Search

Multi-port DC power electronic transformer (PET) is a core equipment for achieving transformation of different voltage levels and flexible interconnection of different DC buses in a DC distribution system. It is capable of bidirectional energy flow, flexible regulation of power flow, port fault isolation, and other functions. A new five-port DC transformer topology based on soft switching technology is proposed in this paper. In this topology, different DC voltage levels can be interconnected efficiently, such as 20 kV, 750 V, ±375 V, and 300 to 500 V adjustable. The control of each port is simple and flexible. The output voltage is stable, and they are independent of each other, which can improve the system reliability. The topology of the proposed multi-port DC transformer is introduced in detail. The working principle, control strategy, and parameter design method of the transformer are analyzed. Simulations and experimental results are provided to validate the theoretical analysis. [ABSTRACT FROM AUTHOR]

A growing interest in offshore wind energy in the Mediterranean Sea has been recently observed thanks to the potential for scale-up and recent advances in floating technologies and dynamic cables: in the Italian panorama, the offshore wind connection requests to the National Transmission Grid (NTG) reached almost 84 GW at the end of September 2024. Starting from a realistic estimate of the offshore wind power plants (OWPPs) to be realized off the southern coasts in a very long-term scenario, this paper presents a novel optimization procedure for meshed AC offshore network configuration, aiming at minimizing the offshore wind generation curtailment based on the DC optimal power flow approximation, assessing the security condition of the whole onshore and offshore networks. The reactive power compensation aspects are also considered in the optimization procedure: the optimal compensation sizing for export cables and collecting stations is evaluated via the AC optimal power flow (OPF) approach, considering a combined voltage profile and minimum short circuit power constraint for the onshore extra-high voltage (EHV) nodes. The simulation results demonstrate that the obtained meshed network configuration and attendant re-active compensation allow most of the offshore wind generation to be evacuated even in the worst-case scenario, i.e., the N1 network, full offshore wind generation output, and summer line rating, testifying to the relevance of the proposed methodology for real applications. [ABSTRACT FROM AUTHOR]

The rising energy demand, coupled with increased integration of distributed energy resources (DERs) and fluctuating renewable generation, underscores the need for effective load management within energy communities. This paper addresses these challenges by implementing effective, constraint-aware load shifting within microgrid-based energy communities. Specifically, the goal of this study is to flatten the electrical load profile of a High-Voltage (HV)/Medium-Voltage (MV) power transformer. The load of a central power transformer includes (a) the diverse, fluctuating electrical and thermal demands of buildings within the energy community and (b) the load of the area supplied by the substation excluding the energy community loads. To achieve a flattened load profile, we apply time shifting to both electrical and heating, ventilation, and air conditioning (HVAC) loads of the energy community, allowing for a redistribution of energy consumption over time. This approach entails shifting non-critical loads, particularly those related to HVAC and other building operations, to off-peak periods. The methodology considers critical operational constraints, such as maintaining occupant thermal comfort, ensuring compliance with building codes, and adhering to technical specifications of HVAC and electrical systems and microgrid organized energy communities. Detailed simulations were conducted to prove the effectiveness of this constraint-aware load-shifting approach. [ABSTRACT FROM AUTHOR]

In the past, providing an online and real-time response to cyber–physical attacks in large-scale power microgrids was considered a fundamental challenge by operators and managers of power distribution networks. To address this issue, an innovative framework is proposed in this paper, enabling real-time responsiveness to cyberattacks while focusing on the techno-economic energy management of large-scale power microgrids. This framework leverages the large change sensitivity (LCS) method to receive immediate updates to the system's optimal state under disturbances, eliminating the need for the full recalculation of power flow equations. This significantly reduces computational complexity and enhances real-time adaptability compared to traditional approaches. Additionally, this framework optimizes operational points, including resource generation and network reconfiguration, by simultaneously considering technical, economic, and reliability parameters—a comprehensive integration often overlooked in recent studies. Performance evaluation on large-scale systems, such as IEEE 33-bus, 69-bus, and 118-bus networks, demonstrates that the proposed method achieves optimization in less than 2 s, ensuring superior computational efficiency, scalability, and resilience. The results highlight significant improvements over state-of-the-art methods, establishing the proposed framework as a robust solution for real-time, cost-effective, and resilient energy management in large-scale power microgrids under cyber–physical disturbances. [ABSTRACT FROM AUTHOR]

A matrix converter (MC) converts an AC source voltage into a variable-voltage variable-frequency AC output voltage (direct AC-AC) without an intermediate DC-link capacitance. By eliminating the traditional DC-link capacitor, MCs can achieve higher power densities and reliability when compared to conventional AC-DC-AC converters. MCs also offer the following characteristics: total semiconductor solution, sinusoidal input and output currents, bidirectional power flow and controllable input power factor. This paper reviews the history, recent developments and commercialization of MCs and discusses several technical requirements and challenges, including bidirectional switches, wide bandgap (WBG) opportunities using GaN and SiC, overvoltage protection, electromagnetic interference (EMI) and ride-through in motor drive applications. MC design solutions and operation are discussed, including a comparison of control and modulation techniques as well as the detailed development of space vector modulation (SVM) to provide a deep insight into the control implementation and results. The paper concludes with compelling motor drive innovation opportunities made possible by advanced MCs including fully integrated and multiphase systems. For conventional MCs, size reductions of 30% are reported, as well as efficiencies of 98% and low input current total harmonic distortion of 3–5%. [ABSTRACT FROM AUTHOR]

