The diploma thesis provides an insight into the remote control and disconnection of DSO supply points phenomenon. The remote or local disconnection/connection of supply point is allowed by the breaker function. The automatic disconnection of supply point is enabled by the limiter function. Due to the anticipated implementation of smart meters in the Czech Republic in the future, this work contains the comprehensive description of breaker/limiter function with proposed possibilities of use in the Czech Republic. The thesis deals with the current breaker/limiter function use in the Czech Republic and in other countries. It introduces the smart meter installation in pilot projects to analyze the breaker/limiter function use in other countries. The thesis is focused on the technical solution of breaker/limiter. Moreover, it describes the ways of connecting the breaker, settting the limiter, connecting/disconnecting a supply point and breaker operation. Further, the thesis introduces the ways of activating the breaker by a customer and defines in which cases it is possible to limit and interrupt the electricity supply in the Czech Republic currently. The main aim of thesis is to describe the specific possibilities of breaker/limiter function use in the Czech Republic. With regard to the function use in other countries and the limiting or interrupting the electricity supply by DSO according to energy law, the possibilities of use are proposed. Each possibility of use is analyzed when implementing the breaker function or the breaker/limiter function. The benefits are defined for a DSO and for a customer. The proposed uses are evaluated in terms of applicability and valid legislation in the Czech Republic. The result of this work is the summary of information about breaker/limiter function which is one of the new features in the implementation of smart metering. The function installation and the implementation of possibilities described in the thesis depends on the DSO decision.

Buildings account for around 40 percent of the primary energy consumption in Europe and in the United States. They also hold tremendous energy savings potential: 15 to 29 percent by 2020 for the European building stock according to a 2009 study from the European Commission. Verifying and predicting the impact of energy conservation measures in buildings is typically done through energy audits. These audits are costly, time-consuming, and may have high error margins if only limited amounts of data can be collected. The ongoing large-scale roll-out of smart meters and wireless sensor networks in buildings gives us access to unprecedented amounts of data to track energy consumption, environmental factors and building operation. This Thesis explores the possibility of using this data to verify and predict the impact of energy conservation measures, replacing energy audits with analytical software. We look at statistical analysis techniques and optimization algorithms suitable for building two regression models: one that maps environmental (e.g.: outdoor temperature) and operational factors (e.g.: opening hours) to energy consumption in a building, the other that maps building characteristics (e.g.: type of heating system) to regression coefficients obtained from the first model (which are used as energy-efficiency indicators) in a building portfolio. Following guidelines provided in the IPMVP, we then introduce methods for verifying and predicting the savings resulting from the implementation of a conservation measure in a building.

[EN] The electricity sector is currently undergoing a process of liberalization and separation of roles, which is being implemented under the regulatory auspices of each Member State of the European Union and, therefore, with different speeds, perspectives and objectives that must converge on a common horizon, where Europe will benefit from an interconnected energy market in which producers and consumers can participate in free competition. This process of liberalization and separation of roles involves two consequences or, viewed another way, entails a major consequence from which other immediate consequence, as a necessity, is derived. The main consequence is the increased complexity in the management and supervision of a system, the electrical, increasingly interconnected and participatory, with connection of distributed energy sources, much of them from renewable sources, at different voltage levels and with different generation capacity at any point in the network. From this situation the other consequence is derived, which is the need to communicate information between agents, reliably, safely and quickly, and that this information is analyzed in the most effective way possible, to form part of the processes of decision taking that improve the observability and controllability of a system which is increasing in complexity and number of agents involved. With the evolution of Information and Communication Technologies (ICT), and the investments both in improving existing measurement and communications infrastructure, and taking the measurement and actuation capacity to a greater number of points in medium and low voltage networks, the availability of data that informs of the state of the network is increasingly higher and more complete. All these systems are part of the so-called