In the villages along the Amazon river, the access to clean drinking water is lacking. In Progreso, the Swedish foundation Ankarstiftelsen and the non-governmental organization Entropika have installed a water purification system to solve this problem. The water used in the purification system is today pumped from a tributary to the Amazon river with a gasoline pump. This comes with social, ecologic and economic problems. To solve these problems, a solar water pumping system has been developed. After a preparing literature study on the topic, a field study was done to find relevant data. From this, an Excel program was made to optimize a suitable solution. Together with suggestions from three companies, two with a surface pump and one with a submersible pump, the system including a submersible pump was considered the most preferable. This mainly due to lower cost, weight and maintenance. Further, the suggestions were used to control the accuracy of the developed Excel program. This program can be used for future optimizations of systems with similar character. / I byarna längs Amazonfloden är tillgången till rent dricksvatten bristfällig. Organisationerna Ankarstiftelsen och Entropika är verksamma i området och arbetar för en ökad levnadsstandard åt lokalbefolkningen. I byn Progreso har organisationerna installerat ett vattenreningssystem för att lösa problemet. Systemet använder flodvatten som renas med sandfilter och sedimentering. Vattnet pumpas idag från en biflod till Amazonfloden med en bensindriven pump. Pumpen är mycket stöldbegärlig och måste därför bäras ner till floden vid varje användning. Den väger 70 kg och utgör en arbetsbörda för vattenmästaren i byn. Utöver det är regelbundna kostanden för drivmedlet ett problem då invånarna saknar en stabil inkomst. Dessutom orsakar den bensindrivna pumpen miljöfarliga utsläpp. För att lösa de sociala, ekonomiska och ekologiska bristerna har ett solvattenpumpssystem dimensionerats. Efter en förberedande litteraturstudie inom ämnet utfördes en fältstudie i Progreso för att hitta relevanta data. Fältstudien bestod av distansmätningar och intervjuer med invånarna. Intervjuerna gav svar på huruvida dagens system fungerar samt det önskade vattenbehovet från det nya systemet. Med funna data kunde beräkningar utföras och ett Excelprogram utvecklas för att optimera ett för platsen passande system. Från tre systemförslag framtagna av företag, två förslag med ytpump och ett med en dränkbar pump, togs beslutet att den dränkbara pumpen var att föredra. Detta främst på grund av lägre kostnad, vikt och underhåll. Vidare användes förslagen för att undersöka pålitligheten hos Excelprogrammet som ämnar till att används för framtida system av liknande karaktär.

[pt] A crescente demanda global por energia e a finitude dos recursos fósseis despertaram grande interesse pelo uso de energias renováveis e tecnologias menos poluentes. Neste contexto, este trabalho introduz uma simulação numérica de um sistema híbrido on-grid de uma unidade combinada de calor e potência (CHP) para microaplicações residenciais e industriais. O sistema é composto por uma célula a combustível tipo membrana polimérica (PEMFC) de 5 kW acoplada a um reformador de gás natural, painéis fotovoltaicos (245 W) e baterias (100 Ah cada) conectados à rede elétrica através de um inversor híbrido bidirecional. Uma análise energética foi desenvolvida para validar a rotina computacional e determinar a vazão de gás natural. Em seguida, foi realizada uma análise econômica baseada na evolução do fluxo de caixa dos usuários e no custo cumulativo total do sistema no horizonte de 2020 a 2040, de forma a investigar a influência das taxas de incremento das tarifas de energia elétrica e gás natural, diferentes configurações do sistema, do número de consumidores e do fator de aproveitamento de créditos na rede. Diferentes tipos de tarifa (convencional e branca) e a possibilidade de cogeração com o rejeito térmico da PEMFC também foram avaliados. Ao final, uma análise ambiental foi desenvolvida para avaliar a contribuição para o potencial de aquecimento global do micro-CHP. Paybacks entre 6 e 20 anos de operação do sistema foram alcançados para diferentes combinações dos parâmetros examinados considerando-se a adesão no ano de 2020. Adicionalmente, fortes reduções no custo cumulativo total foram obtidas levando-se em conta a queda prevista nos custos de aquisição dos componentes para as próximas décadas. Finalmente, emissões equivalentes até 30 porcento inferiores às da eletricidade da matriz energética nacional e do fornecimento de calor por queima de gás natural foram calculadas com uso da cogeração no atendimento da demanda térmica dos usuários. / [en] The growing global energy demand and fossil resources depletion have triggered great interest in the use of renewable energy and low emission technologies. In this context, this work introduces a numerical simulation of an on-grid hybrid system of a combined heat and power unit (CHP) for residential and industrial micro-applications. The system consists of a 5 kW proton-exchange membrane fuel cell (PEMFC) coupled to a natural gas reformer, photovoltaic panels (245 W) and batteries (100 Ah each) connected to the grid through a bidirectional inverter. An energy analysis was carried out to validate the computational routine and assess the natural gas flow and the thermal and electrical efficiencies of the CHP unit. Afterwards, an economic analysis was developed to determine the consumers cash flow progression and the total cumulative cost of the system in an 2020-2040 horizon in order to investigate the influence of increasing natural gas and electricity tariffs, different system configurations, the number of consumers and the reverse metering factor from the grid. Different types of tariffs (conventional and alternative) and the possibility of cogeneration with the thermal rejection from the PEMFC were also evaluated. At last, an environmental analysis was developed to assess the contribution to the global warming potential of the micro-CHP. Paybacks between 6 and 20 years of system s operation were achieved for different combinations of the studied parameters considering beginning of operation in 2020. Additionally, great reductions in the total cumulative cost were obtained considering the predicted decrease in system s components acquisition costs for the next decades. Finally, reductions in CO2 emissions of up to 30 percent compared to those of the electricity from the Brazilian energy matrix and heat supply from burning natural gas were obtained when cogeneration from CHP unit was accounted to meet the consumers thermal demand.

