Residential Smart Meters Research Articles

An Open-Source Framework for Smart Meters: Data Communication and Security Traffic Analysis

In this paper, a structured methodology is proposed to define the architecture for a communication framework with multiframe capability, which can be embedded in a residential smart meter hardware for smart grid applications. This framework is based on the Transmission Control Protocol/Internet protocol, and it is considered that the data are exchanged via wireless technologies. The architectural model was based upon the state-of-the-art in software engineering, making use of the design patterns and the principles of low coupling and high cohesion, which result in a solution that is both more reliable and maintainable. A security module is also provided and the framework performance is evaluated, in terms of packet throughput and computational effort of the security layer on the processing device, via practical experiments of some laboratory scenarios. Furthermore, errors on some frames are introduced in order to evaluate the feasibility of this framework in real field applications. Results show that this framework architecture supports acceptable transfer rates even with extra computational cost due to the use of data encryption and separated processing modules, making it a feasible proposal for the considered application context.

Urban MV and LV Distribution Grid Topology Estimation via Group Lasso

The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution grid. The topology estimation is a critical step to ensure the robustness of distribution grid operation. However, the bus connectivity and grid topology estimation are usually hard in distribution grids. For example, it is technically challenging and costly to monitor the bus connectivity in urban grids, e.g., underground lines. It is also inappropriate to use the radial topology assumption exclusively because the grids of metropolitan cities and regions with dense loads could be with many mesh structures. To resolve these drawbacks, we propose a data-driven topology estimation method for MV and LV distribution grids by only utilizing the historical smart meter measurements. Particularly, a probabilistic graphical model is utilized to capture the statistical dependencies amongst bus voltages. We prove that the bus connectivity and grid topology estimation problems, in radial and mesh structures, can be formulated as a linear regression with a least absolute shrinkage regularization on grouped variables (\textit{group lasso}). Simulations show highly accurate results in eight MV and LV distribution networks at different sizes and 22 topology configurations using PG\&E residential smart meter data.

Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.

C-Vine Copula Mixture Model for Clustering of Residential Electrical Load Pattern Data

The ongoing deployment of residential smart meters in numerous jurisdictions has led to an influx of electricity consumption data. This information presents a valuable opportunity to suppliers for better understanding their customer base and designing more effective tariff structures. In the past, various clustering methods have been proposed for meaningful customer partitioning. This paper presents a novel finite mixture modeling framework based on C-vine copulas (CVMM) for carrying out consumer categorization. The superiority of the proposed framework lies in the great flexibility of pair copulas toward identifying multidimensional dependency structures present in load profiling data. CVMM is compared to other classical methods by using real demand measurements recorded across 2613 households in a London smart-metering trial. The superior performance of the proposed approach is demonstrated by analyzing four validity indicators. In addition, a decision tree classification module for partitioning new consumers is developed and the improved predictive performance of CVMM compared to existing methods is highlighted. Further case studies are carried out based on different loading conditions and different sets of large numbers of households to demonstrate the advantages and to test the scalability of the proposed method.

Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan

Residential smart metering and energy feedback have attracted worldwide attention toward reducing energy consumption and building a sustainable society. Many theoretical studies have suggested the importance of personalized information; however, few feedback demonstrations have focused on household lifestyle. This paper presents a pilot program of energy feedback reports based on analytical methods to show the relationship between electricity consumption and household lifestyle in Japan. One type of report was for households with a night-oriented lifestyle, which were classified by means of frequency analysis; it was evident that such households should shift to a healthy, environmentally friendly, morning-oriented lifestyle. Another type of report was based on cluster analysis: it pinpointed the dates and times when the household consumed much more electricity than with its regular routine. Through panel data regression analysis, it was found that the reports contributed to reducing daily household electricity consumption—as long as a boomerang effect could be avoided. It was also found that the feedback effect was enhanced by activation of consciousness, norms, and motives. It was observed that activation required a good understanding of the characteristics of electricity consumption and lifestyles of each household.

