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Electricity is an undisputed factor supporting human development, while further supporting social wellbeing and fostering economic growth of modern societies. Therefore, the electricity market provides a vivid policy-making arena for the EU regulators, where on-going structural reforms are promoted with the aim to encapsulate and accommodate sustainability aspects. Notably, the EU Member States have adopted the strategic roadmap “Europe 2020” toward reducing greenhouse gas emissions and energy consumption by 20 %, and generating 20 % of energy from renewable sources. However, our research highlights that strategies trying to balance sustainable electricity supply with demand often neglect the societal pricing acceptability of the electricity provided to consumers. Thus far, existing literature focuses on electricity pricing policies that fail to capture the dynamics that have to govern realistic pricing schema in the electricity sector. To that end, this study elaborates the system dynamics (SD) methodological approach to embrace the potential dominant factors within a sustainable electricity system. The proposed SD framework could assist public and private stakeholders in determining a rational electricity pricing policy within a sustainability context. Finally, policy-making interventions are discussed in order to provide managerial insights for the decision-makers.

Electricity is an undisputed factor supporting human development, while further supporting social wellbeing and fostering economic growth of modern societies. Therefore, the electricity market provides a vivid policy-making arena for the EU regulators, where on-going structural reforms are promoted with the aim to encapsulate and accommodate sustainability aspects. Notably, the EU Member States have adopted the strategic roadmap “Europe 2020” toward reducing greenhouse gas emissions and energy consumption by 20 %, and generating 20 % of energy from renewable sources. However, our research highlights that strategies trying to balance sustainable electricity supply with demand often neglect the societal pricing acceptability of the electricity provided to consumers. Thus far, existing literature focuses on electricity pricing policies that fail to capture the dynamics that have to govern realistic pricing schema in the electricity sector. To that end, this study elaborates the system dynamics (SD) methodological approach to embrace the potential dominant factors within a sustainable electricity system. The proposed SD framework could assist public and private stakeholders in determining a rational electricity pricing policy within a sustainability context. Finally, policy-making interventions are discussed in order to provide managerial insights for the decision-makers.

Since a superconductor has no resistance below a certain temperature and can therefore save a large amount of energy dissipated, it is a 'green' material by saving energy loss and hence reducing carbon emissions. Recently the massive manufacture of high-temperature superconducting (HTS) materials has enabled superconductivity to become a preferred candidate to help generation and transportation of cleaner energy. One of the most promising applications of superconductors is Superconducting Magnetic Energy Storage (SMES) systems, which are becoming the enabling engine for improving the capacity, efficiency, and reliability of the electric system. SMES systems store energy in the magnetic field created by the flow of direct current in a superconducting coil. SMES systems have many advantages compared to other energy storage systems: high cyclic efficiency, fast response time, deep discharge and recharge ability, and a good balance between power density and energy density. Based on these advantages, SMES systems will play an indispensable role in improving power qualities, integrating renewable energy sources and energizing transportation systems. This thesis describes an intensive study of superconducting pancake coils wound using second-generation(2G) HTS materials and their application in SMES systems. The specific contribution of this thesis includes an innovative design of the SMES system, an easily calculated, but theoretically advanced numerical model to analyse the system, extensive experiments to validate the design and model, and a complete demonstration experiment of the prototype SMES system. This thesis begins with literature review which includes the introduction of the background theory of superconductivity and development of SMES systems. Following the literature review is the theoretical work. A prototype SMES system design, which provides the maximum stored energy for a particular length of conductors, has been investigated. Furthermore, a new numerical model, which can predict all necessary operation parameters, including the critical current and AC losses of the system, is presented. This model has been extended to analyse superconducting coils in different situations as well. To validate the theoretical design and model, several superconducting coils, which are essential parts of the prototype SMES system, together with an experimental measurement set-up have been built. The coils have been energized to test their energy storage capability. The operation parameters including the critical current and AC losses have been measured. The results are consistent with the theoretical predictions. Finally the control system is developed and studied. A power electronics control circuit of the prototype SMES system has been designed and simulated. This control circuit can energize or discharge the SMES system dynamically and robustly. During a voltage sag compensation experiment, this SMES prototype monitored the power system and successfully compensated the voltage sag when required. By investigating the process of building a complete system from the initial design to the final experiment, the concept of a prototype SMES system using newly available 2G HTS tapes was validated. This prototype SMES system is the first step towards the implementation of future indsutrial SMES systems with bigger capacities, and the knowledge obtained through this research provides a comprehensive overview of the design of complete SMES systems. The full text of this thesis is not available due to ongoing discussions regarding publication