Research in renewable energy sources and microgrid systems is critical for the evolving power industry. This paper examines the operational behavior of both pico- and nano-grids during transitions between grid-connected and islanded modes. Simulation results demonstrate that both grids effectively balance the power flow, regulate the state of charge (SOC), and stabilize the voltage during dynamic operational changes. Specific scenarios, including grid disconnection, load sharing, and weather-based energy fluctuations, were tested and validated. This paper models both pico-grids and nano-grids at the Singapore Institute of Technology Punggol Campus, incorporating solar PVs, energy storage systems (ESSs), power electronic converters, and both DC and AC loads, along with utility grid connections. The pico-grid includes a battery storage system, a single-phase inverter linked to a single-phase grid, and DC and AC loads. The nano-grid comprises solar PV panels, a boost converter, a battery storage system, a three-phase inverter connected to a three-phase grid, and AC loads. Both the pico-grid and nano-grid are configurable in standalone or grid-connected modes. This configuration flexibility allows for a detailed operational analysis under various conditions. This study conducted subsystem-level modelling before integrating all components into a simulation environment. MATLAB/Simulink version R2024b was utilized to model, simulate, and analyze the power flow in both the pico-grid and nano-grid under different operating conditions. [ABSTRACT FROM AUTHOR]

The utilization of Harris Hawks Optimization (HHO) for Multi-Objective Optimal Power Flow (MaO-OPF) challenges presented in this paper is both novel and compelling, as this approach has not been previously applied to these types of optimization problems. HHO, which shares characteristics with ant behavior, demonstrates significant strength in addressing high-dimensional, nonlinear optimization issues within power systems. In this study, HHO is implemented on an IEEE 30-bus power system, optimizing six competing objectives: minimizing total fuel cost, emissions, active power loss, reactive power loss, reducing voltage deviation, and enhancing voltage steady state. The effectiveness of HHO is assessed by comparing its performance to two alternative methods, MOEA/D-DRA and NSGA-III. Experimental results reveal that solutions derived from HHO exhibit superior convergence, enhanced diversity maintenance, and higher quality Pareto-optimal solutions compared to the MOEA/D trail algorithms. The research breaks new ground in the application of the Harris Hawks Optimization (HHO) algorithm to the Multi-Objective Optimal Power Flow (MaO-OPF) problem. The restructuring not only incorporates self-adaptive constraint-handling techniques and dynamic exploration exploitation strategies, but also addresses the more pressing requirements of modern power systems with even better convergence, and both sequential and global computational efficiency than existing skill. This approach proves to be a powerful and effective solution for addressing the complex challenges associated with MaO, enabling power systems to manage multiple conflicting objectives more efficiently. [ABSTRACT FROM AUTHOR]

In this paper, the voltage regulation in power systems is addressed from the perspective of the modern paradigm of control logic supported by phasor measurement units. The information available from measurements is used to better adapt the regulation actions to the actual operation point of the system. The use of the online measurement data allows for identifying the sensitivity matrix and for improving the regulation performances with respect to the fast load variations that increasingly affect modern power systems. With the aim of estimating the sensitivity matrices, a preliminary action is necessary to reconstruct the phases of the network voltages, which are assumed not to be provided by the phasor measurement units. This allows for obtaining a model-free adaptive control method. It is then shown how the regulation problem can be formulated in terms of a linear quadratic Gaussian problem, properly considering the load modeling in terms of the stochastic Ornstein–Uhlenbeck process. This control strategy has the advantage of avoiding dangerous oscillations of power flows, as demonstrated through the results of some simulations on a classical test network. Particularly, the advantage of the proposed approach is shown in the presence of different levels of load disturbances. [ABSTRACT FROM AUTHOR]

Distributed Energy Resources (DERs) aggregation increases the sustainability of the Electric Vehicles (EVs) market. For example, Fast Charging Stations (FCSs) associated with distributed generation and storage systems in a microgrid infrastructure may be beneficial in increasing self-consumption and peak-shaving strategies and mitigating impacts on the grid. However, microgrid sizing planning is a complex challenge, mainly due to numerous factors related to EV market growth and user behavior. This work defines a methodology focusing on sizing planning and analysis of microgrids for FCSs based on quantitative indices formulated according to the Net Zero Energy Building (NZEB) concept, optimizing self-sufficiency and limiting impacts on the primary electrical grid. The methodology is applied to a real case study considering the growth of EVs in southern Brazil. The developed analyses demonstrate that the proposed microgrid meets the energy needs of the FCS and presents the best NZEB indexes within the considered study horizon. Additionally, representative profiles were characterized for different load and generation conditions, complementing the analyses. It was shown that the storage promotes a delay and reduction in the reverse peak power flow, further enhancing the NZEB indexes. [ABSTRACT FROM AUTHOR]

This paper proposes a novel distributed control strategy for DC microgrids using a convex relaxation method to ensure the system operates at the optimal power flow solution. Initially, a suitable convex relaxation technique is applied to transform the non-convex optimal power flow problem into a convex form, with the accuracy of this method being rigorously demonstrated. Next, the Karush–Kuhn–Tucker (KKT) optimality conditions of the relaxed problem are equivalently transformed, and a synchronization term is derived to facilitate the distributed control, thereby ensuring operation under optimal power flow. This paper also analyzes the impacts of communication delay and network structure on the performance of the proposed control strategy. Finally, simulations and numerical experiments are presented to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Transmission networks face continuous electrical and mechanical stresses due to increasing system challenges and power losses. Transmission networks require special focus and detailed studies each time a load or a generator emerges to the grid. The interline power flow controller (IPFC) is a relatively new scheme that is implemented in the transmission network to improve transmission efficiency, decrease transmission losses, and enhance voltage profile. In this paper, the interline power flow controller's impact on transmission network performance is investigated as it is implemented within the IEEE 5-bus, 14-bus, and IEEE 57-bus systems. In addition, the whale optimization algorithm (WOA) is used to optimize the interline power flow controller locations within the system to achieve optimal transmission system performance. WOA performance is also compared to genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, and the superiority of the proposed WOA-based control is proved. The robustness of the optimized system against load variations is investigated and the results introduced affirm the capability of the interline power flow controller to enhance transmission network efficiency. [ABSTRACT FROM AUTHOR]

