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To achieve the carbon peaking and carbon neutrality goals, China promoted county-wide photovoltaic projects (CWPVs) in 2021. A large number of distributed photovoltaic (DPV) installations have expanded the scale of prosumer. It also brings the inequity problem of prosumer, distribution network system operator (DSO) and consumer--DSO and consumer bear the costs such as access cost and depreciation of stock investment caused by DPV access. Combined with China's current electricity tariff system and the promotion of photovoltaic scale development, this paper proposes a standby fee model based on the principle of "beneficiary apportionment". In this model, the standby fee mechanism for self-owned power plants is applied to DPV to reduce the implementation cost of the mechanism, and the principle of "beneficiary apportionment" is adopted to ensure the profitability of DPV. This paper takes the CWPV in Hunan Province, China as an example, calculates the standby fee under various electricity metering methods, and determines the most appropriate standby fee by considering factors such as output uncertainty of DPV. Finally, the difficulties in the implementation of standby fee are sorted out. [ABSTRACT FROM AUTHOR]

Accurate electric energy metering in micro-grid is one of the most urgent problems in the field of electric energy metering. In order to solve the problem that the lackness of electric energy metering can't be accurately calculated with the existing electric energy compensation algorithm when the electric energy metering device is completely faulty, this paper proposes an electric energy compensation algorithm that is limited in micro-grid and is based on standard particle swarm to optimize the deep belief network. Taking the historical electric energy data of each micro source metering point in micro-grid as an example, it is divided into the training set and the test set, the standard particle swarm optimization algorithm is used to determine the optimal learning layers and learning rate of deep belief network, and the electric energy algorithm model is established. The model verification results show that the proposed algorithm is nearly five times lower than the algorithm based on deep belief network solely. Further engineering case comparison shows that lackness of electric energy metering compensated by the algorithm studied in the paper is closer to the theoretical value, and the error is also less than the traditional average electricity algorithm. The real-time power curve is closer to the theoretical value, and has the higher accuracy. [ABSTRACT FROM AUTHOR]

The metering mode is gradually changing from volumetric metering to energy metering under the open-access operation of gas pipelines. The gas component tracking by operating simulation is an effective method to realize the energy metering of gas networks. In this paper, coupling and decoupling transient component tracking models of gas networks are proposed. The pipeline governing equations, compressor equations, valve equations, and node relationship equations are considered in the proposed models. The implicit central difference method and the damped Newtonian method are adopted to solve the models. The coupling model considers the component tracking part and hydraulic-thermal simulation as a whole, while the decoupling model splits the whole process into the gas component tracking part, the parameters calculation part, and the hydraulic-thermal simulation part. In the component tracking part, the spatiotemporal variation of the gas mass fraction and density can be obtained through the input velocity of each node. Then, the parameters in the gas state equation can be solved, the hydraulic-thermal simulation can be performed, and the new velocity of each node can be calculated. The iteration for each time step ends if the deviation of flow velocity in the component tracking process and the hydraulic-thermal process satisfies the requirements. Two cases are studied to verify the effectiveness of the proposed models, and the boundary condition adaptability of the transient simulation models is analyzed. The proposed models can efficiently simulate the spatiotemporal variation of the operating variables and gas components, providing a reference in the operation of natural gas pipeline networks. [Display omitted] • The most accurate gas state equation with 11 parameters is chosen in the models. • A coupling model integrating the T-H process and the GCT process is proposed. • The T-H and GCT sub-models are decoupled to establish the decoupling model. • The proposed models are applied to different boundary conditions combinations. [ABSTRACT FROM AUTHOR]

