Nos sistemas de distribuição, a grande ramificação, radialidade, heterogeneidade, dinâmica das cargas e demais particularidades, impõem dificuldades à localização de faltas, representando um desafio permanente na busca por melhores indicadores de continuidade e confiabilidade no fornecimento de energia elétrica. A regulação incisiva dos órgãos do setor, a penetração de geração distribuída e a tendência de modernização trazida pelas redes inteligentes, demandam detalhados estudos para readequação dos sistemas elétricos a conjuntura atual. Neste contexto, esta tese propõe o desenvolvimento de uma metodologia para localização de faltas em sistemas de distribuição empregando a capacidade dos medidores inteligentes de monitoramento e de aquisição de tensão em diferentes pontos da rede elétrica. A abordagem proposta baseia-se na estimação, por ferramentas de aprendizado de máquina, das impedâncias de sequência zero e positiva entre os pontos de alocação dos medidores inteligentes e de ocorrência de falta, e do estado de sensibilização destes medidores frente a correntes de falta. Assim, calculando-se as respectivas distâncias elétricas em função das impedâncias estimadas e definidas as direções das mesmas em relação a topologia da rede, busca-se identificar o ponto ou área com maior sobreposição de distâncias elétricas como o local ou a região de maior probabilidade da falta em relação aos medidores inteligentes. Para tanto, faz-se uso combinado de ferramentas convencionais e inteligentes pela aplicação dos conceitos de análise de sistemas elétricos, diagnóstico dos desvios de tensão, e classificação de padrões por meio da técnica de aprendizado de máquina denominada Árvore de Decisão. Os resultados obtidos pela aplicação desta metodologia demonstram que o uso de informações redundantes fornecidas pelos medidores inteligentes minimiza os erros de estimação. Além disso, para a maior parte dos casos testados o erro absoluto máximo de localização da falta se concentra entre 200 m e 1000 m, o que reduz a busca pelo local de ocorrência da falta pelas equipes de manutenção da rede elétrica. / In distribution systems, the dense branching, radial pattern, heterogeneity, dynamic of the loads, and other characteristics create several difficulties in defining the fault location, representing a great challenge in the search for better continuity and reliability indicators of the electrical energy supply. The intense government regulations, the increasing use of distributed generation, and the trend towards modernization via smart grids require a detailed study in order to upgrade the current systems. In this context, this thesis proposes a methodology development for fault location in distribution systems with the use of smart meters monitors and the acquisition of voltage at different points in the electrical network. The proposed method is based on the estimation, using machine learning, of the state of awareness of smart meters across the fault currents and of the zero and positive sequence impedance between the location of these meters and of the fault occurrence. Therefore, by calculating the electrical distances as a function of the estimated impedances and defining its the direction in relation to the network topology, the point/region with the biggest superposition of the electrical distances can be assigned as the point/region with the highest probability of fault occurrence in relation to the smart probes. For this purpose, a machine learning technique named decision tree is used to apply concept analyses to the electrical systems, diagnosis of voltage deviations, and pattern recognition of the electrical systems. The results obtained by the application of this methodology demonstrate that the use of redundant information provided by the smart meters minimizes estimation errors. In addition, for most of the cases tested, the maximum absolute error of the fault location is concentrated between 200 m and 1000 m, which reduces the search for the fault location by the maintenance teams of the electrical network.

