Smart Electricity Research Articles

Discriminant analysis classification of residential electricity smart meter data

The objective of this study is to apply machine learning classification to predict building characteristics from electricity smart meter data for the purpose of building stock characterization. Given that there are no publicly available large-scale residential electric smart meter data sets with detailed building characteristics, an open-source virtual smart meter (VSM) data set is used. The VSM data consists of electricity consumption profiles for 200,000 homes with 21 known characteristics, which are used to train predictive models with linear discriminant analysis (LDA). The classification accuracy (CA) is determined for a variety of scenarios where the meter data aggregation and period are varied. The CA depends on the parameter to be classified (the class), the number of data points per building (the features) and the number of buildings used for classification. Reliable classification results are obtained when the number of buildings exceeds the number of features by a significant margin. An application of the developed predictive models to a small data set of 30 real houses illustrates the usefulness of the method but also the challenges in achieving a generalized model with virtual data.

Disain Smart Electricity Penghematan pada Peralatan Listrik Menggunakan Sensor Ultrasonic

Waste of electricity is a problem that often occurs accidentally, especially in household electricity, especially on electrical equipment that is routinely used but forgets to turn it off if there are no users around electrical equipment such as fans, air conditioners, TVs and others. Saving electrical energy is an action to reduce the amount of electrical energy use. This study aims to create a control device for saving electricity consumption. Saving electricity consumption does not mean not using electrical energy for something that is not useful, however, energy savings can be achieved by using energy efficiently where the same benefits are obtained by using less energy. This study uses a design method or is designed to be smart electricity using sensors. ultrasonic with arduino uno, where this tool controls daily use in case of forgetting so that no energy is wasted (waste of electricity) or more practical in controlling electrical equipment. Based on the results of tool testing and data analysis carried out, it was concluded that the working system of this tool succeeded in controlling the use of electronic equipment by detecting the presence of an object / human with the help of an ultrasonic sensor with a horizontal area range. The Radio Frequency Identification sensor is an additional sensor to help the Ultrasonic sensor improve to detect in a wider range of vertical areas by scanning the card. The total cost of the entire expenditure for 1 month is Rp. 203,365 using a smart electricity design tool as a means of saving and the use of electricity based on ultrasonic sensors using Arduino Uno and Rp. 418,175 without tools results in a difference in savings of Rp. 214.810,-

Power Management Analysis of a Photovoltaic and Battery Energy Storage-Based Smart Electrical Car Park Providing Ancillary Grid Services

Future car parks will require significant power to support electric vehicle (EV) charging as there will be both an increase in the penetration of EVs and a higher demand for charging power as battery packs increase in capacity. The effective management of the charging and local battery storage can be installed to help prevent excessive increases in electrical feeder capacity; however, it is inevitable that car parks will attain significant power capability in the future. There is therefore an opportunity for car park owners to utilise this and generate additional revenue by providing frequency response services to the electrical grid. This paper describes the modelling of a car park that utilises photovoltaic power generation, battery storage, and EV charging management strategies to provide a grid frequency response service. The analysis using simulated car park data shows that it can provide a high availability in terms of service but it is dependent on the capacity of the installed generation and storage.

Operational concerns and solutions in smart electricity distribution systems

Interfacing high penetration of Renewable Energy Sources (RES) with energy storage and microgrid control systems is an essential feature of future distribution grids for optimal utilization and management of Distributed Energy Resources (DERs). This feature allows distributed system operators to follow the right path to transform their traditional grids into smart grids. With the continued expansion of DERs, the current distribution grid is moving toward a carbon-free electricity system and greener energy-transportation applications. However, there are some practical concerns as well as technical and operational challenges in an interconnected power network. Therefore, this paper reviews the most prominent issues associated with smart grid, such as large-scale distributed generation integration, energy storage, energy trading, operational planning, service reliability, and cyber resiliency, in light of recent technological advancements. Through reviewing several emerging technologies, this study shows the necessary methodologies for establishing smart energy distribution. Finally, this study highlights future research prospects for a fully functional smart grid system.

