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The increasing threat of cyber-attacks on modern power systems highlights the need for a comprehensive examination through simulations. This study conducts an in-depth simulation of cyber-attacks on critical smart grid components, including smart meters, substation automation, and battery management systems, to expose and analyze potential disruptions to power system operations. We identify vulnerabilities that can lead to severe grid instabilities, such as voltage variations, system collapses, and inverter failures. Our analysis underscores the complex interactions between cyber threats and grid components, revealing how disruptions extend beyond mere load interruptions to affect the core infrastructure. We advocate for integrating established cybersecurity frameworks like NIST, ISO/IEC 27001, and IEC 62443, essential in fortifying grid stability against these dynamic threats. Our findings highlight the urgent need for continuous adaptation and enforcement of these frameworks to enhance resilience and ensure the reliability of modern power grids against cyber-attacks. [ABSTRACT FROM AUTHOR]

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model's precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model's predictive capabilities. Comparing the model's predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach. [ABSTRACT FROM AUTHOR]

Smart grids integrate information technology, decision support systems, communication networks, and sensing technologies. All these components cooperate to facilitate dynamic power adjustments based on received client consumption reports. Although this brings forth energy efficiency, the transmission of sensitive data over the public internet exposes these networks to numerous attacks. To this end, numerous security solutions have been presented recently. Most of these techniques deploy conventional cryptographic systems such as public key infrastructure, blockchains, and physically unclonable functions that have either performance or security issues. In this paper, a fairly efficient authentication scheme is developed and analyzed. Its formal security analysis is carried out using the Burrows–Abadi–Needham (BAN) logic, which shows that the session key negotiated is provably secure. We also execute a semantic security analysis of this protocol to demonstrate that it can resist typical smart grid attacks such as privileged insider, guessing, eavesdropping, and ephemeral secret leakages. Moreover, it has the lowest amount of computation costs and relatively lower communication overheads as well as storage costs. [ABSTRACT FROM AUTHOR]

Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers' privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors' parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers' privacy. [ABSTRACT FROM AUTHOR]

Electricity theft presents a significant financial burden to utility companies globally, amounting to trillions of dollars annually. This pressing issue underscores the need for transformative measures within the electrical grid. Accordingly, our study explores the integration of block chain technology into smart grids to combat electricity theft, improve grid efficiency, and facilitate renewable energy integration. Block chain's core principles of decentralization, transparency, and immutability align seamlessly with the objectives of modernizing power systems and securing transactions within the electricity grid. However, as smart grids advance, they also become more vulnerable to attacks, particularly from smart meters, compared to traditional mechanical meters. Our research aims to introduce an advanced approach to identifying energy theft while prioritizing user privacy, a critical aspect often neglected in existing methodologies that mandate the disclosure of sensitive user data. To achieve this goal, we introduce three distributed algorithms: lower–upper decomposition (LUD), lower–upper decomposition with partial pivoting (LUDP), and optimized LUD composition (OLUD), tailored specifically for peer-to-peer (P2P) computing in smart grids. These algorithms are meticulously crafted to solve linear systems of equations and calculate users' "honesty coefficients," providing a robust mechanism for detecting fraudulent activities. Through extensive simulations, we showcase the efficiency and accuracy of our algorithms in identifying deceitful users while safeguarding data confidentiality. This innovative approach not only bolsters the security of smart grids against energy theft, but also addresses privacy and security concerns inherent in conventional energy-theft detection methods. [ABSTRACT FROM AUTHOR]