Through the Smart Metering Initiative in the Canadian province of Ontario, all residential electricity customers will be converted from a tiered rate regime to a time-of-use (TOU) rate regime by the year 2010. Although TOU rates are designed to be cost-neutral for the average consumer, research suggests that TOU rates may affect consumers differently depending on their socioeconomic characteristics. In an effort to better understand the effects of TOU rates on lower-income and senior-headed households, a case-study in Milton was conducted between June and December of 2007. The overarching thesis question is: What are the behavioural responses to, and financial impacts of, TOU electricity rates on lower-income and senior-headed households? Nine expert interviews were conducted with Ontario professionals working in government, environmental non-profit groups, citizen advocacy organizations and affordable housing associations in order to provide context for the study. Time-differentiated electricity consumption data were then collected from 199 households from two senior housing complexes and two affordable housing complexes in Milton, Ontario between June and December 2007. A questionnaire was also sent to each household to determine some socio-economic and structural characteristics of the households. The electricity consumption data collected from the four sites suggest that the households would not benefit financially from TOU rates given electricity consumption behaviour during the period prior to the implementation of TOU rates in June 2007. Thus, they would have to change their behaviour in order to benefit financially from TOU rates. During this pre-TOU period, Site A, Site B and Site C would have paid more, on average, for their electricity under TOU rates than on tiered rates ($0.34, $0.61 and $0.15 per week, respectively). While Site D, on average, would have seen no change under TOU rates. A conservation effect was detected by comparing the electricity consumption from billing periods in 2006 to corresponding billing periods in 2007 after the implementation of TOU rates. Site A saw a conservation effect during the first corresponding billing period (35%); while Site B saw a conservation effect for three corresponding billing periods (21%, 24% and 9%). Site C saw a conservation effect for the first five corresponding billing periods (ranging from 8% to 21%), while Site D saw a conservation effect for all corresponding billing periods (ranging from 10% to 34%). The presence of a conservation effect at Site D was unexpected, particularly because households at Site D are not responsible for paying their own electricity bills. Although a conservation effect was observed after the implementation of TOU rates, the extent to which it could be attributed to the implementation of TOU rates is unclear, and should be investigated further. There was no considerable shift in the proportion of electricity consumed during each of the peak periods during the summer TOU period for Site A and Site D after the introduction of TOU rates. There was, however, a slight reduction in the portion of electricity consumed during the summer TOU period for Site B and Site C (0.2% and 0.1% per week, respectively). Due to the change in the on-, mid- and off-peak schedule from the summer TOU period to the winter TOU period, the households consume more electricity during the off-peak periods in the winter than they do during the off-peak periods in the summer (even though their patterns of consumption do not change). Similar to the pre-TOU period, during the summer post-TOU period, Site A and Site B, and Site C, on average, paid more for electricity (commodity) under TOU rates than they would have paid if they had continued on tiered rates ($0.38, $0.51 and $0.16 more per week, respectively), while Site D would have seen no change in their electricity costs. In contrast, during the winter post-TOU period several sites paid less for electricity on TOU rates than they would have if they had continued on tiered rates. Site B, Site C and Site D paid, on average, $0.78, $0.16 and $1.76 less per week, respectively. Although Site A paid more under on TOU rates during the winter post-TOU (on average $0.18 more per week), the cost was less than during the summer post-TOU period. The change in costs expressed here does not reflect any reduced costs that may have resulted from conservation. For example, if the households were shown to have a conservation effect, they might have lower electricity costs. Additionally, the changes in costs do not reflect any additional fees or charges that might be attributed to the smart meter installation and the Smart Metering Initiative (e.g., additional fees from Milton Hydro). In conclusion, TOU rates appear to be ineffective at motivating these lower-income and senior-headed households in Milton, Ontario to shift electricity from on-peak periods to off-peak periods, however, a reduction in electricity usage may be attributed to TOU rates. Further research is required to confirm these effects. It is important to note that some of the lower-income and senior-headed households in this study appeared to see an increase in their electricity bill, particularly during the summer TOU period. Lower-income and senior-headed households are thought to be less able to shift electricity consumption, therefore it is important to develop mechanisms to identify households that are at risk of bill increases.

