Diagnóstico de la demanda del consumo de energía eléctrica en un Smart Home, enfocado en el sector residencial de Quito, durante el año 2015, barrio La Kennedy. Caracterización y optimización del consumo de energía eléctrica.

The main goal of this project is to present a possible solution to a problem that is been created by the advance of technology and the implementation of Smart cities “Smart Grid” through studying the characterization and modeling the daily electricity demand curve. This curve considers the growth of technology and therefore the increase of consumption of electricity of residential users, generating a significant impact on the demand curve and the users’ economy. For this reason, the suggested optimization will allow a balance between comfort and energy consumption of users. The research is divided into four chapters: the first one presents the state of art for demand modeling and optimization, the second one develops the research methodology and determines the sample where the surveys will take place. The third chapter deals with tabulation of information obtained in surveys conducted in Kennedy neighborhood in Quito. Also, it includes an analysis of the measurement data by user type energy analyzer. Chapter IV, that is the last one, develops the proposal by modeling the demand by Markov Chains and Monte Carlo (MCMC). This modeling established different scenarios, which characterize the energy consumption and the optimization that was performed by means of the Pareto multi objective method. The problem was solved through experimenting a simulation and modeling a demand to optimize energy, generating a 20% of savings in electricity consumption, generating a benefit to the environment and reducing CO2 emissions; without changing the habits of users.El avance de la tecnología y la implementación de las ciudades Inteligentes Smart Grid presenta un problema por lo que el presente: resolver, estudiar, caracterizar y modelar la curva de demanda eléctrica diaria, la cual considera el crecimiento de la tecnología y por ende el incremento del consumo de energía eléctrica en usuarios residenciales, generando un impacto importante en la curva de la demanda y en la economía de los hogares, razón por lo cual la optimización permitirá tener un equilibrio entre confort y el consumo de energía eléctrica de los usuarios. La investigación se divide en cuatro capítulos el primero el estado de arte para la modelación de la demanda y la optimización, analizando las diferentes investigación, el segundo capítulo se desarrolla la metodología de la investigación y se determina la muestra con la cual se realizarán las encuestas en el barrio La Kennedy de la ciudad de Quito, mediante la aplicación de encuestas y la medición de la energía por medio de analizadores de red, en el tercer capítulo se realizó la tabulación de la información obtenida en las encuestas, además se analizó los datos medidos en un usuario tipo mediante un analizador de energía y finalmente en el capítulo IV se desarrolla la propuesta mediante la modelación de la demanda por Las Cadenas se Markov y Montecarlo (MCMC), esta modelación estableció diferentes escenarios, los cuales caracteriza el consumo de energía, la misma que por medio del método de multiobjetivo de Pareto se realizó la optimización. El problema fue resuelto por la investigación mediante la simulación y modelamiento de la demanda, para luego optimizar la energía, generando un ahorro en el consumo de la electricidad en un 20%, generando un beneficio al medio ambiente y reduciendo las emisiones de CO2.; sin cambiar las costumbres de los usuarios

Realistic Multi-Scale Modelling of Household Electricity Behaviours

To improve the management and reliability of power distribution networks, there is a strong demand for models simulating energy loads in a realistic way. In this paper, we present a novel multi-scale model to generate realistic residential load profiles at different spatial-temporal resolutions. By taking advantage of information from Census and national surveys, we generate statistically consistent populations of heterogeneous families with their respective appliances. Exploiting a Bottom-up approach based on Monte Carlo Non Homogeneous Semi-Markov, we provide household end-user behaviours and realistic households load profiles on a daily as well as on a weekly basis, for either weekdays and weekends. The proposed approach overcomes limitations of state-of-art solutions that do not consider neither the time-dependency of the probability of performing specific activities in a house, nor their duration, or are limited in the type of probability distributions they can model. On top of that, it provides outcomes that are not limited on a per-day basis. The range of available space and time resolutions span from single household to district and from second to year, respectively, featuring multi-level aggregation of the simulation outcomes. To demonstrate the accuracy of our model, we present experimental results obtained simulating realistic populations in a period covering a whole calendar year and analyse our model’s outcome at different scales. Then, we compare such results with three different data-sets that provide real load consumption at household, national and European levels, respectively

Uso eficiente del consumo de energía eléctrica residencial basado en el método Montecarlo