Electric power systems face heightened risks from climate change, on top of existing challenges like aging infrastructure, regulatory shifts, and cybersecurity threats. This paper explores how advanced technologies, including smart grids, artificial intelligence (AI), and machine learning, (ML), enhance the resilience of power systems against climate-driven extreme weather events. Drawing insights from resilience theory, the paper presents a state-of-the-art review of the literature on power system resilience, highlighting the escalating vulnerabilities of energy systems to weather-related disruptions. Although utilities currently use technologies like automated meter reading and advanced metering infrastructure to collect vital grid performance data, the lack of strategic collaboration often impedes effective data governance and sharing, thus undermining efficient responses to climate threats. The paper underscores the significance of distributed energy resources, long-duration energy storage, microgrids, and demand-side management. It further illustrates how AI and ML optimize smart grids to support these strategies. Proactive integration of smart grids with advanced technologies could significantly reduce climate-related costs compared to non-adaptive methods. Such proactive grid resilience strategies not only climate-proof energy infrastructure against climatic changes but also herald a modern, placed-based industrial transformation. Climate change exacerbates challenges in our energy systems, from aging infrastructure and a constantly shifting regulatory environment to cybersecurity risks and diversifying energy portfolios. Addressing these issues requires strategic investment in modern infrastructure, particularly smart grids enhanced by advanced technologies like artificial intelligence (AI) and machine learning (ML). These technologies are vital for enhancing power system resilience against climate impacts. Automated systems such as automated meter infrastructure (AMI) and supervisory control and data acquisition (SCADA) provide real-time data crucial for managing extreme weather events. AI and ML contribute to predictive maintenance, preventing failures and blackouts. They also forecast grid loads during severe weather, facilitating proactive power distribution management to prevent blackouts. This comprehensive improvement in situational awareness promotes economic growth in the energy sector and supports sustainable, climate-resilient transformation. AI and ML not only improve energy distribution and efficiency but also promote conservation efforts and ensure reliable energy amidst a changing climate. Collaboration among utility managers, regulators, and governments is key, focusing on data access, verification, and adaptability. Strategies should be tailored to each utility's unique challenges. Moreover, establishing technical standards is critical for enhancing power grid resilience against climate-induced extreme weather events. [ABSTRACT FROM AUTHOR]

Purpose. To provide a comparative and comprehensive analysis of the smart grid projects funded by the H2020 ENERGY and FP7-ENERGY programs. Methodology. As part of the text analysis, the authors evaluated smart grid projects’ results in a sample using text mining methods. Based on statistical analysis and concept-based method, the most significant outcomes of smart grid projects were identified. Findings. A detailed review of the results shows that project teams of the H2020 ENERGY and FP7-ENERGY programs mostly relied on the existing experience which helped to form further development for standardization of tools, conduct planning, or derive specific management actions aimed at smart energy consumption. The majority of these solutions were applied for digitalizing small commercial consumers and for integrating isolated renewable sources in the most effective way. The projects considered the possibilities of electric vehicles used to solve environmental problems and balancing unstable electricity production from renewable sources with Li-ion stationary batteries, tools for effective interaction of users of smart grids, and integration of isolated renewable sources in centralized energy networks. Originality. Based on statistical and machine analysis, the most significant results of smart grid projects were identified. N­grams of expressed keywords used in the texts of project results were used to present and visualize the textual description of smart grid projects. Practical value. The results might be helpful for the European policymakers and scientific advisers seeking to further promote and ameliorate the pan-European energy system [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has recently become one of the fast‐growing technologies in the world. Integrating existing systems with smart grids still represents a significant challenge arising from the massive cost of upgrading the infrastructure. This paper provides an IoT infrastructure solution based on a newly designed low‐cost microcontroller‐based IoT remote terminal unit (RTU) to integrate new, old, and conventional sites of existing grids with smart grids. The IoT RTU supports Wi‐Fi communication and includes digital/analog inputs and outputs besides the advantage of serial data transmission standard RS‐485 as an industrial field communication (IFC). The Modbus communication protocol is utilized as a serial industrial protocol that can be interfaced over the RS‐485 standard. The advantage of having an IFC protocol integrated into the IoT infrastructure enables to link any supported device and publish the required data to the cloud, besides the ability to add smart metering technologies, which allows Advanced Metering Infrastructure (AMI) in these areas. IoT‐enabled Supervisory Control and Data Acquisition (SCADA) system is integrated with the proposed IoT infrastructure for its advantages of remote monitoring and control. The proposed IoT infrastructure is characterized by proper data security and low power consumption. The IoT infrastructure is simulated with an existing real application for proof of concept, and the results are discussed for different case studies. [ABSTRACT FROM AUTHOR]

This paper presents an integrated methodology for decision making in smart grid investments that assesses the investment plans of stakeholders in local energy communities (LECs). Considering the energy flow exchanges of the LECs and interpreting them in terms of technical benefits and costs, this methodology indicates the most sustainable and profitable solution covering the LEC energy transition plans. A set of specialized tools capturing the energy, environmental, financial, and social impacts are integrated under a common platform called the IANOS Energy Planning and Transition (IEPT) suite. The tools evaluate a set of well-defined key performance indicators that are gathered using a cost–benefit analysis (CBA) module offering multilateral assessment. By upgrading the functionalities of specialized tools, i.e., the energy modeler INTEMA, the life cycle assessment and costing tool VERIFY, and the smart grid-oriented CBA tool, the IEPT suite evaluates the viability of different smart grid investment scenarios from a multi-dimensional perspective at the LEC level. The functionalities of the proposed suite are validated in the LEC of Nisyros island, Greece, where three smart grid-based investment scenarios of different self-consumption levels are evaluated and ranked in terms of benefits and profitability. The results highlight that for a 20-year horizon of analysis, the investment scenario where a 50% self-consumption target is achieved was more financially viable compared to the 80% and 95% scenarios, achieving values of BCR and NPV equal to EUR 2.12 and EUR 4,400,000, respectively. [ABSTRACT FROM AUTHOR]