Effects of Environmental and Electrical Factors on Metering Error and Consistency of Smart Electricity Meters

The smart electricity meter (SEM) is an important part of smart power grid, and the accuracy of SEMs is the basis for power grid operation control and trade settlement between power supply and electricity consumption, but the evolution behaviors of metering error of SEMs under field operation conditions have not yet been identified. The SEMs were installed and operated on site, metering error data were collected under various temperature and current conditions. The influences of current, power coefficient, and temperature on metering error and consistency were analyzed separately with the help of quadratic polynomials, and then an integrated model elaborating the joint effects of multi-stress was developed based on a binary quadratic polynomial. We find that a lower temperature and a larger current result in a higher metering error of SEMs; however, the effects of current on metering error are determined by power coefficients. The results have reference value for remote metrological verification, error monitoring, and the optimization of the operation and maintenance scheme of SEMs.

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

Smart Electricity Meter Prognostics Based on Lithium Battery RUL Prediction

Smart Electricity Meters (SEMs) are widely used in distributed generationsystem, and over 67% of its failure are caused by battery low-voltage.Therefore, it is necessary to study the degradation of battery voltage. Thiswork explores the degradation mechanism of lithium battery and proposed touse voltage as degradation index to estimate the health status of the system.Four groups of batteries of the same type and batch are used for the test.The purpose is to use multiple sets of data to train the model parametersand enhance the robustness of the model. The Particle Filtering (PF) basedapproach is used in this study to estimate the degradation state such that theRemaining Useful Life (RUL) can be predicted. An accurate prediction canprovide the proper maintenance/replacement schedule for the SEMs beforethe failure occurs.

Smart Electric Mobility Operating System Integrated with Off-Grid Solar Power Plants in Tanzania: Vision and Trial Run

To respond to the threat of global warming, countries around the world are promoting the spread of renewable energy and reduction of carbon emissions. In accordance with the United Nation’s Sustainable Development Goal to combat climate change and its impacts, global automakers are pushing for a full transition to electric vehicles within the next 10 years. Electric vehicles can be a useful means for reducing carbon emissions, but in order to reduce carbon generated in the stage of producing electricity for charging, a power generation system using eco-friendly renewable energy is required. In this study, we propose a smart electric mobility operating system integrated with off-grid solar power plants established in Tanzania, Africa. By applying smart monitoring and communication functions based on Arduino-based computing devices, information such as remaining battery capacity, battery status, location, speed, altitude, and road conditions of an electric vehicle or electric motorcycle is monitored. In addition, we present a scenario that communicates with the surrounding independent solar power plant infrastructure to predict the drivable distance and optimize the charging schedule and route to the destination. The feasibility of the proposed system was verified through test runs of electric motorcycles. In considering local environmental characteristics in Tanzania for the operation of the electric mobility system, factors such as eco-friendliness, economic feasibility, ease of operation, and compatibility should be weighed. The smart electric mobility operating system proposed in this study can be an important basis for implementing the SDGs' climate change response.

Context-aware security for the Intra-Electric Vehicle Network under energy constraints

Smart Vehicle applications are generally designed to ensure the driver’s and passenger’s safety as well as to improve the traffic management and provide convenience services. On the other hand, the researchers and industrialists consider Electric Vehicle as a key technology contributing to the reduction of CO2 emissions. Many attacks, such as vehicle remote control and engine management software reboot, can be performed on the Smart Electric Vehicle Network and affect the driver’s safety. To protect the Smart Vehicle traffic, it is necessary to protect all the involved systems (cloud services, applications, automotive components, Intra-Vehicle Network, maintenance and diagnostic tools, etc.). The main objective of this work is to secure the communication of the in-vehicle sensors by proposing a context-aware security solution based on the dynamic driving context. This solution applies a set of security strategies according to the State Of Charge (SOC), the distance to the nearest available charging station, the type of sensors, the sensor resources (memory and processing) and the traffic conditions. Simulation experiments were conducted to evaluate the proposed approach in terms of the trade-off between security and energy.

