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Reliability is a fundamental requirement of wide‐area backup protection (WABP) systems, and it is one of the most crucial performance indicators for such a WABP system. Two definitions are introduced: the reliability of WABP systems and the undetectable probability of a power line. They are determined by the specific placement of communication links (CLs) and phasor measurement units (PMUs). A simultaneous optimization model was developed to minimize the construction cost of WABP systems by optimizing the partitioning and placement of PMUs, protection centers, and CLs. The model considers several constraints, including latency, regional balance, and system reliability. To reduce the computational complexity, a cluster‐based genetic algorithm was developed to determine the optimal solution. Finally, numerical simulations were conducted using IEEE test cases. The results demonstrate that the proposed method can minimize the construction costs of WABP systems while increasing their reliability. [ABSTRACT FROM AUTHOR]

The increasing demand for electricity in daily life highlights the need for Smart Cities (SC) to use energy efficiently. Both technical and Non‐Technical Losses (NTL), particularly those resulting from electricity theft, present powerful obstacles; NTL alone can reach billions of dollars. Although Machine Learning (ML) based approaches for NTL detection have been embraced by numerous utilities, there is still a lack of thorough analysis of these methods. Limited research exists on NTL identification evaluation criteria and unbalanced data management in the context of SC. This research compares ML algorithms and data balancing methods to optimize electricity consumption detection. The given research applied the 15 ML techniques of Logistic regression, Bernoulli naive Bayes, Gaussian naive Bayes, K‐Nearest Neighbour, perceptron, passive‐aggressive classifier, quadratic discriminant analysis, SGD classifier, ridge classifier, linear discriminant analysis, decision tree, nearest centroid classifier, multi‐nomial naive Bayes, complement naive Bayes and dummy classifier. While SMOTE, AdaSyn, NRAS, and CCR are considered for data balancing. AUC, F1‐score, and seven relevant performance metrics were used for comparison. We have also implemented SHapely Additive exPlanations (SHAP) for feature importance and model interpretation. Results show varying classifier performance with different balancing methods, emphasizing data preprocessing's role in NTL detection for smart grid security. [ABSTRACT FROM AUTHOR]

With the development of smart meters and other intelligent electronic devices, more and more data‐driven fault location methods based on wide area measurement are emerging. However, these diagnostic methods for dealing with the whole tested system often appear complex. This paper presents an innovative subsystems‐based fault location strategy in distribution grid by the sparsity promoted Bayesian learning algorithm. To avoid taking measurement for the whole distribution system, the fault‐included subsystem is selected according to the distribution characteristics of negative sequence voltage. Then the data for fault location is measured by allocating meters in subsystem, which can reduce the number of required meters. For accurately estimating the fault location, a sparse prior is proposed for the Bayesian learning, which could improve the accuracy of the fault location algorithm by about 4%. The performance is tested on a 12.66‐kV, 69‐bus distribution system in response to various fault scenarios. The results show that the accuracy of the proposed method for the fault section location can reach 90%. It also verifies the robustness and accuracy for fault line location, faced different fault types, fault resistance, noise, etc. [ABSTRACT FROM AUTHOR]

Electricity theft is a primary concern for utility providers, as it leads to substantial financial losses. To address the issue, a novel extreme gradient boosting (XGBoost)‐based model utilizing the consumers' electricity consumption patterns for analysis is proposed for electricity theft detection (ETD). To remove the imbalance in the real‐world electricity consumption dataset and ensure an even distribution of theft and non‐theft data instances, six different artificially created theft attacks were used. Moreover, the utilization of the XGBoost algorithm for classification, especially to identify malicious instances of electricity theft, yielded commendable accuracy rates and a minimal occurrence of false positives. The proposed model identifies electricity theft specific to the regions, utilizing electricity consumption parameters, and other variables as input features. The authors' model outperformed existing benchmarks like k‐neural networks, light gradient boost, random forest, support vector machine, decision tree, and AdaBoost. The simulation results using the false attacks for balancing the dataset have improved the proposed model's performance, achieving a precision, recall, and F1‐score of 96%, 95%, and 95%, respectively. The results of the detection rate and the false positive rate (FPR) of the proposed XGBoost‐based detection model have achieved 96% and 3%, respectively. [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]

