Sustanability Indicators for DSO

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F

ACULDADE DE

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NGENHARIA DA

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NIVERSIDADE DO

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ORTO

Sustainability Indicators for

Distribution System Operators

José Francisco Pereira Moura Teixeira

Mestrado Integrado em Engenharia Eletrotécnica e de Computadores

Supervisor: Professora Doutora Maria Teresa Costa Pereira da Silva Ponce de Leão Second Supervisor: Professor Doutor Hélder Filipe Duarte Leite

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Resumo

Tendo em conta o impacto da humanidade no meio ambiente, garantir um futuro sustentável é uma prioridade.

A produção e distribuição de eletricidade estão entre as atividades mais impactantes no que diz respeito ao uso de recursos naturais. As sociedades modernas têm cada vez maiores necessidades energéticas, assim cabe aos agentes reguladores equilibrar as dimensões económica, ambiental e social.

Um estudo aprofundado sobre sustentabilidade deve sempre ter em consideração a complexi-dade do problema e a não existência de uma solução ótima para o mesmo.

Os Operadores da Rede de Distribuição (ORD) são protagonistas na obtenção dos objetivos definidos na União Energética Europeia tendo um papel de destaque na Iniciativa 20-20-20 da União Europeia.

Neste trabalho propõe-se um conjunto de Key Performance Indicators (KPI’s) para a análise de ORD’s de acordo com três âmbitos diferentes respetivamente: operacional, ambiental e qualidade no trabalho e impacto social.

Estes KPI são analisados através da Análise de Componentes Principais. As Componentes Principais calculadas neste estudo são usadas na construção de um Índice Composto de Sus-tentabilidade utilizado para analisar o desempenho dos três diferentes ORD’s.

As empresas usadas como objeto de estudo são: EDP Distribuição, Iberdrola e Enel Dis-tribuzione.

Concluímos que, em comparação com os seus pares, a EDP Distribuição tem uma performance de sustentabilidade que tem vindo a aumentar de uma forma consistente e sustentada.

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Abstract

Given the impact that Mankind has on the environment, reassuring a Sustainable future is a priority.

The production and distribution of electricity are amongst the most impactful activities in the use and depletion of natural resources. At the same time modern societies have great energy needs, thus Regulatory Agents need to balance the environmental, social and economical Dimensions.

An in-depth sustainability study should always address the complexity of this concept and realise that there is not one unique solution to the issue of sustainability. A multidimensional ap-proach toward sustainability should be followed, considering three major perspectives, economic, environmental perspective and social.

Distribution System Operators are key players on the European Energy Union objectives, and have a major role on the achievement of the goals defined by the European Union 20-20-20 Stra-tegy.

In this work, we propose a set of Key Performance Indicators (KPI’s), that assess Distribution System Operators in three different scopes, namely, the operational scope, the environmental scope and the working quality and social scope.

This KPI are used in a Principal Component Analysis. The Principal Components computed in this study are used in the construction of a Composite Sustainability Index, which we use to evaluate the overall performance of three different Distribution System Operators.

The companies used in this study are EDP Distribuição, Iberdrola and Enel Distribuzione . We concluded that, in comparison to its peers, EDP Distribuição has superior sustainability performance that has been improving in a consistent way.

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Acknowledgments

First of all, I would like to acknowledge to my thesis supervisor, Professor Doctor Maria Teresa Costa Pereira da Silva Ponce de Leão. Thanks for all the advice, criticism and encouragement throughout the thesis.

I also want to address a special thanks to Professor Doctor Helder Filipe Duarte Leite for all availability, dedication and friendship.

I would also like to thank the institution, the Faculdade de Engenharia da Universidade do Porto, for providing all the conditions for elaborate this Dissertation.

I also want to thank Professor Doctor Joana Pinto Leite César Machado, for all help in the review of the whole thesis.

To my family, especially my parents, for all the support and for always believing in my success during this challenging stage. I have no words to express all of my gratitude.

Other very special thanks to my girlfriend Maria, for being my support in everything.

A huge thank you to all my childhood friends and university friends for all the moments of pleasure and work during my academic life.

José Francisco Pereira Moura Teixeira

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“Para ser grande, sê inteiro: nada Teu exagera ou exclui. Sê todo em cada coisa. Põe quanto és No mínimo que fazes. ”

