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The integration of multimegawatt fast chargers into local distribution grids is becoming increasingly relevant due to recent initiatives to push for higher charging power, especially for applications like heavy-duty vehicles. However, the high-power capacity of these chargers, especially when multiple units operate simultaneously at specific locations, raises several important considerations for the optimal design and integration of multimegawatt fast chargers. These include, for example, power electronics architectures and dedicated designs, grid stability, and the incorporation of renewable energy systems. Thus, this paper provides a comprehensive analysis of the key factors influencing the optimal integration of these ultra-high-power chargers, looking into impacts on medium-voltage (MV) networks, the design considerations for medium-voltage power electronics in DC chargers, and the potential of renewable energy systems to offset grid demand. Additionally, this paper explores the potential high-level communication requirements necessary for efficient and reliable charger operation, including a proposal for a robust communication interface layer stack. This investigation aims to provide a holistic understanding of the challenges and opportunities associated with integrating multimegawatt fast chargers into existing power systems, offering insights into the enhancement of both performance and sustainability. [ABSTRACT FROM AUTHOR]

Abstract Electric vehicles are pivotal for global climate solutions, particularly in emerging markets like Africa. Despite the continent’s clean energy potential, electric vehicle adoption faces unique challenges due to inefficiencies and reliability issues of distribution power grids. Here, we analyze the impacts of expanding electric vehicle fleets—private, commercial, and paratransit—on Nairobi’s power grid. We simulate traffic patterns, charging behaviors, and transformer utilization using local mobility data. Our results show that while electric commercial and paratransit fleets may improve power system efficiency, widespread private EV adoption could significantly strain the grid, increasing peak loads and transformer aging. Smart charging strategies could mitigate these issues, reducing potential transformer replacement costs by up to 40%. Our study highlights the importance of tailored demand management and infrastructure planning to support EV growth in African cities, providing critical insights for policymakers, utilities, and transport planners to facilitate sustainable electric mobility transitions.

The rising electricity costs, cost of space heating, and domestic hot water end up driving consumers toward reducing expenses by generating their electricity through devices like photovoltaic systems and efficient combined heat and power plants. When coupled with thermal systems via an energy management system (EMS) in a Multi-Energy System (MES), this self-produced electricity can effectively lower electricity and heating bills. However, MESs with EMSs can serve various purposes beyond cost reduction via self-consumption, such as reacting to variable electricity prices, meeting special grid connection conditions, or minimizing CO2 emissions. These diverse strategies create unique prosumer profiles, deviating significantly from standard load profiles. The potential threat to the power grid arises as grid operators lack visibility into which consumers employ which control strategies. This paper investigates the impact of controlled MESs on the power grid compared to average households and answers whether new control strategies affect the planning strategies of low voltage grids. It proposes a comprehensive four-step toolchain for the detailed simulation of thermal–electrical load profiles, MES control strategies, and grid dynamics. It includes a new method for the grid impact analysis of extreme and average bulk values. As a result, this study identifies three primary factors influencing distribution power grids by MESs. Firstly, the presence and scale of photovoltaic (PV) systems significantly affect extreme values in the grid. Secondly, MESs incorporating combined heat and power (CHP) and heat pump (HP) units impact the overall grid performance, mainly reflected in bulk values. Thirdly, the placement of an MES with heating systems, especially when concentrated in one feeder, plays a crucial role in grid dynamics. Despite the three distinct factors identified as impactful on the power grid, this study reveals that the various control strategies, despite leading to vastly different grid profiles, do not exhibit divergent impacts on buses, lines, or transformers. Remarkably, the impact of MESs remains consistently similar across the range of control strategies studied. Therefore, different control strategies do not pose an additional challenge to the grid integration of MESs. [ABSTRACT FROM AUTHOR]

Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically heat pumps (HPs) and electric vehicles (EVs). Additionally, it addresses photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to obtain economic benefits for private households and, at the same time, facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with phase change material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of an active distribution grid with eight houses using Power Factory software and DIgSILENT simulation language (DSL). This scalable size of the active distribution network facilitates the careful study of symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours. [ABSTRACT FROM AUTHOR]

The increasing adoption of battery storage units alongside private PV systems may prove to be a new challenge for distribution grid operators. This study explored the potential impact of marketing aggregated battery discharge power as a Fast Frequency Reserve (FFR) and its effect on the distribution grid stability. We investigated the efficacy of Line Voltage Regulators (LVRs) in mitigating voltage surges caused by simultaneous battery activation. For this purpose, a simulation was developed via Matlab (Version R2023a) to simulate the voltage at the nodes of an arbitrary distribution grid, using the feed-in and consumed power of the customers as the input. We applied the model to a distribution grid section in Sundsvall (Sweden). The results confirmed that LVRs can amplify voltage surges when their adjustments are not synchronized with the FFR activation. This study underscored the need for proactive measures to address the voltage maintenance challenges arising from the integration of battery storage units and renewable energy sources. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources such as wind and solar into the distribution grid introduces new complexities and instabilities to traditional electrical grids. This study tackles these challenges by optimizing the configurations of Modular Mobile Battery Energy Storage (MMBES) in urban distribution grids, particularly focusing on capacity-limited areas. Our method investigates five core attributes of energy storage configurations and develops a model capable of adapting to the uncertainties presented by extreme scenarios. This approach not only enhances the adaptability of energy storage systems but also equips decision-makers with proactive and flexible tools for decision-making in complex environments. Empirical evidence from the study shows that modular mobile energy storage significantly improves distribution grid performance by effectively managing the challenges posed by renewable integration. Furthermore, the research confirms that optimizing decision-makers’ cognitive parameters to align with subjective preferences ensures economic viability and enhances grid resilience. This study offers a new perspective and methodology for configuring energy storage, contributing to more flexible and reliable grid operations amidst widespread renewable integration.