Renewable energy sources continue to increase due to the energy transition; thus, the generation output of conventional power sources is decreasing. The installation of renewable energy sources can lead to the concentration of these sources depending on geographical characteristics, which may cause bottlenecks between the renewable energy generation sites and load centers, and such bottlenecks can result in power shortages in load centers and may cause issues that limit the integration of renewable energy. Thus, this paper proposes the application of an LFAC multiport asynchronous grid connection system to solve these problems, where the frequency conversion device uses a variable frequency transformer (VFT). In addition, the installation location of the proposed LFAC multiport asynchronous grid connection system is selected using a grid partitioning technique, and the optimal power flow is performed to minimize the generation costs. The grid partitioning was conducted using the IEEE 39 bus system, and the feasibility of the proposed LFAC multiport asynchronous grid connection system was verified through simulation tests of the generation and load demands in the grid. In addition, the VFT control and optimal power flow control performances were confirmed through MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

As the scale of China's interconnected power grid continues to expand, traditional serial computing methods are no longer sufficient for the rapid analysis and computation of electrical networks with tens of thousands of nodes due to their small scale and low efficiency. To enhance the capability of online grid analysis, this paper introduces an accelerating batched power flow calculation method based on a heterogeneous CPU-GPU platform. This method, based on the fast decoupled method, combined with the tremendous parallel computing capability of GPUs with the multi-threaded parallel processing of CPUs, efficiently resolves the exceeding bus type conversion issues in GPU-batched power flow calculation and improves the accuracy of the power flow calculations. Then, a binary-based power flow data exchange format was introduced, which utilizes a single binary file for data exchange. This format significantly minimizes I/O time overhead and reduces file size, further enhancing the method's efficiency. Case studies on real-world power grids demonstrate its high accuracy and reliability. Compared to the traditional single-threaded power flow calculation method, this method dramatically reduces time consumption in batch power flow calculations. It proves the significant advantages of dealing with large-scale power flow calculations. [ABSTRACT FROM AUTHOR]

Accurate load prediction is critical for boosting high-quality electricity use, as well as safety in energy and power systems. However, the power system is fraught with uncertainty, and cyber-attacks on electrical loads result in inaccurate estimates. In this study, a probability density prediction method is proposed to provide reliable predictions in the face of false data injection (FDI) attacks. The method effectively integrates data-driven and statistical algorithms such as double-layer long short-term memory (DL-LSTM) networks, quantile regression (QR), and kernel density estimation (KDE). To acquire predicted values under diverse conditional quartiles, the FDI-attacked data of different types were first simulated and then utilized as the training set for the QR-DL-LSTM model. A probability density curve was drawn using the Gaussian kernel function, and interval estimates were used to more thoroughly analyze and assess predictive capability. Power load data from a wind farm in northeast China were used to confirm the availability and effectiveness of the QR-DL-LSTM model. The final results show that the proposed model has a 1.13 and 0.26 reduction in MAPE and MSE compared to the original LSTM. According to our research, the suggested model can successfully describe future power systems full of possible risks and uncertainties with great accuracy. [ABSTRACT FROM AUTHOR]

As renewable energy integration scales up, power systems increasingly depend on sources interfaced through power electronic converters, which lack rotating mass and substantially diminish system inertia. This reduction in inertia, coupled with the complex and diverse control strategies governing power electronics, presents significant challenges in accurately assessing the equivalent inertia levels within modern power systems. This paper introduces an online method for estimating the inertia time constant of power nodes, grounded in the node power flow equation, to address these challenges. The approach begins by deriving the rotor motion equation for synchronous generators and defining the inertia time constant of power nodes through an analysis of the power flow equations. Real-time frequency and voltage phasor data are collected from system nodes using phasor measurement units. The frequency state of the power equipment is then characterized using a divider formula, and the equivalent reactance between the power equipment and the node is further derived through the node power flow equation. This enables the real-time estimation of the equivalent inertia time constant for power nodes within the system. The effectiveness of the proposed method is demonstrated through simulations on the WSCC9 system, confirming its applicability for real-time system analysis. [ABSTRACT FROM AUTHOR]

The electrical load and the magnetic load of the transverse flux permanent magnet linear motor (TFPMLM) are decoupled from each other, which makes it easy to achieve modularization, and it has broad application prospects in the field of low speed and high thrust. Firstly, the topology structure of a cylindrical TFPMLM is proposed, and its working principle is introduced. Secondly, the basic electromagnetic performance of the motor is studied. Thirdly, the influence of the structural parameters on the flux linkage, back-EFM, and cogging force has been analyzed by the 3D finite element method (FEM). Then, the kriging agent model is constructed through the DOE experiment, and a set of size parameters with better performance are found by using a multi-objective genetic algorithm. Finally, the prototype and experimental setup have been developed. The calculated results and the measured results are in good agreement. [ABSTRACT FROM AUTHOR]

The quality of power signals is strongly influenced by nonlinear loads in Electrical Power systems. Representation of electrical signals using different Sparse techniques is an interesting area of research as it moderates the volume of data to be stored. The storage of signals in Sparse form will make data storage easier and more efficient. Earlier studies concentrated on blindly choosing Overcomplete Hybrid Dictionaries (OHDs) for Sparse representation. The effect of different dictionaries in representing electrical signals has also not been reviewed in them. This paper presents an investigation of the effect of various dictionaries and the sparsity constant on the representation of electrical signals. The validation for statements presented in this paper is carried out by representing power signals with diverse power line disturbances like Swell, DC offset, and random oscillation, with the help of various dictionaries in the simulation platform. The Sparse representation of the power signals was generated using the Orthogonal Matching Pursuit algorithm. The resultant Sparse representation was then compared with the original signal. The difference between them was found to be negligible with the help of different metrics. The ratio of the obtained signal from Sparse representation, the original signal (A/R ratio), and the Mean Squared Error were taken as the metrics. The MATLAB platform was used for performing the simulation study. [ABSTRACT FROM AUTHOR]

