In this thesis, a novel framework for planning and investment studies pertaining to the implementation of system-wide conservation voltage reduction (CVR) is presented. In the CVR paradigm, optimal voltage profiles at the load buses are determined so as to yield load reductions and hence energy conservation. The system modifications required for CVR is known to be capital intensive; therefore, the proposed model determines the system savings and the appropriate price incentives to offer industries such that a minimum acceptable rate-of-return (MARR) is accrued. In this model, the industrial facilities are represented by a combination of constant impedance, constant current, and constant power loads. A detailed case study for Ontario, Canada, is carried out considering that industrial loads are investing in CVR implementation to reduce their energy costs. The optimal incentives that need be offered by the system planner, over a long-term horizon and across various zones of Ontario, are determined using the presented mathematical model. Furthermore, a comprehensive risk analysis, comprising sensitivity studies and Monte Carlo simulations, is carried out considering the variations in the most uncertain model parameters. In this work, it is shown that savings from CVR are enough so that incentives are not required in Ontario. Sensitivity analysis shows that electricity price and project cost have the highest impact on the incentives, and that electricity price and industrial demand have the most effect on system savings. Monte Carlo simulations show that the expected energy cost savings result in expected incentive rates to be relatively low compared to the average electricity price in Ontario. CVR is shown in this thesis to be a low cost Demand Side Management program to implement from the perspective of the power system planner, and a worthwhile investment for the industrial load.

The main topic of this research is the efficient operation of a modernized distribution grid from both the customer side and utility side. For the customer side, this dissertation discusses the planning and operation of a customer with multiple demand response programs, energy storage systems and distributed generators; for the utility side, this dissertation addresses the implementation and assessment of voltage/VAR control and conservation voltage reduction in a distribution grid with distributed generators. The objectives of this research are as follows: (1) to develop methods to assist customers to select appropriate demand response programs considering the integration of energy storage systems and DGs, and perform corresponding energy management including dispatches of loads, energy storage systems, and DGs; (2) to develop stochastic voltage/VAR control techniques for distribution grids with renewable DGs; (3) to develop optimization and validation methods for the planning of integration of renewable DGs to assist the implementation of voltage/VAR control; and (4) to develop techniques to assess load-reduction effects of voltage/VAR control and conservation voltage reduction. In this dissertation, a two-stage co-optimization method for the planning and energy management of a customer with demand response programs is proposed. The first level is to optimally select suitable demand response programs to join and integrate batteries, and the second level is to schedule the dispatches of loads, batteries and fossil-fired backup generators. The proposed method considers various demand response programs, demand scenarios and customer types. It can provide guidance to a customer to make the most beneficial decisions in an electricity market with multiple demand response programs. For the implementation of voltage/VAR control, this dissertation proposes a stochastic rolling horizon optimization-based method to conduct optimal dispatches of voltage/VAR control devices such as on-load tap changers and capacitor banks. The uncertainties of renewable DG output are taken into account by the stochastic formulation and the generated scenarios. The exponential load models are applied to capture the load behaviors of various types of customers. A new method to simultaneously consider the integration of DGs and the implementation of voltage/VAR control is also developed. The proposed method includes both solution and validation stages. The planning problem is formulated as a bi-level stochastic program. The solution stage is based on sample average approximation (SAA), and the validation stage is based on multiple replication procedure (MRP) to test the robustness of the sample average approximation solutions of the stochastic program. This research applies big data-driven analytics and load modeling techniques to propose two novel methodologies to assess the load-reduction effects of conservation voltage reduction. The proposed methods can be used to assist utilities to select preferable feeders to implement conservation voltage reduction.

