Actual Meter Research Articles

Development of Smart Meter to Monitor Real Time Energy Consumption for Sustainability

This study constructed a Smart Energy Meter where energy consumption can be viewed by the consumers based on rreal time using data from smart meters. Its goals are to increase productivity, make readings more precise, and take less time to determine an individual residence's energy consumption. The device is made up of the PZEM-016 AC Energy Meter, RS-485 UART Serial Converter, NodeMCU ESP8266, Blynk IoT Application, Arduino Uno R3, and LCD Arduino Keypad Module Shield Board. The PZEM-016 is used in this smart energy meter to measure voltage, current, power, frequency, power factor, and energy consumption. Because it lacks its own display, an RS-485 was utilized to communicate with the NodeMCU and Arduino Uno. The NodeMCU sends the parameters to Blynk IoT App as long as it is connected to a fixed mobile WiFi. The Blynk will then display a real-time measurement of the parameters. The Arduino Uno is programmed to display the parameters to the LCD Keypad Module. The device was tested in an actual household. The researchers conducted 48-hour observation on the household where the energy displayed in the Blynk IoT App and the LCD display matches at approximately 9 kWh which is the same as the actual energy meter of the house that is 9kWh. The device is also tested on different appliances which resulted in the same energy consumption in both the Blynk IoT App and LCD display with the ratingsof the appliances. The device was found functional.

Exploring the potential, limitations, and future directions of wireless technologies in smart grid networks: A comparative analysis

Smart Grids play a crucial role in managing electric power in a more efficient, secure, and sustainable manner. These grids rely on information and communication technologies to facilitate two-way communication between energy providers and consumers, enabling better coordination in the production and distribution of electricity. This article aims to compare the main technologies that can be used in Neighborhood Area Networks (NAN) for Smart Grid data communication networks. NANs are critical to Smart Grid infrastructure, providing advanced energy monitoring and control solutions like smart metering and demand management. The article surveys commonly used technologies such as IEEE 802.15.4 g, IEEE 802.11s, LoRa, and LTE, highlighting their pros and cons. Unlike many existing works in the literature, this article does not limit itself to a theoretical comparison but also conducts network simulations using widely-used scientific tools such as ns-3 and OMNeT++. The article evaluates the technologies in two scenarios, a grid-like scenario, and a real-world scenario using actual smart meter locations in Montreal. The simulations’ results are analyzed using widely-used metrics such as Packet Delivery Ratio (PDR), network transit time, and compliant factor, and offer insights into selecting the right technology for NANs in Smart Grids based on various network scenarios.

A mathematical model to forecast solar PV performance

ABSTRACT This work focuses on developing a mathematical model to forecast the performance of solar PV. In the present scenario, solar photovoltaic performance has been envisaged by considering the impact of limited variables subject to certain assumptions and conditions and the current work drives the attention toward the effect of temperature, relative humidity, wind velocity, and panel aging effects. There hasn’t been any breakthrough in the previous research so far while collectively analyzing the efficiency loss in solar photovoltaic, due to the aforementioned parameters. Hence, this work aims to develop a mathematical model in view of the above-mentioned parameters. This work has been experimented using MATLAB software and online simulation tools are used to validate the results. The results are favorable with the deviation of efficiency between the online simulation tool and the proposed mathematical model being 2–4%. Furthermore, a real-time case study has been carried out at a solar power plant installed at Thiagarajar College of Engineering, Madurai, Tamilnadu, to validate the proposed mathematical model. It is observed that the absolute difference in power between the simulation and the actual meter reading is between 30 and 60 Watts with an RMSE between 5 and 16% and MPE of 675 Watts.

전력량계 작업 시 아크플래시 재해 경감방안 연구

About 23 million electronic Watt-hour meters are installed in Korea, and more than 2 million meters are newly installed or replaced every year. Usually, the work is performed on live line and sometimes injuries are occurred due to arc flash by improper behavior or misconnection. In Korea, Watt-hour meter workers are advised to wear personal protective equipment(PPE) level 1, but it is not reviewed well whether they can sufficiently prevent injury caused by arc flash under the guideline. In this paper, we calculate the arc flash energy according to the IEEE 1584 standard that may occur in an actual Watt-hour meter working environment and review the required PPE level by the NFPA 70E for injury prevention. In addition, an economical arc energy reduction solution is proposed instead of wearing a higher PPE level equipment. The effectiveness of the solution is verified through field experiments.

