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Smart grid represents an advanced and interconnected system that incorporates modern technologies to enhance efficiency, reliability and sustainability. In comparison to the conventional grid, the smart grid (SG) uses many cutting-edge technologies. This research introduces a fog computing encryption-based model for privacy preservation in the smart grid model. By using different advanced technologies, our model addresses the balance between privacy, security, effectiveness and functionality. The model facilitates efficient communication and function inquiry while mitigating challenges posed by massive Internet of Things (IoT) systems in the smart grid environment. Specifically, it tackles the secure data consolidation challenge by employing encryption-based techniques for transmitting private data from smart meters to fog devices. These devices consolidate the data before updating to cloud. Conventional data consolidation approaches for SGs have high computation and communication costs and suffer from high storage requirement. The proposed model resolves these issues; algorithms for data consolidation and extraction of data at fog device and cloud respectively to obtain the secure communication has also been included. The performance of the developed mechanism has been computed against existing data consolidation mechanisms GCEDA (Grouping of Clusters for Efficient Data Aggregation), SPPDA (Secure Privacy-Preserving Data Aggregation) and LPDA (Lightweight Privacy-preserving Data Aggregation) for numerous performance parameters. And the results proves that the performance of developed mechanism with respect to bytes of storage, communication cost and ratio of transmission is better than the existing ones. [Display omitted] • Encryption-based data consolidation strategy for 5G in fog computing is presented. • Data consolidation and data extraction algorithm at fog devices and cloud servers. • A comparative analysis based on storage, communication and transmission cost is done. [ABSTRACT FROM AUTHOR]

Global water use has grown steadily in the past decade, and climate change will further worsen the levels of water stress. Another global issue lies in the ageing of water infrastructures, registering conspicuous water losses (40% in the Italian scenario). A great opportunity is represented by the exploitation of new technologies relating to IoT, namely smart meters, which can enable the prediction of water consumption, detect leaks, and customise the service. This paper assesses the economic and environmental impact of smart meters adoption for water consumption, by taking the Italian scenario as a reference context, through the development of an analytical model which considers the benefits and costs associated with their adoption, with the ultimate objective of evaluating the convenience of the investment. Data to feed the model was collected through secondary sources, literature reviews, and interviews with utility companies' employees. Results provide corroborating evidence of the positive impact of smart water meter adoption, both in economic and environmental terms, in particular by increasing the roll-out number, given the higher amount of data available and economies of scale to be exploited. The present study contributes to the academic literature by providing a comprehensive model that considers economic and environmental aspects of smart water adoption, which allows practitioners to have an insightful understanding of the involved variables in such investments. [ABSTRACT FROM AUTHOR]

The exploitation of green hydrogen blending with natural gas (NG) is expected to contribute in lowering greenhouse gas emissions and fostering the share of renewable energy sources (RES). The NG infrastructure, in fact, can act as a storage facility for green hydrogen produced from excess RES, also providing flexibility to the electric system. However, the unbalance of the networks and the raise of protection issues for consumers may occur as a consequence of the potential decay of the metrological performance of gas meters operating with natural gas and hydrogen blends. In this paper, the authors present the results of an experimental campaign aimed at analysing the effect of hydrogen blending on the accuracy of smart domestic gas meters. Experimental tests were performed in the laboratory using air, natural gas and gas-hydrogen blends up to 23%vol. The results show no criticalities occur for diaphragm and ultrasonic gas meters, regardless of the hydrogen content. On the other hand, thermal mass gas meters show good reliability when hydrogen blends are within 2%vol, whereas for higher hydrogen contents, reliability is demonstrated only when they are equipped with a specific new design routine capable to address the gas quality changes. • Hydrogen blending with natural gas is becoming a spread practice for green energy storage and use. • Issues related to the readiness of the gas infrastructure to distribute hydrogen blends and appliances still have to be investigated. • Consumer protection and fairness of the billing is a crucial issue for domestic gas meters. • Accuracy tests of domestic gas meters of different measuring principles with hydrogen admixture up to 23% are presented. [ABSTRACT FROM AUTHOR]

More energy-consuming devices such as household electronics and more production facilities worldwide are causing increases in electricity demand and energy prices. The public service has difficulty maintaining the energy balance. These problems can be overcome by rapidly transforming the traditional electricity grid into a smart grid (SG) infrastructure. Smart meters (SMs) are an essential component of SGs and have vital tasks. This study has developed an Internet of Things (IoT) based SM that can reach high data rate of 38,400 bps or frequency of 160 MHz, using SQL Server for data storage and bidirectional data transmission with 174 W total load. In this way, consumers can track their energy consumption hourly, daily, and monthly, learn how much they spent on consumption, and receive warnings for a power outage. A fuzzy system and mobile application software are integrated into the SM structure for all these purposes. The designed device is believed to contribute significantly to the spread of SGs due to all these features. [ABSTRACT FROM AUTHOR]

As a rapidly evolving paradigm, digital transformation (DT) remains one of today's most significant challenges. Consequently, numerous businesses devote a substantial portion of their resources to this captivating transition. However, this investment's return is difficult to quantify or even unexplored by others. This observation constituted the foundation for Solow's productivity paradox. Even if they are aware of how the KPI measurement affects their performance, managers continue to disregard this evaluation. In terms of academic literature, this field of study is still inadequately developed. To address this limitation, this study uses the NVivo software to conduct a systematic literature review highlighting the KPIs, the methodology used in the corpus, the evolution of related works, and their type. According to our analysis, several works cite this term without describing it or defining a general approach to DT metrics. In addition, the financial and commercial performance KPIs are the most specific. Following a summary of the primary KPIs in the literature, we classified them into two prominent families: generic and specific. This classification represents the originality of this paper. The generic KPIs are multidimensional and applicable to a variety of businesses, while the specific ones pertain to a particular function or industry. [ABSTRACT FROM AUTHOR]

Because privacy-preserving data aggregation protocols provide data privacy and data compression, they have been extensively studied in smart grids. However, most of the existing data aggregation protocols are based on honest-but-curious aggregators, or adopt computationally intensive asymmetric homomorphic encryption, making these protocols only provably secure in weak security models, or resulting in high computational complexity for terminal users. To address the aforementioned issues, in this work, we propose a novel lightweight privacy-preserving data aggregation scheme against malicious aggregators based on our symmetric homomorphic encryption. The proposed scheme can reduce the computational cost of smart meters while also resisting tampering and deletion attacks from malicious aggregators. One of the highlights of the proposed scheme is its ability to withstand deletion attacks from malicious aggregators, which makes it unnecessary for the malicious aggregator to aggregate the readings of all smart meters instead of only a part of them. Therefore, our scheme is more secure than other schemes. It is the first privacy-preserving data aggregation scheme based on a stronger security model without multiple rounds of interaction. Finally, the detailed security analysis shows that the proposed scheme satisfies desirable security properties. Moreover, experimental results demonstrate that the proposed scheme is superior to the other four schemes in terms of computational complexity and communication overhead. [ABSTRACT FROM AUTHOR]

Smart meters are key elements of a smart grid. These data from Smart Meters can help us analyze energy consumption behaviour. The machine learning and deep learning approaches can be used for mining the hidden theft detection information in the smart meter data. However, it needs effective data extraction. This research presents a theft detection dataset (TDD2022) and a machine learning-based solution for automated theft identification in a smart grid environment. An effective theft generator is modelled and used for obtaining a multi-class theft detection dataset from publicly available consumer energy consumption data, owned by the "Open Energy Data Initiative" (OEDI) platform. This is an important and interesting phase to explore in the smart grid field. The proposed dataset can be used for benchmarking and comparative studies. We evaluated the proposed dataset using five different machine learning techniques: k-nearest neighbours (KNN), decision trees (DT), random forest (RF), bagging ensemble (BE), and artificial neural networks (ANN) with different evaluation alternatives (mechanisms). Overall, our best empirical results have been recorded to the theft detection-based RF model scoring an improvement in the performance metrics by 10% or more over the other developed models. [ABSTRACT FROM AUTHOR]