Ricardo Reis

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List of Figures

2.1 Triple-Bottom Line Sustainability Accounting Model (Kenneth Lyngaas, 2013) . 8

3.1 Pressure-State-Response Framework [2] . . . 17

3.2 Driving Forces-Pressures-State-Impact-Response Framework [3] . . . 18

3.3 Stopping Criteria Analysis Example . . . 26

5.1 Total Distributed Energy, EDP . . . 41

5.2 Network Extension, EDP . . . 41

5.3 System Average Interruption Frequency Index, EDP . . . 42

5.4 Network Losses, EDP . . . 42

5.5 Total Waste, EDP . . . 43

5.6 Impact in protected areas, EDP . . . 44

5.7 Number of fatal accidents, EDP . . . 45

5.8 Number of non-fatal accidents, EDP . . . 45

5.9 Percentage of women, EDP . . . 46

5.10 Ratio between entry level wage and national minimum wage, EDP . . . 46

5.11 Stopping Criteria Analysis, EDP . . . 50

6.1 EDP Distribuição Composite Sustainability Index . . . 56

6.2 Iberdrola Composite Sustainability Index . . . 57

6.3 ENEL Composite Sustainability Index . . . 57

6.4 Comparative Analysis between DSO’s using the CSI . . . 58

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List of Tables

3.1 Strenghts and Weaknesses of Multivariate Analysis Algorithms [6] . . . 21

4.1 Operational KPI’s . . . 30

4.2 Environmental KPI’s . . . 30

4.3 Working Quality and Social KPI’s . . . 30

4.4 Operational Relative KPI’s . . . 35

4.5 Environmental KPI’s . . . 35

4.6 Relative Working Quality and Social KPI’s . . . 36

4.7 Economic and Operational GRI Reference . . . 37

4.8 Environmental GRI Reference . . . 37

4.9 Working Quality and Social GRI Reference . . . 38

5.1 Operational KPI’s,EDP . . . 40

5.2 Environmental KPI’s,EDP . . . 43

5.3 Social and Working Quality KPI’s,EDP . . . 44

5.4 Relative Key Performance Indicators, EDP . . . 47

5.5 Statistical Analysis, EDP . . . 48

5.6 Standardise KPI, EDP . . . 49

5.7 Covariance Matrix,EDP . . . 50

5.8 Eigenvalues, EDP . . . 51

5.9 Eigenvectors, EDP . . . 51

5.10 Percentual contribution of the variables, EDP . . . 52

5.11 Observation, EDP . . . 53

A.1 Operational KPI’s, ENEL . . . 61

A.2 Environmental KPI’s,ENEL . . . 61

A.3 Social and Working Quality KPI’s,ENEL . . . 61

A.4 Relative Key Performance Indicators, ENEL . . . 62

A.5 Statistical Analysis, ENEL . . . 62

A.6 Standardise KPI, ENEL . . . 62

A.7 Covariance Matrix, ENEL . . . 63

A.8 Eigenvalues, ENEL . . . 63

A.9 Eigenvectors, ENEL . . . 63

A.10 Percentual contribution of the variables, ENEL . . . 64

A.11 Observation, ENEL . . . 64

B.1 Operational KPI’s, IBERDROLA . . . 65

B.2 Environmental KPI’s, IBERDROLA . . . 65

B.3 Social and Working Quality KPI’s, IBERDROLA . . . 65 xiii

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xiv LIST OF TABLES

B.4 Relative Key Performance Indicators, IBERDROLA . . . 66

B.5 Statistical Analysis, IBERDROLA . . . 66

B.6 Standardise KPI, IBERDROLA . . . 66

B.7 Covariance Matrix, IBERDROLA . . . 67

B.8 Eigenvalues, IBERDROLA . . . 67

B.9 Eigenvectors, IBERDROLA . . . 67

B.10 Percentual contribution of the variables, IBERDROLA . . . 68

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List of acronyms and symbols

CHP Combined Heat and Power

COP Conference Of the Parties

CSI Composite Sustainability Index

CSR Corporate Social Responsibility

CV Coefficient of Variation

DPSIR Driving Forces-Pressure-State-Impact-Response

DRS Driving Forces-State-Response

DSO Distribution System Operator

EED European Union Electricity Directive

EESI European Economic Sustainability Index

EU European Union

GHG Greenhouse Gases

GRI Global Reporting Initiative

ISO International Organisation for Standardisation IUCN International Union for the Conservation of Nature

KPI Key Performance Indicators

OECD Organisation for Economic Co-operation and Development

ORD Operadores da Rede de Distribuição

PC Principal Component

PCA Principal Component Analysis

PSR Pressure State Response

SAIFI System Average Frequency Interruption Index

SDT Spectral Decomposition Theorem

UN United Nations

UNCED United Nations Conference on Environment and Development

UNCHE United Nations Conference on the Human Environment

UNCSD United Nations Conference on Sustainable Development

WCED World Commission on Environment and Development

WCS World Conservation Strategy

WSSD World Summit on Sustainable Development

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Chapter 1

Introduction

1.1 Context

Sustainability in a generic form , can be described as a process that in an optimal scenario can be maintained indefinitely, being directly related to the way that organisations endure and resist.

Human actions since the primordial times are intrinsically related to the availability of ener-getic resources.

In the beginning of the XIX century the Industrial Revolution, with the dawn of the steam ma-chine, led to a breakthrough in the way that energy was produced changing forever the economic, environmental and social paradigms.

This historic milestone lead to an upsurge in the human population and the pressure that hu-manity exerts in the environment. The natural resources started to be consumed in a way that the ecosystems could not cope with. This led to the emergence of, the first concerns related to long term environmental balance, setting the foundations for the sustainability concept.

Although the concept of sustainability is widely used, there is not a unique accepted concep-tualization of sustainability, and this concept is likely to be interpreted in different perspectives, leading to a lack of accuracy in its definition. The starting point for the concept and the basis for most of the related theories, was set in 1987 by the World Commission on Environment and Development (WCED).

Sustainability must be assessed through an integrated and holistic view considering the con-nections between three major dimensions: the economic dimension, the social dimension and the environmental dimension.

The corporate sector, and companies as complex entities, should be aware that besides the major role of profit maximisation, there must be a concern with the way it is achieved, regarding the impact on the society and on the environment.

The Electricity Sector in Europe plays a central role in the European Energy Strategy and in the European Energy Union. Most European countries have followed a liberalisation agenda in the sector. This situation, coupled with further deregulation, demands the setting of international benchmarks to ensure efficiency among electricity market agents.

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2 Introduction

Distribution System Operators (DSO) are key players in the New European Energy Scenario, facing huge challenges such as the flexibility of the Distribution Network, the integration of indi-vidual producers and the use of Renewable Sources of Energy.

Therefore, in this work, DSO’s are chosen as subjects due to their critical impact in both economic, environmental and social scopes.

1.2 Purpose of this thesis

Sustainability as an operative concept can be measured considering a unidirectional analysis where there is a bias towards the importance of one of the three dimensions that define the concept. On the other hand, a multivariate analysis using a composite approach can be considered as an appropriate solution to overcome this problem. Hence, in this work, we decided to implement a multidimensional approach.

The main purpose of this thesis is to create a Composite Sustainability Index that allows to compare different Distribution System Operators, considering a set of Key Performance Indicators (KPI’s) that assess how companies perform on critical Operational, Environmental and Working Quality and Social dimensions.

We have selected Portugal, Spain and Italy DSO’s as subjects of this study, and the purpose is to do a comparison of the sustainability trajectory of the chosen DSO’s during the time window between 2010 and 2015.

The proposed Composite Sustainability Index aims to assess the company’s internal perfor-mance on sustainability, but also to make a comparison with its peers in a homologous time frame.

1.3 Thesis Structure

Chapter 1 presents a brief introduction on the context, the purpose of this thesis and on its structure.

Chapter 2 presents an overview on the concept of sustainability and its historical evolution, and also on the way it can be operationalized.

Chapter 3 describes the data treatment and statistical analysis methodology, explaining the multivariate technique used, the Principal Component Analysis and all the mathematical opera-tions necessary to perform it.

Chapter 4 is concerned with the presentation of the KPI’s used and the rationale behind each one of them. this KPI’s will be used to as variables in Principal Component Analysis and later for the creation of the Composite Sustainability Index (CSI).

Chapter 5 presents the application of the data treatment and statistical analysis methodology, using EDP Distribuição, the Portuguese Distribution System Operator, as example.

In Chapter 6, the Composite Sustainability Index is explained and applied to the data sample. A comparative analysis of the performance of each company is carried out.

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1.3 Thesis Structure 3

Finally, Chapter 7 presents the main conclusions and contributions of this study and the future work perspectives.

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Chapter 2

Sustainability Concept

The concept of Sustainability was first introduced in the World Conservation Strategy pro-duced by the International Union for the Conservation of Nature (IUCN), in 1980. It focused on setting the ecological limits to human activities on Earth.