---With the increasing request for integrating rooftop photovoltaic (PV) systems into the distribution grid, it is crucial to prevent the potential negative impacts of high PV penetration. Considering the uncertainty and variability characteristics of high rooftop PV penetration, a stochastic approach for determining PV hosting capacity (HC)—the maximum capacity of a PV system to receive electricity from the power distribution network before the network reaches an operating limit violation—is necessary. Therefore, this study proposes calculating PV hosting capacity using the Monte Carlo simulation. Unlike previous works, this paper uses real feeders as case studies, representing both urban and rural areas. The feeder representing an urban area with many industrial and business customers has a higher hosting capacity, 31 % of full load, compared to the feeder representing rural areas, which has 18 % of full load. By using actual feeders that represent urban and rural areas, more representative results for specific problems can be achieved without assuming urban and rural feeders have the same specific problems.

The process of electrification and digitalisation that the electricity distribution sector is currently undergoing is a true revolution, imposing more changes in recent years than in dozens of previous years. The electrification process involves the deployment of more and more so-called flexible devices in the distribution network, such as electric vehicles, heat pumps, photovoltaic systems with or without electrical storage. Electrification will bring obvious benefits such as decarbonisation of the energy system and reduction of dependence on fossil fuels, but it is also true that it will put the distribution grid under stress never seen before. The introduction of digital twins for the real-time management of distribution networks, the precise calculation of hosting capacity at each point of the network, the analysis of safety in protections, etc., is already a reality even in low-voltage networks. Some of these methodologies involve the use of precise mathematical models of distribution networks, and this is precisely what is presented in this article. A set of 3 test networks of European type networks, generated with real data representing a 100% urban low voltage distribution system, a 100% rural low voltage distribution system and a mixed medium/low voltage system representing a network in an industrial/commercial environment. As part of the work, OpenDSS models of these networks are attached, which can be of great value to the research community in many types of studies.

Distribution systems have limited observability, as they were a passive grid to consume power. Nowadays, increasing distributed energy resources turns individual customers into “generators,” and two-way power flow between customers makes the grid prone to power outages. This calls for new control methods with performance guarantees in the presence of limited system information. However, limited system information makes it difficult to employ model-based control, making performance guarantees difficult. To gain information about the model, active learning methods propose to disturb the system consistently to learn the nonlinearity. The exploration process also introduces uncertainty for further outages. To address the issue of frequent perturbation, we propose to disturb the system with decreasing frequency by minimizing exploration. Based on such a proposal, we superposed the design with a physical kernel to embed system non-linearity from power flow equations. These designs lead to a highly robust adaptive online policy, which reduces the perturbation gradually but monotonically based on the optimal control guarantee. For extensive validation, we test our controller on various IEEE test systems, including the 4-bus, 13-bus, 30-bus, and 123-bus grids, with different penetrations of renewables, various set-ups of meters, and diversified regulators. Numerical results show significantly improved voltage control with limited perturbation compared to those of the state-of-the-art data-driven methods.

In order to demonstrate the viability of wave energy converters usage in the Mediterranean islands, this paper explores the possibilities of optimized technical integration of wave energy converters into an island's electrical grid, with a particular focus on the island of Vis in the Adriatic Sea. After introduction, a comprehensive definition of wave energy was provided and classifications and technologies associated with wave energy converters were discussed. Additionally, different interface types between wave energy converters and the grid were discussed, as well as the potential impact of these converters on the island's grid and its electrical power system. Power matrices of two different wave energy converters (WECs), namely Wavestar and Wavebob, were adjusted to the specific sea conditions around Vis using the Froude scaling method. Also, Power Factory software tool was used to model the island's power grid, conduct simulations, and present electrical parameter values derived from real-world data collected on Vis. Various scenarios involving the integration of photovoltaic power plants and wave energy converters with the resulting data are both visually and numerically presented. Simulation results indicate a successful possibility of integrating wave energy converters to the microgrid of the island of Vis with the aim to archive near net zero electricity exchange with the mainland grid with improved power (voltage) quality in the island microgrid.

The rising demand for electric vehicles (EVs) in the face of limited grid capacity encourages the development and implementation of smart charging (SC) algorithms. Experimental validation plays a pivotal role in advancing this field. This article formulates a hierarchical mixed integer programming EV SC algorithm designed for low voltage (LV) distribution grid applications. A flexible receding horizon scheme is introduced in response to system uncertainties. It also considers the practical constraints in protocols, such as IEC/ISO 15118 and IEC 61851-1. The proposed algorithm is verified and assessed in a power hardware-in-the-loop testbed that incorporates models of real LV distribution grids. Furthermore, the algorithm's capabilities are examined through eight scenarios, out of which four focus on the uncertainties of the input data and two address the engagement of extra grid capacity restrictions. The results demonstrate that the SC algorithm adequately lowers the EV charging cost while fulfilling the charging demand, and substantially reduces the peak power as well as the overloading duration, even when faced with input data uncertainty. The additional grid restrictions in place are proven to improve peak demand reduction and overloading mitigation further. Finally, the limitations and potentials of the developed algorithm are scrutinized.

