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In the European Union, increasing attention is already being paid not only to the development of renewable energy sources, but also to the establishment of solutions to achieve local energy self-sufficiency while increasing the role of citizens in managing the energy they generate. This approach is expected both to have a positive impact on the environment and the reduction of greenhouse gas emissions, and to enhance energy security—both in economic and civic terms by, i.a., combating energy poverty. The development of local energy communities promoted in the EU is supported i.a. by energy cooperatives. These contribute to the efficient harnessing of renewable energy potential in rural and urban-rural areas, and have been developing in Poland for several years now. In their previous studies, the authors of this research paper attempted to verify the generation (number, type and capacity of installed sources) and consumption (energy demand) configurations in which an energy cooperative would be a viable solution for prosumers who might establish it. However, over the past few years, the conditions for prosumers and the method of their accounting with the electricity seller have changed radically in Poland (shift from net metering to net billing). This situation has opened up space for further research and encouraged the authors to revisit the problem of analyzing the viability of establishing energy cooperatives in relation to the rules of operation of individual prosumers. This research was carried out for three scenarios, and the horizon of the analyses conducted and described extends to 2045. The comparative analysis included energy consumers without their own generation sources, prosumers with a photovoltaic generation installation covered by the net billing model, as well as a scenario involving prosumers' cooperation within an energy cooperative, which by law is settled in the net metering model. Conclusions from the research and simulations made it possible to confirm the claim that, despite changes in the rules of prosumer billing, developing energy independence in the energy community formula results in a significant reduction in the cost of purchasing electricity (even several times lower purchase costs in the timeframe analyzed) and can lead to a reduction in the payback time of investments in generation sources even by a factor of two. The results presented in this research paper open up space for further research. The outcomes allow us to assume that energy cooperatives—in the organizational and institutional model in Poland—are a good tool for reducing the phenomenon of energy poverty on a local scale. [ABSTRACT FROM AUTHOR]

Metrological characterisation of static energy meters under realistic low power quality conditions is a basic requirement for proper grid control and fair energy billing. The paper reports about a new proposed methodology, where the meters are tested under conditions directly recorded at installation sites. The waveforms of voltages and currents are sampled using a portable instrument; they are reproduced in laboratory conditions with a phantom power generator, with a bandwidth covering up to the 40th harmonic. The recording site is a photovoltaic energy production facility, having a a nominal power of 50 kW, at the coupling section to the grid. These waveforms were then reproduced in the laboratory, and tested on different models of single- and three-phase commercial static energy meters; the models chosen represent both energy meters used by energy providers at the point of common-coupling, and also meters typically used for in-line monitoring by end users. The quantity of interest is the reading error of the measured energy, when tested with the conditions reproduced from the on-field measurements, in comparison with a reference meter. All tested energy meter models comply with the present international documentary standards, which require tests under low power quality conditions; nevertheless, there are models that show unacceptable errors (up to 25%) in the measurement of active energy when tested with the on-field recorded waveforms. This suggests that the standardised testing waveforms might, in some cases, be not fully representative of the actual conditions encountered in the field. [ABSTRACT FROM AUTHOR]

Energy is one of the essential inputs for modernization and social development. Energy demand is increasing, and the primary energy source is fossil fuels, which negatively impact the environment. Energy saving and renewables are the potential solutions which could minimize environmental impact. This paper investigates the energy-saving and solar photovoltaic energy potential of an educational institution, Politeknik Sultan Azlan Shah (PSAS), Malaysia. The feasibility analysis was conducted by assuming that PSAS joined the Net Energy Metering (NEM 3.0) program, where PSAS, as a NEM consumer, has a tripartite supply agreement with renewable energy (SARE) with a distribution licensee known as Tenaga National Berhad (TNB). This paper focuses on zero capital expenditure (CAPEX) saving through a 20-year contract. This paper proposes a rooftop solar photovoltaic diagram using a NEM meter installed in the ring distribution system at PSAS. The estimated savings to be obtained by PSAS in the 20 years that the contract is in force are calculated based on the assumption that the installed solar system has a capacity of 688 kW. The maximum value of power generated by the system for a year is 990,720 kWh. The feasibility analysis found that the cumulative net savings estimate for PSAS based on the overall calculation for 21 years of solar use is RM 3,534,250. Meanwhile, the cumulative assessment of carbon emission reduction obtained in the same period is 14,559,760 kg CO2 or 14,559.76 tons of CO2, which would save 363,994 mature trees from being cut down. [ABSTRACT FROM AUTHOR]