The first definition of Sustainable Development and the most commonly used throughout the world was introduced in 1987 by the UN World Commission on Environment and Development (WCED), this report is also called the Brundtland Report it states as follows:

“Development which meets the needs of the present without compromising the ability of future generations to meet their own needs”[4].

It was this report that brought the concept of sustainable development into public debate and concern of the academia.

A good economic analogy to understand the concept is analogous to consuming the capital base of a company instead of only consuming the interest and revenues it generates.

The development that a sustainable approach brings leads to an evolution towards meeting goals for the well-being of the society and the individuals that are part of it. These goals are from three different scopes.

In a quantitative way, a company is more sustainable if it tries to achieve or maximise pre-defined goals. These goals can be self-imposed or stipulated by policies and regulations that are developed internally in the country where the company operates or set by international institutions such as the European Commission (EC)[5].

Sustainable development integrates economic, social and environmental issues through a dy-namic perspective, that will lead to a better future for mankind and the better usage of our re-sources.

The first approach to the concept was done having in account an economic basis. The al-location of resources for the satisfaction of human needs represents a pivotal concern of man’s existence, and there is a growing recognition that the achieving of sustainability rest almost en-tirely on making the economy working well.

As civilisation progresses, the human needs and desires grown more than proportionality to the expansion and the improvement of productive resources [6].

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6 Sustainability Concept

It is accepted that economic growth relies on a more efficient use of resources (capital, work and land), in order to provide wealth, through the production and supply of goods and services.

The carrying capacity of the ecosystems and the depletion of resources only recently began to be considered in setting policy and economic directions.

Arrow and all first analysed the problem in 1995, warming against the general assumption that non-controlled economic growth is beneficial to the environment[7].

Those in favour of sustainable development argue that economic growth is possible in a con-tinued way, if the resources and the energy consumption become increasingly lower, meaning that the impact that a company or a country have on the environment is reduced.

The European Union (EU), in its State of Energy Union proposed the objective of decoupling economic growth from environmental degradation and resource use. Meaning that the usage of resources should not be directly linked to the well being of the people and the increase in life quality [5].

The integration of economic, social and environmental goals is the key for the success of the sustainable development process, since a particular goal should also support all the others. On the other hand, there are normally a conflicting interest between different goals, so the main challenge is to balance the different dimensions [6].

In the last fifty years, sustainable development evolved from an alternative view on the planets development towards a formal political endorsed development model. There are some key confer-ences and documents that are the basis for the implementation and spreading of the concept.

The following milestones are key to the planets development and trajectory towards sustain-ability [6]:

• United Nations Conference on the Human Environment (UNCHE; 1972); •World Conservation Strategy (WCS; 1980);

•Our Common Future (1987);

• United Nations Conference on Environment and Development(UNCED; 1992); •United Nations Millennium Summit (2000);

•Earth Charter(2000);

•United Nations World Summit on Sustainable Development (WSSD; 2002); • Rio +20 United Nations Conference on Sustainable Development (UNCSD; 2012); • Sustainable Development Goals for People and Planet to be held in September; 2017. From an operational point of view, it is possible to define sustainability according to the two following key concepts exposed in the Brundtland Report [4]:

o "the concept of ’needs’, in particular, the essential need of the world’s poor, to which over-tiring priority should be given";

o "the idea of limitations imposed by the state of the technology and social organisation on the environment’s ability to meet present and future needs".

Throughout the literature, there is an agreement about the principles that are fundamental to the concept of sustainability [6]:

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2.1 Triple Bottom Line 7

• Normative Principle - This concept is related to the way that an organisation defines the values in around which it develops its activities.

•Equity Principle - This concept is related to justice, fairness, inter-generational equity, intra-generational equity, geographical equity and inter species equity. When the inter-intra-generational equity is assessed, the main concern is the impact on future generations. This concept was summed in the Brundtland Report accordingly to the following:

"We have not inherit the Earth from our parents, we have borrowed it from our children"[4]. When we are talking about the intra-generational equity, the concept is almost the same as in the inter-generational approach but it concerns the impact in today’s actions, leading to the realisation of social equity.

Geographical equity deals with the way that countries cooperate between themselves begin-ning in a local level and expanding to a more global impact, in a logic of acting local, but thinking in a global sense. Inter-species equity highlights the critical importance of biodiversity and pre-serving of natural habitats.

•Integration Principle: Analysing the concept of sustainability, the fact that we should strive to integrate various development objectives in a harmonious way appears to be a logical conclu-sion. Instead of favouring one specific dimension the approach must be global, and there must not be trade-offs between dimensions [8].

•Dynamism Principle: The interactions between the three dimensions of sustainability are not stable and are subject to a constant flow of change, making the problem an evolutionary process [6].

There is the need to operationalise the concept and to propose a clear way to evaluate the organisation’s performance, according to the following stated definition:

"In essence, sustainable development is a process of change in which the exploitation of re-sources, the direction of investments, the orientation of technological development, and institu-tional change are all in harmony and enhance both current and future potential to meet human needs and aspirations”[4].

The purposed operational way to assess sustainability and one of the most common approach’s is the Triple Bottom Line.

2.1 Triple Bottom Line

The concept of triple bottom line, or the three P’s concept (People, Planet and Profit) ,is the base of sustainability assessment in today’s world. It is composed by three different dimensions. An economic dimension, a social dimension and environmental dimension. It was first introduced in 1994 by John Elkington in his book "Cannibals with Forks". [9] The concept is illustrated in figure2.1.

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Figure 2.1: Triple-Bottom Line Sustainability Accounting Model (Kenneth Lyngaas, 2013)

2.1.1 Economic Dimension

This dimension is directly connected to the economic value that an organisation creates af-ter the deduction of all expenses. It assesses the impact that an organisation might have on its economic environment. Economic sustainability must focus on the long term. The economic dimension of the triple bottom line approach to sustainability refers to the impact of the organisa-tion’s business practices on the economic system . It is related to the ability of the economy as one of the subsystems of sustainability to survive and evolve in order to support future generations. The growth of the organisation is directly tied to the economy and how well it performs.

The main goal of a company is to be profitable. Having this in mind, companies should also be concerned with the environmental and social dimensions. If a company’s vision is biased towards one specific dimension, and if this approach compromises its financial health, the company would not be considered sustainable. The efficient allocation of resources requires that market prices incorporate externalities or "true social costs".

Alfred Marshall, a neoclassical economist was the first to introduce the concept of external costs or externalities, referring to events where a market transition results in unexpected and unin-tentional costs or benefits for a third party not directly involved in the transaction[10].

Let’s take pollution as an example. The goal of a company is not to pollute the environment but to create value, but while doing so, its activities create damage to the system where the company operates. So, companies need to develop an index to access and measure the impact that of their operations. The market value should consider these external costs, otherwise the market operation could lead to a disparity in wealth distribution and aggravate the depletion of resources [11].

