Smart Metering Utility Research Articles

Enhancing Industrial Process Control: Integrating Intelligent Digital Twin Technology with Proportional-Integral-Derivative Regulators

This paper explores the integration of intelligent digital twin technology with PID regulators in industrial process control utilizing smart meter data. It presents a novel approach involving the creation of mathematical models to simulate real-time system behavior, thereby enhancing the PID control loop. The focus is on the development of specialized IT infrastructure to support this integration, which includes data acquisition, processing, and control optimization. This integration aims to not only improve control system efficiency but also introduce a robust predictive maintenance framework, offering significant benefits across a wide range of industrial applications.

Line parameter, topology and phase estimation in three-phase distribution networks with non-[formula omitted]PMUs

The increasing penetration of distributed energy sources demands higher observability of the distribution networks for better scheduling, operation, dispatching and control. However, the huge amounts of nodes in the distribution networks implies that any additional measuring instrument would be of huge costs burden for DSO. In this paper, we proposes a full-scale framework for line parameters, topology, phase label estimation and angle recovery utilizing smart meter measurements only for the non-μPMU distribution networks. Firstly, we linearize the three-phase power flow model and induce the relationship of nodal voltage magnitudes and power injections. Then, a joint estimation method based on linear regression and topology characteristics is proposed to find line parameters, topology, and phase label. Finally, the nodal angle information is recovered by non-linear regression based on the estimation result. Simulations on both single-phase and three-phase networks are implemented. The results suggest that the proposed framework can provide an accurate estimation result with limited measurements.

Evaluation of Exposure to Radiofrequency Electromagnetic Fields from Smart Utility Meters operating at 868 MHz.

Power density and duty factor values were measured around smart utility meters operating at 868 MHz under laboratory-controlled conditions. The maximum 6-min averaged exposure recorded was 0.1 mWm-2 , which is less than 0.0024% of the corresponding 1998 ICNIRP general public reference level. Duty factors measured were less than 2.8%. This study found that the exposure contribution from Zigbee smart meter devices operating at 868 MHz is generally lower than, if not similar to, those operating at 2.4 GHz. © 2023 Crown copyright. Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society.

Learning Reactive Power Control Polices in Distribution Networks Using Conditional Value-at-Risk and Artificial Neural Networks

Scalable coordination of photovoltaic (PV) inverters, considering the uncertainty in PV and load in distribution networks (DNs), is challenging due to the lack of real-time communications. Decentralized PV inverter setpoints can be achieved to address this issue by capitalizing on the abundance of data from smart utility meters and the scalable architecture of artificial neural networks (ANNs). To this end, we first use an offline, centralized data-driven conservative convex approximation of chance-constrained optimal power flow (CVaR-OPF) in which conditional value-at-risk (CVaR) is used to compute reactive power setpoints of PV inverter, taking into account PV and load uncertainties in DNs. Following that, an artificial neural network (ANN) controller is trained for each PV inverter to emulate the optimal behavior of the centralized control set- points of PV inverter in a decentralized fashion. Additionally, the voltage regulation performance of the developed ANN controllers is compared with other decentralized designs (local controllers) developed using model-based learning (regression-based controller), optimization (affine feedback controller), and case-based learning (mapping) approaches. Numerical tests using real-world feeders corroborate the effectiveness of ANN controllers in voltage regulation and loss minimization.

Forecasting Method of Newly-Connected Customer&s Load Curve on LV distribution by Means of Regression Tree Utilizing Smart Meter Data

Maximum current is necessary to be estimated when designing a LV distribution grid, i.e. MV/LV pole-mounted transformers (hereinafter referred to as Pole-Tr) and LV feeders, which supplies customers of several in minimum and dozens in maximum. This is currently estimated from the total contract capacities of the customers supplied by the grid, multiplying diversity and demand factor corresponding to the number of customers. This method is rather efficient and simple because calculations can be done simply by using contract capacities. However, such calculations may not be correct as they were derived statistically from sampled current measurements of customers. Therefore, we analyzed the SM data collected for each customer attribute information and calculated the representative profile. Machine learning is performed using the calculated representative profile as teacher data to generate a regression tree. Using the generated regression trees, we have constructed a proposal method for assuming the maximum power of the Pole-Tr to be connected together with the load curve of the new customer from the customer attribute information obtained when the customer applied for electricity use. This makes it possible to design LV distribution with higher accuracy than the conventional method.