The efficient utilization of household electricity is pivotal in the context of rising energy demands and environmental concerns. This study addresses the critical issue of electricity wastage in Chinese households by applying advanced smart meter technology. Aiming to analyze and optimize electricity consumption patterns, the research utilizes the Smart Energy Utilization Model (SEUM) over a period from January 2020 to December 2022, incorporating methodologies such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), Least Squares Support Vector Regression (LS-SVR), and deep learning. The findings reveal that households equipped with smart meters demonstrated a 15 % reduction in overall energy consumption, a 10 % decrease in peak-hour usage, and a notable improvement in energy-saving behaviors among users. Additionally, the integration of smart meters facilitated better load management and contributed to reducing the carbon footprint. The study also found that smart meter data analytics helped in identifying and rectifying inefficient appliances, resulting in a 12 % reduction in energy waste. Moreover, the increased awareness and real-time feedback provided by smart meters led to a 20 % increase in user engagement with energy-saving initiatives. These findings underscore the potential of smart meter technology, enhanced by advanced analytical methods, to enhance energy efficiency and support sustainable development policies. • Smart meters reduced household energy consumption by 15 %. • Peak-hour electricity usage decreased by 10 % with smart meters. • Energy waste reduced by 12 % through smart meter data analytics. • User engagement in energy-saving initiatives increased by 20 %. [ABSTRACT FROM AUTHOR]

Demand response (DR) is the change in electric consumption by prompting consumers to change their normal consumption patterns in response to financial incentives. In various forms of DR, the contract-based DR is a framework to achieve a certain reduction by the contract with individual participants for their reduction. However, in practice, it is inevitable to encounter a default, implying that some of the participants fail contracted reduction owing to an unexpected event. This study develops a method to detect defaulting participants in contract-based DR. The method iterates two steps: estimating the most suspicious participant and inspecting its smart meter. By utilizing block-sparse structures in the detection problem, it can exactly determine the defaulting participants with a small number of inspections. The performance is evaluated by simulation, which indicates that our method has much higher performance than the conventional method in the sense that the number of inspections approximately 96.8% fewer than that of the conventional one. • An algorithm is developed to detect defaulting participants in demand response. • Four special properties of contract-based demand response are extracted. • Four properties are captured as block sparsity of defaults and utilized in detection. • Our method improves detection performance by 96.8% over the conventional method. [ABSTRACT FROM AUTHOR]

• This research proposes a multifactor index system for evaluating the operating state of smart meters. • An improved combinatorial weighting method is used to estimate the weight of factors. • The research concludes that the proposed method can contribute to improve the efficiency of smart meter detection. As an important terminal of smart grid, smart meter has the functions of equipment health monitoring and power metering. However, it is difficult to quantify the impact of factors such as ex-factory parameters, historical failure rates, electromagnetic interference when conducting an operational condition assessment. Therefore, in view of the large number of influencing factors and the difficulty in quantifying the degree of influence, an improved multifactor evaluation model for the operating status of smart meters is constructed. Firstly, a multifactor index system for evaluating the operating state of smart meters is constructed by analyzing the working mechanism and operating characteristics of smart meters. Then, an improved combinatorial weighting method is proposed by combining the subjective weight and the objective weight. The combinatorial weighting method is used to estimate the weight of factors in the health status evaluation of smart meters, and the improved multifactor evaluation method is presented to evaluate the health status of smart meters. Finally, an example is given to verify and analyze the proposed method. Compared with the other three methods, this method can provide effective evaluation results, and help guide targeted maintenance or replacement to improve the efficiency of smart meter detection. [ABSTRACT FROM AUTHOR]

Encrypted data aggregation can effectively achieve the preservation of users' privacy by aggregating electricity consumption data from multiple-source smart meters, and simultaneously reduce the transmission communication overhead in smart grids. Although encryption operations ensure data confidentiality, as smart meters are resource-constrained devices, compromised private key leakage could likewise threaten users' privacy. In this paper, we devise the fine-grained encrypted data aggregation mechanism with fault tolerance by modifying the Lifted EC-ElGamal encryption and symmetric homomorphic encryption, which could guarantee data confidentiality, even if corresponding private key is leaked. In the mechanism, edge server is integrated in smart grids to receive encrypted multidimensional electricity consumption data sent by smart meters, execute integrity checking, generate and forward corresponding verified aggregated data reports to the control center. To ensure the robustness of smart grids due to network interruption or data packet loss from smart meters to the edge server, we exploit the Shamir secret sharing technique to achieve transmission fault tolerance. Besides, dynamic subset adjustment could be utilized in the mechanism, enabling the control center to dynamically adjust users' multidimensional electricity consumption data collection strategy according to the actual situation. We conduct security analysis and performance evaluation demonstrating the feasibility of the mechanism in the secure deployment of edge-assisted smart grids. [ABSTRACT FROM AUTHOR]

Electrical power is continuously monitored and measured in power systems, industrial applications, and control applications like Static VAr control. Traditionally, power analyzers require measuring the phase shift between voltage and current signals to calculate the power factor and subsequently determine the active or real power in an AC power system. This paper proposes a novel and direct approach for real and reactive power measurement using an analog multiplier and phase-controlled switching. Unlike conventional methods, this approach eliminates the need to calculate the phase shift between current and voltage signals or the frequency. The proposed system is straightforward and can be implemented using commercially available components. It demonstrates that active and reactive power can be accurately calculated by selecting suitable phase switching of the current waveform. The real (V c o s ϕ) and reactive (V s i n ϕ) components of voltage signals are computed directly and instantly, without the need for calculating s i n ϕ or c o s ϕ and their product with voltage. This method is derived mathematically, simulated, and experimentally verified, with experimental error found to be less than 2%. The presented system offers several advantages: linearity of output, simplicity of operation, reduced component count, quick responsiveness, and the potential for online measurement. These attributes make the proposed method highly suitable for integration into modern power analyzers, smart meters, renewable energy systems, industrial automation, power quality analyzers, building energy management systems, and microgrids. [Display omitted] • A novel power meter technique based on controlled rectifiers and the analog multiplier is proposed for power measurements. • The final output is just a multiplication of Vsin ϕ and Vcos ϕ with the current signal, I. • The method uses less components and less parameters to measure power directly. • Proposed method is verified through simulation and experimental setup and tested for all types of loads. [ABSTRACT FROM AUTHOR]

Electricity Theft (ET) causes monetary losses for power utilities in the energy sector. It occurs when electricity is consumed without being billed. Several methods are available for automatically detecting ET. Most of these methods evaluate Electricity Consumption (EC) records. However, these methods either have a low Detection Rate (DR) or a high deployment cost with a high False Positive Rate (FPR). Moreover, it is difficult to identify fraudulent consumers based solely on EC records. In addition, owing to data imbalances, such methods prove to be inefficient for classification. To solve the aforementioned problems, we have proposed a combination of various techniques. The first one is the Fastfood Transform, which is used for dimensionality reduction, along with the Time Series Lag Embedded Network (TLENET) neural network, used for classification between honest and dishonest consumers. The second one is the Wavelet Transform used for dimensionality reduction with TLENET, and the third one is the Nyström method used for dimensionality reduction with TLENET. To tackle the risk of high variance that results in overfitting in Deep Learning (DL) models, a Localized Random Affine Shadowsampling (LoRAS) data balancing technique is used. We have employed various data balancing techniques to analyze the performance of our system model. A game theory based approach, SHapley Additive exPlanations (SHAP), is implemented for explaining the output of our deep model. We have used a real-world dataset, referred as the State Grid Corporation of China (SGCC), to perform the simulations. Our model has achieved 94% accuracy, 92% F1-score, 93% Area Under Curve-Receiver Operating Characteristics (AUC-ROC), and 87% Matthews Correlation Coefficient (MCC) with LoRAS, Wavelet Transform, and TLENET. With dimensionality reduction using Fastfood Transform, our model has achieved 93% accuracy, 92% F1-score, 92% AUC-ROC, and 85% MCC. When the Nyström method is employed for dimensionality reduction, our model has achieved 94% accuracy, 92% F1-score, 90% AUC-ROC, and MCC 85%. Extensive experiments indicate that the proposed model outperforms the existing conventional detectors. • Using Fast Food Transform with Time Series Lag Embedded Network (TLENET). • Using Wavelet Transform and Nystroem method with TLENET. • Using LoRAS for tackling the problem of high variance. • Data balancing techniques are applied to solve the class imbalance issue. • A real-world dataset, SGCC, is used to perform simulations. [ABSTRACT FROM AUTHOR]

