<p>An increasing concern over environmental impacts of fossil fuels and sustainability of energy resources is leading to significant changes in the electric power systems. Decentralized power generation, in particular, is emerging as one of the most effective and promising tools in addressing these concerns.</p> <p>Microgrids are small-scale electricity grids with elements of load, generation and storage. Microgrids have emerged as an essential building block of a future smart grid, and an enabling technology for distributed power generation and control. This thesis presents an optimization-based approach for the design and control of energy management systems (EMS) for electric microgrids. A linear programming formulation of power/energy management is proposed to minimize energy cost for a microgrid with energy storage and renewable energy generation, by taking advantage of time-of-use (TOU) pricing. The thesis also addresses the issue of sizing of the battery storage and solar power generation capacity by formulating and solving a mixed integer linear programming (MILP) problem. The aim of the optimization is to minimize the combined capital and electricity usage cost subject to applicable physical constraints. Several case scenarios are analyzed for grid-connected microgrids in residential, commercial and industrial settings, as well as a case of an islanded microgrid intended for a remote community.</p> <p>Finally, the thesis investigates circuit level control of a microgrid with EMS. A finite state machine based control logic is proposed that enables outage ride through and smooth transition between islanded and grid connected operation. Simulation results are provided to demonstrate the effectiveness of the proposed controller under various possible scenarios.</p> / Master of Applied Science (MASc)

The rapid escalation in plug-in electric vehicles (PEVs) and their uncoordinated charging patterns pose several challenges in distribution system operation. Some of the undesirable effects include overloading of transformers, rapid voltage fluctuations, and over/under voltages. While this compromises the consumer power quality, it also puts on extra stress on the local voltage control devices. These challenges demand a well-coordinated and power network-aware charging approach for PEVs in a community. This paper formulates a realtime electric vehicle charging scheduling problem as a mixed-integer linear program (MILP). The problem is to be solved by an aggregator that provides charging services in a residential community. The proposed formulation maximizes the profit of the aggregator, enhancing the utilization of available infrastructure. With prior knowledge of load demand and hourly electricity prices, the algorithm uses a moving time horizon optimization approach, allowing an unknown number of arriving vehicles. In this realistic setting, the proposed framework ensures that power system constraints are satisfied and guarantees the desired PEV charging level within the stipulated time. Numerical tests on an IEEE 13-node feeder system demonstrate the computational and performance superiority of the proposed MILP technique. / M.S. / There is an enhanced rate of global warming due to emissions and increased usage of fossil fuels in the transportation sector. As a feasible solution, electrification of transportation has become a necessary step towards an environment-friendly future. The escalation in plug-in electric vehicles (PEVs) has increased the impact on loading and voltage fluctuations in the distribution grid due to uncoordinated charging. This puts on extra stress on the grid system and compromises the system performance. As a measure to control the vehicle charging in a residential setup, a real-time optimal charging scheduling algorithm is developed which is implemented at the neighborhood level. To increase the charging performance with the limited available resources, an aggregator is introduced. The charging profit is maximized as the PEV charging problem is solved optimally by the aggregator. This facilitates the reduction in night-time grid congestion and maximization of number of PEVs getting charged with limited dependency on communication to avoid long delays in charging control. The proposed technique guarantees the complete charging of the selected PEVs in the stipulated time while considering the power grid operational constraints. It also reduces the impact of peak load demand by flattening the base load demand curve. To demonstrate the efficiency of the proposed mixed integer linear programming optimization algorithm, numerical tests for an IEEE 13 node feeder are performed. The results are discussed to give an outlook on the balance between system and user requirements by meeting the demand of the PEV users.

