<p>Prior knowledge about the possible characteristics of demand and supply is vital in the planning and operation of economically sustainable isolated rural power systems. System modelling and simulation is one of the tools that can be used in planning and assessing the performance of these systems. This thesis is presenting a Monte Carlo simulation methodology for modelling, simulating and analysing the performance of isolated rural electricity markets applicable in developing countries. The definitions of possible power system operators managing these markets are introduced based on different economic objectives of operating the systems. The two system operators considered in the thesis are: altruistic and profit maximising operators. The concept used to define types of isolated rural electricity markets is combining the definitions of operators and the possible combinations of power supply options (purely thermal or hybrid system). It is anticipated that the rural electricity markets under consideration comprise of uncertainties in demand and supply (both demand and generation are modelled as random variables from assumed or estimated probability distributions).</p><p>Demand is price sensitive and modelled as a product of two random variables, relative demand and peak demand. The price sensitivity of demand is shown by representing peak demand using an economic price-demand function. The parameters (price sensitivity and demand factor) of this function are modelled as random variables which reflect the randomness of consumers’ preferences.</p><p>The simulation algorithm is based on the theory of correlated sampling, in order to compare the performance of systems under different operators. The thesis introduces the concept of nested Monte Carlo simulation to be able manage the simulation of different operators subjected to the same market conditions. The performance of electricity markets is assessed by analysing three parameters (tariffs, profit and reliability), which are random variables presented using probability distributions in form of duration curves.</p><p>The methodology is tested on a theoretical case study system using load data obtained from a rural community in Africa.  The case study illustrates how to use the model, preparation of the input variables and how to use the output to estimate and assess the possible performance of isolated rural power systems under different power system operators. It is anticipated that the proposed methodology can be used by researchers, planners and academia as a tool for planning, estimating and assessing the performance of rural power systems in isolated areas of developing countries</p>

Prior knowledge about the possible characteristics of demand and supply is vital in the planning and operation of economically sustainable isolated rural power systems. System modelling and simulation is one of the tools that can be used in planning and assessing the performance of these systems. This thesis is presenting a Monte Carlo simulation methodology for modelling, simulating and analysing the performance of isolated rural electricity markets applicable in developing countries. The definitions of possible power system operators managing these markets are introduced based on different economic objectives of operating the systems. The two system operators considered in the thesis are: altruistic and profit maximising operators. The concept used to define types of isolated rural electricity markets is combining the definitions of operators and the possible combinations of power supply options (purely thermal or hybrid system). It is anticipated that the rural electricity markets under consideration comprise of uncertainties in demand and supply (both demand and generation are modelled as random variables from assumed or estimated probability distributions). Demand is price sensitive and modelled as a product of two random variables, relative demand and peak demand. The price sensitivity of demand is shown by representing peak demand using an economic price-demand function. The parameters (price sensitivity and demand factor) of this function are modelled as random variables which reflect the randomness of consumers’ preferences. The simulation algorithm is based on the theory of correlated sampling, in order to compare the performance of systems under different operators. The thesis introduces the concept of nested Monte Carlo simulation to be able manage the simulation of different operators subjected to the same market conditions. The performance of electricity markets is assessed by analysing three parameters (tariffs, profit and reliability), which are random variables presented using probability distributions in form of duration curves. The methodology is tested on a theoretical case study system using load data obtained from a rural community in Africa.  The case study illustrates how to use the model, preparation of the input variables and how to use the output to estimate and assess the possible performance of isolated rural power systems under different power system operators. It is anticipated that the proposed methodology can be used by researchers, planners and academia as a tool for planning, estimating and assessing the performance of rural power systems in isolated areas of developing countries