• This research investigates the smart meter rollout in New Zealand. • A mixed-method approach is used to gather evidence. • The main positive and negative aspects of the rollout are discussed. • An experience that differs from usual European/American rollouts is presented. [ABSTRACT FROM AUTHOR]

• A system-level distribution power system integrating energy source, EV consumers and smart meters is modeled for the EVs charging and discharging scenario, leading a new research perspective for studying EVs charging and discharging with consideration of the vehicle owners' information exchange and interactions in the power system framework. • A two-stage optimization problem with priority for EVs charging and discharging is solved with a hierarchical optimal method, which decompose the problem as high-priority load shifting problem and low-priority cost minimization problem. • A penalty-based consensus algorithm is proposed to solve the cost minimization problem, in which two equational constraints are split and two similar optimization sub-problems which can be solved by a consensus distributed algorithm, and the convergence analysis of the algorithm is given mathematically. In addition, this paper combines the hierarchical algorithm with receding horizon method to deal with a more practical situation, where the EVs arrive and leave randomly. In preparing for the widespread adoption of electric vehicles (EVs), this paper investigates a two-stage optimization problem with priority for EV charging and discharging scheduling, while taking into account power load stability, economic objectives, and technical constraints of the distribution network. To this end, a distribution power system integrating energy source and EV owners is modeled. Considering the disturbance or even loss of the communication link, a periodical connected topology is adopted for information exchange among EV owners with the deployed smart meters, which is substantially different from the existing works. The main challenges of the problem are that the information flow among EV owners is time-varying and asymmetric, and each EV owner's demand should be satisfied throughout the whole scheduling horizon. A novelty distributed hierarchical optimal algorithm, combined with iterative water-filling-based method and penalty-based consensus method, is proposed for the two-stage optimization problem with priority. The convergence and optimality of the algorithm are presented mathematically. In addition, the proposed method combined with receding horizon optimization is performed for EVs random arrival and departure scenarios in the system. Finally, we demonstrate advantages of the proposed algorithm in both theory and simulation. [ABSTRACT FROM AUTHOR]

In residential microgrids, rooftop solar power generation is becoming increasingly common. A new concept in electricity markets, such as peer-to-peer (P2P) auctions, is also emerging. In these markets, consumers and prosumers can exchange locally generated power directly with each other without relying on an intermediary. This approach promotes sustainable development. However, data security is a major concern in energy trading. Consequently, the use of blockchain technology in power markets has become more widespread. Blockchain can facilitate the trading of power from P2P sources. This paper presents the design and algorithms for automating energy trading through smart contracts, utilizing Home Energy Management Systems (HEMS) and Smart Meter Gateways (SM) for prosumers, consumers, and utilities. Simulations across various microgrid setups validated the peer-to-peer (P2P) energy market on the blockchain network. This framework increases profits for prosumers and consumers trading with each other, the necessity of blockchain operators for updating smart contracts and managing participants, the economic impracticality of current high gas costs on Ethereum, and the benefit of a closer bidding range for profit generation in P2P energy markets. [ABSTRACT FROM AUTHOR]

Smart meters are the primary source of energy consumption data in the smart grid network, which can record energy usage with fine granularity. The use of smart meters expands the interaction between the energy supplier and the consumer. Security for smart meters and privacy for the users are, therefore, paramount. The study of smart-meter data, particularly security concerns, is a very active research area. A high-frequency smart meter captures and transmits energy usage data in small bursts (per second or minute). Maintaining a high level of security while processing data in such a short period of time is critical for resource-limited devices like smart meters. To address this issue, this work presents a privacy-preserving protocol for high-frequency smart meters (P3HF) using difference expansion-based reversible watermarking and Paillier homomorphic encryption. The proposed protocol significantly increases the security of high-frequency smart meters by introducing a unique encryption server and using joint watermarking and encryption to protect real-time data transmission. The acquired results, which include experiments conducted on a real hardware platform using Arduino UNO Rev.3, show that the proposed scheme ensures security and user data privacy while consuming a low amount of energy and time for execution. Additionally, a comparative analysis demonstrates that the proposed protocol performs better than earlier research works concerning requirements for data and privacy, resilience to possible attacks, and capacity to overcome their limitations. [ABSTRACT FROM AUTHOR]

In this work, we propose a non-intrusive and training free method to detect behind-the-meter (BTM) electric vehicle (EV) charging events from the data measured by advanced metering infrastructure (AMI) such as smart meters. By leveraging the contextual information of EV charging, we formulate a mixed-integer convex quadratic program (MICQP) to detect EV charging events from customers' daily meter data. No labeled training data or hyperparameter tuning are required, and the MICQP can be efficiently solved. By collecting information about the start time, the charging duration, and the power level of each detected charging event, we infer customers' charging patterns in terms of probabilities of charging profiles through a data-driven approach using one year's meter data. In a numerical case study, we use the proposed approach to extract EV charging events from a test dataset of customers' meter data, and we demonstrate that similar detection accuracy is achieved as that of other learning-based approaches which use high-solution meter data. Finally, impacts of EV charging on the IEEE-8500 test feeder are presented in the case study by using the inferred charging patterns. • A method to detect electric vehicles (EVs) charging from meter data is proposed. • A mixed-integer convex quadratic program is formulated. • EV owners' charging patterns are inferred in terms of probability distributions. • Comparable accuracy is achieved with those of learning-based approaches. • EV charging impacts to distribution networks are studied using the inferred patterns. [ABSTRACT FROM AUTHOR]

Reliable integration and operation of renewable distributed energy resources requires accurate distribution grid models. However, obtaining precise models by field inspection is often prohibitively expensive, given their large scale and the ongoing nature of grid operations. To address this challenge, considerable efforts have been devoted to harnessing abundant consumption data for automatic model inference. The primary result of the paper is that, while the impedance of a line or a network can be estimated without synchronized phase angle measurements in a consistent way, the admittance cannot. Furthermore, a detailed statistical analysis is presented, quantifying the expected estimation errors of four prevalent admittance estimation methods. Such errors constitute fundamental model inference limitations that cannot be resolved with more data. These findings are empirically validated using synthetic data and real measurements from the town of Walenstadt, Switzerland, confirming the theory. The results contribute to our understanding of grid estimation limitations and uncertainties, offering guidance for both practitioners and researchers in the pursuit of more reliable and cost-effective solutions. • Review of the methods for distribution grid identification using smart meter data. • Without phasor measurements, admittance estimation is biased while impedance is not. • Real world experiments confirm the statistical challenges due to the unknown phase. [ABSTRACT FROM AUTHOR]

Unlike transmission systems, distribution systems historically lack enough measurements, making their real-time monitoring almost impossible. Recent deployment of diverse types of devices such as phasor measurement units (PMUs), smart meters, solar inverters and weather information sensors opens up new ways of monitoring these systems, with the assistance of customized machine learning (ML) applications. The paper describes a grid-model-informed machine learning (ML) tool which integrates heterogeneous data streams and creates synchronous measurement snapshots to be used by a hybrid robust state estimator (SE) which provides not only accurate state estimates but also real-time feedback for ML model refinement. Improved monitoring performance due to the use of developed computational framework is experimentally observed by simulated scenarios on an electric utility's distribution system. • A new framework for monitoring distribution systems is proposed. • A robust and scalable state estimator is developed. • A new method that can pinpoint line outages in distribution systems based on sparse PMU measurements. [ABSTRACT FROM AUTHOR]