Energy consumption rates have been dramatically increasing on a global scale within the last few decades. A significant role in this increase is subjected by the recent high temperature levels especially at summer time which caused a rapid increase in the air conditioning demands. Such phenomena can be clearly observed in developing countries, especially those in hot climate regions, where people depend mainly on conventional air conditioning systems. These systems often show poor performance and thus negatively impact the environment which in turn contributes to global warming phenomena. In recent years, the demand for urban or district cooling technologies and networks has been increasing significantly as an alternative to conventional systems due to their higher efficiency and improved ecological impact. However, to obtain an efficient design for district cooling systems is a complex task that requires considering a wide range of cooling technologies, various network layout configuration possibilities, and several energy resources to be integrated. Thus, critical decisions have to be made regarding a variety of opportunities, options and technologies. The main objective of this thesis is to develop a tool to obtain preliminary design configurations and operation patterns for district cooling energy systems by performing roughly detailed optimizations and further, to introduce a decision-making approach to help decision makers in evaluating the economic aspects and environmental performance of urban cooling systems at an early design stage. Different aspects of the subject have been investigated in the literature by several researchers. A brief survey of the state of the art was carried out and revealed that mathematical programming models were the most common and successful technique for configuring and designing cooling systems for urban areas. As an outcome of the survey, multi objective optimization models were decided to be utilized to support the decision-making process. Hence, a multi objective optimization model has been developed to address the complicated issue of decision-making when designing a cooling system for an urban area or district. The model aims to optimize several elements of a cooling system such as: cooling network, cooling technologies, capacity and location of system equipment. In addition, various energy resources have been taken into consideration as well as different solar technologies such as: trough solar concentrators, vacuum solar collectors and PV panels. The model was developed based on the mixed integer linear programming method (MILP) and implemented using GAMS language. Two case studies were investigated using the developed model. The first case study consists of seven buildings representing a residential district while the second case study was a university campus district dominated by non-residential buildings. The study was carried out for several groups of scenarios investigating certain design parameters and operation conditions such as: Available area, production plant location, cold storage location constraints, piping prices, investment cost, constant and variable electricity tariffs, solar energy integration policy, waste heat availability, load shifting strategies, and the effect of outdoor temperature in hot regions on the district cooling system performance. The investigation consisted of three stages, with total annual cost and CO2 emissions being the first and second single objective optimization stages. The third stage was a multi objective optimization combining the earlier two single objectives. Later on, non-dominated solutions, i.e. Pareto solutions, were generated by obtaining several multi objective optimization scenarios based on the decision-makers’ preferences. Eventually, a decision-making approach was developed to help decision-makers in selecting a specific solution that best fits the designers’ or decision makers’ desires, based on the difference between the Utopia and Nadir values, i.e. total annual cost and CO2 emissions obtained at the single optimization stages. / Die Energieverbrauchsraten haben in den letzten Jahrzehnten auf globaler Ebene dramatisch zugenommen. Diese Erhöhung ist zu einem großen Teil in den jüngst hohen Temperaturniveaus, vor allem in der Sommerzeit, begründet, die einen starken Anstieg der Nachfrage nach Klimaanlagen verursachen. Solche Ereignisse sind deutlich in Entwicklungsländern zu beobachten, vor allem in heißen Klimaregionen, wo Menschen vor allem konventionelle Klimaanlagensysteme benutzen. Diese Systeme verfügen meist über eine ineffiziente Leistungsfähigkeit und wirken sich somit negativ auf die Umwelt aus, was wiederum zur globalen Erwärmung beiträgt. In den letzten Jahren ist die Nachfrage nach Stadt- oder Fernkältetechnologien und -Netzwerken als Alternative zu konventionellen Systemen aufgrund ihrer höheren Effizienz und besseren ökologischen Verträglichkeit satrk gestiegen. Ein effizientes Design für Fernkühlsysteme zu erhalten, ist allerdings eine komplexe Aufgabe, die die Integration einer breite Palette von Kühltechnologien, verschiedener Konfigurationsmöglichkeiten von Netzwerk-Layouts und unterschiedlicher Energiequellen erfordert. Hierfür ist das Treffen kritischer Entscheidungen hinsichtlich einer Vielzahl von Möglichkeiten, Optionen und Technologien unabdingbar. Das Hauptziel dieser Arbeit ist es, ein Werkzeug zu entwickeln, das vorläufige Design-Konfigurationen und Betriebsmuster für Fernkälteenergiesysteme liefert, indem aureichend detaillierte Optimierungen durchgeführt werden. Zudem soll auch ein Ansatz zur Entscheidungsfindung vorgestellt werden, der Entscheidungsträger in einem frühen Planungsstadium bei der Bewertung städtischer Kühlungssysteme hinsichtlich der wirtschaftlichen Aspekte und Umweltleistung unterstützen soll. Unterschiedliche Aspekte dieser Problemstellung wurden in der Literatur von verschiedenen Forschern untersucht. Eine kurze Analyse des derzeitigen Stands der Technik ergab, dass mathematische Programmiermodelle die am weitesten verbreitete und erfolgreichste Methode für die Konfiguration und Gestaltung von Kühlsystemen für städtische Gebiete sind. Ein weiteres Ergebnis der Analyse war die Festlegung von Mehrzieloptimierungs-Modelles für die Unterstützung des Entscheidungsprozesses. Darauf basierend wurde im Rahmen der vorliegenden Arbeit ein Mehrzieloptimierungs-Modell für die Lösung des komplexen Entscheidungsfindungsprozesses bei der Gestaltung eines Kühlsystems für ein