Today, the Internet of Things (IoT) has an important role for deploying power and energy management in the smart grids as emerging trend for managing power stability and consumption. In the IoT, smart grids has important role for managing power communication systems with safe data transformation using artificial intelligent approaches such as Machine Learning (ML), evolutionary computation and meta-heuristic algorithms. One of important issues to manage renewable energy consumption is intelligent aggregation of information based on smart metering and detecting the user behaviors for power and electricity consumption in the IoT. To achieve optimal performance for detecting this information, a context-aware prediction system is needed that can apply a resource management effectively for the renewable energy consumption for smart grids in the IoT. Also, prediction results from machine learning methods can be useful to manage optimal solutions for power generation activities, power transformation, smart metering at home and load balancing in smart grid networks. This paper aims to design a new periodical detecting, managing, allocating and analyzing useful information regarding potential renewable power and energy consumptions using a context-aware prediction approach and optimization-based machine learning method to overcome the problem. In the proposed architecture, a decision tree algorithm is provided to predict the grouped information based on important and high-ranked existing features. For evaluating the proposed architecture, some other well-known machine learning methods are compared to the evaluation results. Consequently, after analyzing various components by solving different smart grids datasets, the proposed architecture's capacity and supremacy are well determined among its traditional approaches. [ABSTRACT FROM AUTHOR]

Smart meters are among the fundamental components of smart grids due to their features such as high-accuracy energy measurement, remote reading, and control. They are also utilized to reduce the rate of illegal electrical energy use in some distribution areas. This paper presents the development of a Wi-Fi smart meter and designing a smart plug-in device for customer-side energy management. In this way, a method that does not require additional infrastructure and extra investment for remotely reading the energy consumption in environments with common Wi-Fi networks, such as large shopping malls, business centers, community buildings, villa cities, and university campuses has been proposed. The Wi-Fi smart meter and smart energy metering module (SEMM) support various user-friendly applications in accordance with the infrastructure of the smart grid and smart building applications. The SEMM is capable of measuring, monitoring, and remotely managing each electrical device. Thanks to the developed mobile application, customers can monitor their Wi-Fi smart meter data and also the consumption of each electrical device through the SEMMs as well as remotely manage these devices. The application examples presented in this study are also aimed at increasing awareness of energy saving for customers. [ABSTRACT FROM AUTHOR]

Due to the high inherent uncertainty of renewable energy, probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities. However, the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data. This article proposes a physics-informed artificial intelligence (AI) surrogates method to augment the incomplete dataset and quantify its uncertainty to improvewind power forecasting performance. The incomplete dataset, built with numerical weather prediction data, historical wind power generation, and weather factors data, is augmented based on generative adversarial networks. After augmentation, the enriched data is then fed into a multiple AI surrogates model constructed by two extreme learning machine networks to train the forecasting model for wind power. Therefore, the forecasting models' accuracy and generalization ability are improved by mining the implicit physics information from the incomplete dataset. An incomplete dataset gathered from a wind farm in North China, containing only 15 days of weather and wind power generation data with missing points caused by occasional shutdowns, is utilized to verify the proposed method's performance. Compared with other probabilistic forecastingmethods, the proposed method shows better accuracy and probabilistic performance on the same incomplete dataset, which highlights its potential for more flexible and sensitive maintenance of smart grids in smart cities. [ABSTRACT FROM AUTHOR]

The rising interest in the security of network infrastructure, including edge devices, the Internet of Things, and smart grids, has led to the development of numerous machine learning-based approaches that promise improvement to existing threat detection solutions. Among the popular methods to ensuring cybersecurity is the use of data science techniques and big data to analyse online threats and current trends. One important factor is that these techniques can identify trends, attacks, and events that are invisible or not easily detectable even to a network administrator. The goal of this paper is to suggest the optimal method for feature selection and to find the most suitable method to compare results between different studies in the context of imbalance datasets and threat detection in ICT. Furthermore, as part of this paper, the authors present the state of the data science discipline in the context of the ICT industry, in particular, its applications and the most frequently employed methods of data analysis. Based on these observations, the most common errors and shortcomings in adopting best practices in data analysis have been identified. The improper usage of imbalanced datasets is one of the most frequently occurring issues. This characteristic of data is an indispensable aspect in the case of the detection of infrequent events. The authors suggest several solutions that should be taken into account while conducting further studies related to the analysis of threats and trends in smart grids. [ABSTRACT FROM AUTHOR]