The Energy Efficiency Directive (EED, Directive 2012/27/EU) has made mandatory the installation of individual metering systems in the case of buildings with centralized heating/cooling and hot water sources (multi-apartment and multi-purpose buildings), provided it is economically and technically feasible. Individual metering of heating/cooling systems is mainly based on thermal energy meters (TEM), which are widely used for direct metering in heating applications. On the other hand, direct metering of energy consumption in cooling systems still represents a challenge, given the different types of cooling units and the lack of regulations from the technical and legal points of view. In this context, this paper briefly overviews the available centralized cooling systems and the possible solutions for metering and sub-metering, which depend on the specific application. Vapour Compression Refrigeration (VCR) systems are spreading worldwide for air conditioning applications. Particular attention has been paid to the direct metering of cooling energy and specifically to refrigerant flow rate measurement, which represents a critical issue because of the small-diameter pipes and the different thermodynamic properties of the fluid used. Thus, an experimental campaign has been developed and carried out in order to compare a clamp-on ultrasonic flowmeter with a more accurate Coriolis one in a direct expansion (DE) system. The experimental tests have been performed at two different temperature conditions, showing a relative error in the mass flow rate measurements within ±10%. [ABSTRACT FROM AUTHOR]

Solar PV and battery energy storage (BES) costs for domestic consumers are constantly diminishing. On top of this, the end of the Feed-in-Tariff programme has significantly increased interest in combined PV + BES systems. In this paper, we explore the economics of domestic PV + BES systems, extending the current literature on the topic via the use of a large smart meter dataset and a demographic comparison. Predictably, time-varying tariffs and higher electricity prices generally strengthen the economic arguments for PV + BES systems, however our consumption data yields a wide range of Net Present Values for different consumers. In terms of demographics, we find that batteries are more favourable for more affluent households due to their larger consumption levels, though profitability becomes more uniform if the batteries are tailored to individual households. This is an important point for policy, since it indicates that if PV + BES systems become widely profitable this is unlikely to help the financial situation of lower-income households. [ABSTRACT FROM AUTHOR]

Various metrological aspects for the correct measurements of electrical energy that is consumed by energy-saving (mainly LED) single phase loads are discussed in this paper. One of the most important problems presented here concerns the question of how strong distortions of the current waveform, introduced by typical LED lighting, affects the operation of electronic energy meters. Measurement results for the energy consumption of different LED lamps used in households in various conditions, alongside comparative results that were obtained by electronic and electromechanical energy meters, were also offered and the appropriate conclusions were then drawn. [ABSTRACT FROM AUTHOR]

Considering the engineering problem of electric energy meter automatic verification and scheduling, this paper proposes a novel scheduling scheme based on an improved Q-learning algorithm. First, by introducing the state variables and behavior variables, the ranking problem of combinatorial optimization is transformed into a sequential decision problem. Then, a novel reward function is proposed to evaluate the pros and cons of the different strategies. In particular, this paper considers adopting the reinforcement learning algorithm to efficiently solve the problem. In addition, this paper also considers the ratio of exploration and utilization in the reinforcement learning process, and then provides reasonable exploration and utilization through an iterative updating scheme. Meanwhile, a decoupling strategy is introduced to address the restriction of over estimation. Finally, real time data from a provincial electric energy meter automatic verification center are used to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper the idea of functioning of Building Management Systems and Object Management Systems in intelligent buildings is presented. New functionalities of intelligent buildings resulting from the introduction of microgeneration are described. Low-Power Wide-Area Networks (LPWAN) are characterized and compared. The selected Long-Range (LoRaWAN) technology is tested for its use for communication with energy meters and monitoring the power supply network in intelligent buildings. In the paper a new system for reading and monitoring the network is proposed, consisting of hardware, communication, and application layers. A key element of the system is a specially developed converter, which has been designed and tested in a real urban environment. Using our solution in practice could allow to change the architecture of a measurement data acquisition system to much more flexible and efficient. [ABSTRACT FROM AUTHOR]