Stadtgebiet oder einen Bezirk entwickelt. Das Modell zielt darauf ab, mehrere Elemente des Kühlsystems zu optimieren, wie beispielsweise Kühlnetzwerke, Kühltechnologien sowie Kapazität und Lage der Systemtechnik. Zusätzlich werden verschiedene Energiequellen, auch solare wie Solarkonzentratoren, Vakuum-Solarkollektoren und PV-Module, berücksichtigt. Das Modell wurde auf Basis der gemischt-ganzzahlig linearen Optimierung (MILP) entwickelt und in GAMS Sprache implementiert. Zwei Fallstudien wurden mit dem entwickelten Modell untersucht. Die erste Fallstudie besteht aus sieben Gebäuden, die ein Wohnviertel darstellen, während die zweite Fallstudie einen Universitätscampus dominiert von Nichtwohngebäuden repräsentiert. Die Untersuchung wurde für mehrere Gruppen von Szenarien durchgeführt, wobei bestimmte Designparameter und Betriebsbedingungen überprüft werden, wie zum Beispiel die zur Verfügung stehende Fläche, Lage der Kühlanlage, örtliche Restriktionen der Kältespeicherung, Rohrpreise, Investitionskosten, konstante und variable Stromtarife, Strategie zur Einbindung der Solarenergie, Verfügbarkeit von Abwärme, Strategien der Lastenverschiebung, und die Wirkung der Außentemperatur in heißen Regionen auf die Leistung des Kühlsystems. Die Untersuchung bestand aus drei Stufen, wobei die jährlichen Gesamtkosten und die CO2-Emissionen die erste und zweite Einzelzieloptimierungsstufe darstellen. Die dritte Stufe war ein Pareto-Optimierung, die die beiden ersten Ziele kombiniert. Im Anschluss wurden nicht-dominante Lösungen, also Pareto-Lösungen, erzeugt, indem mehrere Pareto-Optimierungs-Szenarien basierend auf den Präferenzen der Entscheidungsträger abgebildet wurden. Schließlich wurde ein Ansatz zur Entscheidungsfindung entwickelt, um Entscheidungsträger bei der Auswahl einer bestimmten Lösung zu unterstützen, die am besten den Präferenzen des Planers oder des Entscheidungsträgers enstpricht, basierend auf der Differenz der Utopia und Nadir Werte, d.h. der jährlichen Gesamtkosten und CO2-Emissionen, die Ergebnis der einzelnen Optimierungsstufen sind.

The content of greenhouse gases in the atmosphere is increasing resulting in climate change and efforts to stop the negative trend need to be intensified. The energy use in the Swedish residential and service sector constitutes 40 % of the total energy use of 378 TWh in the country. Nationally there is a target to reduce the energy use per heated area with 20 % to 2020 and 50 % to 2050. Energy renovation of buildings from the Million Programme is foreseen to be able to contribute to achieving the targets owing to the large building stock and energy efficiency potential. In the master thesis cost optimal energy renovation strategies are investigated for two multi-family buildings in Linkoping built during the Million Programme, one with an unheated attic and one with a heated attic. The thesis is carried out by using life-cycle cost optimisation (LCC-optimisation) by utilising the software OPERA-MILP, developed at Linkoping University. The aim of the thesis is to obtain the energy renovation strategy that is optimal from an LCC-perspective and to investigate the energy reduction and LCC. Optimal energy renovation strategies are also investigated for energy renovation to levels of the Energy Classes of the National Board of Housing, Building and Planning in Sweden and the stricter limits for nearly zero-energy buildings (NZEB) that will likely come into force in 2021. Greenhouse gas emissions and primary energy use are also investigated for the different cases with the purpose of putting energy renovation in relation to climate impact. Local environmental factors are used for district heating while electricity is assigned values based on the Nordic electricity mix and Nordic marginal electricity respectively. The current LCC and annual energy use is 2 945 kSEK and 133 MWh for the building with an unheated attic and 3 511 kSEK and 162 MWh for the building with a heated attic. The result shows that LCC can be reduced by approximately 70 kSEK and 90 kSEK respectively. The optimal solution constitutes of a window change from windows with U=3,0 W/m2°C to windows with U=1,5 W/m2°C and results in a reduction of the energy use by 13 % and 15 % respectively. LCC increases with 240 kSEK for the building with unheated attic and decreases with 18 kSEK for the other building when Energy Class D is reached. Energy Class C is attained through an increase in LCC by 300 – 590 kSEK and Energy Class B through an increase by 1610 – 1800 kSEK. It is not possible to reach Energy Class A or the future requirements for NZEB (55 kWh/m2Aheated) with the energy renovation measures that are implemented in OPERA-MILP. The largest energy reduction that can be attained is approximately 60 %. The most cost optimal insulation measure is additional insulation of the attic floor/pitched roof followed by additional insulation of the ground concrete slab. It was shown to be more cost efficient to change to windows with U=1,5 W/m2°C in combination with additional insulation compared to changing to windows with better energy performance. For greater energy savings additional insulation on the inside of the external wall is applied, while insulation on the outside of the external wall is never cost optimal. To reach Energy Class B installation of HRV is required which gives a large increase in cost. Less extensive energy renovation is needed to reach the energy classes for the building with heated attic compared to the building with unheated attic. The annual use of primary energy in the reference case is 22 MWh for the building with an unheated attic and 26 MWh for the building with a heated attic. The emissions of greenhouse gases are 18 tonnes CO2e and 22 tonnes CO2e per year respectively when the emission factor of the Nordic electricity mix is applied and 20 tonnes CO2e and 25 tonnes CO2e respectively when the Nordic marginal electricity is applied. The yearly primary energy use can be reduced with up to 7 MWh through energy renovation. When the energy renovation leads to an increase in electricity use the primary energy can however increase with up to 12 MWh. The yearly greenhouse gas emissions can be decreased with up to 14 tonnes CO2e. When Nordic marginal electricity is applied to estimate the emissions of greenhouse gases for an energy renovation strategy that leads to an increase in electricity use the result is less beneficial from a climate perspective compared to when Nordic electricity mix is applied.

