Modelos de programação matemática para o gerenciamento de energia em modernos sistemas de distribuição de energia elétrica
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Data
2017-02-17
Autores
Ñahuis, Fernando Vladimir Cerna [UNESP]
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Universidade Estadual Paulista (Unesp)
Resumo
Nesta tese são apresentados três modelos de programação matemática que abordam os problemas de otimização relacionados ao gerenciamento da energia nos sistemas de distribuição de energia elétrica (SDEE), como: 1) Programação ótima das entregas e carregamento dos veículos elétricos (VEs) durante a navegação em um mapa de cidade, 2) Gerenciamento ótimo pelo lado da demanda considerando um sistema fotovoltaico híbrido (SFH) em uma residência em baixa tensão (RBT) no SDEE, e 3) O melhoramento do fator de carga (FC) do SDEE através do controle da demanda. O primeiro problema visa minimizar os custos relacionados com a manutenção e geração de horas extra durante a operação de uma frota de VEs, levando em conta um conjunto de entregas pre-especificadas, assim como, pontos de carregamento alocados ao longo de cada via urbana (principal e/ou secundária) pertencente ao mapa da cidade. No segundo problema, para uma residência em baixa tensão é planejado um perfil ótimo de consumo para o dia seguinte. Este perfil de consumo é obtido através de um programa de gerenciamento pelo lado da demanda (GLD) que considera uma estrutura tarifária e um esquema de operação que otimiza os recursos energéticos vindos de um SFH e o SDEE. Para cada problema de otimização é apresentado o seu correspondente modelo de programação não linear inteiro misto (PNLIM). O terceiro problema visa minimizar os custos por compra de energia (consumo e perdas de potência ativa) da concessionária, levando em conta, o controle da demanda dos usos-finais, presentes nas unidades consumidoras (residenciais, comerciais, e industriais) no SDEE. As incertezas na utilização dos usos-finais nas unidades consumidoras são simuladas através de um algoritmo Monte Carlo. Além disso, o modelo proposto PIMRQ é rodado dentro de um processo iterativo, que visa a melhoria do FC do SDEE. Por outro lado, através destes modelos não-lineares, a solução ótima global não é garantida, enquanto o uso de modelos equivalentes (para o primeiro e segundo problema, sendo um modelo aproximado para o terceiro) de programação linear inteira mista (PLIM) resolvidos por ferramentas de otimização clássica existentes garantem a convergência para a solução ótima global. Por conseguinte, para resolver este inconveniente, os seus modelos MILP equivalentes são obtidos e explicados em detalhe. Os modelos propostos foram implementados na linguagem de modelagem algébrica AMPL e resolvidos usando o solver comercial CPLEX. Além disso, algoritmos de simulação para representar as incertezas dos tempos de demora na operação dos VEs e os hábitos de utilização dos usos-finais durante o dia, são desenvolvidos. Um grafo unidirecional de 71 nós, uma rede elétrica IEEE de 34 nós, e 21 usos-finais (incluído um VE plug-in para o carregamento na residência) residenciais são utilizados para testar a precisão e a eficiência, assim como, também técnica de solução dos modelos propostos para cada problema.