The regulation, control and optimization in residential workloads are the main issues that now need to be evaluated and taken into account in the decision making in the future growth of residential loads, since this represents not only the losses by amento appliances but also losses by heat. This paper proposes a procedure using the Monte Carlo model whereby optimization taking into account charges and energy production is taken as random variables, this paper intends to produce a probabilistic optimization model, which will be taking into account the costs of lost, which usually losses tend to decrease when the load tends to decrease, or be the opposite case to increase losses. Aim is to move the characteristic curve of load according to user's needs without disrupting its comforts, this necessity arises for the reason that there is no way of accumulating power and must be consumed at the moment that is being generated in real-time. For this purpose it is necessary to have a load curve as defined users through applications that they have to daily and he did it through surveys of residential load in different part of the city, once with tabulated data is defined higher demand schedules and equipment that are to be carried out which should be turned off and not given use in schedules defined to be able to move the loads.La regulación, control y optimización en las cargas residenciales son los principales temas que en la actualidad necesitan ser evaluadas y tomadas en cuenta para la tomas de decisiones en el futuro crecimiento de las cargas residenciales, ya que esto representa no solo las perdidas por amento de aparatos eléctricos sino también de pérdidas por calor. En este trabajo se propone un procedimiento mediante el método de Montecarlo por lo cual la optimización se toman en cuenta las cargas y la producción de energía se toma como variables aleatorias, en este trabajo se pretende realizar un modelo probabilístico de optimización, que será tomando en cuenta los costos de perdidas, que generalmente las perdidas suelen disminuir cuando la carga tiende a disminuir, o sea este el caso contrario al aumentar perdidas. Lo que se pretende es desplazar la curva característica de carga en función de las necesidades de usuario sin interrumpir sus comodidades, esta necesidad surge por el motivo de que no existe la manera de acumular la energía y tiene que consumirse en el momento que se está generando en tiempo real. Para ello es necesario tener un curva de carga ya definida de los usuarios por medio de los usos que tienen a diario y se lo consiguió mediante las encuestas de carga residencial en diferentes punto de la ciudad, una vez con los datos tabulados se define los horarios de mayor demanda y los equipos que se van a realizar el control los mismos que deberán ser apagados y no dados de uso en horarios definidos para poder desplazar las cargas

Modelamiento para el almacenamiento y aporte de energía a la red en horas pico de demanda mediante un prototipo

This draft thesis develops a modeling allows to study the behavior of daily electricity demand residential with which the possibility of including a prototype storage and supply of energy, the model is programmed and presented at the MATLAB software tool analyzes ; first the behavior of the occupants of a dwelling is modeled by the Markov method and its respective power equivalent using the Monte Carlo method, the model obtained daily residential demand optimizes manually looking for a reference limit by which energy is obtained which provides the prototype and storing it at another time of day, another way to optimize energy presented in this study is entering an amount of money and that the model yield results that much energy is stored and provides network.El presente proyecto de tesis desarrolla un modelamiento que permita estudiar el comportamiento de la demanda eléctrica diaria residencial con el cual se analice la posibilidad de la inclusión de un prototipo de almacenamiento y aporte de energía, el modelo es programado y presentado en la herramienta informática MATLAB; primeramente se modela el comportamiento de los ocupantes de una vivienda mediante el método de Markov y su respectiva potencia equivalente mediante el método de Montecarlo, el modelo obtenido de demanda residencial diaria se lo optimiza manualmente buscando un límite de referencia mediante el cual se obtiene la energía que aporta el prototipo y almacenándola a otra hora del día, otra forma de optimizar energía que presenta este estudio es ingresando un monto de dinero y que el modelo arroje resultados que cuanta energía se almacena y aporta a la red

Unsupervised Learning Based on Markov Chain Modeling of Hot Water Demand Processes