Summing up, the Triple Bottom Line Economic dimension ties the growth of the organisation to the way that it impacts the economic system where the company exerts its activity. The Triple Bottom Line focuses on the economic value provided by the organisation to the surrounding

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sys-2.1 Triple Bottom Line 9

tem in a way that the activities of the company will promote the well-being and support future generations.

2.1.2 Social Dimension

The social dimension of sustainability is related to the fair and beneficial business operations towards labour quality and the community of the region under which the company carries its activities[9].

The practises of the company should provide benefits to the community. This means that the company should be aware of the surroundings where it exerts its activities. The social dimension of sustainability gives weight not only to just making profit, but also to being responsible for how the company’s human capital is treated.

The social dimension is related to the balance of economic power in the society. Besides the competition and the profit objectives, it is also important for companies to have a corporate social responsibility (CSR) [12].

CSR introduces a concept where companies focus not only on themselves but also on their surroundings, proposing that companies should be responsible and choose to be self-regulated in what concerns social impact and ethics. Companies should not only comply with the laws and policies of the country where it operates, but they should go beyond in order to meet self-imposed goals.

Even though companies are legally bonded to the juridical framework of the regions they oper-ate on, CSR introduces the concept of ethics in business, meaning that the companies stakeholders have to comply with an ethical code. The existence of this code will provide employees and cus-tomers a more equal society, and contribute to a more sustainable approach to development [13].

The aim is to increase long-term profits and shareholder trust through positive public relations and high ethical standards, to reduce business and legal risk by taking responsibility for corporate actions.

2.1.3 Environmental Dimension

The environmental dimension of sustainability is related to the impact that companies have on surrounding areas of its activities [9].

Albeit having in mind the profit maximisation, there must be a real concern on the companies impact and deplete the resources, having in mind future generations.

The companies must endeavour to benefit the natural order as much as possible and to carry out their activities with as low impact as possible.

A sustainable company must have environmental concerns and to align itself with the global goals when it counts to pollutive activities.

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2.2 Multivariate Analysis Importance

The measurement of a complex phenomenon like sustainability cannot be done through single indicators. Sustainability is related with activities from such diverse areas as the environment, the society and the economy. The interrelationships between them depend on more general and specific factors, which determine the dynamics of each system.

Corporations may have a different perspective on sustainability, given their different charac-teristics, activities, stakeholders and interrelations.

In a globalised world, a company is challenged to face sustainability issues and to be respon-sible for the way it conducts its activities. Employees, consumers and society are increasingly valuing companies with social and environmental concerns, and that contribute to the wellbeing of the society in the long run.

On the other hand, companies with no environmental or social concerns may incur in direct or indirect losses, which usually, on the long run, lead to very negative impacts on their credibility, corporate image and market share.

The pressure to deliver results in environmental and social areas has led companies to seek new ways to collect and communicate this information, namely through the reporting of corporate non-financial performance.

The development of guidelines allows to systematise the reporting of business performance and provide a better understanding of corporate achievements in respect to the environment and society..

It is a fact that the assessment of such a complex concept as sustainability demands the use of several indicators[14]. It is important to realise to what extent the chosen KPI’s will be used, since their collection requires effort and resource use.

As the basis of reporting is crucial, the clearance of the main issues affecting present and future generations should be disclosed by companies. In this regard, the core of sustainability reporting is linked with specific KPI’s related with relevant sectors . Business indicators should be focused on key sector sustainable issues and integrated with financial and economic indicators [15].

There is a clear multilateral relationship between all the mentioned dimensions, while high-lighting the role that balanced interactions may play in building and enhancing long term compa-nies survival. The vision of corporate sustainability applied in the present work meets three basic lines: the operative efficiency and the compliance of the objectives of reliability and continuity of service; the social improvement and the environmental neutrality; the building of a sustainable business should respect the assumption of corporate responsibility in the short term, while meeting the definition of strategic guidelines for the future.

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2.3 Sustainable Development in Distribution System Operators 11

2.3 Sustainable Development in Distribution System Operators

Distribution System Operators (DSO’s) in the European Union Internal Electricity Market are responsible for providing and operating low, medium and high voltage networks for regional distribution of electricity as well as for supply of lower-level distribution systems and directly connected customers (Articles 2 and 25 of Directive 2009/72/EC of the European Parliament and of the Council of 13 July 2009 concerning common rules for the internal market in electricity and repealing Directive 2003/54/EC).

Electricity is a vital component of infrastructures and an essential part of modern day life, playing a critical role in the economies of most countries. Electricity has become a prime mover for productivity and jobs throughout the world.

First and foremost, like other commercial enterprises, electric utilities add economic value, and this is the economic rationale for their existence and the basis of their commercial performance.

We must have in mind, that electricity is not like other commodities, because electric utilities provide value as public service providers. Another technical issue is the impossibility to store elec-tricity for later use, and a credible supply of elecelec-tricity is a prerequisite for economic development, social security and public welfare.

As an example of the impact that an electric utility can have on a countries economy, we can use the blackout, even a short-term blackout may have tremendous negative social and economic impacts.

Electric utilities are corporate citizens of the countries in which they operate, meaning that they have social responsibilities and they influence the physical environmental surrounding their assets and operations.

In an economic perspective, electric utilities are part of the commercial matrix that comprises a modern economy, the industry creates economic value through the technical and commercial processes involved in the generation and distribution of electricity, and its subsequent application in end uses. Electric Utilities redistribute this value at a community level and a broader societal level, for example through the payment of wages to workers, dividends to shareholders and rents to land owners [16].

The electricity sector is normally a large sector in the country’s economy, and therefore it creates a significant amount of jobs, related to their direct operations and indirectly through the procurement of goods and services from other business.

Because of the technical difficulty of power generation, transport and distribution, electrical utilities are repositories of technological competence and a source of innovation, that contributes to the country’s economic development and higher level of technical formation.

In an environmental perspective, different power generation technologies have different en-vironmental implications, for example fossil-fuelled generation results in the greenhouse gases emissions, nuclear power raises issues regarding the handling and storage of radioactive waste, and hydro power can have environmental consequences such was impacts on the river systems, wetlands and biodiversity. Regarding the transmission and distribution of electricity, they can

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have impact, regarding the usage of protected areas to implement assets, leading to a decrease of the value of the landscape[16].

Concerning a social perspective, in addition to conventional economic value, electricity util-ities are providers of a commodity that can impact the life of the citizens of a country, meaning that a reliable and consistence supply is a must in a developed country. On the other hand, in developing countries where access to electricity remains low, social basic services, such as health and education are constrained for the lack of energy.