As the next generation of green power system, smart grids have gradually enhanced the operation efficiency of power system. Meanwhile, the application of communication and intelligent technologies make the power grid more vulnerable to the emerging cyber‐physical attacks, such as the false data injection attack (FDIA). Particularly, the deception property of the FDIA on the output measurement estimation can fool the current security mechanism without triggering an alarm. Motivated by this problem, this paper aims at developing a novel detection and recovery mechanism against FDIA in smart grid. Based on the established state space grid model derived from the three‐phase sinusoidal voltage equations, an improved principal component analysis (PCA)‐based detection method is proposed. By introducing the mathematical transformation principle method, the detection performance such as detection rate and false positive rate can be improved. To keep the stable running of power system, a genetic optimization algorithm‐based linear quadratic regulator (LQR) defense method is developed. In addition, to improve the response performance to external attacks, an artificial intelligence method named genetic optimization algorithm is introduced to optimize the robust performance of the proposed defense method. Finally, the simulation results on the IEEE 6‐bus and 118‐bus grid system demonstrate the superiority of the proposed genetic algorithm optimization‐based LQR defense method. [ABSTRACT FROM AUTHOR]

The privacy of electricity consumers has become one of the most critical subjects in designing smart meters and their proliferation. In this work, a multilayer architecture has been proposed for anonymous data collection from smart meters, which provides: (1) The anonymity of information for third‐party data consumers; (2) Secure communication to utility provider network for billing purposes; (3) Online control of data sharing for end‐users; (4) Low communication costs based on available Internet of things (IoT) communication protocols. The core elements of this architecture are, first, the digital twin equivalent of the cyber‐physical system and, second, the Tangle distributed ledger network with IOTA cryptocurrency. In this architecture, digital twin models are updated in real‐time by information received from trusted nodes of the Tangle distributed network anonymously. A small‐scale laboratory prototype based on this architecture has been developed using the dSPACE SCALEXIO real‐time simulator and open‐source software tools to prove the feasibility of the proposed solution. The numerical results confirm that after a few seconds of anomaly detection, the microgrid was fully stabilized around its operating point with less than 5% deviation during the transition time. [ABSTRACT FROM AUTHOR]

Many progressed information scientific strategies, particularly Artificial Intelligence (AI) and profound learning methods, have been proposed and tracked down wide applications in our general public. This proposition creates information driven arrangements by utilizing the most recent profound learning and AI innovation, including outfit learning, meta‐learning and move learning, for energy the executives framework issues. Genuine world datasets are tried on proposed models contrasted and best in class plans, which exhibit the predominant presentation of the proposed model. In this proposition, the engineering of the Smart Grid testbed is additionally planned and created by using ML calculations and true remote correspondence frameworks to such an extent that constant plan necessities of Smart Grid testbed is met by this reconfigurable system with stacking of full convention in medium access control (MAC) and physical layers (PHY). The proposed engineering has the reconfiguration property in view of the organization of remote correspondence and trend setting innovations of Information and communication technologies (ICT) which incorporates Artificial Intelligence (AI) calculation. The fundamental plan objectives of the Smart Grid testbed is to make it simple to construct, reconfigure and scale to address the framework level prerequisites and to address the ongoing necessities. [ABSTRACT FROM AUTHOR]