In this study, firstly, the balance between the exploration and exploitation capabilities of the weighted mean of vectors (INFO) algorithm was developed using the fitness–distance balance (FDB) method. Then, the FDB-INFO algorithm was developed with a hyper-heuristic method to create the beginning optimal population by using Linear Population Reduction Success History-based Adaptive Differential Evolution (LSHADE) and a novel Hyper-FDB-INFO algorithm was presented. Finally, the developed Hyper-FDB-INFO algorithm was applied to solve the optimal placement and sizing of FACTS devices for the optimal power flow (OPF) problem incorporating wind energy sources. Moreover, determining the placement and sizing of FACTS devices is an additional problem to minimize the total cost of generation and reducing the power losses of the power system. The experimental results showed that the Hyper-FDB-INFO algorithm is a more effective solver than the SHADE-SF, INFO, FDB-INFO and Hyper-INFO algorithms for wind power and FACTS devices integrating the OPF problem. [ABSTRACT FROM AUTHOR]

The heating energy consumption in public buildings in cold regions is notably significant, presenting substantial scope for energy savings and emission reductions. Flexible loads can actively participate in controlling the operation of the power grid, improving the energy utilization and the economy of the system. This study introduces flexible loads into the operation optimization of energy systems, establishing mathematical models for flexible thermal and electrical loads. A two-stage operation optimization method is proposed: the first stage simulates the starting and stopping control conditions of equipment at varying temperatures and times, selecting the optimal time period to regulate the thermal loads; the second stage employs a multi-objective particle swarm optimization algorithm to optimize the scheduling of the system's electrical load. Finally, an empirical analysis is carried out in a public building in Shenyang City as an example, and the results indicate that optimal scheduling of flexible thermal and electrical loads reduces the daily operating cost of the energy supply system by RMB 124.12 and decreases carbon emissions by 22.7%. [ABSTRACT FROM AUTHOR]

As the proportion of new energy access increases year by year, the resulting energy imbalance and voltage/trend distribution complexity of the distribution network system in the spatio-temporal dimension become more and more prominent. The joint introduction of electromagnetic rotary power flow controller (RPFC) and energy router (ER) can improve the high proportion of new active distribution network (ADN) consumption and power supply reliability from both spatial and temporal dimensions. To this end, the paper proposes an ADN expansion planning method considering RPFC and ER access. A two-layer planning model for RPFC and ER based on spatio-temporal characteristics is established, with the upper model being the siting and capacity-setting layer, which takes the investment and construction cost of RPFC and ER as the optimization objective, and the lower model being the optimal operation layer, which takes the lowest operating cost of the distribution network as the objective. The planning model is solved by a hybrid optimization algorithm with improved particle swarm and second-order cone planning. The proposed planning model and solving algorithm are validated with the IEEE33 node example, and the results show that the joint access of RPFC and ER can effectively improve the spatial-temporal distribution of voltage in the distribution network and has the lowest equivalent annual value investment and operation cost. [ABSTRACT FROM AUTHOR]

Achieving transportation decarbonization and reducing carbon emissions are global initiatives that have attracted a lot of effort. The use of electric vehicles (EVs) has experienced a significant increase lately, which will have a considerable impact on current power systems. This study develops a framework to evaluate/mitigate the negative impact of increasing EV charging on urban power distribution systems. This framework includes data analytics of actual residential electrical load and EV charging profiles, and the development of optimal EV charging management and AC load flow models using an actual residential power distribution system in Saskatchewan, Canada. We use statistical methods to identify a statistically-extreme situation for a power system, which a power utility needs to prepare for. The philosophy is that if the power system can accommodate this situation, the power system will be stable 97.7% of the time. Simulation results show the house voltage and transformer loading at various EV penetration levels under this statistically-extreme situation. We also identify that the particular 22-house power distribution system can accommodate a maximum number of 11 EVs (representing 50% EV penetration) under this statistically-extreme situation. The results also show that the proposed optimal EV charging management model can reduce the peak demand by 43%. Since we use actual data for this study, it reflects the current real-world situation, which presents a useful reference for power utilities. The framework can also be used to evaluate/mitigate the impact of EV charging on power systems and optimize EV infrastructure development. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources (RESs) into the Italian electricity market has heightened inter-zonal congestion challenges as power flows vary across importing and exporting zones. Utilizing a Multinomial Logistic Regression model as an empirical approach, this study investigates the key factors driving inter-zonal congestion between zonal pairs from 2021 to 2023, focusing on how local and neighboring zones' RES generation (wind, solar, and hydropower) and demand dynamics impact congestion probabilities. The findings reveal that increased local RES generation generally reduces the likelihood of congestion for importing regions but increases it for exporting zones. Specifically, higher wind and solar production in importing zones like CNOR and CSUD alleviates congestion by reducing the need for imports, while in exporting zones, such as NORD and CALA, increased RES generation can exacerbate congestion due to higher export volumes. Hydropower production shows similar trends, with local production mitigating congestion in importing zones but increasing it in exporting zones. In addition to the effects of local generation and demand within each zonal pair, the generation and demand from neighboring zones also have a notable and statistically significant impact. Although their marginal effects tend to be smaller, the contributions from neighboring zones are essential for comprehending the overall congestion dynamics. These insights underscore the need for strategic RES placement to enhance market efficiency and minimize congestion risks across the Italian zonal electricity market. [ABSTRACT FROM AUTHOR]