Due to the increasing threat of global warming, today’s grid system faces large changes and challenges as more renewable sources are being implemented in the grid. In order to handle these changes and secure future distribution, new technologies and components are necessary. This study investigates the innovation – IT/OT integration and its rate of adoption among potential adopters – Distribution System Operators. Based upon 8 expert interviews, 19 interviews with Swedish DSOs and literature, the study has concluded the following: - Increased micro production in the Swedish electricity grid system is the main drivers for IT/OT integration. IT Security and Swedish Energy Market Inspectorates current pricing model are two of the main inhibitors for IT/OT integration. - Key factors, such as perceived attributes of the innovation and business transformation speed are of high importance when analyzing rate of adoption. - Medium-sized DSOs with high ambition are likely to adopt before other customer segments. Thus, they are potential target customers for suppliers, such as Ericsson. The thesis contributes to literature by providing research of a technical innovation within a complex market. Future research of interest is to apply similar methodology for predicting rate of adoption of IT/OT integration in other nations, since drivers and regulations might differ. / Det ökade hotet från klimatförändringar har medfört att dagens elnätssystem står inför stora förändringar och utmaningar då allt fler förnyelsebara källor implementeras i elnätet. För att hantera denna förändring och säkra framtidens eldistribution krävs att ny teknik och nya komponenter implementeras i elnätet. Denna rapport undersöker innovationen - IT/OT integration och hur denna sprids bland potentiella kunder – elnätsdistributörer. Baserat på 8 expertintervjuer, 19 intervjuer med svenska elnätsdistributörer och litteratur har studien kommit fram till följande slutsatser: - Ökad mikroproduktion i det svenska elnätet är den främsta drivaren för IT/OT integration. IT säkerhet och Energimarknadsinspektionens nuvarande regleringsmodell är idag två av de främsta barriärerna för IT/OT integration. - Huvudfaktorer, så som förväntade uppfattningen av innovationen och företags omvandlingshastighet är av stor betydelse för att uppskatta spridningshastigheten av innovationen. - Mellanstora DSOer med höga ambitioner kommer troligast ta till sig tekniken tidigare än andra kundsegment och bör därför vara potentiell målgrupp för leverantörer, så som Ericsson. Rapporten bidrar till forskningen genom att en teknisk innovation analyserats i en komplex marknad. Vidare undersökningar som kan genomföras är att applicera motsvarande metodik för estimera spridningen av IT/OT integration i andra länder, då drivare och regleringar där kan skilja sig från Sverige.

Europe requires significant transmission grid expansions to foster the integration of electricity markets, enhance security of supply and integrate renewable energies. However, next to lengthy authorization processes, transmission system operators (TSOs) in Europe are currently facing extreme public opposition in their transmission line projects leading to significant project delays. These delays imply significant additional costs for TSOs as well as society as a whole and put the transformation of the European energy system at risk. Existing scientific literature currently lacks comprehensive studies that have tried to identify generalizable success factors to overcome public opposition in transmission line projects. The goal of work at hand was to close this research gap. Potential success factors were collected through extensive literature review and interviews throughout Europe with respective stakeholders such as citizen action groups, NGOs or energy experts. Experiences from analogue large infrastructure projects like wind parks, carbon capture and storage facilities, hydro dams, nuclear waste repositories, etc. were also used to form hypotheses. The findings were transformed into a structural equation model and tested through a questionnaire answered by almost all European TSOs. Results revealed that people’s trust in the TSO is of utmost importance for less public opposition. It can be regarded as the critical success factor per se. TSOs can create trust through stakeholder participation, sufficient communication, proper organizational readiness and liaison with stakeholders. Furthermore, appropriate technical planning can help to reduce public opposition in transmission line projects. In total 18 concrete and actionable success factors were identified for TSO management to facilitate the establishment of these aforementioned aspects. They will help European TSOs to reduce public opposition and thus accelerate the implementation of new transmission lines. Interestingly, economic benefits for people did not turn out to be a Significant success factor in reducing their opposition against new transmission lines.