Le milieu hospitalier est un monde à la fois sensible et complexe, sensible parce que la vie humaine est en jeu et complexe parce que les équipements médicaux augmentent en nombre et en complexité technique. Ainsi, afin de préserver le bon état de fonctionnement de ces équipements et à un niveau élevé de disponibilité, leur entretien est devenu l'une des préoccupations majeures des responsables de l’hôpital. L’objectif de cette thèse est de proposer, aux responsables de maintenance biomédicale dans les établissements de soins, des outils d’aide à la décision qui permettent une meilleure maitrise des coûts. Ceci en assurant la sécurité des patients et des utilisateurs et en maintenant des performances optimales de l’ensemble des équipements médicaux. Tout d’abord, une heuristique a été proposée pour le choix de l’internalisation ou de l’externalisation de la maintenance et pour la sélection du contrat adéquat. La sélection du contrat est basée sur un ensemble de critères tout en considérant la contrainte du budget disponible. Ensuite, afin d’améliorer la procédure proposée, nous avons proposé des outils d’aide à la décision multicritère pour le choix adéquat d’une stratégie de maintenance. Pour l’étude de la criticité des équipements médicaux et le choix de la maintenance, sept critères ont été étudiés en proposant un couplage de l’approche AHP « Analytical Hierarchy Process » à la technique TOPSIS « Technique for Order Performance by Similarity to Ideal Solution ». Comme les experts du service de maintenance présentaient une certaine incertitude dans leurs jugements, nous avons intégré l’évaluation linguistique floue dans l’étude de la criticité des équipements et dans la sélection de la stratégie de maintenance (Fuzzy AHP couplée avec Fuzzy TOPSIS). Un modèle mathématique MILP a été développé pour la définition des limites de la criticité afin de caractériser les trois stratégies de maintenance. Le bon choix de ces limites permet d’optimiser le coût de la maintenance en respectant le budget disponible. Enfin, un deuxième modèle mathématique MILP a été développé en se basant sur l’heuristique proposée. Ce modèle permet de sélectionner pour chaque équipement, la stratégie de maintenance, internaliser ou externaliser la maintenance et le type du contrat tout en considérant le budget disponible et la charge/capacité du service maintenance / The hospital is a world that is both sensitive and complex, sensitive because the human life is involved and complex because medical facilities are growing in number and in technical complexity. Then, the problem of the medical equipment maintenance in order to keep them in safe, reliable and with high level of availability has become a major preoccupation of the hospital. The objective of this thesis is to provide tools to help the biomedical maintenance service of the hospital to make decisions that allow a better control of costs, while ensuring patient and user safety and maintaining optimal performance of medical equipment. First, a heuristic has been proposed for the choice of internalization or outsourcing maintenance and for the selection of the appropriate contract. The selection of the contract is based on a set of criteria while considering the available budget constraint. Then, to improve the proposed procedure, we proposed multi-criteria decision-making tools to select the appropriate maintenance strategies. Seven criteria have been designed to study the criticality of medical equipment and the choice of maintenance by providing a coupling of the AHP approach "Analytical Hierarchy Process" with TOPSIS technique "Technique for Order Performance by Similarity to Ideal Solution." As the expert judgments of the maintenance department presented some uncertainty, we integrated the fuzzy language assessment of the criticality of the equipment and the selection of the maintenance strategy (Fuzzy AHP coupled with Fuzzy TOPSIS). A mixed integer linear programming model (MILP) was developed to define thresholds of criticality to characterize the three maintenance strategies. According to these thresholds, maintenance cost can be optimized within the available budget. Finally, a second mixed integer linear programming model (MILP) was developed based on the proposed heuristic. This model allows selecting for each equipment, the maintenance strategy, the internalization or the outsourcing of the maintenance and the type of contract while considering the available budget and the workload / capacity of the maintenance department

