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Direct current microgrid (DCMG) clusters are gaining popularity in power systems due to their simplicity and high efficiency. However, DCMG clusters are susceptible to minor disturbances due to low system inertia. This paper proposes a method to enhance the small‐signal stability of a DCMG cluster by optimizing the main control parameters of the system. This paper presents a small‐signal state‐space model of a DCMG cluster system at the system level, considering a multi‐bus network topology. Then, the control parameters that significantly affect the small‐signal stability of the DCMG are selected using the participation factor method. To enhance the system damping, the Pareto‐optimal frontier of the bi‐objective problem was determined using the elite non‐dominated sorting genetic algorithm (NSGA‐II). The optimal compromise is determined by using the fuzzy membership function method to extract it from the generated Pareto optimal front. The proposed method has been verified on a three‐sub DCMG test system with droop control. [ABSTRACT FROM AUTHOR]

The integration of renewable energy into power grid planning poses significant challenges. In this investigation, planning strategies across eight key indicators encompassing economic, reliability, environmental, and adaptability are assessed. Leveraging the analytical hierarchy process alongside the entropy weight method, subjective and objective weights are ascertained and comprehensive weights with parsimonious discriminative data are synthesized. Scores for planning strategies are then derived utilizing these weights. To illustrate and validate the efficacy of the proposed methodology, it was applied to four schemes within the IEEE‐6 node system, incorporating wind and photovoltaic power plants. [ABSTRACT FROM AUTHOR]

For Years, Experts have expressed growing concerns that critical infrastructures are vulnerable to cyberattacks. Remotely transmitted malware and viruses can compromise interconnected power systems, airports, hospitals, military systems, and any Internet-connected computers used to manage critical functions. As the world becomes more digitalized, integrating new communication technologies offers unlimited possibilities for the accumulation of information, creating a fundamental dependence on proper functioning in all areas of society. As such reliance could offer great opportunities for a smart and efficient world, it also poses significant threats to the operation of critical infrastructures and vital facilities. Specifically, safety issues from illegal acts that violate the integrity, availability, and confidentiality of information in cyberspace are expected to be the central theme in the digitalized world. Exploitation of cyberspace to advance political, social, and military agendas is of particular concern. [ABSTRACT FROM AUTHOR]

One of the problems that are associated to power systems is islanding condition, which must be rapidly and properly detected to prevent any negative consequences on the system's protection, stability, and security. This paper offers a thorough overview of several islanding detection strategies, which are divided into two categories: classic approaches, including local and remote approaches, and modern techniques, including techniques based on signal processing and computational intelligence. Additionally, each approach is compared and assessed based on several factors, including implementation costs, non-detected zones, declining power quality, and response times using the analytical hierarchy process (AHP). The multi-criteria decision-making analysis shows that the overall weight of passive methods (24.7%), active methods (7.8%), hybrid methods (5.6%), remote methods (14.5%), signal processing-based methods (26.6%), and computational intelligent-based methods (20.8%) based on the comparison of all criteria together. Thus, it can be seen from the total weight that hybrid approaches are the least suitable to be chosen, while signal processing-based methods are the most appropriate islanding detection method to be selected and implemented in power system with respect to the aforementioned factors. Using Expert Choice software, the proposed hierarchy model is studied and examined. [ABSTRACT FROM AUTHOR]

Abstract The role of cybersecurity in cyber‐physical power systems (CPPS) is reviewed, focusing on the applications of dynamic state estimation (DSE) techniques. These DSE techniques are particularly relevant with the integration of phasor measurement units (PMUs) and advanced communication infrastructure. A comprehensive review on DSE techniques and applications to efficiently protect CPPS against cyberattacks is classified into three cyber resilience phases including prevention, detection, and mitigation. The DSE techniques in the prevention phase are surveyed to improve the observability of the CPPS by the robust design of the Kalman filter and optimal protection of PMUs. The DSE techniques in the detection phase are surveyed to improve the adaptability of CPPS in various attack detection scenerios and optimise the detection accuracy. The DSE techniques in the mitigation phase are surveyed to enhance the flexibility of CPPS resource utilisation with compensation‐based, isolation‐based, and scheduling‐based strategies. Finally, the benefits and limitations of each DSE technique are summarised with potential suggestions on research directions for enhancing the cyber resilience of CPPS.

Abstract Direct current microgrid (DCMG) clusters are gaining popularity in power systems due to their simplicity and high efficiency. However, DCMG clusters are susceptible to minor disturbances due to low system inertia. This paper proposes a method to enhance the small‐signal stability of a DCMG cluster by optimizing the main control parameters of the system. This paper presents a small‐signal state‐space model of a DCMG cluster system at the system level, considering a multi‐bus network topology. Then, the control parameters that significantly affect the small‐signal stability of the DCMG are selected using the participation factor method. To enhance the system damping, the Pareto‐optimal frontier of the bi‐objective problem was determined using the elite non‐dominated sorting genetic algorithm (NSGA‐II). The optimal compromise is determined by using the fuzzy membership function method to extract it from the generated Pareto optimal front. The proposed method has been verified on a three‐sub DCMG test system with droop control.

