Concentrated Solar Power (CSP) constitutes one suitable solution for exploiting solar resources for power generation. In this context, parabolic dish systems concentrate the solar radiation onto a point focusing receiver for small-scale power production. Given the modularity feature of such system, the scale-up is a feasible option; however, they offer a suitable solution for small scale off-grid electrification of rural areas. These systems are usually used with Stirling engines, nevertheless the coupling with micro-gas turbines presents a number of advantages, related to the reliability of the system and the lower level of maintenance required. The OMSoP project, funded by the European Union, aims at the demonstration of a parabolic dish coupled with an air-driven Brayton cycle. By looking at the integrated system, a key-role is played by the solar receiver, whose function is the absorption of the concentrated solar radiation and its transfer to the heat transfer fluid. Volumetric solar receivers constitute a novel and promising solution for such applications; the use of a porous matrix for the solar radiation absorption allows reaching higher temperature within a compact volume, while reducing the heat transfer losses between the fluid and the absorption medium. The aim of the present work is to deliver a set of optimal design specifications for a volumetric solar receiver for the OMSoP project. The work is based on a Multi-Objective Optimization algorithm, with the objective of the enhancement of the receiver thermal efficiency and of the reduction of the pressure drop. The optimization routine is coupled with a detailed analysis of the component, based on a Computational Fluid Dynamics model and a Mechanical Stress Analysis. The boundary conditions are given by the OMSoP project, in terms of dish specifications and power cycle, whilst the solar radiation boundary is modelled by means of a Ray Tracing routine. The outcome of the analysis is the assessment of the impact on the receiver performance of some key design parameters, namely the porous material properties and the receiver geometrical dimensions. From the results, it is observed a general low pressure drop related to the nominal air mass flow, with several points respecting the materials limitations. One design point is chosen among the optimal points, which respects the OMSoP project requirements for the design objectives, i.e. a minimum value of efficiency of 70%, and pressure losses below 1%. The final receiver configuration performs with an efficiency value of 86%, with relative pressure drop of 0.5%, and it is based on a ceramic foam absorber made of silicon carbide, with porosity value of 0.94.  Moreover, the detailed analysis of one volumetric receiver configuration to be integrated in the OMSoP project shows promising results for experimental testing and for its actual integration in the system.

Sustainable engineering within product development is becoming increasingly important with the ever-growing amounts of resources used to sustain the human way of life in modern times. An effective way of helping to deal with this problem is to reduce the resources used in products and components across the world. This thesis explores the effectiveness of the topology optimization method in achieving significant material reductions whilst maintaining structural strength and integrity when designing an aircraft component. The part is an engine handling mounting bracket which will be optimized to be produced by additive manufacturing, and so restrictions imposed by traditional manufacturing methods are not considered, allowing for larger material reductions to be achieved. The original bracket part was provided by GE Electric, and the computer software Abaqus computer aided engineering with integrated TOSCA was used to solve the problem. Two trials were conducted, with the first being used to gain knowledge and understanding of the optimization features of the software. The basic requirements for the optimized design were that it should be able to withstand four given static load cases without undergoing plastic deformation, and these load cases were applied separately in trial 1 for simplicity. The second trial was conducted with a higher complexity, utilising multi-objective topology optimization which allowed the load cases to be weighted individually whilst being applied simultaneously during optimization. The resulting bracket part that was created with the help of the optimized topology from trial 2 reduced the volume of the original part by over 75%. This also left potential for further material reductions as the optimized part did not undergo plastic deformation when subject to any of the four load cases of the study. In conclusion, topology optimization seems to be extremely helpful when designing components that have clearly defined load cases, producing results that designers and engineers can have confidence in. The method does however have its flaws, such as difficulties in utilising the optimized topology directly to create a computer aided design part file. The post-processing process needed to achieve such a part is also time-consuming although it must be implemented to create a digital part that can be analysed and verified by FEA.

