Biomass is one of the renewable energy sources being used widely for power generation. This research work includes developing a comprehensive model for a biomass based power generation system as well as analyzing the technical, economical, and environmental impacts. The research objectives include modeling of the system, stability studies, and sensitivity analysis using MATLAB/Simulink. A mathematical model for the gas turbine has been developed and successfully interconnected with the distribution network. Transient stability of the power system has been carried out for four bus and six bus test case systems. Maximum rotor speed deviation, oscillation duration, rotor angle, and mechanical power have been taken as the stability indicators to analyze the system characteristics. Additionally, the sensitivity of the system to the changes of gas turbine parameters has been investigated under balanced and unbalanced fault scenarios. The economical and environmental impacts of the biomass have been analyzed using HOMER software developed by the National Renewable Energy Laboratory (NREL). The net present cost of the four biomass resources namely agricultural resources, forest residues, animal waste, and energy crops were obtained and the comparison of the costs of the biomass fuels as well as the diesel have been carried out. To investigate the environmental impact, carbon emissions of the different biomass fuels have been explored using HOMER.

Since the last United Nations Climate Change Conference in 2015 in Paris (the COP 21), world leaders acknowledged climate change. There is no need any more to justify the switch from fossil fuel-based to renewable energy sources. Nevertheless, this transition is far from being straightforward. Besides technologies that are not yet mature -- or at least not always financially viable in today's economy -- the power grid is currently not ready for a rapid and massive integration of renewable energy sources. A main challenge for the power grid is the inadequacy between electric production and consumption that will rise along with the integration of such sources. Indeed, due to their dependence on weather, renewable energy sources are intermittent and difficult to forecast with today's tools. As a commodity, electricity is a quite distinct good for which there must be perfect adequacy of production and consumption at all time and characterized by a very inelastic demand. High shares of renewable energy sources lead to high price volatility and a higher risk to jeopardize the security of supply. Additionally, the switch to renewable energy sources will lead to an electrification of loads and transportation, and thus the emergence of new higher-consumption loads such as electric vehicles and heat pumps. These new and higher-consumption loads, combined with the population growth, will cause over-rated power load increases with less predictable load patterns in the future.This work focuses on issues specific to the distribution power grid in the context of the current energy transition. Traditional low-voltage grids are perhaps the most passive circuits in power grids. Indeed, they are designed primarily using a fit and forget approach where power flows go from the distribution transformer to the consumers and no element has to be operated or regularly managed. In fact, low-voltage networks completely lack observability due to very low monitoring. The distribution grid will especially undergo drastic changes from this energy transition. Distributed sources and new high-consumption -- and uncoordinated -- loads result in new power flow patterns, as well as exacerbated evening peaks for which it is not designed. The consequences are power overloads and voltage imbalances that deteriorate grid components, such as a main asset like the medium-to-low voltage transformer. Additionally, the distribution grid is characterized by end-users that pay a price for electricity that does not reflect the grid situation -- that is, mostly constant over a year -- and allow little to no actions on their consumption.These issues have motivated authorities to propose a global approach to ensure security of electricity supply at short and medium-term. The latter requires, among others, the development of demand response programs that encourage users to take advantage of load flexibility. First, we propose adequate electricity pricing structures that will allow users to unlock the potential of such demand response programs; namely, dynamic pricings combined with a prosumer structure. Second, we propose a fast and robust two-level optimization, formulated as a mixed-integer linear program, that coordinates flexible loads. We focus on two types of loads; electric vehicles and heat pumps, in an environment with solar PV panels. The lower level aims at minimizing individual electricity bills while, at the second level, we optimize the power load curve, either to maximize self-consumption, or to smoothen the total power load of the transformer. We propose a parametric study on the trade-off between only minimizing the individual bills versus only optimizing power load curves, which have proven to be antagonist objectives. Additionally, we assess the impact of the rising share of flexible loads and renewable energy sources for scenarios from today until 2050. A macro-analysis of the results allows us to assess the benefits of load flexibility for every actor of the distribution grid, and depending on the choice of a pricing structure. Our optimization has proved to prevent evening peaks, which increases the lifetime of the distribution transformer by up to 200%, while individual earnings up to 25% can be made using adequate pricings. Consequently, the optimization significantly increases the power demand elasticity and increases the overall welfare by 10%, allowing the high shares of renewable energy sources that are foreseen. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-02-05T17:32:25Z No. of bitstreams: 1 diegonascimentoarcanjo.pdf: 1706072 bytes, checksum: 2329ddd810b5aca8da733c7793937d65 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-02-26T11:52:47Z (GMT) No. of bitstreams: 1 diegonascimentoarcanjo.pdf: 1706072 bytes, checksum: 2329ddd810b5aca8da733c7793937d65 (MD5) / Made available in DSpace on 2016-02-26T11:52:47Z (GMT). No. of bitstreams: 1 diegonascimentoarcanjo.pdf: 1706072 bytes, checksum: 2329ddd810b5aca8da733c7793937d65 (MD5) Previous issue date: 2014-07-24 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Neste trabalho é proposto uma metodologia multi-estágio utilizando algoritmos bio-inspirados para a resolução do processo de Restabelecimento de Sistemas Elétricos de Distribuição. O primeiro estágio consiste na solução de uma função multi-objetivo visando a determinação da configuração final das chaves do sistema após isolados os ramos defeituosos (configuração de pós-contingência). Neste estágio, a modelagem da função multi-objetivo busca uma configuração adequada de chaves para minimizar a carga não suprida, as perdas do sistema, o número de chaveamentos, penalizando as violações aos limites operativos do sistema e considerando a presença de consumidores prioritários. Adicionalmente, a restrição de radialidade é assegurada em cada configuração utilizando, caso necessário, uma técnica de abertura de laço. A partir da configuração final obtida no primeiro estágio, são identificadas as chaves que foram manobradas. O segundo estágio da metodologia busca a determinação da sequência de chaveamento levando em conta a minimização da energia não suprida. Essa formulação permite que o tempo de manobra das chaves possa ser considerado. Sendo necessário, é realizado, ainda neste estágio, cortes mínimos discretos de carga para cada manobra executada. Em ambos os estágios foram utilizadas algoritmos bio-inspirados como métodos de solução dos respectivos problemas de otimização não-lineares inteiros mistos. As técnicas utilizadas são: Algoritmos Genéticos, Método da Eco Localização de Morcegos (Bat Algorithm) e Método da Reprodução dos Pássaros Cuco (Cuckoo Search). Os desenvolvimentos do algoritmo proposto foi implementado no ambiente MatLab®. Os resultados obtidos foram comparados com outras metodologias conhecidas da literatura comprovando a eficiência e robustez da técnica proposta. / This dissertation proposes a methodology for solving multi-stage process of Restoration on Power Distribution Systems using Nature-Inspired Algorithms. The first stage consists in solving a fitness multi-objective function in order to determine the final configuration of the switches after the faulted branches were isolated (post-contingency configuration). In this stage the multi-objective function seeks through the suitable configuration to minimize the undelivered power, the power losses, the number of switching, penalizing for violation in the system operational limits and taking in consideration the presence of priority load in the system. Additionally the radiality constraint is improved using an open loop technique. After the final configuration is obtained, for the first stage, the switches which were maneuvered are identified. The second stage of the methodology is to determine the sequence of switching taking into account the minimization of energy not supplied. This formulation allows to consider the switching operation time. If necessary, the minimum discrete load shedding procedure is made for each maneuvered switch. In both stages Nature-Inspired Algorithms to solve mixed integer nonlinear programming problems were used. The techniques used are: Genetic Algorithms, Bat Algorithm and Cuckoo Search. The developments of the proposed algorithm were implemented in MatLab ® environment. The results obtained were compared with other well-known methodologies showing the efficiency and robustness of the proposed technique.

Motivated by the need for less polluting energy production, the recent increase in renewable electricity production is reshaping classical power systems. Initially unidirectional and constant power flow becomes multi-directional and dynamic. As one of the many consequences, classical power system fault location methods might become outdated.To this extent, the development of new methods as well as improvement of already existing methods is of great interest. Additionally, robust and fast means of fault location strengthen power system reliability by improving recovery time. Since most of the faults occur at the distribution level, a study of the main fault location methods in distribution power systems is first conducted. Relevant information about their respective advantages and drawbacks put into light the need to improve classical fault location methods or to develop new methods. The main objective of the thesis is to develop a prototype data-driven ground fault location method that aims to improve the robustness and accuracy offault location in the power system, as well as offer new solutions for fault location. An 11-bus 20 kV distribution power system with distributed generation is modeled to test the method. As a requirement for data-driven methods, the dataset is provided through simulation where time-domain three-phase voltages at the system substation during fault are generated. This data is then processed using dyadic discrete wavelet transform, a powerful signal processing method, to extract useful information of the signal, after what relevant features are found from the wavelet coefficients. To predict the location ofthe fault, neural networks are trained to find potential correlations between computed features and the distance of the fault from the substation. After testing and comparing different combinations of neural networks, results are analyzed, and eventually, challenges and potential improvements for further development and application of the method are introduced.