It is evident that renewable energy sources (RES), will soon be considered as primary energy source in electrical networks. However, the increased penetration of RES along with the variable charging profile of electric vehicles in the distribution grid will pose serious technical challenges such as network instability, protection malfunctioning, aggravated line, and low- and high-voltage ride through capabilities. Moreover, the frequent occurrence of natural disasters has posed different dimensional problems for distribution system engineers. Hence, owing to the immediate demand for resilient electrical systems, many researchers have presented planning and operating techniques for resiliency enhancement. Consequently, application of low-voltage ride through (LVRT) during grid transformation in small clusters has significant role. However, the implementation of LVRT in operational methods during the transformation of an electric grid after a natural disaster has a lean share in the literature. Moreover, the applicability of grid code during microgrid formation due to extreme events also drives the planning and operational methodology. Therefore, this article first substantiates the importance of grid codes and IEEE standards for the interconnection of RES plants into a grid. Additionally, the paper presents a succinct summary on various control strategies to overcome the challenges of fault ride through capability, reactive power support, and anti-islanding in integrated wind and solar plants. Thenceforth, the importance of LVRT visibility in resiliency enhancement validated by simulating the urban electrical grid using the DIgSILENT PowerFactory Software.

The integration of multimegawatt fast chargers into local distribution grids is becoming increasingly relevant due to recent initiatives to push for higher charging power, especially for applications like heavy-duty vehicles. However, the high-power capacity of these chargers, especially when multiple units operate simultaneously at specific locations, raises several important considerations for the optimal design and integration of multimegawatt fast chargers. These include, for example, power electronics architectures and dedicated designs, grid stability, and the incorporation of renewable energy systems. Thus, this paper provides a comprehensive analysis of the key factors influencing the optimal integration of these ultra-high-power chargers, looking into impacts on medium-voltage (MV) networks, the design considerations for medium-voltage power electronics in DC chargers, and the potential of renewable energy systems to offset grid demand. Additionally, this paper explores the potential high-level communication requirements necessary for efficient and reliable charger operation, including a proposal for a robust communication interface layer stack. This investigation aims to provide a holistic understanding of the challenges and opportunities associated with integrating multimegawatt fast chargers into existing power systems, offering insights into the enhancement of both performance and sustainability.

Distribution grids are complex networks containing multiple pieces of equipment. These components are interconnected, and each of them is described by various attributes. A knowledge graph is an interesting data format that represents pieces of information as nodes and relations between the pieces as edges. In this paper, we describe the proposed vocabulary used to build a distribution system knowledge graph. We identify the concepts used in such graphs and a set of relations to represent links between concepts. Both provide a semantically rich representation of a system. Additionally, we offer a few illustrative examples of how a distributed system knowledge graph can be utilized to gain more insight into the operations of the grid. We show a simplified analysis of how outages can influence customers based on their locations and how adding DERs can influence/change it. These demonstrative use cases show that the graph-based representation of a distribution grid allows for integrating information of different types and how such a repository can be efficiently utilized. Based on the experiments with distribution system knowledge graphs presented in this article, we postulate that graph-based representation enables a novel way of storing information about power grids and facilitates interactive methods for their visualization and analysis. [ABSTRACT FROM AUTHOR]

Active distribution grids that contain energy sources (so-called distributed generation or DG) are nowadays a reality. Besides the many benefits DGs bring to the distribution grid, some challenges are associated with their integration. Since there are DGs now in the distribution grid, the occurrence of islanding operation is possible. Since an islanding operation can be dangerous, it is necessary to have an effective method to detect it. In the last decade, scientists have made a great effort to develop and test various islanding detection methods (IDMs). Many approaches have been tested, and the methods based on computational intelligence (CI) have shown great potential. Among them, artificial neural networks (ANNs) gained most of the research attention. This paper focuses on ANN application for islanding detection. It gives an exhaustive review of the ANN types used for islanding detection, the types of input data, and their transformation to fit the ANNs. Furthermore, various applications based on specific input data, preprocessing types, different learning algorithms, real-time implementation, and various distribution models used for ANN are reviewed. This paper investigates the potential of ANNs to enhance islanding detection accuracy, reduce non-detection zone (NDZ), and contribute to an overall efficient detection method. [ABSTRACT FROM AUTHOR]

This research presents an improved calculation method for grid power losses, particularly focusing on the challenges posed by new energy access models. With the integration of electric vehicles and the rise of data centers, the demand for electrical energy has surged, leading to increased strain on grid stations and subsequent power losses. The proposed model aimed at reducing these power losses, while also examining existing systems to mitigate and analyze such issues. A significant contribution of this work is the application of the Random Forest machine learning algorithm, which enables efficient and accurate power flow calculations essential for optimizing grid performance. The proposed method is expected to enhance the grid’s ability to handle future energy demands and contribute to the sustainable development of electrical energy systems.

EVs are becoming more popular and widely used worldwide due to their environmentally friendliness as part of the world efforts to decrease the effects of climate change. Moreover, more users are buying EVs due to governmental incentives, development of charging technologies and cheaper maintenance costs. Thus, the increased electrical loads on the distribution grid caused by the charging of EVs can have negative impacts such as high voltage fluctuations, power losses and power overloads. Thus, a power system management solution is required to protect the distribution grid from the harmful effects of EVs charging through the regulation of the charging of EVs. In this paper, a deep RL-based EVs charging management solution is presented, while considering fast charging, conventional charging and V2G operation, in order to satisfy the requirements of the user and the utility. Deep RL is utilized to model the EV chargers and the EV users. The EV chargers are considered the RL environment and the EV users are considered the RL agent. Finally, the system was tested with a range of case studies using real-life EVs charging data, which proved the effectiveness and reliability of the system to protect the distribution grid and satisfy the EV user’s charging requirements.

Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically heat pumps (HPs) and electric vehicles (EVs). Additionally, it addresses photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to obtain economic benefits for private households and, at the same time, facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with phase change material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of an active distribution grid with eight houses using Power Factory software and DIgSILENT simulation language (DSL). This scalable size of the active distribution network facilitates the careful study of symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours.

Under the vision of carbon neutrality, carbon dioxide emission allowance targets are gradually decreasing, and clean power sources will penetrate in an ultra-high proportion. The traditional distribution grid dispatching model needs to solve the problems of carbon emission compliance and strong intermittent leveling of clean power sources. Based on the analysis of the coupling relationship between carbon emission index and the economic cost of electric power, this paper proposes a novel dispatching model for the future state distribution grid with carbon and electric power couping, proposes an optimal dispatching strategy for distribution grid based on the second-order cone planning model for several scenarios of increasing carbon emission cost of system operaion, and verifies the effectiveness of the proposed method in the improved IEEE 33-node system. The example results show that the output and power generation cost of each distribution network change after considering the carbon emission index.

Abstract For the past few years, the world has seen a great shift toward renewable energy resources from conventional ones. But the ever‐increasing integration of distributed generation (DG) to the electrical network leads to integration limiting constraints like overvoltage, under voltage, harmonics, equipment ampacity violations, and failure of protection schemes. Therefore, an extensive investigation of the methodologies in which DGs can be incorporated into the electrical network is presented in this manuscript. This article provides an extensive review of all the hosting capacity (HC) terms, references, limiting constraints of the studied networks, geographical segregation, and their determination methodologies. Moreover, the factors defining the HCs of various networks and the architectures employed to increase them, are also explained briefly in the conducted review study.

Demand response plays an important role in improving the balance of power generation and consumption between the distribution grid and photovoltaic (PV) microgrids. However, due to the uncertainty and volatility of PV output, as well as the different operation goals of PV microgrids, a conventional single-tier optimization approach is infeasible to realize the coordinated interaction between the distribution grid and PV microgrids. To address these challenges, we propose a second-order cone and improved consensus algorithm-based hybrid bilevel optimization algorithm for the interaction between the distribution grid and PV microgrids. First, we construct price-based and incentive-based differentiated demand response models to deal with various supply and demand dynamics of the distribution grid and PV microgrids. Building upon this foundation, we construct a hybrid bilevel optimization model. In the lower level, distributed optimization is adopted, and an improved consensus algorithm is used to optimize power output of PV microgrids to maximize the revenue based on output power of upper-level generator sets. In the upper level, centralized optimization is adopted, and second-order cone programming is employed to minimize the grid loss in the distribution grid based on the power output of lower-level PV microgrids. Hybrid bilevel optimization is iterated until the convergence condition is satisfied. Simulation results verify the proposed algorithm for achieving a coordinated interaction between the distribution grid and PV microgrids.

Most existing models for estimating electric system impacts from windstorms tend to have detailed representation only for the electric or only for the meteorological system. As a result, there is little evidence on how models with detailed electric systems and realistic wind gust field representations would perform in different windstorm cases. This work explores the evidence for the ability of such a fragility-based model to generate realistic spatiotemporal lost load profiles for the most impactful windstorm cases in Finland. The literature review shows multiple driving factors for windstorm impacts that are difficult to assess analytically, and similarities between the most impactful windstorms. All the available interruption data for thirteen years were analyzed, with their grouping by individual storm and calm periods. The fixing of time distribution fits for these periods show most faults as being within the 20% uncertainty bounds of the severity-dependent distribution trendlines. The medium-voltage electricity grid impact model with national coverage was applied for the three most impactful and most recent windstorm cases, with the model calibrated for one case. The generated spatiotemporal lost load profiles in all cases recreate historic profiles within the similar error margins of approximately 20%. [ABSTRACT FROM AUTHOR]

The increasing demand for precise load forecasting for distribution networks has become a crucial requirement due to the continual surge in power consumption. Accurate forecasting of peak loads for distribution networks is paramount to ensure that power grids operate smoothly and to optimize their configuration. Many load forecasting methods do not meet the requirements for accurate data and trend fitting. To address these issues, this paper presents a novel forecasting model called Prophet-LSTM, which combines the strengths of the Prophet model's high trend fitting and LSTM model's high prediction accuracy, resulting in improved accuracy and effectiveness of peak load forecasting. The proposed algorithm models the distribution network peak load using the Prophet-LSTM algorithm. The researchers then analyzed the experimental data and model of the algorithm to evaluate its effectiveness. We found that the Prophet-LSTM algorithm outperformed the Prophet and LSTM models individually in peak load prediction. We evaluate the proposed model against commonly used forecasting models using MAE (mean absolute error) and RMSE (root mean square error) as evaluation metrics. The results indicate that the proposed model has better forecasting accuracy and stability. As a result, it can predict the peak load of distribution networks more accurately. In conclusion, this study offers a valuable contribution to load forecasting for distribution networks. The proposed Prophet-LSTM algorithm provides a more precise and stable prediction, making it a promising approach for future applications in distribution network load forecasting. [ABSTRACT FROM AUTHOR]