Through the Smart Metering Initiative in the Canadian province of Ontario, all residential electricity customers will be converted from a tiered rate regime to a time-of-use (TOU) rate regime by the year 2010. Although TOU rates are designed to be cost-neutral for the average consumer, research suggests that TOU rates may affect consumers differently depending on their socioeconomic characteristics. In an effort to better understand the effects of TOU rates on lower-income and senior-headed households, a case-study in Milton was conducted between June and December of 2007. The overarching thesis question is: What are the behavioural responses to, and financial impacts of, TOU electricity rates on lower-income and senior-headed households? Nine expert interviews were conducted with Ontario professionals working in government, environmental non-profit groups, citizen advocacy organizations and affordable housing associations in order to provide context for the study. Time-differentiated electricity consumption data were then collected from 199 households from two senior housing complexes and two affordable housing complexes in Milton, Ontario between June and December 2007. A questionnaire was also sent to each household to determine some socio-economic and structural characteristics of the households. The electricity consumption data collected from the four sites suggest that the households would not benefit financially from TOU rates given electricity consumption behaviour during the period prior to the implementation of TOU rates in June 2007. Thus, they would have to change their behaviour in order to benefit financially from TOU rates. During this pre-TOU period, Site A, Site B and Site C would have paid more, on average, for their electricity under TOU rates than on tiered rates ($0.34, $0.61 and $0.15 per week, respectively). While Site D, on average, would have seen no change under TOU rates. A conservation effect was detected by comparing the electricity consumption from billing periods in 2006 to corresponding billing periods in 2007 after the implementation of TOU rates. Site A saw a conservation effect during the first corresponding billing period (35%); while Site B saw a conservation effect for three corresponding billing periods (21%, 24% and 9%). Site C saw a conservation effect for the first five corresponding billing periods (ranging from 8% to 21%), while Site D saw a conservation effect for all corresponding billing periods (ranging from 10% to 34%). The presence of a conservation effect at Site D was unexpected, particularly because households at Site D are not responsible for paying their own electricity bills. Although a conservation effect was observed after the implementation of TOU rates, the extent to which it could be attributed to the implementation of TOU rates is unclear, and should be investigated further. There was no considerable shift in the proportion of electricity consumed during each of the peak periods during the summer TOU period for Site A and Site D after the introduction of TOU rates. There was, however, a slight reduction in the portion of electricity consumed during the summer TOU period for Site B and Site C (0.2% and 0.1% per week, respectively). Due to the change in the on-, mid- and off-peak schedule from the summer TOU period to the winter TOU period, the households consume more electricity during the off-peak periods in the winter than they do during the off-peak periods in the summer (even though their patterns of consumption do not change). Similar to the pre-TOU period, during the summer post-TOU period, Site A and Site B, and Site C, on average, paid more for electricity (commodity) under TOU rates than they would have paid if they had continued on tiered rates ($0.38, $0.51 and $0.16 more per week, respectively), while Site D would have seen no change in their electricity costs. In contrast, during the winter post-TOU period several sites paid less for electricity on TOU rates than they would have if they had continued on tiered rates. Site B, Site C and Site D paid, on average, $0.78, $0.16 and $1.76 less per week, respectively. Although Site A paid more under on TOU rates during the winter post-TOU (on average $0.18 more per week), the cost was less than during the summer post-TOU period. The change in costs expressed here does not reflect any reduced costs that may have resulted from conservation. For example, if the households were shown to have a conservation effect, they might have lower electricity costs. Additionally, the changes in costs do not reflect any additional fees or charges that might be attributed to the smart meter installation and the Smart Metering Initiative (e.g., additional fees from Milton Hydro). In conclusion, TOU rates appear to be ineffective at motivating these lower-income and senior-headed households in Milton, Ontario to shift electricity from on-peak periods to off-peak periods, however, a reduction in electricity usage may be attributed to TOU rates. Further research is required to confirm these effects. It is important to note that some of the lower-income and senior-headed households in this study appeared to see an increase in their electricity bill, particularly during the summer TOU period. Lower-income and senior-headed households are thought to be less able to shift electricity consumption, therefore it is important to develop mechanisms to identify households that are at risk of bill increases.