In order to make the sustainability concept usable in an operational way, it is necessary to define principles and objectives that the electricity utilities must meet.

Distribution System Operators are a crucial element in the EU 20-20-20 Policy, this being related to the 20 % reduction of Green House Gas Emissions (GHG) regarding 1990 levels, the improvement by 20 % of Energy efficiency and the increasing of the percentage of renewable production in the European Energy mix in 20 %. All these goals are to be obtained until 2020.

Article 25 of the Electricity EU Directive (EED) from 2009 lays down the tasks of the DSOs and states that they must carry out their responsibilities with due regard to energy efficiency. Ac-cording to Article 25(7) of the Electricity Directive, DSOs must consider energy efficiency/demand-side management measures or distributed generation that might supplant the need to upgrade or replace electricity capacity when planning the development of the distribution network.

In this context, Article 2(29) of the EED defines energy efficiency/demand-side management as a global or integrated approach aimed at influencing the amount and timing of electricity con-sumption in order to reduce primary energy concon-sumption and peak loads by giving precedence to investments in energy efficiency measures, or other measures, such as interruptible supply con-tracts, over investments to increase generation capacity, if the former are the most effective and economical option, taking into account the positive environmental impact of reduced energy con-sumption and the security of supply and distribution costs aspects related to it’.[17]

Under EU law, therefore, both electricity from renewable sources and electricity from high efficiency CHP enjoy the right of priority dispatch under certain conditions. Limitations on priority access and dispatch can be applied when this is needed for the maintenance of the reliability and the safety of the grid and to ensure its secure operation.

In the context of the requirement for Member States to promote access to and participation of demand response, the EED (Article 15(8)) provides that Member States must require national regulatory authorities, or TSOs and DSOs where the national regulatory systems so require, to promote access to and participation of demand response in balancing, reserve and other system services markets. This requires clarifying, and if necessary changing, what the technical or con-tractual requirements for participation in those markets are, e.g. minimum required capacity, tim-ing and duration of demand response activation, notice time for activation, etc., in a way that is appropriate for demand side participation. The promotion of access to and participation of demand response should also include dedicated provisions organising the relationships between relevant stakeholders, in particular between demand response service providers (e.g. aggregators or energy saving companies –ESCOs–) and balance responsible parties. These may be part of

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2.3 Sustainable Development in Distribution System Operators 13

technical or contractual arrangements or any other participation procedures and be further defined and harmonised taking into account future network codes developed pursuant to Regulation (EC) No 714/2009.

Although the concept of sustainability is somewhat recent, it has quickly attracted the attention of consumers, companies, scholars, institutions and the society in general. Mankind has realised that we are rapidly reaching a breaking point when it comes to our survival as a species. From now on, we must consider our actions carefully because they will have irreversible consequences in the future.

As usual, the first approaches to sustainability were related strictly to economic issues. How-ever, the concept has been widened, and it is now generally agreed that there are other concerns to be considered when analysing the sustainability of any project, company or country. An example is the Triple Bottom Line concept, which we follow in our analysis.

After dabbling in the concept and dimensions to be considered in our analysis, one can under-stand that integrating different objectives will prove difficult. The main obstacle to setting policies or measures will be to determine how to accomplish one goal without hurting the others. This ev-idences the importance of a multivariate analysis, in order to unveil how each dimension is related to the remaining ones, and which trade-offs one can expect from different plans.

Sustainability concerns are increasingly central to a companies survival, due to new laws im-posed by governments and the scrutiny of their customers. More and more companies have started to report about their environmental and social performance in response.

In particular, DSOs play a vital role in sustainability matters due to their importance in the economic, social and environmental spheres in their respective countries. This importance is high-lighted in the EU, for example, through its 20-20-20 Policy and the mentioned directives.

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Chapter 3

Methodology for the Evaluation of Key

Performance Indicators

This works assesses the sustainable development of Distribution System Operators (DSO’s) by a developed algorithm.

The assessment of Sustainable Development is not a linear problem and the search for exact parameters to compare companies between themselves, relies on the use of pre-made reporting tools such as the ones presented in the Global Reporting Initiative (GRI) [18].

The developed algorithm used the mathematical model for corporate sustainable analysis based on the work presented in the OECD Handbook on Constructing Composite Indicators, and implements the use of multivariate data analysis[19].

The data analysis and statistical methodology is composed by two different sections. The first section is related to the framework used in the assessment of a company’s sustainable development. The second section comprises the mathematical analysis of the data set.

3.1 Framework Background

The framework and the context where the company operates can change throughout the time frame, depending on the scientific knowledge of the problem, the environmental concerns and the policy priorities of the company’s stakeholders[20]. Therefore, in this work, we will present the evolution of the frameworks used for sustainability analysis. The first framework that uses a more simplistic approach is the Pressure State Response framework (PSR framework), followed by the Driving Forces-State-Response (DSR Framework) and the final evolution of the Driving Forces-Pressure-State-Impact-Response Framework (DPSIR Framework) [21].

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16 Methodology for the Evaluation of Key Performance Indicators

Pressure-State-Response Framework (PSR )[2]

This framework was developed by OECD for the classification of environmental indicators, and it is based on the causality concept. The human activity exerts pressure in the environment, changing the quantity and quality of the natural resources. The initial state of the system will change leading to a response in the form of economic, environmental and social measures [2].

This framework defines three types of key performance indicators:

• Environmental State Indicators - these KPI’s are related to the STATE box of figure3.1 and are associated to the reporting of environmental aspects regarding the quality and quantity of natural resources.

• Environmental Pressure Indicators - these KPI’s are related to the PRESSURES box of figure 3.1. They describe the pressures that are exerted in the system, and the way the human activity affects the environment. These indicators can be divided into two categories: direct pres-sure indicators and indirect prespres-sure indicators. The first type considers phenomena that can be directly measured, such as emissions of greenhouse gas (GHG) or the usage of natural resources. The second category reports on phenomena that can lead to environmental threats.

• Social Pressure Indicators - these KPI’s correspond to the RESPONSE box of figure3.1 and give information on how the society is responding to the changes in the system and to the concerns that are derived from these changes. This category, considers the collective or individual actions that could prevent or mitigate the negative impacts in the system.

This framework considers the analysis of the different sectors in the system in an aggregated way related to their principal characteristics and according to the following principles:

Functional Meaning - aggregates in one sector different activities according to their environ-mental impact and their role in pollution. As an example, we can consider the transport sector for which we should consider not only the GHG gas emission by the vehicles but also the impacts related to the automotive industry.