Smart Energy (SE) has emerged as a critical technology in tackling global challenges like climate change while addressing the rising energy demands driven by today’s data-intensive industrial revolution. SE integrates information and communication technologies into energy systems, optimizing them to meet these challenges effectively. At the core of SE operations are smart meters, playing a fundamental role in ensuring efficient functionality. These devices collect data, which is then leveraged to derive Business Intelligence (BI) for operations across the entire spectrum, from the sensing infrastructure to the cloud, primarily utilizing the Internet of Things (IoT) technology framework. With the increasing complexity of operations and the growing demand for optimization and enhanced functionality, the SE technology stack is evolving to integrate across all layers and domains. This integration has led to the stratification of computational load across IoT layers, intensifying the dependence on smart meter data for BI. Consequently, smart meters themselves have evolved to become more functional and complex. This paper’s novelty lies in its comprehensive exploration of the integration of BI with smart meter data. It delves into various aspects, including the different layers of intelligent operations within SE systems, the current state of the art, and diverse implementations of smart meters and their applications across operational locations, ranging from consumers to fog computing. The paper concludes by identifying research gaps and future directions, offering insights into the evolving requirements for the next generation of SE systems and the necessary adaptations in smart metering infrastructure to support these roles. This work contributes to a better understanding of the evolving landscape of data and computation in the context of SE, facilitating more efficient and effective energy management solutions.

In 2022, Thailand's Demand Response (DR) business model was shifting from the Traditional Utility (TU) model to the Load Aggregator (LA) model in accordance with Thailand's smart grid master plan. This research studied the current demand response model and mechanism to draw possible gaps in operations. This research deals with the data system owned by the individual load aggregator. The load aggregators collect meter data and evaluate demand adaptations before sending the results to claim compensation on behalf of their customers. This approach lacks transparency and facilitates distortion of the facts. Hence, this research introduces the data execution by smart contracts and data records on the blockchain that enhance transparent data sharing among multiple parties and maintain data integrity. Moreover, the proposed bidding algorithm allows customers to offer an expected price under the maximum incentive payment determined by the avoided costs of running the peaking power plants. Hence, the bidding helps reflect the DR operation costs on the customer side and control the budget for incentive payments. This study emphasized the smart contracts and decentralized application layer, so the public blockchain is a reasonable network for the test. However, implementation in real cases using the public blockchain requires careful considerations, such as network fees, transaction speeds, and the security of smart contract codes. [ABSTRACT FROM AUTHOR]

Recent years have seen the rapid development of technologies in Smart Grids (SGs) to enhance electricity networks with digital and data communication technologies. SGs can proactively detect, react, and respond to dynamic changes in the network. SGs can also enhance the efficiency and reliability of electricity supplies and promote the integration of renewable energy sources. Smart Meters (SMs) are often seen as the first step to a successful implementation of SGs. While SMs enable Utility Providers and consumers to obtain near real-time information of energy consumption, they can also be exploited to infer sensitive consumer data. Therefore, privacy preservation in SMs is paramount in ensuring the widespread and successful deployment of SGs. In this paper, we present a comprehensive survey of the state-of-the-art SM privacy-preserving techniques published in the literature over the past decade. We categorize these techniques based on the attack types and their objectives. We aim to offer a unique perspective in this survey article through the lens of privacy preservation, cross-cutting the wide range of techniques presented in the literature. We conclude by identifying the challenges and highlighting key future research directions in the field. [ABSTRACT FROM AUTHOR]

Increasing energy demands due to factors such as population, globalization, and industrialization has led to increased challenges for existing energy infrastructure. Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable, cheap, and easily available sources of energy. Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions. But the integration of distributed energy sources and increase in electric demand enhance instability in the grid. Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid. Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control, reinforcement of the grid demand, and generation balancing with cost reduction. But accurate forecasting of electrical data is a very challenging task due to the non-stationary and nonlinearly nature of the data. Machine learning and artificial intelligence have recognized more accurate and reliable load forecasting methods based on historical load data. The purpose of this study is to model the electrical load of Jajpur, Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression (GPR). The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past, current, and future dependent variables, factors, and the relationship among data. Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead. The study is very helpful in grid stability and peak load control management. [ABSTRACT FROM AUTHOR]