Electricity is a fundamental energy carrier for modern life and for economic prosperity. All kinds of equipment use electricity as their power source, including domestic and industrial applications. There is a trend to adopting more electricity-based equipment in all areas. The modern power infrastructures can sufficiently supply most cities and developed areas. However, certain rural areas are still unable to get access to electric power due to the inconvenient locations or less developed economy. This makes the living conditions in such areas extremely inconvenient and further hinders the economic development in those areas.Electrification for rural areas has been a critical task for some developing countries. To accomplish this task, the options are limited to build a stand-alone power system or construct a power transmission line for the chosen location. A stand-alone power system has commonly been based on fossil fuel, such as a diesel generator, with low capital cost compared to a long connection, but with significant running cost of fuel. Recent improvements of renewable sources and storage, and more efficient loads, have made renewable sources much more competitive than before for a stand-alone electricity supply. The choice between different renewable energies depends on the local natural resources. It is a more flexible way to providing the electricity and a more efficient and environmental-friendly way since the energy loss caused by transmission is eliminated. On the other hand, the grid connection option involves building a transmission line to connect the rural area to the national grid, which is a more traditional approach to provide power. The cost of this method depends on the relative distance between the rural area and the nation grid.The choice between the above two mentioned electrification options is the first step when considering providing power to the rural area. This thesis focuses on the electrification for rural areas and comparing the above two methods, finding out the break-even point. It is of current interest as the technology for both options is changing, and the break-even is also changing.In this thesis, a mathematical model for on-grid electrification is proposed and simulated on MATLAB. The off-grid option is simulated by HOMER. The results show how the LCOE of on-grid and off-grid electrification as well as the off-grid configuration are affected by different parameters like the distance to grid, load demand level, PV cost, WT cost, storage cost, the diesel price and so on. By comparing the results, the break-even point of two options is also presented. / Elektricitet är den viktigaste energibäraren för det moderna livet och för ekonomiskt välstånd. Många typer av utrustning använder el som sin kraftkälla, i hushållet såväl som I industrin, och det finns en tendens att öka användning av el inom alla områden. Moderna elnät levererar till de flesta städer och utvecklade områden. Dock har vissa landsbygdsområden fortfarande inte elförsörjning, på grund av svårtillgängliga områden och mindre utvecklade ekonomier. Detta gör att levnadsförhållandena i sådana områden är lägre än om man hade haft tillgång till el, och ytterligare hindrar den ekonomiska utvecklingen i dessa områden.Elektrifiering för landsbygdsområden har varit en viktig uppgift för vissa utvecklingsländer. Två extrema fall är att bygga ett fristående lokalt kraftsystem, eller att bygga nya kraftledningar för att ansluta till ett befintligt elnät. Ett fristående kraftsystem har historiskt sett typiskt berott på fossila bränslen, till exempel med en dieselgenerator, vilket ger lägre kapitalkostnad än en lång ledning, fast med betydande driftskostnader för bränsle. De senaste förbättringarna av förnybara källor och lagring, samt effektivare laster, har gjort förnybara källor mycket mer konkurrenskraftiga än tidigare för en fristående elförsörjning.Valet mellan de två ovannämnda alternativen är det första steget när man elektrifierar ett landsbygdsområde. Denna uppsats fokuserar på elektrifiering för landsbygdsområden och jämför dessa två metoder. Det är av aktuellt intresse eftersom tekniken för båda alternativen är i förändring.I denna uppsats, en matematisk modell för on-grid elektrifiering är föreslås och simuleras på MATLAB. Alternativet off-grid simuleras av HOMER. Resultaten visar hur LCOE av on-grid och off-grid elektrifiering såväl som nätverkskonfigurationen påverkas av olika parametrar som avståndet till rutnätet, lastbehovsnivå, PV kostnad, WT kostnad, lagerkostnad, dieselpriset och så vidare. Genom att jämföra resultaten, jämnpunkten av två alternativ är också presenterad.