Municipal energy systems in Nordic environments face multiple challenges: the cold climate, large-scale industries, a high share of electric heating and long distances drive energy consumption. While actions on the demand side minimize energy use, decarbonization efforts in mining, industries, the heating and the transport sector can increase the consumption of electricity and biofuels. Continued growth of intermittent wind and solar power increases supply, but the planned phase out of Swedish nuclear power will pose challenges to the reliability of the electricity system in the Nordic countries. Bottlenecks in the transmission and distribution grids may restrict a potential growth of electricity use in urban areas, limit new intermittent supply, peak electricity import and export. Environmental concerns may limit growth of biomass use. Local authorities are committed in contributing to national goals on mitigating climate change, while considering their own objectives for economic development, increased energy self-sufficiency and affordable energy costs. Given these circumstances, this thesis investigates existing technical and economic potentials of renewable energy (RE) resources in the Nordic countries with a focus on the northern counties of Finland, Norway and Sweden. The research further aims to provide sets of optimal solutions for sustainable Nordic municipal energy systems, where the interaction between major energy sectors are studied, considering multiple objectives of minimizing annual energy system costs and reducing carbon emissions as well as analyzing impacts on peak electricity import and export. This research formulates an integrated municipal energy system as a multi-objective optimization problem (MOOP), which is solved by interfacing the energy system simulation tool EnergyPLAN with a multi-objective evolutionary algorithm (MOEA) implemented in Matlab. In a first step, the integration or coupling of electricity and heating sectors is studied, and in a second step, the study inquires the impacts of an increasingly decarbonized transport sector on the energy system. Sensitivity analysis on key economic parameters and on different grid emission factors is performed. Piteå (Norrbotten County, Sweden) is a typical Nordic municipality, which serves as a case study for this research. The research concludes that significant techno-economic potentials exist for the investigated resources. Optimization results show that CO2 emissions of a Nordic municipal energy system can be reduced by about 60% without a considerable increase in total energy system costs and that peak electricity import can be reduced by up to 38%. The outlook onto 2030 shows that the transport sector could be composed of high electrification shares and biofuels. Technology choices for optimal solutions are highly sensitive to electricity prices, discount rates and grid emission factors. The inquiries of this research provide important insights about carbon mitigation strategies for integrated energy sectors within a perspective on Nordic municipalities. Future work will refine the transport model, develop and apply a framework for multi-criteria decision analysis (MCDA) enabling local decision makers to determine a technically and economically sound pathway based on the optimal alternatives provided, and analyze the existing policy framework affecting energy planning of local authorities. / Kommunala energisystem i nordiska miljöer möter flera utmaningar: det kalla klimatet, storskaliga industrier, en stor andel elvärme och långa distanser driver energiförbrukningen. Medan åtgärder vidtas på efterfrågesidan för att minimera energianvändningen, kan utsläppsminskande åtgärder inom gruvdrift, industrier, uppvärmningen och transportsektorn öka förbrukningen av el och biobränslen. Fortsatt tillväxt av intermittent vind- och solkraft ökar elproduktion, men den planerade avvecklingen av svensk kärnkraft kommer att utmana tillförlitligheten i elsystemet i de nordiska länderna. Flaskhalsar i överförings- och distributionsnäten kan begränsa en potentiell tillväxt av elanvändningen i stadsområden, begränsa ny intermittent utbud, och påverka elutbyte mellan länderna. Miljöhänsyn kan begränsa ökad användning av biomassa. Lokala myndigheter är engagerade i att bidra till nationella klimatmål, samtidigt som de följer sina egna mål för ekonomisk utveckling, ökad självförsörjning av energi och överkomliga energikostnader. Mot bakgrund av dessa omständigheter undersöker denna avhandling befintliga tekniska och ekonomiska potentialer för förnybar energi i Norden med fokus på de nordliga länen i Finland, Norge och Sverige. Forskningen syftar vidare till att utveckla optimala lösningar för hållbara nordiska kommunala energisystem, där samspelet mellan stora energisektorer studeras, med tanke på att minimera årliga energisystemkostnader och samtidigt minska koldioxidutsläppen samt analysera påverkan på elimport till och export från kommunen. Denna forskning formulerar ett integrerad kommunalt energisystem som multimåloptimeringsproblem (multi-objective optimisation problem - MOOP), som löses genom att kombinera simuleringsverktyget EnergyPLAN med en evolutionär algoritm implementerad i Matlab. I ett första steg studeras kopplingen av el- och värmesektorerna, och i ett andra steg effekterna av en integrerad och alltmer förnybar transportsektor