Two-Tier Prediction of Solar Power Generation with Limited Sensing Resource

This paper considers a typical solar installations scenario with limited sensing resources. In the literature, there exist either day-ahead solar generation prediction methods with limited accuracy, or high accuracy short timescale methods that are not suitable for applications requiring longer term prediction. We propose a two-tier (global-tier and local-tier) prediction method to improve accuracy for long term (24 hour) solar generation prediction using only the historical power data. In global-tier, we examine two popular heuristic methods: weighted k-Nearest Neighbors (k-NN) and Neural Network (NN). In local-tier, the global-tier results are adaptively updated using real-time analytical residual analysis. The proposed method is validated using the UCLA Microgrid with 35kW of solar generation capacity. Experimental results show that the proposed two-tier prediction method achieves higher accuracy compared to day-ahead predictions while providing the same prediction length. The difference in the overall prediction performance using either weighted k-NN based or NN based in the global-tier are carefully discussed and reasoned. Case studies with a typical sunny day and a cloudy day are carried out to demonstrate the effectiveness of the proposed two-tier predictions

Reduction of power grid fluctuations by communication between smart devices

The increase of electric demand and the progressive integration of renewable sources threatens the stability of the power grid. To solve this issue, several methods have been proposed to control the demand side instead of increasing the spinning reserve on the supply side. Here we focus on dynamic demand control (DDC), a method in which appliances can delay its scheduled operation if the electric frequency is outside a suitable range. We have recently shown that DDC effectively reduces small and medium-size frequency fluctuations but, due to the need of recovering pending tasks, the probability of large demand peaks, and hence large frequency fluctuations, may actually increase. Although these events are very rare they can potentially trigger a failure of the system and therefore strategies to avoid them have to be addressed. In this work, we introduce a new method including communication among DDC devices belonging to a given group, such that they can coordinate opposite actions to keep the group demand more stable. We show that for this method the amount of pending tasks decreases by a factor 10 while large frequency fluctuations are significantly reduced or even completely avoided

Óptima respuesta de la demanda residencial, usando tarifas dinámicas basadas en el negawatt

A flexible and efficient behavior of consumers in the energy use is vital for the adequate and environmentally supportive operation of the electrical system, that’s why new management techniques are developed. Historically, the electric pricing of residential consumers has been flat and constant in order to simplify billing processes, unfortunately this has provoked the separation between the paid price by the residential user and the hourly changing energy prices in the electric market, leading to its excessive use, especially at certain times of the day or seasons of the year, making necessary to invest more in the electricity system to supply the demand. In contrast to what has been described, this document proposes the implementation of dynamic TPRTP tariffs (Two Parts Real Time Price) related to hourly changes in energy costs and in addition to the amount of "Negawatts" (theoretical unit that represents the watts saved when RD is implemented) that each consumer can "generate" and that come either from the decrease in the use of energy called "Cutting Negawatts" or the "Deferred Negawatts" that are the result of different and optimal energy use, its importance lies in their effectively collaborate in the descent of the peaks of demand and come from deferrable charges whose change of use does not affect transcendentally in consumer "comfort".Un comportamiento flexible y eficiente de los consumidores en el uso de la energía, es vital para que el sistema eléctrico funcione adecuadamente y sea solidario con el medio ambiente, es por esta razón que se desarrollan nuevas técnicas de gestión para lograrlo. Históricamente la tarificación eléctrica de los consumidores residenciales ha sido del tipo plana y constante con el fin de simplificar los procesos de facturación, lamentablemente esto ha provocado la desvinculación entre el precio pagado por el usuario residencial y la variación horaria de precios de la energía en el mercado eléctrico, conllevando a su uso desmesurado sobretodo en ciertas horas del día o temporadas del año, haciendo necesaria mayor inversión en el sistema eléctrico para suplir la demanda. Este documento plantea la implementación de tarifas dinámicas TPRTP (Two Parts Real Time Price) relacionadas con los cambios horarios de los costos de energía y con la cantidad de “Negawatts” (unidad teórica que representa los watts ahorrados al implementarse RD) que cada consumidor pueda “generar” y que provienen ya sea de la disminución en el uso de energía por parte de los usuarios (“Negawatts de recorte”) o los “Negawatts de diferimiento” que son el resultado del uso diferente y óptimo de la energía, su importancia radica en que colaboran efectivamente en el descenso de los picos de demanda y provienen de cargas diferibles cuyo cambio de uso no afectan de forma trascendental en el “confort” del consumidor