The dissertation presents a generalized control structure for two-stage power converters operated in a renewable energy power system for smart grid and micro grid systems. The generalized control structure is based on the two-loop average-mode-control technique, and created by reconstructing the conventional control structure and feedback configuration. It is broadly used for both dc-dc and dc-ac power conversion based on the two-stage converter architecture, while offering several functionalities required for renewable energy power systems. The generalized control structure improves the performance and reliability of renewable energy power systems with multiple functionalities required for consistent and reliable distributed power sources in the applications of the smart grid and micro grid system. The dissertation also presents a new modeling approach based on a modification of the subsystem-integration approach. The approach provides continuous-time small-signal models for all of two-stage power converters in a unified way. As a result, a modeling procedure is significantly reduced by treating a two-stage power converter as a single-stage with current sinking or sourcing. The difficulty of linearization caused by time-varying state variables is avoided with the use of the quasi-steady state concept. The generalized control structure and modeling approach are demonstrated using the two-stage dc-dc and dc-ac power conversion systems. A battery energy storage system with a thermoelectric source and a grid-connected power system with a photovoltaic source are examined. The large-signal averaged model and small-signal model are developed for the two demonstrated examples, respectively. Based on the modeling results, the control loops are designed by using frequency domain analysis. Various simulations and experimental tests are carried out to verify the compensator designs and to evaluate the generalized control structure performance. From the simulation and experimental results, it is clearly seen that the generalized control structure improves the performance of a battery energy storage system due to the unified control concept. The unified control concept eliminates transient over-voltage or over-current, extra energy losses, power quality issues, and complicated decision processes for multiple-mode control. It is also seen that the generalized control structure improves the performance of a single-phase grid-connected system through increased voltage control loop bandwidth of the active ripple current reduction scheme. As a result of the increased loop bandwidth, the transient overshoot or undershoot of the dc-link voltage are significantly reduced during dynamic load changes. / Ph. D.