Abstract Having correct distribution network topology information is essential for system state estimation, line loss analysis, electricity theft detection and fault location. At present, with continuous deployment of smart sensors, a large amount of monitoring data is collected, which enables refined management for distribution network. A data‐driven low voltage (LV) distribution network topology identification method is proposed, which realises transformer‐customer pairing and customer phase identification for distribution network with relatively balanced power supplies. Firstly, an integrated similarity coefficient of voltage curve is proposed, which can reflect the neighbourhood relationship within stations while increase the distinction between stations; the K‐Nearest Neighbour (KNN) algorithm is used to propagate the service transformer labels to complete transformer‐customer association. Then, the influence of power fluctuation on voltage curve is analysed and a dynamic sliding window model is adopted to search for voltage segments with significantly difference among three phase feeders to formulate a voltage time series to identify customer phase. Finally, the results are corrected and verified based on the principle of network power balance. The proposed algorithm is tested in two different real substations in China and Europe and shows high accuracy and robustness especially in distribution network with relatively balanced power supplies.

Abstract In order to optimise resource allocation within the province, a two‐stage scheduling model for provincial‐level power grids, encompassing day‐ahead and intra‐day stages is proposed. Firstly, a Conditional Generative Adversarial Network is employed to generate scenarios for load and new energy output. Based on the generated scenario set, the model takes into account the uncertainty and permissible error intervals of new energy and load, utilising conditional value at risk to measure the system scheduling risk. In the day‐ahead stage, an optimisation model is proposed, considering intra‐provincial power purchase demands, with the goal of minimising system operating costs, including risk costs. It optimises day‐ahead scheduling and contingency plans to ensure economic efficiency and robustness of the system based on extreme scenarios. During the training phase, the dataset is enhanced using Conditional Generative Adversarial Network and updated daily, improving the training effectiveness of the multi‐agent proximal policy optimisation intra‐day scheduling model. In the intra‐day stage, the intra‐day scheduling model utilises ultra‐short‐term forecasting data as input to generate contingency plans for dispatching reserve units. Experiments conducted on the IEEE 39‐node system validate the feasibility and effectiveness of the proposed approach.

Abstract During the fourth energy revolution, artificial intelligence implementation is necessary in all fields of technology to meet the increasing energy demands and address the diminishing fossil fuel reserves, necessitating the shift towards smart grids. The authors focus on predicting parameters accurately to minimise loss and improve power generation capacity in smart grids, given that accurate parameter prediction is essential for traditional power grid stations converting to smart grids. The authors employ an artificial intelligence‐based machine learning model, namely the long short‐term memory, to predict parameters of a solar power plant. After analysing the results obtained from the long short‐term memory model in graphical visualisation, the model is further improved using two different techniques namely, a convolutional neural network‐long short‐term memory and the authors proposed an autoencoder long short‐term memory. Comparing the results of these models, the study finds that autoencoder long short‐term memory outperforms the convolutional neural network‐long short‐term memory as well as simple long short‐term memory. Thus, the use of artificial intelligence in this study substantially enhances the precision of parameter prediction by augmenting the performance of rudimentary machine learning models, thereby facilitating the attainment of a resilient and resourceful power system that overcomes power losses and ameliorates production capacity in the context of Smart Grids.

Abstract With the rapid increase of electric vehicle (EV) ownership, the impact of EV charging load on the power grid is becoming more and more prominent. To reasonably guide EV charging/discharging to participate in Demand Response (DR) and help the power grid achieve peak cutting and valley filling, the charge‐discharge compensation pricing strategy of EV Aggregator (EVA) considering user response willingness from the perspective of Stackelberg game is proposed. Firstly, EVA, as the leader, provides charge‐discharge compensation price, to maximise its income within a day, taking into account user satisfaction constraints. Secondly, a user response willingness model is established. User engagement is used to describe the change in the number of EV responses with the change of the charge‐discharge compensation price by EVA and select the random EV set that accepts EVA charge‐discharge guidance. Finally, EV, as a follower, conducts charging/discharging behaviour to minimise the charging cost. By using the Karush–Kuhn–Tucker (KKT) condition, strong duality theory and iterative method, the strategy equilibrium solution is solved. The results show that considering the user response willingness can effectively reduce the decision risk when EVA participates in bidding. Although EVA income slightly decreases considering the response willingness, the average user satisfaction increases by 0.1.

The trend towards equipping buildings with active space cooling technology is expected to continue, due to the warming climate. In addition, the electrification of mobility and industry, and the massive rollout of photovoltaics, may put Austria’s power grids under stress if adequate mechanisms are not implemented in time. This study investigates the magnitudes of the loads induced by this increasing demand for cooling, together with the relief that passive cooling measures can provide. Data from current climate models, measured real load profiles from households, building stock models, and dynamic building simulations are utilised, alongside stakeholder and expert opinions, to fine-tune the analysis. This article suggests plausible scenarios for the development of the demand for cooling energy in Austria until 2050, and analyses the aggregated effect of the different options for cooling on the electricity power grid. The integration of cooling loads is expected to cause overloads or voltage violations only for a few types of grid within the periods investigated here. Subsidy programs in the building sector often do not properly consider cooling demand. Building regulations and subsidies should therefore focus on passive measures, and only when these are not sufficient should measures related to active cooling be implemented.

Abstract Cyber–physical distribution systems with renewable energy should have high resilience under extreme conditions. However, in a distribution system with deep cyber–physical coupling, the propagation of information risk impairs the resilience of the system. This paper proposes a strategy to improve the resilience of cyber–physical distribution systems that considers cross‐space propagation of information risk. First, the mechanism for interaction between the cyber space and physical space is analyzed, and an information risk cross‐space propagation model is proposed. Second, the risk status of key device nodes is quantified according to the risk propagation model, and an optimization strategy for information risk control is obtained, whose objective function is the minimum risk of the coupling network. Third, a fault restoration strategy that considers cyber space risk is proposed. Finally, the paper reports experiments using the modified IEEE 33‐node and 123‐node distribution networks to simulate the proposed strategy and verify its effectiveness.