Computational complexities in modern power systems are reportedly increasing daily, and it is anticipated that traditional computers might be inadequate to provide the computation prerequisite in future complex power grids. In that given context, quantum computing (QC) can be considered a next‐generation alternative solution to deal with upcoming computational challenges in smart grids. The QC is a relatively new yet promising technology that leverages the unique phenomena of quantum mechanics in processing information and computations. This emerging paradigm shows a significant potential to overcome the barrier of computational limitations with better and faster solutions in optimization, simulations, and machine learning problems. In recent years, substantial progress in developing advanced quantum hardware, software, and algorithms have made QC more feasible to apply in various research areas, including smart grids. It is evident that considerable research has already been carried out, and such efforts are remarkably continuing. As QC is a highly evolving field of study, a brief review of the existing literature will be vital to realize the state‐of‐art on QC for smart grid applications. Therefore, this article summarizes the research outcomes of the most recent papers, highlights their suggestions for utilizing QC techniques for various smart grid applications, and further identifies the potential smart grid applications. Several real‐world QC case studies in various research fields besides power and energy systems are demonstrated. Moreover, a brief overview of available quantum hardware specifications, software tools, and algorithms is described with a comparative analysis. [ABSTRACT FROM AUTHOR]

With the desire to improve the efficiency and the reliability of the power system as well as the advancement of smart meter and communication networks, the demand response (DR) program has been facilitated to become a key component in the smart grid. Many current researches focus on incentives‐based DR research orientation. In addition, accurately quantifying the incentive payment is also a significant challenge because of the fact that the incentive payment is currently determined depending on knowledge, experience, and experimental results. This paper presents a method of pricing incentive payments for the incentive‐based DR program through social welfare maximization framework which ensures the benefits of all participants involved. Besides, this study also proposed the quantification of incentive payments by a theoretical approach based on the satisfaction level of electricity consumers which is approximated through a fuzzy logic model. Moreover, the impact of renewable energy sources on the pricing mechanism and benefits of all parties participating in the DR program is also discussed through the calculation cost for renewable energy integration and emission cost function. The results show the validity and feasibility of the method. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the feasibility of smart metering system based on narrowband power line communications (NB‐PLC) by using low‐voltage (LV) and medium‐voltage (MV) power line channels. The LV and MV networks of two different sites are selected to carry out comprehensive field measurements to investigate the characteristics of NB‐PLC channels for narrowband frequencies. The NB‐PLC channels of both sites are classified into two categories, that is, bad and good channels of LV and MV networks by considering their characteristics and performance. On the basis of measured results, the simulation models for the modeling of LV and MV NB‐PLC channels are developed and thus proposed. The measured and simulated results are compared to validate the efficiency of proposed methods. A state of the art mechanism for the modelling of capacitative coupling device and impedances of LV and MV networks is developed. It is proposed that simulation‐based NB‐PLC channel models and analyses can play a vital role in examining the residential and industrial types of channels. This research work will pave the way to standardize the future smart metering equipment and sensing devices of power system protection. [ABSTRACT FROM AUTHOR]

With the wide deployment of smart meters in the end-user side, demand response (DR) is gaining prominence. Estimating the potential response is a preliminary step to the DR implementation. However, how to select proper features, how to protect privacy, and how to capture the response uncertainty remains three challenges to the customer response potential estimation. This study provides a detailed response quantity estimation for each customer. The authors proposed a probabilistic response quantity estimation framework and solved the problem by alternating direction method of multipliers (ADMM) in a fully-distributed way. In particular, by utilising a similar consumption pattern matching principle, the feature for each DR day of each customer is selected based on the matched DR participants. Then, the pinball loss-guided ridge regression is formulated, so the quantiles are obtained to cope with the uncertainty. The training process can be solved by each customer at the local site in a fully-distributed way, which protects the privacy and reduces the centralised processing burden. Finally, in the case study, the assumption behind a similar consumption pattern matching principle is validated empirically. Also, the proposed method is confirmed to have good convergence performance and can produce reasonable estimation results. [ABSTRACT FROM AUTHOR]

