Context: A District Heating System (DHS) uses a central heating plant to produce and distribute hot water in a community. Such a plant is connected with consumers’ premises to provide them with hot water and space heating facilities. Variations in the consumption of heat energy depend upon different factors like difference in energy prices, living standards, environmental effects and economical conditions etc. These factors can manage intelligently by advanced tools of Information and Communication Technology (ICT) such as smart metering. That is a new and emerging technology; used normally for metering of District Heating (DH), district cooling, electricity and gas. Traditional meters measures overall consumption of energy, in contrast smart meters have the ability to frequently record and transmit energy consumption statistics to both energy providers and consumers by using their communication networks and network management systems. Objectives: First objective of conducted study was providing energy consumption/saving suggestions on smart metering display for accepted consumer behavior, proposed by the energy providers. Our second objective was analysis of financial benefits for the energy provides, which could be expected through better consumer behavior. Third objective was analysis of energy consumption behavior of the residential consumes that how we can support it. Moreover, forth objective of the study was to use extracted suggestions of consumer behaviors to propose Extended Smart Metering Display for improving energy economy. Methods: In this study a background study was conducted to develop basic understanding about District Heat Energy (DHE), smart meters and their existing display, consumer behaviors and its effects on energy consumption. Moreover, interviews were conducted with representatives of smart heat meters’ manufacturer, energy providers and residential consumers. Interviews’ findings enabled us to propose an Extended Smart Metering Display, that satisfies recommendations received from all the interviewees and background study. Further in this study, a workshop was conducted for the evaluation of the proposed Extended Smart Metering Display which involved representatives of smart heat meters’ manufacture and residential energy consumers. DHE providers also contributed in this workshop through their comments in online conversation, for which an evaluation request was sent to member companies of Swedish District Heating Association. Results: Informants in this research have different levels of experiences. Through a systematic procedure we have obtained and analyzed findings from all the informants. To fulfill the energy demands during peak hours, the informants emphasized on providing efficient energy consumption behavior to be displayed on smart heat meters. According to the informants, efficient energy consumption behavior can be presented through energy consumption/saving suggestions on display of smart meters. These suggestions are related to daily life activities like taking bath and shower, cleaning, washing and heating usage. We analyzed that efficient energy consumption behavior recommended by the energy providers can provide financial improvements both for the energy providers and the residential consumers. On the basis of these findings, we proposed Extended Smart Metering Display to present information in simple and interactive way. Furthermore, the proposed Extended Smart Metering Display can also be helpful in measuring consumers’ energy consumption behavior effectively. Conclusions: After obtaining answers of the research questions, we concluded that extension of existing smart heat meters’ display can effectively help the energy providers and the residential consumers to utilize the resources efficiently. That is, it will not only reduce energy bills for the residential consumers, but it will also help the energy provider to save scarce energy and enable them to serve the consumers better in peak hours. After deployment of the proposed Extended Smart Metering Display the energy providers will able to support the consumers’ behavior in a reliable way and the consumers will find/follow the energy consumption/saving guidelines easily. / mcs294@yahoo.com, shahid_yousaf27@yahoo.com

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This dissertation presents an analysis of economic viability of smart meters installation, building scenarios of probable behaviors of the residential consumers and the hourly rate which includes the concept of smart grids. The study takes account the smart meters implementation costs under the consumer point of view, using investment economic analysis concepts. The scenarios obtained from PROCEL INFO graphics and excel spreadsheets calculations compare conventional fare to different hourly rates using smart meters. From this context, it is possible observe the monthly savings that were obtained and the investments return period. The results show the investments economic viability and the priority of this new technology installation, considering that the installations shall be made first in the most viable regions. These results may turn possible a strategy that creates an order to the smart meters implementation. / Esta dissertação apresenta uma análise da viabilidade econômica da instalação de medidores inteligentes, construindo cenários de prováveis comportamentos dos consumidores residenciais e a tarifa horária que contempla o conceito de redes inteligentes. O estudo leva em consideração os custos de implantação destes medidores sob a ponto de vista do consumidor, utilizando conceitos de análise econômica de investimentos. Os cenários, construídos a partir dos gráficos do PROCEL INFO e com cálculos efetuados com o auxílio de planilhas do Excel, comparam a tarifa convencional com as tarifas horárias diferenciadas utilizando medidores inteligentes. Dentro deste contexto, é possível observar a economia mensal que é obtida e o prazo de retorno do investimento. Os resultados obtidos indicam viabilidade econômica de investimento e uma prioridade na instalação desta nova tecnologia, considerando as regiões do Brasil mais viáveis como sendo as primeiras à implantação. Com as contribuições deste estudo, por exemplo, permite-se traçar estratégias de implantação de medidores inteligentes.