Smart radial distribution grids will include advanced metering infrastructure (AMI) and significant distributed generators (DGs) connected close to loads. DGs in these radial distribution systems (RDS) introduce bidirectional power flows (BPFs) and contribute to fault current. These BPFs may cause unwanted tripping of existing overcurrent (OC) protection devices and result in permanent outages for a large number of customers. This thesis presents a protection approach that modified an existing overcurrent protection scheme to reduce the number of customers affected by faults in RDS with DGs. Further, a technique is presented that utilizes customers loading information from smart meters in AMI to improve the sensitivity of substation OC relays by adaptively changing the pickup settings. The modified protection approach involves predefining zones in RDS with DGs and installing directional OC relays and circuit breakers at the zonal boundaries. Zonal boundary relays determine faulted zones by sharing information on the direction of detected faults current using binary state signals over a communication medium. The technique to adapt the substation relay pickup settings uses the demand measurements from smart meters for two 12-hour intervals from the previous day to determine the maximum diversified demand at the relay?s location. The pickup settings of the substation relay for the two 12-hour intervals during the following day for the zone supplied by the substation are adaptively set based on the current that corresponds to the maximum diversified demand from the previous day. The techniques were validated through simulations in EMTP/PSCAD using an expanded IEEE 34 node radial test feeder that included DGs and a secondary distribution level. By decentralizing the control of the zonal boundary breakers, the single point of failure was eliminated in the modified protection approach. The cases studied showed that the modified protection approach allows for selective identification and isolation of the faulted zones. Also, the sensitivity of the substation OC relay was improved by at least 24% by using the pickup settings for the two 12-hour intervals from the smart meter demand measurements compared to the pickup settings computed using the conventional methodology based on the maximum loading of the zone.

Nos sistemas de distribuição, a grande ramificação, radialidade, heterogeneidade, dinâmica das cargas e demais particularidades, impõem dificuldades à localização de faltas, representando um desafio permanente na busca por melhores indicadores de continuidade e confiabilidade no fornecimento de energia elétrica. A regulação incisiva dos órgãos do setor, a penetração de geração distribuída e a tendência de modernização trazida pelas redes inteligentes, demandam detalhados estudos para readequação dos sistemas elétricos a conjuntura atual. Neste contexto, esta tese propõe o desenvolvimento de uma metodologia para localização de faltas em sistemas de distribuição empregando a capacidade dos medidores inteligentes de monitoramento e de aquisição de tensão em diferentes pontos da rede elétrica. A abordagem proposta baseia-se na estimação, por ferramentas de aprendizado de máquina, das impedâncias de sequência zero e positiva entre os pontos de alocação dos medidores inteligentes e de ocorrência de falta, e do estado de sensibilização destes medidores frente a correntes de falta. Assim, calculando-se as respectivas distâncias elétricas em função das impedâncias estimadas e definidas as direções das mesmas em relação a topologia da rede, busca-se identificar o ponto ou área com maior sobreposição de distâncias elétricas como o local ou a região de maior probabilidade da falta em relação aos medidores inteligentes. Para tanto, faz-se uso combinado de ferramentas convencionais e inteligentes pela aplicação dos conceitos de análise de sistemas elétricos, diagnóstico dos desvios de tensão, e classificação de padrões por meio da técnica de aprendizado de máquina denominada Árvore de Decisão. Os resultados obtidos pela aplicação desta metodologia demonstram que o uso de informações redundantes fornecidas pelos medidores inteligentes minimiza os erros de estimação. Além disso, para a maior parte dos casos testados o erro absoluto máximo de localização da falta se concentra entre 200 m e 1000 m, o que reduz a busca pelo local de ocorrência da falta pelas equipes de manutenção da rede elétrica. / In distribution systems, the dense branching, radial pattern, heterogeneity, dynamic of the loads, and other characteristics create several difficulties in defining the fault location, representing a great challenge in the search for better continuity and reliability indicators of the electrical energy supply. The intense government regulations, the increasing use of distributed generation, and the trend towards modernization via smart grids require a detailed study in order to upgrade the current systems. In this context, this thesis proposes a methodology development for fault location in distribution systems with the use of smart meters monitors and the acquisition of voltage at different points in the electrical network. The proposed method is based on the estimation, using machine learning, of the state of awareness of smart meters across the fault currents and of the zero and positive sequence impedance between the location of these meters and of the fault occurrence. Therefore, by calculating the electrical distances as a function of the estimated impedances and defining its the direction in relation to the network topology, the point/region with the biggest superposition of the electrical distances can be assigned as the point/region with the highest probability of fault occurrence in relation to the smart probes. For this purpose, a machine learning technique named decision tree is used to apply concept analyses to the electrical systems, diagnosis of voltage deviations, and pattern recognition of the electrical systems. The results obtained by the application of this methodology demonstrate that the use of redundant information provided by the smart meters minimizes estimation errors. In addition, for most of the cases tested, the maximum absolute error of the fault location is concentrated between 200 m and 1000 m, which reduces the search for the fault location by the maintenance teams of the electrical network.

