time-consuming detailed investigation, which often involves confidentiality issues. Alternatively, some studies relate the economic losses incurred by voltage dips with customer interruption cost (CIC). CIC is the economic damage to customers caused by power interruptions (outages) of a specified duration. Customer damage functions due to power interruptions are well studied and reasonably well documented, and thus provide a convenient reference for voltage dip-related cost analysis.

Basically, CIC can be obtained from survey results obtained from a large number of customers of various industrial sectors. This information is then analyzed, aggregated per sector, and averaged to give plant-level costs per voltage dip or cost per kW of power per voltage dip (normalized cost) [10] for various industrial sectors. Studies that use cost per event for voltage-dip economic analysis include [8, 9, 15, 16, 29], while [10, 18, 29] use cost per kW power per voltage dip. A PQ index that uses CIC/kWh for financial loss assessment is proposed in [30].

Weighted cost per dip method is used [7, 21, 24, 31-33] where different weighting factors are assigned to different magnitudes of voltage dips. In this way, the cost of severe dips are equal to the cost of

interruptions, while less-severe dips incur a fraction of the cost of interruption.

There are also indirect ways to estimate the economic impact of voltage dips and short interruptions. Some studies [14] use the power of a customer plant as basis for cost evaluation. The losses incurred

because of voltage dips and short interruptions are estimated as a percentage of the annual cost of power consumption. Other common methods of indirect economic analysis are the willingness to pay (WTP) and willingness to accept compensation (WTA) methods [34].

2.1.3. IEEE Guidelines

The IEEE Standard 1346-1998 [19] provides guidelines to assess economic losses at customer facilities due to voltage dips. The aspects considered there include the voltage-dip performance at the utility and the industrial plant level, the equipment susceptibility to voltage dips, and economic evaluation of the losses incurred because of process-disruptive dips. A method of representing a voltage-dip profile at the customer facility using contour lines and comparing it with equipment voltage-tolerance curves to obtain the number of disruptive dips is presented. In terms of financial aspect, a list of all direct and indirect costs was given in a standard cost of disruption evaluation form [19] to aid economic assessment of the losses incurred.

Initially, a voltage-dip profile of the facility concerned is acquired from either utility data, measurements, monitoring, or prediction. Using this dip data, the supply dip performance contours are drawn, where each contour represents the number of voltage dips per year. Next, equipment sensitivities (voltage-tolerance curves) are overlaid on the supply dip performance contours to form dip-coordination charts. The sensitivity of the process is defined by the most sensitive component, with the knee point located at the upper most left hand portion of the chart. The subsequent step involves cost estimation of process disruption. All losses involved are listed in a cost of disruption evaluation form, which should be completed by those who are familiar with the operational impact of process stoppage (frontline workers, supervisors), finance, accounting, sales, and marketing personnel to ensure that all aspects of economic losses are considered. Briefly, the costs of disruption in industrial processes are made up of downtime-related costs (lost production, idled labor, equipment damage, recovery cost), product quality-downtime-related costs (scrap and rework costs), and other indirect costs (customer dissatisfaction, employee and customer safety, fines and penalties). Finally, the total financial losses of the facility are obtained by multiplying the cost of process disruption and the number of disruptive dips per year.

The method proposed by this standard is useful for estimation of economic losses due to voltage dips. However, there are a few important issues yet to be addressed. These issues include:

• The sensitivity of the entire industrial process is determined by the most sensitive equipment in the process. This assumption may not be appropriate because the process sensitivity also depends on the function and significance of the equipment involved. Tripping of the most sensitive equipment does not necessarily disrupt the entire process.

• The interconnections between equipment and sub-processes could have significant impact on process operation, but these are not considered in this standard.

• It is shown that all equipment types have a range of voltage-tolerance curves. This range (of uncertainty) is not considered in the method when evaluating the number of disruptive dips.

• The cost values used for economic assessment are based on historical data or experience; this may not be useful for evaluation of new industries at the planning stage.