Implementing a proper clustering algorithm and a high accuracy classifier for applying on electricity smart meter data is the first stage in analyzing and managing electricity consumption. In this paper, Random Forest (RF) classifier optimized by Artificial Bee Colony (ABC) which is called Artificial Bee Colony-based Random Forest (ABC-RF) is proposed. Also, in order to determine the representative load curves, the Convex Clustering (CC) is used. The solution paths generated by convex clustering show relationships among clusters that were hidden by static methods such as k-means clustering. To validate the proposed method, a case study that includes a real dataset of residential smart meters is implemented. The results evidence that the proposed ABC-RF method provides a higher accuracy if compared to other classification methods. • To investigate the application of convex clustering on smart meter data. • To present ABC-RF as an accurate classifier for smart meter data • To introduce a demand response approach based on the proposed data mining technique. [ABSTRACT FROM AUTHOR]

The continuous improvement of the energy and sustainability performances of buildings is stimulated by mandatory rules, economic incentives and voluntary sustainability pathway of which energy efficiency is one of the most important components. The tools to evaluate the energy, sustainability, automation and energy metering performances of buildings, to check the status and lead the investments, are often complex, slow, expensive and related to the specific Countries, making difficult the job for the facility management departments of national or international organizations in EU. The present paper wants to give a contribute to existing literature by providing a new energy, sustainability, automation and energy metering label system to quickly, simply and economically evaluate the performances of office buildings in Europe. This tool has been tested on a case study, a research center of the European Space Agency (ESA-ESRIN) in Rome, Italy. The results of the test show that the tool has been successfully applied on the buildings of the research center using the data provided by the facility management department of ESA-ESRIN and the energy audit of the center. The labels can correctly represent the state of the buildings in a more cost-effective way if compared to the national energy certification and to international sustainability protocols. This study provides the facility management departments of public and private companies in EU - especially if they manage buildings in different EU nations - a reliable tool to quicky evaluate the energy, sustainability, automation and energy metering performances of their buildings. [Display omitted] • Energy and sustainability performances of buildings are evaluated through protocols. • Facility managers need quick tools: the proposed labeling tool for offices can help. • It labels energy, sustainability, automation and energy metering performances. • This tool considers Trans-national European Union differences (primary energy, CO 2). • The first application has been on the European Space Agency Italian research center. [ABSTRACT FROM AUTHOR]

The data of the power Internet of Things (IOT) system is transferred from the IaaS layer to the SaaS layer. The general data preprocessing method mainly solves the problem of big data anomalies and missing at the PaaS layer, but it still lacks the ability to judge the high error data that meets the timing characteristics, making it difficult to deal with heterogeneous power inconsistent issues. This paper shows this phenomenon and its physical mechanism, showing the difficulty of building a quantitative model forward. A data-driven method is needed to form a hybrid model to correct the data. The research object is the electricity meter data on both sides of a commercial building transformer, which comes from different power IOT systems. The low-voltage side was revised based on the high-voltage side. Compared with the correction method based on purely using neural networks, the combined method, Linear Regression (LS) + Differential Evolution (DE) + Extreme Learning Machine (ELM), further reduces the deviation from approximately 4% to 1%. • The heterogeneous electrical data from complex IOT system is inconsistent. • The positive mechanism of inconsistency has been revealed, but not easy to correct. • Reverse data correction used a combination of simple methods and the effect is better. [ABSTRACT FROM AUTHOR]

In the current deregulated electricity market, flexible consumers are more active in participating in market activities via the representation of electricity retailers. However, without an effective communication infrastructure, the connection between retailers and the consumers they serve is incomplete. Nowadays, smart meters are being rolled out worldwide to enhance the connection and data exchanges between retailers and consumers. Specifically, smart meters enable retailers to provide customers with detailed information about retail tariffs and their energy usage at different times of the day, which in turn enables customers to manage their energy use more proactively. This paper drops this assumption and makes use of data acquired from smart meters to design a personalized retail pricing scheme for different types of consumers. To formulate this problem, a bi-level optimization model is proposed, with the upper-level problem representing the pricing decision made by the retailer and two lower-level problems representing the demand response of consumers and the wholesale market clearing process, respectively. Afterward, we convert this bi-level optimization model into a single-level mathematical program with equilibrium constraints by using its Karush Kuhn Tucker optimality conditions and complementary conditions. The scope of the examined case studies is fourfold. First, consumers are classified based on their daily load profiles using the advanced clustering method. Second, the physical benefit of fully exploring the consumer's demand flexibility as well as the economic benefits of increasing retailers' profitability and reducing consumers' energy bills are evaluated with respect to the traditional uniform retail pricing scheme. Third, the impacts of consumers' demand flexibility on electricity market outcomes and business cases are investigated. Finally, the proposed personalized retail pricing scheme is verified to relieve the strategic retailer's market power reduction caused by the flexibility of demand, which is beneficial to the retailer's profitability. [ABSTRACT FROM AUTHOR]