Distribution grids play an important role in delivering electricityto end users. Electricity customers would like to have a continuouselectricity supply without any disturbance. For customerssuch as airports and hospitals electricity interruption may havedevastating consequences. Therefore, many electricity distributioncompanies are looking for ways to prevent power outages.Sometimes the power outages are caused from the grid sidesuch as failure in transformers or a break down in power cablesbecause of wind. And sometimes the outages are caused bythe customers such as overload. In fact, a very high peak inelectricity consumption and irregular load profile may causethese kinds of failures.In this thesis, we used an approach consisting of two mainsteps for detecting customers with irregular load profile. In thefirst step, we create a dictionary based on all common load profileshapes using daily electricity consumption for one-monthperiod. In the second step, the load profile shapes of customersfor a specific week are compared with the load patterns in thedictionary. If the electricity consumption for any customer duringthat week is not similar to any of the load patterns in thedictionary, it will be grouped as an anomaly. In this case, loadprofile data are transformed to symbols using Symbolic AggregateapproXimation (SAX) and then clustered using hierarchicalclustering.The approach is used to detect anomaly in weekly load profileof a data set provided by HEM Nät, a power distributioncompany located in the south of Sweden.

No description available.

The energy transition is accompanied by massive electrification of uses and sectors such as transport. As a result, the pressure on the electricity grid is increasing, and the time to connect to the power system is lengthening. Deploying new infrastructure is a laborious and expensive process but there are alternatives to exploit the flexibility of the power grid. The deployment of smart meters opens the door to many applications related to flexibility on the consumer side, to reduce peak loads that threaten grid capacity. Targeting the right consumers for Demand-Side Management (DSM) is a prerequisite to maximizing the chances of success of such programs. This degree project replicates and adapts the method developed in [14] to segment residential customers. It consists of encoding Daily Load Curves (DLC) using a dictionary of Typical Load Profiles (TLP) and grouping consumers according to the distribution of their TLP. A temporal analysis of the main TLP reveals different consumption behaviors. Customers are segmented into groups that reflect the degree of volatility of their consumption. This enables a classification based on the potential for Energy Efficiency (EE) or Demand Response (D/R) programs. We address the issue of attribute detection using the distribution of TLP of customers. In particular, several classification algorithms are compared to detect TLP characteristic of Electric Vehicle (EV). The obtained load shapes show consumption peaks at night, which may correspond to the charging time of EV. The method is discussed, especially the choice of the number of load profiles to be included in the dictionary of TLP. It proves to be useful to group consumers with similar consumption profiles and opens the door to applications such as individual household consumption forecasting. / Energiomställningen kräver en massiv elektrifiering av användningsområden och sektorer som t.ex. transportsektorn. Detta leder till att trycket på elnätet ökar och att tiden för att ansluta sig till elnätet blir allt längre. Att bygga ut ny infrastruktur är en mödosam och dyr process, men det finns alternativ för att utnyttja elnätets flexibilitet. Utplaceringen av smarta mätare öppnar dörren för många tillämpningar som rör flexibilitet på konsumentsidan, för att minska toppbelastningar som hotar nätkapaciteten. Att rikta in sig på rätt konsumenter för DSM är en förutsättning för att maximera chanserna att lyckas med sådana program. I detta examensarbete replikeras och anpassas den metod som utvecklats i [14] för att segmentera hushållskunder. Den består av att koda DLC med hjälp av ett lexikon av TLP och gruppera konsumenter enligt fördelningen av deras TLP. En tidsmässig analys av de viktigaste TLP avslöjar olika konsumtionsbeteenden. Kunderna delas in i grupper som återspeglar graden av volatilitet i deras konsumtion. Detta möjliggör en klassificering baserad på potentialen för EE eller D/R-program. Vi tar upp frågan om attributdetektering med hjälp av fördelningen av TLP hos kunderna. I synnerhet jämförs flera klassificeringsalgoritmer för att upptäcka TLP som är karakteristiska för EV. De erhållna belastningsformerna visar konsumtionstoppar på natten, vilket kan motsvara laddningstiden för EV. Metoden diskuteras, särskilt valet av antalet belastningsprofiler som ska ingå i ordlistan för TLP. Metoden visar sig vara användbar för att gruppera konsumenter med liknande förbrukningsprofiler och öppnar dörren för tillämpningar som prognostisering av enskilda hushålls förbrukning.