The increasing spread of photovoltaic systems for private households (PVs) and electric vehicles (EVs) in order to reduce carbon emissions significantly impacts operation conditions in existing distribution networks. Variable and unpredictable PVs can stress distribution network operation, mainly manifested in voltage violations during the day. On the other hand, variable loads such as EV chargers which have battery storage in their configuration have the ability of storying a surplus energy and, if it is necessary, support a distribution network with energy, commonly known as vehicle-to-grid concept (V2G), to help voltage stability network enhancement. This paper proposes an optimal power flow (OPF)-based model for EV charging to minimize power exchange between the superior-10 kV grid and the observed distribution feeder. The optimization procedure is realized using the co-simulation approach that connects power flow analysis software and optimization method. Three different scenarios are observed and analysed. The first scenario is referred to as a base case without optimization. The second and third scenarios include optimal EV charging and discharging patterns under different constraints. To test the optimization model, a 90-bus unbalanced distribution feeder modelled based on real-life examples is used. The obtained results suggest that this optimization model does not only significantly reduce the power exchange between an external network and the distribution feeder but also improves voltage stability and demand curve in the distribution feeder. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the operational characteristics of three-phase AC–DC matrix converters, which operate as rectifiers but possess a range of additional capabilities. These include the ability to reverse the power flow, control the input power factor, and generate a multilevel output voltage. The analysis yields a modulation strategy that minimizes switching losses. The proposed modulation strategy is compared with seven well-known space vector modulation strategies in terms of efficiency and distortion of input and output current. The performance is experimentally validated. [ABSTRACT FROM AUTHOR]

Accurate identification of network topology and line parameters is essential for effective management of distribution systems. An innovative joint estimation method for distribution network topology and line parameters is presented, utilizing a power flow graph convolutional network (PFGCN). This approach addresses the limitations of traditional methods that rely on costly voltage phase angle measurements. The node correlation principle is applied to construct a node correlation matrix, and a minimum distance iteration algorithm is proposed to generate candidate topologies, which serve as graph inputs for the parameter estimation model. Based on the topological dependencies and convolutional properties of AC power flow equations, a PFGCN model is designed for line parameter estimation. Parameter refinement is achieved through an alternating iterative process of pseudo-trend calculation and neural network training. Training convergence and loss function values are used as feedback to filter and validate candidate topologies, enabling precise joint estimation of both topologies and parameters. The proposed method's accuracy, transferability, and robustness are demonstrated through experiments on the IEEE-33 and modified IEEE-69 distribution systems. Multiple metrics, including MAPE, IAE, MAE, and R2, highlight the proposed method's advantages over Adaptive Ridge Regression (ARR). In the C33 scenario, the proposed method achieves MAPEs of 4.6% for g and 5.7% for b, outperforming the ARR method with MAPEs of 7.1% and 7.9%, respectively. Similarly, in the IC69 scenario, the proposed method records MAPEs of 3.0% for g and 5.9% for b, surpassing the ARR method's 5.1% and 8.3%. [ABSTRACT FROM AUTHOR]

Accurate and fast transient stability assessment (TSA) of power systems is crucial for safe operation. However, deep learning-based methods require long training and fail to simultaneously extract the spatiotemporal characteristics of the transient process in power systems, limiting their performance in prediction. This paper proposes a novel TSA method based on a spatiotemporal graph convolutional network with graph simplification. First, based on the topology and node information entropy of power grids, as well as the power flow of each node, the input characteristic matrix is compressed to accelerate evaluation. Then, a high-performance TSA model combining a graph convolutional network and a Gated Convolutional Network is constructed to extract the spatial features of the power grid and the temporal features of the transient process. This model establishes a mapping relationship between spatiotemporal features and their transient stability. Finally, the focal loss function has been improved to dynamically adjust the influence of samples with different levels of difficulty on model training, adaptively addressing the challenge of sample imbalance. This improvement reduces misclassification rates and enhances overall accuracy. Case studies on the IEEE 39-bus system demonstrate that the proposed method is rapid, reliable, and generalizable. [ABSTRACT FROM AUTHOR]

Coordinated voltage control enables the active management of voltage levels throughout electricity distribution networks by leveraging the voltage support capabilities of existing grid-connected PV inverters. The efficient management of power flows and precise voltage regulation through coordinated voltage control schemes facilitate the increased adoption of rooftop PV systems and enhance the hosting capacity of electricity distribution networks. The research work presented in this paper proposes a coordinated voltage control scheme and evaluates the enhanced hosting capacity utilizing a deep reinforcement learning-based approach. A comparative analysis of the proposed algorithm is presented, and the performance is benchmarked against existing local voltage control schemes. The proposed coordinated voltage control scheme in this paper is evaluated using simulations on a real-world low-voltage electricity distribution network. The evaluation involves quasi-static time series power flow simulations for assessing performance. Furthermore, a discussion is presented that reflects on the strengths and limitations of the proposed scheme based on the results observed from the case study. [ABSTRACT FROM AUTHOR]

This paper introduces an open-source tool capable of performing the Composite System Reliability evaluation developed in the high-level, dynamic Julia™ programming language. Employing Monte Carlo Simulation and parallel computing, the tool evaluates probabilistic adequacy indices for combined generation and transmission systems, focusing on both individual delivery points and the broader system. Proficiency in Optimal Power Flow problem formulations is demonstrated through two distinct methods: DC and linearized AC, enabling comprehensive resource and transmission adequacy analysis with high-performance solvers. Addressing replicability and the insufficiency of available software, the tool supports diverse analyses on a unified platform. The paper discusses the tool's design and validation, particularly focusing on the two optimal power flow problem formulations. These insights significantly contribute to understanding transmission system performance and have implications for power system planning. [ABSTRACT FROM AUTHOR]