RÉSUMÉ L’ensemble des questions analysées dans ce mémoire dérive d’un important projet de recherche multidisciplinaire appelé smartDESC et réalisé à l’École Polytechnique de Montréal entre les années 2012 et 2016. L’objectif général du projet smartDESC était d’utiliser le stockage associé à certains types de charges d’électricité, et naturellement présent de manière distribuée chez des consommateurs, en vue d’aider à compenser les déséquilibres temporaires entre génération et demande de puissance électrique. Ces derniers sont appelés à devenir de plus en plus fréquents avec la fraction d’énergies renouvelables de type intermittent (énergies solaire et éolienne) dans le mélange de sources d’énergie des réseaux électriques modernes où l’écologie occupe une place de plus en plus importante. Au sein de cet effort général, les chauffe-eau électriques constituent un type de charges d’intérêt particulier vu leur ubiquité et la capacité globale de stockage d’énergie significative à laquelle ils sont associés. Partant d’un ensemble de mesures rendues anonymes de volumes d’extraction d’eau chaude aux 5 minutes, sur une période de plusieurs mois, et fourni par le laboratoire LTE de l’Institut de recherche d’Hydro-Québec, le but de notre recherche était de développer des algorithmes permettant de regrouper des clients individuels en classes de consommation relativement homogènes et dépendantes à la fois du temps de la journée et du jour de la semaine, dans un objectif subséquent de commande coordonnée. Ce faisant, nous devions faire face à trois défis: (i) automatiser la partition des données en segments temporels de durée suffisante pour être statistiquement significatifs, et durant lesquels les statistiques d’extraction d’eau puissent être considérées comme relativement stationnaires; (ii) À l’intérieur de chaque segment temporel, développer des algorithmes d’estimation de paramètres de modèles de chaînes de Markov à deux états (On et Off) d’extraction d’eau avec un paramètre constant par morceaux de taux moyen d’extraction d’eau dans l’état On; (iii) À la lumière des résultats en (ii), développer des algorithmes de classification des usagers en groupes de consommation relativement proches en termes de propriétés statistiques de consommation, selon l’heure de la journée et le jour de la semaine. Dans ce mémoire, des outils de la théorie de l’apprentissage machine, de statistiques, et de la théorie des processus stochastiques sont proposés pour répondre aux trois défis en question.----------ABSTRACT The set of problems tackled in this master thesis is an offshoot of a large multidisciplinary research project called smartDESC or smart Distribution Energy Storage Controller, which was carried out at École Polytechnique de Montréal between 2012 and 2016. The general thrust of the smartDESC project was the coordinated use of storage associated with electric loads at customer sites; the objective of this coordination was to smooth out the uncontrolled generation variability brought about by ecologically friendly, yet intermittent, energy sources such as wind and solar. In that global effort, one particular class of loads of interest because of their ubiquity, and their significant overall energy storage capacity, is that of electric water heaters. We start with a data set consisting of anonymized measurements of hot water extraction volumes in 5 minute samples, over a period of several months, for 73 Quebec households. This data is provided by the LTE laboratory of Institut de recherche d’Hydro-Québec. The goal of the research was to develop approaches to cluster individual users into time of the day and day of the week. We intend to cluster users to relatively homogeneous classes from the point of view of timing and volume of water extraction statistics. Other part of smartDESC is to use these homogeneous clusters to implement coordinated control. In doing so three challenges were to be met: (i) to automate the partition of time of the day into segments of sufficient duration for statistical significance, but relatively stationary hot water extraction statistics; (ii) within each one of the time segments considered, to develop for each user estimation algorithms for two-state (On-Off) Markov chain stochastic models of water extraction with a piecewise constant rate of extraction when On, and validate the results; (iii) In light of the results in (ii), to develop clustering approaches to group users into time of the day and day of the week time intervals where they display relative statistical homogeneity as consumers. In the master thesis, tools from machine learning, statistics and the theory of stochastic processes are used to propose solutions to each of the above three challenges

Implications of Consumer Lifestyle Changes and Behavioral Heterogeneity on U.S. Energy Consumption and Policy