This study investigates the feasibility of advanced metering infrastructure (AMI) based on narrowband power line communications (NB-PLC) by incorporating all possible types of low voltage (LV) and medium voltage (MV) power line channels. The extensive field measurements are carried out on six selected sites that include residential, commercial, and industrial LV and MV NB-PLC channels. On the basis of measured results, NB-PLC channels of various under evaluation sites are segregated into two classes i.e. worst and good channels for both LV and MV networks. The simulation models for the modelling of LV and MV NB-PLC channels by considering the practical data of measured sites are developed and thus proposed. The proposed methods are validated by comparing the measured and simulated results. A sophisticated mechanism for the modelling of LV and MV network impedances and capacitive coupling device is formulated. The proposed analyses and simulation-based NB-PLC channel models can play a key role in examining the characteristics of various types of NB-PLC channels. This research work will help to standardise the future smart metering equipment and sensing devices of power system protection. [ABSTRACT FROM AUTHOR]

This study proposes a novel machine learning‐based methodology to estimate the air‐conditioning (AC) load from the hourly smart meter data. The commonly employed approaches for disaggregating the share of the AC load from the total consumption are either using data obtained from dedicated sensors or high‐frequency data that cannot be provided by conventional smart meters. In the present work, an alternative approach is proposed, in which a machine learning‐based pipeline is first optimised and trained using the data obtained from a set of households in a period, including both smart meter data and the AC load measurements along with corresponding weather conditions. The obtained optimal pipeline is then utilised to estimate the AC load in another set of buildings in the same period of the year, while providing it with only the smart meter data and weather conditions. As the first step of the pipeline implementation, several features are extracted from the smart meters and weather data. The most promising algorithm is then determined, the corresponding hyper‐parameters are optimised and the most influential parameters are finally determined. The proposed method leads to a lower monitoring system's cost, lower user privacy concerns and fewer data processing complexity compared to the conventional energy disaggregation approaches, while providing an acceptable accuracy. [ABSTRACT FROM AUTHOR]

With growing connection of distributed energy resources, availability of demand side response technologies, deployment of smart meters, the distribution system needs advanced network automation for running the system efficiently. State estimation is the core driver of network automation. While the output from SMs will make the state estimation more accurate, advanced metering infrastructures come with several challenges such as noisy, erroneous measurement including lost or missed measurements, exposure to cyber attack and so on. This study proposes a three-phase unbalanced distribution system state estimation which is robust against noisy distribution system measurements, bad data attacks and missing or delayed measurements. This method considers measurement from hybrid sources such as SCADA, micro-phasor measurement units (${\mu}$μ PMUs) and SMs. Kalman smoother is used to fill the missing measurements and expectation-maximisation based forecasting is used to interpolate the hybrid measurements to a common timestamp and compensate for the delay in SM measurements. Extensive numerical comparisons are made on IEEE 13, 37 and 123 bus systems to test the robustness of the proposed DSSE against delayed SM measurements and bad or noisy data. An IEEE 24 bus system is modelled and real-time measurement devices are interfaced to it in Hypersim. The data from the hybrid measurement devices of IIT Kanpur smart grid is also used to test the robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Cloud computing has been envisioned as the main technology capable of integrating and managing many systems in a smart grid (SG). Thus, this research aims to develop a cloud computing infrastructure to store and manipulate smart distribution system data. To do this, extensible architecture with essential services was proposed to be integrated into an advanced metering infrastructure to host new applications in smart distribution systems. Based on this proposition, a cloud computing platform was developed using open source tools. A new application to reduce multiple estimation for fault location in radial distribution systems using data mining techniques over smart meter data was implemented. An optimised version of the data mining tool known as DAMICORE (DAta MIning of COde REpositories) was implemented as an extension to the proposed architecture basic services. The new cloud application was tested using hundreds of fault simulations on a test feeder and was able to reduce multiple estimation by more than 90% in the simulated fault cases. The results show that the proposed cloud computing architecture and infrastructure enable new smart distribution system applications, contributing to the development of SGs and diffusion of cloud computing in this context. [ABSTRACT FROM AUTHOR]