Increased sensing and data collection in electric power systems from utility to minigrid to individual household scale are resulting in an explosion of data collection about users and providers of electricity services. In the push to expand energy access for poor communities, the collection, use, and curation of these data have historically taken a back seat to the goal of expanding energy access but are increasingly being recognized as important issues. We review the nascent literature on this topic, characterize current data management practices, and examine how expanding access to data and data sharing are likely to provide value and pose risks to key stakeholders: end users of electricity, microutilities, macroutilities, governments, development institutions, and researchers. We identify the key opportunities and tensions and provide recommendations for the design and implementation of new data-sharing practices and platforms. Our review and analysis suggest that although a common and open platform for sharing technical data can mitigate risks and enable efficiency, fewer benefits are likely to be realized from sharing detailed financial data. We also recommend codesigning practices with each stakeholder group, increasing legal protections for end users of electricity and using deep qualitative data in addition to quantitative metrics.

This paper provides a comprehensive review of the applications of smart meters in the control and optimisation of power grids to support a smooth energy transition towards the renewable energy future. The smart grids become more complicated due to the presence of small-scale low inertia generators and the implementation of electric vehicles (EVs), which are mainly based on intermittent and variable renewable energy resources. Optimal and reliable operation of this environment using conventional model-based approaches is very difficult. Advancements in measurement and communication technologies have brought the opportunity of collecting temporal or real-time data from prosumers through Advanced Metering Infrastructure (AMI). Smart metering brings the potential of applying data-driven algorithms for different power system operations and planning services, such as infrastructure sizing and upgrade and generation forecasting. It can also be used for demand-side management, especially in the presence of new technologies such as EVs, 5G/6G networks and cloud computing. These algorithms face privacy-preserving and cybersecurity challenges that need to be well addressed. This article surveys the state-of-the-art of each of these topics, reviewing applications, challenges and opportunities of using smart meters to address them. It also stipulates the challenges that smart grids present to smart meters and the benefits that smart meters can bring to smart grids. Furthermore, the paper is concluded with some expected future directions and potential research questions for smart meters, smart grids and their interplay. [ABSTRACT FROM AUTHOR]

Background: Smart technologies, such as smart grids, are emerging as indispensable aspects of an energy transformation and come with hopes of more sustainable resource use. A substantial amount of research has examined the technical, economic, and environmental implications of these technologies, but less attention has been paid to their social aspects. For the smart grid projects to be realised, studies that include the actors who are supposed to implement the visions are needed. Results: A semi-structured literature review was conducted to investigate the state of social science literature on smart grids and identify the main research avenues and research gaps by addressing a broad research question: "What kind of knowledge is produced in social science studies on smart grids?" We retrieved peer-reviewed articles from the Scopus and Web of Science (WoS) databases up until 2022 and mapped them in terms of features such as topic, design, method, and theory. Conclusions: We found that knowledge development in social science studies on smart grids followed a pattern where most research focused on visions; professionals and users; and smart technologies in homes with a geographical focus on Europe or the USA. We identified six research gaps related to an overly vague definition of the smart grid and the need to include more diverse actors and geographical places to advance our understanding of the smart grid. There is also a lack of studies relating to energy democracy, the resistance of smart grids and the centralised–decentralised nexus of the smart grid. These less studied areas can bring in new knowledge that enhances a deployment of a smart grids supporting not only technological development, but also society and users. [ABSTRACT FROM AUTHOR]

Smart grid operations require accurate information on network topology and electrical equipment parameters. This paper proposes estimating such information with data from the smart grid. Assuming that the availability of bus voltage data is restricted to their magnitude, a linear model of the relationship between these data and the parameters of the admittance matrix is derived in a way that does not involve bus voltage angles. A regression optimizer is then proposed to minimize the deviation between data and values estimated by the linear model. Results on the IEEE 33 bus system are presented to illustrate the model accuracy and efficiency when used to estimate parameters of medium-voltage, three-phase balanced grids. [ABSTRACT FROM AUTHOR]

The modest objective is to check the integrated effect of energy storage systems (ESSs) and distributed generations (DGs) and compare the optimization of the size and location of ESS and DG to explore its challenges for smart grids (SGs) modernization. The research enlisted different algorithms for cost-effectiveness, security, voltage control, and less power losses. From this perspective, optimization of the distribution network's energy storage and capacity are being performed using a variety of methods, including the particle swarm, ant-lion optimization, genetic, and flower pollination algorithms. The experimental outcomes demonstrate the effectiveness of these techniques in lowering distribution network operating costs and controlling system load fluctuations. The efficiency and dependability of the distribution network (DN) are both maximized by these strategies. [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]

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]

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]

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]