In the context of the green energy transition, the rapid expansion of flexible resources such as distributed renewable energy, electric vehicles (EVs), and energy storage has significantly impacted the operation of distribution networks. This paper proposes a multi-objective optimization approach for active distribution networks (ADNs) based on analytical target cascading (ATC). Firstly, a dynamic optimal power flow (DOPF) calculation method is developed using second-order conic relaxation (SOCR) to address power flow and voltage issues in the distribution network, incorporating active management (AM) elements. Secondly, this study focuses on aggregating the power of flexible resources within station areas connected to distribution network nodes and incorporating these resources into demand response (DR) programs. Finally, a two-layer model for collaborative multi-objective scheduling between station areas and the active distribution network is implemented using the ATC method. Case studies demonstrate the model's effectiveness and validity, showing its potential for enhancing the operation of distribution networks amidst the increasing integration of flexible resources. [ABSTRACT FROM AUTHOR]

To alleviate the power flow congestion in active distribution networks (ADNs), this paper proposes a two-stage load transfer optimization model based on Neo4j-Dueling DQN. First, the Neo4j graph model was established as the training environment for Dueling DQN. Meanwhile, the power supply paths from the congestion point to the power source point were obtained using the Cypher language built into Neo4j, forming a load transfer space that served as the action space. Secondly, based on various constraints in the load transfer process, a reward and penalty function was formulated to establish the Dueling DQN training model. Finally, according to the ε − g r e e d y action selection strategy, actions were selected from the action space and interacted with the Neo4j environment, resulting in the optimal load transfer operation sequence. In this paper, Python was used as the programming language, TensorFlow open-source software library was used to form a deep reinforcement network, and Py2neo toolkit was used to complete the linkage between the python platform and Neo4j. We conducted experiments on a real 79-node system, using three power flow congestion scenarios for validation. Under the three power flow congestion scenarios, the time required to obtain the results was 2.87 s, 4.37 s and 3.45 s, respectively. For scenario 1 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by about 56.0%, 76.0% and 55.7%, respectively. For scenario 2 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 41.7%, 72.9% and 56.7%, respectively. For scenario 3 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 13.6%, 47.1% and 37.7%, respectively. The experimental results show that the trained model can quickly and accurately derive the optimal load transfer operation sequence under different power flow congestion conditions, thereby validating the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Hydrodynamic tools reveal information as to the behaviour of a device in the presence of waves but provide little information on how to improve or optimise the device. With no recent work on the transfer of power (energy flux) from a wave field through the body surface of a wave energy converter (WEC), we introduce the energy flux method to map the flow of power. The method is used to develop an open-source tool to visualise the energy flux density on a WEC body surface. This energy flux surface can also be used to compute the total power capture by integrating over the surface. We apply the tool to three WEC classes: a heaving cylinder, a twin-hulled hinged barge, and pitching surge devices. Using the flux surfaces, we investigate power efficiency in terms of power absorbed to power radiated. We visualise the hydrodynamic consequence of sub-optimal damping. Then, for two pitching surge devices with similar resonant peaks, we reveal why one device has a reduced power performance in a wave spectrum compared to the other. The results show the effectiveness of the energy flux method to predict power capture compared to motion-based methods and highlight the importance of assessing the flux of energy in WECs subjected to different damping strategies. Importantly, the tool can be adopted for a wide range of applications, from geometry optimisation and hydrodynamic efficiency assessment to structural design. [ABSTRACT FROM AUTHOR]

In a deregulated power system, managing congestion is crucial for effective operation and control. The goal of congestion management is to alleviate transmission line congestion while adhering to system constraints at minimal cost. This research proposes a hybrid Harris Hawk Optimization–Sine Cosine Algorithm (hHHO-SCA) for an efficient generation rescheduling approach to achieve the lowest possible congestion cost. The hybridization has been performed by introducing the features of SCA in the HHO to boost the exploration and exploitation steps of HHO, providing an efficient global solution and effectively optimizing rescheduled power output. The effectiveness of this methodology is evaluated using IEEE 30 and IEEE 118-bus test systems, taking into account system parameters. The potency of the proposed method is analyzed by comparing the results of the hHHO-SCA with those from other recent optimization techniques. The findings show that the hHHO-SCA outperforms other methods by avoiding local optima and demonstrating promising convergence characteristics. [ABSTRACT FROM AUTHOR]

To ensure the constant availability of electrical energy, power companies must consistently maintain a balance between supply and demand. However, electrical load is influenced by a variety of factors, necessitating the development of robust forecasting models. This study seeks to enhance electricity load forecasting by proposing a hybrid model that combines Sorted Coefficient Wavelet Decomposition with Long Short-Term Memory (LSTM) networks. This approach offers significant advantages in reducing algorithmic complexity and effectively processing patterns within the same class of data. Various models, including Stacked LSTM, Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM), and Convolutional Long Short-Term Memory (ConvLSTM), were compared and optimized using grid search with cross-validation on consumption data from Lome, a city in Togo. The results indicate that the ConvLSTM model outperforms its counterparts based on Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and correlation coefficient (R2) metrics. The ConvLSTM model was further refined using wavelet decomposition with coefficient sorting, resulting in the WT+ConvLSTM model. This proposed approach significantly narrows the gap between actual and predicted loads, reducing discrepancies from 10–50 MW to 0.5–3 MW. In comparison, the WT+ConvLSTM model surpasses Autoregressive Integrated Moving Average (ARIMA) models and Multilayer Perceptron (MLP) type artificial neural networks, achieving a MAPE of 0.485%, an RMSE of 0.61 MW, and an R2 of 0.99. This approach demonstrates substantial robustness in electricity load forecasting, aiding stakeholders in the energy sector to make more informed decisions. [ABSTRACT FROM AUTHOR]