Smart Grids, or intelligent networks of the future, a future which is not so far. One such source of information comes from the energy consumption of customers, measured on a regular basis (every hour, half hour or quarter-hour) and sent to the Distribution System Operators from the Smart Meters making use of Advanced Metering Infrastructure (AMI). This way, there is an increasingly amount of information on the energy consumption of customers, being stored in Big Data systems. This growing source of information demands specialized techniques which can take benefit from it, extracting a useful and summarized knowledge from it. This thesis deals with the use of this information of energy consumption from Smart Meters, in particular on the application of data mining techniques to obtain temporal patterns that characterize the users of electrical energy, grouping them according to these patterns in a small number of groups or clusters, that allow evaluating how users consume energy, both during the day and during a sequence of days, allowing to assess trends and predict future scenarios. For this, the current techniques are studied and, proving that the current works do not cover this objective, clustering or dynamic segmentation techniques applied to load profiles of electric energy consumption from domestic users are developed. These techniques are tested and validated on a database of hourly energy consumption values for a sample of residential customers in Spain during years 2008 and 2009. The results allow to observe both the characterization in consumption patterns of the different types of residential energy consumers, and their evolution over time, and to assess, for example, how the regulatory changes that occurred in Spain in the electricity sector during those years influenced in the temporal patterns of energy consumption. / [ES] El sector eléctrico se halla actualmente sometido a un proceso de liberalización y separación de roles, que está siendo aplicado bajo los auspicios regulatorios de cada Estado Miembro de la Unión Europea y, por tanto, con distintas velocidades, perspectivas y objetivos que deben confluir en un horizonte común, en donde Europa se beneficiará de un mercado energético interconectado, en el cual productores y consumidores podrán participar en libre competencia. Este proceso de liberalización y separación de roles conlleva dos consecuencias o, visto de otra manera, conlleva una consecuencia principal de la cual se deriva, como necesidad, otra consecuencia inmediata. La consecuencia principal es el aumento de la complejidad en la gestión y supervisión de un sistema, el eléctrico, cada vez más interconectado y participativo, con conexión de fuentes distribuidas de energía, muchas de ellas de origen renovable, a distintos niveles de tensión y con distinta capacidad de generación, en cualquier punto de la red. De esta situación se deriva la otra consecuencia, que es la necesidad de comunicar información entre los distintos agentes, de forma fiable, segura y rápida, y que esta información sea analizada de la forma más eficaz posible, para que forme parte de los procesos de toma de decisiones que mejoran la observabilidad y controlabilidad de un sistema cada vez más complejo y con más agentes involucrados. Con el avance de las Tecnologías de Información y Comunicaciones (TIC), y las inversiones tanto en mejora de la infraestructura existente de medida y comunicaciones, como en llevar la obtención de medidas y la capacidad de actuación a un mayor número de puntos en redes de media y baja tensión, la disponibilidad de datos sobre el estado de la red es cada vez mayor y más completa. Todos estos sistemas forman parte de las llamadas Smart Grids, o redes inteligentes del futuro, un futuro ya no tan lejano. Una de estas fuentes de información proviene de los consumos energéticos de los clientes, medidos de forma periódica (cada hora, media hora o cuarto de hora) y enviados hacia las Distribuidoras desde los contadores inteligentes o Smart Meters, mediante infraestructura avanzada de medida o Advanced Metering Infrastructure (AMI). De esta forma, cada vez se tiene una mayor cantidad de información sobre los consumos energéticos de los clientes, almacenada en sistemas de Big Data. Esta cada vez mayor fuente de información demanda técnicas especializadas que sepan aprovecharla, extrayendo un conocimiento útil y resumido de la misma. La presente Tesis doctoral versa sobre el uso de esta información de consumos energéticos de los contadores inteligentes, en concreto sobre la aplicación de técnicas de minería de datos (data mining) para obtener patrones temporales que caractericen a los usuarios de energía eléctrica, agrupándolos según estos mismos patrones en un número reducido de grupos o clusters, que permiten evaluar la forma en que los usuarios consumen la energía, tanto a lo largo del día como durante una secuencia de días, permitiendo evaluar tendencias y predecir escenarios futuros. Para ello se estudian las técnicas actuales y, comprobando que los trabajos actuales no cubren este objetivo, se desarrollan técnicas de clustering o segmentación dinámica aplicadas a curvas de carga de consumo eléctrico diario de clientes domésticos. Estas técnicas se prueban y validan sobre una base de datos de consumos energéticos horarios de una muestra de clientes