The question of finding the optimal size for battery energy storage systems (BESS) to be used for energy arbitrage and peak shaving has gained more and more interest in recent years. This is due to the increase in variability of electricity prices caused by the increase of renewable but also variable electricity production units in the electricity grid. The problem of finding the optimal size for a BESS is of high complexity. It includes many factors that affect the usefulness and the economic value of a BESS. This study includes a thorough literature study regarding different methods and techniques used for finding optimal size (both capacity and power) for a BESS. From the literature study two meta-heuristic algorithms were found to have been used with success for similar problems. The two algorithms were Genetic algorithm (GA) and Firefly algorithm (FF). These algorithms have in this thesis been tested in a case study optimizing the BESS capacity and power to either maximising the net present value (NPV) of investing in a Li-ion BESS of the LPF type or minimizing the levelized cost of storage (LCOS) for the BESS, with a project lifetime of 10 years. The BESS gains monetary value from energy arbitrage by being a middleman between a large residential house complex seen as the "user" with a predefined hourly electricity load demand and the electricity grid. For the case study a simplified charge and discharge dispatch schedule was implemented for the BESS with the focus of maximising the value of energy arbitrage. The case study was divided into 3 different cases, the base case where no instalment of a BESS was done. Case 2 included the instalment of the BESS whilst case 3 included installing both a BESS and an electrical heater (ELH). The electrical heater in case 3 was implemented to shift a heating load from the user to an electrical load, to save money as well as reduce CO2 emissions from a preinstalled gas heater used in the base case. The results showed that overall GA was a better optimization algorithm for the stated problem, having lower optimization time overall between 60%-70% compared to FF and depending on the case. For case 2, GA achieves the best LCOS with a value of 0.225 e/kWh, being 11.4% lower compared to using FF. Regarding NPV for case 2, FF achieves the best solutions at the lowest possible value in the search space for the capacity and power (i.e., 0.1 kWh for capacity and 0.1 kW for power), with an NPV at -51.5e, showing that for case 2 when optimizing for NPV an investment in a BESS is undesirable. GA finds better solutions for case 3 for both NPV and LCOS at 954,982e and 0.2305 e/kWh respectively, being 35.7% larger and 9.1% lower respectively compared to using FF. For case 3 it was shown that the savings from installing the ELH stands for a large portion of the profits, leading to a positive NPV compared to case 2 when it was not implemented. Finally, it was found that the GA can be a useful tool for finding optimal power and capacity for BESS instalments, compared to FF that got stuck at local optimums. However, it was seen that the charge and discharge dispatch schedule play an important role regarding the effectiveness of installing a BESS. As for some cases the BESS was only used 17% of all hours during a year (case 2, when optimizing for NPV). Therefore, further research is of interest into the schedule function and its role regarding finding the optimal BESS size. / Frågan angående hur man hittar den optimal storleken på en energilagringsenhet av batteritypen (BESS) som skall användas för energiarbitrage samt "peak shaving" har fått mer och mer uppmärksamhet de senaste åren. Detta sker på grund av en ökning av variabiliteten av elpriser, vilket i sig delvis kommer från ett ökat installerande av förnyelsebar, men då också variabla energiproduktionsenheter till elnätet. Problemet med att hitta den optimala storleken för en BESS är på grund av komplexitet i frågan. Det innehåller många faktorer som påverkar effektiviteten samt det ekonomiska värdet av en BESS. Denna avhandling innehåller en litteraturstudie om olika tekniker och metoder som används för att hitta den optimal lösningen för optimal storlek (kapacitet och kraft) på en BESS. Från litteraturstudien hittades två meta-heuristiska algoritmer som använts med succés på liknande problem. De två algoritmerna var "Genetic algorithm" (GA) och "Firefly algorithm (FF). Dessa algoritmer har i denna avhandling blivit testade i en fallstudie för att optimera kapacitet och kraft för en BESS genom att antingen maximera nettonuvärdet (NPV) som fås av att investera i en Li-ion BESS av typen LPF eller att minimera "levelized cost of storage" (LCOE) för en BESS med en livstid på 10 år. Detta genom att man får monetärt värde från att använda en BESS för energiarbitrage genom att vara en mellanhand mellan ett stort bostadskomplex som ses vara en "användare" med ett förbestämt elanvändningsmönster och elnätet. För fallstudien användes en simpel metodologi för laddnings- och urladdninsgschema för att maximera energiarbitrage. Fallstudien delades upp i tre olika fall, ett basfall där ingen installation av en BESS gjordes. I fall 2 installerades bara en BESS medans för fall 3 installerades både en BESS samt en elektrisk värmare (ELH) för att omvandla användarens termiska energianvändning till mer elektrisk energianvändning. Genom detta kan monetära besparingar göras samt reducera mängden CO2 utsläpp som annars hade kommit från en redan installerade gasvärmare, i basfallet.  