In the context of constructing new power systems, the intermittency and volatility of high-penetration renewable generation pose new challenges to the stability and secure operation of power systems. Enhancing the ramping capability of power systems has become a crucial measure for addressing these challenges. Therefore, this paper proposes a bi-level peak regulation optimization model for power systems considering ramping capability and demand response, aiming to mitigate the challenges that the uncertainty and volatility of renewable energy generation impose on power system operations. Firstly, the upper-level model focuses on minimizing the ramping demand caused by the uncertainty, taking into account concerned constraints such as the constraint of price-guided demand response, the constraint of satisfaction with electricity usage patterns, and the constraint of cost satisfaction. By solving the upper-level model, the ramping demand of the power system can be reduced. Secondly, the lower-level model aims to minimize the overall cost of the power system, considering constraints such as power balance constraints, power flow constraints, ramping capability constraints of thermal power units, stepwise ramp rate calculation constraints, and constraints of carbon capture units. Based on the ramping demand obtained by solving the upper-level model, the outputs of the generation units are optimized to reduce operation cost of power systems. Finally, the proposed peak regulation optimization model is verified through simulation based on the IEEE 39-bus system. The results indicate that the proposed model, which incorporates ramping capability and demand response, effectively reduces the comprehensive operational cost of the power system. [ABSTRACT FROM AUTHOR]

Flexible user-side resources are of great potential in providing power regulation so as to effectively address the challenges of reverse power flow and overvoltage issues in distribution networks characterized by high photovoltaic (PV) penetration. However, existing distributed algorithms typically implement control signals after the convergence of the algorithms, making it difficult to track frequent and rapid fluctuations in PV power outputs in real time. Given this background, an online-distributed control algorithm for the real-time power regulation of flexible user-side resources is proposed in this paper. The objective of the established control model is to minimize network losses by dynamically adjusting active power outputs of flexible user-side resources and reactive power outputs of PV inverters while respecting branch power flow and voltage magnitude constraints. Furthermore, by deconstructing the centralized problem into a primal–dual one, a distributed control strategy based on the dual ascent method is implemented. With the proposed method, agents can achieve global optimality by exchanging limited information with their neighbors. The simulation results verify the good balance between economic efficiency and voltage control performance of the proposed method. [ABSTRACT FROM AUTHOR]

The integration of renewable sources, such as hydro, wind, and solar power, into electrical systems has profoundly transformed the sector's dynamics. The inherent intermittency of these energy sources, due to the uncertainty associated with inflows, winds, and solar irradiation, introduces considerable challenges in the operation and planning of the electrical system. In this context, demand response emerges as a promising solution to handle the fluctuations in renewable generation and maintain system stability and reliability. Therefore, this study presents a new approach to the demand response program through the modeling of an optimal power flow problem to minimize operational costs, considering the uncertainties in hydro, wind, and solar generation by applying the Conditional Value-at-Risk (CVaR) risk metric. The mathematical modeling of the problem was conducted, and the problem was solved using the MINOS solver. To validate the model, simulations were carried out using modified IEEE systems of 14, 30, 57, and 118 buses, considering operation planning for the next 24 h. Furthermore, sensitivity analyses were performed by altering the CVaR parameters. As a result of the simulations, the total operational cost, electrical losses, and hourly generation at each bus by source type were determined, highlighting how CVaR impacts the operation of this type of system. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic homotopy technique that can be used to calculate a preliminary result for a power flow problem (PFP). This result can then be used as an initial estimate to efficiently solve the PFP using either the classical Newton-Raphson (NR) method or its fast decoupled version (FDXB) while still maintaining high accuracy. The preliminary stage for the dynamic homotopy problem is formulated and solved by employing integration techniques, where implicit and explicit schemes are studied. The dynamic problem assumes an initial condition that coincides with the initial estimate for a traditional iterative method such as NR. In this sense, the initial guess for the FPF is adequately set as a flat start, which is a starting for the case when this initialization is of difficult assignment for convergence. The static homotopy method requires a complete solution of a PFP per homotopy pathway point, while the dynamic homotopy is based on numerical integration methods. This approach can require only one LU factorization at each point of the pathway. Allocating these points properly helps avoid several PFP resolutions to build the pathway. The hybrid technique was evaluated for large-scale systems with poor conditioning, such as a 109,272-bus model and other test systems under stressed conditions. A scheme based on the implicit backward Euler scheme demonstrated the best performance among other numerical solvers studied. It provided reliable partial results for the dynamic homotopy problem, which proved to be suitable for achieving fast and highly accurate solutions using both the NR and FDXB solvers. [ABSTRACT FROM AUTHOR]

Accurate electrical load forecasting is crucial for the stable operation of power systems. However, existing forecasting models face limitations when handling multidimensional features and feature interactions. Additionally, traditional metaheuristic algorithms tend to become trapped in local optima during the optimization process, negatively impacting model performance and prediction accuracy. To address these challenges, this paper proposes a short-term electrical load forecasting method based on a parallel Temporal Convolutional Network–Gated Recurrent Unit (PTCN-GRU) model, optimized by an improved Dung Beetle Optimization algorithm (IDBO). This method employs a parallel TCN structure, using TCNs with different kernel sizes to extract and integrate multi-scale temporal features, thereby overcoming the limitations of traditional TCNs in processing multidimensional input data. Furthermore, this paper enhances the optimization performance and global search capability of the traditional Dung Beetle Optimization algorithm through several key improvements. Firstly, Latin hypercube sampling is introduced to increase the diversity of the initial population. Next, the Golden Sine Algorithm is integrated to refine the search behavior. Finally, a Cauchy–Gaussian mutation strategy is incorporated in the later stages of iteration to further strengthen the global search capability. Extensive experimental results demonstrate that the proposed IDBO-PTCN-GRU model significantly outperforms comparison models across all evaluation metrics. Specifically, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were reduced by 15.01%, 14.44%, and 14.42%, respectively, while the coefficient of determination (R2) increased by 2.13%. This research provides a novel approach to enhancing the accuracy of electrical load forecasting. [ABSTRACT FROM AUTHOR]