Institutional Meaning - aggregates the companies according to their economic activity. We can correlate in a systematic way environmental pressures and different sectors of activity, meaning that a profile for every typical problem could be created, in which the environmental pressure that leads to it is analysed and also its impact on the entire system [2].

This framework has the advantage of emphasising the existent relation between the three pre-sented categories. However, it could lead the user to think that the existent relations are linear. To complement this method, the Driving Force-State-Response Framework (DSR Frame- work) was created. In this framework, the Pressures category was changed to the Driving Forces category.

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3.1 Framework Background 17

Figure 3.1: Pressure-State-Response Framework [2]

Driving Forces-State-Response Framework (DSR)[20]

DSR framework was developed by the United Nations Organisation (UN), to control the sus-tainable development. It constitutes an evolution of the PSR framework. The term Pressures was changed to Driving Forces, leading to the incorporation of KPI’s that can be used in the analysis of the social, economic and institutional components [20].

One of the main characteristics of this framework is that the Driving Forces parameter could be considered as positive or negative.

The framework is presented to the user in the form of a table, that horizontally includes three types of indicators and vertically analyses four dimensions of sustainable growth, according to the following dimensions:

• Social dimension;

• Environmental dimension; • Economic dimension; • Institutional dimension.

This methodology considers three components of analysis:

o Driving Forces activities and human processes that affect the system in study, changing is sustainable development;

o State indicators that give information about the state of the system in relation to sustainable growth;

o The response given by the governments and civil society, in the form of policies.

The use of Driving Forces is an appropriate way to describe socio-economic needs and moti-vations that impel the existence of human activity. A driving force describes social, demographic and economic development in society.

The effort to optimise the DSR Framework and the need to create a better separation between KPI’s that are connected to Driving Forces and those which are related to State and Response led

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to the creation of the Driving Force-Pressure-State-Impact-Response (DPSIR) framework. In this framework two more categories are introduced, namely, Impacts and Pressures.

Driving Forces-Pressures-State-Impact-Response Framework (DPSIR )[3]

Figure 3.2: Driving Forces-Pressures-State-Impact-Response Framework [3]

DPSIR Framework for sustainability assessment was created by the UN as an evolution of the DPSR Framework. It can be defined as a chain of causal relations between entities as can be seen in figure3.2.

This framework is widely spread across the world and nowadays is the most used for sustain-ability assessment of organisations and countries [3].

The components of this framework are the following:

• Driving Forces – this category indicates the needs of the organisation stakeholders (e.g. in the industrial sector, the objective of profit maximisation and low operating costs, could be considered as driving forces);

• Pressures – according to the needs defined in the system, the actions taken to achieve them exert pressures in the environment. These pressures are related to the depletion of natural resources and can be distinguished in three different categories:

o Excessive use of resources;

o Changing in land use and in its morphology;

o Emissions of noxious elements that could lead to the contamination of soils, the atmo-sphere, or that can change the properties of the water reserves.

• State - the results of the exert pressures in the environment lead to changes. Thus, it is necessary to assess how the environment changed in terms of quality, through a comparison against pre-defined quality parameters;

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3.2 Key Performance Indicators Data Structure 19

• Impact - when the system state changes, due to pressures, these will always have an impact. This impact could be categorised in a positive or negative way and it will lead to changes in the way the system works;

• Response – the changes in the system lead to a response of the civil society and policy makers. This category is defined as the reaction to an undesired impact in the system and could have an effect in any part of the previously presented chain.

The DPSIR Framework can describe the relationships between the origins and the conse-quences of the problems that affect the system, meaning that the system can only be deeply under-stood if the interactions between every element are studied.

3.2 Key Performance Indicators Data Structure

Composite sustainability indexes largely derive from the quality of their input data, meaning that ideally the chosen KPI’s should comply with aspects related to their quality and should be done with robust gathering techniques [19].

The raw data used in this work was collected from public sources, such as the companies’websites, annual reports and annual reports on sustainability.

The collection period used for this work comprises the period from 2010 to 2015. The reason to use this time window is to ensure enough data to allow the study of the trend that the company’s composite sustainability index follows and to comply with the rules of the quantity of data needed to make the chosen method robust.

There are different values for the same indicator, thus, we considered the most recent published information, since sometimes there is a correction on the information of previous years. Whenever this was possible, and since there are indicators where data is available from more than one source, we have done a verification using multiple sources.

All the companies used in this study are part of EU, meaning that the currency used throughout the work is EURO Currency (e).

The use of only public sources brings accuracy problems, such as the following: o Errors of measurement;

o Observational errors;

o Different data collecting methods; o Confidential data.

Dealing with different sustainability reports from different DSO’s has showed that standards for measurement are essential. In order to minimise this type of errors, we used GRI indicators and a GRI equivalence table in the used reports.

The comparison of companies from different countries deals with different legal frameworks and national accounting systems. The breakdown of indicators by geographical area is a difficulty due to the fact that the DSO’s are part of a larger group of companies.

To overcome these difficulties the following methods were used: o Consolidation of information from different sources;

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o Definition and standardisation of data; o Definition and standardisation of metrics; o Definition of concepts for global use;

o Definition of formulas for calculating the KPI’s.

A more detailed perspective on each of the KPI’s is presented in chapter4.

The application of the most advanced approaches to the development of a composite sustain-ability index based on incoherent or incorrect data would not produce good quality results. On the other hand, the quality of the composite sustainability index will largely be determined by the appropriateness of the used indicators.

3.3 Multivariate Analysis Techniques

The most common approaches to sustainability assessment use a single dimension approach, which can lead to indexes that overwhelm, confuse and mislead decision makers and the public. The creation of an index using only a single approach cannot provide a broad picture that considers the three distinct aspects of sustainability. As an example we can use indexes that only take in account one dimension, such as the Human Development Index, which is only related to a country’s social performance, or the European Economic Sustainability Index (EESI), which only assess how a country performs in specific economic metrics such as deficit, national debt and economic growth [22].

The alternative to an unidimensional analysis is a multivariate analysis, that assumes that us-ing a broader variety of KPI’s, which are aggregated into an index, can lead to a more complete view of the system, showing at a glance a “simplified, coherent, multidimensional view of a sys-tem”, according to [23]. The objectives and characteristics that a robust index must meet are the following:

• Monitor and evaluate sustainable development and environmental pressure; • Aggregate complex or multidimensional issues to support policy making; • Highlight the factors which are most responsible for driving the system; • Formulate strategies and communicate ideas.

The first studies that used a multivariate analysis in the assessment of sustainability, applied the method to countries not companies. This work aims to implement the same logic and algorithms whilst using as subject of study DSOs.

Multivariate analysis techniques are used in this work to study the overall structure of the data set and assess its quality.