Abstract As the proportion of renewable energy penetrated in power systems further increases, the optimal operation faces a large obstacle to satisfying various conflict interests simultaneously. This paper first formulates a many‐objective optimal operation problem of the new power system considering a comprehensive set of economic, environmental, and reliable objectives. To figure out this problem efficiently, this paper proposes a probability confidence correlation analysis method (PCCA) utilizing a t‐distributed stochastic neighbor embedding (T‐SNE) standard to greatly preserve the distribution information and separability of the objectives. Consequently, the objectives can be aggregated and the objective dimension is reduced based on the important sorting and correlation of the objectives. Based on the outcome of the T‐SNE, the adopted optimal operation problem with low dimensional objectives is solved by a multiple producer group search optimizer (GSOMP) with less computation burden, to obtain the Pareto‐optimal solution set. Simulation studies are conducted on IEEE 30‐bus, 39‐bus and 57‐bus systems to investigate the performance of the proposed PCCA in terms of efficiency and accuracy, making comparisons of the existing many‐objective optimization algorithms.

Abstract The rapid growth in the number of electric vehicles (EVs), driven by the ‘double‐carbon’ target, and the impact of uncontrolled charging and discharging behaviour and discharged battery losses severely limit electric vehicles’ low carbon characteristics. Existing research on systemic low‐carbon emissions and electric vehicle charging and discharging issues is usually determined by considering only carbon trading markets or charging and discharging management on the source side. In this regard, a coordinated and optimized operation model that considers the participation of electric vehicle clusters in deep peaking and the source network load and storage adjustable resources is proposed. The upper layer establishes a real‐time price‐based demand response mechanism for the load side with the minimum net load fluctuation as the objective function; the middle layer establishes a comprehensive operation mechanism for the source and storage side that includes an orderly charging and discharging peaking compensation mechanism for electric vehicles, and a deep peaking mechanism that takes into account clean emission, and constructs an optimal operation model with the minimum comprehensive operating cost as the objective function; the lower layer establishes a distribution network loss minimization model for the network side that takes into account the orderly charging and discharging of electric vehicle as the objective function. The optimal load model with the objective function of minimizing the distribution network loss is established at the lower level. Finally, the original problem is transformed into a mixed integer linear programming problem, and the model's effectiveness is verified by setting different scenarios. The model reduces the total cost by 22.22%, improves the wind power consumption rate by 19.55%, reduces the actual carbon emission by 16.66%, and reduces the distribution network loss by 13.91% compared to the basic model.

Abstract The integration of the renewable energy sources (RESs) into the power grid, drives a significant transformation in the conventional power generation landscape. This transition from traditional synchronous generators to inverter based RESs introduces unique challenges in maintaining the grid frequency stability due to the reduced system inertia. The inherent stochastic nature of the RES power generation, load demand, and grid inertia includes further complexity in the assessment of frequency stability. Existing studies have limitations, including neglecting the stochastic nature of RES generation and load demand fluctuations, relying on limited metrics, and lacking a comprehensive day‐to‐day assessment. To address these shortcomings of the existing approaches, this paper introduces a novel methodology for assessing frequency stability in power grids with high RES penetration. It proposes three indices for evaluating grid frequency sensitivity, resiliency, and permissibility amidst varying RES integration. Utilizing a stochastic approach, the study incorporates uncertainties in RES generation and load demand, offering a comprehensive framework for day‐to‐day frequency stability analysis. Additionally, it presents a systematic method to ascertain the necessary inertial support for maintaining desired frequency reliability in RES‐dominated grids. The effectiveness of these methodologies is validated through a case study on a modified IEEE 39‐bus test system, demonstrating their applicability in ensuring reliable grid operation under high RES scenarios.

Abstract The DC‐side stability of the grid‐tied converter under different control modes is fully investigated using electrical torque analysis. The small‐signal model of a single converter connected to an ideal DC bus under various control modes is formulated. Accordingly, the damping and synchronising coefficient contributed by the DC network and controllers of grid‐tied converter are separately accessed using the electrical torque analysis method and the stabilising conditions of the grid‐tied converter operating under different control modes are further derived. The system stability mainly corresponds with DC network dynamics under constant active power control mode. On the contrary, the grid‐tied converter under constant DC‐link voltage control mode has no stability problem. Generally, elevating the DC‐link capacitance or decreasing the droop gain can greatly improve the stability margin reserve of the VSC‐HVDC links. In addition, the control gains of the classical PQ controller are proven to have limited impacts on DC‐side system stability. Finally, the results of numerical simulation prove the validity of the proposed stability analysis method and the stable boundary for the grid‐tied converter with different control modes.

Abstract With the vigorous development of the energy Internet, all kinds of user information data are increasing day by day. How to comprehensively and deeply mine the effective information of users, develop a model to predict the behaviour characteristics of big data users, distinguish customer relationships, and provide an accurate basis for the next behaviour of users for various platforms have become one of the research hotspots of big data analysis of user behaviour. The data is sampled according to the feature vector of power user. The portrait mining of power user is conducted, and the user screening and analysis are conducted by using the measure of decision tree node purity in the model. The decision tree variable of the up–down stopping rule is generated. Then the results of the model and the Logistics model are tested and analysed, which can effectively predict the behaviour of power user. The proposed user strategy based on the characteristics of power consumption behaviour is analysed to verify the effectiveness of the scheme. The example shows that the model has a strong ability to distinguish and good stability than the traditional Logistics model, which can effectively predict the user's behaviour in advance, reduce user complaints, and help enterprises and users to form a long‐term mechanism of mutual benefit and reciprocity, which has a strong practical significance. This paper analyses the panorama of users through power big data technology and proposes a maturity model to evaluate the priority of users' electricity consumption. It emphasises the use of resources and methods provided in the power big data technology package to solve the practical problems of users' electricity consumption, and helps power companies to avoid market risks and improve service levels, which has strong practical significance.