Understanding the relationship between consumer lifestyle and energy use is essential to solving many of the energy and sustainability challenges. By studying shifts in consumer lifestyle over time and behavior heterogeneity, this dissertation provides valuable insights into understanding energy consumption trends and improving energy efficiency programs. Technologies continue to change our daily lifestyles, influencing energy demand. In the first part of the dissertation, changes in how people spend their time (time-use) patterns are used as an indicator of lifestyle shifts. Using decomposition analysis changes in energy use due to these lifestyle shifts are measured. The results show that for an average American, time spent in residences increased at the rate of 3.1 minutes per day per year while time spent for travel and other non-residential activities decreased (-0.4 min/day/year and -2.7 min/day/year respectively). The time-use shifts induced a net energy change of -1,722 trillion BTU, 1.8% of national primary energy consumption in 2012. The lifestyle/energy shifts are interpreted as primarily driven by information and communication technology: people are spending more time at home with online entertainment and services. Information provided to consumers and energy efficiency rebate programs generally assume characteristics of an average consumer. There is, however, substantial heterogeneity in behavior, energy prices and impacts of electricity use. To understand the impact of heterogeneity on rebate programs, in the second part, the economic and carbon benefits of efficient choices of three household technologies (television, clothes washer and dryer) are assessed for different locations and usage patterns. For some households, an efficient energy washers and dryers do not save money, but brings substantial economic benefits to others. Viewing utility appliance rebate programs as tools for carbon abatement, abatement cost of carbon was assessed. At current rebate levels, for an average household, the abatement cost for carbon exceeds social cost of carbon (SCC). However, subpopulations with abatement cost less than SCC exists: 4%, 6%, and 41% for televisions, washers and dryers respectively. Therefore, abatement programs can benefit from targeted intervention. For targeted intervention, it would be useful to identify groups with high energy use and characterize their demographics. To achieve this, in the third analysis, time-use survey data is used to characterize patterns of TV watching. Using cluster analysis, the population was divided into three groups, the high-energy use cluster has 14% of the population and spends an average of 7.7 hours per day on TV. This relatively small group, due to high use, accounts for 34% of total television energy consumption. This group tends to be older, not in the work force and/or poorly educated. A high-use household purchasing an efficient television saves more than three times the energy of an average household. The main policy implications of these results are that more targeted information and policies have potential to enhance adoption by household who will benefit the most economically as well as reduce more carbon. In the management of utility efficiency programs, the results make a case for variable rebates or tiered communication programs

Gestion de stockage d'énergie thermique d'un arc de chauffe-eaux par une commande à champ moyen

RÉSUMÉ Dans un contexte actuel de transition énergétique, le smartgrid et la gestion de la charge électrique sont des champs de recherche de plus en plus actifs. Le projet smartDESC dans lequel ce mémoire s’inscrit, s’intéresse plus spécifiquement au contrôle d’appareils électroménagers chauffant (chauffe-eau, chauffe-espace) permettant de moduler la charge domestique. Ainsi, en utilisant la théorie des jeux à champ moyen, le projet smartDESC veut convertir les chauffe-eaux (et éventuellement les chauffe-espace) en des réservoirs d’énergie intelligents. Pour ce faire, un ensemble de "modules" a été développé. Certains permettent la génération d’une commande optimale et son interprétation en champ moyen, d’autres permettent la simulation numérique d’un chauffe-eau, du processus aléatoire de tirage d’eau ou d’un réseau de télécommunications. Durant la maîtrise de recherche, tous ces modules ont été intégrés, testés, interfacés et réglés dans un simulateur commun. Ce simulateur a pour but de réaliser des simulations de réseau électriques complètes allant du fonctionnement individuel de chaque chauffe-eau jusqu’à des considérations plus générales tel que la charge globale électrique. Après l’interfaçage de l’ensemble des modules, un ensemble de simulations ont été réalisées. Ces simulations permettent d’analyser la réponse d’un parc de chauffe-eaux connectés à différents types de contrôle et de situation. Dans un premier temps, les différents modules sont décrits précisément d’un point de vue théorique et pratique. Ensuite, différents types de contrôle sont appliqués à une population uniforme de maisons équipées de chauffe-eaux et de dispositifs de commande. Les résultats de chacun de ces contrôles sont analysés et comparés afin d’en comprendre les points forts et faibles. Enfin, un étude est menée afin d’analyser les capacités de résilience d’un contrôle champ-moyen. Ce rapport tend à montrer la possibilité d’effectuer un contrôle basé sur l’utilisation de la théorie des jeux à champ moyen. Ce contrôle offre une bonne résilience aux imprévus pouvant perturber le réseau. Il est aussi démontré que l’utilisation d’un contrôle champs-moyen pour absorber les variations dues à la production éolienne est possible. Ainsi, en réduisant la variabilité de la charge électrique du secteur résidentiel, le contrôle en champ moyen participe à accroître la stabilité générale du réseau.----------ABSTRACT In today’s energy transition, smart grids and electrical load control are very active research fields. This master’s thesis is an offshoot of the SmartDesc project which aims at using energy storage capability of electric household appliances, such as water heaters and electric heaters to mitigate the fluctuations of system loads and renewable generation. The smartDESC project aims at demonstrating that the mean field game theory (MFG), as new mathematical theory, can be used to convert and control water heaters (and possibly space heater) into smart thermal capacities. Thus, a set f "modules" has been developed. These modules are used to generate the optimal control and locally interpret it, to simulate the water-heater thermophysics or water draw event, or to virtualize a telecommunication mesh network. The different aspects of the project have been first studied and developed separately. During the course of this master’s research, the modules have been integrated, tested, interfaced and tuned in a common simulator. This simulator is designed to make complete electrical network simulations with a multi-scale approach (from individual water heater to global electric load and production). Firstly, the modules are precisely described theoretically and practically. Then, different types of control are applied to an uniform population of houses fitted with water heaters and controllers. The results of these controls are analysed and compared in order to understand their strengths and weaknesses. Finally, a study was conducted to analyse the resilience of a mean field control. This report demonstrates that mean field game theory in coordination with a system level aggregate model based optimization program, is able to effectively control a large population of water heaters to smooth the overall electrical load. This control offers good resilience to unforeseen circumstances that can disrupt the network. It is also demonstrated that a mean field control is able to absorb fluctuations due to Wind power production. Thus, by reducing the variability of the residential sector’s electrical charge, the mean field control plays a role in increasing power system stability in the face of high levels of renewable energy penetration. The next stage of smartDESC project is now to set up an intelligent electric water heater prototype. This prototype, in progress since January 2016 at École Polytechnique in Montreal,is aimed at proving concretely the theories developed in the project