The ongoing digitalisation of the district heating sector, particularly the installation of smart heat meters (SHMs), is generating data with unprecedented extent and temporal resolution. This data offers potential insights into heat energy use at a large scale, supporting policymakers and district heating utility companies in transforming the building sector. Clustering is crucial for representing this wealth of data in human-understandable groups, necessitating consideration of seasonality. Advancing current research in clustering SHM data, this work applies an established co-clustering approach, FunLBM, considering seasonal variation without fixed season definitions. Furthermore, to enhance the understanding of differentiating factors between clusters, the possibility to understand cluster memberships based on 26 building characteristics was analysed using classification and variable selection methods. Applying FunLBM on a large-scale hourly dataset from single-family houses revealed six well-separated energy use clusters each distributed over six-temporal clusters, which are correlated with the exterior temperature, yet not following fixed seasons. Variable selection and classification showed that building characteristics describing the building with a high level of detail are insufficient to explain cluster membership (Matthew's correlation coefficient (MCC) ≈ 0.3). By merging the energy use clusters based on profile and magnitude similarities, classification performance significantly improved (MCC ≈ 0.5). In both cases, simple and readily available building characteristics yield similar insights to detailed ones, emphasising their cost-effectiveness and practicality. [Display omitted] • Co-clustering of smart heat meter data to establish season-independent clusters. • Analysis of energy use clusters based on 26 building characteristics. • Classification and variable selection to identify the minimum information needed. • Statistical data leads to the same insight as detailed building data. • Prediction of energy use clusters with building characteristics has a low accuracy. [ABSTRACT FROM AUTHOR]

The increasing impacts of human-induced climate change in developing countries have spurred government policies, activism, and sustainability research aimed at reducing energy consumption. Understanding the electricity usage of buildings is crucial to cutting carbon emissions and achieving cost savings. This study addresses the challenge of establishing realistic and relevant energy benchmarks for educational institutions in developing countries, specifically focusing on the Western Cape, South Africa. Schools in this region exhibit significant differences in energy intensities but are currently assessed using the same reference standard. A top-down analysis was performed using descriptive statistics to develop energy performance benchmarks tailored to unique patterns of energy consumption in schools. Data from 31 less affluent schools were collected using smart meters to ensure accuracy. The proposed reference benchmarks, ranging from 12 to 37 kWh/m 2 per year, are significantly lower than the existing 60 kWh/m 2 per year benchmark, demonstrating substantial potential for energy savings. This nuanced benchmarking approach accounts for seasonal and term variations in energy usage, providing a more accurate comparison across schools. The research introduces a novel, context-sensitive benchmarking method that extends beyond existing standards by incorporating these variations. It underscores the importance of localized benchmarks for achieving school energy efficiency, contributing to environmental preservation and financial savings. The proposed benchmarks offer a robust framework for policymakers, standard bureaus, and education departments to craft energy efficiency policies that drive progress in the education sector. By addressing the unique energy usage patterns of schools, this approach facilitates targeted interventions, leading to improved energy management and sustainability. • Smart meter data for 31 schools across affluence spectrum analyzed for the first time. • Energy usage from 25 to 97 MWh per year observed across schools. • Various benchmarks explored to improve on current flat 0 kWh/m 2 benchmark. • Proposed full-year benchmark recommended at 22 kWh/m 2 , 14 out of term and 28 in term. • Percentile benchmarks range from 13 to 37 kWh/m 2 , and clustered from 19 to 29 kWh/m 2. [ABSTRACT FROM AUTHOR]

Due to the scarcity of measurement devices, distribution systems often suffer from unobservability. The recent expansion of smart meters (SMs) provides the means to enable system observability. To ensure continuous situational awareness of distribution grids, strategic planning of advanced metering infrastructure (AMI) is crucial. However, conventional AMI planning approaches only consider the economically optimal placement of communication devices without considering the need for observability even in case of failures. To address this challenge, this paper proposes a wireless AMI network planning framework that guarantees the observability of distribution grids in the face of potential communication failures. Specifically, given the failure scenario, a closed-form observability criterion is formulated and its observability fortification strategy is derived, based on which the AMI network planning problem is formulated as an integer linear programming (ILP) problem. In addition, a heuristic decomposition technique is applied to the ILP problem in order to address the scalability issues of large-size networks. Finally, case studies demonstrate the robustness and effectiveness of the proposed AMI system planning framework. The findings of this work assist distribution utilities in developing a reliable and economical AMI, while providing guaranteed situational awareness of their assets. • A wireless AMI network planning framework to ensure observability of distribution grids against communication failures. • A closed-form observability criterion is formulated to assist the fortification strategy. • The fortification strategy provides communication routes with flexible redundancy. • A heuristic decomposition technique is proposed to address scalability issues of large networks. [ABSTRACT FROM AUTHOR]

With the rise of user energy consumption data as a significant data asset, data privacy has emerged as a critical concern. To address users' diverse attitudes towards data sharing and their varied usage requirements, this paper introduces an adaptive privacy protection framework for user energy data based on dictionary learning and watermarking techniques. Central to this framework is an innovative digital watermark anonymization method designed to meet the dual objectives of encryption and anonymous data sharing. This method employs sparse dictionary decomposition to embed confidential user information within sparse coefficients, significantly enhancing computational efficiency while minimally impacting the integrity of the original data. Additionally, through sparse data representation, the framework achieves effective data compression, addressing the challenges of extensive storage requirements inherent in maintaining original energy consumption data, encryption results, and the data sharing process. Security analysis and case studies demonstrate the proposed method's robustness against eavesdropping, unauthorized access, and other security threats. • A hierarchical privacy-preserving scheme considering diverse use of smart meter data. • An efficient compression and secure sharing method based on cloud storage. • Flexible anonymous control for energy consumption data based on blind watermark. • The adaptive blind watermark method has lower distortion and controllable capacity. • The scheme robustness against eavesdropping and unauthorized access. [ABSTRACT FROM AUTHOR]

Smart grids offer numerous possibilities for developing and executing sustainable and efficient electricity supply chains that exhibit increased resilience to disturbances. The exploration of the Electric Supply Chain Network (ESCND) design problem using smart grids has been limited. This article aims to tackle this challenge through the application of a robust multi-objective optimization approach, encompassing three economic goals (maximizing profit), environmental goals (minimizing greenhouse gas emissions), and resilience goals (maximizing network resilience). Additionally, the optimization process takes into account the dual objectives of maximizing efficiency and minimizing energy loss by incorporating relevant cost considerations. The proposed methodology incorporates distinctive features of smart grids, such as demandside management programs, microgrid structures, bidirectional distribution lines, and consumer–supplier nodes. It addresses various interconnected decisions, including location placement, capacity expansion, load allocation, and pricing. To solve the formulated model, a potent combination of multi-objective optimization methods based on cutting-plane algorithms and AUGMECON2 is introduced. Subsequently, the proposed model and solution approach are applied to a practical case study, and the obtained results are comprehensively examined. The findings suggest that, despite potential contradictions between economic and environmental objectives, the implementation of smart grids can concurrently improve environmental performance and network flexibility. • Strategic deployment of EV charging stations in energy hubs. • Developing LSTM model to forecast load demands and inform decisions. • Deployment plan is optimized using SGD. [ABSTRACT FROM AUTHOR]

Residential water demands vary with a diurnal pattern, and peak hour demands lead to inefficiencies in the operation and management of urban water distribution systems. Peak hourly demands generate significant costs due to the energy requirements of producing and pumping large volumes of potable water. High peak demands also require large investments in infrastructure expansion to support urban growth and economic development. This research develops an agent-based model to assess a demand-side management strategy that shifts high hourly demands. This research envisions a gamification system that rewards players through a leaderboard for shifting water end uses for laundry, irrigation, and bathing from on-peak to off-peak periods. Gamification of demand shifting behaviors is enabled through advanced metering infrastructure, which measures hourly or sub-hourly demands at the account level, and can be analyzed to assess water use behaviors. This research develops an Agent-based Modeling approach based on the Opinion Dynamics theory to calculate the changing opinion of household agents toward shifting water demands, based on utility broadcasts, observations of neighbor water use, social media, and participation in a leaderboard. The model is applied for eight months of hourly demand data collected through smart meters at a participating utility. Results show that a high level of participation in the leaderboard increases the adoption rate of customers shifting the time of specific end uses and may account for up to a 37 % reduction of the peak volume. The diurnal curve is flattened as consumers shift water use from on-peak to off-peak hours. The Agent-Based Model - Opinion Dynamics approach can assist water utilities in designing and evaluating gamification approaches that mitigate peak demands to support water infrastructure management. [ABSTRACT FROM AUTHOR]