The promotion of smart meters has led to the exponential growth of monitoring data. Consequently, challenges have emerged for utilities to analyse and interpret big data at the demand side. In this study, an adaptive weighted‐feature K‐means‐affinity propagation (AP) clustering algorithm is proposed to analyse customer electricity consumption behaviours. First, a comprehensive feature set is constructed, i.e. the time‐domain, frequency‐domain and fluctuation features of load curves. Second, the feature set is applied to a divide‐and‐conquer framework to analyse customer behaviours. In the local modelling, adaptive K‐means algorithm is adopted to cluster load curves by considering the time‐domain and fluctuation features; the former is objectively weighed by entropy weighting. In the global modelling, AP clustering algorithm is introduced and clustering results are obtained by combining the weighted time‐domain and frequency‐domain features. The performance tests on the simulation data and electricity consumption behaviour analyses on the big dataset are conducted to verify the effectiveness of the proposed models and approaches. The divide‐and‐conquer framework is practical, and the proposed features are not only beneficial for clustering the load curves, but also conducive to consumption behaviour analysis. [ABSTRACT FROM AUTHOR]

This work presents a cooperative demand‐side management (DSM) scenario in a low‐voltage network considering a context of liberalised electricity markets. The authors show that introducing an additional inter‐supplier cooperation mechanism among consumers enhances a better use of the flexibility consented by each individual, hence aiming at reaching a global optimum instead of optimising the costs of a few ones. To that end, a real‐time pricing (RTP) scheme is explored based on cost functions differentiated in both time and consumption level that should reflect the true energy cost. The authors apply to each consumer a commodity cost function shared among the set of cooperative users of its respective supplier as well as one common network cost function shared by all cooperative users of all suppliers in the considered network. Each individual runs, through its smart meter (SM), a decentralised optimisation algorithm defining an energy consumption schedule for a set of flexible appliances (FAs). The mechanism the authors propose ensures that a fair cost distribution between all users is achieved by reaching the Nash equilibrium. To assess their proposition, they confront it to intermediate consumption strategies through a benchmark. The results confirm that their inter‐supplier cooperation mechanism always leads to the minimum total cost. [ABSTRACT FROM AUTHOR]

Losses on low-voltage networks are often substantial. For example, in the UK they have been estimated as being 4% of the energy supplied by low-voltage networks. However, the breakdown of the losses to individual conductors and their split over time are poorly understood as generally only the peak demands and average loads over several months have been recorded. The introduction of domestic smart meters has the potential to change this. How domestic smart meter readings can be used to estimate the actual losses is analysed. In particular, the accuracy of using 30 min readings compared with 1 min readings, and how this accuracy could be improved, were investigated. This was achieved by assigning the data recorded by 100 smart meters with a time resolution of 1 min to three test networks. Smart meter data from three sources were used in the investigation. It was found that 30 min resolution data underestimated the losses by between 9 and 24%. By fitting an appropriate model to the data, it was possible to reduce the inaccuracy by ~50%. Having a smart meter time resolution of 10 min rather than 30 gave little improvement to the accuracy. [ABSTRACT FROM AUTHOR]

Since feeders have complicated laterals connected with unbalanced loads, the location of a ground fault is a challenge in radial distribution networks. Furthermore, lower short‐circuit current due to the high value of fault resistance, which is common in the distribution network, will make it even more challenging to identify the fault location. Taking advantage of increasing number of intelligent electronic devices installed in the modern distribution network, the monitoring, and automation of distribution systems can be improved by smart feeder meters. On the basis of the voltage information from the downstream sub‐laterals, a novel fault location method involving distributed voltage measurement is proposed in this study. The first step in the proposed method is to identify the fault lateral in the main lateral. In a zero‐sequence components network, the position of nodes in the main lateral is iterated to judge if the calculated voltage at the end matches the measured one by a smart feeder meter. The second step is to identify the fault section accurately by combining the voltage information from main lateral or other sub‐laterals. Finally, a precise fault location can be attained via an iteration search in the fault section. Two distribution test systems simulated in power systems computer‐aided design/electro‐magnetic transient design and control with 34‐node and 134‐node have been employed to evaluate the effectiveness of the proposed algorithm under various ground fault scenarios. The simulation results show that the proposed method is robust since it offered a good performance for ground faults identification with low and high values of impedance occurring at different locations. Furthermore, it is also suitable for the system with distributed generations and dynamic loads. [ABSTRACT FROM AUTHOR]