The current study uses a data-driven method for Nontechnical Loss (NTL) detection using smart meter data. Data augmentation is performed using six distinct theft attacks on benign users' samples to balance the data from honest and theft samples. The theft attacks help to generate synthetic patterns that mimic real-world electricity theft patterns. Moreover, we propose a hybrid model including the Multi-Layer Perceptron and Gated Recurrent Unit (MLP-GRU) networks for detecting electricity theft. In the model, the MLP network examines the auxiliary data to analyze nonmalicious factors in daily consumption data, whereas the GRU network uses smart meter data acquired from the Pakistan Residential Electricity Consumption (PRECON) dataset as the input. Additionally, a random search algorithm is used for tuning the hyperparameters of the proposed deep learning model. In the simulations, the proposed model is compared with the MLP-Long Term Short Memory (LSTM) scheme and other traditional schemes. The results show that the proposed model has scores of 0.93 and 0.96 for the area under the precision–recall curve and the area under the receiver operating characteristic curve, respectively. The precision–recall curve and the area under the receiver operating characteristic curve scores for the MLP-LSTM are 0.93 and 0.89, respectively. [ABSTRACT FROM AUTHOR]

As one of the most successful industrial realizations of Internet of Things, a smart grid is a smart IoT system that deploys widespread smart meters to capture fine-grained data on residential power usage. Unfortunately, it always suffers diverse privacy attacks, which seriously increases the risk of violating the privacy of customers. Although some solutions have been proposed to address this privacy issue, most of them mainly rely on a trusted party and focus on the sanitization of metering masurements. Moreover, these solutions are vulnerable to advanced attacks. In this paper, we propose a decentralized mechanism for privacy-preserving computation in smart grid called DDP, which leaverages the differential privacy and extends the data sanitization from the value domain to the time domain. Specifically, we inject Laplace noise to the measurements at the end of each customer in a distributed manner, and then use a random permutation algorithm to shuffle the power measurement sequence, thereby enforcing differential privacy after aggregation and preventing the sensitive power usage mode informaton of the customers from being inferred by other parties. Extensive experiments demonstrate that DDP shows an outstanding performance in terms of privacy from the non-intrusive load monitoring (NILM) attacks and utility by using two different error analysis. [ABSTRACT FROM AUTHOR]

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ‘15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. In this paper, we address the problem of allowing third parties to apply $K$ -means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset. [ABSTRACT FROM AUTHOR]

In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples' nature. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary. In order to cope with a Theft Case scenario, theft data is ascertained and synthesized randomly by using six theft data variants. Theft data variants are benign class appertaining data samples which are modified and manipulated to synthesize malicious samples. Furthermore, a K-means minority oversampling technique is used to tackle the class imbalance issue. In addition, to enhance the detection of the classifier, abstract features are engineered using a stochastic feature engineering mechanism. Moreover, to carry out affine training of the model, balanced data are inputted in order to mitigate class imbalance issues. An integrated hybrid model consisting of Bi-Directional Gated Recurrent Units and Bi-Directional Long-Term Short-Term Memory classifies the consumers, efficiently. Afterwards, robustness performance of the model is verified using an attack vector which is subjected to intervene in the model's efficiency and integrity. However, the proposed model performs efficiently on such unseen attack vectors. [ABSTRACT FROM AUTHOR]

Accurate day-ahead load forecasting is an important task in smart energy communities, as it enables improved energy management and operation of flexibilities. Smart meter data from individual households within the communities can be used to improve such forecasts. In this study, we introduce a novel hybrid bi-directional LSTM-XGBoost model for energy community load forecasting that separately forecasts the general load pattern and peak loads, which are later combined to a holistic forecasting model. The hybrid model outperforms traditional energy community load forecasting based on standard load profiles as well as LSTM-based forecasts. Furthermore, we show that the accuracy of energy community day-ahead forecasts can be significantly improved by using smart meter data as additional input features. [ABSTRACT FROM AUTHOR]

Nowadays, with the overconsumption of energy, researchers attempt to optimally manage energy consumption and prevent it from being wasted. One beneficial way to manage energy consumption is to integrate the energy grid with information technology, so that data can be exchanged between producers and consumers in a two-way communication network, which is called a smart grid. This data transfer must take place in a secure environment. However, many protocols have been designed to establish a secure channel to transfer data in the smart grid environment, each of which has some disadvantages that still keep the problem open for this field of research. Recently, Kumar et al. proposed a lightweight authentication and key agreement protocol based on elliptic curve cryptography (ECC). In this article, we show that the proposed protocol does not provide the anonymity of smart meters and is vulnerable to smart meter tracing attack. We also propose a new lightweight ECC-based authenticated key agreement protocol that is resistant against all known attacks including the attack presented in this article. Also, the proposed scheme is more efficient than the recent related scheme. We show that the proposed protocol is semantically secure and also we simulate our protocol using the AVISPA. [ABSTRACT FROM AUTHOR]