The electric power grid is evolving in terms of generation, transmission and distribution network architecture. On the generation side, distributed energy resources (DER) are participating at a much larger scale. Transmission and distribution networks are transforming to a decentralized architecture from a centralized one. Residential and commercial buildings are now considered as active elements of the electric grid which can participate in grid operation through applications such as the Demand Response (DR). DR is an application through which electric power consumption during the peak demand periods can be curtailed. DR applications ensure an economic and stable operation of the electric grid by eliminating grid stress conditions. In addition to that, DR can be utilized as a mechanism to increase the participation of green electricity in an electric grid. The DR applications, in general, are passive in nature. During the peak demand periods, common practice is to shut down the operation of pre-selected electrical equipment i.e., heating, ventilation and air conditioning (HVAC) and lights to reduce power consumption. This approach, however, is not optimal and does not take into consideration any user preference. Furthermore, this does not provide any information related to demand flexibility beforehand. Under the broad concept of grid modernization, the focus is now on the applications of data analytics in grid operation to ensure an economic, stable and resilient operation of the electric grid. The work presented here utilizes data analytics in DR application that will transform the DR application from a static, look-up-based reactive function to a dynamic, context-aware proactive solution. The dynamic demand response framework presented in this dissertation performs three major functionalities: electrical load forecast, electrical load disaggregation and peak load reduction during DR periods. The building-level electrical load forecasting quantifies required peak load reduction during DR periods. The electrical load disaggregation provides equipment-level power consumption. This will quantify the available building-level demand flexibility. The peak load reduction methodology provides optimal HVAC setpoint and brightness during DR periods to reduce the peak demand of a building. The control scheme takes user preference and context into consideration. A detailed methodology with relevant case studies regarding the design process of the network architecture of a deep learning algorithm for electrical load forecasting and load disaggregation is presented. A case study regarding peak load reduction through HVAC setpoint and brightness adjustment is also presented. To ensure the scalability and interoperability of the proposed framework, a layer-based software architecture to replicate the framework within a cloud environment is demonstrated. / Doctor of Philosophy / The modern power grid, known as the smart grid, is transforming how electricity is generated, transmitted and distributed across the US. In a legacy power grid, the utilities are the suppliers and the residential or commercial buildings are the consumers of electricity. However, the smart grid considers these buildings as active grid elements which can contribute to the economic, stable and resilient operation of an electric grid. Demand Response (DR) is a grid application that reduces electrical power consumption during peak demand periods. The objective of DR application is to reduce stress conditions of the electric grid. The current DR practice is to shut down pre-selected electrical equipment i.e., HVAC, lights during peak demand periods. However, this approach is static, pre-fixed and does not consider any consumer preference. The proposed framework in this dissertation transforms the DR application from a look-up-based function to a dynamic context-aware solution. The proposed dynamic demand response framework performs three major functionalities: electrical load forecasting, electrical load disaggregation and peak load reduction. The electrical load forecasting quantifies building-level power consumption that needs to be curtailed during the DR periods. The electrical load disaggregation quantifies demand flexibility through equipment-level power consumption disaggregation. The peak load reduction methodology provides actionable intelligence that can be utilized to reduce the peak demand during DR periods. The work leverages functionalities of a deep learning algorithm to increase forecasting accuracy. An interoperable and scalable software implementation is presented to allow integration of the framework with existing energy management systems.

The idea of two elements overlapping to create a third element is a very simple idea, yet one imbued with possibility. It can be as simple as two colors combining to create a new color: yellow + blue = green. This new, third element can stand alone, but it always retains traces of the two original elements. This third element is enriched by the two primary elements, and they, in turn, are enriched by this connection. 1 + 1 = 3 The place where two elements come together can become an integral part of both elements. It can become a central space where ideas meet and intermingle. In such a way, a school of architecture and design centers around its studio. The studio is, for the student, the place where living and learning come together. Here, the practicality of materials meets the theoretical concepts of the classroom. It is often, quite literally, "home away from home" for the student, who spends many hours working on studio projects, gathering with students and faculty, trying to bring design theories into practice. With this idea in mind, a Girls' School of Design is postulated. / Master of Architecture

A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities. In the presence of grid users who are autonomous, rational, and intelligent, there is an overall degradation of the total efficiency of the computational grid in comparison to what can be achieved when the participating users are centrally coordinated . This loss in efficiency might arise due to an unwillingness on the part of some of the grid resource providers to either not perform completely or not perform to the fullest capability, the computational jobs of other users in the grid. In this thesis, our attention is focused on designing grid resource procurement mechanisms which a grid user can use for procuring resources in a computational grid based on bids submitted by autonomous, rational, and intelligent resource providers. Specifically, we follow a game theoretic and mechanism design approach to design three elegant, different incentive compatible procurement mechanisms for this purpose: G-DSIC (Grid-Dominant Strategy Incentive Compatible) mechanism which guarantees that truthful bidding is a best response for each resource provider, irrespective of what the other resource providers bid G-BIC (Grid-Bayesian Nash Incentive Compatible) mechanism which only guarantees that truthful bidding is a best response for each resource provider whenever all other resource providers also bid truthfully G-OPT (Grid-Optimal) mechanism which minimizes the cost to the grid user, satisfying at the same time, (1) Bayesian Incentive Compatibility (which guarantees that truthful bidding is a best response for each resource provider whenever all other resource providers also bid truthfully) and (2) Individual Rationality (which guarantees that the resource providers have non-negative payoffs if they participate in the bidding process). We evaluate the relative merits and demerits of the above three mechanisms using game theoretical analysis and numerical experiments. The mechanisms developed in this thesis are in the context of parameter sweep type of jobs, which consist of multiple homogeneous and independent tasks. We believe the use of the mechanisms proposed transcends beyond parameter sweep type of jobs and in general, the proposed mechanisms could be extended to provide a robust way of procuring resources in a computational grid where the resource providers exhibit rational and strategic behavior.