• Technical procedures and standards for subsequent verification of thermal energy meters are still not available in several EU countries. • Strict error and uncertainty limits are required for thermal energy meters verification. • Relevant deviation between in laboratory and on field results and outcomes have been found. • In laboratory verification shows good reliability at all conditions whereas on the field critical issues arise. Individual metering for heating and cooling has been recognized by EU as an effective tool to improve energy efficiency. Hence, thermal energy meters are widely spreading in district heating networks and in buildings. While type approval and initial verification of thermal energy meters are clearly regulated, no technical standards are available in EU for subsequent verification both in laboratory and in-field. Nevertheless, verification of thermal energy meters is a difficult task, due to the complex measuring chain and to the need to set appropriate verification points combined in flow-rate and temperature difference. In this paper, the authors present the results of an experimental campaign aimed at analysing the metrological issues and compatibility between subsequent verification of a thermal energy meter performed in the laboratory and in-field. The obtained results demonstrate that in-field verification outcomes in some cases are not consistent with the in-laboratory ones. [ABSTRACT FROM AUTHOR]

Electric vehicles as indicated by the recent developments have established a foundation for sustainable transportation. The amount of electricity consumed by the EVs adds a significant amount of loading to the grid. Thus, necessitating the need for simultaneous implementation of vehicle-to-grid infrastructure to protect the grid. In the above circumstances, a bidirectional energy metering system is needed to record the import & export of energy between the grid and the vehicle. Presently the meters are installed on the AC side of the chargers, therefore, losses that occurred during the rectification process are not measured precisely. In this paper, the role of a bidirectional DC measurement system in vehicle-to-grid operation is investigated. The author's aim is to provide accurate billing to end customers by keeping the DC meter on the battery side. In addition, a net metering scheme is implemented to generate the net bill at the end of the billing period. The proposed DC net meter is developed in compliance with the guidelines mentioned in IEC 61000/60255/62052 standards for low voltage applications. It also has a real time clock to implement the Time of Usage (TOU) tariff. The precision of the DC net meter can be seen from the experimental results obtained from the two bench configuration. [ABSTRACT FROM AUTHOR]

Buildings represent 39% of global greenhouse gas emissions, thus reducing carbon emissions in buildings is of importance to greenhouse gas emissions reductions. This requires understanding how electricity is utilized in the buildings, then optimizing electricity management to seek conservation of energy. Non-intrusive load monitoring (NILM) is a technique that disaggregates a house's total load to estimate each appliance's electric power usage. Several strategies for estimating one appliance at a time (one-to-one model) have been presented and experimentally proven to be effective, with two mainstream trends: appliance transfer learning and cross-domain transfer learning. The former refers to the transfer between different types of appliances in the same data domain, while the latter refers to the transfer between different data domains for the same type of appliance. Different from the previous work, this paper explores the approach of adopting one model for all appliances (one-to-many model) and proposes a novel transfer learning scheme, that incorporates appliance transfer learning and cross-domain transfer learning. Thus, a well-trained model can be transferred and utilized to effectively estimate the power consumption in another data set for all appliances, which demands fewer measurements and only one model. Three public data sets, REFIT, REDD, and UK-DALE, are used in our experiments. Further, a set of smart electricity meters was deployed in a practical non-residential building to validate the proposed method. The results demonstrate the accuracy and practicality compared to start-of-the-art one-to-one NILM transferred models. • An adaptive one-to-many NILM model is designed (one model for all appliances). • A novel transfer one-to-many NILM strategy is proposed for the first time. • The number of parameters for NILM model is significantly reduced. • A novel metric, Overall Disaggregation Proportion Error (ODPE), is used. • The proposed model is validated on both public and self-collected datasets. [ABSTRACT FROM AUTHOR]