residenciales en España durante los años 2008 y 2009. Los resultados permiten observar tanto la caracterización en consumos de los distintos tipos de consumidores energéticos residenciales, como su evolución en el tiempo, y permiten evaluar, por ejemplo, cómo influenciaron en los patrones temporales de consumos los cambios regulatorios que se produjeron en España en el sector eléctrico durante esos años. / [CA] El sector elèctric es troba actualment sotmès a un procés de liberalització i separació de rols, que s'està aplicant davall els auspicis reguladors de cada estat membre de la Unió Europea i, per tant, amb distintes velocitats, perspectives i objectius que han de confluir en un horitzó comú, on Europa es beneficiarà d'un mercat energètic interconnectat, en el qual productors i consumidors podran participar en lliure competència. Aquest procés de liberalització i separació de rols comporta dues conseqüències o, vist d'una altra manera, comporta una conseqüència principal de la qual es deriva, com a necessitat, una altra conseqüència immediata. La conseqüència principal és l'augment de la complexitat en la gestió i supervisió d'un sistema, l'elèctric, cada vegada més interconnectat i participatiu, amb connexió de fonts distribuïdes d'energia, moltes d'aquestes d'origen renovable, a distints nivells de tensió i amb distinta capacitat de generació, en qualsevol punt de la xarxa. D'aquesta situació es deriva l'altra conseqüència, que és la necessitat de comunicar informació entre els distints agents, de forma fiable, segura i ràpida, i que aquesta informació siga analitzada de la manera més eficaç possible, perquè forme part dels processos de presa de decisions que milloren l'observabilitat i controlabilitat d'un sistema cada vegada més complex i amb més agents involucrats. Amb l'avanç de les tecnologies de la informació i les comunicacions (TIC), i les inversions, tant en la millora de la infraestructura existent de mesura i comunicacions, com en el trasllat de l'obtenció de mesures i capacitat d'actuació a un nombre més gran de punts en xarxes de mitjana i baixa tensió, la disponibilitat de dades sobre l'estat de la xarxa és cada vegada major i més completa. Tots aquests sistemes formen part de les denominades Smart Grids o xarxes intel·ligents del futur, un futur ja no tan llunyà. Una d'aquestes fonts d'informació prové dels consums energètics dels clients, mesurats de forma periòdica (cada hora, mitja hora o quart d'hora) i enviats cap a les distribuïdores des dels comptadors intel·ligents o Smart Meters, per mitjà d'infraestructura avançada de mesura o Advanced Metering Infrastructure (AMI). D'aquesta manera, cada vegada es té una major quantitat d'informació sobre els consums energètics dels clients, emmagatzemada en sistemes de Big Data. Aquesta cada vegada major font d'informació demanda tècniques especialitzades que sàpiguen aprofitar-la, extraient-ne un coneixement útil i resumit. La present tesi doctoral versa sobre l'ús d'aquesta informació de consums energètics dels comptadors intel·ligents, en concret sobre l'aplicació de tècniques de mineria de dades (data mining) per a obtenir patrons temporals que caracteritzen els usuaris d'energia elèctrica, agrupant-los segons aquests mateixos patrons en una quantitat reduïda de grups o clusters, que permeten avaluar la forma en què els usuaris consumeixen l'energia, tant al llarg del dia com durant una seqüència de dies, i que permetent avaluar tendències i predir escenaris futurs. Amb aquesta finalitat, s'estudien les tècniques actuals i, en comprovar que els treballs actuals no cobreixen aquest objectiu, es desenvolupen tècniques de clustering o segmentació dinàmica aplicades a corbes de càrrega de consum elèctric diari de clients domèstics. Aquestes tècniques es proven i validen sobre una base de dades de consums energètics horaris d'una mostra de clients residencials a Espanya durant els anys 2008 i 2009. Els resultats permeten observar tant la caracterització en consums dels distints tipus de consumidors energètics residencials, com la seua evolució en el temps, i permeten avaluar, per exemple, com van influenciar en els patrons temporals de consums els canvis reguladors que es van produir a Espanya en el sector elèctric durant aquests anys. / Benítez Sánchez, IJ. (2015). Dynamic segmentation techniques applied to load profiles of electric energy consumption from domestic users [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59236

Cette thèse présente une méthode pour désagréger les appareils électriques dans le profil des bâtiments résidentiels de charge. Au cours des dernières années, la surveillance de l’énergie a obtenu beaucoup de popularité dans un environnement privé et industriel. Avec des algorithmes de la désagrégation, les données mesurées à partir de soi-disant compteurs intelligents peuvent être utilisés pour fournir de plus amples informations de la consommation d’énergie. Une méthode pour recevoir ces données est appelé non-intrusifs charge identification. La majeure partie de la thèse peut être divisée en trois parties. Dans un premier temps, un système de mesure propre a été développé et vérifié. Avec ce système, les ensembles de données réelles peuvent être générés pour le développement et la vérification des algorithmes de désagrégation. La deuxième partie décrit le développement d’un détecteur