Impact assessment of COVID-19 on electricity demand by utilizing smart meter

Since the beginning of 2020, the whole world has been suffering from COVID-19. We tried to visualize how much effect of staying home and refraining from daily activities from the aspect of electricity demand by utilizing data from smart meters. At the same time, we tried to recognize where the difficulties and constraints exist in terms of data utilization. We acquired the data of smart meters for business district and shopping district, and visualize the effects of COVID-19 by comparing (1) within the state of emergency declaration, (2) outside the state of emergency on the same days of the same month, and (3) the weekday average of the same month of the previous year. After the Declaration, the activities in the entertainment district had been declined. We could see the constraints of data utilization, such as data authorization which are constrains such as it takes 60 minutes to acquire data, the accuracy of the data is about 95%, and the necessity to build additional servers. As a result, we were able to visualize the impact of COVID-19 on society by using smart meter data, and we were able to confirm the restrictions on using the data.

Multiple symbol detection for convolutional coded O-QPSK signals in smart metering utility networks without channel state information

In this paper, a multiple symbol detection (MSD) algorithm is proposed for convolutional coded offset quadrature phase shift keying (O-QPSK) in IEEE 802.15.4 g smart metering utility networks (SUNs) when the channel state information (CSI) is unavailable at the receiver. Specifically, we first develop the derivation of exact formulas for the log-likelihood ratio (LLR) with imperfect CSI under the Rayleigh fading channel. Then, the complexity-intensive Bessel function is successfully simplified, and a simple expression without CSI is finally developed that allows for an effective implementation. Performance simulation and comprehensive analysis are carried out in terms of detection performance, robustness and complexity. The simulation results show that the proposed LLR is simple to be implemented, and the corresponding detection performance is effectively improved when comparing to the symbol-by-symbol coded system. It is noticeable that the results of the paper are applicable to other digital receivers that perform soft decision decoding or iterative decoding.

Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

The key advantage of smart meters over traditional metering devices is their ability to transfer consumption information to remote data processing systems. Besides enabling the automated collection of a customer’s electricity consumption for billing purposes, the data collected by these devices makes the realization of many novel use cases possible. However, the large majority of such services are tailored to improve the power grid’s operation as a whole. For example, forecasts of household energy consumption or photovoltaic production allow for improved power plant generation scheduling. Similarly, the detection of anomalous consumption patterns can indicate electricity theft and serve as a trigger for corresponding investigations. Even though customers can directly influence their electrical energy consumption, the range of use cases to the users’ benefit remains much smaller than those that benefit the grid in general. In this work, we thus review the range of services tailored to the needs of end-customers. By briefly discussing their technological foundations and their potential impact on future developments, we highlight the great potentials of utilizing smart meter data from a user-centric perspective. Several open research challenges in this domain, arising from the shortcomings of state-of-the-art data communication and processing methods, are furthermore given. We expect their investigation to lead to significant advancements in data processing services and ultimately raise the customer experience of operating smart meters.

Reduced-Complexity Multiple-Symbol Detection of O-QPSK Signals in Smart Metering Utility Networks

In this work, an implementation-friendly multiple-symbol detection (MSD) scheme is proposed for the IEEE 802.15.4g offset quadrature phase shift keying (O-QPSK) receivers over the slow fading channel. The full MSD scheme presents better detection performance than the symbol-by-symbol detection (SBSD) scheme, yet its complexity increases exponentially as the observation window length increases. We introduce a simplified MSD scheme based on two powerful strategies. We first seek the optimal and suboptimal decisions in each symbol position with the standard SBSD procedure. Then, the aforementioned optimal and suboptimal decisions instead of all candidates are jointly searched with the standard MSD procedure. That is, only the most and second most reliable candidates in each symbol position are selected to participate in the final detection. The simulation results demonstrate that the new MSD scheme can achieve more encouraging energy gain than the SBSD scheme, while the high complexity of full MSD is also effectively reduced. A more legitimate compromise between detection performance and complexity is thus accomplished, which enables smart metering utility networks (SUN) nodes to achieve energy saving and maximum service life.