With the development of distributed generation and power electronics technology, DC microgrids have made certain progress with their advantages of simple structure, economic efficiency, and easy control. Considering factors such as the geographical location of some residential areas, microgrids with fuel cells that can operate independently are also receiving increasing attention. In this paper, an online control method named virtual hydrogen consumption is proposed based on an independent DC microgrid which solves the problem of electro‐hydrogen conversion successfully. By solving the overall consumption function, the analytical solution is further obtained to realize the online control. Based on the results of RT‐LAB platform, the proposed method can maintain the output of fuel cell system in its high efficiency range with a lower volatility. [ABSTRACT FROM AUTHOR]

As the proportion of renewable energy penetrated in power systems further increases, the optimal operation faces a large obstacle to satisfying various conflict interests simultaneously. This paper first formulates a many‐objective optimal operation problem of the new power system considering a comprehensive set of economic, environmental, and reliable objectives. To figure out this problem efficiently, this paper proposes a probability confidence correlation analysis method (PCCA) utilizing a t‐distributed stochastic neighbor embedding (T‐SNE) standard to greatly preserve the distribution information and separability of the objectives. Consequently, the objectives can be aggregated and the objective dimension is reduced based on the important sorting and correlation of the objectives. Based on the outcome of the T‐SNE, the adopted optimal operation problem with low dimensional objectives is solved by a multiple producer group search optimizer (GSOMP) with less computation burden, to obtain the Pareto‐optimal solution set. Simulation studies are conducted on IEEE 30‐bus, 39‐bus and 57‐bus systems to investigate the performance of the proposed PCCA in terms of efficiency and accuracy, making comparisons of the existing many‐objective optimization algorithms. [ABSTRACT FROM AUTHOR]

Cyber–physical distribution systems with renewable energy should have high resilience under extreme conditions. However, in a distribution system with deep cyber–physical coupling, the propagation of information risk impairs the resilience of the system. This paper proposes a strategy to improve the resilience of cyber–physical distribution systems that considers cross‐space propagation of information risk. First, the mechanism for interaction between the cyber space and physical space is analyzed, and an information risk cross‐space propagation model is proposed. Second, the risk status of key device nodes is quantified according to the risk propagation model, and an optimization strategy for information risk control is obtained, whose objective function is the minimum risk of the coupling network. Third, a fault restoration strategy that considers cyber space risk is proposed. Finally, the paper reports experiments using the modified IEEE 33‐node and 123‐node distribution networks to simulate the proposed strategy and verify its effectiveness. [ABSTRACT FROM AUTHOR]

The expansion of renewable electricity storage technologies, including green hydrogen storage, is spurred by the need to address the high costs associated with hydrogen storage and the imperative to increase storage capacity. The initial section of the paper examines the intricacies of storing electricity generated from renewable sources, particularly during peak periods, through green hydrogen. Two primary challenges arise: firstly, the complexity inherent in the storage technology and its adaptation for electricity reproduction; and secondly, the cost implications throughout the technological chain, resulting in a significant increase in the price of the reproduced energy. Electric energy storage emerges as a pivotal solution to accommodate the growing proportion of renewable energy within contemporary energy systems, which were previously characterized by high stability. During the transition to renewable-based energy systems, optimizing energy storage technology to manage power fluctuations is crucial, considering both initial capital investment and ongoing operational expenses. The economic analysis primarily focuses on scenarios where electricity generated from renewable sources is integrated into existing power grids. The subsequent part of this paper explores the possibility of localizing excess electricity storage within a specific system, illustrated by domestic hot water. [ABSTRACT FROM AUTHOR]

The rapid growth in the number of electric vehicles (EVs), driven by the 'double‐carbon' target, and the impact of uncontrolled charging and discharging behaviour and discharged battery losses severely limit electric vehicles' low carbon characteristics. Existing research on systemic low‐carbon emissions and electric vehicle charging and discharging issues is usually determined by considering only carbon trading markets or charging and discharging management on the source side. In this regard, a coordinated and optimized operation model that considers the participation of electric vehicle clusters in deep peaking and the source network load and storage adjustable resources is proposed. The upper layer establishes a real‐time price‐based demand response mechanism for the load side with the minimum net load fluctuation as the objective function; the middle layer establishes a comprehensive operation mechanism for the source and storage side that includes an orderly charging and discharging peaking compensation mechanism for electric vehicles, and a deep peaking mechanism that takes into account clean emission, and constructs an optimal operation model with the minimum comprehensive operating cost as the objective function; the lower layer establishes a distribution network loss minimization model for the network side that takes into account the orderly charging and discharging of electric vehicle as the objective function. The optimal load model with the objective function of minimizing the distribution network loss is established at the lower level. Finally, the original problem is transformed into a mixed integer linear programming problem, and the model's effectiveness is verified by setting different scenarios. The model reduces the total cost by 22.22%, improves the wind power consumption rate by 19.55%, reduces the actual carbon emission by 16.66%, and reduces the distribution network loss by 13.91% compared to the basic model. [ABSTRACT FROM AUTHOR]