2.1.4. Analytical Economic Analysis

In the past decade, new methodologies have been continually developed with the promise of improved accuracy in assessment. In analytical economic analysis, losses due to PQ disturbances are often

calculated or estimated through detailed assessment processes. These assessment processes may consider the probability of PQ events occurring, characteristics of events, equipment and process sensitivities to events, cost of process disruption, cost and benefit of mitigations, and other indirect costs subsequent to the event.

2.1.4.1. Assessment of Equipment and Process Sensitivity

For most PQ disturbances, economic losses are incurred when equipment or industrial processes are tripped or disrupted. Hence, equipment and process response to PQ disturbances directly influence the magnitude of economic losses. However, predicting equipment and process response to disturbances is not entirely straightforward due to various uncertainties involved in equipment sensitivity. Therefore, properly representing these uncertainties helps reduce assessment error.

Probabilistic Assessment of Financial Losses due to Interruptions and Voltage Dips

This methodology [15, 16] uses a probabilistic approach to assess the economic losses due to

interruptions and voltage dips. The cost of interruptions and voltage dips are assessed separately and then combined to estimate the total economic losses in the network. This methodology can be used to assess both customer-level losses and network level losses. It takes into account the uncertainties associated with voltage-dip calculation, equipment sensitivity, interconnection of equipment within an industrial process, and customer type and location of the process in the network.

The fault positions method is used to obtain voltage-dip performance of the system. Process equipment is grouped into four categories of equipment types, and the voltage-tolerance curves of these equipment types are obtained through a series of laboratory tests. The main equipment types tested are personal computer (PC), programmable logic controllers (PLCs), adjustable-speed drives (ASDs), and AC contactors. The impact of voltage dips at a particular site in the network is estimated through three basic steps. They include fault analysis, voltage-dip analysis, and economic analysis. Fault analysis is typically performed using the fault positions method to simulate various types of faults at various locations throughout the system network. The corresponding voltage magnitude and duration during of each fault is determined.

Voltage-dip analysis is performed at the point of common coupling (PCC) between the network and the buses of interest. The historical fault performance (fault per kilometre per year) of all network buses, overhead lines, and underground cables is then used to determine frequency of dips of specified magnitude and duration over a period of interest. Dip durations depend on fault-clearing times of protection devices used in the network.

The economic analysis is performed in two stages. First, sensitive equipment is classified into various categories based on device type. The voltage tolerance characteristic of four main equipment types, namely personal computer (PC), PLC, ASD, and AC contactors are determined through a series of laboratory tests. General voltage-tolerance characteristic is used to represent each equipment type. Next, dip performance charts of the network buses of interest are prepared using the results from voltage-dip analysis (Step 2). The dip-performance charts are compared with the equipment voltage-tolerance curves to determine equipment response (failure probability of equipment) to voltage dips. After obtaining the failure probabilities of equipment, the probability of a process trip is calculated. Finally, the total economic losses can be determined using (2.1).

Total financial loss Total process trips Cost per trip

=

×

A major advantage of this method is that the uncertainties regarding equipment sensitivity are represented using probability density functions. Probabilistic representation is more realistic and efficient as

compared to the deterministic approach, especially when a large number of equipment is to be evaluated. Furthermore, this methodology provides the flexibility for different equipment sensitivity levels to be represented using different probability density functions.

This methodology is probably one of the most comprehensive methodologies developed so far that takes into account many aspects of the system. An example of its application is given in Appendix 2-A. However, there are still some problems that were not fully resolved even with this relatively

comprehensive methodology. They include the choice of appropriate probability distribution functions for equipment sensitivity evaluation (which are yet to be determined) and interdependence between

equipment controlling a process or sub-process.

Prob-A-Dip Method

The Prob-A-Dip method [21] manipulates two-dimensional arrays to represent all parameters for economic loss management. This method allows equipment sensitivity to be represented using both discrete states (on or off state) and probabilistic values. Different cost values can be assigned to voltage dips of different characteristics, which enables more realistic evaluation of losses as costs such as equipment damage that occurs only for a certain characteristic of voltage dip. The method also takes into account the interconnections between equipment in a probabilistic manner and the effect of mitigation devices.