på energisystemet. Känslighetsanalys på viktiga ekonomiska parametrar och på olika utsläppsfaktorer utförs. Piteå (Norrbottens län, Sverige) är en typisk nordisk kommun som fungerar som en fallstudie för detta arbete. Forskningens slutsatser innebär att det finns betydande teknisk-ekonomiska potentialer för de undersökta förnybara resurserna. Optimeringsresultaten visar att koldioxidutsläppen från ett nordiskt kommunalt energisystem kan minskas med cirka 60% utan en avsevärd ökning av de totala energisystemkostnaderna och att den högsta elimporten kan minskas med upp till 38%. Resultat för år 2030 visar att transportsektorn kan ha en mycket hög elektrifieringsgrad och samtidigt används biobränslen i tunga fordon. Optimala lösningar är mycket känsliga för elpriser, räntor och utsläppsfaktorer. Detta arbete ger viktiga insikter om strategier för koldioxidminskning för integrerade energisektorer i ett perspektiv på nordiska kommuner. Min framtida forskning kommer att förfina transportmodellen, utveckla och tillämpa ett ramverk för beslutsanalys med flera kriterier (multi-criteria decision analysis - MCDA) som ska stödja lokala myndigheter att fastställa tekniskt och ekonomiskt hållbara lösningar i deras energiplanering.

In the recent years there has been a great deal of attention on the optimal demand and supply side strategy. The increase in renewable energy sources and the expansion in demand response programmes has shown the need for a robust power system. These changes in power system require the control of the uncertain generation and load at the same time. Therefore, it is important to provide an optimal scheduling strategy that can meet an adequate energy mix under demand response without affecting the system reliability and economic performance. This thesis addresses the following four aspects to these changes. First, a renewable obligation model is proposed to maintain an adequate energy mix in the economic dispatch model while minimising the operational costs of the allocated spinning reserves. This method considers a minimum renewable penetration that must be achieved daily in the energy mix. If the renewable quota is not achieved, the generation companies are penalised by the system operator. The uncertainty of renewable energy sources are modelled using the probability density functions and these functions are used for scheduling output power from these generators. The overall problem is formulated as a security constrained economic dispatch problem. Second, a combined economic and demand response optimisation model under a renewable obligation is presented. Real data from a large-scale demand response programme are used in the model. The model finds an optimal power dispatch strategy which takes advantage of demand response to minimise generation cost and maximise renewable penetration. The optimisation model is applied to a South African large-scale demand response programme in which the system operator can directly control the participation of the electrical water heaters at a substation level. Actual load profile before and after demand reduction are used to assist the system operator in making optimal decisions on whether a substation should participate in the demand response programme. The application of these real demand response data avoids traditional approaches which assume arbitrary controllability of flexible loads. Third, a stochastic multi-objective economic dispatch model is presented under a renewable obligation. This approach minimises the total operating costs of generators and spinning reserves under renewable obligation while maximising renewable penetration. The intermittency nature of the renewable energy sources is modelled using dynamic scenarios and the proposed model shows the effectiveness of the renewable obligation policy framework. Due to the computational complexity of all possible scenarios, a scenario reduction method is applied to reduce the number of scenarios and solve the model. A Pareto optimal solution is presented for a renewable obligation and further decision making is conducted to assess the trade-offs associated with the Pareto front. Four, a combined risk constrained stochastic economic dispatch and demand response model is presented under renewable obligation. An incentive based optimal power dispatch strategy is implemented to minimise generation costs and maximise renewable penetration. In addition, a risk-constrained approach is used to control the financial risks of the generation company under demand response programme. The coordination strategy for the generation companies to dispatch power using thermal generators and renewable energy sources while maintaining an adequate spinning reserve is presented. The proposed model is robust and can achieve significant demand reduction while increasing renewable penetration and decreasing the financial risks for generation companies. / Thesis (PhD (Electrical Engineering))--University of Pretoria, 2020. / Electrical, Electronic and Computer Engineering / PhD (Electrical Engineering) / Unrestricted