Experimental and numerical assessment for HVAC management in an industrial building: a preliminary optimization

COVID-19 emergency has caused major changes in everyday life in the last months, and it also affected the management of buildings. In particular, indoor air quality and ventilation have been considered to play a key role in the spreading of the infection, causing national and international subjects to draw up specific guidelines on ventilation and air recirculation rate in AHUs. The paper deals with the “Loccioni Leaf Lab”, an industrial building that hosts offices and workers operating on test benches. The building features high performance envelope, solar photovoltaic systems, groundwater heat pumps and a high-technology control and monitoring system and it is connected to a thermal and electric smart grid. A validated model of the building, implemented with the software DesignBuilder and EnergyPlus, was used to carry out numerical simulations to optimize the management of the HVAC through the Building Management System. Different working conditions have been simulated, and the numerical output has been used together with experimental data collected from the Company monitoring system. It has been possible to investigate how the extra ventilation required by the new guidelines would affect the total energy consumption and to compare, in terms of energy efficiency, the different HVAC management strategies that could be used to ensure occupants health safety and indoor air quality.

Optimal Performance and Modeling of Wireless Technology Enabling Smart Electric Metering Systems Including Microgrids.

This work is focused on the performance analysis and optimal routing of wireless technology for intelligent energy metering, considering the inclusion of micro grids. For the study, a geo-referenced scenario has been taken into account, which will form the structure of a graph to be solved using heuristic-based algorithms. In the first instance, the candidate site of the world geography to perform the case study is established, followed by deploying infrastructure devices and determining variables and parameters. Then, the model configuration is programmed, taking into account that a set of nodes and vertices is established for proper routing, resulting in a preliminary wireless network topology. Finally, from a set of restrictions, a determination of users connected to the concentrator and optimal routing is performed. This procedure is treated as a coverage set problem. Consequently, to establish the network parameters, two restrictions are specifically considered, capacity and range; thus, can be determined the best technology to adapt to the location. Finally, a verification of the resulting network topologies and the performance of the infrastructure is done by simulating the wireless network. With the model created, scenarios are tested, and it is verified that the optimization model demonstrates its effectiveness.

Atomically Thin Materials for Next-Generation Rechargeable Batteries.

Atomically thin materials (ATMs) with thicknesses in the atomic scale (typically <5 nm) offer inherent advantages of large specific surface areas, proper crystal lattice distortion, abundant surface dangling bonds, and strong in-plane chemical bonds, making them ideal 2D platforms to construct high-performance electrode materials for rechargeable metal-ion batteries, metal-sulfur batteries, and metal-air batteries. This work reviews the synthesis and electronic property tuning of state-of-the-art ATMs, including graphene and graphene derivatives (GE/GO/rGO), graphitic carbon nitride (g-C3N4), phosphorene, covalent organic frameworks (COFs), layered transition metal dichalcogenides (TMDs), transition metal carbides, carbonitrides, and nitrides (MXenes), transition metal oxides (TMOs), and metal-organic frameworks (MOFs) for constructing next-generation high-energy-density and high-power-density rechargeable batteries to meet the needs of the rapid developments in portable electronics, electric vehicles, and smart electricity grids. We also present our viewpoints on future challenges and opportunities of constructing efficient ATMs for next-generation rechargeable batteries.