On the trade-off between timeliness and accuracy for low voltage distribution system grid monitoring utilizing smart meter data

Due to limited bandwidth and high delays in access to Smart Meter measurements, it is not possible in most cases to access measurements from the complete set of smart meters in a low-voltage grid area for distribution grid monitoring. Distribution system state estimation can be performed based on measurements of voltage and active and reactive power from a subset of selected smart meters. Increasing the number of selected smart meters will, on the one hand, increase the accuracy of distribution system state estimation, while on the other hand, it will degrade timeliness of the monitoring data. This paper proposes to utilize part of the idle time of the legacy periodic smart meter data collection for access to measurements from the subset of selected smart meters for distribution system state estimation. It subsequently proposes a methodology on how to quantitatively analyze this trade-off. The methodology is applied to an example LV grid area with 20 customers using a weighted least square state estimation with support of pseudo-measurements obtained during the regular smart meter collection cycle.

Complexity Analysis of Data Aggregation and Routing Algorithms for Automated Utility Management Using WSN

At present most of the houses in the country have the traditional electromechanical or digital utility usage meters, water meter, and gas meters. Presently most of the utility meter reading, billing system and utility management is not automated. The recent advances in the wireless sensor networks (WSNs) have made strong impact on the development of low cost remote monitoring systems. The WSN based remote automated utility management, remote meter reading and billing for future smart cities will enhance the quality and service by government. This increases the revenue of government. Due to unpleasant trend in the growth of congestion in urban areas, the smart utility meter data traffic aggregation and routing faces more challenges in the traditional automated service departments. In this work we propose an integrated architecture that include electricity, water and gas utility meters and discuss the methodology for Data Aggregation and Routing for Integrated Public Utility Services (IPUS-DAR) using WSN network. This work aims to integrate three types of utility meters and minimize redundant routing data in the network by applying data aggregation that improves traffic performance. We discuss the computational complexity of proposed Data Routing with Data Aggregation algorithm. The comparative analysis of proposed Data Routing with Data Aggregation methodology with previous methods is analyzed. We investigate performance metrics which include packet delivery ratio, end-to-end delay, jitter, throughput and energy consumption with respect to varying network size using QUALNET.

A new method utilizing smart meter data for identifying the existence of air conditioning in residential homes

Climate change, urbanization, and economic growth are expected to drive increases in the installation of new air conditioners, as well as increases in utilization of existing air conditioning (AC) units, in the coming decades. This growth will provide challenges for a diversity of stakeholders, from grid operators charged with maintaining a reliable and cost-effective power system, to low-income communities that may struggle to afford increased electricity costs. Despite the importance of building a quantitative understanding of trends in existing and future AC usage, methods to estimate AC penetration with high spatial and temporal resolution are lacking. In this study we develop a new classification method to characterize AC penetration patterns with unprecedented spatiotemporal resolution (i.e. at the census tract level), using the Greater Los Angeles Area as a case study. The method utilizes smart meter data records from 180 476 households over two years, along with local ambient temperature records. When spatially aggregated, the overall AC penetration rate of the Greater Los Angeles Area is 69%, which is similar to values reported by previous studies. We believe this method can be applied to other regions of the world where household smart meter data are available.