Since a superconductor has no resistance below a certain temperature and can therefore save a large amount of energy dissipated, it is a 'green' material by saving energy loss and hence reducing carbon emissions. Recently the massive manufacture of high-temperature superconducting (HTS) materials has enabled superconductivity to become a preferred candidate to help generation and transportation of cleaner energy. One of the most promising applications of superconductors is Superconducting Magnetic Energy Storage (SMES) systems, which are becoming the enabling engine for improving the capacity, efficiency, and reliability of the electric system. SMES systems store energy in the magnetic field created by the flow of direct current in a superconducting coil. SMES systems have many advantages compared to other energy storage systems: high cyclic efficiency, fast response time, deep discharge and recharge ability, and a good balance between power density and energy density. Based on these advantages, SMES systems will play an indispensable role in improving power qualities, integrating renewable energy sources and energizing transportation systems. This thesis describes an intensive study of superconducting pancake coils wound using second-generation(2G) HTS materials and their application in SMES systems. The specific contribution of this thesis includes an innovative design of the SMES system, an easily calculated, but theoretically advanced numerical model to analyse the system, extensive experiments to validate the design and model, and a complete demonstration experiment of the prototype SMES system. This thesis begins with literature review which includes the introduction of the background theory of superconductivity and development of SMES systems. Following the literature review is the theoretical work. A prototype SMES system design, which provides the maximum stored energy for a particular length of conductors, has been investigated. Furthermore, a new numerical model, which can predict all necessary operation parameters, including the critical current and AC losses of the system, is presented. This model has been extended to analyse superconducting coils in different situations as well. To validate the theoretical design and model, several superconducting coils, which are essential parts of the prototype SMES system, together with an experimental measurement set-up have been built. The coils have been energized to test their energy storage capability. The operation parameters including the critical current and AC losses have been measured. The results are consistent with the theoretical predictions. Finally the control system is developed and studied. A power electronics control circuit of the prototype SMES system has been designed and simulated. This control circuit can energize or discharge the SMES system dynamically and robustly. During a voltage sag compensation experiment, this SMES prototype monitored the power system and successfully compensated the voltage sag when required. By investigating the process of building a complete system from the initial design to the final experiment, the concept of a prototype SMES system using newly available 2G HTS tapes was validated. This prototype SMES system is the first step towards the implementation of future indsutrial SMES systems with bigger capacities, and the knowledge obtained through this research provides a comprehensive overview of the design of complete SMES systems. The full text of this thesis is not available due to ongoing discussions regarding publication

ABSTRACT Tire selection has an important impact on the operational costs of heavy-goods vehicles (HGVs). HGV tires are designed on a tradeoff between wear resistance, rolling resistance, and adhesion (skid resistance). High wear resistance tires (high mileage) are replaced less often but use more fuel during operation, and vice versa for low rolling resistance tires. Presently, finding the optimal tire to minimize replacement costs and fuel consumption (greenhouse gas emissions) is challenging due to the difficulty in predicting tire wear for a given operation, since its rate varies with different vehicle configurations (e.g., load, vehicle length, number of axles, type of axle, etc.) and road types (e.g., motorways/highways, minor roads, urban roads, etc.). This article presents a novel empirical tire-wear model that can be used to predict the wear for multi-axle vehicles based on route data and a vehicle model. The first part of the article presents the analytical and experimental development of the model. The second part presents the experimental validation of the model based on 10 months of in-service data totaling 37,000 km of operation. The model predicts tire tread depth within 8% (average error of 2%).

ABSTRACT Tire selection has an important impact on the operational costs of heavy-goods vehicles (HGVs). HGV tires are designed on a tradeoff between wear resistance, rolling resistance, and adhesion (skid resistance). High wear resistance tires (high mileage) are replaced less often but use more fuel during operation, and vice versa for low rolling resistance tires. Presently, finding the optimal tire to minimize replacement costs and fuel consumption (greenhouse gas emissions) is challenging due to the difficulty in predicting tire wear for a given operation, since its rate varies with different vehicle configurations (e.g., load, vehicle length, number of axles, type of axle, etc.) and road types (e.g., motorways/highways, minor roads, urban roads, etc.). This article presents a novel empirical tire-wear model that can be used to predict the wear for multi-axle vehicles based on route data and a vehicle model. The first part of the article presents the analytical and experimental development of the model. The second part presents the experimental validation of the model based on 10 months of in-service data totaling 37,000 km of operation. The model predicts tire tread depth within 8% (average error of 2%).