de flanc. Différentes méthodes sont présentées et évaluées, avec lequel les temps de commutation des appareils peuvent être détectés dans le profil de la charge. La dernière partie décrit un procédé de classification. Différents critères sont utilisés pour la classification. Le classificateur reconnaît et étiquette les appareils individuels de la courbe de charge. Pour les classifications différentes structures de réseaux de neurones artificiels sont comparés. / This thesis presents a method to disaggregate electrical appliances in the load profile of residential buildings. In recent years, energy monitoring has obtained significantly popularity in private and industrial environment. With algorithms of the disaggregation, the measured data from so-called smart meters can be used to provide more information of the energy usage. One method to receive these data is called non-intrusive appliance load monitoring.The main part of the thesis can be divided into three parts. At first, an own measurement system was developed and verified. With that system, real data sets can be generated for the development and verification of the disaggregation algorithms. The second part describes the development of an event detector. Different methods are presented and evaluated, with which the switching times of the appliances can be detected in the load profile. The last part describes a classification method. Different features are used for the classification. The classifier recognizes and labels the individual appliances in the load profile. For the classification different structures of artificial neural network (ANN) are compared.

Smart metering residential demand management programs, such as consumption feedback, variable pricing regimes and the remote control of appliances, are being used to respond to the resource management problems of peak electricity demand, climate change and water shortages. Like other demand management programs, these strategies fail to account for (and respond to) the reasons why people consume resources in their homes, namely to carry out everyday practices such as bathing, laundering, heating and cooling. In particular, comfort and cleanliness practices together constitute most of Australia's potable water consumption in urban centres, and represent most of household energy consumption. In addition, new household cooling practices involving air-conditioning appliances are the major contributor to the nation's rising peak electricity demand, which overloads the electricity system on hot days, costing consumers millions of dollars each year. The oversight of comf ort and cleanliness practices in smart metering demand management programs is concerning because these practices are continuing to shift and change, often in more resource-consuming directions, potentially negating the resource savings achieved through demand management programs. This thesis aims to bridge the problematic divide between the policies and strategies of demand managers, and the day-to-day practices which constitute everyday life. Using the empirical 'hook' of smart metering demand management programs and the everyday practices of comfort and cleanliness, this thesis develops a practice-based conceptual framework to study, understand and analyse these practices and the ways in which smart metering demand management programs reconfigure or further entrench them. A series of qualitative methods were employed in studying 65 households across four research groups, focusing specifically on the household practices of heating, cooling, bathing, laundering, toilet flushing and house cleaning. In addition, 27 interviews were conducted with smart metering industry stakeholders involved or implicated in delivering demand management strategies. Together, these lines of inquiry are used to analyse householders' existing and changing comfort and cleanliness practices, the role of several smart metering demand management strategies in reconfiguring these practices, and potential avenues and opportunities for further practice change in less resource-intensive directions. In particular, this thesis highlights the inherent contradictions and problems in accounting for everyday practices within the dominant demand management paradigm, and offers an alternative paradigm termed the co-management of everyday practices. The thesis concludes by briefly identifying the ways in which smart metering could potentially constrain or catalyse a transition towards this new paradigm.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2015-01-13T10:55:38Z No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Dissertação - Gustavo Batista de Castro Souza - 2014.pdf: 11760996 bytes, checksum: 8245af285d79ff9e8079bafddb72e690 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-01-13T10:56:54Z (GMT) No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Dissertação - Gustavo Batista de Castro Souza - 2014.pdf: 11760996 bytes, checksum: 8245af285d79ff9e8079bafddb72e690 (MD5) / Made available in DSpace on 2015-01-13T10:56:54Z (GMT). No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Dissertação - Gustavo Batista de Castro Souza - 2014.pdf: 11760996 bytes, checksum: 8245af285d79ff9e8079bafddb72e690 (MD5) Previous issue date: 2014-07-17 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Smart Grids systems have become