Respuesta a la Demanda para Smart Home Utilizando Procesos Estocásticos

The increase in energy consumption, especially in residential consumers, means that the electrical system should grow at pair, in infrastructure and installed capacity, the energy prices vary to meet these needs, so this paper uses the methodology of demand response using stochastic methods such as Markov, to optimize energy consumption of residential users. It is necessary to involve customers in the electrical system because in this way it can be verified the actual amount of electric charge that exists on the network at a given time, and this helps electrical systems to become more reliable and efficient, providing security when an energy supply is given. In addition, to optimize energy consumption lower CO2 emissions is achieved for the environment by relying less on plants using fossil fuels, which implies a reduction in global pollution, an issue that is very important today. Although there are models for energy optimization, the reality is that the consumption at home is much more complex because it has variables such as: geographical location, architecture, materials used for the design, arrangement of windows, number of occupants, weather, and season. Therefore, to apply the response to the demand in residential settings, it is important to take into account basic criteria, such as maintaining the comfort of the user and in this way a sustained participation of demand response, having individual participation, it would require a great investment in technology of control and communication.El incremento del consumo de energía en los usuarios finales, en especial en los residenciales, implica que el sistema eléctrico crezca a la par, tanto en infraestructura como en potencia instalada, además los precios de la energía varían para poder satisfacer estas necesidades, por lo que el presente trabajo utiliza la metodología de respuesta a la demanda utilizando métodos estocásticos como Markov para poder optimizar el consumo de energía en los usuarios residenciales. Es necesaria la participación de los clientes en el sistema eléctrico, ya que de esta manera se logra verificar la cantidad de carga real que existe en la red en determinado tiempo, y esto ayuda a los sistemas eléctricos a ser más confiables y eficientes, dando garantías a la hora de dar un suministro energético. Además, al optimizar el consumo energético se logra una menor emisión de CO2 al medio ambiente al depender menos de centrales que utilizan combustibles fósiles, lo cual implica una reducción en la contaminación global, un tema que es de primordial importancia en la actualidad. Aunque existen modelados para la optimización energética, la realidad es que el consumo de una vivienda es mucho más complejo, ya que tiene variables como la ubicación geográfica, la arquitectura, los materiales usados para el diseño, la disposición de las ventanas, el número de ocupantes, el clima, la estación del año. Entonces, al aplicar la respuesta a la demanda en entornos residenciales, es importante tomar en cuenta criterios básicos, como por ejemplo mantener el confort del usuario final ya que de esta manera se logra una participación sostenida de la respuesta de la demanda, al tener participación individual, se requeriría una gran inversión en tecnología de control y comunicación

Gestión energética mediante procesos estocásticos para la respuesta a la demanda

Population increase and industrial development have forced a greater energy demand, this forces the generating companies to increase their infrastructure of energy production, which is very expensive; for this reason, is sought to implement Demand Response Policies, including Distributed Generation, on this way, consumption will be achieved more efficient, like transferring non-representative loads of peak hours to other time periods. Also, it’s possible to implement Energy Management Systems, in other words, adopt policies where is necessary to plan an optimal consumption and applying to general or industrial electrical system. These policies will help to energy company improving its competitiveness, it includes reduction of energy consumption using more efficient equipment. It should be considered that improving equipment technology is not enough in fact to have energy efficiency, this should be accompanied by an optimal use of energy resources and their optimal use with skillful and precise ways. To solve the problem of energy consumption, it has been used stochastic processes, where an optimization of consumption is done through Demand Response Policies, then, through economic dispatch, it’s possible to find the lowest generation cost, using cheaper and less polluting generators, since it will be left aside part of thermal generation, reducing the costs of electrical production and also contributing to the reduction of environmental pollution with less CO2 emissions.El aumento de la población y el desarrollo industrial, han obligado a que exista una mayor demanda energética, esto obliga a las empresas generadoras a aumentar su infraestructura de producción de energía, lo cual es muy costoso, por esta razón se busca implementar políticas de respuesta a la demanda (RD), incluyendo generación distribuida (GD), así se logrará un consumo energético más eficiente, ya que se podrán trasladar cargas no representativas de horarios pico de consumo, a otros periodos de tiempo. Además, se puede implementar sistemas de gestión energética (GE), es decir, adoptar políticas en donde se haga una planificación de consumo óptimo y esta sea aplicada a la industria o al sistema eléctrico en general, esta política ayudaría a la empresa a que mejore su competitividad, ya que los gastos por consumo energético se reducirían, al tener equipos más eficientes. Se debe considerar que mejorar la tecnología en equipos no es suficiente para tener eficiencia energética, esto debe ir acompañado de un óptimo uso de recurso energéticos y su utilización de forma hábil y precisa. Para resolver el problema de consumo energético se ha utilizado procesos estocásticos, en donde se hace una optimización de consumo mediante RD, luego mediante un despacho económico se encontrará el menor costo de generación, utilizando generadores más económicos y menos contaminantes, ya que se dejará de lado parte de la generación térmica, reduciendo así los costos de producción eléctrica y además se contribuye a la disminución de la contaminación ambiental generando menos emisiones de CO2