Resultatet visade att totalt sätt var GA en bättre optimeringsalgoritm för det specifika problemet, med lägre optimeringstid på 60%-70% jämfört med FF och beroende på fall. För fall 2 hittar GA det lägsta värdet på LCOS på 0.225 e/kWh, och var då 11.4% lägre jämfört med FF. Angående NPV för fall 2 hittar FF den bästa lösningen på det minsta möjliga värdet på kraft och kapacitet i sökutrymmet (det vill säga 0.1 kWh för kapacitet och 0.1 kW för kraft), med ett NPV värde på -51.5e, vilket visar att för fall 2 när man optimerar för NPV så finns ingen ekonomisk vinning av att investera i en BESS. GA hittar den bästa lösningen för fall 3, både för NPV och LCOS på 954,982e och 0.2305 e/kWh respektivt, vilket är 35.7% större och 9.1% lägre respektivt jämfört när man använder FF. För fall 3 visade resultaten att besparingarna från att installera en ELH stod för den större delen av alla vinster, vilket ledde till positiva värden för NPV. Slutligen visade resultaten att GA kan vara ett användbart verktyg för att hitta den optimala lösningen för storleken på en BESS, jämfört med FF som fastande på lokal optimala lösningar. Dock kunde resultaten också visa att laddnings- och urladdninsgschemat använt i fallstudien spelade en viktig roll angående effektiviteten med att installera en BESS. I vissa fall så användes BESS:en så lite som 17% av alla timmar på ett år (fall 2, optimering av NPV). Därför är det ett stort intresse att göra fortsatt forskning på andra laddnings- och urladdninsgscheman och dess roll med att hitta en optimal storlek på en BESS.

An option, a type of a financial derivative, is a contract that creates an opportunity for a market player to avoid risks involved in investing, especially in equities. An investor desires to know the accurate value of an option before entering into a contract to buy/sell the underlying asset (stock). There are various techniques that try to simulate real market conditions in order to price or evaluate an option. However, most of them achieved limited success due to high uncertainty in price behavior of the underlying asset. In this study, I propose two new Firefly variant algorithms to compute accurate worth for European and American option contracts and compare them with popular option pricing models (such as Black-Scholes-Merton, binomial lattice, Monte-Carlo, etc.) and real market data. In my study, I have first modelled the option pricing as a multi-objective optimization problem, where I introduced the pay-off and probability of achieving that pay-off as the main optimization objectives. Then, I proposed to use a latest nature-inspired algorithm that uses the bioluminescence of Fireflies to simulate the market conditions, a first attempt in the literature. For my thesis, I have proposed adaptive weighted-sum based Firefly algorithm and non-dominant sorting Firefly algorithm to find Pareto optimal solutions for the option pricing problem. Using my algorithm(s), I have successfully computed complete Pareto front of option prices for a number of option contracts from the real market (Bloomberg data). Also, I have shown that one of the points on the Pareto front represents the option value within 1-2 % error of the real data (Bloomberg). Moreover, with my experiments, I have shown that any investor may utilize the results in the Pareto fronts for deciding to get into an option contract and can evaluate the worth of a contract tuned to their risk ability. This implies that my proposed multi-objective model and Firefly algorithm could be used in real markets for pricing options at different levels of accuracy. To the best of my knowledge, modelling option pricing problem as a multi-objective optimization problem and using newly developed Firefly algorithm for solving it is unique and novel. / October 2016