Various factors, particularly an increasing share in underground cable lines, can lead to excessive reactive power demand of distribution grids to their respective transport grids and, thus, to the need of costly compensation measures. At the same time, the occurrence of battery-electric cars is considerably increasing. It is, therefore, conceivable to provide reactive power as an inexpensive "waste product" by the charging converters of the cars as a solution to the aforementioned problem. In this research work, the idea is examined for its feasibility by means of extensive time series simulation using the example of a real existing distribution grid. It was found that, with a realistic number of charging points—especially public ones with integrated converter electronics—not only can the reactive power demand be reduced to an acceptable level but also additional reactive power can be provided as a service to the transport grid. [ABSTRACT FROM AUTHOR]

The widespread adoption of electric vehicles poses certain challenges to the distribution grid, which refers to the network of power lines, transformers, and other infrastructure that delivers electricity from power plants to consumers. This higher demand can strain the distribution grid, particularly in areas with a high concentration of electric vehicles. Grid operators need to ensure that the grid infrastructure can handle this additional load and prevent overloading and consequences in terms of additional losses. As part of the task, a methodology was developed for the assessment of the electricity consumption of battery electric vehicles in Slovenia. The approach used for the calculation includes the number of electric cars, average consumption, distance travelled and efficiency of the system. Additionally, the results of the modelling approach for an integrated distribution grid model in terms of steady-state simulations are presented. The regular situation of the power losses within the distribution grid is managed together with an optimal result. In this sense, an application of the particle swarm optimisation-based strategy is suggested to minimise reliance on grid systems. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical Digital Twin architecture and implementation that uses real-time simulation to emulate the physical grid and support grid planning and operation. With the demand for detailed grid information for automated grid operations and the ongoing transformation of energy systems, the Digital Twin can extend data acquisition by establishing a reliable real-time simulation. The system uses observer algorithms to process model information about the voltage dependencies of grid nodes, providing information about the dynamic behavior of the grid. The architecture implements multiple layers of data monitoring, processing, and simulation to create node-specific Digital Twins that are integrated into a real-time Hardware-in-the-Loop setup. The paper includes a simulation study that validates the accuracy of the Digital Twin, in terms of steady-state conditions, dynamic behavior, and required processing time. The results show that the proposed architecture can replicate the physical grid with high accuracy and corresponding dynamic behavior. [ABSTRACT FROM AUTHOR]

The growing integration of renewable energies into electricity grids leads to an increase of grid congestions. One countermeasure is the curtailment of renewable energies, which has the disadvantage of wasting energy. Forecasting congestion provides valuable information for grid operators to prepare and instruct countermeasures to reduce these energy losses. This paper presents a novel approach for congestion prediction in distribution grids (i.e. up to 110 kV) considering the n-1 security criterion. For this, our method considers node injections and power flow and combines three artificial neural network models. The analysis of study results shows that the implemented neural networks within the presented approach perform better than naive forecasts models. In the case of vertical power flow, the artificial neural networks also show better results than comparable parametric models: average values of the mean absolute errors relative to the parametric models range from 0.89 to 0.21. A high level of accuracy can be achieved for the neural network that predicts the loading of grid components with a F1 score of 0.92. Further, also with a F1 score of 0.92, this model shows higher accuracy for the distribution grid components than for those of the transmission grid, which achieve a F1 score of 0.84. The presented approaches show good potential to support grid operators in congestion management.

Recent studies have developed a power electronic device known as the power router, able to fully control the power flows over the connected power lines. The present paper aims to extend the application of power routers to an electrical grid and explore its characteristics and limitations. It defines the power router grid concept, including the conditions for interconnection and architecture; it proposes a mathematical modelling methodology, analyses its operational requirements, and explores the degree of flexibility the proposed grid architecture provides. Finally, two case studies are presented to show the application of the power router grid in a practical example.

In the distribution grids, grounding fault transfer devices have been widely used to suppress the transient arc grounding fault. Based on the grounding fault transfer device, a high sensitivity faulty section location method is proposed in this paper. When an arc grounding fault occurs, the fault phase bus is earthed by the grounding fault transfer device, which can simultaneously realize arc suppression and faulty section location. There are two grounding transient processes in this method, including fault grounding and active grounding of the faulty phase bus. During the aforementioned processes, the similarity degree of the zero mode current on both directions of the faulty section varies from low to high, whereas there is almost no change in the healthy section. The ratio of S transform energy relative entropy is used to represent the level of change of the similarity degree on both directions of each section. The section with the maximal entropy ratio is determined as the faulty section. The proposed method does not need to set the threshold and presents higher sensitivity for an arc fault. Simulation proves the sensitivity and accuracy of the proposed method.

The journey towards transportation electrification started with electric vehicles and has attracted more and more attention on a global scale in recent years. EVs are seen as a substantial, effective, and urgent solution for transportation electrification. In this paper, we investigate the operation requirements for integrating charger stations into the distribution grid in Vietnam. We also propose a simple evaluation method for assessing the feasibility of integrating planned unidirectional EV chargers into the distribution grid. The assessment method is applied to two main distribution feeders in Danang, Vietnam, where the new charger stations are already planned to be deployed in 2025 and 2030. The results showed that with addition of pre-planned EV chargers, both feeders still meet operation requirements in 2025 and 2030. However, the feeder with voltage indices close to the limit needs to be considered for an upgrade in configuration. [ABSTRACT FROM AUTHOR]

For the past few years, the world has seen a great shift toward renewable energy resources from conventional ones. But the ever‐increasing integration of distributed generation (DG) to the electrical network leads to integration limiting constraints like overvoltage, under voltage, harmonics, equipment ampacity violations, and failure of protection schemes. Therefore, an extensive investigation of the methodologies in which DGs can be incorporated into the electrical network is presented in this manuscript. This article provides an extensive review of all the hosting capacity (HC) terms, references, limiting constraints of the studied networks, geographical segregation, and their determination methodologies. Moreover, the factors defining the HCs of various networks and the architectures employed to increase them, are also explained briefly in the conducted review study. [ABSTRACT FROM AUTHOR]