It is worth noting that Prob-A-Dip is more of a management tool rather than an assessment tool. All input values have to be acquired through other means before they can be processed by this Prob-A-Dip. Basically, the user has to acquire information regarding equipment sensitivity, annual dip frequency, process-related information, and the consequential economic loss associated with different voltage dips. With this information, the Prob-A-Dip method can be used to determine sensitivity of the industrial process, the frequency of dip-induced plant interruptions, and the total economic losses incurred due to voltage dips and short interruptions. The main advantages of this method include the following [21]: • All quantities are presented in a uniform format.

• It is applicable to all environments from a single customer to a complete power distribution system. • It delivers flexible accuracy, from a rough estimate to exact values.

• It allows probabilistic processing of data.

• It can be implemented in power system software platforms.

• It enables assessment of the effectiveness of mitigating solutions to a certain degree.

Estimating the Economic Impact of Voltage Dips

The methodology for estimating the economic impact of voltage dips proposed in [24] was built on the assumption that different voltage dips have different impacts on customer process. It is postulated that the behavior of process equipment varies with the severity of voltage dips, thus causing different failure modes of the customer plant.

First, in order to characterize process equipment behavior when exposed to a voltage dip, the voltage-tolerance curves of different equipment are obtained and converted into a component behavior function. The function represents the state (On or Off) of the equipment when subjected to a voltage dip with specific dip magnitude and duration. Next, the behavior of the customer load is categorized into different failure modes, where certain combinations of equipment behaviors and dip conditions (causes) will lead to certain failure modes (consequence). The “cause” and “consequence” are related using fault trees. A cost function is assigned to each failure mode. The economic impact of a voltage dip is estimated by combining the cost function with information regarding voltage-dip frequency.

The use of fault tree analysis provides more space for subjective judgement in process sensitivity evaluation. The user would not need to deal with complex equipment interconnections to determine the consequences of equipment failure. Using different failure modes for different voltage-dip levels would also yield more realistic results. However, it is worth noting that the equipment, even of the same type and brand, exhibit very different responses to a voltage dip. So, it is virtually impossible to generalize equipment behavior into a common working state. Also, the number of failure modes increases rapidly with size of customer plant, and hence increases the complexity of evaluation.

PQ Index Based on Equipment Sensitivity, Cost, and Network Vulnerability

The ideas of load drop index (LDI) and load drop cost (LDC) [18] are proposed with the objective of capturing load vulnerability and the cost impact of voltage dips. These indices are calculated using customer equipment composition data, load information, equipment sensitivity curves, and historical-derived cost data.

Basically, voltage dips are first categorized into various duration classes (instantaneous, momentary, temporary, and sustained interruption) consistent with the classification of interruption events given in IEEE Standard 1159 [35]. For each duration class, various regions of voltage dip class areas are defined based on the voltage-tolerance curves of sensitive equipment involved. Next, the historical voltage-dip profile of the bus of interest is processed to obtain the number of events that fall in each defined area. With this information, the load drop index for each duration class k is calculated using (2.2) [18].

( )

,

1, 2,...

LDI k

N L

j

r

=

∑

=

Where Lkj represents load composition ratio and Nkj represents the number of events that falls in the area

Different cost indices are derived for different duration classes based on average cost of interruption in each duration class. These cost indices are multiplied by the corresponding LDI of each duration class to obtain LDC [18].

( )

LDC

C LDI k

=

∑

Where Ck is the cost index reflecting the average cost of interruption in a given duration class. The

average cost of power interruptions reported in [18] are given in Table I-1 of Appendix 2-I.

LDC is particularly useful for assessment of economic losses due to voltage dips and interruption events. As a PQ index, it can be easily translated into cost figures for evaluation of all general industrial plants. The merits of using LDI and LDC also include:

• Instead of using the most sensitive equipment to define process sensitivity, the impact of a voltage dip on all equipment types is considered.

• Composition ratios of equipment are considered.

• Capable of processing probabilistic values of equipment sensitivity.

• Different economic impacts of different voltage-dip severities (dip duration) are considered. • Only involves data processing and does not require additional instrumentation.