This article introduces an enhanced Radial, Equivalent, Independent (REI) model for power system reduction, aiming to enhance computational efficiency while preserving accuracy. Unlike the conventional REI method, which divides the system into two regions, the proposed model partitions the grid into more suitable regions, maintaining regional control centers for a more effective reduction process. The model significantly decreases the number of busbars, transmission lines, generators, and load models while retaining the overall geographical structure of the grid. An 8-step application process is outlined for implementing this approach. Validation on a 48-bus power system and the IEEE 39-bus system demonstrates its efficiency. Application on the Turkish transmission system results in a 57% reduction in busbars (from 162 to 70) and a 49% reduction in transmission lines, simplifying the model while achieving an impressive average active load flow error of only 3.51%. The study concludes that the extended REI model offers improved computational efficiency without compromising accuracy, making it promising for applications like network planning, market simulations, and capacity management. [ABSTRACT FROM AUTHOR]

The integration of the renewable energy sources (RESs) into the power grid, drives a significant transformation in the conventional power generation landscape. This transition from traditional synchronous generators to inverter based RESs introduces unique challenges in maintaining the grid frequency stability due to the reduced system inertia. The inherent stochastic nature of the RES power generation, load demand, and grid inertia includes further complexity in the assessment of frequency stability. Existing studies have limitations, including neglecting the stochastic nature of RES generation and load demand fluctuations, relying on limited metrics, and lacking a comprehensive day‐to‐day assessment. To address these shortcomings of the existing approaches, this paper introduces a novel methodology for assessing frequency stability in power grids with high RES penetration. It proposes three indices for evaluating grid frequency sensitivity, resiliency, and permissibility amidst varying RES integration. Utilizing a stochastic approach, the study incorporates uncertainties in RES generation and load demand, offering a comprehensive framework for day‐to‐day frequency stability analysis. Additionally, it presents a systematic method to ascertain the necessary inertial support for maintaining desired frequency reliability in RES‐dominated grids. The effectiveness of these methodologies is validated through a case study on a modified IEEE 39‐bus test system, demonstrating their applicability in ensuring reliable grid operation under high RES scenarios. [ABSTRACT FROM AUTHOR]

The article deals with the development of a method and algorithm for calculation of individual compo-nents of electricity flows in the branches of an electric power grid circuit caused by generation and con-sumption in its nodes. In particular, it is about estimating the share of renewable energy sources (RES) in the electricity consumption of a given consumer. The method is based on methods and algorithms for the calculation of steady-state modes of power grids, integrating a mathematical model for determining the components of current flows in the branches of the power system circuit. The model employs distribution coefficients of currents in the circuit branches from RES nodes and node voltages to form a matrix of cur-rent distribution coefficients for RES across the power grid branches. Since RES in electric power systems (EPS) use general power grids to transmit the electricity they generate, accurate determination of their power share within overall flows enables the impact of RES on the EPS mode parameters to be considered. The peculiarity of the algorithm for estimating the share of RES in the electricity consumption of a given consumer is its consideration of physical processes in the power system. The input data for the calculation are the power system parameters, the node loads (specified by capacity or schedules), as well as arrays of RES generation and centralized power supply nodes and consumers with a controlled value of the Guar-anteed Origin of electricity from RES. The voltages during the formation of the matrix of power distribu-tion coefficients at nodes along the branches of the power grid are determined by the calculation results of the power grid steady-state modes or by experimental measurement data. The method's efficacy and the accuracy of estimating the RES share in the power consumption of a given consumer are verified and demonstrated through a practical example. Determination of the share of the consumer's electricity con-sumption, which origin from renewable sources can be guaranteed, creates an enabling environment that would foster producers and consumers to increase the use of renewable energy sources and promotes the development of the green energy market. Ref. 18. Fig.3. [ABSTRACT FROM AUTHOR]

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Abstract The integration of renewable energy into power grid planning poses significant challenges. In this investigation, planning strategies across eight key indicators encompassing economic, reliability, environmental, and adaptability are assessed. Leveraging the analytical hierarchy process alongside the entropy weight method, subjective and objective weights are ascertained and comprehensive weights with parsimonious discriminative data are synthesized. Scores for planning strategies are then derived utilizing these weights. To illustrate and validate the efficacy of the proposed methodology, it was applied to four schemes within the IEEE‐6 node system, incorporating wind and photovoltaic power plants.

This article introduces an innovative application of the Enhanced Gorilla Troops Algorithm (EGTA) in addressing engineering challenges related to the allocation of Thyristor Controlled Series Capacitors (TCSC) in power grids. Drawing inspiration from gorilla group behaviors, EGTA incorporates various methods, such as relocation to new areas, movement towards other gorillas, migration to specific locations, following the silverback, and engaging in competitive interactions for adult females. Enhancements to EGTA involve support for the exploitation and the exploration, respectively, through two additional strategies of periodic Tangent Flight Operator (TFO), and Fitness-based Crossover Strategy (FCS). The paper initially evaluates the effectiveness of EGTA by comparing it to the original GTA using numerical CEC 2017 single-objective benchmarks. Additionally, various recent optimizers are scrutinized. Subsequently, the suitability of the proposed EGTA for the allocation of TCSC apparatuses in transmission power systems is assessed through simulations on two IEEE power grids of 30 and 57 buses, employing various TCSC apparatus quantities. A comprehensive comparison is conducted between EGTA, GTA, and several other prevalent techniques in the literature for all applications. According to the average attained losses, the presented EGTA displays notable reductions in power losses for both the first and second systems when compared to the original GTA. Specifically, for the first system, the proposed EGTA achieves reductions of 1.659 %, 2.545 %, and 4.6 % when optimizing one, two, and three TCSC apparatuses, respectively. Similarly, in the second system, the suggested EGTA achieves reductions of 6.096 %, 7.107 %, and 4.62 %, respectively, when compared to the original GTA's findings considering one, two, and three TCSC apparatuses. The findings underscore the superior effectiveness and efficiency of the proposed EGTA over both the original GTA and several other contemporary systems.