Advanced Demand Response Solutions for Capacity Markets

International audience—The Internet of Things (IoT) paradigm brings an opportunity for advanced Demand Response (DR) solutions. Indeed, it enables visibility and control on the various appliances that may consume, store or generate energy within a home. It has been shown that a centralized control on the appliances of a set of households leads to efficient DR mechanisms; unfortunately, such solutions raise privacy and scalability issues. In this paper we propose an IoT-based DR approach that deals with these issues. Specifically, we propose and analyze a scalable two levels control system where a centralized controller allocates power to each house on one side and, each household implements an IoT-based DR local solution on the other side. A limited feedback to the centralized controller allows to enhance the performance with little impact on privacy. The solution is proposed for the general framework of capacity markets

Predicción con series de tiempo para la optimización de la demanda eléctrica residencial

This draft develops a modeling allows to predict the behavior of annual electricity demand residential, is to say to forecast values to take in certain times. In this investigation draft alone is considered to residential level, having as need to minimize costs of energy electric consumption to that be representative for the user, for the reason that currently reading of electromechanical and electronic counters are done manually and usually monthly, to determine the energy billing. The development of the investigation is carried out through time 2 series, where it be identify that electric demand is not static, because varies through time by so that was chosen the method of static prognosis for time series, where is determined the main components of a time series. For the modeling of residential demand it was required determine a linear regression with on data from the electricity consumption of historical data from 2013 provided by Empresa Eléctrica Quito, to later to make the prediction for 2016, scheduled and presented in the Matlab tool informatic, showing the prediction of individual and total demand. For optimization it is manually developed considering a referential consumption limit heuristically or by the simplex method to minimize a function, to simulate the reduction in peak response curve of the forecasted demand.El presente proyecto desarrolla un modelamiento que permita predecir el comportamiento de la demanda eléctrica residencial anual, es decir para pronosticar valores que toma en determinados tiempos. En este proyecto de investigación solo se considera a nivel residencial, teniendo como en la necesidad la minimización de costos en el consumo de energía eléctrica para que sea representativo para el usuario, por el motivo que actualmente la lectura de contadores electromecánicos y electrónicos se realiza de forma manual y por lo general mensual, para determinar la facturación de energía. El desarrollo de la investigación se lleva a cabo a través de series de tiempo, donde se identifica que la demanda eléctrica no es estática, porque varía a través del tiempo por lo cual se eligió el método de pronóstico estático por series de tiempo, donde se determina la principales componente de una serie de tiempo. Para la modelación de la demanda residencial se requiere determinar una regresión lineal realizada con datos del consumo de energía eléctrica de datos históricos desde el 2013 facilitados por la Empresa Eléctrica Quito, para posteriormente realizar la predicción para el año 2016, programado y presentado en la herramienta informática Matlab, mostrando la predicción de la demanda individual y total. Para la optimización se desarrolla manualmente planteándose un límite referencial de consumo de forma heurística o por el método simplex para minimizar una función, para poder simular la reducción en los picos de la curva de respuesta de la demanda pronosticada

Demand Response Management in Smart Grid Networks: a Two-Stage Game-Theoretic Learning-Based Approach

In this diploma thesis, the combined problem of power company selection and Demand Response Management in a Smart Grid Network consisting of multiple power companies and multiple customers is studied via adopting a distributed learning and game-theoretic technique. Each power company is characterized by its reputation and competitiveness. The customers who act as learning automata select the most appropriate power company to be served, in terms of price and electricity needs’ fulfillment, via a distributed learning based mechanism. Given customers\u27 power company selection, the Demand Response Management problem is formulated as a two-stage game theoretic optimization framework, where at the first stage the optimal customers\u27 electricity consumption is determined and at the second stage the optimal power companies’ pricing is calculated. The output of the Demand Response Management problem feeds the learning system in order to build knowledge and conclude to the optimal power company selection. A two-stage Power Company learning selection and Demand Response Management (PC-DRM) iterative algorithm is proposed in order to realize the distributed learning power company selection and the two-stage distributed Demand Response Management framework. The performance of the proposed approach is evaluated via modeling and simulation and its superiority against other state of the art approaches is illustrated

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