Global trends such as the growing share of renewable energy sources in the generation mix, electrification, e-mobility, and the increasing number of prosumers reshape the electricity value chain, and distribution systems are necessarily affected. These systems were planned, developed, and operated as a passive structure for decades with low level of observability. Due to the increasing number of system states, real time operation planning and flexibility services are the key in transition to an active grid management. In this pathway, distribution system state estimation (DSSE) has a great potential, but the real demonstration of this technique is in an early stage, especially on low-voltage level. This paper focuses on the gap between theory and practice and summarizes the limits of low-voltage DSSE implementation. The literature and the main findings follow the general structure of a state estimation process (meter placement, bad data detection, observability, etc.) giving a more essential and traceable overview structure. Moreover, the paper provides a comprehensive mapping of the possible use-cases state estimation and evaluates 27 different experimental sites to conclude on the practical applicability aspects. [ABSTRACT FROM AUTHOR]

Estimating line impedance is relevant in transmission and distribution networks, in particular for planning and control. The large number of deployed PMUs has fostered the use of passive indirect methods based on network model identification. Electrified railways are a particular example of a distribution network, with moving highly dynamic loads, that would benefit from line impedance information for energy efficiency and optimization purposes, but for which many of the methods used in industrial applications cannot be directly applied. The estimate is carried out onboard using a passive method in a single-point perspective, suitable for implementation with energy metering onboard equipment. A comparison of two methods is carried out based on the non-linear least mean squares (LMS) optimization of an over-determined system of equations and on the auto- and cross-spectra of the pantograph voltage and current. The methods are checked preliminarily with a simulated synthetic network, showing good accuracy, within 5%. They are then applied to measured data over a 20 min run over the Swiss  16.7  Hz railway network. Both methods are suitable to track network impedance in real time during the train journey; but with suitable checks on the significance of the pantograph current and on the values of the coefficient of determination, the LMS method seems more reliable with predictable behavior. [ABSTRACT FROM AUTHOR]

This effort to make the power grid more intelligent is tightly coupled with the deployment of advanced metering infrastructure (AMI) as an integral part of the future vision of smart grid. The goal of AMI is to provide necessary information for the consumers and utilities to accurately monitor and manage energy consumption and pricing in real time. Immediate benefits are enhanced transparency and efficiency of energy usage and the improvement of customer services. Although the road map toward successful AMI deployment is clearly defined, many challenges and issues are to be solved regarding the design of AMI. In this paper, a multi-agent AMI based on the fog-computing approach is presented. Architecture follows structural decomposition of AMI functionalities encapsulated in a form of local and area-specific service components that reside at the different tiers of hierarchically organized AMI deployment. Fog computing concepts provide the framework to effectively solve the problems of creating refined and scalable solutions capable of meeting the requirements of the AMI as a part of future smart grid. On the other hand, agent-based design enables concurrent execution of AMI operations across the distributed system architecture, in the same time improving performance of its execution and preserving the scalability of the AMI solution. The real-time performance of the proposed AMI solution, related to the periodic and on-demand acquisition of metering data from the connected electricity meters, was successfully verified during one year of pilot project operation. The detailed analysis of the performance of AMI operation regarding data collection, communication and data availability across the deployed pilot AMI, covering several transformer station areas with diverse grid topologies, is also presented. [ABSTRACT FROM AUTHOR]