Lately, adequate protection strategies need to be developed when Microgrids (MGs) are connected to smart grids to prevent undesirable tripping. Conventional relay settings need to be adapted to changes in Distributed Generator (DG) penetrations or grid reconfigurations, which is a complicated task that can be solved efficiently using Artificial Intelligence (AI)-based protection. This paper compares and validates the difference between conventional protection (overcurrent and differential) strategies and a new strategy based on Artificial Neural Networks (ANNs), which have been shown as adequate protection, especially with reconfigurable smart grids. In addition, the limitations of the conventional protections are discussed. The AI protection is employed through the communication between all Protective Devices (PDs) in the grid, and a backup strategy that employs the communication among the PDs in the same line. This paper goes a step further to validate the protection strategies based on simulations using the MATLABTM platform and experimental results using a scaled grid. The AI-based protection method gave the best solution as it can be adapted for different grids with high accuracy and faster response than conventional protection, and without the need to change the protection settings. The scaled grid was designed for the smart grid to advocate the behavior of the protection strategies experimentally for both conventional and AI-based protections. [ABSTRACT FROM AUTHOR]

The rapid advancements in smart grid (SG) technology extend a large number of applications including vehicle charging, smart buildings, and smart cities through the efficient use of advanced communication architecture. However, the underlying public channel leads these services to be vulnerable to many threats. Recently, some security schemes were proposed to counter these threats. However, the insecurities of some of these schemes against key compromise impersonation (KCI) and related attacks or compromise on efficiency calls for a secure and efficient authentication scheme for SG infrastructure. A new scheme to secure SG communication is presented in this article to provide a direct device-to-device authentication among smart meter and neighborhood area network gateway. Designed specifically to resists KCI and related attacks, the proposed scheme is more secure and completes the authentication procedure by using the least communication cost as compared with related schemes, which is evident through security and efficiency comparisons. [ABSTRACT FROM AUTHOR]

Maintaining proper operation of adaptive protection schemes is one of the main challenges that must be considered for smart grid deployment. The use of reliable cyber detection and protection systems boosts the preparedness potential of the network as required by National Infrastructure Protection Plans (NIPPS). In an effort to enhance grid cyber-physical resilience, this paper proposes a tool to enable attack detection in protective relays to tackle the problem of compromising their online settings by cyber attackers. Implementing the tool first involves an offline phase in which Monte Carlo simulation is used to generate a training dataset. Using rough set classification, a set of If-Then rules is obtained for each relay and loaded to the relays at the initialization stage. The second phase occurs during online operation, with each updated setting checked by the corresponding relay's built-in tool to determine whether the settings received are genuine or compromised. A test dataset was generated to assess tool performance using the modified IEEE 34-bus test feeder. Several assessment measures have been used for performance evaluation and their results demonstrate the tool's superior ability to classify settings efficiently using physical properties only. [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]

Recent years have seen the rapid development of technologies in Smart Grids (SGs) to enhance electricity networks with digital and data communication technologies. SGs can proactively detect, react, and respond to dynamic changes in the network. SGs can also enhance the efficiency and reliability of electricity supplies and promote the integration of renewable energy sources. Smart Meters (SMs) are often seen as the first step to a successful implementation of SGs. While SMs enable Utility Providers and consumers to obtain near real-time information of energy consumption, they can also be exploited to infer sensitive consumer data. Therefore, privacy preservation in SMs is paramount in ensuring the widespread and successful deployment of SGs. In this paper, we present a comprehensive survey of the state-of-the-art SM privacy-preserving techniques published in the literature over the past decade. We categorize these techniques based on the attack types and their objectives. We aim to offer a unique perspective in this survey article through the lens of privacy preservation, cross-cutting the wide range of techniques presented in the literature. We conclude by identifying the challenges and highlighting key future research directions in the field.