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 overarching question of this dissertation is in what ways complementary currencies (CC) systems can contribute to environmental sustainability from an institutional and ecological economics perspective. More particularly, the research focuses on household energy consumption, as it is an important target for policy makers in the EU. The first three chapters focus on the emerging trend of using CC systems as top-down instruments for environmental sustainability. Our findings relate to developing a taxonomy of these systems, designing new top-down CC systems adapted to the context of energy savings in the household sector, exploring the influence of these new systems on the quality of motivation in the light of Self-Determination Theory and investigating the acceptability and effectiveness of these systems. In the fourth and last chapter of this dissertation, we approach our research question from a bottom-up angle and, using an inductive methodology, we explore how community currencies could act as an organising instrument capable of helping cohousing communities achieve their energy-efficiency potential. Finally, we conclude by highlighting our contributions regarding the structure and important features of these systems, how they can be used in an ecological economics paradigm, and how they can set rules for collective action in an institutional perspective. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

Efforts to combat climate change through limiting our greenhouse gas emissions (GHG),is the main driver of the energy transition and entail strategies to decarbonize the energy sector. However, these strategies come with various social and technical challenges whichare important to address, especially regarding emerging consumer participation in the system as producers and consumers i.e., prosumers. The aim of this study is to investigate how smart technical solutions can be better diffused to improve participation of consumers in the energy transition through building knowledge and direct engagement as prosumers. Similarly, this study aims to investigate if there is potential for such smart technical solutions to help improve social acceptance of renewables and smart meter devices which also support “prosumerism”, through exploring Ferroamp, and its unique smart solution modular system as an example in the Swedish context.  This study conducted three qualitative interviews, one with a solar seller who acts as a representative and interacts with a large consumer base as well as 2 customers of the Ferroamp system. These interviews led to conclusions regarding the opportunity of smart solutions to facilitate greater knowledge and participation in the Swedish Energy Transition; however, key challenges regarding technological awareness and economic knowledge of the consumer base remain, which has implications for actors trying to increase smart technology penetration in Sweden. Therefore, this study highlights the importance of increasing consumers’ technological awareness and better communicating economic benefits if we are to increase consumer acceptance, participation, and engagement in the energy transition moving forward.

The power grid has changed a great deal from what has been generally viewed as a traditional power grid. The modernization of the power grid has seen an increase in the integration and incorporation of computing and communication elements, creating an interdependence of both physical and cyber assets of the power grid. The fast-increasing connectivity has transformed the grid from what used to be primarily a physical system into a Cyber- Physical System (CPS). The physical elements within a power grid are well understood by power engineers; however, the newly deployed cyber aspects are new to most researchers and operators in this field. The new computing and communications structure brings new vulnerabilities along with all the benefits it provides. Cyber security of the power grid is critical due to the potential impact it can make on the community or society that relies on the critical infrastructure. These vulnerabilities have already been exploited in the attack on the Ukrainian power grid, a highly sophisticated, multi-layered attack which caused large power outages for numerous customers. There is an urgent need to understand the cyber aspects of the modernized power grid and take the necessary precautions such that the security of the CPS can be better achieved. The power grid is dependent on two main cyber infrastructures, i.e., Supervisory Control And Data Acquisition (SCADA) and Advanced Metering Infrastructure (AMI). This thesis investigates the AMI in power grids by developing a testbed environment that can be created and used to better understand and develop security strategies to remove the vulnerabilities that exist within it. The testbed is to be used to conduct and implement security strategies, i.e., an