Extracting Users: Regimes of Engagement in Norwegian Smart Electricity Transition

Recent efforts to involve digital technologies and renewables in the electricity grid have placed users at center stage in the legitimation of energy transitions. This move has been paralleled by an emphasis on users and energy practices in social studies of energy related to science and technology studies. This article builds on an eighteen-month Living Lab exploration of energy practices with smart electricity users in Bergen, Norway. We make two interrelated arguments. First, energy production and distribution in Norway and elsewhere is shifting toward greater automation of tasks, possibly bypassing the “active user” concept. Energy sector practices are evolving from simply extracting natural resources (Extraction 1.0) toward extraction of users’ behavioral data (Extraction 2.0), and privacy thus emerges as a key component in the stabilization of energy systems. Second, we reflect on displacements of the roles and possibilities of users (or “energy citizens”) thereby enabled, especially their normative (political and regulatory) aspects. We propose that conceptualization of energy practices be supported by the concept of regimes of engagement from pragmatist sociology. Relatedly, we argue that market, civic, ecological, and industrial regimes are being actively merged through digital innovation and what we call the techno-epistemic network of smart electricity.

A Thermal Discomfort Index for Demand Response Control in Residential Water Heaters

Demand-response techniques are crucial for providing a proper quality of service under the paradigm of smart electricity grids. However, control strategies may perturb and cause discomfort to clients. This article proposes a methodology for defining an index to estimate the discomfort associated with an active demand management consisting of the interruption of domestic electric water heaters. Methods are applied to build the index include pattern detection for estimating the water utilization using an Extra Trees ensemble learning method and a linear model for water temperature, both based on analysis of real data. In turn, Monte Carlo simulations are applied to calculate the defined index. The proposed approach is evaluated over one real scenario and two simulated scenarios to validate that the thermal discomfort index correctly models the impact on temperature. The simulated scenarios consider a number of households using water heaters to analyze and compare the thermal discomfort index for different interruptions and the effect of using different penalty terms for deviations of the comfort temperature. The obtained results allow designing a proper management strategy to fairly decide which water heaters should be interrupted to guarantee the lower discomfort of users.

Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter

The problem of energy disaggregation is the separation of an aggregate energy signal into the consumption of individual appliances in a household. This is useful, since the goal of energy efficiency at the household level can be achieved through energy-saving policies towards changing the behavior of the consumers. This requires as a prerequisite to be able to measure the energy consumption at the appliance level. The purpose of this study is to present some initial results towards this goal by making heavy use of the characteristics of a particular din-rail meter, which is provided by Meazon S.A. Our thinking is that meter-specific energy disaggregation solutions may yield better results than general-purpose methods, especially for sophisticated meters. This meter has a 50 Hz sampling rate over 3 different lines and provides a rather rich set of measurements with respect to the extracted features. In this paper we aim at evaluating the set of features generated by the smart meter. To this end, we use well-known supervised machine learning models and test their effectiveness on certain appliances when selecting specific subsets of features. Three algorithms are used for this purpose: the Decision Tree Classifier, the Random Forest Classifier, and the Multilayer Perceptron Classifier. Our experimental study shows that by using a specific set of features one can enhance the classification performance of these algorithms.

Automatic Detection of Display Defects for Smart Meters based on Deep Learning

The smart meter is an essential part of an intelligent grid system. Defects in the LCD screen the smart meters affect their use. Therefore, detection of LCD screen defects of smart meters is of great significance for management and use of smart electricity meters. At present, detection methods are mainly realized by manual detection and automatic detection based on machine vision. However, performance of these two methods is not satisfactory. The fault detection task of a smart meter LCD screen can be divided into two parts: smart meter LCD localization and LCD fault detection. Therefore, this paper proposes a twostage system based on deep learning, which combines YOLOv5 with ResNet34. YOLOv5 is used for smart meter LCD localization and the classification network based on ResNet34 for LCD fault detection. We have constructed an LCD screen localization dataset and an LCD screen defect detection dataset to train and test our model. As a result, our model achieves a defect detection accuracy of 98.9% on the dataset proposed in this paper and can accurately detect the common defects of an LCD screen.