This research focuses on modeling and simulating voltage control of passive islanding detections with distributed generation. This research presents how reactive power generation and/or absorption can be utilized to partake voltage control in medium voltage distribution through multi-microgrids for passive islanding detection with rule-based mathematical model and artificial neural network (ANN). With the increased emphasis on renewable energy, modern power grids have become more reliant to smaller distributed generation units. Unlike traditional power grids that rely on larger centralized units, the detection of islanding events in these grids is more complex. However, it is still important to maintain the connection to the power grid to maintain high stability of the system and is also important to deenergize the grid when an islanding event happens, to protect the workers that may be working on the grid to clear the cause of the islanding. Islanding detection methods are categorized into passive, active and communication based. Active methods detect islanding by measuring the influence of noise they add to the grid, to predict the size of the grid, which reduces the quality of the power provided on that grid. Communication-based methods are expensive and highly reliable on the communications infrastructure, which limits their application. Unlike existing methods, the proposed method of ANN relies on the instantaneous current and voltage values to detect any islanding with an accuracy of 97.89% precisely. These values are directly fed to the ANN, i.e., without applying any feature extraction, so that, faster and more accurate decisions are made. The proposed method is expected to combine the high accuracy of ANNs with the faster change in instantaneous values, so that, faster and more accurate detection can be achieved. [ABSTRACT FROM AUTHOR]

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This paper presents a method for integrating industrial consumers owning compensation systems as alternative reactive power sources into grid operating processes. In remuneration, they receive a market-based provision of reactive power. The aim is to analyze the potential of reactive power compensation systems of industrial companies connected to medium-voltage (10 kV–30 kV) AC grids in order to increase the reactive power ability of distribution grids. Measurement methods and reactive power potential results of six industrial companies are presented to characterize the amount and temporal availability of their reactive power potential. The presented approach for using the decentralized reactive power potential is a centralized reactive power control method and is based on optimal power flow (OPF) calculations. An optimization algorithm based on linear programming is used to coordinate a reactive power retrieval tuned to the actual demand. The influencing quantities are the current grid status (voltage and load flow capacity reserves at grid nodes and power lines) and the current reactive power potential of the reactive power sources. The compensation impact of six measured industrial companies on an exemplary medium-voltage grid is shown by an application example. [ABSTRACT FROM AUTHOR]

Active distribution grids that contain energy sources (so-called distributed generation or DG) are nowadays a reality. Besides the many benefits DGs bring to the distribution grid, some challenges are associated with their integration. Since there are DGs now in the distribution grid, the occurrence of islanding operation is possible. Since an islanding operation can be dangerous, it is necessary to have an effective method to detect it. In the last decade, scientists have made a great effort to develop and test various islanding detection methods (IDMs). Many approaches have been tested, and the methods based on computational intelligence (CI) have shown great potential. Among them, artificial neural networks (ANNs) gained most of the research attention. This paper focuses on ANN application for islanding detection. It gives an exhaustive review of the ANN types used for islanding detection, the types of input data, and their transformation to fit the ANNs. Furthermore, various applications based on specific input data, preprocessing types, different learning algorithms, real-time implementation, and various distribution models used for ANN are reviewed. This paper investigates the potential of ANNs to enhance islanding detection accuracy, reduce non-detection zone (NDZ), and contribute to an overall efficient detection method.

Distribution grids are complex networks containing multiple pieces of equipment. These components are interconnected, and each of them is described by various attributes. A knowledge graph is an interesting data format that represents pieces of information as nodes and relations between the pieces as edges. In this paper, we describe the proposed vocabulary used to build a distribution system knowledge graph. We identify the concepts used in such graphs and a set of relations to represent links between concepts. Both provide a semantically rich representation of a system. Additionally, we offer a few illustrative examples of how a distributed system knowledge graph can be utilized to gain more insight into the operations of the grid. We show a simplified analysis of how outages can influence customers based on their locations and how adding DERs can influence/change it. These demonstrative use cases show that the graph-based representation of a distribution grid allows for integrating information of different types and how such a repository can be efficiently utilized. Based on the experiments with distribution system knowledge graphs presented in this article, we postulate that graph-based representation enables a novel way of storing information about power grids and facilitates interactive methods for their visualization and analysis.

Harmonic resonance endangers seriously safe and reliable operation of power grid and an appropriate resonance harmonic analysis method is of great importance. The traditional mode analysis method is a kind of main method to analyze harmonic resonance of power grid so to obtain various harmonic information such as resonance frequency, maximum excitation node and observation node as well as participation factor. However, its calculation efficiency is lower and has no way to judge accurately the maximum excitation node in the harmonic resonance of the distribution power grid. The improved mode analysis method proposed in this paper combines the frequency spectrum analysis with the traditional mode analysis method, and the condition of whether the harmonic resonance could occur at the determined excitation node at resonance frequency is added. Taking the distribution power grid in Bonan Oil area of Shengli Oilfield as an example, this paper analyzes the harmonic resonance of distribution network by using traditional mode analysis method and improved mode analysis method respectively. By comparing the analysis results of the two methods, the superiority of the improved mode analysis method is verified. The analysis of harmonic resonance of distribution network in Bonan Oil area shows that the improved mode analysis method proposed in this paper can accurately determine the harmonic resonance frequency and the highest excitation node of distribution network, while the traditional mode analysis method could not accurately judge the highest excitation node. In addition, the calculation time of the improved mode analysis method is only 17% of that of the traditional mode analysis method, which greatly improves the analysis efficiency.