It is worth noting that the effects of equipment interconnections and the importance level of individual equipment in process operation are not modeled in LDI and LDC. This might prevent accurate assessment because equipment interconnections and importance are significant factors that affect process

vulnerability to voltage dips.

Unified Reliability and PQ Index

The unified reliability and PQ index method proposed in [36] combines the costs incurred by interruption, voltage dip, voltage deviation, and harmonics into a unified reliability and PQ index. In terms on voltage-dip cost, the factors considered include voltage voltage-dip rate, the load size at customer busbar, and sector customer damage function for voltage dips (SCDF(dip)) at customer busbar.

The dip rate is calculated utilizing sustained interruption rate and momentary interruption rate at the customer busbar. Two types of system configuration are considered (loop and radial), both protected by a reclosing system. SCDF(dip) depends on the sector where the cost is to be assessed. Seven sectors are classified, namely large user, industrial, commercial, agricultural, residential, government installation, and office buildings. This methodology is suitable for fast estimation of economic losses at network level. Due to the fact that many important factors are not considered, the accuracy of estimation is not too high.

2.1.4.2. The Cost of Process Interruption

Regardless of the type of disturbance in an industrial process (voltage dip, transients, short interruption, or long interruption), economic losses are incurred every time the process trips. The cost per trip should include only those costs that are above and beyond the normal production costs, net of potential savings. An example of how economic losses for an industrial customer can be determined is suggested in [37] and elaborated on in more detail in Appendix 2.

2.1.5. Indirect Economic Analysis

When the information required for analytical economic analysis is not available, indirect economic analysis is the only option to estimate the financial losses due to PQ disturbances. Common ways of analysis include the customer willingness to pay method [34], customer willingness to accept method [34], and cost estimation from the size and value of mitigating solutions.

2.1.5.1. Customer’s Willingness to Pay

The customer’s willingness to pay (WTP) method has been used in several studies [34, 38, 39] to obtain the costs of power supply interruptions. Usually, customers are given several hypothetical outage scenarios and asked to express the amount of money that they are willing to pay in order to avoid each outage scenarios. In terms of PQ, customers are asked to express their willingness to pay for different levels of PQ improvements. Though the WTP method may not be as technical as the analytical approaches, it reflects the value customers place on electricity supply and PQ.

However, one should anticipate the amount a customer is willing to pay to be lower than the actual financial damage caused by a PQ disturbance [34]. This is because economic benefit could only be achieved if the financial damage due to power interruption is more than the amount paid to avoid the damage. Therefore, from the customers’ point of view, the WTP amount will always be less than the actual damage due to PQ disturbances.

Besides, the WTP method makes sense only when customers understand the damaging effects of power supply interruptions on their processes. Usually, the effects of a total power interruption are more apparent and well known. However, the effects of other PQ disturbances such as voltage dips that cause partial disruption of processes are not straightforward. In most cases, customers do not know the financial damages due to PQ disturbances, and therefore cannot place an accurate WTP value on them.

In the customer’s willingness to accept (WTA) method, electricity users are given various imaginary outage scenarios and asked to estimate the amount of compensation that they are willing to accept for each outage scenario. The WTA is similar to the WTP method because they both require customers to place a monetary value on hypothetical outage scenarios. However, in most cases, the WTA method gives substantially larger values compared to the WTP method. According to [34], the reason behind this is that customers consider electricity supply as a social right rather than a market commodity. It is also

recommended in [34] that the two methods can be used together to produce upper and lower limits for power interruption costs.

Both WTP and WTA methods are heavily dependent upon the customer’s subjectivity in placing a value on PQ costs, and may be influenced by other considerations, such as the customer’s perception of the electricity supply, their knowledge on PQ disturbances, and their ability to pay.

2.1.5.2. Cost Estimation from Historical Events

Over the years, numerous surveys have been carried out around the world to gather information regarding economic losses of various industrial, agricultural, commercial, and even residential customers. By reviewing the past studies, the financial loss information can be gathered and aggregated to represent different customer types and sizes. This information can be conveniently used to estimate PQ-related costs of a particular customer.