Ensuring the reliability of electrical distribution networks is a pressing concern, especially given the power outages due to surface contamination on insulating components. Surface contamination can elevate surface conductivity, thereby resulting in failures that can lead to power shutdowns. Addressing this challenge, this paper proposes an approach for real-time monitoring of electrical distribution grids to prevent such incidents. A hypertuned version of the you only look once (YOLO) model is tailored for this application. We refine the model's hyperparameters by integrating a genetic algorithm to maximize its detection performance. The EigenCAM technique enhances the visual interpretability of the model's outcomes, providing operators with actionable insights for maintenance and monitoring tasks. Benchmark tests reveal that the proposed Hypertuned-YOLO outperforms Detectron (Masked R-CNN), YOLOv5, and YOLOv7 models. The Hypertuned-YOLO achieves an F1-score of 0.867 and a mAP@0.5 of 0.922, validating its robustness and efficacy.

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The traditional approach to planning the distribution grid has focused on reliability in the context of gradual and reasonably predictable load growth. Forecasts of load growth, combined with asset management practices, were used by system planners to identify upgrades to the system to maintain or improve reliability. The decisions, typically based within load flow analysis tools, included considerations about contingency scenarios and corporate forecasts (i.e., top-down predictions at a summary level of what would happen in a particular area that could impact load growth and behavior). Today, this traditional approach no longer fits all purposes.

Due to the deteriorating condition of the power grids and the increasing number of power outages, research on technical solutions to prevent blackouts in the Ukrainian energy system is becoming an extremely relevant and necessary task. This study aims to analyse technical solutions aimed at increasing the resilience of Ukraine’s energy system to prevent blackouts and ensure reliable electricity supply to the population. The methods used include the analytical method, classification method, functional method, statistical method, synthesis method, and others. The study analysed modern technical solutions to improve the resilience of the Ukrainian energy system. The study included a detailed analysis of innovative approaches to modernizing the energy infrastructure. The primary objective was to study and evaluate modern technologies for designing new power lines with improved transmission characteristics. The study emphasized the importance of expanding automation systems to effectively monitor and control the state of the power grid. The study also identified the importance of using energy storage, developing renewable energy sources and improving electricity consumption forecasting systems. Flexible load management measures and improved cybersecurity systems were identified as critical. It is noted that the implementation of the project to provide electricity to the conditional area will be a key step in improving the lives of the local population and providing the necessary conditions for the development of the area’s infrastructure and economy. In addition, the operation of generators for electricity supply will help to reduce the feeling of lack of electricity among residents and ensure reliability in the use of electricity for all sectors of the district’s economy. The overall conclusion is that the implementation of such technical solutions will significantly increase the resilience of Ukraine’s energy system. The results obtained make it possible to effectively avoid blackouts and ensure a sustainable electricity supply to the population. This allows energy companies, government agencies and other stakeholders to optimize energy resource management and make informed decisions to ensure the sustainable and efficient operation of Ukraine’s energy system in the face of a possible blackout risk

As renewable energy sources are integrated into power systems, the complexity of their management increases. For the analysis of modern power systems, clustering algorithms have become indispensable tools. The representation of an electric power transmission system involves conceptualizing it as a graph—a mathematical structure depicting a set of nodes and edges. In the context of power systems, these nodes correspond to buses, and the edges represent branches. Weighting may be assigned to the edges to denote the strength of their connections. In this paper, spectral clustering and hierarchical agglomerative clustering have been employed to analyze the Italian transmission network. Spectral clustering relies on the eigenvalues and eigenvectors derived from the Laplacian matrix associated with the network. The hierarchical agglomerative clustering, based on Thévenin impedance to define the distances, merges the two closest clusters at each step. The primary objective of this research is to identify distinct areas within the network based solely on its topology. In pursuit of this goal, admittances and Thévenin impedances have been employed as weights in the graph representation. By utilizing these approaches, the research aims at uncovering meaningful structures and patterns within the power transmission system based on the inherent connectivity and strength of the network elements. The techniques have been used on two real network models to demonstrate the performance of the algorithms with different grid configurations.

Natural disasters and health pandemics have occurred more frequently in the 21st century. Research show that the large blackouts caused by natural disasters are more intractable, especially striking during health pandemics. In this sense, power grid resilience becomes a topical area both in industry and academia. The resilience can be enhanced by making the grid “Smarter”, utilizing advanced techniques such as distributed generation, microgrids, sensing, communication, and computing. In this paper, a structured review framework for decision support for smart distribution system (SDS) resilience against natural disasters during health pandemics is proposed. The challenges and negative impacts of natural disasters during health pandemics on SDS resilience are well illustrated. The stochastic impact assessment methods of natural disasters and human activities on SDSs are extensively discussed. The SDS planning and operation models against natural disasters during health pandemics are comprehensively reviewed. The strategic decision-making for resilient SDS against natural disasters during health pandemics are extensively surveyed. Future research directions are also presented.

Abstract Frequency security is the premise of realizing the net‐zero transition. A novel assessment scheme is proposed to quantify the frequency security levels of transmission systems under different inverter‐based resources (IBRs) and their control parameter combinations. A novel system frequency dynamic model is proposed as a parametric optimization method, where the saturation of generators, energy storage systems, and renewable energy sources is incorporated as differential algebra equations. This problem is further reformulated as a mixed‐integer linear programming problem to generate a sufficient amount of data under different IBR integration and control parameters. The frequency security assessment problem is formulated as a data‐driven multivariate classification problem, which is solved by the optimal classification tree (OCT) algorithm with better interptretionability. Simulations are conducted on a transmission system. Numerical results indicate that the proposed system frequency dynamic model can capture the frequency dynamic under different IBR reinforcement plans and the OCT can realize accurate classification of the synthetic data regarding frequency security.