• For different types of electricity theft has obvious characteristics. • The proposed method can detect specific types of electricity theft. • Real-time electricity theft detection without historical data. Electricity theft detection is very important for the economic benefits of power companies and the effectiveness of safe operation of power systems. At present, the traditional power theft detection method can only identify whether the user has power theft, but cannot perform rapid and accurate inspections for various types of power theft users. Aiming at the characteristics of medium-voltage users with large power consumption and regular power consumption, this paper proposed a method for detecting the type of power theft in medium-voltage distribution lines based on robust regression and convolutional neural network. Firstly, considering the existence of abnormal data due to factors such as communication delay interruption, a robust regression algorithm is used to reduce its impact and improve the accuracy of regression analysis. Secondly, the correction coefficient and error term of each user obtained by regression are taken as the characteristics of user stealing electricity, and input into the convolutional neural network model for training to complete the identification of stealing electricity type. Finally, the method is verified by simulation and measured data. The results show that under different disturbance conditions, the proposed method can accurately identify different types of power stealing behaviors, which can better assist on-site investigation, narrow the investigation scope and improve the verification rate. [ABSTRACT FROM AUTHOR]

The recent European energy crisis has underscored the need for improvements in the electricity market design, emphasising the necessity for enhanced liquidity, flexibility, and risk-hedging instruments. In addition, the ongoing transformation of power systems with more active participation from the demand-side requires revisions of rates and market designs. As a result, demand response (DR) schemes have started to be deployed over the past ten years in many jurisdictions. The objective of this study is to describe current trends and highlight key differences in DR programs to understand whether the most recent programs have achieved their primary objective of integrating consumers in electricity markets and whether DR has been effective in limiting the recent power crises in Europe. A comparative analysis of demand response (DR) programs is performed in different electricity markets: France, Germany, and the Pennsylvania-New Jersey-Maryland (PJM) Interconnection in the United States. Despite the acknowledged critical role of demand response in the future, a global approach to its integration in electricity markets is still lacking. This jeopardises the full potential of demand-side flexibility and prevents benefits for consumers and the grid. However, recent DR programs have also demonstrated their effectiveness during the 2021–2023 energy crisis, notably in France, despite the long-lasting operational and economic challenges. These findings underscore the importance of further research and policy development in this area. • Extensive review of demand-response (DR) in France, Germany and PJM in the US. • Slow uptake in demand response despite recent smart meter roll-out achievement. • Numerous activations during the 2021–2023 power market crisis in France. • Remaining lack of directionality of DR, focused only on peak-shaving capabilities. • Lack of consistent approach and uncertain cost-effectiveness prevent DR development. [ABSTRACT FROM AUTHOR]

With the advent of integrated sensor technology (smart flow meters and pressure sensors), various new numerical algorithms for leak localization (a core element of water distribution system operation) have been developed. However, there is a lack of theory regarding the limitations of leak localization. In this work, we contribute to the development of such a theory by introducing an example water network structure with parallel pipes that is tractable for analytical treatment. We define the leak localization problem for this structure and show how many sensors and what conditions are needed for the well-posedness of the problem. We present a formula for the leak position as a function of measurements from these sensors. However, we also highlight the risk of finding false but plausible leak positions in the multiple pipes. We try to answer the questions of how and when the leaking pipe can be isolated. In particular, we show that nonlinearities in the pipes' head loss functions are essential for the well-posedness of the isolation problem. We propose procedures to get around the pitfall of multiple plausible leak positions. [ABSTRACT FROM AUTHOR]

This paper presents a novel Iterative Bidirectional Gradient Boosting Model (IBi-GBM) for estimating the baseline of Conservation Voltage Reduction (CVR) programs. In contrast to many existing methods, we treat CVR baseline estimation as a missing data retrieval problem. The approach involves dividing the load and its corresponding temperature profiles into three periods: pre-CVR, CVR, and post-CVR. To restore the missing load profile during the CVR period, the method employs a three-step process. First, a forward-pass GBM is executed using data from the pre-CVR period as inputs. Subsequently, a backward-pass GBM is applied using data from the post-CVR period. The two restored load profiles are reconciled, considering pre-calculated weights derived from forecasting accuracy, and only the leftmost and rightmost points are retained. The newly restored points are then included as inputs for the subsequent iteration. This iterative procedure continues until the original load data in the CVR period is fully restored. We develop IBi-GBM using actual smart meter and Supervisory Control and Data Acquisition (SCADA) data. Our results demonstrate that IBi-GBM exhibits robust performance across various data resolutions and in different seasons and outperforms existing methods by achieving a 1%–2% reduction in normalized Root Mean Square Error (nRMSE). • Propose an Iterative Bidirectional Gradient Boosting Model for CVR baseline estimation. • The methodology includes selecting similar days, training the GB model, and generating the baseline. • Illustrate the impact of diverse data sources on CVR baseline recovery using bottom-up and top-down methods at different resolutions. [ABSTRACT FROM AUTHOR]

Smart meters, being a vital component in the advanced metering infrastructure (AMI), provide an opportunity to remotely monitor and control power usage and act like a bridge between customers and utilities. The installation of millions of smart meters in the power grid is a step forward towards a green transition. However, it also constitutes a massive cybersecurity vulnerability. Cyberattacks on AMI can result in inaccurate billing, energy theft, service disruptions, privacy breaches, network vulnerabilities, and malware distribution. Thus, utility companies should implement robust cyber-security measures to mitigate such risks. In order to assess the impact of cybersecurity breaches on AMI, this paper presents a cyber-attack scenario on grid measurements obtained via smart meters and assesses the stochastic grid estimations under attack. This paper also presents an efficient method for the detection and identification of anomalous data within the power grid by leveraging the distance between measurements and the confidence ellipse centered around the estimated value. To assess the proposed method, a comparative analysis is done against the chi-square test for detection and the largest normalized distribution test for the identification of bad data. Furthermore, by using a Danish low-voltage grid as a base case, this paper introduces two test cases to evaluate the performance of the proposed method under single and multiple-node cyber-attacks on the grid state estimation. Results show a notable improvement in accuracy when using the proposed method. Additionally, based on these numerical results, protective countermeasures are presented for the grid. [ABSTRACT FROM AUTHOR]

Electricity consumption has been intensifying due to population growth, climate change, urbanization, and the growing use of electronic devices, which are increasingly non-linear loads that cause poor power quality conditions. The trend of the Internet of Things has led to the creation of devices that encourage the efficient and effective utilization of electrical power. This in turn facilitates the development of modern power distribution structures such as smart grids. Consequently, this paper presents in detail the design, construction, and validation of a three-phase IoT smart meter intended to form part of the end-user demand side of a smart grid. The compact embedded system, with a manufacturing cost below $80 USD, features a unique electronic design that enables its installation in any load center and employs a straightforward IoT structure that includes WiFi technology for Internet communication. Also, a deployed web application was developed specifically to display the smart meter measurements. Unlike other smart meters, the proposed meter not only provides the amount of active energy consumption, but total and fundamental RMS current and voltage, active, reactive, and apparent power, reactive energy, power factor, and some power quality parameters such as, line frequency, amplitude of 64 current harmonics, and total harmonic distortion. Additionally, this study shows that the prototype achieves an absolute error of less than 1% in all its measurements. Finally, real-life applications of the developed device are demonstrated in residential environments. • A smart meter embedded in a light IoT platform that sends data directly to the cloud. • Original electronic design for multiple 3-phase parameters without invasive tools. • Smart meter validated using different loads and tested in real-life environments. [ABSTRACT FROM AUTHOR]