Short-term electrical load forecasting through heuristic configuration of regularized deep neural network

An accurate electrical load forecasting is essential for optimal grid operation. The paper presents a methodology for the short-term commercial building electrical load forecasting through a regularized deep neural network: Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). Detailed heuristic analysis regarding relevant input feature selection, the volume of training data, hyperparameter tuning and regularizer selection of an optimal LSTM-RNN network configuration is presented. The regularized LSTM-RNN is used to forecast 30-min and 24-h ahead electrical loads of two commercial buildings in Virginia, USA. The forecast is performed for one week each over four different months in 2019: January, April, July and October to represent four different seasons in North America. The performance of electrical load forecasts has been compared against actual smart meter data from the electric utility of these buildings. For the case study presented, Mean Absolute Percentage Error (MAPE%) with the regularized LSTM-RNN is 4.9%, compared to 6.4%, 9.2% and 13.3% with Shallow-ANN (Artificial Neural Network), Support Vector Regression (SVR) and Linear Regression (LR) respectively for 30-min ahead electrical load forecast. For 24-h ahead electrical load forecast, MAPE (%) is 11.6%, compared to 12.7%, 13.4% and 14.3% with shallow-ANN, SVR and LR respectively. The methodology to configure a deep neural network (LSTM-RNN) for electrical load forecasting presented in this paper can be utilized for optimal forecasting performance.

Conditional Multivariate Elliptical Copulas to Model Residential Load Profiles From Smart Meter Data

The development of thorough probability models for highly volatile load profiles based on smart meter data is crucial to obtain accurate results when developing grid planning and operational frameworks. This paper proposes a new top-down modeling approach for residential load profiles (RLPs) based on multivariate elliptical copulas that can capture the complex correlation between time steps. This model can be used to generate individual and aggregated daily RLPs to simulate the operation of medium and low voltage distribution networks in flexible time horizons. Additionally, the proposed model can simulate RLPs conditioned to an annual energy consumption and daily weather profiles such as solar irradiance and temperature. The simulated daily profiles accurately capture the seasonal, weekends, and weekdays power consumption trends. Five databases with actual smart meter measurements at different time resolutions have been used for the model's validation. Results show that the proposed model can successfully replicate statistical properties such as autocorrelation of the time series, and load consumption probability densities for different seasons. The proposed model outperforms other multivariate state-of-the-art methods, such as Gaussian Mixture Models, by one order of magnitude in two different distance metrics for probability distributions.

A Data-Driven Global Sensitivity Analysis Framework for Three-Phase Distribution System With PVs

Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution system. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of load and PV power injections. The sampling-based method estimates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-bus distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework.

Networking of Smart Meters Based on Time-Varying Feature of Low-Voltage Power Line Channel in Microgrid

In order to manage the electricity consumption information of microgrid users, the reliability of electricity information collection is studied in this paper. The normal communication between the acquisition terminal and the smart meter is a key factor affecting the accurate collection of power information; it is the basis for ensuring the operation of the microgrid as well. In order to improve the reliability of the low power line communication between the acquisition terminal and smart meters, this article first uses the static networking method to layer the smart meters and select relays from them and then select the optimal communication path based on integrating communication quality and relay forwarding number dynamically, which could avoid the signal conflict problem caused by simultaneous communication. Finally, by analyzing the influence of the time-varying power line channel on the smart meter communication, a method based on the integrated communication quality and the relay number to consider the time variability of low power line communication is proposed. Choosing the optimal path of the smart meters when the communication path is abnormal can not only establish a new communication path for communication in time, but also avoid communication failures caused by the time-varying channel. Through MATLAB simulation, the time-varying dynamic network of the power line channel is introduced in this paper, which improves the reliability of the smart meter communication and has certain guiding significance for the actual smart meter network construction in microgrid.