Three-phase state estimation in the medium-voltage network with aggregated smart meter data

In distribution networks, the lack of measurement data is usually thought to be an inevitable bottleneck of conventional grid operation and planning. Recently, the availability of smart meters in the distribution network has provided an opportunity to improve the network observability. In medium-voltage (MV) distribution networks, there is an increasing demand to use aggregated smart meter data for the state estimation, instead of adopting pseudo-measurements with a low level of accuracy. However, the performance of an estimator requires good knowledge of the available measurements, in terms of both expected values and associated uncertainties. Therefore, this paper intends to firstly pave a new way of utilizing smart meter data gathered from the low-voltage (LV) feeders in a concrete and reliable manner. For the purpose of state estimation in MV distribution networks, smart meter data is to be processed through three steps: phase identification, data aggregation and uncertainty evaluation. The feasibility of the proposed method is verified on the IEEE European LV Test Feeder with a set of real-world smart meter data. Afterwards, the influence of the aggregated smart meter data on the three-phase state estimation are investigated on the modified IEEE 13-node test system and IEEE 34-node test system. Simulation results show that the effect of aggregated smart meter data on the accuracy of state estimators is dependent on both the accuracy level of the aggregated data and the measurement configuration in the network. Furthermore, the use of aggregated smart meter data is shown to be able to provide improved state estimation.

DSRC Based Sensor-Pooling Protocol for Connected Vehicles in Future Smart Cities

Smart cities are racing to create a more connected Intelligent Transportation Systems (ITS) that rely on collecting data from every possible sensor such as a smart utility meter or a smart parking meter. The use of more sensors resulted in generating a lot of information that maps the smart city environment conditions to more real time data points that needed to be shared and analyzed among smart city nodes. One possibility, to carry and share the collected data, is in autonomous vehicles systems, which use the Dedicated Short Range Communications (DSRC) technology. For example, in a Car-to-Parking-Meter or a Vehicle-to-Vehicle (V2V) communications, short-range embedded sensors such as Bluetooth, Cameras, Lidar send the collected data to the vehicle’s Electronic Control Unit (ECU) or to a road side gateway for making collaborative decisions and react to the environment’s surrounding conditions.The goal of this research is to develop and test a DSRC based sensor-pooling protocol for vehicles to cooperatively communicate inclement weather or environment conditions. Five simulation experiments are setup using PreScan and Simulink to validate and study the scalability of the proposed solution. PreScan is an automotive simulation platform that is used for developing and testing Advanced Driver Assistance System (ADAS). The research findings proved that the DSRC can be used to effectively stream the short range sensors’ collected data over a long distance communications link.

Assessment of exposure to radio frequency electromagnetic fields from smart utility meters in GB; part II) numerical assessment of induced SAR within the human body.

Human body exposure to radiofrequency electromagnetic waves emitted from smart meters was assessed using various exposure configurations. Specific energy absorption rate distributions were determined using three anatomically realistic human models. Each model was assigned with age- and frequency-dependent dielectric properties representing a collection of age groups. Generalized exposure conditions involving standing and sleeping postures were assessed for a home area network operating at 868 and 2,450 MHz. The smart meter antenna was fed with 1 W power input which is an overestimation of what real devices typically emit (15 mW max limit). The highest observed whole body specific energy absorption rate value was 1.87 mW kg-1 , within the child model at a distance of 15 cm from a 2,450 MHz device. The higher values were attributed to differences in dimension and dielectric properties within the model. Specific absorption rate (SAR) values were also estimated based on power density levels derived from electric field strength measurements made at various distances from smart meter devices. All the calculated SAR values were found to be very small in comparison to International Commission on Non-Ionizing Radiation Protection limits for public exposure. Bioelectromagnetics. 39:200-216, 2018. © 2017 Wiley Periodicals, Inc.

Automatic recognition of electric loads analyzing the characteristic parameters of the consumed electric power through a Non-Intrusive Monitoring methodology

Non-Intrusive Load Monitoring (NILM) consists in measuring the electricity consumption using a power consumption data acquisition system, typically placed in the main supply of the building. Relying on a single point of measure it is also called one-sensor metering in contrast to the common metering hardware that can be embedded in each appliance (electronic metering ore-metering) and in differentiation with the common utility smart meters. NILM is the process in which you are able to disaggregate a set of energy readings over a period of time to determine exactly what appliances have used the power and how much power each appliance has used during that time period. In this work a supervised classification method was employed for offline appliances classification, based on low frequency power consumption. The classification feature set consists of the true power, reactive power, and the step changing of the true power. Multiple classifiers were tested and evaluated, such as Decision Tree, Nearest Neighbor, Discriminant Analysis, and the multilayer Feed-forward Neural Network classifier. The methods were tested on the ACS-F2 appliance consumption signatures database.