Priority-based energy scheduling is proposed, whereby the data utility of measured user information in smart meters, including the priority and power demand of power consumers and the maximum power supply of power suppliers, is leveraged to ensure that in the case of limited power resources within islanded microgrids, energy is allocated to power consumers in the order of high to low priority. Nonetheless, in terms of data utility, most existing strategies only consider high-priority power consumers, neglecting the priority of power suppliers and low-priority power consumers, thus damaging the fairness of low-priority consumers and the interests of power suppliers, to the detriment of these groups. Moreover, smart meters are vulnerable to data integrity attacks, which may result in the manipulation of measured user information during the forwarding process, thereby disrupting normal priority-based energy scheduling. Therefore, focusing on data utility without considering data security is insufficient. Researchers have extensively investigated how to secure smart meters within advanced metering infrastructure (AMI), primarily employing cryptography-based methods to encrypt user information in measured meters and decrypt it at the microgrid central controller (MGCC), thereby protecting the data confidentiality of user information during the forwarding process and preventing data tampering. However, the prevailing cryptographic methods used to secure smart meters are extremely complex, and make smart meters and local controllers on the forwarding path unable to obtain any user information in which embedded consumers' priority and cannot leverage the data utility of priority to forward information accordingly. In other words, existing models cannot simultaneously achieve data confidentiality and data utility during the forwarding process. To solve these issues, this study investigates a Lightweight_PAEKS-based energy scheduling model considering priority in microgrid (LPESCP). As a lightweight solution, the LPESCP comprehensively considers data confidentiality and data utility, as well as the priority and fairness of all users, to ensure the security of smart meters and optimize energy scheduling. In particular, the data utility problem is solved by using an optimization model that aims to maximize the global satisfaction degree of all users in terms of priority and fairness. The Lightweight_PAEKS and Paillier encryption scheme are used so that nodes on the forwarding path can successfully match priority-related keyword and forward information in order of the keyword corresponding emergency coefficient indicating the urgency levels of information, ensuring data utility, while do not know the specific keyword, emergency coefficient and other information, ensuring data confidentiality. To verify the effectiveness of the LPESCP , experiments are conducted for three cases generated based on random numbers. The results show that the LPESCP model can effectively ensure energy supply to consumers, comprehensively considering data confidentiality and utility as well as priority and fairness of all users. In addition, the global satisfaction degree and time overhead are introduced as metrics to verify that the LPESCP strategy is more effective in providing greater global satisfaction degree and lower time overhead than existing priority-based energy scheduling models. [ABSTRACT FROM AUTHOR]

The smart grid is an emerging power system that can realize the information exchange between users and power companies in two-way communication and adjust power companies' power supply according to the real-time power requests from smart meters on the users' side. However, since malicious attackers can eavesdrop on two-way communication to collect and transmit information, they may leak users' information. Therefore, this paper proposes a new certificateless blind signature (CLBS) scheme with batch verification function and improves a power request system model for the smart grid to protect users' privacy. In addition, the proposed CLBS scheme simplifies the certificate management process in the traditional public key cryptosystem and eliminates the hidden key escrow in the identity-based public key cryptosystem. The unforgeability of the scheme is proved through the random oracle model, and the scheme's security can be reduced to the intractability of the elliptic curve discrete logarithm problem (ECDLP). The analysis results show that our scheme does not use the time-consuming bilinear pairing operation, and has obvious advantages in computing performance compared with the existing signature schemes. [ABSTRACT FROM AUTHOR]

• Rigorous study of optimization to the smart meter technology for thermal building modeling based on thermal-electric analogy. • Model is formulated in terms of a random ordinary differential equation. • A general function space setting for the model and its discretization is developed for uncertainty quantification. • A novel data-driven algorithm for simulating the temperature of a residential building is presented. • The performance of the algorithm is validated on real data from two residential buildings. This work studies the thermal behavior of residential buildings by using the data provided by smart thermostats and weather forecast data. We consider an equivalent circuit model depending on four parameters related to the heater power, the solar energy, heat capacity, and the thermal resistance of the building. We employ a random ordinary differential equation to overcome the natural model uncertainty. We develop a differential equation constrained least-squares-based parameter identification approach for uncertainty quantification. We provide results related to the analytic properties of the parameter-to-solution map involving a derivative characterization. Furthermore, using a discretization scheme adapted to the given data, we derive discrete formulas for the deterministic identification model and compute the statistical moments of the random model. Following a machine learning approach, we propose an algorithm that consists of three phases. The first is a training phase where we identify the parameter uncertainties on a training dataset. In the second phase, we establish a normal distribution of the parameters using these uncertainties. In the final phase, we simulate the temperature on a test dataset by solving the random model. We test this algorithm on real data from two residential buildings. The detailed numerical experiments show the feasibility and the efficacy of the developed framework. [ABSTRACT FROM AUTHOR]

While it has been concluded that climate change poses a significant threat to worldwide supply of freshwater resources, it is unclear if and how demand for water would also be affected. To fill this knowledge gap, we leverage on 'big data' collected using smart water meters from over 40,000 Chinese urban households, spanning nine years and ten provinces to examine the relationship between daily household water usage and climate variability. At the baseline, we find that municipal water is not only a coping mechanism for heat, but its usage is accelerated during heatwave events. Heterogeneity analyses reveal that households from lower-valued properties are more likely to substitute water for electricity to counter heat. Importantly, we find evidence of adaptation behaviors where over time, households are using increasingly more water to cope with high-temperature days. In all, after feeding our results into climate projection models, it is estimated that household water usage will increase by around 7–44% in the long-term (2080–2099) under emissions scenarios of SSP245 and SSP370. Our findings are especially relevant for water-scarce countries such as China as well as developing countries where water is a cheaper and more accessible resource to cope with heat. • Households in China use water to cope with heat, with higher usage during heatwaves. • Those from lower-valued properties are more reliant on water as a coping mechanism. • Water is likely a substitute for electricity in coping with heat. • Future household water usage will increase by 7%−44% under climate change scenarios. [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]

Air conditioning (AC) is an important tool for combatting the adverse health effects of heat, but its use can also drive surges of high demand for electricity. To better understand these effects, there is a need for non-intrusive methods of estimating AC access and operation. In this study, we use a novel methodology to identify residential AC ownership rates using smart meter data from 200,000 customers in Southern California, and find that 79 % of all customers in the region have AC. In contrast to previous methods, we classify AC ownership using hourly, rather than daily, electricity consumption records and directly account for the potential presence of electric heating. We then adapt and apply an algorithm to determine in which hours these households operate their AC. We estimate that the average customer runs their AC during 8.3 % of all hours in the two-year study period, but census-tract level averages range from 1 to 23 % of all hours. Lastly, we combine our estimates of AC ownership and use to analyze cooling behavior spatially and temporally, and are able to identify pockets of high cooling demand, areas lacking in access or the ability to use their AC, and potential targets for cooling-related DR programs. • Three-part methodology to characterize patterns of residential AC ownership and use. • Smart meter dataset of ∼200,000 households in Southern California. • Estimated AC Ownership Rate of 79 % and average AC Operation rate of 8.3 %. • Average AC Operation Rate ranges from 1 to 23 % of total hours across census tracts. • Evaluate energy insecurity using Net AC Utilization, a function of ownership & use. [ABSTRACT FROM AUTHOR]