To obtain a realistic cost estimation from historical events, one would have to use historical values from the customer type that best resemble the customer of concern. Ideally, the historical values used should be obtained from customers of similar type and size, and within the same geographical region as the

customer of concern. Unfortunately, information gathered from historical events available today is still insufficient to meet the abovementioned requirements. Most studies produced cost values for total power interruption, not considering the impact of other PQ disturbances. Some studies managed to produce cost values of voltage dips but have yet to obtain cost values for different severity levels of voltage dips. Overall, cost estimates from historical events without considering sensitive equipment in a customer’s plant and process sensitivities will not produce accurate financial loss values.

In [29] an original approach was used to estimate economic losses for industrial users. The authors designed a questionnaire, in the form of a journal. The main innovative aspects of this instrument are: 1. The questionnaire was not of the usual scenario type. Instead, it entailed the registration, for a

specified time period (at least three months), of the consequences experienced by the end-user during process disruptions caused by very short interruptions and voltage dips;

2. The questionnaire required the end-users to provide a structured description of “what happened” at the production site during the voltage disturbance (see below), together with per-unit economic data (for instance, hourly wages). It did not request direct cost estimations from the respondents.

3. The questionnaire did not demand to identify precisely what type of voltage disturbance brought the process to a halt. This information was extracted, at a later stage, from the data recorded by a power quality recorder, installed at the customer’s connection point.

The questionnaire included a technical section, to be filled in by personnel working on the production line(s). The description requested was structured in a time sequence:

• At the occurrence of the event: damaged equipment and defective WIP (and its destination: recycling, second-hand goods, etc.).

• During downtime: duration and number of workers inactive (or engaged in restarting the process). • During the restarting of the process: defective WIP and lost production (if the process requires time

to return to the nominal production quality and speed).

• After the event: time and number of workers necessary to recover lost production (or other means to recover it).

The questionnaire included also an economic section, to be filled in by a manager or by an accountant. This section investigated the per-unit costs of several production inputs, such as raw material, labor, and energy (but also the cost of repairing the damaged equipment).

With this approach, the authors were able to retain full control over the cost assessment: they did not ask industrial users to estimate their costs, because it is normally done in surveys, but to provide a structured description of the process disruptions, together with “per-unit” economic information. The calculation of costs was performed by the authors according to a standardized methodology. Overall, the approach resulted in an improvement in terms of feasibility and robustness with respect to previous surveys.

2.1.6. Reported PQ-Related Losses from Around the World

In the past decade, many studies have been conducted around the world to determine the cost of voltage dips and short interruptions. The experience gained from these studies is very valuable and can be used for carrying out similar studies in the future. This section summarizes the major findings of those studies. A few studies that focused on obtaining the cost of interruptions (outages) only are also included in this summary as the information gathered can be further post-processed and used for the assessment of cost of voltage dips and short interruptions.

Numerical data from the surveys is reported in Appendix 2. The surveys confirm high sensitivity to short interruptions and voltage dips in many processes, with particularly high losses in the production of electrical and electronic equipment, chemical products, food products, and motor vehicles. Generally, survey results are presented in the following ways:

• Direct cost per kW of plant per disturbance (Table -J-1) • Direct cost per kVA of plant per disturbance (Table J-2) • Direct cost per disturbance event (Table J-3)

• Annual cost of disturbance (Table J-4)

• Cost per hour of process interruption (Table J-5)

Though high losses, processes are commonly identified in most surveys, the magnitude of the losses is rather inconsistent. For example, huge differences in losses can be seen in different surveys reported for chemical products and electrical products manufacturing. This disparity is due to the difference in circumstances while conducting the surveys. In particular, there are differences in the country in which the surveys are conducted, the categorization of industries, the type of disturbances included, the year of survey, the size of the industries involved, and the base currency used for loss representation. These differences prevent the surveys from being compared effectively and meaningfully.

With increasing need for accurate loss estimation for the industrial sector, a common standard in conducting surveys is crucial to ensure a consistent outcome in future surveys. In the meantime, a methodology capable of grouping and analyzing the surveys is urgently required.