Smart electricity meter failures triggered by low-output-voltage accounted for 79 % of on-site meter failures. It is significant to predict the event of low-output-voltage of the clock battery. Still, it is also challengeable due to the complexity of the failure causes and the diversity of component reliability data. In this paper, multi-causes of battery low-output-voltage failure were analyzed and incorporated to develop a system reliability model for the clock battery circuit. The lifetime distribution of the clock battery circuit was computed using time-varying weights to integrate diverse forms of component data, including degradation data, lifetime distribution data, lifetime observation data, and failure rate data from datasheet. Finally, an engineering example is used to verify the effectiveness of the developed method. The developed reliability model of clock-battery systems can also be applied in other systems where power is supplied from batteries. [ABSTRACT FROM AUTHOR]

Energy meters provide valuable information that can be used to determine important features such as energy consumption of electrical devices and consumption habits in corporate, residential or public institutions. This information is crucial to establish energy saving strategies. With this aim, different approaches have been proposed in the literature, including non-intrusive load monitoring techniques, which enable the energy disaggregation of appliances and devices through a centralized measurement taken at panel level using a metering infrastructure. Generally, the accuracy of these techniques increases as more information is available on the analyzed signals or through subsequent post-computed values. Active power, reactive power, or even current harmonics measurements can be used for this task. However, the use of these and other recently proposed power and current features increases the dimensionality and, therefore, the complexity of the algorithms involved in the disaggregation process. Therefore, it is necessary to apply advanced techniques to reduce the dimensionality of the data, as well as the possible linear dependence between variables. This paper compares the performance of 8 data pretreatment methods and 6 dimensionality reduction techniques to data retrieved by an advanced metering infrastructure in a real environment consisting of 10 different home appliances. Results obtained from the comparative analysis show that the information provided by raw data can be enhanced by using pretreatment techniques and dimensionality reduction methods, especially when a custom combination of active power and current harmonics measures is considered. [ABSTRACT FROM AUTHOR]

Smart scheduling of domestic appliances based on user demand is an important aspect of any home energy management system. Such a demand driven operation can be performed only by accurately detecting the presence and the location of users in their residence. IoT and other enabling technologies can transform a smart home into an energy aware entity by judicious utilization of the available data. This paper presents a survey of various occupancy detection and localization schemes and evaluates them on the basis of various factors that decide their suitability for home energy management system. Wireless technologies have been historically employed for detecting unknown targets in indoor environments. The pervasiveness of WiFi access points in urban buildings render it as a preferred candidate for occupancy detection and localization. Design of device-free sensing techniques, using commercially available WiFi routers can offer an affordable solution with minimum disturbance to the inhabitants. Detection accuracy maybe enhanced by combining wireless techniques with smartphone inertial sensors and other passive detection schemes that utilize smart energy meters or environmental sensors. In contrast to commercial buildings, several challenges must be addressed for occupancy detection in home energy management system. The impact of these design challenges are investigated and feasible solutions are suggested. • Occupant centric appliance control can result in energy savings up to 40%. • Device-free sensing aids in non intrusive domestic occupancy detection. • Diverse sensing modalities can be combined using IoT for context acquisition. • An occupant centric Home Energy Management System (HEMS) design unfolds new research challenges. [ABSTRACT FROM AUTHOR]