Europe requires significant transmission grid expansions to foster the integration of electricity markets, enhance security of supply and integrate renewable energies. However, next to lengthy authorization processes, transmission system operators (TSOs) in Europe are currently facing extreme public opposition in their transmission line projects leading to significant project delays. These delays imply significant additional costs for TSOs as well as society as a whole and put the transformation of the European energy system at risk. Existing scientific literature currently lacks comprehensive studies that have tried to identify generalizable success factors to overcome public opposition in transmission line projects. The goal of work at hand was to close this research gap. Potential success factors were collected through extensive literature review and interviews throughout Europe with respective stakeholders such as citizen action groups, NGOs or energy experts. Experiences from analogue large infrastructure projects like wind parks, carbon capture and storage facilities, hydro dams, nuclear waste repositories, etc. were also used to form hypotheses. The findings were transformed into a structural equation model and tested through a questionnaire answered by almost all European TSOs. Results revealed that people’s trust in the TSO is of utmost importance for less public opposition. It can be regarded as the critical success factor per se. TSOs can create trust through stakeholder participation, sufficient communication, proper organizational readiness and liaison with stakeholders. Furthermore, appropriate technical planning can help to reduce public opposition in transmission line projects. In total 18 concrete and actionable success factors were identified for TSO management to facilitate the establishment of these aforementioned aspects. They will help European TSOs to reduce public opposition and thus accelerate the implementation of new transmission lines. Interestingly, economic benefits for people did not turn out to be a Significant success factor in reducing their opposition against new transmission lines.:Contents I List of tables VIII List of figures IX List of abbreviations XI List of symbols XV List of country codes XVI 1 Introduction 1 1.1 Problem statement 1 1.2 Thematic classification and research gap 2 1.3 Objective, research questions and scop e of work 3 1.4 Methodology and structure of work 5 2 Fundamentals of electricity transmission line planning 7 2.1 History of the European electricity transmission network 7 2.2 Transmission technologies 9 2.2.1 High-voltage alternating current (HVAC) 9 2.2.1.1 High - voltage alternating current overhead lines (HVAC OHL) 9 2.2.1.2 High - voltage alternating underground cables (HVAC UGC) 10 2.2.2 High - voltage direct current (HVDC) 12 2.2.2.1 High - voltage direct current overhead lines (HVDC OHL) 12 2.2.2.2 High - voltage direct current underground cables (HVDC UGC) 13 2.2.3 Gas - insulated lines (GIL) 14 2.3 Major players 15 2.3.1 European Transmission System Operators (TSOs) and related associations 15 2.3.1.1 National Transmission System Operators (TSOs) 15 2.3.1.2 ENTSO - E 16 2.3.2 Energy regulators and related associations 18 2.3.2.1 National regulatory authorities (NRA) 18 2.3.2.2 European associations of energy regulators 19 2.4 Development of new transmission lines 20 2.4.1 Planning objectives 20 2.4.2 Planning process 21 2.4.2.1 Identification of needs 22 2.4.2.2 Feasibility study 23 2.4.2.3 Spatial planning 24 2.4.2.4 Strategic Environmental Assessment (SEA) 25 2.4.2.5 Environmental Impact Assessment (EIA) 26 2.4.2.6 Permitting procedure 28 2.4.2.7 Securing land rights and way - leaves 28 2.4.2.8 Construction, commissioning and operation 29 2.5 Project delays and obstacles 31 2.5.1 Project delays 31 2.5.2 Rationales for delay 33 2.5.2.1 Minor obstacles 34 2.5.2.2 Public opposition 35 2.5.2.3 Insufficient authorization procedures 36 2.5.3 Excursus: Recent governmental measures to overcome delays 38 2.5.3.1 Austria 38 2.5.3.2 Denmark 38 2.5.3.3 Germany 39 2.5.3.4 Great Britain 41 2.5.3.5 Netherlands 42 2.5.3.6 European Union 43 2.5.3.7 Further recommendations 48 2.6 Interim conclusion on the fundamentals of transmission line planning 49 3 Fundamentals of social acceptance 51 3.1 Definition and classification 51 3.2 Contextual factors that influence stakeholders’ attitudes 54 3.2.1 Proximity of stakeholders to a facility 54 3.2.2 Risk perception of individuals 55 3.2.3 Individual knowledge base 56 3.2.4 Existing and marginal exposure 56 3.2.5 Land valuation