The global energy and environmental crisis promotes the development of electric vehicles (EVs), and the rational planning of EV fast charging stations is an important influencing factor for their development. In this paper, for the EV fast charging station capacity planning problem, a joint-optimization model for optimal planning of EV fast charging stations and the economic operation of a distribution network is constructed, considering the impact of user preference selection and EV access on the regional distribution network. To address the problems of low efficiency and local convergence found in traditional heuristic optimization algorithms, an improved krill swarm optimization algorithm (CKHA) that introduces chaotic optimization parameters to make the initial population as uniformly distributed as possible is proposed to find the optimal planning scheme for EV fast charging stations. The case results show that the optimal planning model and its solution method are effective. [ABSTRACT FROM AUTHOR]

The need to evolve cleaner, decentralized, and digitalized energy distribution systems and services includes the electrification of means of transport as Electric Vehicles (EVs) achieve a greater market share. In this context, this work presents and applies, through a case study, the proposal of a new methodology for calculating the hosting capacity of EVs in low- and medium-voltage distribution systems. The proposal of a new methodology that combines deterministic and stochastic methods, while considering several operational criteria, as well as being applicable in both low and medium voltage, shows itself as a more germane and innovate approach. The results obtained demonstrated that the hosting capacity of EVs for the transformers pertinent to the distribution system under study is 100% for more than 50% of the simulations performed. The conductor overload criterion is the main limiting factor, representing 36.69% of violations for the 3.6 kW charger and 52.14% for the 7 kW charger. According to the executed evaluated projections, the distribution system under investigation will posses the capacity to host the growth of EVs in any of the scenarios presented in this study until 2025 for the 3.6 kW charger. [ABSTRACT FROM AUTHOR]

Improving air quality, reducing greenhouse gas emissions, and achieving independence from fossil fuels have led most countries towards deploying solar photovoltaics (PV) in the power distribution grid and electrifying the transportation fleet. Internal combustion engine (ICE) vehicles are, in particular, one of the main culprits of injecting greenhouse gas emissions into the atmosphere, making electric vehicles (EVs) an important tool in combating climate change. Despite their considerable environmental and economic benefits, the integration of PVs and EVs can introduce unique operational challenges for the power distribution grid. If not coordinated, high penetration of PVs and EVs can result in variety of power quality issues, such as instances of overvoltage and undervoltage, frequency fluctuations, and/or increased losses. This paper proposes a mixed-integer multi-objective nonlinear optimization model for optimal energy dispatch in a power distribution grid with high penetration of PV and EV resources. The model proposed here is an extension of the traditional voltage and var optimization (VVO) into a comprehensive and coordinated control of voltage, active power, and reactive power. A modified version of the IEEE 123-bus test distribution system is used to demonstrate the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

This paper analyses the topology of a regional power system distribution grid. This research improves the efficiency of the electric power system's operation by using upgrading the redesign (reengineering) methods of topological structures within distribution grids. The research object is an electric power system that consists of generation, transmission and distribution parts and requires reengineering. The subject of research is the re-engineering of the topology of the power system distribution grid. To achieve the research purpose, modifications of k-means algorithm as well as the small step algorithm based on the statistical analysis, clustering and minimum spanning tree development methods of Prim and Kraskal are used. The modifications described in this paper allow for optimization of the network based on user needs, properties of the operating grid elements, and other additional constraints for flexibility and generality. Given the varying parameters, this method provides the means to redesign parts of the distribution grid, keeping its certain elements safe from displacement, but also the means to redesign the whole distribution grid, including changes to the number and location of transformer substations and transmission lines. Conclusions. To solve the problem of determining the territorially close groups of consumers in the paper, it was proposed to use the k-means clustering algorithm. This algorithm allows us to divide consumer sets into clusters, so that coordinates of their centres will be recommended as locations of transformer substations. The modernization of the k-means algorithm was proposed by developing procedures for adding and combining clusters using different strategies for determining starting centroids. Based on this, a method for reengineering the topological structures of regional electric power systems in terms of the possibility of their fundamental restructuring was developed. The results of this research may be useful to various enterprises, organizations or institutes dealing with the elaboration or design of electric power system development on the corporate, regional or local level.

In recent years, global energy systems have experienced multiple disruptions from known threats at unprecedented severity, causing costly impacts in broader and unexpected domains. This work attempts to improve the understanding of such severity-dependent impact change for the historically unprecedented but meteorologically plausible windstorm on the present Finnish electricity grid. The wind gust speed field of the unprecedented windstorm is obtained by scaling the field of the historically most impactful windstorm upwards by 24%, a value obtained with the extreme-value-theory-based method. Windstorm impacts on the electricity supply are computed with a fragility-based electricity grid impact model, accounting for medium voltage line faults and repairs throughout the country. The lost load from 24% higher wind gust speeds increases tenfold. Impacts are limited by the significant cabling of powerlines done since 2011. The obtained spatiotemporal lost load profile provides a basis for the identification and quantification of extreme windstorm costs as well as a realistic case for broader emergency preparedness exercises. The former application is illustrated by the preliminary cost-benefit assessment for cabling in the case of an unprecedented windstorm. Finally, the reevaluation of currently used cost rates calls for an account of time dependency, critical services, and impacts on smaller economic and population segments. • 24% stronger windstorm increases the number of line faults fivefold and LL tenfold • Unprecedented LL: peak – 45% of vulnerable; after a week – 32% of historical peak • Unprecedented VoLL savings from line cabling cover at least half of its costs • New method to generate spatiotemporal value field for unprecedented windstorm • Socioeconomic bases for reevaluation of cost driving factor in a major disruption [ABSTRACT FROM AUTHOR]