• Electric Vehicle's usage has been disaggregated from energy meter's data. • EV's charging state as well as State of Charge (SOC) value has been estimated. • A Hidden Markov Model (HMM) based NILM algorithm has been used for this purpose. • Model probabilities were estimated using the Baum-Welch algorithm. • The state sequence was found out using the Viterbi algorithm. Due to a surge in the usage of Electric Vehicles (EV), the burden on the power grid utilities have increased in the recent past. But the time-shiftable nature of the charging process of these EVs allow them to participate in Demand Response Programs (DRP) which offers a whole new dimension of flexibility in a Power system. In order to identify the availability of EV in a household, the grid operator can employ the concerned household's consumption data that is available through the smart meter installed there. A Hidden Markov Model (HMM) based approach similar to Machine Learning has been proposed in this paper for identifying houses with EV. For operational purpose, this algorithm helps in desegregation of EV charging from smart meter's energy consumption data along with the determination of their charging/discharging state, and SOC estimation. This has been done in a non-intrusive way and is aimed towards the identification of time-flexible or shiftable loads for Demand Response (DR) participation. Data from the smart meters of over 1000 households have been taken into account at different sampling frequencies. Several of these households have also incorporated roof top PV to make them self-reliant. The practicality of the proposed approach is ascertained by the simulation results that are based on real-world situations. An error analysis has been performed to observe the effectiveness of this method. The scope of participation for the identified EVs in DR programs has been discussed in the end. [ABSTRACT FROM AUTHOR]

Microgrids with renewable power are becoming a widespread alternative for decarbonizing the electrical sector in light of climate change and global warming. However, such widespread penetration of renewables degrades some parameters of power quality along the low voltage utility grid. This research conducts an experiment with an advanced metering infrastructure of a power utility grid with hundreds of thousands of smart grid devices. The experiment identifies the location of the power quality disturbances. The results of the measurements have shown that the power quality parameters are mainly degraded along the downstream section of the low voltage power utility grid, despite the fulfillment of the regulatory quality requirements in the upstream high voltage substation. Thus, the novelty of the approach presented in the paper consists in the use of relatively low capacity energy storage units to locally mitigate the power quality disturbances, keeping them as close as possible to their source in the low voltage grid, instead of using large energy storage units connected to the medium voltage grid. Moreover, the research evaluated the parameters of power capacity, charge–discharge rate, weight, size, and the Levelized Cost Of Energy (LCOE) implemented with five types of energy storage technology. Lithium-Ion Capacitor (LIC) presents the lowest LCOE. However, service type and location parameters also play an essential role in selecting other different types of energy storage technology. This research shows that locating small energy storage units close to the source of power quality disturbance is cost-effective and offers excellent potential for widespread implementation in the low voltage distribution grid. [Display omitted] • High penetration of renewables causes power quality degradation. • The advanced metering system locates low voltage disturbances. • Control methods for mitigation of disturbances with energy storage are proposed. • Fluctuation of low voltage profile decreases with energy storage. [ABSTRACT FROM AUTHOR]

An accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network. This model is built on the so-called Q-algorithm and fed with real data from 42 smart energy meters located in 42 buildings connected to the DH in Tartu (Estonia). These meters deliver heat consumption data with a 1-h frequency. Heat load profiles are analysed, and a model based on supervised clustering methods in combination with multiple variable regression is proposed. The model makes use of four climatic variables, including outdoor ambient temperature, global solar radiation and wind speed and direction, combined with time factors and data from smart meters. The model is designed for deployment over large sets of the building stock, and thus aims to forecast heat load regardless of the construction characteristics or final use of the building. The low computational cost required by this algorithm enables its integration into machines with no special requirements due to the equations governing the model. The data-driven model is evaluated both statistically and from an engineering or energetic point of view. R2 values from 0.70 to 0.99 are obtained for daily data resolution and R2 values up to 0.95 for hourly data resolution. Hourly results are very promising for more than 90% of the buildings under study. • Data driven model for heat-load prediction of buildings is developed. • Real data from 42 buildings connected to the district-heating in Tartu is used. • Different building typologies with different energy demand profiles are used. • The present model is applied to hourly and daily data. • R2 for hourly data range between 0.47 and 0.95 and for daily data range between 0.70 and 0.99 [ABSTRACT FROM AUTHOR]