Rapid advances in integration technology have tremendously increased the design complexity of very large scale integrated (VLSI) circuits, necessitating robust optimization techniques in many stages of VLSI design. A genetic algorithm (GA) is a stochastic optimization technique that uses principles derived from the evolutionary process in nature. In this work, genetic algorithms are used to alleviate the hardware design process of VLSI application specific integrated circuits (ASICs) and reconfigurable hardware. VLSI ASIC design suffers from high design complexity and a large number of optimization objectives requiring hierarchical design approaches and multi-objective optimization techniques. The floorplanning stage of the design cycle becomes highly important in hierarchical design methods. In this work, a multi-objective genetic algorithm based floorplanner has been developed with novel crossover operators to address the multi-objective floorplanning problem for VLSI ASICs. The genetic floorplanner achieves significant wirelength savings (>19% on average) with little or no increase in area ( < 3% penalty) over previous floorplanners that perform simultaneous area and wirelength minimization. Hardware implementation of genetic algorithms is gaining importance because of their proven effectiveness as optimization engines for real-time applications. Earlier hardware implementations suffer from major drawbacks such as absence of GA parameter programmability, rigid pre-defined system architecture, and lack of support for multiple fitness functions. A compact IP core that implements a general purpose GA engine has been designed to realize evolvable hardware in field programmable gate array devices. The designed GA core achieved a speedup of around 5.16x over an analogous software implementation. Novel reconfigurable analog architectures have been proposed to realize extreme environment analog electronics. In this work, a digital framework has been developed to realize self reconfigurable analog arrays (SRAA) where genetic algorithms are used to evolve the required analog functionality and compensate performance degradation in extreme environments. The framework supports two methods of compensation, namely, model based lookup and genetic algorithm based compensation and is scalable in terms of the number of fitness evaluation modules. The entire framework has been implemented as a digital ASIC in a leading industrystrength silicon-on-insulator (SOI) technology to obtain high performance and a small form factor.

Les réseaux de capteurs sans fil sont déployés pour assurer la surveillance de phénomènes physiques. La précision de l’information recueillie dépend fortement des positions des capteurs déployés. Ces positions doivent satisfaire les exigences de l’application en termes de couverture et de connectivité. Par conséquent, les algorithmes de déploiement sont nécessaires pour déterminer les positions optimales des capteurs. Cette thèse se concentre sur le déploiement de capteurs sans fil, d’une part lorsque ces noeuds sont capables de se positionner de manière autonome, et d’autre part lorsque leur déploiement est assisté par des robots mobiles. Dans les deux cas, ce déploiement doit, non seulement répondre aux exigences de l’application en termes de couverture et connectivité, mais aussi minimiser le nombre de capteurs nécessaires tout en satisfaisant diverses contraintes (e.g. obstacles, énergie, connectivité tolérant les défaillances). Nous avons proposé des algorithmes de déploiement autonome qui évitent les oscillations des noeuds : ADVFA, GDVFA et OA-DVFA pour surveiller des zones 2D et 3D-DVFA pour surveiller un espace 3D. Lorsque le déploiement est assisté par robots mobiles, nous avons proposé deux approches pour optimiser la durée du déploiement. La première se base sur la théorie de jeux pour optimiser la durée des trajectoires de deux robots (TRDS) et la deuxième se base sur l'optimisation multi-objectifs avec un nombre de robots supérieur ou égale à 2 (MRDS). Les objectifs à satisfaire sont : optimiser la durée de la trajectoire la plus longue, équilibrer les durées des trajectoires des robots et minimiser le nombre de robots utilisés, en évitant les obstacles. / Wireless sensor networks are deployed to monitor physical phenomena. The accuracy of information collected depends on sensor node positions. These positions must meet the application requirements in terms of coverage and connectivity. Therefore, deployment algorithms are necessary to determine the optimal positions of sensor nodes. This thesis focuses on the deployment of wireless sensor nodes, first when these nodes are autonomous, and secondly when they are static and the deployment is assisted by mobile robots. In both cases, this deployment must not only meet the application requirements in terms of coverage and connectivity, but also minimize the number of sensors needed while satisfying various constraints (e.g. obstacles, energy, fault-tolerant connectivity). We proposed autonomous deployment algorithms that avoid node oscillations: ADVFA, GDVFA and OA-DVFA to monitor 2D areas and 3D-DVFA to monitor a 3D space. In case of a deployment assisted by mobile robots, we proposed two approaches to optimize the deployment duration. The first one is based on game theory to optimize the length of the paths of two robots (TRDS) and the second is based on the multi-objective optimization, with a number of robots greater than or equal to 2 (MRDS). The objectives to be met are: optimizing the duration of the longest tour duration, balancing the durations of the robot tours and minimizing the number of robots used, while bypassing obstacles.