This thesis focuses on the design and implementation of a software tool able to achieve electricity grid optimal management in a dynamic pricing environment, at the residential level, and from a customer's perspective. The main drivers encouraging a development of energy management at the home level are analyzed, and a system architecture modeling power, thermodynamic and economic subsystems is proposed. The user behavior is also considered. A mathematical formulation of the related energy management optimization problem is proposed based on the linear programming theory. Several cases involving controllable and non-controllable domestic loads as well as renewable energy sources are presented and simulation scenarios illustrate the proposed optimization strategy in each case. The performance of the controller and the changes in energy use are analyzed, and ideas for possible future work are discussed.

A finite-volume procedure, comprising a gradient-reconstruction technique and a multidimensional limiter, has been proposed for upwind algorithms on unstructured grids. The high-resolution strategy, with its inherent dependence on a wide computational stencil, does not suffer from a catastrophic loss of accuracy on a grid with poor connectivity as reported recently is the case with many unstructured-grid limiting procedures. The continuously-differentiable limiter is shown to be effective for strong discontinuities, even on a grid which is composed of highly-distorted triangles, without adversely affecting convergence to steady state. Numerical experiments involving transient computations of two-dimensional scalar convection to steady-state solutions of Euler and Navier-Stokes equations demonstrate the capabilities of the new procedure.

Conventionally, to support varying power demand, the utility company must prepare to supply more electricity than actually needed, which causes inefficiency and waste. With the increasing penetration of renewable energy which is intermittent and stochastic, how to balance the power generation and demand becomes even more challenging. Demand response, which reschedules part of the elastic load in users' side, is a promising technology to increase power generation efficiency and reduce costs. However, how to coordinate all the distributed heterogeneous elastic loads efficiently is a major challenge and sparks numerous research efforts. In this thesis, we investigate different methods to provide demand response and improve power grid efficiency. First, we consider how to schedule the charging process of all the Plugged-in Hybrid Electrical Vehicles (PHEVs) so that demand peaks caused by PHEV charging are flattened. Existing solutions are either centralized which may not be scalable, or decentralized based on real-time pricing (RTP) which may not be applicable immediately for many markets. Our proposed PHEV charging approach does not need complicated, centralized control and can be executed online in a distributed manner. In addition, we extend our approach and apply it to the distribution grid to solve the bus congestion and voltage drop problems by controlling the access probability of PHEVs. One of the advantages of our algorithm is that it does not need accurate predictions on base load and future users' behaviors. Furthermore, it is deployable even when the grid size is large. Different from PHEVs, whose future arrivals are hard to predict, there is another category of elastic load, such as Heating Ventilation and Air-Conditioning (HVAC) systems, whose future status can be predicted based on the current status and control actions. How to minimize the power generation cost using this kind of elastic load is also an interesting topic to the power companies. Existing work usually used HVAC to do the load following or load shaping based on given control signals or objectives. However, optimal external control signals may not always be available. Without such control signals, how to make a tradeoff between the fluctuation of non-renewable power generation and the limited demand response potential of the elastic load, and to guarantee user comfort level, is still an open problem. To solve this problem, we first model the temperature evolution process of a room and propose an approach to estimate the key parameters of the model. Then, based on the model predictive control, a centralized and a distributed algorithm are proposed to minimize the fluctuation and maximize the user comfort level. In addition, we propose a dynamic water level adjustment algorithm to make the demand response always available in two directions. Extensive simulations based on practical data sets show that the proposed algorithms can effectively reduce the load fluctuation. Both randomized PHEV charging and HVAC control algorithms discussed above belong to direct or centralized load shaping, which has been heavily investigated. However, it is usually not clear how the users are compensated by providing load shaping services. In the last part of this thesis, we investigate indirect load shaping in a distributed manner. On one hand, we aim to reduce the users' energy cost by investigating how to fully utilize the battery pack and the water tank for the Combined Heat and Power (CHP) systems. We first formulate the queueing models for the CHP systems, and then propose an algorithm based on the Lyapunov optimization technique which does not need any statistical information about the system dynamics. The optimal control actions can be obtained by solving a non-convex optimization problem. We then discuss when it can be converted into a convex optimization problem. On the other hand, based on the users' reaction model, we propose an algorithm, with a time complexity of O(log n), to determine the RTP for the power company to effectively coordinate all the CHP systems and provide distributed load shaping services. / Graduate