No description available.

No contexto de Smart Grids, determinar a correta localização das fontes causadoras de Variação de Tensão de Curta Duração (VTCD) não é uma tarefa simples, devido à curta duração destes eventos e também, por sua rápida propagação nas redes de distribuição de energia elétrica. Neste sentido, esse trabalho apresentou um método híbrido recursivo baseado em ferramentas da área de aprendizado de máquinas (algoritmo de agrupamento e base de regras), o qual é capaz de localizar as fontes de VTCD, a partir da análise dos das características dos distúrbios disponibilizadas pelos smart meters instalados no sistema. Assim, o trabalho destinouse ao desenvolvimento de uma plataforma em hardware para aquisição, detecção e classificação dos distúrbios, através de um Sistema Operacional de Tempo Real. Em seguida o algoritmo de agrupamento (k-means) agrupou os dados dos medidores de forma a definir dois clusters, onde um deles correspondeu aos medidores que estão longe da região que ocorreu o distúrbio e o outro, correspondeu aos medidores que estavam localizados próximos da região de ocorrência do distúrbio. Na segunda etapa, um sistema baseado em regras determinou qual dos clusters abrangeu o nó de origem. No entanto, quando o algoritmo determinou uma região muito grande, essa região é introduzida recursivamente, como entrada da metodologia desenvolvida, para refinar a região de localização. O sistema resultante foi capaz de estimar a região de localização com uma taxa de acerto acima de 90%. Assim, o método teve sua concepção adequada ao empregado nos centros de controle e operações de concessionárias de energia elétrica, visando apoiar a decisão do corpo técnico para que ações corretivas fossem estabelecidas de forma assertiva. / In the Smart Grids context, the correct location of short-duration voltage variations sources is not a trivial task, because of the short duration of these events and for rapid propagation in the distribution feeder. In this sense, aiming to develop a recursive hybrid method based on machine learning area tools (clustering algorithm and rule base) that is able to locate the sources of short-duration voltage variations, it was used data from smart meters installed along the distribution feeder. The recursive hybrid method, as input, received the disturbance characteristics provided by the meters installed in the system. Thus, this thesis aimed to development of a measurement hardware for signal acquisition, detection, classification through a realtime operating system. Then, k-means clustering algorithm grouped the meters data in order to define two clusters, where one of them corresponded to the meters that were distant from the region that occurred the disturbance and the other one corresponded to the meters, which were located near to the disturbance occurrence region. In a second step, a rule-based system determined which of the clusters corresponded to the source node. When the algorithm determined a very large region, that region was recursively introduced as input of the developed methodology to decrease its size. The resulting system was able to estimate the location region with a accuracy above 90%. Therefore, this method showed a suitable design for employment by operation control centers of power sector concessionaires, aiming to support technical staff decision to stablish assertive corrective actions.