A Real-World Test Distribution System With Appliance-Level Load Data for Demand Response and Transactive Energy Studies

Research on demand response and transactive energy systems often require granular, appliance-level demand data. However, there is no existing test system with such appliance-level data with proper temporospatial diversity in a realistic distribution system. This paper develops a 240-node real distribution test system with appliance-level demand data for responsive loads. The residential appliance-level demand data are derived from smart meters connected to 1,120 homes in a real distribution system from Iowa State, hereafter called Midwest 240-Node test distribution system. A queueing load model was used to derive the appliance-level data from the smart meter data. The Midwest 240-Node test distribution system provides granular appliance-level information for all homes in the distribution system (i.e., individual appliances that constitute the home load), and the aggregate of all customer load emulates the actual smart meter data. The performance of the Midwest 240-Node test distribution system is evaluated by comparing the aggregated appliance-level demand with the actual measured smart meter data from the utility. The one-year appliance data has a mean absolute percentage error of 2.58% compared to the measured smart meter data. The test system is modeled in OpenDSS and GridLAB-D and is openly available to researchers to enable demand response and transactive energy studies with active end-users.

Connectivity Verification in Distribution Systems Using Smart Meter Voltage Analytics: A Cloud-Edge Collaboration Approach

Distribution topology is oftentimes changed to cope with the development of local power load. Therefore, connectivity verification has become a critical task for optimal grid operation. In this article, a novel cloud-edge collaboration approach is presented to identify outlier users and correct connections. In this article, based on the smart meter voltage analytics, an affinity propagation clustering-based local outlier factor (AP-LOF) algorithm is proposed for the voltage outlier identification and verification of the edge transformer. Compared to traditional methods, it can effectively identify the outlier user groups with high internal voltage correlation. Besides, a recommendation mechanism is developed in the cloud center, which repositions the identified outlier users by coordinating the information exchange between the edge transformers and the cloud center. Numerical tests are conducted using the actual smart meter voltage data. The results show that the proposed AP-LOF algorithm exhibits a better performance, which is suitable for the identification of various outlier users. Compared to a centralized architecture, 66% savings in calculation time is achieved by the cloud-edge collaboration approach. It further demonstrates the effectiveness and practicability of the proposed method in terms of identification accuracy and verification efficiency.

Numerical modeling and assessment of human exposure to radiofrequency electromagnetic fields used for wireless communication system installed on a smart meter

AbstractWe assessed human exposure to radiofrequency (RF) electromagnetic fields used for a wireless communication system installed on a smart meter for communication in the 920 MHz band. In an exposure guideline, the specific absorption rate (SAR) is used as a measure of the thermal effects of human exposure to electromagnetic fields. Although the low‐power wireless communication device used for the smart meter inherently complies with the exposure guideline, the assessment of the RF electromagnetic field exposure is useful to deepen our understanding of human safety. For the exposure assessment, first, a computational smart meter model was constructed and validated by comparison with the measured data of electric fields around an actual smart meter. Using the computational smart meter model, we calculated the SAR in the head of an anatomical human model that was located very close to the smart meter. The maximum SAR in the head was obtained when the antenna was placed in front of a human eye, which was calculated to be 10.3 mW/kg for an input power of 20 mW. It was shown that the maximum SAR was 200 times smaller than the basic restriction level in the exposure guideline.

An Automatic Electricity Meter Reading System Based on Neural Vision Location

Remote meter automatic reading and smart meter reading is a vital part of smart grid and has been widely concerned. Traditional manual method is manpower consuming. Developing an automatic electricity ether reading system is necessary in practice. Two particular aspects in this task should be concerned. One is different-perspective problem, which causes by the variations of camera position and leads to pool display localization. Another is information diversity problem. Namely, the electrical meter used in this paper needs to recognize effective information from diversity information. This paper describes a novel automatic electricity meter reading system, which can automatically locate and recognise effective information. For the camera perspective variability problem, we utilize a new localization method based on neural network. Via actual meter monitoring experiment, the result shows our system can efficient recognition and achieve 97% accuracy.