Otimização estrutural é uma área relativamente nova que vem sendo cada vez mais explorada. Existem muitos métodos clássicos, e outros mais recentes vem surgindo para disputar em eficiência, confiabilidade e rapidez na obtenção de um resultado ótimo. Os algoritmos são classificados em algoritmos determinísticos, que utilizam a informação do gradiente, ou seja, usam os valores das funções e suas derivadas, e os meta-heurísticos, algoritmos de otimização aleatórios que são métodos probabilísticos não baseados em gradiente, ou seja, usam somente a avaliação da função objetivo. São apresentados dois algoritmos meta-heurísticos relativamente recentes: o Harmony Search, baseado na improvisação musical em busca da harmonia perfeita, e o Firefly Algorithm, que é inspirado no comportamento da luz dos vagalumes. Vários exemplos clássicos de treliças 2-D e 3-D considerando otimização paramétrica e de forma, com restrições de tensão, deslocamento, flambagem e frequência natural, são apresentados para demonstrar a eficiência dos métodos. Os resultados são comparados aos de outros autores usando diferentes métodos encontrados na literatura. Os resultados indicam que os algoritmos de otimização estudados neste trabalho são melhores ou tão eficientes quanto os demais. Por fim, os métodos são aplicados à estrutura de um projeto de engenharia adaptado. / Structural optimization is a relatively new area that has been increasingly exploited. There are many classical methods, and newer are emerging to compete on efficiency, reliability and speed in obtaining an optimal result. The algorithms are classified into deterministic algorithms, which use the gradient information, i.e., use the values of the functions and their derivatives, and meta-heuristic algorithms, random optimization methods which are probabilistic methods not based on gradient, i.e., they use only objective function evaluation. Two relatively recent meta-heuristics algorithms are presented, Harmony Search, based on musical improvisation in search of the perfect harmony, and Firefly Algorithm, which is inspired by the behavior of the light of fireflies. Several benchmarks of 2-D and 3-D trusses considering size and shape optimization, with stress, displacement, buckling and natural frequency constraints, are presented to demonstrate the effectiveness of the methods. The results are compared to the others authors using different methods found in the literature. The results indicate that optimization algorithms studied in this work are better than or as efficient as others. Finally, the methods are applied to the structure of an adapted engineering design.

Otimização estrutural é uma área relativamente nova que vem sendo cada vez mais explorada. Existem muitos métodos clássicos, e outros mais recentes vem surgindo para disputar em eficiência, confiabilidade e rapidez na obtenção de um resultado ótimo. Os algoritmos são classificados em algoritmos determinísticos, que utilizam a informação do gradiente, ou seja, usam os valores das funções e suas derivadas, e os meta-heurísticos, algoritmos de otimização aleatórios que são métodos probabilísticos não baseados em gradiente, ou seja, usam somente a avaliação da função objetivo. São apresentados dois algoritmos meta-heurísticos relativamente recentes: o Harmony Search, baseado na improvisação musical em busca da harmonia perfeita, e o Firefly Algorithm, que é inspirado no comportamento da luz dos vagalumes. Vários exemplos clássicos de treliças 2-D e 3-D considerando otimização paramétrica e de forma, com restrições de tensão, deslocamento, flambagem e frequência natural, são apresentados para demonstrar a eficiência dos métodos. Os resultados são comparados aos de outros autores usando diferentes métodos encontrados na literatura. Os resultados indicam que os algoritmos de otimização estudados neste trabalho são melhores ou tão eficientes quanto os demais. Por fim, os métodos são aplicados à estrutura de um projeto de engenharia adaptado. / Structural optimization is a relatively new area that has been increasingly exploited. There are many classical methods, and newer are emerging to compete on efficiency, reliability and speed in obtaining an optimal result. The algorithms are classified into deterministic algorithms, which use the gradient information, i.e., use the values of the functions and their derivatives, and meta-heuristic algorithms, random optimization methods which are probabilistic methods not based on gradient, i.e., they use only objective function evaluation. Two relatively recent meta-heuristics algorithms are presented, Harmony Search, based on musical improvisation in search of the perfect harmony, and Firefly Algorithm, which is inspired by the behavior of the light of fireflies. Several benchmarks of 2-D and 3-D trusses considering size and shape optimization, with stress, displacement, buckling and natural frequency constraints, are presented to demonstrate the effectiveness of the methods. The results are compared to the others authors using different methods found in the literature. The results indicate that optimization algorithms studied in this work are better than or as efficient as others. Finally, the methods are applied to the structure of an adapted engineering design.