Optimisation and Integration of Variable Renewable Energy Sources in Electricity Networks

The growing penetration of renewable energy sources (RESs) into the electricity power grid is profitable from a sustainable point of view and provides economic benefit for long-term operation. Nevertheless, balancing production and consumption is and will always be a crucial requirement for power system operation. However, the trend towards increasing RESs penetration has raised concerns about the stability, reliability and security of future electricity grids. The clearest observation in this regard is the intermittent nature of RESs. Moreover, the location of renewable generation tends to be heavily defined by meteorological and geographical conditions, which makes the generation sites distant from load centres. These facts make the analysis of electricity grid operation under both dynamic and the steady state more difficult, posing challenges in effectively integrating variable RESs into electricity networks. The thesis reports on studies that were conducted to design efficient tools and algorithms for system operators, especially transmission system operators for reliable short-term system operation that accounts for intermittency and security requirements. Initially, the impact of renewable generation on the steady state is studied in the operation stage. Then, based on the first study, more sophisticated modeling on the electricity network are investigated in the third and fourth chapters. Extending the previous studies, the fourth chapter explores the potential of using multiple microgrids to support the main grid’s security control. Finally, the questions regarding the computational efficiency and convergence analysis are addressed in chapter 5 and a DSM model in a real-time pricing environment is introduced. This model presents an alternative way of using flexibility on the demand side to compensate for the uncertainties on the generation side

Optimal Demand Response Strategy in Electricity Markets through Bi-level Stochastic Short-Term Scheduling

Current technology in the smart monitoring including Internet of Things (IoT) enables the electricity network at both transmission and distribution levels to apply demand response (DR) programs in order to ensure the secure and economic operation of power systems. Liberalization and restructuring in the power systems industry also empowers demand-side management in an optimum way. The impacts of DR scheduling on the electricity market can be revealed through the concept of DR aggregators (DRAs), being the interface between supply side and demand side. Various markets such as day-ahead and real-time markets are studied for supply-side management and demand-side management from the Independent System Operator (ISO) viewpoint or Distribution System Operator (DSO) viewpoint. To achieve the research goals, single or bi-level optimization models can be developed. The behavior of weather-dependent renewable energy sources, such as wind and photovoltaic power generation as uncertainty sources, is modeled by the Monte-Carlo Simulation method to cope with their negative impact on the scheduling process. Moreover, two-stage stochastic programming is applied in order to minimize the operation cost. The results of this study demonstrate the importance of considering all effective players in the market, such as DRAs and customers, on the operation cost. Moreover, modeling the uncertainty helps network operators to reduce the expenses, enabling a resilient and reliable network.A tecnologia atual na monitorização inteligente, incluindo a Internet of Things (IoT), permite que a rede elétrica ao nível da transporte e distribuição faça uso de programas de demand response (DR) para garantir a operação segura e económica dos sistemas de energia. A liberalização e a reestruturação da indústria dos sistemas de energia elétrica também promovem a gestão do lado da procura de forma otimizada. Os impactes da implementação de DR no mercado elétrico podem ser expressos pelo conceito de agregadores de DR (DRAs), sendo a interface entre o lado da oferta e o lado da procura de energia elétrica. Vários mercados, como os mercados diário e em tempo real, são estudados visando a gestão otimizada do ponto de vista do Independent System Operator (ISO) ou do Distribution System Operator (DSO). Para atingir os objetivos propostos, modelos de otimização em um ou dois níveis podem ser desenvolvidos. O comportamento das fontes de energia renováveis dependentes do clima, como a produção de energia eólica e fotovoltaica que acarretam incerteza, é modelado pelo método de simulação de Monte Carlo. Ainda, two-stage stochastic programming é aplicada para minimizar o custo de operação. Os resultados deste estudo demonstram a importância de considerar todos os participantes efetivos no mercado, como DRAs e clientes finais, no custo de operação. Ainda, considerando a incerteza no modelo beneficia os operadores da rede na redução de custos, capacitando a resiliência e fiabilidade da rede