Abstract A novel method is introduced to determine cost‐optimized active power curtailment (APC) of renewable energy sources (RES) considering weather‐dependent dynamic line rating (DLR). A new formulation of the security‐constrained optimal power flow (SC‐OPF) is developed and applied in a case study. The reduction of the required preventive APC in the (N-0) case for (N-1) secure grid operation due to the consideration of thermal inertia of overhead power lines is demonstrated. Considering thermal inertia allows relying on curative APC that is activated only after an (N-1) situation occurs. The overhead power line temperature, calculated with the temperature‐dependent power flow (TDPF), acts as a new limit for line loading and releases unused transmission reserves. The TDPF algorithm is integrated in the open‐source Python library pandapower, providing access to it to the broader community. The power system can be utilized to a greater extent, with a reduced demand for congestion management.

Abstract Peak‐valley period partition of load curve is a key aspect of time‐of‐use (ToU) tariff to improve power load characteristics, such as shifting peak loads towards valley time periods. Fuzzy clustering algorithm is an effective and popular method commonly used to solve the peak‐valley period partition of load curves, but it still encounters the difficulty of dividing some data within the boundary regions of different time periods. Therefore, this paper presents a peak‐valley period partition and abnormal time correction scheme for ToU tariffs under typical daily load curves based on improved fuzzy C‐means (FCM) clustering algorithm. In order to improve the accuracy of peak‐valley period partition, modified fuzzy membership functions are proposed to improve the initialization of FCM clustering, and a loss function‐based method is presented for calculating the fuzzy parameters of those membership functions. To resolve the problem of abnormal time partitioning within the boundaries of different time periods, an abnormal time period recognition model and a correction model based on fuzzy subsethood are proposed to obtain the final corrected peak‐valley time period partitioning results. On the MATLAB R2020b platform, the effectiveness of the proposed method is verified through two real daily load curves with a time resolution of 5 min.

Abstract Modular multilevel converters (MMC) are widely used in power systems. However, relevant studies and reports show that MMC connected with the grid may cause oscillation problems. When the bridge arm resistance is large enough, the operating conditions will be stable because of overdamping in the system. According to this idea, a virtual damping strategy is proposed, which is equivalent to increasing the resistance of the bridge arm without any actual power loss. Meanwhile, from the perspective of the impedance‐based analysis method, it is explained that this virtual damping control strategy can improve the stability margin of the system to realize the suppression of oscillation. In order to verify the effectiveness of the control method, the MMC simulation model is established under Matlab/Simulink. The simulation results show that the proposed virtual damping control can effectively suppress the MMC oscillation problem.

Abstract Given the short time intervals in which short‐circuit faults occur in a power system, a certain time delay between the moment of a fault's inception in the system to the moment in which the fault is actually detected is introduced. In this small time margin, the high amplitudes of the fault current can deal significant damage to the power system. A technique to characterise different types of short circuit faults in a power system for real‐time detection, namely AB, BC, CA, ABC, AG, BG and CG faults (and normal operation), is presented based on the geometry of the curve generated by the Clarke transform of the three‐phase voltages of the power system. The process was conducted in real time using the HIL402 system and a Raspberry Pi 3, and all programming done in the Python programming language. It was concluded that the tested types of faults can be accurately characterised using the eigenvalues and eigenvectors of the matrix that characterises an ellipse associated with each fault: eigenvalues can be used to determine the fault inception distance and eigenvectors can be used to determine the type of fault that occurred. Next, the design of a machine learning model was done based on the previously mentioned characterisation technique. The model was embedded into a Raspberry Pi 3, thus enabling fault detection and classification in a base power system in real time. Finally, the accuracy of the model was tested under different measurement conditions, yielding satisfactory results for a selected set of conditions and overcoming the shortcomings presented in the current research, which do not perform detection and classification in real time.

Abstract A trilateral control for LCL filter‐based system is introduced by the authors with a single grid current sensor in weak grid conditions. The LCL filter increases the complexity when the uncertain nature of the grid comes into the picture. Moreover, the traditional three‐loop control technique requires three current sensors on the inverter side, three voltage sensors to sense voltage across the capacitor, and three current sensors on the grid side combined for sensing. A novel trilateral control technique utilising a single sensor is implemented to sense the grid current. This technique has reduced a considerable number of current sensors and voltage sensors. The α axis of grid current is proportional to sensed ‘a’ phase grid current. The β current in the utility grid is acquired by employing the controller reference quantities of the grid current. The computation of another variable, that is, the current in the inverter side inductor and the voltage across the capacitor, is executed by an estimation algorithm. The proposed technique provides the feature of reducing implementation financial value and weight that reduces the complexity and size of hardware. The synchronisation technique is executed by a modified dual second‐order generalised integrator digital phase‐locked loop for the grid‐connected converter. The implemented system offers the advantage of ease of implementation, good performance, and high stability. The validity of the proposed scheme in the implemented system is demonstrated by the simulated waveform obtained on the MATLAB/Simulink platform. Finally, the effectiveness of the proposed system is further justified by the experimental waveform procured from a prototype developed in the laboratory.