The gas crisis of 2022 put pressure on electricity prices in Europe, prompting the Swiss government to launch a national energy-saving campaign. To effectively quantify potential savings and guide timely decision-making, this campaign called for rigorous near-real-time modeling of changes in electricity consumption habits. The proposed approach estimates national electricity consumption at an hourly resolution across three consumer categories using thousands of streamed smart-meter load curves. These curves are aggregated to produce a national consumption estimate using scaling factors that account for differences among Swiss distributors. These factors are derived by regressing historical annual consumption against public socio-economic variables. The obtained national load curve is adjusted for the influence of weather conditions, the calendar and global trends, in order to compare different periods with a reference scenario. Such external effects are modeled with splines using Generalized Additive Models, trained on a 5-year dataset, to precisely measure each contribution on the national consumption and evaluate the consumers' response to the saving plan. The results indicate a reduction of approximately 4.8% of the adjusted electricity consumption during winter 2022–2023, equivalent to an average monthly savings of 246 GWh, distributed across residential, service, and industrial sectors. • Swiss electricity demand is estimated from load curves and public socioeconomic data. • A 4.8% energy saving in winter 2022 is observed compared to the pre-COVID-19 period. • Model reveals differences in the savings behavior of three consumer groups over time. • The method enables real-time monitoring and savings opportunities for practitioners. • The scaling method can be easily adapted for use in different regions or countries. [ABSTRACT FROM AUTHOR]

The power consumption data from residential households collected by smart meters exhibit a diverse pattern temporally and among themselves. It is challenging to distinguish between regular consumer behavior and injected falsified measurements into the data stream with the intent of energy theft or compromising the security of the associated measurement infrastructure. This work identifies the challenges of detecting falsified measurements in smart meter data aggregated at geographically hierarchical levels and proposes a novel graph attention network (GAT)-based unsupervised learning framework to detect false data injection attacks (FDIA) from the moving statistics of the power consumption data in real-time, namely MOVSTAT-GAT. The proposed technique is capable of detecting falsified measurements at both 9-digit and 5-digit ZIP code labels in an unsupervised manner, solely from smart meter power consumption data with no additional meters. Moreover, the proposed technique offers a visualization technique to assist the operator in identifying the localization characteristics of the attack and proposes an automated localization strategy for localized FDIAs. Experiments suggest the effectiveness of the proposed framework, especially for localized FDIA or external anomalies, such as power outages and denial-of-service (DoS). Additionally, a detailed discussion regarding the implementation of MOVSTAT-GAT in the industrial environment has been provided. • Analyzes spatial–temporal aspects of smart meter power consumption data. • Models energy theft, power outages, and meter DOS under the FDIA framework. • Proposed novel MOVSTAT-GAT technique excels in unsupervised FDIA detection. • Enables localization of clustered attacks. • Provides a visualization tool to detect and localize FDIAs for operators. [ABSTRACT FROM AUTHOR]

With increasingly prevalence of distributed renewable energy sources, Peer-to-Peer (P2P) energy trading has become an active research direction. This study explores the role of the participants' Socio-Demographic Characteristics (SDCs) in the decision-making process P2P energy trading by proposing a personalized P2P energy trading system. The system periodically collects the participants' bids and pair energy sellers and buyers to form transactions. An attention-based SDC inference system is developed, which identifies a participant's SDCs from the on-site historical smart meter readings. Followed by this, the system analyzes the importance of energy buyers' demands based on their SDCs, and an alternative current network constrained P2P energy market clearing model is formulated to maximize the participant population's social warfare by considering their energy demand importance and economic benefits. Simulations based on real-world datasets are conducted to validate the proposed system. • To propose a new Socio-Demographic Characteristic Inference (SDCI) system, which enables the market operator to understand the participants' social-demographic characteristics from the power consumption data collected by smart meters deployed in the participants' sites. • To propose a new P2P energy trading system, which periodically performs market clearing to form energy trading transactions by considering the participants' socio-demographic characteristics. • A market clearing model is established for maximizing the social welfare of the participants while satisfying the Alternative Current (AC) constraints of the underlying power distribution network. [ABSTRACT FROM AUTHOR]

Smart grids are proposed to enable the integration of renewables and facilitate the energy transition. Households have been pointed out as a significant resource for demand response, that is to adapt their electricity consumption based on the status in the grid. This article analyses narrative mismatches in the context of smart grid implementation in Sweden. We compare policy narratives on the role of homes in the future energy system with home personas, emerging from interviews with households. The policy narratives envision households to become either actively engaged in time-shifting motivated by information and incentives, or bypassed through automation. The home personas, although seemingly similar, show great diversity, being well informed about their electricity use, concerned regarding the safety of technology, preferring to manage flexibility themselves, and reluctant to give up control. Several dissonances are identified between narratives and the home personas regarding smart meter communication, energy awareness, trust, agency, and control, that need further attention for demand response to be realised. The analysis illustrates how policy visions of the home in the future grid would encounter severe challenges in living up to values and characteristics of real households. Policy thus needs to acknowledge households as a diverse group to ensure a sustainable and democratic energy transition. We encourage the use of home personas to substantiate this diversity. • We create two future narratives based on Swedish policy documents emphasising the active consumer and the automated future. • Based on interviews with Swedish households, we present four home personas to illustrate some diversity of households • When situating home personas against narratives, we discover mismatches of sustainability, agency and participation [ABSTRACT FROM AUTHOR]

Understanding how and when residential electricity is used throughout the day is integral to the successful implementation of potential residential demand management strategies. Our analysis characterizes the daily hourly load profiles of approximately 160,000 residential electricity customers across the Southern California Edison (SCE) service area during the period spanning 2015 to 2016 and 2018 to 2019 across weekends, weekdays, seasons, and climate zones. We find that total daily electricity usage was highest in the hottest months of the year compared to milder months, particularly for households located in the hottest climate zones. The most energy-consumptive hours occurred during the mid-afternoon during the hottest months, in contrast to early evening high consumption in cooler months. We find that customers with average daily consumption at or above the 80th percentile cumulatively consume over 40% of electricity during the hottest months of the year residential load, while the bottom half of customers cumulatively consume <25% of the total residential load. The disparities in electricity usage across SCE households are higher in the mid-day, especially in milder months across all regions, and in mild climate zones compared to hotter climate zones since loads are not as dependent on high HVAC loads. • Hourly smart-meter data of 160,000 homes were used to construct diurnal load profiles. • Disparities in hourly loads between households were measured using the Gini index. • The top 20% of households consumed about 40% of the daily electricity load. • The bottom half of consumers used <25% of the daily electricity load. • Customers in mild climates show less seasonal variability than those in hot climates. [ABSTRACT FROM AUTHOR]

This paper aims to estimate the three-phase voltage sensitivity matrix and the network topology of a low-voltage distribution network from smart meter data, which measures voltage magnitude, current magnitude, and power factor with a lead/lag indicator. The targeted networks are three-phase networks with single-phase loads. Understanding network sensitivity and topology is crucial for fault detection, unmetered load identification, and addressing Dynamic Operating Envelope problems. The problem is formulated as a constrained optimization problem to estimate both the three-phase voltage sensitivity and the low-voltage transformer voltage. The estimated voltage sensitivity is used to further identify the network topology, by implementing an enhanced Recursive-Grouping and Backtracking algorithm, as well as a candidate topology selection technique. The proposed method is tested on the 55-node European feeder and several synthetic networks. Compared to the state-of-the-art, the results show a four-fold improvement in the accuracy of voltage sensitivity estimation and substantially fewer mistakes in the topology estimates. The result underscores the efficacy of a three-phase network model and voltage angle approximation in enhancing estimation accuracy. • Estimating voltage sensitivity to current of low-voltage distribution network. • Estimation uses smart meter measurements only. • Transformer voltage is estimated simultaneously. • Approximating unknown phase angle and considering coupling effect on neutral line. • A fast Recursive-Grouping and Backtracking algorithm to find topology. [ABSTRACT FROM AUTHOR]