Intrusion Detections Systems (IDS), creating an emulated environment to best resemble the environment of the AMI system. A DoS flooding attack and an IDS are implemented on the emulated testbed to show the effectiveness and validate the performance of the emulated testbed. / M.S. / The power grid is becoming more digitized and is utilizing information and communication technologies more, hence the smart grid. New systems are developed and utilized in the modernized power grid that directly relies on new communication networks. The power grid is becoming more efficient and more effective due to these developments, however, there are some considerations to be made as for the security of the power grid. An important expectation of the power grid is the reliability of power delivery to its customers. New information and communication technology integration brings rise to new cyber vulnerabilities that can inhibit the functionality of the power grid. A coordinated cyber-attack was conducted against the Ukrainian power grid in 2015 that targeted the cyber vulnerabilities of the system. The attackers made sure that the grid operators were unable to observe their system being attacked via Denial of Service attacks. Smart meters are the digitized equivalent of a traditional energy meter, it wirelessly communicates with the grid operators. An increase in deployment of these smart meters makes it such that we are more dependent on them and hence creating a new vulnerability for an attack. The smart meter integration into the power grid needs to be studied and carefully considered for the prevention of attacks. A testbed is created using devices that emulate the smart meters and a network is established between the devices. The network was attacked with a Denial of Service attack to validate the testbed performance, and an Intrusion detection method was developed and applied onto the testbed to prove that the testbed created can be used to study and develop methods to cover the vulnerabilities present.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-06T18:59:56Z No. of bitstreams: 1 PEDRO YÓSSIS SILVA BARBOSA - DISSERTAÇÃO PPGCC 2014..pdf: 17089632 bytes, checksum: 4623777c293a51dbb1b392f37d2dd75e (MD5) / Made available in DSpace on 2018-08-06T18:59:56Z (GMT). No. of bitstreams: 1 PEDRO YÓSSIS SILVA BARBOSA - DISSERTAÇÃO PPGCC 2014..pdf: 17089632 bytes, checksum: 4623777c293a51dbb1b392f37d2dd75e (MD5) Previous issue date: 2014-02-27 / Capes / Companhias de energia começaram a substituir os medidores de energia tradicionais pelos Smart Meters, que podem transmitir valores de consumo para as companhias em curtos intervalos de tempo. Com uma insfraestrutura de Smart Meters, existem muitas motivações para as concessionárias de energia coletarem dados de consumo em alta resolução. Entretanto, isto implica em informações bastante detalhadas sobre os consumidores sendo monitoradas. Consequentemente, um problema sério precisa ser resolvido: como preservar a privacidade dos consumidores sem afetar a prestação de certos serviços pelas concessionárias? Claramente, este é um tradeoff entre privacidade e utilidade. Existem diversas abordagens para preservar a privacidade, porém muitas delas afetam a utilidade dos dados ou possuem um alto custo computacional. Neste trabalho, nós propomos e avaliamos uma abordagem computacionalmente barata que preserva a privacidade e utilidade dos dados através de adição de ruído. Para validar a privacidade, nós avaliamos possíveis ataques (tal como Monitoramento Não-Intrusivo de Carga de Eletrodomésticos - NIALM, do inglês Non-Intrusive Appliance Load Monitoring) utilizando dados reais de consumidores. Para validar a utilidade, nós avaliamos a influência da abordagem em vários benefícios que podem ser providos com o uso de Smart Meters. / Power providers have started replacing traditional electricity meters for Smart Meters, which can transmit power consumption levels to the provider within short intervals. With a Smart Metering infrastructure, there are many motivations for power providers to collect highresolution data of electricity usage from consumers. However, this implies in very detailed information about the consumers being monitored. Consequently, a serious issue needs to be addressed: how to preserve the privacy of consumers but making the provision of certain services still possible? Clearly, this is a tradeoff between privacy and utility. There are several approaches for privacy preserving, but many of them affect the data usefulness or are computationally expensive. In this work, we propose and evaluate a lightweight approach for privacy and utility based on the addition of noise. To validate the privacy, we evaluate possible attacks (such as a NIALM - Non-Intrusive Appliance Load Monitoring) using real consumers' data. To validate the utility, we analyze the influence of the approach in various benefits that can be provided through the use of Smart Meters.