In the electrical installations of buildings, there is an increasing number of non-linear energy receivers, which introduce strong distortions of the load current of electronic energy meters. Since the readings of these meters are the basis for financial settlements of electricity consumers, it is very important to determine how much the distortion of the receiver current affects the correct operation of commonly used electronic energy meters. The article will present exemplary results and analyses of research work on the impact of individual current harmonics on the readings and errors of selected energy meters. [ABSTRACT FROM AUTHOR]

Regarding the modern power smart grid, distribution consumers and prosumers are highly concerned about power quality (PQ). In fact, they would prefer to pay higher prices for a reliable and good quality power supply. Unfortunately, utility operators still aim for reliability alone, ignoring the quality of supply voltage and current. There are no clear guidelines for monitoring, penalizing, or implementing PQ-based tariff schemes in LV distribution systems. In addition, the implementation of a PQ-based tariff requires a real-time measuring mechanism at the user end, which is very expensive and difficult to understand for a domestic consumer. This paper presents a novel, low-cost, efficient, and user-friendly smart PQ meter to overcome these issues and limitations. It is essentially a PQ analyzer with energy metering functionality, which implements a novel PQ-based tariff scheme that penalizes consumers violating the PQ limits and provides incentives for a good PQ profile. It measures as many as 28 parameters and keeps track of the PQ for both the consumer and the grid in real-time. This paper demonstrates the specifications, design, and testing of the meter and proves the validity of the concept by practical implementation. The meter is practical, feasible, and economical for implementing PQ-based tariff schemes in LV distribution systems or smart grids. [ABSTRACT FROM AUTHOR]

In this paper, an introduction and comprehensive analysis have been presented for the implementation and application of modern smart meters which include Unbundled Smart Meters (USM) and Next-Generation Open Real-Time Smart Meters (NORM). This article also contributes to methods through which USM and NORM could provide a better perspective to the already available technologies for grid-tied-inverter controlled feeding renewables to the grid. The research proposes a next-generation smart meter model with the feature of a phasor measurement unit. The meter is further integrated with a controller board that controls the power injection from the inverter to the grid based on the real-time data obtained from the smart meter. The inverter is simulated with an open-circuit fault and is controlled to provide non-oscillatory power to the grid based on an instantaneous grid power factor or phase requirement. The proposed meter has the flexibility to add additional features to control the inverter based on other grid requirements such as active and reactive power control, tariff implementation, etc. This manuscript provides the analytical aspects of the use of smart meters in efficient energy management and also addresses the need for smart technologies for grid modernization. [ABSTRACT FROM AUTHOR]

The Smart Energy Research Lab (SERL) Observatory dataset described here comprises half-hourly and daily electricity and gas data, SERL survey data, Energy Performance Certificate (EPC) input data and 24 local hourly climate reanalysis variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) for over 13,000 households in Great Britain (GB). Participants were recruited in September 2019, September 2020 and January 2021 and their smart meter data are collected from up to one year prior to sign up. Data collection will continue until at least August 2022, and longer if funding allows. Survey data relating to the dwelling, appliances, household demographics and attitudes were collected at sign up. Data are linked at the household level and UK-based academic researchers can apply for access within a secure virtual environment for research projects in the public interest. This is a data descriptor paper describing how the data were collected, the variables available and the representativeness of the sample compared to national estimates. It is intended to be a guide for researchers working with or considering using the SERL Observatory dataset, or simply looking to learn more about it. [ABSTRACT FROM AUTHOR]