In smart home services, non-intrusive load monitoring (NILM) can reveal individual appliances' power consumption from the aggregate power and requires only one measurement point at the entrance by a smart meter. Most of the existing load disaggregation methods are based on deep and complex neural networks, and excessively long input sequences could increase the model disaggregation time. Meanwhile, constructing representative features and designing effective disaggregation model is becoming increasingly important. Therefore, we utilize a gramian summation difference angular field (GASDF) image, taking any two power sample points' temporal correlations as input to our baseline model, to better recognize different appliances from the aggregate power sequence. Then, since GASDF could not provide statistical characteristics, we further build the expert feature encoder (EFE) to realize the multi-dimensional representation of power by encoding both current aggregate power and statistical characteristics from historical data as prior knowledge. Afterwards, a batch-normalization (BN)-based normalization fusion (NF) method is proposed to lower the disaggregation error incurred by the distribution difference between GASDF and prior knowledge. Finally, to verify the proposed method's effectiveness, named ApplianceFilter, experiments are conducted on the UK-DALE and REDD data, showing that load disaggregation is improved using prior knowledge fusion, superior to the existing end-to-end neural network model. • A prior knowledge idea is introduced into the proposed baseline model to improve the effectiveness of the disaggregation model. • Proposed prior knowledge to provide spatial and correlation features different from those extracted from GASDF images. • Proposed a fusion method to lower disaggregation error caused by distribution difference between the GASDF feature map and expert feature. [ABSTRACT FROM AUTHOR]

• To reduce the impact of outliers on the reliability assessment of electric metering devices (EMDs), a novel bi-directional (BD) outlier detection approach is proposed, which employs MCD robust analysis and Thompson tau to identify horizontal and longitudinal outliers in the measurement error (ME) observations, respectively. The BD detection method can effectively avoid misdetections and omissions. • Then, a Wiene-based multi-stress fusion nonlinear degradation (MFND) model is constructed to establish the relationship between multiple stresses and the ME data. It can provide future input values for environmental stresses according to the characteristics of the environmental stress fusion term. The fluctuation trend of ME under time-varying stresses can be effectively tracked using MFND, and its parameters can be greatly interpreted via affine invariant ensemble MCMC (GWMCMC). • A reliability assessment framework that can characterize the dynamic performance of EMDs is proposed by combining the BD outlier detection method and the MFND model, which can accurately predict the trend of ME data. Then, the reliability of batch devices is assessed based on the predicted pseudo-life, and recommendations are made for maintenance intervals. Finally, we conduct extensive experimental tests using ME data from smart meters, and the results demonstrate that our framework possesses exceptional predictive capability in real-world harsh environments, affirming its effectiveness. Reliability assessment for electric metering devices (EMDs), which includes environmental stress analysis, measurement error (ME) prediction, and reliability estimation, can be utilized for predictive instrument maintenance, especially under harsh environmental stresses. Nevertheless, the actual evaluation process is limited by data noise and the unavailability of future environmental inputs for the degradation model. To this end, we first extract the main environmental factors affecting ME and then fuse them using the weighted principal component analysis (WPCA) method to provide future environmental input values for the prediction model. Next, a bi-directional (BD) outlier detection method based on MCD robust analysis and the Thompson tau method is proposed to detect outliers from horizontal and longitudinal perspectives. Then, an ME prediction method called multi-stress fusion nonlinear degradation (MFND) is put forward, which considers the cyclic variation of ME over time and the effect of environmental stresses on ME. Real-world datasets from a high-dry-hot area manifest that our proposed BD-based MFND reliability assessment framework enjoys pleasurable prediction performance. Compared to some well- known methods, our framework excels in outlier detection and ME prediction. [ABSTRACT FROM AUTHOR]

The paper presents an innovative approach to identifying voltage fluctuation sources in power networks, also considering the localization understood as the indication of supply points of disturbing loads. The presented approach considers disturbance sources that change their operating state with a frequency higher than the power frequency. The implementation of the proposed solution is also proposed in such a way that its implementation in the smart meter infrastructure allows automatic localization of disturbance sources without additional expert knowledge. In the proposed approach, the modulation signal is estimated using a carrier signal estimator, which allows the estimation of a modulation signal with a frequency higher than the power frequency. The estimated modulating signal is decomposed into component signals associated with individual disturbing loads by decomposition by approximation using pulse waves. The decomposition process allows for the estimation of selected parameters associated with disturbing loads, on the basis of which the assessment of propagation of voltage fluctuations associated with the impact of individual disturbance sources is performed, which allows for the indication of their supply point. The proposed approach was verified in numerical simulation studies using MATLAB/SIMULINK and in experimental studies carried out in a real low-voltage power grid. • A new method using effective demodulation and decomposition techniques allowing identification of disturbing loads in power networks. • The proposed approach allows the identification of disturbing loads that change their operating state with a frequency higher than the power frequency. • The proposed approach allows the identification of disturbing loads that have comparable changes in their operating state. • A verification of the approach and comparison with other methods using data that considers actual conditions occurring in modern power networks. • A possibility of automatic identification of sources of voltage fluctuations without additional expert knowledge. [ABSTRACT FROM AUTHOR]

Smart Water Metering Networks (SWMNs) stand as pivotal infrastructure, crucial for communities and industries. The escalating value of water resources due to climate change and overexploitation underscores the urgency of optimizing these networks for efficiency and resilience. This study focuses on identifying anomalies within SWMNs to address challenges impeding efficient water resource management. Leveraging a comprehensive 72-month dataset from Windhoek, Namibia, this research employs a meticulous analytical approach to unveil diverse anomaly types prevalent within SWMNs. Anomalies, including irregular consumption patterns, leakages, and inaccurate meters, contribute significantly to both apparent and real losses. By scrutinizing this dataset, the study reveals nuanced anomaly patterns like persistent zero consumption and unexpected fluctuations, highlighting the pervasive nature of these issues within the network. The findings not only shed light on these multifaceted anomalies but also lay the groundwork for future advancements in machine learning-based anomaly detection techniques. This research holds promise beyond academia, offering practical implications for water utility management. Identifying and understanding these anomalies serves as a stepping stone toward developing robust detection systems, ultimately fostering heightened efficiency and resilience in water networks. This study serves as a catalyst for strategic improvements, enabling more sustainable and efficient utilization of water resources amidst evolving environmental challenges. • SWMNs are vital for managing water efficiently. • Climate change intensifies the need for network optimization. • This study identifies prevalent anomalies within SWMNs. • Anomalies contribute to substantial water losses. • Findings pave the way for better anomaly detection techniques. [ABSTRACT FROM AUTHOR]

The digitization of the water sector has led to the emergence of Smart Water Metering Networks (SWMNs), which enable automated and continuous water consumption measurement. However, challenges persist in efficiently managing and transmitting the vast amount of data generated by these networks. To address this, researchers have proposed anomaly detection techniques to identify and detect anomalies within SWMNs. In the realm of anomaly detection, a substantial body of research has emerged. However, there is a notable gap in the literature on a comprehensive synthesis of Machine Learning (ML) applications for anomaly detection in SWMNs. To bridge this knowledge gap, this study evaluated thirty-two research papers written between 2016 and 2023, focusing on ML applications for anomaly detection in smart water systems, smart water grids, water distribution networks, and water networks. From an initial pool of 725 research papers, those directly related to ML-based anomaly detection techniques were selected and analysed using both quantitative and qualitative data analysis. The study revealed that ML techniques such as k-Nearest Neighbors (kNN), Autoencoders, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Random Forest, Decision Trees, and Support Vector Machines (SVM) have made significant contributions to anomaly detection within these networks. Notably, researchers frequently employed multiple ML techniques to enhance accuracy. Performance metrics analysis demonstrated that F1 score, precision, accuracy, and recall were commonly used to assess the quality of ML anomaly detection techniques. This review aims to provide researchers with recommendations for selecting suitable ML anomaly detection techniques in SWMNs. The findings underscore the promising nature of ML applications for anomaly detection in SWMNs and highlight the need for further research in this area. • Water networks use Cyber Physical Systems, leveraging adaptability for benefits. • Machine Learning (ML) detects diverse anomalies in Water Networks effectively. • Data availability crucial for robust ML anomaly detection in Water Networks. • ML techniques enable real-time anomaly detection, identifying Water Network issues. • Research on ML anomaly detection in SWMNs lacks systematic literature review. [ABSTRACT FROM AUTHOR]