Poor building energy performance often arises from suboptimal operations such as inappropriate control sequences or hard faults in heating, ventilation, and air-conditioning (HVAC) systems. Furthermore, such deficiencies are often left unaddressed due to a lack of accessible analytical tools that can derive insights which identify energy-saving measures using multiple data resources. This paper presents a novel multi-source, data-driven building energy management toolkit as a synthesis of established inverse energy modelling, anomaly detection and diagnostics, load disaggregation, and occupancy and occupant complaint analytics methods in the literature. The toolkit contains seven functions that input HVAC controls, energy meter, Wi-Fi-based occupancy, and work order log data to detect hard and soft faults, improve sequences of operation, and monitor energy flows, occupancy patterns, and occupant satisfaction. The toolkit's unique multifaceted analytical approach was demonstrated on a case study building as a proof of concept. Five faults pertaining to the air handling units' mode of operation and heating coil valves were identified and the generated insights were used to pinpoint operational deficiencies stemming from inappropriate zone temperature overheating thresholds and perimeter heating devices. The toolkit, along with the data from the case study, is open-source and accessible through GitHub (Markus, 2021) to initiate and facilitate future development. [ABSTRACT FROM AUTHOR]

• Electricity fraud detection in conventional meters. • Combining ML: supervised and unsupervised algorithms. • Extensive feature engineering. • Multivariate Gaussian distribution for anomaly detection. • Light Gradient Boosting algorithm: performance and tuning. Although fraud in electricity consumption is easier to detect when consumption is recorded hourly by smart meters, in most developing countries, where the propensity for fraud is higher, conventional meters are not yet affordable. Fraud detection is easier with time series data-logging due to the periodicity and variability of consumption that reveals deviations from a regular consumption pattern. In contrast, fraud detection with conventional meters remains a significant challenge because anomalies in consumption are well hidden within the normal consumption of other consumers. In this paper, large datasets regarding consumers and invoice data from Tunisia are combined and investigated with several Machine Learning (ML) classification algorithms, to detect irregularities in electricity consumption. By performing extensive feature engineering, including multivariate Gaussian distribution, the efficiency of ensemble classifiers such as Light Gradient Boosting (LGB) outperforms other algorithms and achieves realistic performance from challenging, unbalanced and uncorrelated input datasets. [ABSTRACT FROM AUTHOR]

• The proposed methodology allocates aggregator units in low voltage distribution networks. • The units are allocated considering the minimization of their installation costs. • The proposal can allocate the aggregators where they can better characterize the net load curves. • The proposal improves the characterization of the power losses in the aggregator coverage area. The aggregator units are essential in implementing smart grids since they are responsible for connecting smart meters installed on the consumers and ensuring exchanging information with the utility control centers. This paper presents a methodology to allocate these aggregators in low voltage distribution systems with solar photovoltaic generation. The proposed method is composed of two stages. In the first one, the aggregators are allocated considering the minimization of the distance to the remote terminal unit and their installation costs using a method based on Particle Swarm Optimization. In the second stage, the smart meters are connected to each allocated aggregator using a graph method. These connections are performed, taking into account a function, which depends on the distance between the meters and the load density within the aggregator coverage area. The proposed methodology was tested using a 400 V low voltage distribution system with 45 residential loads and 12 photovoltaic generators. The results showed that the proposal allocates aggregators at points where the consumer groups' load curves into the aggregator coverage area are better characterized. The values of the average voltage profile and the system's power losses in each aggregator coverage area presented an excellent match with values obtained with the test system without installing aggregators. From this characterization, it is possible to get useful information for operational planning and expansion planning of low voltage networks with photovoltaic generators by aggregation areas. [ABSTRACT FROM AUTHOR]