Energy consumption rates have been dramatically increasing on a global scale within the last few decades. A significant role in this increase is subjected by the recent high temperature levels especially at summer time which caused a rapid increase in the air conditioning demands. Such phenomena can be clearly observed in developing countries, especially those in hot climate regions, where people depend mainly on conventional air conditioning systems. These systems often show poor performance and thus negatively impact the environment which in turn contributes to global warming phenomena. In recent years, the demand for urban or district cooling technologies and networks has been increasing significantly as an alternative to conventional systems due to their higher efficiency and improved ecological impact. However, to obtain an efficient design for district cooling systems is a complex task that requires considering a wide range of cooling technologies, various network layout configuration possibilities, and several energy resources to be integrated. Thus, critical decisions have to be made regarding a variety of opportunities, options and technologies. The main objective of this thesis is to develop a tool to obtain preliminary design configurations and operation patterns for district cooling energy systems by performing roughly detailed optimizations and further, to introduce a decision-making approach to help decision makers in evaluating the economic aspects and environmental performance of urban cooling systems at an early design stage. Different aspects of the subject have been investigated in the literature by several researchers. A brief survey of the state of the art was carried out and revealed that mathematical programming models were the most common and successful technique for configuring and designing cooling systems for urban areas. As an outcome of the survey, multi objective optimization models were decided to be utilized to support the decision-making process. Hence, a multi objective optimization model has been developed to address the complicated issue of decision-making when designing a cooling system for an urban area or district. The model aims to optimize several elements of a cooling system such as: cooling network, cooling technologies, capacity and location of system equipment. In addition, various energy resources have been taken into consideration as well as different solar technologies such as: trough solar concentrators, vacuum solar collectors and PV panels. The model was developed based on the mixed integer linear programming method (MILP) and implemented using GAMS language. Two case studies were investigated using the developed model. The first case study consists of seven buildings representing a residential district while the second case study was a university campus district dominated by non-residential buildings. The study was carried out for several groups of scenarios investigating certain design parameters and operation conditions such as: Available area, production plant location, cold storage location constraints, piping prices, investment cost, constant and variable electricity tariffs, solar energy integration policy, waste heat availability, load shifting strategies, and the effect of outdoor temperature in hot regions on the district cooling system performance. The investigation consisted of three stages, with total annual cost and CO2 emissions being the first and second single objective optimization stages. The third stage was a multi objective optimization combining the earlier two single objectives. Later on, non-dominated solutions, i.e. Pareto solutions, were generated by obtaining several multi objective optimization scenarios based on the decision-makers’ preferences. Eventually, a decision-making approach was developed to help decision-makers in selecting a specific solution that best fits the designers’ or decision makers’ desires, based on the difference between the Utopia and Nadir values, i.e. total annual cost and CO2 emissions obtained at the single optimization stages. / Die Energieverbrauchsraten haben in den letzten Jahrzehnten auf globaler Ebene dramatisch zugenommen. Diese Erhöhung ist zu einem großen Teil in den jüngst hohen Temperaturniveaus, vor allem in der Sommerzeit, begründet, die einen starken Anstieg der Nachfrage nach Klimaanlagen verursachen. Solche Ereignisse sind deutlich in Entwicklungsländern zu beobachten, vor allem in heißen Klimaregionen, wo Menschen vor allem konventionelle Klimaanlagensysteme benutzen. Diese Systeme verfügen meist über eine ineffiziente Leistungsfähigkeit und wirken sich somit negativ auf die Umwelt aus, was wiederum zur globalen Erwärmung beiträgt. In den letzten Jahren ist die Nachfrage nach Stadt- oder Fernkältetechnologien und -Netzwerken als Alternative zu konventionellen Systemen aufgrund ihrer höheren Effizienz und besseren ökologischen Verträglichkeit satrk gestiegen. Ein effizientes Design für Fernkühlsysteme zu erhalten, ist allerdings eine komplexe Aufgabe, die die Integration einer breite Palette von Kühltechnologien, verschiedener Konfigurationsmöglichkeiten von Netzwerk-Layouts und unterschiedlicher Energiequellen erfordert. Hierfür ist das Treffen kritischer Entscheidungen hinsichtlich einer Vielzahl von Möglichkeiten, Optionen und