There are diverse ways to do a multivariate analysis, but the ones that are most often used in the literature on the creation of composite indexes are the following [19]:

• Principal Component Analysis; • Cronbach Coefficient Alpha; • Cluster Analysis.

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3.4 Principal Component Analysis (PCA) 21

When comparing the different techniques we find strengths and weakness in all of them as shown in , table3.1.

Table 3.1: Strenghts and Weaknesses of Multivariate Analysis Algorithms [6]

Algorithm Strengths Weaknesses

Principal Component Analysis

-Able to summarise a set of individual indicators while preserving the maximum possible proportion of the total variation of the data set -Largest Factor Loadings are assigned to the individual indicators that have the largest variation across DSOs

-Correlations do not necessarily represent the real influence of the individual indicators on the phenomenon being used -Sensitive to modifications in the basic data -Sensitive to the presence of outliers -Minimization of the contribution of individual indicators which do not move with other individual indicators

Cronbach Coefficient Alpha

-Measures the internal consistency in the set of individual indicators

-Correlations do not necessarily represent the real influence of the individual indicators on the phenomenon expressed by the composite indicators -Meaningful only when the composite indicator is computed as a scale

Cluster Analysis -Offer unique way to

group companies -Purely Descriptive Tool

From the techniques presented in, table3.1in this work we chose to use Principal Component Analysis (PCA), having in mind that the preservation of the maximum information regarding the variance is very important for the creation of the composite sustainability index.

3.4 Principal Component Analysis (PCA)

The main goal of PCA is to describe how different variables change in relation to each other and how they are associated. This is achieved by transforming correlated variables into a new set of uncorrelated ones using a covariance matrix, or its normalised form, which is called the correlation matrix [19].

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In this work, PCA is used as a descriptive technique, meaning that there is no reason for the variables to be of any particular type. As explained in chapter4, the variables are a mixture of continuous and ordinal variables.

3.4.1 Mathematical Model of PCA

The main objective of PCA is to study the variance of the gathered data, through the creation of linear combinations of the original dataset [24].

Let’s consider that there are Q variables, x1,x2,. . . ,xQ, much of the raw data variation could be studied using only a small group of variables, these are the Principal Components.

Principal components are linear and uncorrelated combinations of the original data.

The next step is to select the first Z<Q Principal Components that preserve the highest amount of cumulative variance of the original data [19].

PC1= a11∗ x1+ a12∗ x2+ ... + a1Q∗ xQ

PC₂= a21∗ x1+ a22∗ x2+ ... + a2Q∗ xQ

PC₃= a31∗ x1+ a32∗ x2+ ... + a3Q∗ xQ

PCz= az1∗ x1+ az2∗ x2+ ... + azQ∗ xQ

(3.1)

The lack of correlation in the Principal Components is a useful property, meaning that each one is measuring a different statistical dimension of the data.

If the variation of Q original variables could be accounted for a small number of Z variables, there will be a reduction in the time of data analysis and data processing. PCA, cannot always reduce the dimension, indeed if the original variables are uncorrelated, the analysis does not lead to a matrix size reduction, on the other hand, if the variables are highly correlated the dimension will be largely reduced without the loss of information.

The weights ai j in equation3.1, are designated factor loading’s, and are applied to each of the

study variable, xj, and are chosen so that PC Zisatisfy the following conditions:

I. They are uncorrelated, and orthogonal between themselves

II. The first PC, accounts for the maximum possible proportion of the variance of the set of variables x, the second PC accounts for the maximum of the remaining variance, and so on until the last of PC absorbs all the remaining variance not accounted for by the preceding components and that the sum of the factor loadings squared are equal to one accordingly to the formulation in equation3.2.

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3.4.1.1 Normalisation Method

The different original variables normally have different measurement units, so in order to combine them in a composite index there is the need to normalise them [25].

To prevent problems of mixing different units variables are transformed to a common basis. Another reason to normalise variables is to avoid problems of outliers or extreme values [19].

There are different techniques of normalisation, such has Min-Max Normalisation, Standard-isation (Z-Scores) and Ranking NormalStandard-isation [19]. The normalisation method should take in account the data properties, as well as the objectives of the composite indicator.

Min-Max Normalisation change variables to have an identical rage [0,1] by subtracting the minimum value and dividing by the range of the indicator values accordingly to equation3.3. This technique is not usable in cases where there are outliers or extreme values in the data set.

xinorm=

xi− minc(xi)

maxc(xi) − minc(xi)

(3.3) • xinorm - normalised value of the indicator for a given year

• xi- raw value of the indicator for a given year

• minc(xi)- Minimum of the indicator i, across the year

• maxc(xi) -Maximum of the indicator i, across the years

Standardisation is done accordingly to equation3.4. For each of individual KPI, Xi, the mean

across the years ¯Xiand the standard deviation σiare calculated. The standardisation is done so that

all the KPI’s have a similar dispersion across the years. There is also to notice that the standardise values have mean zero and standard deviation one.

Xnorm=

Xi− ¯Xi

σi

(3.4) In this work it is used as normalisation method the standardisation of data.

3.4.1.2 Covariance Matrix

Principal Component Analysis, implies the analysis of the eigenvalues of the covariance matrix of the original data. The eigenvalues will be designated λj, with (1 < j < Q) meaning that there

are as much eigenvalues as study variables Q.

CM=       cm₁₁ cm₁₂ ... cm_1Q cm21 cm22 ... cm2Q ... cm_Q1 cm_Q2 ... cmQQ       (3.5)

The matrix presented in equation3.5, is designated as covariance matrix. In this study, each of its elements is analysed in a detailed way.

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Covariance is useful to find out how much one of the dimensions in analysis varies relatively to the mean. Covariance is always measured between two dimensions, and so if we calculate the covariance between one measure and itself, we get the variance of that dimension.

A useful way to get all the possible variance values between all the different dimensions is to calculate them all and present them in the form of a matrix. Dealing with a n-dimensional data set, the covariance matrix has n rows and columns, meaning that it is squared and each entry in the matrix is the result of calculating the covariance between two separate dimensions [24].

There are some useful characteristics in this matrix: the elements in the positive diagonal, type cmii, give information about the covariance of the variables; the other elements of the matrix,

type cmi j, show the covariance between variables xi and xj; the correlation matrix is symmetric

regarding the main diagonal.

The interpretation of every value in the covariance matrix, is done using its sign according to the following three rules:

1. If the sign is positive it indicates that both dimensions increase together;

2. If the sign is negative it indicates that when one dimension increases the other decreases; 3. If the covariance values are zero, it indicates that the two dimensions are independent to each other.