State Estimation of Three-Phase Four-Conductor Distribution Systems With Real-Time Data From Selective Smart Meters

Distribution system state estimation (DSSE) has recently been tested and experimentally deployed in some practical distribution networks. Distinct features of distribution systems, such as diverse and unsymmetrical configurations as well as limited real-time measurements, prohibit the direct application of mature state estimation methods for transmission systems. Targeting at three-phase four-conductor configured unsymmetrical medium-voltage distribution systems (MDS) with neutral conductors and ground resistances, this paper proposes a weighted least square-based DSSE approach, in which voltages are chosen as state variables and load pseudo measurements of low-voltage distribution systems (LDS) are considered to compensate insufficient real-time measurements in MDS. Both rectangular and polar coordinates are studied, and voltage variables of neutrals and zero-injection phases are eliminated to reduce the scale of the DSSE problem. Moreover, in order to enhance the load pseudo measurement accuracy of LDSs, a clustering and partial least square regression-based load estimation model is proposed to leverage the real-time communication ability of smart meters. Case studies on a modified IEEE 123-bus distribution system with actual smart meter data illustrate the effectiveness of the proposed approaches.

Social Information Filtering-Based Electricity Retail Plan Recommender System for Smart Grid End Users

Rapid growth of data in smart grids provides great potentials for the utility to discover knowledge of demand side and design proper demand side management schemes to optimize the grid operation. The overloaded data also impose challenges on the data analytics and decision making. This paper introduces the service computing technique into the smart grid, and proposes a personalized electricity retail plan recommender system for residential users. The proposed personalized recommender system (PRS) is based on the collaborative filtering technique. The energy consumption data of users are firstly collected from the smart meter, and then key energy consumption features of the users are extracted and stored into a user knowledge database (UKD), together with the information of their chosen electricity retail plans. For a target user, the recommender system analyzes his/her energy consumption pattern, find users having similar energy consumption patterns with him/her from the UKD, and then recommend most suitable pricing plan to the target user. Experiments are conducted based on actual smart meter data and retail plan data to verify the effectiveness of the proposed PRS.

A Cluster-Based Baseline Load Calculation Approach for Individual Industrial and Commercial Customer

Demand response (DR) in the wholesale electricity market provides an economical and efficient way for customers to participate in the trade during the DR event period. There are various methods to measure the performance of a DR program, among which customer baseline load (CBL) is the most important method in this regard. It provides a prediction of counterfactual consumption levels that customer load would have been without a DR program. Actually, it is an expected load profile. Since the calculation of CBL should be fair and simple, the typical methods that are based on the average model and regression model are the two widely used methods. In this paper, a cluster-based approach is proposed considering the multiple power usage patterns of an individual customer throughout the year. It divides loads of a customer into different types of power usage patterns and it implicitly incorporates the impact of weather and holiday into the CBL calculation. As a result, different baseline calculation approaches could be applied to each customer according to the type of his power usage patterns. Finally, several case studies are conducted on the actual utility meter data, through which the effectiveness of the proposed CBL calculation approach is verified.

Model predictive control for building loads connected with a residential distribution grid

Aggregated control of electrical loads in a large cluster of buildings has been a challenge due to the complexity of the system involving generators, grid constraints, load serving entities complex load models, and people behavior. This paper introduces a novel load aggregation method in an electricity distribution system with Model Predictive Controlled (MPC) loads. This method closes the control loop from power generation to people behavior, resulting in a more stable and efficient integrated buildings-to-grid system. A behavior-driven price-based MPC is introduced for a residential building energy management system, which controls the air conditioner (AC), electric vehicle (EV), water heater, and battery energy storage system. A nodal pricing method is introduced representing power generation and distribution costs, which is mathematically proven to stabilize the system with MPC controlled loads. The method is tested in a 342-node residential building distribution network with 15,000 buildings which is inverse sampled from hundreds of actual smart meter data. The results show a 21% reduction in generation cost, 17% reduction in peak load, and reduced nodal voltage drop from the coordinated control system.