Technologien unabdingbar. Das Hauptziel dieser Arbeit ist es, ein Werkzeug zu entwickeln, das vorläufige Design-Konfigurationen und Betriebsmuster für Fernkälteenergiesysteme liefert, indem aureichend detaillierte Optimierungen durchgeführt werden. Zudem soll auch ein Ansatz zur Entscheidungsfindung vorgestellt werden, der Entscheidungsträger in einem frühen Planungsstadium bei der Bewertung städtischer Kühlungssysteme hinsichtlich der wirtschaftlichen Aspekte und Umweltleistung unterstützen soll. Unterschiedliche Aspekte dieser Problemstellung wurden in der Literatur von verschiedenen Forschern untersucht. Eine kurze Analyse des derzeitigen Stands der Technik ergab, dass mathematische Programmiermodelle die am weitesten verbreitete und erfolgreichste Methode für die Konfiguration und Gestaltung von Kühlsystemen für städtische Gebiete sind. Ein weiteres Ergebnis der Analyse war die Festlegung von Mehrzieloptimierungs-Modelles für die Unterstützung des Entscheidungsprozesses. Darauf basierend wurde im Rahmen der vorliegenden Arbeit ein Mehrzieloptimierungs-Modell für die Lösung des komplexen Entscheidungsfindungsprozesses bei der Gestaltung eines Kühlsystems für ein Stadtgebiet oder einen Bezirk entwickelt. Das Modell zielt darauf ab, mehrere Elemente des Kühlsystems zu optimieren, wie beispielsweise Kühlnetzwerke, Kühltechnologien sowie Kapazität und Lage der Systemtechnik. Zusätzlich werden verschiedene Energiequellen, auch solare wie Solarkonzentratoren, Vakuum-Solarkollektoren und PV-Module, berücksichtigt. Das Modell wurde auf Basis der gemischt-ganzzahlig linearen Optimierung (MILP) entwickelt und in GAMS Sprache implementiert. Zwei Fallstudien wurden mit dem entwickelten Modell untersucht. Die erste Fallstudie besteht aus sieben Gebäuden, die ein Wohnviertel darstellen, während die zweite Fallstudie einen Universitätscampus dominiert von Nichtwohngebäuden repräsentiert. Die Untersuchung wurde für mehrere Gruppen von Szenarien durchgeführt, wobei bestimmte Designparameter und Betriebsbedingungen überprüft werden, wie zum Beispiel die zur Verfügung stehende Fläche, Lage der Kühlanlage, örtliche Restriktionen der Kältespeicherung, Rohrpreise, Investitionskosten, konstante und variable Stromtarife, Strategie zur Einbindung der Solarenergie, Verfügbarkeit von Abwärme, Strategien der Lastenverschiebung, und die Wirkung der Außentemperatur in heißen Regionen auf die Leistung des Kühlsystems. Die Untersuchung bestand aus drei Stufen, wobei die jährlichen Gesamtkosten und die CO2-Emissionen die erste und zweite Einzelzieloptimierungsstufe darstellen. Die dritte Stufe war ein Pareto-Optimierung, die die beiden ersten Ziele kombiniert. Im Anschluss wurden nicht-dominante Lösungen, also Pareto-Lösungen, erzeugt, indem mehrere Pareto-Optimierungs-Szenarien basierend auf den Präferenzen der Entscheidungsträger abgebildet wurden. Schließlich wurde ein Ansatz zur Entscheidungsfindung entwickelt, um Entscheidungsträger bei der Auswahl einer bestimmten Lösung zu unterstützen, die am besten den Präferenzen des Planers oder des Entscheidungsträgers enstpricht, basierend auf der Differenz der Utopia und Nadir Werte, d.h. der jährlichen Gesamtkosten und CO2-Emissionen, die Ergebnis der einzelnen Optimierungsstufen sind.

In face of climate change, Europe and other global actors are in the process of transitioning to carbon-neutral economies, aiming to phase out of fossil fuels and power industries with renewable energies. Hydrogen is going to play a crucial role in the transition, replacing fossil fuels in hard-to-decarbonize industries and acting as energy carrier and energy storage for renewable electricity. However, the hydrogen production method with the lowest carbon intensity, water electrolysis in combination with renewable electricity, is often not cost competitive to other production methods. Even though policies and initiatives are providing subsidies to scale up low-carbon hydrogen production, companies hesitate to invest due to the complexity of hydrogen production systems and the uncertainties of cost competitiveness. This research aims to develop a tool for optimizing the capacity of a water electrolysis system to produce low-carbon hydrogen and to lay the groundwork for optimizing the operation of electrolysis hydrogen production plants. The objective is to find the optimal plant capacity to achieve the lowest cost of hydrogen production for a defined hydrogen demand and energy supply. The scope is limited to the electrolysis system as optimizing asset which is modeled with technology-specific costs and characteristics, gained from manufacturer interviews and internal company data. This includes the often neglected characteristics of load-dependent efficiency and degradation effects. Further, the tool is enabled to buy and sell electricity on the spot market according to predicted prices in order to minimize the electricity costs. The developed tool is tested, benchmarked