In this paper, three main approaches (univariate, multivariate and multistep) for electricity consumption forecasting have been investigated. In fact, three major algorithms (XGBOOST, LSTM and SARIMA) have been evaluated in each approach with the main aim to figure out which one performs the best in forecasting electricity consumption. The motivation behind this work is to assess the forecasting accuracy and the computational time/complexity for an embedded forecasting and model training at the smart meter level. Moreover, we investigate the deployment of the most efficient model in our platform for an online electricity consumption forecasting. This solution will serve for deploying predictive control solutions for efficient energy management in buildings. As a proof of concept, an already existing public dataset has been used. These data were mainly collected thanks to the usage of already deployed sensors. These provide accurate data related to occupancy (e.g., presence) as well as contextual data (e.g., disaggregated electricity consumption of equipment). Experiments have been conducted and the results showed the effectiveness of these algorithms, used in each approach, for short-term electricity consumption forecasting. This has been proved by performance evaluation and error calculations. The obtained results mainly shed light on the challenging trade-off between embedded forecasting model training and processing for being deployed in smart meters for electricity consumption forecasting. [ABSTRACT FROM AUTHOR]

Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities. [ABSTRACT FROM AUTHOR]

This challenge was investigated by the authors in Lahariya et al. [[12]], who propose a synthetic data generator to create samples of realistic EV session data, where each session is defined by an arrival time, a departure time, and the required energy. In addition to measuring electrical consumption data at much greater temporal and amplitude resolutions than offered by traditional metering devices, smart meters can communicate collected data to external service providers and, thus, enable the creation of novel energy data-based services. Most research works in the domain of smart meter data analytics rely on the use of real-world datasets to evaluate the performance of newly proposed methods and algorithms. [Extracted from the article]

This paper is the first country-specific analysis of the market-driven Smart Metering innovation system, covering technologies, actors, and policies. It provides new insights on the key enablers and barriers in the rollout of electricity Smart Meters (SMs) without binding regulatory mandate. The presented research is based on the Technology Innovation System (TIS) analysis for Poland, where an obligation scheme for the rolling out of SMs has been introduced very recently. Still, the number of SMs installed places the country in the top 10 Member States of the European Union. The implementation of SMs is progressing in a complex, multi-actor innovation system, shaped by the leading role of Distribution System Operators (DSOs). The article analyses the key elements of the SM innovation system (technologies and infrastructures, actors and networks, institutions and policies) and characterizes their interaction based on desk research and a critical assessment of regulations, statistics, and literature. The major enablers of the rollout are DSOs expectations of benefits, which have been instigated by the market regulator's benevolence in tariffs approval. On the other hand, the major barriers are delayed and incomplete public policy instruments. Results of the study can inform the development of other market-driven SM deployments around the world. [ABSTRACT FROM AUTHOR]

Currently, in China's power grid, the accounting of carbon emissions has shortcomings such as unclear accounting boundaries, slow updating of carbon emission factors (EFs), and a lack of spatiotemporal characteristics. In this study, a dynamic accounting model for carbon emission was constructed based on carbon flow theory and the QIO (Quasi-Input-Output) model using the transmission side, the substation side, and the distribution side as accounting nodes. By utilizing the electricity metering data and carbon EF on the input side of the node, the total carbon emissions flowing into the node could be calculated. Furthermore, based on the electricity metering data on the output side of the node, the carbon emissions and carbon EF flowing out of the node could be calculated. The accounting results of carbon emissions and carbon EF are characterized by flexibility and dynamicity in both spatial and temporal dimensions. Finally, the practicality of the method has been demonstrated through a substation node. The accounting model has a positive impact on accurate carbon emission accounting for the power grid, better tracing of carbon emissions, and effective user guidance on active carbon emission reduction. [ABSTRACT FROM AUTHOR]