The potential benefits of direct current (DC) network in low‐voltage system promote smart grids and facilitate increasing penetration of distributed generation (DG). Correct identification of faulted section of a distribution network by an automation system reduces power outage time and improves reliability indices of such systems. With the onset of a fault in a DC distribution system, data recorded at an outage management system are used to identify the faulted section. With DG units active in a distribution system, there will be bidirectional power flow and faulted section identification becomes challenging. Using voltage and current data from consumer‐end smart meters and the substation, a method is proposed to identify the faulted section in DC distribution system. The Hadamard product of fault‐affected sections (obtained from network topology and voltage status matrices) is used to identify the faulted section correctly. Using power systems computer‐aided design/electro‐magnetic transient design and control simulation, the accuracy of the proposed method is verified for different fault situations including simultaneous faults, fault close to a bus and with rooftop photovoltaic integration to a DC distribution system. The method has the capability to provide information on the fault segment that connects feeder pillar to the consumer end also. [ABSTRACT FROM AUTHOR]

Illegal use of electricity has been a major concern in power system industries for a long time. Fraudulent large‐scale consumption of electricity may result in an unbalanced demand–supply gap. This study proposed a data‐driven electricity theft detector that is based on random matrix theory with the widespread use of smart meters and advanced metering infrastructure. The application of an augmented matrix as the data source is the key step of the proposed method, indicating the correlations between power consumption and system operating states under abnormal conditions of electricity use. Moreover, the regional/individual theft detection algorithm and the abnormal consumption pattern signal are designed for shortlisting regions with a high probability of theft and selecting suspected fraudulent customers in real time. Numerical case studies using simulated case studies conducted on the IEEE 34‐bus system and real data collected from the power system of a Chinese city are used to investigate the correctness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

The authors propose, design, and implement a low‐cost universal smart energy meter (USEM) with demand‐side load management. The meter can be used in the postpaid and prepaid modes with flexible tariff plans such as time of use, block rate tariff, and their combination. The smart meter comprises of a potential transformer, current transformer, and microcontroller unit with an embedded communication module. The connectivity among the utility authority, the smart meter, and consumer is established by authority identification number, meter identification number, and user identification number using the cellular network. The load management option of the meter controls electrical loads and provides emergency power during the power shortage. The USEM can be configured and reconfigured remotely simply by short message service without changing hardware. Besides, energy consumption status, meter tampering, and fault at the distribution end can be monitored with the proposed metering system. Here, a prototype of the smart meter is presented, and its effectiveness, flexibility, and versatility are experimentally demonstrated. [ABSTRACT FROM AUTHOR]

In smart grid, the smart meters improve the grids' efficiency but imply the sensitive residential information. Hence, how to prevent privacy leakage of smart meter data has drawn lots of researchers' attentions. Yet, it is non‐trivial to quantify the relation between privacy protection behaviours and system utility loss. To this end, the authors leverage the notion of differential privacy (DP) to measure the privacy‐protection strength, under the framework of optimal power flow (OPF). Specifically, once the noise is injected to hide the actual demand, the solutions of OPF problem are probably affected, which undermine the grid utility. In this study, the authors are the first quantitatively investigating DP preserving OPF problem. Starting with re‐modelling the noise‐injected OPF problem, the authors rigorously prove OPF solution's sensitivity with respect to the uncertainty of demand. Moreover, aiming at OPF‐based pricing mechanism, locational marginal pricing (LMP), the respective privacy‐protection's contribution on LMPs is explicitly expressed. Subsequently, based on the extensive experiments, it is illustrated that the quantitative correlation between the privacy‐protection strength and the gird system performance. Furthermore, by combining the grid topology and privacy‐protection strength, a novel billing system to fairly charge the extra payment to subsidise the privacy‐insensitive customers is proposed. [ABSTRACT FROM AUTHOR]