widespread around the world. The RF mesh communication systems have contributed to make power systems smarter and reliable with implementation of Distributed Automation and Demand Response technologies. This work presents a methodology for positioning of GPRS concentrators in a energy meter ZigBee mesh network in order to attain the average network delay, thus aiming to improve the performance of the communication service. The proposed algorithm determines the amount and placement of concentrators using Integer Linear Programming and a Queuing Model for the Mesh Network. The solutions given by the proposed algorithm are validated by verifying the network performance through computer simulations based on real network scenarios. / Smart Grids tem se difundido em todo o mundo. Sistemas de comunicação RF Mesh (em malha) tem contribuído para deixar sistemas de potência mais inteligentes e confiáveis com a implantação de tecnolgias de Automação da Distribuição e Resposta à Demanda. Este trabalho apresenta um metodologia de posicionamento de concentradores GPRS em uma rede ZigBee mesh de medidores de energia elétrica com o objetivo de limitar o delay médio da rede e assim otimizar o desempenho do serviço de comunicação. O algoritmo proposto determina a quantidade e a localização de concentradores utilizando Programação Linear Inteira e um Modelo de Filas para Redes Mesh. As soluções obtidas pelo algoritmo proposto são validadas verificando o desempenho da rede através de simulações computacionais baseadas em cenários reais de redes.

Orientador: Gustavo Fraidenraich / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-26T03:16:11Z (GMT). No. of bitstreams: 1 FloresRodriguez_AndreaCarolina_M.pdf: 1415054 bytes, checksum: 6b986968e8d7ec4e6459e4cea044d379 (MD5) Previous issue date: 2014 / Resumo: A compressão dos dados de consumo residencial de energia elétrica registrados torna-se extremadamente necessária em Smart Metering, a fim de resolver o problema de grandes volumes de dados gerados pelos medidores. A principal contribuição desta tese é a proposta de um esquema de representação teórica da informação registrada na forma mais compacta, sugerindo uma forma de atingir o limite fundamental de compressão estabelecido pela entropia da fonte sobre qualquer técnica de compressão disponibilizada no medidor. A proposta consiste na transformação de codificação dos dados, baseado no processamento por segmentação: no tempo em taxas de registros de 1/900 Hz a 1 Hz, e nos valores de consumo residencial de energia elétrica. Este último subdividido em uma compressão por amplitude mudando sua granularidade e compressão dos dados digitais para representar o consumo com o menor número de bits possíveis usando: PCM-Huffman, DPCM-Huffman e codificação de entropia supondo diferentes ordens de distribuição da fonte. O esquema é aplicado sobre dados modelados por cadeias de Markov não homogêneas para as atividades dos membros da casa que influenciam no consumo elétrico e dados reais disponibilizados publicamente. A avaliação do esquema é feita analisando o compromisso da compressão entre as altas taxas de registro, distorção resultante da digitalização dos dados, e exploração da correlação entre amostras consecutivas. Vários exemplos numéricos são apresentados ilustrando a eficiência dos limites de compressão. Os resultados revelam que os melhores esquemas de compressão de dados são encontrados explorando a correlação entre as amostras / Abstract: Data compression of recorded residential electricity consumption becomes extremely necessary on Smart Metering, in order to solve the problem of large volumes of data generated by meters. The main contribution of this thesis is to propose a scheme of theoretical representation of recorded information in the most compact form, which suggests a way to reach the fundamental limit of compression set by the entropy of the source, of any compression technique available in the meter. The proposal consists in the transformation of data encoding, based on the processing by segmentation: in time by registration rate from 1/900 Hz to 1 Hz, and in the values of residential electricity consumption. The latter is subdivided into compression: by amplitude changing their regularity, and digital data compression to represent consumption as few bits as possible. It is using PCM-Huffman, DPCM-Huffman and entropy encoding by assuming different orders of the source. The scheme is applied to modeled data by inhomogeneous Markov chains to create the activities of household members that influence electricity consumption, and real data publicly available. The assessment scheme is made by analyzing the trade off of compression between high registration rates, the distortion resulting from the digitization of data, and analyzing the correlation of consecutive samples. Several examples are presented to illustrate the efficiency of the compression limits. The analysis reveals that better data compression schemes can be found by exploring the correlation among the samples / Mestrado / Telecomunicações e Telemática / Mestra em Engenharia Elétrica