Smart energy has evolved over the years to include multiple domains integrated across multiple technology themes, such as electricity, smart grid, and logistics, linked through communication technology and processed in the cloud in a holistic way to deliver on global challenges. Advances in sensing, communication, and computation technologies have been made that enable better smart system implementations. In smart energy systems, sensing technologies have spanned multiple domains with newer techniques that are more accurate, have greater dynamic ranges, and are more reliable. Similarly, communication techniques have now evolved into very high-speed, flexible, and dynamic systems. Computation techniques have seen a quantum leap with greater integration, powerful computing engines, and versatile software stacks that are easily available and modifiable. Finally, the system integration has also seen advances in the form of management, automation, and analytics paradigms. Consequently, smart energy systems have witnessed a revolutionary transformation. The complexity has correspondingly grown exponentially. With regard to smart meters, the measurement component has to scale up to meet the demands of the evolved energy eco-system by relying on the advancements offered. The internet of things (IoT) is a key technology enabler in this scenario, and the smart meter is a key component. In recent years, metering technology has evolved in both complexity and functionality. Therefore, it must use the advances offered by IoT to deliver a new role. The internet of things (IoT) is a key technology enabler in this scenario and the smart meter a key component. In recent years, metering technology has evolved in both complexity and functionality. To deliver on its new role, it must use the advances offered by IoT. In this review, we analyze the smart meter as a combination of sensing, computing, and communication nodes for flexible and complex design paradigms. The components are, in turn, reviewed vis-à-vis the advances offered by IoT. The resultant gaps are reported for future design challenges in the conclusion. The identified gaps are the lack of usage of the full spectrum of the available technology and the lack of an inter-disciplinary approach to smart meter design. [ABSTRACT FROM AUTHOR]

Smart meters in road lighting systems create new opportunities for automatic diagnostics of undesirable phenomena such as lamp failures, schedule deviations, or energy theft from the power grid. Such a solution fits into the smart cities concept, where an adaptive lighting system creates new challenges with respect to the monitoring function. This article presents research results indicating the practical feasibility of real-time detection of anomalies in a road lighting system based on analysis of data from smart energy meters. Short-term time series forecasting was used first. In addition, two machine learning methods were used: one based on an autoregressive integrating moving average periodic model (SARIMA) and the other based on a recurrent network (RNN) using long short-term memory (LSTM). The algorithms were tested on real data from an extensive lighting system installation. Both approaches enable the creation of self-learning, real-time anomaly detection algorithms. Therefore, it is possible to implement them on edge computing layer devices. A comparison of the algorithms indicated the advantage of the method based on the SARIMA model. [ABSTRACT FROM AUTHOR]

Reducing office buildings' energy consumption can contribute significantly towards carbon reduction commitments since it represents ∼40% of total energy consumption. Major components of this are lighting, electrical equipment, heating, and central cooling systems. Solid evidence demonstrates that individual occupants' behaviors impact these energy consumption components. In this work, we propose the methodology of using virtual choreographies to identify and prioritize behavior-change interventions for office users based on the potential impact of specific behaviors on energy consumption. We studied the energy-related office behaviors of individuals by combining three sources of data: direct observations, electricity meters, and computer logs. Data show that there are behaviors with significant consumption impact but with little potential for behavioral change, while other behaviors have substantial potential for lowering energy consumption via behavioral change. [ABSTRACT FROM AUTHOR]

Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed. The accuracy of the previous residential load monitoring approach relies heavily on the data acquisition frequency of the energy meters. It brings high overall cost issues, and furthermore, the differentiating algorithm becomes much more complicated. Based on this, we proposed a novel non-Intrusive residential load disaggregation method that only depends on the regular data acquisition speed of active power measurements. Additionally, this approach brings some novelties to the traditionally used denoising Auto-Encoder (dAE), i.e., the reconfiguration of the overlapping parts of the sliding windows. The median filter is used for the data processing of the overlapping window. Two datasets, i.e., the Reference Energy Disaggregation Dataset (REDD) and TraceBase, are used for test and validation. By numerical testing of the real residential data, it proves that the proposed method is superior to the traditional Factorial Hidden Markov Model (FHMM)-based approach. Furthermore, the proposed method can be used for energy data, disaggregation disregarding the brand and model of each appliance. [ABSTRACT FROM AUTHOR]