and applied to two different industry-based test scenarios in Germany and Portugal. The test scenario in Germany describes a mid-scale hydrogen production case for a transport application with a demand increase over 10 years (80 to 1,800 tons per year) and regional renewable energy supply via power purchase agreements. The lowest costs of hydrogen production for this scenario can be reached with an alkaline electrolysis system of a capacity of 16 MWel considering only renewable energy sources, achieving a LCOH of 4.75 €/kg of green hydrogen. The second test scenario describes a large-scale production case in Portugal for application in the refinery industry. The yearly hydrogen demand increases from 5,000 tons up to 17,100 tons within three years and is assumed to stay constant for the residual years. The electricity for the electrolysis process is secured through large solar PV and offshore wind power purchase agreements. Utilizing the alkaline electrolysis technology with a capacity of 128 MWel, a LCOH of 3.31 €/kg of green hydrogen can be achieved at the output point of the plant. The study concludes that the optimal solution and the achievable hydrogen production costs are highly dependent on the hydrogen demand (quantity and profile), the energy supply (quantity, profile, costs), and the chosen technology (efficiency, degradation, costs) and need to be evaluated under the case-specific prerequisites. The thesis further highlights the significant impact of the electrolysis system efficiency and capital expenditures on the capacity decision and achievable hydrogen production costs. / Mot bakgrund av klimatförändringarna håller Europa och andra globala aktörer på att ställa om till koldioxidneutrala ekonomier, med målet att fasa ut fossila bränslen och driva industrier med förnybara energikällor. Vätgas kommer att spela en avgörande roll i omställningen genom att ersätta fossila bränslen i industrier som är svåra att koldioxidneutralisera och fungera som energibärare och energilagring för förnybar el. Den metod för vätgasproduktion som har lägst koldioxidintensitet, vattenelektrolys i kombination med förnybar el, är dock ofta inte kostnadsmässigt konkurrenskraftig i förhållande till andra produktionsmetoder. Även om politik och initiativ tillhandahåller subventioner för att skala upp koldioxidsnål vätgasproduktion, tvekar företagen på grund av komplexiteten i vätgasproduktionssystemen och osäkerheten kring konkurrenskraften. Denna forskning syftar till att utveckla ett verktyg för att optimera kapaciteten hos ett vattenelektrolyssystem för att producera grön vätgas och att lägga grunden för att optimera driften av elektrolysanläggningar för vätgasproduktion. Målet är att hitta den optimala anläggningskapaciteten för att uppnå den lägsta kostnaden för vätgasproduktion för en definierad vätgasefterfrågan och definierad energiförsörjning. Omfattningen är begränsad till elektrolyssystemet som en optimerande tillgång som modelleras med teknikspecifika kostnader och egenskaper, hämtade från tillverkar-intervjuer och från företags interna marknadsdata. Detta inkluderar de ofta försummade egenskaperna hos lastberoende effektivitet och degraderingseffekter. Vidare kan verktyget köpa och sälja el på spotmarknaden enligt förutspådda priser för att minimera elkostnaderna. Det utvecklade verktyget testas, jämförs och tillämpas på två olika industribaserade testscenarier i Tyskland och Portugal. Testscenariot i Tyskland beskriver en medelstor vätgasproduktion för en transporttillämpning där efterfrågan ökar över 10 år (80 till 1 800 ton per år) och regional förnybar energiförsörjning via energiköpsavtal (power purchase agreements). De lägsta kostnaderna för vätgasproduktion för detta scenario kan uppnås med ett alkaliskt elektrolyssystem med en kapacitet på 16 MWel som endast använder förnyelsebara energikällor och uppnår en LCOH på 4,75 €/kg grön vätgas. Det andra testscenariot beskriver en storskalig vätgasproduktion i Portugal för tillämpning inom raffinaderi-industrin. Det årliga vätgasbehovet ökas från 5 000 ton till 17 100 ton inom tre år och antogs förbli konstant under de återstående åren. El för elektrolysprocessen säkras genom stora energiköpsavtal (power purchase agreements) för solceller och havsbaserad vindkraft. Genom att använda alkalisk elektrolysteknik med en kapacitet på 128 MWel kan en LCOH på 3,31 €/kg grön vätgas uppnås vid anläggningens utgångspunkt. Studien visar att den optimala lösningen och de uppnåbara vätgasproduktionskostnaderna är starkt beroende av vätgasbehovet (mängd och profil), energiförsörjningen (mängd, profil, kostnader) och den valda tekniken (effektivitet, nedbrytning, kostnader) och måste utvärderas utifrån de fallspecifika förutsättningarna. Avhandlingen belyser vidare den betydande inverkan som elektrolyssystemets effektivitet och kapitalutgifter har på kapacitetsbeslutet och de uppnåeliga kostnaderna för vätgasproduktion.