and heritage 57 3.2.6 Trust in project developer 58 3.2.7 Energy system development level 59 3.3 The history of social movement against infrastructure facilities 60 3.4 Forms of public opposition 61 3.5 Interim conclusion on the fundamentals of social acceptance 63 4 Fundamentals and methodology of success factor research 64 4.1 The goal of success factor research 64 4.2 Defining success factor terminology 64 4.2.1 Success 64 4.2.2 Success factors 65 4.3 Success factor research history and current state 67 4.4 Classification of success factor studies 67 4.4.1 Specificity 68 4.4.2 Causality 69 4.5 Success factor identification approaches 70 4.5.1 Systematization of success factor identification approaches 70 4.5.2 Approach assessment 72 4.6 Criti cism to success factor research 73 4.7 Interim conclusion on the fundamentals of success factor research 75 5 Success factor res earch on social acceptance in transmission line planning – a combination of research streams 77 5.1 State of research 77 5.1.1 Social acceptance in electricity transmission line planning (A) 77 5.1.2 Success factor research on social acceptance (B) 83 5.1.3 Success factor research in transmission line planning (C) 89 5.2 Value add and classification of this work 89 5.3 Research design 90 5.3.1 Identification of potential success factors through a direct, qualitative - explorative approach 92 5.3.1.1 Overview of methodologies 92 5.3.1.2 Survey 93 5.3.2 Quantitative - confirmatory approach to validate potential success factors 95 5.3.2.1 Overview of statistical methodologies 95 5.3.2.2 Structural equation modeling (SEM) 96 5.3.2.2.1 Path analysis 97 5.3.2.2.2 Structure of SEM 99 5.3.2.2.3 Methods for SEM estimation 102 5.3.2.2.4 PLS algorithm 106 6 Identification of reasons for public opposition and derivation of potential success factors 112 6.1 Conducted interviews 112 6.1.1 Selection of interviewees 112 6.1.2 Preparation, conduction and documentation of interviews 115 6.2 Reasons for public opposition 117 6.2.1 Health and safety issues 118 6.2.1.1 Electric and magnetic fields (EMF) 118 6.2.1.2 Falling ice 124 6.2.1.3 Toppled pylons and ruptured conductors 125 6.2.1.4 Flashover 125 6.2.2 Reduced quality of living 126 6.2.2.1 Visual impact 126 6.2.2.2 Noise 128 6.2.3 Economic unfairness 130 6.2.3.1 Devaluation of property and insufficient compensation 130 6.2.3.2 Expropriation 131 6.2.3.3 Negative impact on tourism 132 6.2.3.4 Lack of direct benefits and distributional unfairness 132 6.2.3.5 Agricultural disadvantages 133 6.2.4 Lack of transparency and communication 135 6.2.4.1 Insufficient justification of line need 135 6.2.4.2 Insufficient, inaccurate and late information 137 6.2.4.3 Intransparent decision making 138 6.2.4.4 Inappropriate appearance 138 6.2.4.5 Expert dilemma 139 6.2.5 Lack of public participation 140 6.2.5.1 Lack of involvement 140 6.2.5.2 One - way communication 141 6.2.5.3 Lack of bindingness 141 6.2.5.4 Inflexibility 142 6.2.6 Environmental impact 142 6.2.6.1 Flora 143 6.2.6.2 Fauna 145 6.2.7 Distrust 146 6.3 Potential success factors to reduce public opposition 147 6.3.1 Communication 149 6.3.1.1 Communication strategy 149 6.3.1.2 Early communication 150 6.3.1.3 Line justification 150 6.3.1.4 Direct personal conversation 151 6.3.1.5 Appropriate communication mix 153 6.3.1.6 Comprehensibility 156 6.3.1.7 Sufficient and honest information 157 6.3.1.8 Stakeholder education 158 6.3.1.9 Post - communication 159 6.3.2 Participation 160 6.3.2.1 Pre - polls 160 6.3.2.2 Participation possibilities 161 6.3.2.3 Participation information 164 6.3.2.4 Macro - planning involvement 165 6.3.2.5 Pre - application involvement 166 6.3.2.6 Neutral moderation/mediation 166 6.3.2.7 Joint fact finding 169 6.3.2.8 Flexibility, openness and respect 170 6.3.2.9 Commitment and bindingness 171 6.3.2.10 Transparent decision making 172 6.3.3 Economic benefits 173 6.3.3.1 Local benefits 173 6.3.3.2 Individual compensations 174 6.3.3.3 Muni cipality compensations 176 6.3.3.4 Socio - economic benefits 177 6.3.3.5 Excursus: Social cost - benefit analysis of a new HVDC line between France and Spain 177 6.3.4 Organizational readiness 182 6.3.4.1 Stakeholder analysis and management 182 6.3.4.2 Qualification and development 184 6.3.4.3 Sufficient resources 186 6.3.4.4 Internal coordination 187 6.3.4.5 Cultural change 187 6.3.4.6 Top - management support 188 6.3.4.7 Best practice exchange 188 6.3.5 Stakeholder liaison 189 6.3.5.1 Stakeholder cooperation 189 6.3.5.2 Supporters / Multiplicators 190 6.3.5.3 Local empowerment 191 6.3.6 Technical planning 191 6.3.6.1 Line avoidance options 191 6.3.6.2 Route alternatives 194 6.3.6.3 Transmission technology options 194 6.3.6.4 Piloting of innovations 198 6.3.6.5 Excursus: Exemplary transmission line innovations 198 6.3.6.6 Avoidance of sensitive areas 206 6.3.6.7 Bundling of infrastructure 206 6.3.6.8 Line deconstruction 207 6.3.6.9 Regulatory overachievement 208 7. Development of research model 