The real-time monitoring of electric distribution grids via state estimation is a fundamental requirement to deploy smart automation and control in the distribution system. Due to the large size of distribution networks and the poor coverage of measurement instrumentation on the field, designing fast state estimation algorithms and achieving accurate results are two major challenges associated to distribution system state estimation. In this paper, an efficient and accurate solution for performing state estimation in multi-feeder radial distribution grids is presented. The proposed algorithm is based on a two-step approach. In the first step, state estimation is performed in parallel on the different feeders suitably processing the available measurements and pseudo-measurements and taking into account their uncertainty characteristics. In the second step, the results on each feeder are post-processed to refine the estimations and to improve the accuracy performance. To this purpose, the second step considers how measurement uncertainties propagate towards the final estimates and how measurements shared among the feeders could adversely affect the final estimation. Performed tests show that the conceived design leads to accuracy performance very close to those achievable by running state estimation on the full grid. At the same time, the parallelization of the estimation process on the different feeders allows decentralizing the state estimation problem, with the associated benefits in terms of computation time and distribution of the communication and storage requirements.

Due to the ongoing expansion of renewable energies and the increasing energy demand of household consumers, e.g., due to the electrification of the mobility and heating sectors, grid bottlenecks and voltage band violations in low-voltage grids in Germany are expected to occur more frequently in the future. Dynamic pricing, implemented through digitization, is seen as one option to incentivize flexible and responsive electricity use of household consumers. Flexible consumption of household consumers could make grid utilization more efficient. So far, most studies on the effects of dynamic pricing focus on only one tariff for all household consumers within one low-voltage grid, whereas, in reality, households are free to choose between many different static and dynamic electricity tariffs. As previous studies have shown, the choice of tariff plays a major role in how flexibility is used. To address this research gap, we developed a model which integrates households and their static and flexible consumption and generation units into a home energy management system (HEMS) and minimizes the households’ power purchase costs. The model considers a range of dynamic tariffs that are already available today and includes the decision-making of heterogeneous household customers regarding the choice of tariff. Load flow calculations are then used to determine and analyze the effects on a low-voltage grid area. With this methodology including the free choice of tariff we go beyond the state of the art and paint a more realistic picture of the effects of dynamic tariffs on low-voltage grids. Our results show that, even though dynamic tariffs lead to increased peak demand at the level of individual households, peak loads are spread more widely within a grid area as the result of households choosing different tariffs based on economic considerations. Due to these effects, dynamic pricing has positive effects on grid utilization and could reduce the need for grid expansion.

As the distribution grid moves toward a tightly monitored network, it is important to automate the analysis of the enormous amount of data produced by the sensors to increase the operators situational awareness about the system. In this paper, focusing on microphasor measurement unit (μPMU) data, we propose a hierarchical architecture for monitoring the grid and establish a set of analytics and sensor fusion primitives for the detection of abnormal behavior in the control perimeter. Due to the key role of the μPMU devices in our architecture, an optimal μPMU placement with limited number of sensors is also described that finds the best location of the devices with respect to our rules. The effectiveness of the proposed methods are tested through the synthetic and real μPMU data.

We consider the field of machine learning and where it is both useful, and not useful, for the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper we consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors - such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals - such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis are becoming significant, with more data and multi-objective concerns. Efficient applications of analysis and the machine learning field are being considered in the loop. This paper describes benefits and limits of present machine-learning applications for use on the grid and presents a series of case studies that illustrate the potential benefits of developing advanced local multi-variate analytics machine-learning-based applications.

As the distribution grid moves toward a tightly monitored network, it is important to automate the analysis of the enormous amount of data produced by the sensors to increase the operators situational awareness about the system. In this paper, focusing on microphasor measurement unit (μPMU) data, we propose a hierarchical architecture for monitoring the grid and establish a set of analytics and sensor fusion primitives for the detection of abnormal behavior in the control perimeter. Due to the key role of the μPMU devices in our architecture, an optimal μPMU placement with limited number of sensors is also described that finds the best location of the devices with respect to our rules. The effectiveness of the proposed methods are tested through the synthetic and real μPMU data.

Local Energy Markets (LEMs) were recently proposed as a measure to coordinate an increasing amount of distributed energy resources on a distribution grid level. A variety of market models for LEMs are currently being discussed; however, a consistent analysis of various proposed grid tariff designs is missing. We address this gap by formulating a linear optimization-based market matching algorithm capable of modeling a variation of grid tariff designs. A comprehensive simulative study is performed for yearly simulations of a rural, semiurban, and urban grids in Germany, focusing on electric vehicles, heat pumps, battery storage, and photovoltaics in residential and commercial buildings. We compare energy-based grid tariffs with constant, topology-dependent and time-variable cost components and power-based tariffs to a benchmark case. The results show that grid tariffs with power fees show a significantly higher potential for the reduction of peak demand and feed-in (30–64%) than energy fee-based tariffs (8–49%). Additionally, we show that energy-based grid tariffs do not value the flexibility of assets such as electric vehicles compared to inflexible loads. A postprocessing of market results valuing the reduction of power peaks is proposed, enabling a compensation for the usage of asset flexibility. [ABSTRACT FROM AUTHOR]