Distribution networks are becoming increasingly 'smarter', as well as more complex, with the addition of power electronic devices, information and communication technologies, smart meters, and more. As a result, advanced control strategies to manage such networks are becoming necessary. These strategies need to be thoroughly tested and validated, before they can be implemented in a real network. For this reason, a smart grid control algorithm testing chain is proposed, that aims to gradually test control algorithms, in all their development stages, using increasingly advanced laboratory setups. In addition, the interfacing options and challenges of each stage of the chain are highlighted. The proposed testing chain is substantiated in an optimal centralised coordinated voltage control (CVC) algorithm and the final stage of the chain, namely the combination of control and power hardware‐in‐the‐loop simulation, is presented in this study. As a specific example, the management technique for a storage system is implemented as part of the CVC algorithm. The laboratory results demonstrate that the proposed setup, despite its high complexity, enables the algorithm to be effectively and realistically tested as part of the overall system. [ABSTRACT FROM AUTHOR]

Smart grids introduce new technological elements into power systems which take prevalent challenges to a new level by shaping parameters of power systems towards a complex regime of uncertainties. Rapid proliferation of advanced metering infrastructure (AMI) and integration of renewable energy sources in smart grids increase system‐wide complexities. This study proposes an innovative approach to classify the energy consumptions of smart meter customers with typical profiles by processing with multi‐layered clustering of energy consumption data of smart consumers extracted from the AMI. There are two stages for the approach of which the first stage analyses the data for intra‐cluster similarity of energy consumption patterns and in case the patterns do not have a high intra‐cluster similarity, they are fed back for re‐clustering with multi‐layered clustering process until the clearly identifiable energy patterns with high intra‐cluster similarity is obtained. The second stage linearises the complex energy patterns using interpolant and curve fitting techniques until stabilised profiles are obtained. This study also proposes a methodology for smart meter load modelling for Monte Carlo (MC) simulation applications to reduce the computing time compared with traditional alternatives. This study validates the robustness of the approach and provides the corroboration of the method for MC simulation applications in a smart grid environment. [ABSTRACT FROM AUTHOR]

Demand side management is one of the key topics of smart grids. This study integrates the service computing paradigm in smart grid domain and proposes a demand side personalised recommendation system (PRS). The proposed PRS employs service recommendation techniques to infer residential users' potential interests and needs on energy saving appliances, and then it recommends energy saving appliances to users, therefore potentially creating opportunities to save energy for the grid. The proposed approach starts by applying a non‐intrusive appliance load monitoring (NILM) method based on generalised particle filtering to disaggregate the end users' household appliance utilisation profiles from the smart meter data. Based on the NILM results, several inference rules are applied to infer the preferences and energy consumption patterns, and to form the user profile. In parallel, information retrieval techniques are applied to extract keywords from the textual appliance advertisements (Ads), and to define the appliance profile. Finally, the similarity measurement method is applied to compare the user profile and appliance profile, to rank the appliance Ad, and to make the recommendations. Experiments are conducted to validate the proposed system. [ABSTRACT FROM AUTHOR]