スマートメータデータからの実需要推定による在・不在判定の精度改善手法

In this paper, we propose high-accuracy occupancy detection using low-resolution electricity consumption data. In Japan, residential smart meters, which automatically read and transmit energy consumption data at each household to electric power companies, have started to be installed and will be set up in 80 percent of households by 2020. Occupancy detection is one of the major techniques leveraging electricity consumption data and is applicable various services such as ambient assisted living and peak load shifting. However, it is difficult to conduct high-accurate occupancy detection using the transmitted smart meter data because they are in 30-minute interval and truncated to 100Wh units. Especially, truncation makes difficult to analyze the change of demand by absence. Therefore, we propose a machine-learning based occupancy detection method combined with the estimation of the actual consumption from the truncated data using total variation regularization. In experiments, our method shows the performance is comparable to the result using the raw demand data in 1W unit.

State estimation of medium voltage distribution networks using smart meter measurements

Distributed generation and low carbon loads are already leading to some restrictions in the operation of distribution networks and higher penetrations of e.g. PV generation, heat pumps and electric vehicles will exacerbate such problems. In order to manage the distribution network effectively in this new situation, increased real-time monitoring and control will become necessary. In the future, distribution network operators will have smart meter measurements available to them to facilitate safe and cost-effective operation of distribution networks. This paper investigates the application of smart meter measurements to extend the observability of distribution networks. An integrated load and state estimation algorithm was developed and tested using residential smart metering measurements and an 11kV residential distribution network. Simulation results show that smart meter measurements, both real-time and pseudo measurements derived from them, can be used together with state estimation to extend the observability of a distribution network. The integrated load and state estimation algorithm was shown to produce accurate voltage magnitudes and angles at each busbar of the network. As a result, the algorithm can be used to enhance distribution network monitoring and control.

Towards Dynamic Demand Response On Efficient Consumer Grouping Algorithmics

The widespread monitoring of electricity consumption due to increasingly pervasive deployment of networked sensors in urban environments has resulted in an unprecedentedly large volume of data being collected. Particularly, with the emerging Smart Grid technologies becoming more ubiquitous, real-time and online analytics for discovering the underlying structure of increasing-dimensional (w.r.t. time) consumer time series data are crucial to convert the massive amount of fine-grained energy information gathered from residential smart meters into appropriate demand response (DR) insights. In this paper we propose READER and OPTIC, that are real-time and online algorithmic pre-processing frameworks respectively, for effective DR in the Smart Grid. READER (OPTIC) helps discover underlying structure from increasing-dimensional consumer consumption time series data in a provably optimal real-time (online) fashion. READER (OPTIC) catalyzes the efficacy of DR programs by systematically and efficiently managing the energy consumption data deluge, at the same time capturing in real-time (online), specific behavior, i.e., households or time instants with similar consumption patterns. The primary feature of READER (OPTIC) is a real-time (online) randomized approximation algorithm for grouping consumers based on their electricity consumption time series data, and provides two crucial benefits: (i) time efficiently tackles high volume, increasing-dimensional time series data and (ii) provides provable worst case grouping performance guarantees. We validate the grouping and DR efficacy of READER and OPTIC via extensive experiments conducted on both, a USC microgrid dataset as well as a synthetically generated dataset.

Analysis and Clustering of Residential Customers Energy Behavioral Demand Using Smart Meter Data

Clustering methods are increasingly being applied to residential smart meter data, which provides a number of important opportunities for distribution network operators (DNOs) to manage and plan low-voltage networks. Clustering has a number of potential advantages for DNOs, including the identification of suitable candidates for demand response and the improvement of energy profile modeling. However, due to the high stochasticity and irregularity of household-level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper, we present in-depth analysis of customer smart meter data to better understand the peak demand and major sources of variability in their behavior. We find four key time periods, in which the data should be analyzed, and use this to form relevant attributes for our clustering. We present a finite mixture model-based clustering, where we discover ten distinct behavior groups describing customers based on their demand and their variability. Finally, using an existing bootstrap technique, we show that the clustering is reliable. To the authors’ knowledge, this is the first time in the power systems literature that the sample robustness of the clustering has been tested.