No contexto de Smart Grids, determinar a correta localização das fontes causadoras de Variação de Tensão de Curta Duração (VTCD) não é uma tarefa simples, devido à curta duração destes eventos e também, por sua rápida propagação nas redes de distribuição de energia elétrica. Neste sentido, esse trabalho apresentou um método híbrido recursivo baseado em ferramentas da área de aprendizado de máquinas (algoritmo de agrupamento e base de regras), o qual é capaz de localizar as fontes de VTCD, a partir da análise dos das características dos distúrbios disponibilizadas pelos smart meters instalados no sistema. Assim, o trabalho destinouse ao desenvolvimento de uma plataforma em hardware para aquisição, detecção e classificação dos distúrbios, através de um Sistema Operacional de Tempo Real. Em seguida o algoritmo de agrupamento (k-means) agrupou os dados dos medidores de forma a definir dois clusters, onde um deles correspondeu aos medidores que estão longe da região que ocorreu o distúrbio e o outro, correspondeu aos medidores que estavam localizados próximos da região de ocorrência do distúrbio. Na segunda etapa, um sistema baseado em regras determinou qual dos clusters abrangeu o nó de origem. No entanto, quando o algoritmo determinou uma região muito grande, essa região é introduzida recursivamente, como entrada da metodologia desenvolvida, para refinar a região de localização. O sistema resultante foi capaz de estimar a região de localização com uma taxa de acerto acima de 90%. Assim, o método teve sua concepção adequada ao empregado nos centros de controle e operações de concessionárias de energia elétrica, visando apoiar a decisão do corpo técnico para que ações corretivas fossem estabelecidas de forma assertiva. / In the Smart Grids context, the correct location of short-duration voltage variations sources is not a trivial task, because of the short duration of these events and for rapid propagation in the distribution feeder. In this sense, aiming to develop a recursive hybrid method based on machine learning area tools (clustering algorithm and rule base) that is able to locate the sources of short-duration voltage variations, it was used data from smart meters installed along the distribution feeder. The recursive hybrid method, as input, received the disturbance characteristics provided by the meters installed in the system. Thus, this thesis aimed to development of a measurement hardware for signal acquisition, detection, classification through a realtime operating system. Then, k-means clustering algorithm grouped the meters data in order to define two clusters, where one of them corresponded to the meters that were distant from the region that occurred the disturbance and the other one corresponded to the meters, which were located near to the disturbance occurrence region. In a second step, a rule-based system determined which of the clusters corresponded to the source node. When the algorithm determined a very large region, that region was recursively introduced as input of the developed methodology to decrease its size. The resulting system was able to estimate the location region with a accuracy above 90%. Therefore, this method showed a suitable design for employment by operation control centers of power sector concessionaires, aiming to support technical staff decision to stablish assertive corrective actions.

The 2008 Climate Change Act set legally-binding carbon reduction targets. Demand side management (DSM) includes energy use reduction and peak shaving and offers significant potential to reduce the amount of carbon used by the electricity grid. The demand side management (DSM) schemes that have tried to meet this challenge have been dominated by engineering-based approaches and so favour tools like automation (which aims to make shifting invisible) and pricing (which requires customer response) to shift demand. These approaches tend to focus on the tools for change and take little account of people and energy-use practices. This thesis argues that these approaches are limited and therefore unlikely to produce the level of response that will be needed in future. The thesis therefore investigates the potential for time-shifting domestic energy demand but takes a different angle by trying to understand how people use energy in their daily lives, whether this use can be shifted and some of the implications of shifting it. The centrepiece of the work is an empirical study of eleven households energy-use practices. The interdisciplinary methodology involved in-house observations, interviews, photographs, metered energy data and disruptive interventions. The data was collected in two phases. Initially, a twenty-four hour observation was carried out in each household to find out how energy was implicated in everyday practices. Next, a series of three challenges were carried out, aimed at assessing the implications of disrupting practices by time-shifting food preparation, laundry and work/ leisure. A practice theory approach is used to shift the focus of attention from appliances, tools for change, behaviour or even people, to practices. The central finding of this work is that practices were flexible. This finding is nuanced, in the light of the empirical research, by an extended discussion on the nature of practices; in particular, the relationship between practices and agency and the temporal-spatial locatedness of practices. The findings demonstrate that, in this study at least, expanding the range of demand response options was possible. The research suggests numerous possibilities for extending the potential of practices to shift in time and space, shift the energy used in practices or substitute practices for other non-energy-using practices, though there are no simple technological or behavioural fixes . More profoundly, however, the thesis concludes that infrastructures of provision , such as the electricity grid and the companies that run it, underpin and facilitate energy-use practices irrespective of the time of day and year. In this context technology-led demand response schemes may ultimately contribute to the problem they purport to solve. A more fundamental interrogation of demand and the infrastructures that serve it is therefore necessary and is almost entirely absent from the demand response debate.

This thesis focuses on the design and implementation of a software tool able to achieve electricity grid optimal management in a dynamic pricing environment, at the residential level, and from a customer's perspective. The main drivers encouraging a development of energy management at the home level are analyzed, and a system architecture modeling power, thermodynamic and economic subsystems is proposed. The user behavior is also considered. A mathematical formulation of the related energy management optimization problem is proposed based on the linear programming theory. Several cases involving controllable and non-controllable domestic loads as well as renewable energy sources are presented and simulation scenarios illustrate the proposed optimization strategy in each case. The performance of the controller and the changes in energy use are analyzed, and ideas for possible future work are discussed.