In recent years, the demand for electricity has increased in households with the use of different appliances. This raises a concern to many developed and developing nations with the demand in immediate increase of electricity. There is a need for consumers or people to track their daily power usage in houses. In Sweden, scarcity of energy resources is faced during the day. So, the responsibility of human to save and control these resources is also important. This research work focuses on a Smart Metering data for distributing the electricity smartly and efficiently to the consumers. The main drawback of previously used traditional meters is that they do not provide information to the consumers, which is accomplished with the help of Smart Meter. A Smart Meter helps consumer to know the information of consumption of electricity for appliances in their respective houses. The aim of this research work is to measure and analyze power consumption using Smart Meter data by conducting case study on various households. In addition of saving electricity, Smart Meter data illustrates the behaviour of consumers in using devices. As power consumption is increasing day by day there should be more focus on understanding consumption patterns i.e. measurement and analysis of consumption over time is required. In case of developing nations, the technology of employing smart electricity meters is still unaware to many common people and electricity utilities. So, there is a large necessity for saving energy by installing these meters. Lowering the energy expenditure by understanding the behavior of consumers and its correlation with electricity spot prices motivated to perform this research. The methodology followed to analyze the outcome of this study is exhibited with the help of a case analysis, ARIMA model using XLSTAT tool and a flattening technique. Based on price evaluation results provided in the research, hypothesis is attained to change the behavior of consumers when they have better control on their habits. This research contributes in measuring the Smart Meter power consumption data in various households and interpretation of the data for hourly measurement could cause consumers to switch consumption to off-peak periods. With the results provided in this research, users can change their behavior when they have better control on their habits. As a result, power consumption patterns of Smart electricity distribution are studied and analyzed, thereby leading to an innovative idea for saving the limited resource of electrical energy. / +91 9908265578

The diploma thesis describes the actual situation of smart water metering, an overview of water meters suitable for remote data reading and individual components for application of remote data transmission including transmission itself. The thesis also contains the characteristics of available wireless data communication technologies and detailed solutions according to two companies specializing in remote transmission of water meter data. Subsequently, the pilot projects of large water company are presented, including practical findings. The practical part deals with the implementation of smart water metering in three specific municipalities, including a description of the area of interest, water supply system specification and possible limitations, the recommended technology, the requirements for putting in into operation and the pricing of technology and services according to two specialized companies. Finally, the possibilities of other using of smart water meter technology are discussed.

Smart metering and precise measurement of energy consumption levels have brought more detailed information and interest on the actual load profile of a house which continues to improve consumer-retailer relationships. Participation in demand response (DR) programs is one of these relationships but studies have shown that there are considerable impacts resulting to some level of discomfort on consumers as they aim to follow a suggested load profile. This research therefore investigates the impact on consumers while participating in DR programs by evaluating various perspectives that includes:  Modelling the causes discomfort during participation in DR programs;  Evaluation of user participation capabilities in DR programs;  Identification of schedulable and non-schedulable loads and opportunities;  Application of load scheduling mechanism which caters for specific user concerns.  Investigation towards ensuring a secure and robust system design. The key source of information that enhances this work is obtained from data on historical user behavior which can be stored within a smart controller installed in the home and optimised using genetic algorithm implemented on MATLAB. Results show that user participation in DR programs can be improved and effectively managed if the challenges facing home owners are adequately understood. This is the key contribution of this work whereby load schedules created are specifically tailored to meet the need of the users hence minimizing the impact of discomfort experienced due to participation in DR programs. Finally as part of the test for robustness of the system design in order to prevent or minimize the impact of any event of a successful cyber-attack on the load or price profiles, this work includes means to managing any such attacks thereby mitigating the impact of such attacks on users who participate in demand response programs. Solutions to these attacks are also proffered with the aim of increasing robustness of the grid by being sufficiently proactive.