Otimização estrutural é uma área relativamente nova que vem sendo cada vez mais explorada. Existem muitos métodos clássicos, e outros mais recentes vem surgindo para disputar em eficiência, confiabilidade e rapidez na obtenção de um resultado ótimo. Os algoritmos são classificados em algoritmos determinísticos, que utilizam a informação do gradiente, ou seja, usam os valores das funções e suas derivadas, e os meta-heurísticos, algoritmos de otimização aleatórios que são métodos probabilísticos não baseados em gradiente, ou seja, usam somente a avaliação da função objetivo. São apresentados dois algoritmos meta-heurísticos relativamente recentes: o Harmony Search, baseado na improvisação musical em busca da harmonia perfeita, e o Firefly Algorithm, que é inspirado no comportamento da luz dos vagalumes. Vários exemplos clássicos de treliças 2-D e 3-D considerando otimização paramétrica e de forma, com restrições de tensão, deslocamento, flambagem e frequência natural, são apresentados para demonstrar a eficiência dos métodos. Os resultados são comparados aos de outros autores usando diferentes métodos encontrados na literatura. Os resultados indicam que os algoritmos de otimização estudados neste trabalho são melhores ou tão eficientes quanto os demais. Por fim, os métodos são aplicados à estrutura de um projeto de engenharia adaptado. / Structural optimization is a relatively new area that has been increasingly exploited. There are many classical methods, and newer are emerging to compete on efficiency, reliability and speed in obtaining an optimal result. The algorithms are classified into deterministic algorithms, which use the gradient information, i.e., use the values of the functions and their derivatives, and meta-heuristic algorithms, random optimization methods which are probabilistic methods not based on gradient, i.e., they use only objective function evaluation. Two relatively recent meta-heuristics algorithms are presented, Harmony Search, based on musical improvisation in search of the perfect harmony, and Firefly Algorithm, which is inspired by the behavior of the light of fireflies. Several benchmarks of 2-D and 3-D trusses considering size and shape optimization, with stress, displacement, buckling and natural frequency constraints, are presented to demonstrate the effectiveness of the methods. The results are compared to the others authors using different methods found in the literature. The results indicate that optimization algorithms studied in this work are better than or as efficient as others. Finally, the methods are applied to the structure of an adapted engineering design.

Mobile learning (m-learning) is a relatively new technology that helps students learn and gain knowledge using the Internet and Cloud computing technologies. Cloud computing is one of the recent advancements in the computing field that makes Internet access easy to end users. Many Cloud services rely on Cloud users for mapping Cloud software using virtualization techniques. Usually, the Cloud users' requests from various terminals will cause heavy traffic or unbalanced loads at the Cloud data centers and associated Cloud servers. Thus, a Cloud load balancer that uses an efficient load balancing technique is needed in all the cloud servers. We propose a new meta-heuristic algorithm, named the dominant firefly algorithm, which optimizes load balancing of tasks among the multiple virtual machines in the Cloud server, thereby improving the response efficiency of Cloud servers that concomitantly enhances the accuracy of m-learning systems. Our methods and findings used to solve load imbalance issues in Cloud servers, which will enhance the experiences of m-learning users. Specifically, our findings such as Cloud-Structured Query Language (SQL), querying mechanism in mobile devices will ensure users receive their m-learning content without delay; additionally, our method will demonstrate that by applying an effective load balancing technique would improve the throughput and the response time in mobile and cloud environments.