In this thesis, a novel framework for planning and investment studies pertaining to the implementation of system-wide conservation voltage reduction (CVR) is presented. In the CVR paradigm, optimal voltage profiles at the load buses are determined so as to yield load reductions and hence energy conservation. The system modifications required for CVR is known to be capital intensive; therefore, the proposed model determines the system savings and the appropriate price incentives to offer industries such that a minimum acceptable rate-of-return (MARR) is accrued. In this model, the industrial facilities are represented by a combination of constant impedance, constant current, and constant power loads. A detailed case study for Ontario, Canada, is carried out considering that industrial loads are investing in CVR implementation to reduce their energy costs. The optimal incentives that need be offered by the system planner, over a long-term horizon and across various zones of Ontario, are determined using the presented mathematical model. Furthermore, a comprehensive risk analysis, comprising sensitivity studies and Monte Carlo simulations, is carried out considering the variations in the most uncertain model parameters. In this work, it is shown that savings from CVR are enough so that incentives are not required in Ontario. Sensitivity analysis shows that electricity price and project cost have the highest impact on the incentives, and that electricity price and industrial demand have the most effect on system savings. Monte Carlo simulations show that the expected energy cost savings result in expected incentive rates to be relatively low compared to the average electricity price in Ontario. CVR is shown in this thesis to be a low cost Demand Side Management program to implement from the perspective of the power system planner, and a worthwhile investment for the industrial load.

[pt] Os novos elementos conectados nos sistemas de distribuição de energia elétrica aumentam a complexidade do planejamento e operação destas redes. Os benefícios da implementação de técnicas clássicas, como Conservation Voltage Reduction (CVR), combinadas com uma operação coordenada dos recursos energéticos distribuídos, podem contribuir para o aumento de eficiência nos sistemas de distribuição de energia elétrica e reduzir o consumo de energia. Na técnica CVR, as tensões são reduzidas objetivando redução de picos de demanda e consumo de energia. Este trabalho propõe um modelo de otimização para o planejamento da operação do dia seguinte nos sistemas de distribuição de energia elétrica, considerando sistemas desequilibrados e com penetração de geração distribuída (GD) fotovoltaica. A técnica CVR será aplicada em uma abordagem determinística, estocástica e robusta, considerando a incerteza nos seus parâmetros e, consequente, na modelagem de carga. O modelo de otimização proposto considera a atuação de elementos de controle tradicionais, como transformador On Load Tap Changers (OLTC) na subestação e bancos de capacitores (BC), além de elementos modernos, como inversores fotovoltaicos inteligentes, para minimização do consumo de energia observado na subestação. O problema, fundamentalmente de programação não-linear inteira mista, é transformado em um problema de programação linear de natureza contínua. Os resultados são avaliados no sistema teste IEEE 123-barras para as diferentes estratégias modeladas. A economia de energia obtida foi significativa nas abordagens propostas, mas o modelo de otimização robusta se mostrou mais adequado para reduzir os riscos de violação de tensão. / [en] The new elements connected in electrical distribution systems increase the complexity of grids planning and operating. The benefits of classical techniques, such as Conservation Voltage Reduction (CVR), combined with a coordinated operation of distributed energy resources, can contribute to increasing efficiency and reducing energy consumption of the distribution systems. In the CVR technique, voltages are reduced in order to reduce peak demand and energy consumption. This paper proposes an optimization model for the day-ahead operation planning of unbalanced distribution systems with photovoltaic distributed generation (DG) penetration. The CVR technique will be applied in deterministic, stochastic and robust approach, considering the uncertainty in its parameters and consequently, in the load modeling. The proposed optimization model considers the operation of traditional control elements, such as On Load Tap Changers (OLTC) at substation and capacitor banks (CB), in addition to modern elements, such as intelligent photovoltaic inverters, to minimize the energy consumption at the substation. The problem, originally of mixed-integer nonlinear programming, is transformed into a continuous linear programming problem. The results are evaluated in the IEEE123-bus test system for the different optimization approaches. The energy savings obtained were significant in all the proposed approaches, but the robust optimization model proved to be more adequate since it reduces the risk of voltage violations.