Dynamic Model-Driven Energy Retrofit of Bøgevangen and Runevej Daycare Centers in Aarhus

With the huge ambition to achieve a fossil fuel-free energy sector by 2050, the Danish government has targeted the building stock as a priority, offering large potential for feasible and cost-effective energy savings. Around 75% of the existing buildings in Denmark were constructed before 1979 and therefore a special focus has been directed in the recent years towards buildings’ energy renovation especially non-residential and public buildings. A comprehensive strategy for energy renovation of the existing Danish building stock was established with multiple initiatives to enhance cost-effective and energy-efficient renovation of buildings. In this context, Aarhus Municipality is investing 450 million DKK for public buildings energy renovation covering an area of about 1 million m2 and aiming to cut CO2 emissions by 40%. Thus, two daycare centers, Bøgevangen 101 and Runevej 107-109, have been selected as case studies under the international research project COORDICY aiming to improve the centers energy performance implementing renovation techniques. A detailed energy model was developed for each center employing a package of Google Sketchup, Open Studio and Energy Plus energy modelling and simulation tools. The energy model was calibrated against actual meter data. Using the developed model, multiple energy management and retrofit combinations were analyzed and evaluated from both technical and economic perspectives. Conventional envelope insulation and windows upgrade were found unfavorable from the economic perspective and thus were not prioritized. It was shown that employing a retrofit package consisting of installing LED lights, implementing demand-controlled ventilation, and installing efficient equipment could lead to an overall reduction of 30.2% and 31.1% on the energy consumption in Bøgevangen and Runevej respectively, improving the thermal comfort and indoor air quality with an acceptable payback period of around 3 years.

A Cluster-Based Method for Calculating Baselines for Residential Loads

Demand response (DR) programs provide incentives for load reductions during event periods. Measuring these reductions requires estimating load if there was no event, known as baselines. Differences in metered load below the baseline are assumed to be load reductions. Baselines are typically calculated from historical data, sometimes with adjustments from pre-event periods. This paper presents a cohort-based baseline method that utilizes event-day metered load. The cohorts, a group of consumers with a similar consumption pattern, are developed from correlations between normal nonevent day loads. Baselines for DR participants are established using event-day metered load of nonparticipating cohort members. This method implicitly incorporates weather and other complex factors into the baseline. Our method is compared with several traditional methods using actual utility meter data. Baselines are calculated for each nonevent day and compared with the actual meter data on these days. The results show that cohort-based baselines are significantly more accurate than traditional methods.

A Data-Driven Approach for Detection and Estimation of Residential PV Installations

The number of photovoltaic (PV) systems in the electric grid is growing at an unprecedented speed. This is rapidly transforming the ways in which the traditional distribution grid is being planned and operated. A problem faced by utilities is that, in many cases, the PV system installed does not correspond to the size or type filed with the installation permit, or simply the installation took place without a permit. In order to maintain grid reliability and safety, utilities must be able to detect and monitor all PV installations in their network. This paper proposes a data-driven approach for the detection, verification, and estimation of residential PV system installations. We use a change-point detection algorithm to screen out abnormal energy consumption behaviors including unauthorized PV installations. Then the existence of the unauthorized PV installation is further verified through a statistical inference known as permutation test with Spearman’s rank coefficient. The proposed hypothesis test takes the customer’s load profiles before and after the detected change-point as inputs, which are estimated through Gaussian kernel density method. Finally, the local cloud cover index is integrated with smart meter measurements to estimate the size of the PV system. The proposed method has been tested and validated with actual smart meter measurements under several scenarios.

Frequency versus time domain immunity testing of Smart Grid components

Abstract. Smart Grid components often are subject to considerable conducted current disturbances in the frequency range 2–150 kHz and, as a consequence, it is necessary to provide reliable immunity test methods. The relevant basic standard IEC 61000-4-19 that is currently under discussion focusses on frequency domain test methods. It is remarked in this contribution that in the context of frequency domain testing the chosen frequency spacing is related to the resonance response of the system under test which, in turn, is characterized in terms of resonance frequencies and quality factors. These notions apply well to physical system but it is pointed out by the example of an actual smart meter immunity test that smart grid components may exhibit susceptibilities that do not necessarily follow a resonance pattern and, additionally, can be narrowband. As a consequence it is suggested to supplement the present frequency domain test methods by time domain tests which utilize damped sinusoidal excitations with corresponding spectra that properly cover the frequency range 2–150 kHz, as exemplified by the military standard MIL-STD-461.