Combined cycle gas turbine power plants (CCGT), as part of the electricity generation fleet, are required to improve their flexibility to help balance the power system under new scenarios with high shares of variable renewable sources. Among the different possibilities to enhance the power plant performance, an inlet air conditioning unit offers the benefit of power augmentation and “minimum environmental load” reduction by controlling the gas turbine intake temperature using cold thermal energy storage and a heat pump. In this thesis, an evaluation of the conditioning unit impact over a power-oriented CCGT under a day-ahead optimized operation strategy is presented. To establish the hourly dispatch of the power plant and the right operation mode of the inlet condition unit bringing the desired benefits, a mixed-integer linear optimization was formulated aiming to maximize the operational profit of the plant within a 24 hours horizon. To assess the impact of the proposed unit operating under this control strategy, annual simulations of a reference power plant were developed with and without the unit, allowing to a comparison of their performance by means of technical and economic indicators. Furthermore, a case study changing equipment sizes was performed in order to identify trends of the power plant performance related to such parameters; and lastly, a sensitivity analysis on market conditions to test the control strategy response was included. The results indicate that the inlet conditioning unit together with the dispatch optimization increase the power plant operational profit trough the gain of power variation over peak and off-peak periods. For the specific case study in northern Italy, it is shown that a power plant integrated with the conditioning unit is more profitable in terms of net present value based on the undertaken investment figures. Related to the technical performance, it also shows that the unit reduces by 1,34% the minimal environmental load when part-load operations are required and that it can increase the net power output by 0.17% annually. All in all, this study presents the benefits of a dispatch optimization strategy when couple to a novel solution to increase CCGT flexibility. / Elproducerande kombikraftverk (CCGT) förväntas förbättra sin flexibilitet för att kunna bidra till stabilisering av elnätet i framtida scenarier med ökande andel variabla, förnybara energikällor. Av de diverse metoder som finns att tillgå för att förbättra ett kraftverks prestanda, erbjuder en inluftsbehandlingsenhet både fördelar med kraftförbättring samt minskning av “minimun environmental load”; genom att med hjälp av kall termisk energilagring och en värmepump kontrollera gasens inluftstemperatur till gasturbinen. I den här uppsatsen undersöks hur en sådan inluftsbehandlingsenhet påverkar prestandan hos en kraftproduktionsfokuserad CCGT när en optimerad driftsstrategi introduceras. För att bestämma kraftverkets elproduktion vid varje timme och det korrekta driftläget för luftbehandlingsenheten (för att uppnå tidigare nämnda eftersökta fördelar) formulerades ett linjärt optimeringsproblem med syfte att maximera kraftverkets driftsförtjänst under ett 24-timmars tidsspann. För att bedöma den föreslagna inluftsbehandlingsenhetens inverkan under den optimerade driftsstrategin genomfördes simuleringar av ett referenskraftverk med och utan nämnda enhet, varpå en jämförelse med avseende på teknisk prestanda och ekonomi genomfördes. Vidare genomfördes en fallstudie där storlek på diverse utrustning varierades för att kunna identifiera trender i kraftverksprestanda baserat på dessa parametrar; slutligen genomfördes en känslighetsanalys rörande hur luftbehandlingsenheten och kontrollstrategin reagerar vid olika marknader.. Resultaten indikerar att en inluftsbehandlingsenhet tillsammans med en optimerad driftsstrategi ökar kraftverkets driftsvinning genom en ökad variation i kraftuttag över peak och off-peak timmar. För fallstudien i norra Italien fanns att ett kraftverk med integrerad luftbehandlingsenhet är mer lönsamt sett till nuvärdesanalys. Gällande teknisk prestanda visade resultaten att enheten minskar den minsta miljöbelastningen med 1,34 % när delbelastningsdrift fordras, och att det kan öka nettokraftuttag med 0,17% årligen. Sammanfattningsvis presenterar denna studie fördelarna med ett driftsoptimerat kraftverk kopplat till en ny lösning för att öka flexibilitet hos CCGT:er.

Autonomous coordination among multiple aerial vehicles to ensure a collision free airspace is a critical aspect of today’s airspace. With the rise of Unmanned Aerial Vehicles (UAVs) in the military and commercial sectors, obstacle avoidance in a densely populated airspace is necessary. This thesis investigates finding optimal or near-optimal trajectories in real-time for aircraft in complex airspaces containing a large number of obstacles. The solution for the trajectories is described as a linear program subject to mixed integer constraints, known as a Mixed Integer Linear Program (MILP). The resulting MILP problem is solved in real time using a well-known, public domain MILP solver. In addition, an Exhaustive, Breadth-First Search algorithm was implemented and is used for comparison in terms of execution time and flight path optimality. The Exhaustive Search algorithm is comprised of a multi-branch tree structure that iterates through all possible flight paths from source to target. The MILP solution was implemented in both PC based and embedded system environments. The embedded system environment was implemented on an onboard processor to develop trajectories for each individual aircraft in real time.

The thesis focuses on a mixed integer linear programming (MILP) formulation for a bi-level mathematical program with equilibrium constraints (MPEC) considering chance constraints. The particular MPEC problem relates to a power producer’s bidding strategy: maximize its total benefit through determining bidding price and bidding power output while considering an electricity pool’s operation and guessing the rival producer’s bidding price. The entire decision-making process can be described by a bi-level optimization problem. The contribution of our thesis is the MILP formulation of this problem considering the use of chance constrained mathematical program for handling the uncertainties. First, the lower-level poor operation problem is replaced by Karush-Kuhn-Tucker (KKT) optimality condition, which is further converted to an MILP formulation except a bilinear item in the objective function. Secondly, duality theory is implemented to replace the bilinear item by linear items. Finally, two types of chance constraints are examined and modeled in MILP formulation. With the MILP formulation, the entire MPEC problem considering randomness in price guessing can be solved using off-shelf MIP solvers, e.g., Gurobi. A few examples and a case study are given to illustrate the formulation and show the case study results.