The increasing awareness on the effect of carbon emissions in our planet has led to several countries to adopt targets for their reduction. One way of contributing to this aim is to use and distribute electricity more efficiently. In this context, Conservation Voltage Reduction (CVR), a well-known technique that takes advantage of the positive correlation between voltage and demand to reduce energy consumption, is gaining renewed interest. This technique saves energy by only reducing customer voltages, without relying on customer actions and, therefore, can be controlled by the Distribution Network Operator (DNO). CVR not only brings benefits to the electricity system by reducing generation requirements (fewer fossil fuel burning and carbon emissions), but also to customers, as energy bill reductions. The extent to which CVR can bring benefits mainly depends on the customers load composition and their voltages. While the former dictates the voltage-demand correlation, the latter constraints the voltage reduction that can be applied without violating statutory limits. Although CVR has been studied for many years, most of the studies neglect the time-varying voltage-demand characteristic of loads and/or do not assess end customer voltages. While these simplifications could be used to estimate CVR benefits for fixed and limited voltage reductions, realistic load and network models are needed to assess the performance of active CVR schemes, where voltages are actively managed to be close to the minimum limit. Moreover, distribution networks have been traditionally designed with limited monitoring and controllability. Therefore, CVR has been typically implemented by adopting conservative voltage reductions from primary substations, for both American and European-style networks. However, as new infrastructure is deployed in European-style LV networks (focus of this work), such as monitoring and on-load tap changers (OLTCs), the opportunity arises to actively manage voltages closer to end customer (unlocking further energy savings). Although these technologies have shown to effectively control voltages in LV networks, their potential for CVR has not been assessed before. Additionally, most CVR studies were performed in a context where distributed generation (DG) was not common. However, this has changed in many countries, with residential photovoltaic (PV) systems becoming popular. As this is likely to continue, the interactions of residential PV and CVR need to be studied. This thesis contributes to address the aforementioned literature gaps by: (i) proposing a simulation framework to characterise the time-varying voltage-demand correlation of individual end customers; (ii) developing a process to model real distribution networks (MV and LV) from DNO data; (iii) adopting a Monte Carlo-based quantification process to cater for the uncertainties related to individual customer demand; (iv) assessing the CVR benefits that can be unlocked with new LV infrastructure and different PV conditions. To accomplish (iv), first, a simple yet effective rule-based scheme is proposed to actively control voltages in OLTC-enabled LV networks without PV and using limited monitoring. It is demonstrated that by controlling voltages closer to customers, annual energy savings can increase significantly, compared to primary substation voltage reductions. Also, to understand the effect of PV on CVR, a centralized, three-phase AC OPF-based CVR scheme is proposed. This control, using monitoring, OLTCs and capacitors across MV and LV networks, actively manages voltages to minimize energy consumption in high PV penetration scenarios whilst considering MV-LV constraints. Results demonstrate that without CVR, PV systems lead to higher energy imports for customers without PV, due to higher voltages. Conversely, the OPF-based CVR scheme can effectively manage voltages throughout the day, minimising energy imports for all customers. Moreover, if OLTCs at secondary substations are available (and managed in coordination with the primary substation OLTC), these tend to regulate customer voltages close to the minimum statutory limit (lower tap positions), while the primary OLTC delivers higher voltages to the MV network to also reduce MV energy losses.

Submitted by DARWIN ALEXIS QUIJANO RODEZNO (alexisqr@yahoo.es) on 2018-05-08T15:12:06Z No. of bitstreams: 1 TeseDarwinQuijano.pdf: 3515679 bytes, checksum: f52d72089eda5a3bb15b367d3afbf33a (MD5) / Approved for entry into archive by Cristina Alexandra de Godoy null (cristina@adm.feis.unesp.br) on 2018-05-08T17:48:35Z (GMT) No. of bitstreams: 1 quijanorodezno_da_dr_ilha.pdf: 3515679 bytes, checksum: f52d72089eda5a3bb15b367d3afbf33a (MD5) / Made available in DSpace on 2018-05-08T17:48:35Z (GMT). No. of bitstreams: 1 quijanorodezno_da_dr_ilha.pdf: 3515679 bytes, checksum: f52d72089eda5a3bb15b367d3afbf33a (MD5) Previous issue date: 2018-04-19 / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Atualmente, existe uma tendência para aumentar a participação da Geração Distribuída (GD) baseada em Fontes de Energia Renováveis (FER) no suprimento do consumo global de energia elétrica. Esta tendência está sendo impulsionada principalmente por iniciativas governamentais destinadas a aumentar a eficiência energética, aumentar o uso da energia proveniente das FER e reduzir as emissões de gases de efeito estufa. No entanto, à medida que seu nível de penetração aumenta, a GD pode dar origem a um sistema