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<p>  </p> <p>In the last years, lithium-ion batteries (LIBs) have become the most important energy storage system for consumer electronics, electric vehicles, and smart grids. The development of lithium-ion batteries (LIBs) based on current practice allows an energy density increase estimated at 10% per year. However, the required power for portable electronic devices is predicted to increase at a much faster rate, namely 20% per year. Similarly, the global electric vehicle battery capacity is expected to increase from around 170 GWh per year today to 1.5 TWh per year in 2030--this is an increase of 125% per year. Without a breakthrough in battery design technology, it will be difficult to keep up with the increasing energy demand. To that end, a design methodology to accelerate the LIB development is needed. This can be achieved through the integration of electro-chemical numerical simulations and machine learning algorithms.</p> <p><br></p> <p>To help this cause, this study develops a design methodology and framework using Simcenter Battery Design Studio® (BDS) and Bayesian optimization for design and optimization of cylindrical cell type 18650. The materials of the cathode are Nickel-Cobalt-Aluminum (NCA)/Nickel-Manganese-Cobalt-Aluminum (NMCA), anode is graphite, and electrolyte is Lithium hexafluorophosphate (LiPF6). Bayesian optimization has emerged as a powerful gradient-free optimization methodology to solve optimization problems that involve the evaluation of expensive black-box functions. The black-box functions are simulations of the cyclic performance test in Simcenter Battery Design Studio. </p> <p>The physics model used for this study is based on full system model described by Fuller and Newman. It uses Butler-Volmer Equation for ion-transportation across an interface and solvent diffusion model (Ploehn Model) for Aging of Lithium-Ion Battery Cells. The BDS model considers effects of SEI, cell electrode and microstructure dimensions, and charge-discharge rates to simulate battery degradation. Two objectives are optimized: maximization of the specific energy and minimization of the capacity fade. We perform global sensitivity analysis and see that thickness and porosity of the coating of the LIB electrodes that affect the objective functions the most. As such the design variables selected for this study are thickness and porosity of the electrodes. The thickness is restricted to vary from 22 micron to 240 microns and the porosity varies from 0.22 to 0.54. </p> <p>Two case studies are carried out using the above-mentioned objective functions and parameters. In the first study, cycling tests of 18650 NCA cathode Li-ion cells are simulated. The cells are charged and discharged using a constant 0.2C rate for 500 cycles. In the second case study a cathode active material more relevant to the electric vehicle industry, Nickel-Manganese-Cobalt-Aluminum (NMCA), is used. Here, the cells are cycled for 5 different charge-discharge scenarios to replicate charge-discharge scenario that an EVs battery module experiences. The results show that the design and optimization methodology can identify cells to satisfy the design objective that extend and improve the pareto front outside the original sampling plan for several practical charge-discharge scenarios which maximize energy density and minimize capacity fade. </p>

In the last years the steady development of autonomous driving technology has enabled the deployment of more mature autonomous vehicles. These vehicles have been applied in several pilot projects worldwide, most commonly in the form of small buses. At the same time, the amount of people traveling in especially urban areas is continuously growing, resulting in more trips in the transportation system. An efficient transportation system is therefore required to serve the growing passenger demand. Autonomous buses (AB) are assumed to have lower operational costs and with that public transport (PT) systems can potentially be designed more efficiently to facilitate the increased demand better. In this study, an AB specific simulation-based optimization framework is proposed which allows analyzing the impacts AB have on line-based PT systems. The thesis focuses on the transition from existing PT systems towards line-based PT systems operated partially or exclusively by AB. Existing work on PT service design is extended so that realistic AB systems can be investigated. This is achieved by (i) using AB specific operator cost formulations, (ii) integrating