A two-stage decomposition method for integrated optimization of islanded AC grid operation scheduling and network reconfiguration

A restructuring of electricity systems promises energy resilience, prosperity, and sustainability for communities. In particular, Reconfigurable networks, such as distribution systems or microgrids, as smart electricity systems are more controllable and flexible compared to traditional systems. In this study, we integrate network reconfiguration decisions into a multi-period grid operation scheduling problem to minimize the operational costs while taking into account both technical and inter-temporal constraints for distributed energy resources, physical constraints on alternating current (AC) network, and system reliability constraints. We develop a comprehensive optimization model for the grid operation planning problem by taking into account the operation scheduling and economic dispatch decisions from earlier cycles and grid operational constraints while determining the optimal network topology. Due to the NP-hard nature of the problem, we introduce a two-stage decomposition algorithm where the solution of the operation scheduling problem (without switching variables) feeds the network reconfiguration problem embedding the dispatch problem for each time period. The performance of the proposed two-stage decomposition algorithm is demonstrated on three different available IEEE testbeds against CPLEX and COUENNE solvers for benchmarking purposes. The selected IEEE testbeds have been widely adopted by previous literature for the aforementioned grid operation planning problem due to their capability in reproducibility. The proposed decomposition algorithm outperforms CPLEX in terms of both solution quality and computational times, particularly our algorithm provides 0.5% and 3.17% better solutions in mid-size and large-size networks, respectively.

Sustainable energy systems in the making: A study on business model adaptation in incumbent utilities

Delivering a low-carbon future depends significantly on the decarbonization of the electricity industry. Increasingly, electric utilities have experienced pressure to redefine their business model amid the need to transition to a sustainable energy system. In this study, we focus on how utilities have changed their business model to adapt to the emergence of sustainable energy innovations in the energy system and which value creation drivers they draw on. By framing the business model as an activity system, we capture how utilities expand the boundaries of their business to integrate sustainable energy activities. We analyze 756 boundary-spanning transactions (mergers and acquisitions, joint ventures, and strategic alliances) of 20 European utilities from 1990 to 2019. We find that utilities pursued 20 distinct sustainable energy activities across renewable electricity generation, smart electricity management, emerging technologies, and sustainable mobility. Preference for renewable energy activities, particularly wind generation is observed. The combination of renewable electricity generation and smart electricity management indicates a focus on systems integration. We also find preference for integrating activities through mergers and acquisitions. Utilities focus on acquiring sustainable energy activities leading to a novel bundling of activities contributing to decarbonization while reinforcing the efficiency and lock-in of their traditional business model.

Energy Re-Shift for an Urbanizing World

This essay considers the rural-to-urban transition and correlates it with urban energy demands. Three distinct themes are inspected and interrelated to develop awareness for an urbanizing world: internal urban design and innovation, technical transition, and geopolitical change. Data were collected on the use of energy in cities and, by extension, nation states over the last 30 years. The urban population boom continues to pressure the energy dimension with heavily weighted impacts on less developed regions. Sustainable urban energy will need to reduce resource inputs and environmental impacts and decouple economic growth from energy consumption. Fossil fuels continue to be the preferred method of energy for cities; however, an increased understanding is emerging that sustainable energy forms can be implemented as alternatives. Key to this transition will be the will to invest in renewables (i.e., solar, wind, hydro, tidal, geothermal, and biomass), efficient infrastructure, and smart eco-city designs. This essay elucidates how the technical transition of energy-friendly technologies focuses on understanding the changes in the energy mix from non-renewable to renewable. Smart electricity storage grids with artificial intelligence can operate internationally and alleviate some geopolitical barriers. Energy politics is shown to be a problematic hurdle with case research examples specific to Central and Eastern Europe. The energy re-shift stressed is a philosophical re-thinking of modern cities as well as a new approach to the human-energy relationship.

Research Tools for Smart Electric Vehicle Charging: An introduction to the adaptive charging network research portal

Millions of Electric Vehicles (EVs) will enter service in the next decade, generating gigawatthours of additional energy demand. Charging these EVs cleanly, affordably, and without excessive stress on the grid will require advances in charging system design, hardware, monitoring, and control. Collectively, we refer to these advances as smart charging.