incapaz de fornecer energia de forma confiável e de acordo com os padrões de qualidade. Nesse cenário, a Gestão de Redes Ativas (GRA) surge como uma alternativa para a integração de grandes montantes de GD. A GRA promove a disponibilização de instrumentos comerciais e regulatórios, e o fornecimento das redes de distribuição com tecnologias de automação para procurar serviços ancilares e flexibilidade a partir da GD. A GRA requer o desenvolvimento de ferramentas computacionais para coordenar a implementação de esquemas de controle inteligentes, chamados de esquemas de GRA, a fim de otimizar a utilização e operação das redes. Neste trabalho, são propostos modelos de otimização e técnicas de solução para a GRA considerando a integração de GD solar fotovoltaica e eólica e a eficiência energética. O primeiro modelo é desenvolvido para determinar a capacidade máxima de GD que pode ser alocada em uma rede de distribuição quando se considera o efeito da tensão na eficiência das cargas. No segundo modelo, o procedimento de controlar os níveis de tensão para reduzir a demanda das cargas é implementado para economia de energia e para o balanço de geração e demanda, através de uma estratégia projetada para o planejamento da operação de redes de distribuição ativas. Em ambos os modelos as incertezas são consideradas através de formulações de programação estocásticas de dois estágios. Os esquemas de GRA considerados são o controle coordenado da tensão através de reguladores de tensão e transformadores com comutador de tap sob carga, suporte de potência reativa através da GD, e corte de geração. A técnica de solução envolve a discretização das funções de densidade de probabilidade que definem os parâmetros incertos através de um processo de geração e redução de cenários. Depois, o método de decomposição de Benders é aplicado para reduzir o esforço computacional necessário para resolver os problemas formulados. Os algoritmos desenvolvidos foram testados em dois sistemas teste IEEE e os resultados mostraram benefícios importantes para a integração de GD e a eficiência energética. / Nowadays, there is a trend to increase the participation of distributed generation (DG) based on renewable energy sources in supplying the global electricity consumption. This trend is being driven mainly by government initiatives to increase energy efficiency, convert the energy use to renewable sources and reduce greenhouse gas emissions. However, as its penetration level increases, the DG can give rise to a system unable to deliver energy reliably and according to quality standards. In this scenario, active network management (ANM) emerges as an alternative for the integration of large amounts of DG. ANM promotes the availability of commercial and regulatory instruments and the provision of distribution networks with automation technologies for procuring ancillary services and flexibility from the DG. ANM requires the development of computational tools to coordinate the implementation of intelligent control schemes, called ANM schemes, in order to optimize the utilization and operation of distribution networks. In this work, optimization models and solution techniques are proposed for ANM considering the integration of solar and wind-based DG and energy efficiency. The first model is developed to determine the maximum capacity of DG that can be allocated in a distribution network when considering the effect of voltage on load efficiency. In the second model, the procedure of controlling the voltage levels to reduce the load demand is implemented for energy saving and for balancing the demand and generation, in a strategy designed for the operation planning of active distribution networks. In both models the uncertainties are considered through two-stage stochastic programming formulations. The ANM schemes considered are the coordinated voltage control through voltage regulators and transformers with on-load tap changer, reactive power support from the DG, and DG generation curtailment. The solution technique involves the discretization of the probability density functions that define the uncertain parameters through a scenario generation and reduction process. Then, the Benders decomposition method is applied in order to reduce the computational effort required to solve the formulated problems. The developed algorithms were tested in two IEEE test systems and the results showed important benefits for the integration of DG and energy efficiency. / 2014/14201-0 e 2015/12911-3

Orientador: Antonio Padilha Feltrin / Resumo: Atualmente, existe uma tendência para aumentar a participação da Geração Distribuída (GD) baseada em Fontes de Energia Renováveis (FER) no suprimento do consumo global de energia elétrica. Esta tendência está sendo impulsionada principalmente por iniciativas governamentais destinadas a aumentar a eficiência energética, aumentar o uso da energia proveniente das FER e reduzir as emissões de gases de efeito estufa. No entanto, à medida que seu nível de penetração aumenta, a GD pode dar origem a um sistema incapaz de fornecer energia de forma confiável e de acordo com os padrões de qualidade. Nesse cenário, a Gestão de Redes Ativas (GRA) surge como uma alternativa para a integração de grandes montantes de GD. A GRA promove a disponibilização de instrumentos comerciais e regulatórios, e o fornecimento das redes de distribuição com tecnologias de automação para procurar serviços ancilares e flexibilidade a partir da GD. A GRA requer o desenvolvimento de ferramentas computacionais para coordenar a implementação de esquemas de controle inteligentes, chamados de esquemas de GRA, a fim de otimizar a utilização e operação das redes. Neste trabalho, são propostos modelos de otimização e técnicas de solução para a GRA considerando a integração de GD solar fotovoltaica e eólica e a eficiência energética. O primeiro modelo é desenvolvido para determinar a capacidade máxima de GD que pode ser alocada em uma rede de distribuição quando se considera o efeito da tensão na eficiência das cargas.... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Nowadays, there is a trend to increase the participation of distributed generation (DG) based on renewable energy sources in supplying the global electricity consumption. This trend is being driven mainly by government initiatives to increase energy efficiency, convert the energy use to renewable sources and reduce greenhouse gas emissions. However, as its penetration level increases, the DG can give rise to a system unable to deliver energy reliably and according to quality standards. In this scenario, active network management (ANM) emerges as an alternative for the integration of large amounts of DG. ANM promotes the availability of commercial and regulatory instruments and the provision of distribution networks with automation technologies for procuring ancillary services and flexibility from the DG. ANM requires the development of computational tools to coordinate the implementation of intelligent control schemes, called ANM schemes, in order to optimize the utilization and operation of distribution networks. In this work, optimization models and solution techniques are proposed for ANM considering the integration of solar and wind-based DG and energy efficiency. The first model is developed to determine the maximum capacity of DG that can be allocated in a distribution network when considering the effect of voltage on load efficiency. In the second model, the procedure of controlling the voltage levels to reduce the load demand is implemented for energy saving and for b... (Complete abstract click electronic access below) / Doutor

No description available.

[pt] Para simular, prever e controlar os sistemas de energia elétrica, engenheiros precisam de ferramentas computacionais para modelar os componentes dessa rede interconectada altamente complexa. Muitos esforços ao longo do século passado foram dedicados a desenvolver modelos matemáticos para geradores, linhas de transmissão, compensadores de potência reativa, transformadores e assim por diante. Os principais componentes dos sistemas de potência são representados precisamente através de modelos matemáticos, mas as cargas ainda são uma fonte de incerteza nas simulações, devido à sua característica de aleatoriedade. Modelos de carga conservadores superestimam a resposta de potência a desvios de tensão, enquanto modelos de carga excessivamente otimistas podem subestimar as margens de estabilidade, deixando o sistema muito próximo do seu limite operacional. É preciso estabelecer representações de cargas tão próximas da realidade quanto possível, a fim de explorar os recursos de rede de modo mais eficiente. Este trabalho fornece uma metodologia para modelagem de carga, investigando e resumindo as etapas do processo, que podem ser implementadas de diversas maneiras. O tratamento de dados, a escolha de uma representação matemática do modelo de carga e sua estimação de parâmetros são apresentados através de estudos de caso reais, tanto para uma aplicação focada na dinâmica do sistema elétrica, quanto para uma aplicação em regime permanente. Discute-se como otimização e conceitos de inferência estatística podem ser ferramentas efetivas para alcançar melhores aproximações sobre como a carga responderá a perturbações causadas por variações de tensão, sejam estas variações espontâneas, ou devido a ações de controle, ou causadas por curtos-circuitos. / [en] To simulate, predict and control Electric Power Systems (EPS), engineers need tools to model the components of this highly complex interconnected network. Many efforts over the past century were dedicated to develop mathematical models for generators, transmission lines, reactive power compensators, transformers and so on. The main components of the power systems are precisely represented by mathematical models, but the loads are still a source of uncertainty in the simulations, due to their random characteristics. It is well known that conservative load models super estimate power response to voltage deviations, and, on the other hand, over-optimistic load models may underestimate stability margins, leading a system to operate too close to its limit. It is necessary to stablish load representations as close to reality as possible, in order to fully exploit grid resources. This work provides a methodology for load modeling, investigating and summarizing the steps of the process, whose can be implemented in a wide variety of ways. Data treatment, the choice of a load model representation and their parameters estimation are presented through real case studies, both for dynamic simulation and a steady state application. It is discussed how optimization and statistical inference concepts can be effective tools to reach better approximations on how load will respond to disturbances caused by voltage variations, whether these were spontaneous, due to control actions, or caused by short-circuits.