Yes / A compact planar monopole antenna is proposed for ultra-wideband applications. The antenna has a microstrip line feed and band-rejected characteristics and consists of a ring patch and partial ground plane with a defective ground structure of rectangular shape. An annular strip is etched above the radiating element and two slots, one C-shaped and one arc-shaped, are embedded in the radiating patch. The proposed antenna has been optimized using bio-inspired algorithms, namely Particle Swarm Optimization and the Firefly Algorithm, based on a new software algorithm (Antenna Optimizer). Multi-objective optimization achieves rejection bands at 3.3 to 3.7 GHz for WiMAX, 5.15 to 5.825 GHz for the 802.11a WLAN system or HIPERLAN/2, and 7.25 to 7.745 GHz for C-band satellite communication systems. Validated results show wideband performance from 2.7 to 10.6 GHz with S11 ˂ -10 dB. The antenna has compact dimensions of 28 × 30 mm2. The radiation pattern is comparatively stable across the operating band with a relatively stable gain except in the notched bands. / This work was supported in part by the United Kingdom Engineering and Physical Science Research Council (EPSRC) under Grant EP/E022936A, TSB UK under grant application KTP008734 and the Iraqi Ministry of Higher Education and Scientific Research.

Na presente tese é resolvida a equação de difusão de nêutrons estacionária, bem como problemas de cinética, em geometria unidimensional cartesiana multi-região considerando o modelo de multigrupos de energia. Um dos objetivos e inovação neste trabalho é a obtenção de uma solução aproximada com estimativa de erro, controle de precisão e na forma de uma expressão analítica. Com esse tipo de solução não há a necessidade de recorrer a esquemas de interpolação, geralmente necessários em caso de discretizações do domínio. O fluxo de nêutrons é expandido em uma série de Taylor cujos coeficientes são encontrados utilizando a equação diferencial e as condições de contorno e interface. O domínio é dividido em várias células, cujo tamanho e o grau do polinômio são ajustáveis de acordo com a precisão requerida. Para resolver o problema de autovalor é utilizado o método da potência. A metodologia é aplicada em um benchmark que consiste na solução da equação de difusão como condição inicial e na solução de problemas de cinética para diferentes transientes. Os resultados são comparados com sucesso com resultados da literatura. A convergência da série é garantida pela aplicação de um raciocínio baseado no critério de Lipschitz para funções contínuas. Cabe ressaltar que a solução obtida, em conjunto com a análise da convergência, mostra a solidez e a precisão dessa metodologia. / In the present dissertation the one-dimensional neutron diffusion equation for stationary and kinetic problems in a multi-layer slab has been solved considering the multi-group energy model. One of the objectives and innovation in this work is to obtain an approximate solution with error estimation, accuracy control and in the form of an analytical expression. With this solution there is no need for interpolation schemes, which are usually needed in case of discretization of the domain. The neutron flux is expanded in a Taylor series whose coefficients are found using the differential equation and the boundary and interface conditions. The domain is divided into several layers, whose size and the polynomial order can be adjusted according to the required accuracy. To solve the eigenvalue problem the conventional power method has been used. The methodology is applied in a benchmark problem consisting of the solution of the diffusion equation as an initial condition and solving kinetic problems for different transients. The results are compared successfully with the ones in the literature. The convergence of the series is guaranteed by applying a criterion based on the Lipschitz criterion for continuous functions. Note that the solution obtained, together with the convergence analysis, shows the robustness and accuracy of this methodology.