Smart grids, together with the Internet of Things, are considered to be the future of the electric energy world. This is possible through a two-way communication between nodes of the grids and computer processing. It is necessary that the communication is easy and safe, and the distance between a point of demand and supply is short, to reduce the electricity loss. All these requirements should be met at the lowest possible cost. In this paper, we study a two-dimensional rectangular grid graph which is considered to be a model of a smart grid; nodes of the graph represent points and devices of the smart grid, while links represent possible ways of communication and energy transfer. We consider the problem of choosing the lowest possible number of locations (nodes, points) of the grid which could serve as energy sources (or a source of different resources) to other nodes in such a way that we ensure reduction in electricity loss and provide safe communication and resistance to failures and increases in energy demand.Therefore, we study minimum doubly connected dominating sets in grid graphs. We show that the proposed solutions are the best possible in terms of the number of source points for the case of narrow grid graphs and we give upper and lower bounds for the case of wide grid graphs. [ABSTRACT FROM AUTHOR]

With the progressive advancement of the smart grid paradigm, electricity systems worldwide are deploying advanced metering technologies in residential sectors. Smart meters can monitor and process the electricity use of households at very high sampling rates. The availability of hundreds of thousands of time-series load profiles opens the possibility of applying clustering methods for grouping consumers with similar consumption patterns. [ABSTRACT FROM AUTHOR]

A smart grid uses automation and information communication technologies to guarantee its integrity. The first step of smart grid construction is the deployment of an advanced metering infrastructure system, which can read meter data automatically and maximize energy efficiency. Messages are collected at hubs distributed throughout the smart grid and sent through limited network resources. The advanced metering infrastructure system comprises a meter data management system, concentrators, relay stations between a set of smart meters and the control center, and smart meters that can collect the information of thousands of users. Concerns regarding this system are the loading on the control center, the volume of data traffic, and the quantity of the data. This study analyzes various situations of data traffic, uses Poisson process, and proposed two schemes to reduce the transmission frequency and the total data volume. A concentrator is determined to reduce the transmission frequency and compress the data from smart meters to the control center, thereby reducing the total data volume. Furthermore, the proposed scheme can reduce the loading on the control center. [ABSTRACT FROM AUTHOR]

Smart grids are a reality in distribution systems. They have assisted in the operation, control, and most of all, the protection of urban networks, significantly solving the contingencies of these networks. This paper treats the initial stage of implementing smart grid switching devices in distribution networks. In this stage, smart grid technologies need to operate with the traditional protection elements (such as fuses, reclosers, and sectionalizers). This fact can create trouble in the protection schemes because there are two distinctive philosophies. In some companies, especially those without substantial capital, these two protection philosophies can run together for many years. The most popular intelligent electronic devices (IEDs) available in the market are studied to verify their features and the possibility to incorporate techniques to allow the two philosophies to work together. After that, the proposed approach shows how the existing IEDs can interact with the traditional devices. Special functions can also be incorporated to inform the control center of an operational problem, increasing the observability of the network. With the proposed approach, the IEDs are transformed into intelligent agents. Practical examples using real distribution systems are presented and discussed, proving the efficacy of the proposed methodology. [ABSTRACT FROM AUTHOR]

Various smart meter data aggregation protocols have been developed in the literature to address the rising privacy threats against customers' energy consumption data. However, most of these protocols require a smart meter (installed at the consumer's end) to either maintain a secret key or to run an authenticated key establishment scheme for interacting with the aggregator. Both of these approaches create additional requirements for the system. To address this issue, this article first proposes a machine-learning-based ultra-light-weight data aggregation scheme for smart grids that does not require a secret key to be maintained for communicating with the aggregator. In particular, unlike existing data aggregation schemes, in the proposed data aggregation scheme, neither the server nor the smart meter needs to store any secret. Instead, for every round of data aggregation, each smart meter uses an embedded PUF for generating a unique random response for a given challenge. On the other hand, the server maintains a PUF model for each smart meter for producing the same random response. This unique secret key is used to ensure the privacy of the metering data. Next, we propose an optimized data aggregation scheme using collaborative learning to enhance the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

Smart grid is a network created through information technology, communication technology and electrical power systems. It is simply a "smarter" power grid which ensures a two-way communication between user and the power supplier. This work proposes a smart microgrid design hybrid renewable energy system based on solar, grid and wind energy resources. The optimization has been performed using homer software programme to get the best and optimal operation system. Hybrid system combines several energy systems together which offer increased energy reliability and security, and carry a large economic opportunity in terms of cost saving. A typical microgrid system would also include intelligent management that interfaces with the equipment via wired or wireless communication protocols. In the present work the optimization model has been developed for the optimal operation of the system. The modelled system collects meteorological and load data from a town. The optimal hybrid system design is realized by satisfying the load demand, nonlinear seasonal variations and equipment constraints. The main focus of the system is on operation of smart microgrids to maximize usage of green energy, reduction of environmental emissions, decrement of levelized cost of electricity and intelligent management of overall system. [ABSTRACT FROM AUTHOR]