Abstract In this article, the stability of a synchronous motor generator pair (SMGP) used for improving the inertia of grid‐connected renewable energy systems is investigated. The useful operational regime for different sets of system parameters is identified, such as electromagnetic torque, damping co‐efficient, and inertia by employing bifurcation analysis to detect stability boundaries. For the first time, the existence of bi‐stable regimes for the SMGP with non‐linear stability analysis is revealed. The authors' analysis unravels the possibility of the system getting transited to unsafe operation even when the system is in the linearly stable region. The existence of the bistable regime indicates the possibility of the system becoming unstable even when the eigenvalues are in the left half plane. The authors also identify globally stable and globally unstable regimes in the parameter space. The safe operating range of inertia and damping co‐efficient values helps in the design of a suitable MGP set that is robust to frequency deviations, even with a low inertia source. With the recommended values of electromagnetic torque, the authors' analysis provides a safe operational regime for power generation from renewable energy sources.

Abstract The control technology of virtual synchronization generator (VSG) based on energy storage system is proposed to compensate for the inertia and damping loss caused by the grid connection of distributed generators. However, with the addition of virtual inertia and damping, VSG is prone to generate frequency and power oscillation, and the regulating ability is weakened. For the above problems, this paper analyzes the inertia and damping characteristics of the synchronous motor in the rotation of synchronous motors, and applies artificial neural network control to VSG. In intelligent control strategy, a multi‐input‐output radial basis function (RBF) neural network is first proposed to adaptively adjust virtual inertia and damping. Finally, an experimental platform with a rated power of 1 kW and a variable load of 1 kW was built to validate the proposed control strategy. The experimental results show that the artificial neural network control strategy has a better regulation ability.

Abstract In the three‐phase grid‐connected current‐source inverters (CSIs), the resonance result from the AC‐side CL filter and the quality of the grid‐current waveform under the unbalanced and harmonic grid voltage conditions are two issues deserving attention. To solve the two problems, a continuous control set‐model predictive control (CCS‐MPC) method based on the optimization theory is proposed in the two‐phase synchronous coordinate frame in this paper. The constructed cost function takes grid‐connected current and output current of the inverter as control target, and obtains its optimal solution under the least‐squares method, where the optimal inverter‐side current reference is composed of inductor‐current proportional feedback, capacitor‐voltage proportional feedback, and inverter‐side current steady‐state value feedforward. The proposed method can easily realize the normal operation of three‐phase grid‐connected CSIs because the method can be determined by only one parameter under the unbalanced and distorted grids. Furthermore, in the proposed method, the proportional feedback of capacitor voltage can effectively suppress the CL filter resonance. Finally, simulation and experimental results verify the effectiveness of the proposed control strategy.

Frequency security is the premise of realizing the net‐zero transition. A novel assessment scheme is proposed to quantify the frequency security levels of transmission systems under different inverter‐based resources (IBRs) and their control parameter combinations. A novel system frequency dynamic model is proposed as a parametric optimization method, where the saturation of generators, energy storage systems, and renewable energy sources is incorporated as differential algebra equations. This problem is further reformulated as a mixed‐integer linear programming problem to generate a sufficient amount of data under different IBR integration and control parameters. The frequency security assessment problem is formulated as a data‐driven multivariate classification problem, which is solved by the optimal classification tree (OCT) algorithm with better interptretionability. Simulations are conducted on a transmission system. Numerical results indicate that the proposed system frequency dynamic model can capture the frequency dynamic under different IBR reinforcement plans and the OCT can realize accurate classification of the synthetic data regarding frequency security. [ABSTRACT FROM AUTHOR]

A quadratic converter is now a days being investigated for high boost topologies. This paper presents the quadratic converter-based single-phase grid-connected system. For this system, the DC side control loop is regulated using a symmetrical shoot-through (SST)-based PWM. AC side current controller is implemented by the variation of modulation index in a nearly decoupled manner. The advantage of the suggested system is in terms of several controls particularly for input variations and load changes which are most beneficial in the case of grid application. The experimental results are presented to support the simulation results. The simulation results are achieved using MATLAB/SIMULINK. [ABSTRACT FROM AUTHOR]

Due to the deteriorating condition of the power grids and the increasing number of power outages, research on technical solutions to prevent blackouts in the Ukrainian energy system is becoming an extremely relevant and necessary task. This study aims to analyse technical solutions aimed at increasing the resilience of Ukraine's energy system to prevent blackouts and ensure reliable electricity supply to the population. The methods used include the analytical method, classification method, functional method, statistical method, synthesis method, and others. The study analysed modern technical solutions to improve the resilience of the Ukrainian energy system. The study included a detailed analysis of innovative approaches to modernizing the energy infrastructure. The primary objective was to study and evaluate modern technologies for designing new power lines with improved transmission characteristics. The study emphasized the importance of expanding automation systems to effectively monitor and control the state of the power grid. The study also identified the importance of using energy storage, developing renewable energy sources and improving electricity consumption forecasting systems. Flexible load management measures and improved cybersecurity systems were identified as critical. It is noted that the implementation of the project to provide electricity to the conditional area will be a key step in improving the lives of the local population and providing the necessary conditions for the development of the area's infrastructure and economy. In addition, the operation of generators for electricity supply will help to reduce the feeling of lack of electricity among residents and ensure reliability in the use of electricity for all sectors of the district's economy. The overall conclusion is that the implementation of such technical solutions will significantly increase the resilience of Ukraine's energy system. The results obtained make it possible to effectively avoid blackouts and ensure a sustainable electricity supply to the population. This allows energy companies, government agencies and other stakeholders to optimize energy resource management and make informed decisions to ensure the sustainable and efficient operation of Ukraine's energy system in the face of a possible blackout risk. [ABSTRACT FROM AUTHOR]