A otimização de amortecedores é uma área nova que vem sendo explorada nos últimos anos. Existem vários métodos clássicos e outros mais recentes que estão disputando em confiabilidade, eficiência e rapidez na obtenção de um resultado ótimo. Os algoritmos de otimização são classificados em determinísticos, que utilizam a informação do gradiente, ou seja, usam os valores das funções e suas derivadas, e os meta-heurísticos são algoritmos aleatórios que são métodos probabilísticos não baseados em gradiente, utilizando somente a avaliação da função objetivo. O Firefly Algorithm é um algoritmo meta-heurístico relativamente recente inspirado no comportamento da luz dos vagalumes. Este trabalho propõe um método para a otimização de amortecedores por atrito utilizando algoritmo meta-heurístico. O método proposto é testado em dois edifícios, de nove e dezesseis andares, submetidos a duas excitações sísmicas cada. A otimização tem um objetivo principal: diminuir a resposta dinâmica em termos do deslocamento máximo no topo das estruturas obtido através de um algoritmo programado baseado no método das diferenças finitas centrais, otimizando o local de um número máximo de amortecedores e as forças de atrito dos mesmos. Para o caso da estrutura de nove andares o número máximo de amortecedores é de quatro e para o caso da estrutura de dezesseis andares o número máximo é seis. Os resultados demostraram que, para os dois casos estudados, o deslocamento no topo da estrutura diminui em mais de 50%, concluindo-se que o método programado é eficaz assim como o Firefly Algorithm é adequado para obter as posições e as forças de atrito ótimas. Portanto, acredita-se que o método proposto poderá ser utilizado como uma ferramenta útil para auxiliar no projeto de amortecedores por atrito. / The damper’s optimization is a new area that has been explored in recent years. There are several classics and newer methods that are competing in reliability, efficiency and speed in achieving a great result. The algorithms are classified as deterministic, using gradient information, or use the function values and their derivatives, and meta- heuristic optimization algorithms are random probabilistic methods that are not based on gradient using only the evaluation of the objective function. The Firefly Algorithm is a relatively new meta-heuristic algorithm inspired on the behavior of the light of fireflies. This work proposes a method for the friction damper’s optimization using meta-heuristic algorithm. The proposed method is tested in two structures: a nine story building and a sixteen story building. They were submitted to two seismic excitations each. The optimization has one main goal: to reduce the dynamic response in terms of the maximum displacement at the top of the structures obtained by a programmed algorithm based on the central finite difference method, optimizing the location of a maximum number of dampers and their friction’s forces. In the case of the nine story building, the maximum number of dampers is four, and in the case of the sixteen story building the maximum number is six. The results showed that for the two cases studied, the displacement at the top of the structure decreases by more than 50%, concluding that the programmed method is effective and the Firefly Algorithm is appropriate to get the positions and friction’s forces optimal. Therefore, it is believed that the proposed method can be used as a tool to aid in the design of friction dampers.

Na presente tese é resolvida a equação de difusão de nêutrons estacionária, bem como problemas de cinética, em geometria unidimensional cartesiana multi-região considerando o modelo de multigrupos de energia. Um dos objetivos e inovação neste trabalho é a obtenção de uma solução aproximada com estimativa de erro, controle de precisão e na forma de uma expressão analítica. Com esse tipo de solução não há a necessidade de recorrer a esquemas de interpolação, geralmente necessários em caso de discretizações do domínio. O fluxo de nêutrons é expandido em uma série de Taylor cujos coeficientes são encontrados utilizando a equação diferencial e as condições de contorno e interface. O domínio é dividido em várias células, cujo tamanho e o grau do polinômio são ajustáveis de acordo com a precisão requerida. Para resolver o problema de autovalor é utilizado o método da potência. A metodologia é aplicada em um benchmark que consiste na solução da equação de difusão como condição inicial e na solução de problemas de cinética para diferentes transientes. Os resultados são comparados com sucesso com resultados da literatura. A convergência da série é garantida pela aplicação de um raciocínio baseado no critério de Lipschitz para funções contínuas. Cabe ressaltar que a solução obtida, em conjunto com a análise da convergência, mostra a solidez e a precisão dessa metodologia. / In the present dissertation the one-dimensional neutron diffusion equation for stationary and kinetic problems in a multi-layer slab has been solved considering the multi-group energy model. One of the objectives and innovation in this work is to obtain an approximate solution with error estimation, accuracy control and in the form of an analytical expression. With this solution there is no need for interpolation schemes, which are usually needed in case of discretization of the domain. The neutron flux is expanded in a Taylor series whose coefficients are found using the differential equation and the boundary and interface conditions. The domain is divided into several layers, whose size and the polynomial order can be adjusted according to the required accuracy. To solve the eigenvalue problem the conventional power method has been used. The methodology is applied in a benchmark problem consisting of the solution of the diffusion equation as an initial condition and solving kinetic problems for different transients. The results are compared successfully with the ones in the literature. The convergence of the series is guaranteed by applying a criterion based on the Lipschitz criterion for continuous functions. Note that the solution obtained, together with the convergence analysis, shows the robustness and accuracy of this methodology.