The development of smart metering technologies makes it possible to apply the thermostatically controlled loads (TCLs) into the demand response programs such as direct load control. To characterise the dynamics of a large number of TCLs, the computationally efficient sate‐queuing (SQ) model is designed in some recent research works. However, very few attentions have been given to its accuracy. This study reviews the existing SQ modelling approach, and reveals its limitations in the accuracy by comprehensive comparison and analysis. To improve the accuracy of the SQ model, a modification method is specially designed based on the optimisation technique. Numerous simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The cyber‐physical security of power grid has gained more attention in the research community due to integration of information and communication technologies. Smart meters are vulnerable to cyber threats and if the security of these meters is compromised then the consequence can be devastating. It is necessary to study all the possible impacts that cyber attacks may have on the power grid in order to make the grid immune to such intrusions. With more and more renewable energy and information technology integration, electricity companies must make sure that they are not paying for spoofed electricity. In this study, the authors propose a new attack through which a private actor injects false data into multiple meters to deceive the system operator with new modified system state to gain momentary profit by projecting higher energy export than actual. Assuming real power injection measurement to be secured at all the generator buses, the attack is simulated for IEEE 14‐bus and IEEE 30‐bus system. From the system operator's perspective, the most vulnerable buses are obtained and ranked based on the severity and minimum set of meters required to launch an attack. [ABSTRACT FROM AUTHOR]

This study presents a smart volt/VAR control (VVC) technique for smart distribution systems, which is designed to be integrated into a distributed real‐time analysis and control framework implemented with the use of multiple processing units equipped with wireless communication transceivers. The distributed processing units collaborate to perform power flow analysis based on smart meter measurements for controlling and coordinating the switched capacitor banks and voltage‐regulating transformers. The objective is to maintain acceptable voltage levels along the distribution feeder, minimise system losses, and limit the number of switching operations. GNU‐Octave simulations are employed as a means of evaluating the performance of the proposed smart VVC technique with respect to loss reduction and number of switching operations. The network simulator ns‐3 is used to simulate the distributed processing units that execute the proposed smart VVC technique. Worldwide interoperability for microwave access (WiMAX) and long‐term evolution (LTE) communication networks are employed in the ns‐3 simulations in order to provide data connectivity among the distributed processing units. The performance of the communication network is evaluated in terms of the execution time of the smart VVC technique, the average packet delay and the average packet delivery ratio. [ABSTRACT FROM AUTHOR]

Power delivery systems that use power lines can communicate with other electrical systems, monitor the quality of electrical energy and provide an economical solution for automatic meter reading (AMR). Energy meters can be connected to a low‐voltage power system by power line carriers for all consumers. Illegal electricity usage in the power delivery system can be easily detected if AMR is installed in the system, which consists of a terminal smart meter (TSM) and a gateway smart meter (GSM). This study proposes a novel remote detection method for illegal electricity usage that uses both TSM and GSM, where the TSM and the GSM are installed on the power system of each consumer and on the node of the power system feeder, respectively. TSM and GSM simultaneously disconnect an electrical source for a very short period of time. After this, the proposed detection method sends a low‐voltage signal with a high frequency from the TSM to the GSM during the time of disconnection. Once the signal has passed, the proposed detection method detects illegal electricity usage by measuring the amount of time it was disconnected. This study presents the structure of the proposed detection method as well as the mathematical model, proof, simulation and experimental results. [ABSTRACT FROM AUTHOR]

Data size reduction techniques may be helpful in the process of categorising the electrical load consumption patterns on the basis of their shape. Starting from a macro‐class of consumers defined according to certain general criteria on the type of consumers and the period of the year or week, the representative load pattern (RLP) of each consumer can be built by considering the data points in the time domain or a reduced number of features. This study exploits the effects of using the symbolic aggregate approximation (SAX) method to form the reduced set of features. The portion of the time–amplitude plane defining the RLP is partitioned into sub‐portions on the basis of the characteristics of the whole data set. A specific partitioning of the time axis is proposed on the basis of the cumulative distribution function of the RLP variations in time. Each RLP is then coded according to the SAX principles. The resulting codes are then used into a hierarchical clustering procedure. The validity of the clustering results obtained by using the SAX data representation with the proposed non‐uniform partitioning of the time axis is presented and discussed, including comparisons with the results obtained from other data size reduction techniques. [ABSTRACT FROM AUTHOR]