A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts

This work uses a probabilistic method to combine two unique datasets of real world electric vehicle charging profiles and residential smart meter load demand. The data was used to study the impact of the uptake of Electric Vehicles (EVs) on electricity distribution networks. Two real networks representing an urban and rural area, and a generic network representative of a heavily loaded UK distribution network were used. The findings show that distribution networks are not a homogeneous group with a variation of capabilities to accommodate EVs and there is a greater capability than previous studies have suggested. Consideration of the spatial and temporal diversity of EV charging demand has been demonstrated to reduce the estimated impacts on the distribution networks. It is suggested that distribution network operators could collaborate with new market players, such as charging infrastructure operators, to support the roll out of an extensive charging infrastructure in a way that makes the network more robust; create more opportunities for demand side management; and reduce planning uncertainties associated with the stochastic nature of EV charging demand.

Citizen Perspectives on the Customization/Privacy Paradox Related to Smart Meter Implementation

This paper employs the framework of contextual integrity related to privacy developed by as a tool to understand citizen response to implementation of residential smart metering technology. To identify and understand specific changes in information practices brought about by the introduction of smart meters, citizens were interviewed, read a description of planned smart grid/meter implementation, and were asked to reflect on changes in the key actors involved, information attributes, and principles of transmission. Areas where new practices emerge with the introduction of the smart grid were then highlighted as potential problems (privacy violations). Issues identified in this study included concern about unauthorized use and sharing of personal data, data leaks or spoofing via hacking, the blurring distinction between the home and public space, and inferences made from new data types aggregated with other personal data that could be used to unjustly discriminate against individuals or groups.

Reducing household electricity demand through smart metering: The role of improved information about energy saving

The international roll out of residential smart meters has increased considerably in recent years. The improved consumption feedback provided, and in particular, the installation of in-house displays, has been shown to significantly reduce residential electricity demand in some international trials. This paper attempts to uncover the underlying drivers of such information-led reductions by exploring two research questions. First, does feedback improve a household's stock of information about potential energy reducing behaviours? And second, do improvements in such information help explain the demand reductions associated with the introduction of smart metering and time-of-use tariffs? Data is from a randomised controlled smart metering trial (Ireland) which also collected extensive information on household attitudes towards energy conservation and self-reported stocks of information related to energy saving. As with previous results in Ireland, we find that participation in a smart metering programme with time-of-use tariffs significantly reduces demand. Although treated households also increased their self-reported energy-reducing information, such improvements are not correlated with demand reductions in the short-run. Given this result, it is possible that feedback and other information provided in the context of smart metering are mainly effective in reducing and shifting demand because they act as a reminder and motivator.

Effectiveness of Reactive Power Capability of Photo Voltaic Inverters to Maintain Voltage Profile in a Residential Distribution Feeder

AbstractAbstract—Large penetration of rooftop PVs has resulted in unacceptable voltage profile in many residential distribution feeders. Limiting real power injection from PVs to alleviate over voltage problem is not feasible due to loss of green power and hence corresponding revenue loss. Reactive capability of the PV inverter can be a solution to address over voltage and voltage dip problems to some extent. This paper proposes an algorithm to utilize reactive capability of PV inverters and investigate their effectiveness for voltage improvement based on R/X ratio of the feeder. The length and loading level of the feeder for a particular R/X ratio to have acceptable voltage profile is also investigated. This can be useful for suburban design and residential distribution planning. Furthermore, coordination among different PVs using residential smart meters via a substation based controller is also proposed.

Modelling of a smart micro grid with renewable energy for rural area based on power line communication

Renewable energy conversion systems are main factor driving research on smart grid technology. Smart grid represents the paradigm to enable highly efficient power generation, transmission, distribution and consumption from the source to consumer. Bangladesh is facing energy crisis like other developing countries. High energy demands and environmental concerns force the transformation of existing power grid into smart micro grid. This paper proposes power line communication-based (PLC) platform on dynamic demand response and distributed generation including renewable power generation management approach in the context of smart micro grid model for residential and industrial consumptions. The implementation of smart micro grid to a rural area promotes the utilisation of renewable resources such as solar and wind energy. In this model, deployed PLC networks, data management system to integrate sensors, switchgears, transformers and other utility devices are used to interconnect smart homes to smart grids. Analytical results show the effectiveness of the proposed system to optimise residential renewable energy generation and smart meters to improve electrical grid control and energy conservation system.