Structured Literature Review of Electricity Consumption Classification Using Smart Meter Data

Smart meters for measuring electricity consumption are fast becoming prevalent in households. The meters measure consumption on a very fine scale, usually on a 15 min basis, and the data give unprecedented granularity of consumption patterns at household level. A multitude of papers have emerged utilizing smart meter data for deepening our knowledge of consumption patterns. This paper applies a modification of Okoli’s method for conducting structured literature reviews to generate an overview of research in electricity customer classification using smart meter data. The process assessed 2099 papers before identifying 34 significant papers, and highlights three key points: prominent methods, datasets and application. Three important findings are outlined. First, only a few papers contemplate future applications of the classification, rendering papers relevant only in a classification setting. Second; the encountered classification methods do not consider correlation or time series analysis when classifying. The identified papers fail to thoroughly analyze the statistical properties of the data, investigations that could potentially improve classification performance. Third, the description of the data utilized is of varying quality, with only 50% acknowledging missing values impact on the final sample size. A data description score for assessing the quality in data description has been developed and applied to all papers reviewed.

Exposure to electromagnetic fields from smart utility meters in GB; part I) laboratory measurements.

Laboratory measurements of electric fields have been carried out around examples of smart meter devices used in Great Britain. The aim was to quantify exposure of people to radiofrequency signals emitted from smart meter devices operating at 2.4 GHz, and then to compare this with international (ICNIRP) health-related guidelines and with exposures from other telecommunication sources such as mobile phones and Wi-Fi devices. The angular distribution of the electric fields from a sample of 39 smart meter devices was measured in a controlled laboratory environment. The angular direction where the power density was greatest was identified and the equivalent isotropically radiated power was determined in the same direction. Finally, measurements were carried out as a function of distance at the angles where maximum field strengths were recorded around each device. The maximum equivalent power density measured during transmission around smart meter devices at 0.5 m and beyond was 15 mWm-2 , with an estimation of maximum duty factor of only 1%. One outlier device had a maximum power density of 91 mWm-2 . All power density measurements reported in this study were well below the 10 W m-2 ICNIRP reference level for the general public. Bioelectromagnetics. 2017;38:280-294. © 2017 Crown copyright. BIOELECTROMAGNETICS © 2017 Wiley Periodicals, Inc.

Development of an Intelligent System for Smart Home Energy Disaggregation Using Stacked Denoising Autoencoders

Energy sustainability remains one of the biggest challenges for the Philippines’ energy sector with 51% of the demand coming from the residential and commercial sectors. Intelligent energy monitoring systems play a key role with the opportunity to contribute sizeable amount of energy savings by providing meaningful consumption feedback to home owners. While smart meters provide an ideal ubiquitous energy monitoring solution, these devices lack appliance-level feedback. In this study, we present a concept of augmenting smart utility meters with an energy disaggregation method using Stacked Denoising Autoencoders to provide appliance-level feedback to home owners. We evaluate the results using absolute mean loss and proportion of energy correctly assigned as metrics for the signal disaggregation. The results show that the model was able to decompose an aggregate appliance signal and provide an itemized appliance-level power consumption.

Distribution System Distributed Quasi-Dynamic State Estimator

With increasing deployment of smart meters and other smart grid sensors in distribution systems, the amount of available measurements has been growing. This change enables implementation of comprehensive distribution system state estimators, a basic tool that provides the real-time model and operating conditions of the distribution network. The performance of many smart grid applications is dependent upon an accurate and reliable real-time model. This paper presents the development of a distributed quasi-dynamic state estimator (DS-DQSE) for distribution systems utilizing smart meter and other sensor data, as well as traditional SCADA data. The DS-DQSE implementation described in this paper works for both radial or meshed distribution networks and variable configuration systems, as distribution systems may be reconfigured following the clearing of a fault. This paper provides the problem formulation, solution methodology, modeling issues unique to distribution systems, and test results on multiple distribution circuits.