209 7.1 Procedure 209 7.2 Development of hypotheses on causal relationships 209 7.2.1 Stakeholder liaison 209 7.2.2 Participation 210 7.2.3 Communication 210 7.2.4 Organizational readiness 211 7.2.5 Economic benefits 212 7.2.6 Technical planning 212 7.2.7 Trust 213 7.2.8 Summary of hypotheses 213 7.3 Development of path diagram and model specification 214 7.3.1 Structural model 214 7.3.2 Measurement model 215 7.3.2.1 Formative measurements 215 7.3.2.2 Reflective measurements 2 7.4 Identifiability of model structure 217 8 Empirical validation of potential success factors 219 8.1 Data acqu isition 219 8.1.1 Concept of using questionnaires for data acquisition 219 8.1.2 Target group and sample size 220 8.1.3 Questionnaire design 222 8.1.3.1 Form and structure 222 8.1.3.2 Operatio nalization 224 8.1.3.2.1 Operationalization of potential success factors 224 8.1.3.2.2 Operationalization of construct TRUST 225 8.1.3.2.3 Operationalization of construct REDUCED PUBLIC OPPOSITION 226 8.1.3.2.4 Operationalization of control variables 226 8.1.3.3 Bias 227 8.1.3.3.1 Common method bias 227 8.1.3.3.2 Key i nformation bias 229 8.1.3.3.3 Hypothetical bias 229 8.1.4 Pretest 230 8.1.5 Questionnaire return and data preparation 231 8.2 Model estimation 236 8.2.1 Software selection for modeling 236 8.2.2 Estimation results 237 8.3 Model evaluation 239 8.3.1 Evaluat ion of reflective measurement models 240 8.3.1.1 Content validity 240 8.3.1.2 Indicator reliability 243 8.3.1.3 Construct validity 245 8.3.1.3.1 Convergent validity 245 8.3.1.3.1.1 Average var iance extracted (AVE) 245 8.3.1.3.1.2 Construct reliability 245 8.3.1.3.2 Discriminant validity 247 8.3.1.3.2.1 Fornell/Larcker criterion 247 8.3.1.3.2.2 Cross loadings 248 8.3.2 Evaluation of formative measurement models 250 8.3.2.1 Content validity 250 8.3.2.2 Indicator reliability / relevance 250 8.3.2.2.1 Indicator weights and significance 250 8.3.2.2.2 Multicollinearity 254 8.3.2.3 Construct validity 256 8.3.3 Evaluation of structural model 256 8.3.3.1 Multicollinearity 256 8.3.3.2 Explanatory power 257 8.3.3.3 Predictive relevance 259 8.3.4 Evaluation of total model 260 8.4 Verification of hypotheses and discussion of results 260 8.5 Success factors for reducing public opposition in transmission line planning: Recommendations for TSO management 264 8.5.1 Measures to create stakeholder trust 266 8.5.1.1 Sufficient stakeholder participation 266 8.5.1.2 Proper stakeholder communication 267 8.5.1.3 TSO’s organizational readiness for stakeholder management 267 8.5.1.4 Creating liaison with stakeholders 268 8.5.2 Important aspects in technical planning 268 8.5.3 Consolidated overview 269 9 Concluding remarks 270 9.1 Summary of results 270 9.2 Contribution, limitations, and directions for further research 272 10 Appendix 276

Physical asset management in the electric power sector encompasses the scheduling of the maintenance and replacement of grid components, as well as decisions about investments in new components. Data plays a crucial role in these decisions. The importance of data is increasing with the transformation of the power system and its evolution toward smart grids. This thesis deals with questions related to data management as a way to improve the performance of asset management decisions. Data management is defined as the collection, processing, and storage of data. Here, the focus is on the collection and processing of data. First, the influence of data on the decisions related to assets is explored. In particular, the impacts of data quality on the replacement time of a generic component (a line for example) are quantified using a scenario approach, and failure modeling. In fact, decisions based on data of poor quality are most likely not optimal. In this case, faulty data related to the age of the component leads to a non-optimal scheduling of component replacement. The corresponding costs are calculated for different levels of data quality. A framework has been developed to evaluate the amount of investment needed into data quality improvement, and its profitability. Then, the ways to use available data efficiently are investigated. Especially, the possibility to use machine learning algorithms on real-world datasets is examined. New approaches are developed to use only available data for component ranking and failure prediction, which are two important concepts often used to prioritize components and schedule maintenance and replacement. A large part of the scientific literature assumes that the future of smart grids lies in big data collection, and in developing algorithms to process huge amounts of data. On the contrary, this work contributes to show how automatization and machine learning techniques can actually be used to reduce the need to collect huge amount of data, by using the available data more efficiently. One major challenge is the trade-offs needed between precision of modeling results, and costs of data management. / <p>QC 20210330</p>