infrastructure costs required for AB operations, (iii) utilizing a dynamic, stochastic and schedule-based passenger assignment model for the simulation of PT networks and by (iv) formulating a multi-objective optimization problem allowing to investigate the stakeholder-specific impacts of AB. In Paper I the effects of AB, concerning service frequency and vehicle capacity, on fixed-line PT networks are investigated. Among other metrics, the changes are evaluated based on differences in level of service and passenger flow. Additionally, the sequential introduction of AB in existing PT systems is studied. The framework addresses a case study in Kista, Sweden. The study confirmed the initial hypothesis that the deployment of AB leads to an increase in service frequency and a marginal reduction in vehicle capacity. Furthermore, it could be seen that the deployment of AB increases the passenger load on AB lines and that passengers can shift from other PT modes towards the AB services. Paper II incorporates a multi-objective heuristic optimization algorithm in the simulation framework. The study investigates changes in transport network design based on the deployment of AB. The differences in user-focused and operator-focused network design are analyzed and the impact of AB on these is quantified. This study is applied to a case study in Barkarby, Sweden where a full-sized, line-based PT network is designed to exclusively operate AB. Among other findings, we show that the autonomous technology reduces the number of served bus stops and reduces the total PT network size. Additionally, average passenger waiting time can be reduced when deploying AB on user-focused PT networks, which in turn leads to a further reduction of user cost. / De senaste årens framsteg inom autonom körteknik har lett till mer mogna autonoma fordon. Dessa fordon har setts tillämpas i flera pilotprojekt över hela världen, oftast i form av små bussar. Samtidigt växer mängden människor som reser, särskilt i stadsområden, kontinuerligt vilket resulterar i fler resor i transportsystemet. Därför krävs ett effektivt transportsystem för att tillgodose det växande antalet passagerare. Autonoma bussar (AB) antas ha lägre driftskostnader och därmed kan system för kollektivtrafik (public transport, PT) potentiellt utformas mer effektivt för att underlätta den ökade efterfrågan bättre. I denna studie föreslås ett AB-specifikt simuleringsbaserat optimeringsramverk som gör det möjligt att analysera effekterna AB har på linjebaserade PT-system. Avhandlingen fokuserar på övergången från befintliga PT-system till linjebaserade PT-system som delvis eller uteslutande drivs av AB. Befintligt arbete med PT-tjänstdesign utvidgas så att realistiska AB-system kan undersökas. Detta uppnås genom att (i) använda AB-specifika operatörskostnadsformuleringar, (ii) integrera infrastrukturkostnader som krävs för AB-verksamhet, (iii) använda en dynamisk, stokastisk och schemabaserad modell för att tilldela passagerare vid simulering av PT-nät samt genom att (iv) formulera ett multifunktionellt optimeringsproblem som gör det möjligt att undersöka AB: s intressespecifika effekter. I artikel I undersöks effekterna av AB, med avseende på servicefrekvens och fordonskapacitet, på fasta linjer i PT-nät. Förändringar utvärderas bland annat utifrån skillnader i servicenivå och passagerarflöde. Dessutom studeras den sekventiella introduktionen av AB i befintliga PT-system. Det föreslagna ramverket tillämpas på en fallstudie i Kista, Sverige. Studien bekräftade den initiala hypotesen att utplaceringen av AB leder till en ökning av servicefrekvensen och en marginell minskning av fordonens kapacitet. Vidare kunde man se att utplaceringen av AB ökar passagerarbelastningen på AB-linjer och att passagerare kan skifta från andra PT-former mot AB-tjänsterna. Artikel II integrerar en multifunktionell heuristisk optimeringsalgoritm i ramverket för simuleringen. Studien undersöker förändringar i transportnätverkets design baserat på implementeringen av AB. Skillnaderna i användarfokuserad och operatörsfokuserad nätverksdesign analyseras och AB: s inverkan på dessa kvantifieras. Denna studie tillämpas på en fallstudie i Barkarby, Sverige, där ett fullstort linjebaserat PT-nät är utformat för att exklusivt driva AB. Vi visar bland annat att den autonoma tekniken reducerar antalet använda busshållplatser och reducerar den totala PT-nätstorleken. Dessutom kan implementeringen av AB på användarfokuserade PT-nät ytterligare förbättra servicenivån främst genom att minska den genomsnittliga väntetiden per passagerare.