Electricity Market Designs for Demand Response from Residential Customers

The main purpose of this dissertation is to design an appropriate tariff program for residential customers that encourages customers to participate in the system while satisfying market operators and utilities goals. This research investigates three aspects critical for successful programs: tariff designs for DR, impact of renewable on such tariffs, and load elasticity estimates. First, both categories of DR are modeled based on the demand-price elasticity concept and used to design an optimum scheme for achieving the maximum benefit of DR. The objective is to not only reduce costs and improve reliability but also to increase customer acceptance of a DR program by limiting price volatility. A time of use (TOU) program is considered for a PB scheme designed using a monthly peak and off peak tariff. For the IBDR, a novel optimization is proposed that in addition to calculation of an adequate and a reasonable amount of load change for the incentive also finds the best times to request DR. Second, the effect of both DR programs under a high penetration of renewable resources is investigated. LMP variation after renewable expansion is more highly correlated with renewable’s intermittent output than the load profile. As a result, a TOU program is difficult to successfully implement; however, analysis shows IBDR can diminish most of the volatile price changes in WECC. To model risk associated with renewable uncertainty, a robust optimization is designed considering market price and elasticity uncertainty. Third, a comprehensive study to estimate residential load elasticity in an IBDR program. A key component in all demand response programs design is elasticity, which implies customer reaction to LSEs offers. Due to limited information, PB elasticity is used in IBDR as well. Customer elasticity is calculated using data from two nationwide surveys and integrated with a detailed residential load model. In addition, IB elasticity is reported at the individual appliance level, which is more effective than one for the aggregate load of the feeder. Considering the importance of HVAC in the aggregate load signal, its elasticity is studied in greater detail and estimated for different customer groupings

ANOMALY INFERENCE BASED ON HETEROGENEOUS DATA SOURCES IN AN ELECTRICAL DISTRIBUTION SYSTEM

Harnessing the heterogeneous data sets would improve system observability. While the current metering infrastructure in distribution network has been utilized for the operational purpose to tackle abnormal events, such as weather-related disturbance, the new normal we face today can be at a greater magnitude. Strengthening the inter-dependencies as well as incorporating new crowd-sourced information can enhance operational aspects such as system reconfigurability under extreme conditions. Such resilience is crucial to the recovery of any catastrophic events. In this dissertation, it is focused on the anomaly of potential foul play within an electrical distribution system, both primary and secondary networks as well as its potential to relate to other feeders from other utilities. The distributed generation has been part of the smart grid mission, the addition can be prone to electronic manipulation. This dissertation provides a comprehensive establishment in the emerging platform where the computing resources have been ubiquitous in the electrical distribution network. The topics covered in this thesis is wide-ranging where the anomaly inference includes load modeling and profile enhancement from other sources to infer of topological changes in the primary distribution network. While metering infrastructure has been the technological deployment to enable remote-controlled capability on the dis-connectors, this scholarly contribution represents the critical knowledge of new paradigm to address security-related issues, such as, irregularity (tampering by individuals) as well as potential malware (a large-scale form) that can massively manipulate the existing network control variables, resulting into large impact to the power grid