3.4.1.3 Eigenvalues and Eigenvectors

The mathematical definition of eigenvalue can be explained according to equation3.6, if we have a n x n matrix P, there are n eigenvalues λ1,λ2,λ3,. . . ,λn.

|P − λ ∗ I| = 0 (3.6)

• P- Square matrix P, sizes [nxn] • I-Identity Matrix, size [nxn] • λ - Eigenvalues Vector.

On the left side of equation3.6, there is matrix P minus the eigenvector multiplied by the Iden-tity matrix. The next step is to calculate the determinant of the resulting matrix, this calculation leads to a polynomial of order n.

The last step of the calculation to obtain the eigenvalues, is setting the n order polynomial equal to zero and solving for λ . The result of equation3.7will have n solutions and so there are n eigenvalues.

(P − λn∗ I) ∗ en= 0 (3.7)

The corresponding eigenvectors are calculated according to equation3.7leading to the finding of the eigenvectorenassociated with eigenvalue λn.

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The covariance matrix can be described as a function of its eigenvectors and their correspond-ing eigenvalues, havcorrespond-ing in mind the Spectral Decomposition Theorem (SDT) accordcorrespond-ing to equation 3.8. CM= p

∑

n=1 λn∗ en∗ e0n (3.8)

The interpretation of equation3.8, is that the covariance matrix can be written as the product of the eigenvectors multiplied by its transpose, summed over the n eigenvalues.

The calculus of the eigenvalues is done accordingly to equation 3.6, where I is the identity matrix, it has the same order as the CM matrix, and λ , corresponds to the eigenvalues vector.

One of the main properties of the eigenvalues is that is sum is equal to the sum of the CM matrix diagonal values, meaning that the sum of the PC variance is equal to the sum of the original variables variance.

Having explained the method of calculation of the eigenvalues and eigenvector and the impor-tance of the correlation matrix the next section will be about the method of finding the coefficients ai j.

3.4.1.4 PCA Coefficients

The way to calculate the PCA coefficients ai j, involves the eigenvalues, eigenvectors and the

covariance matrix.

First let’s consider that the eigenvalues of the covariance matrix will be designated (λ )j, with

1 < j < Q, ordering from the largest to the smallest accordingly to equation3.9.

(λ1≥ (λ2≥ ... ≥ λn (3.9)

The eigenvectors of the covariance matrix will be designated en, the elements for these

eigen-vectors will be the coefficients of the PC.

The variance for the nth PC is equal to the ntheigenvalue, and the PC are uncorrelated with each other.

var(Zi) = var(en1∗ X1 + en2∗ X2 + ... + eni∗ X p) = (λn (3.10)

cov(Zi, Z j) = 0 (3.11)

3.4.1.5 Components Loadings and Scores

The correlation coefficients between the PC’s, Z, and the variables X are called components loadings. In the case of uncorrelated variables, the loadings are equal to the variable weights.

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The component score for a given year is calculated by taking the case’s standardised value on each variable, multiplying by the corresponding loadings of the variables for the given principal component factor, and summing these products.

Interpretation of the PC, is one of the most important steps in the algorithm, it consists in analysing the weights that every indicator has on a PC.

After analysing this relation, it is possible to understand which indicators dominate each Prin-cipal Component, meaning that is possible to directly connect a PC to a dimension of the company approach to sustainable development.

3.4.1.6 Stopping Criteria

The question of how many principal components should be retained in the analysis without losing too much information could be chosen using the following principles:

• Kaiser Rule: The main idea behind this rule is that any PC which the variance is lower than one, contains less information than one of the original variables and so it is not worth retaining [24].

• Scree Plot: This method was proposed by Cattel in 1966, is considered to be an even more subjective, because it involves looking at a plot of the component number against the eigenvalues, and deciding at which value of k, the slopes of lines joining the plotted points are steeper to the left than they are to the right of the point. The point k defining by these characteristic, is then taken, and the amount of component numbers that are before this point are the m components to be retained in the analysis [24].

• Variance Explained Criteria: Some researchers simply use the stopping rule of keeping enough factor to account for 90% of the variance[19].

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This chapter can be summed up as the toolbox that will be used to create the composite sus-tainability index for the analysed companies.

The first part of the chapter is related to the most commonly used frameworks in sustainability assessment and how they evolved through time. The sustainability concept is complex and re-quires the understanding of more than one dimension, so there is the need to use multidimensional analysis.

The used technique is Principal Component Analysis, from this chapter we can understand how the eigenvalues and eigenvectors analysis of the covariance matrix is useful to calculate the Principal Components and how it can lead to a reduction in the data set, but also how it can be used to asses the data structure.

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Chapter 4

Key Performance Indicators (KPI)

After reading through the sustainability and annual reports in different Distribution System Operators (DSO’s), the method for assessment of the utilities will be presented next.

In the first part of the data gathering, we created a database using the economic and operational reports of the companies in study. This database comprises a bundle of twenty key performance indicators, having as subject three companies, between the period of 2010 to 2015.

So that we can compare companies between themselves, and since each company is different in size and business volume, we need to create a group of fourteen relative key performance indicators.

It is very difficult to measure and assess the sustainability of a company using a large basis of indicators, so the integration of financial and non-financial information into one composite indicator, that allows to calculate the overall index, is a very convenient tool, that can be used by policy makers to complement their decisions and the information that is given by the economic information [26].

All the chosen KPI’s have met the following criteria[26]: • Significance; • Representativeness; • Measurability; • Availability of Data; • Comparability of Data; • Information Value; • Simplicity of calculation.

The indicators are distributed in three distinct categories, according to the type of information they report on. The categories used are the following:

o Operational Category: This category reports on the technical aspects of the company, regarding quality, safety and reliability of service to every costumer in the Distribution Network. The KPI’s that are part of this category are presented in table4.1;

o Environmental Category: This category reports on the environmental information and on the impact that the activity of the company has on the environment. The assessment made

Sustanability Indicators for DSO

F

E

U

P

Sustainability Indicators for

Distribution System Operators

José Francisco Pereira Moura Teixeira

Resumo

Abstract

Acknowledgments

Contents

List of Figures

List of Tables

List of acronyms and symbols

Chapter 1

Introduction

1.1

Context

1.2

Purpose of this thesis

1.3

Thesis Structure

Chapter 2

Sustainability Concept

2.1

Triple Bottom Line

2.2

Multivariate Analysis Importance

2.3

Sustainable Development in Distribution System Operators

Chapter 3

Methodology for the Evaluation of Key

Performance Indicators

3.1

Framework Background

3.2

Key Performance Indicators Data Structure

3.3

Multivariate Analysis Techniques

3.4

Principal Component Analysis (PCA)

∑

Chapter 4

Key Performance Indicators (KPI)