This thesis presents three mathematical programming models to address the optimization problems related to the energy management in the electricity distribution systems (EDSs), such as: 1) Optimal delivery scheduling and charging of electric vehicles (EVs) in the navigation of a city map, 2) Optimal demand side management of an EDS considering a hybrid photovoltaic system (HPS) in a residential low voltage (RLV), and 3) Load factor improvement through the demand control in the EDS. The first problem aims at minimizing the costs related to the maintenance and generation of extra hours during the operation of a EVs fleet, taking into account a number of prespecified deliveries, as well as charging points allocated along each urban road (main or secondary) belongs to the city map. In the second problem, for a RLV, an optimal consumption profile of a day-ahead is planned. This consumption profile is obtained through a demand side management (DSM) program that considers a tariff structure and an operating scheme that optimizes the energy resources coming from HFS and EDS. The third problem aims at minimizing the costs of energy purchase (consumption and active energy losses) of the company, taking into account, the demand control of the end-uses, presents in the consumers units (residential, commercial, and industrial) in the EDS. Uncertainties in the use of the end-uses in the different consumer units are simulated through a Monte Carlo algorithm that determines a habitual consumption profile for EDSs. Based on this habitual profile, the proposed MIPRQ model determines an optimal profile for EDSs. This model uses an iterative process that aims to improve the load factor of the EDS. For each optimization problem the corresponding non-linear mixed integer programming (NLMIP) model is presented. On the other hand, via these nonlinear models, the global optimal solution is not guaranteed, while using the equivalent mixed-integer linear (MILP) models (for the first and second problems, being an approximate model for the third) and solving them by existing classical optimization tools ensures convergence to global optimal solution. Therefore, in order to address this drawback, their equivalent mixed integer linear programming (MILP) models are obtained and explained in detail. The proposed models are implemented in the algebraic modeling language AMPL and solved using the commercial CPLEX solver. Moreover, simulations algorithms to represent the uncertainties of delay times in the operation of EVs and usage habits of end-uses during the day, are developed. A multidirectional graph with 71 nodes, an electrical network IEEE 34 nodes, and a quantity of 21 residential end-uses (including an EV plug-in for residential charging) are used to test the precision and the efficiency, as well as the solution technique of the models proposed for each problem.
This thesis presents three mathematical programming models to address the optimization problems related to the energy management in the electricity distribution systems (EDSs), such as: 1) Optimal delivery scheduling and charging of electric vehicles (EVs) in the navigation of a city map, 2) Optimal demand side management of an EDS considering a hybrid photovoltaic system (HPS) in a residential low voltage (RLV), and 3) Load factor improvement through the demand control in the EDS. The first problem aims at minimizing the costs related to the maintenance and generation of extra hours during the operation of a EVs fleet, taking into account a number of prespecified deliveries, as well as charging points allocated along each urban road (main or secondary) belongs to the city map. In the second problem, for a RLV, an optimal consumption profile of a day-ahead is planned. This consumption profile is obtained through a demand side management (DSM) program that considers a tariff structure and an operating scheme that optimizes the energy resources coming from HFS and EDS. The third problem aims at minimizing the costs of energy purchase (consumption and active energy losses) of the company, taking into account, the demand control of the end-uses, presents in the consumers units (residential, commercial, and industrial) in the EDS. Uncertainties in the use of the end-uses in the different consumer units are simulated through a Monte Carlo algorithm that determines a habitual consumption profile for EDSs. Based on this habitual profile, the proposed MIPRQ model determines an optimal profile for EDSs. This model uses an iterative process that aims to improve the load factor of the EDS. For each optimization problem the corresponding non-linear mixed integer programming (NLMIP) model is presented. On the other hand, via these nonlinear models, the global optimal solution is not guaranteed, while using the equivalent mixed-integer linear (MILP) models (for the first and second problems, being an approximate model for the third) and solving them by existing classical optimization tools ensures convergence to global optimal solution. Therefore, in order to address this drawback, their equivalent mixed integer linear programming (MILP) models are obtained and explained in detail. The proposed models are implemented in the algebraic modeling language AMPL and solved using the commercial CPLEX solver. Moreover, simulations algorithms to represent the uncertainties of delay times in the operation of EVs and usage habits of end-uses during the day, are developed. A multidirectional graph with 71 nodes, an electrical network IEEE 34 nodes, and a quantity of 21 residential end-uses (including an EV plug-in for residential charging) are used to test the precision and the efficiency, as well as the solution technique of the models proposed for each problem.
Descrição
Palavras-chave
Modelos de programação matemática, Mapa de cidade, Operação de uma frota de VEs, Gerenciamento pelo lado da demanda, Sistema fotovoltaico híbrido, City map, Demand side management, Hybrid photovoltaic system, Mathematical programming models, Operation of a fleet of EVs