The transportation industry is experiencing a switch towards electrification. Availability of electric vehicle (EV) charging infrastructure is very critical for broader acceptance of EVs. The increasing use of OBCs, due to their cost-effectiveness and ease of installation, necessitates addressing key challenges. These include achieving high efficiency and power density to overcome space limitations and reduce charging times. Additionally, the growing interest in bidirectional power flow, allowing EVs to supply power back to the grid, highlights the importance of innovative OBC solutions. This review article provides a thorough analysis of the current advancements, challenges, and prospects in EV on-board charger technology. It aims to offer a comprehensive review of OBC architectures, components, technologies, and emerging trends, guiding future research and development. Addressing these challenges is essential to enhance the efficiency, reliability, and integration of OBCs within the broader EV ecosystem. [ABSTRACT FROM AUTHOR]

With the increase in the proportion of photovoltaic and wind power access, the scale and form of distribution networks are becoming more and more complex. The traditional single distribution network vulnerability assessment method is difficult to use to identify the vulnerable links in the distribution network. Therefore, this paper proposes a method for identifying and evaluating vulnerable links in distribution networks based on minimum discriminant information. First, considering the influence of distributed grid connection, an improved probabilistic power flow calculation method is proposed, which improves the calculation efficiency and accuracy. Second, considering the correlation degree, transmission capacity, and voltage stability of branches in the distribution network, the identification index of vulnerable lines is defined. Based on power quality and operating state, the identification index of vulnerable nodes in a distribution network is defined. Finally, based on the indicators of vulnerable nodes and vulnerable lines, the vulnerable links in the distribution network are comprehensively evaluated based on the principle of minimum discriminant information, and the vulnerable links of the entire distribution network are evaluated according to different degrees of vulnerability. The rationality and effectiveness of the proposed method are verified via an example analysis of actual power grid data. [ABSTRACT FROM AUTHOR]

A new energy industry represented by photovoltaic and wind power has been developing rapidly in recent years, and its randomness and volatility will impact the stable operation of the power system. At present, it is proposed to enrich the regulation of the power grid by tapping the regulation potential of load-side resources. This paper evaluates the overall voltage regulation capability of substations under the premise of considering the impact on network voltage security and providing a theoretical basis for the participation of load-side resources of distribution networks in the regulation of the power grid. This paper proposes a Zbus linear power flow model based on Fixed-Point Power Iteration (FFPI) to enhance power flow analysis efficiency and resolve voltage sensitivity expression. Establishing the linear relationship between the voltages of PQ nodes, the voltage of the reference node, and the load power, this paper clarifies the impact of reactive power compensation devices and OLTC (on-load tap changer) tap changes on the voltages of various nodes along the feeder. It provides theoretical support for evaluating the voltage regulation range for substations. The day-ahead focus is on minimizing network losses by pre-optimizing OLTC tap positions, calculating the substation voltage regulation boundaries within the day, and simultaneously optimizing the total reactive power compensation across the entire network. By analyzing the calculated examples, it was found that a pre-scheduled OLTC (on-load tap changer) can effectively reduce network losses in the distribution grid. Compared with traditional methods, the voltage regulation range assessment method proposed in this paper can optimize the adjustment of reactive power compensation devices while ensuring the voltage safety of all nodes in the network. [ABSTRACT FROM AUTHOR]

To realize two-way power transfer without physical connections under a grid-connected application, bidirectional wireless power transfer (BDWPT) is introduced. This paper proposes an LCC-S compensated BDWPT system based on dual-active-bridge (DAB) topology with the minimum component counts. LCC-S is designed to be a constant voltage (CV) network. To obtain the power transmission characteristics of the system, a mathematical model based on the fundamental harmonic approximation (FHA) method is established, and the result shows that the direction and amount of transfer power can be controlled by changing the magnitude of output voltages of either/both side of H-bridges. The reactive power of the system can be controlled to be zero when the output voltages of two H-bridges are in the same phase. Compared with DAB-based BDWPT systems with constant current (CC) compensation networks, the proposed structure has better transfer power regulation capability and easier control of the direction of power flow. A 1.1 kW experimental prototype is built in the laboratory to verify the characteristics of the proposed system. The results indicate that the power transfer characteristics of the proposed BDWPT system match its mathematical derivation results based on the FHA model. [ABSTRACT FROM AUTHOR]

The ongoing replacement of synchronous machine generators (SMs) with converter-interface generators (CIGs) is raising the voltage unbalance of power systems, affecting power quality and grid stability. This paper focuses on a key power quality index for power systems, i.e., the voltage unbalance factor. The purpose of this work is twofold. First, it presents the generalization of a three-phase power flow algorithm developed by University of Padova, named PFPD_3P, to assess the voltage unbalance factors of power systems supplied by CIGs. In particular, it is demonstrated that CIGs can be modelled as three-phase PV/PQ constraints embedding their positive-, negative- and zero-sequence admittances. Then, the concept of three-phase contingency analysis is introduced. Indeed, for static security evaluation, the classical single-phase contingency analysis may no longer be sufficient, as it lacks power quality computations, e.g., voltage/current unbalance factors. Numerical simulations evaluating the unbalance factors due to different generation mix scenarios and contingencies are tested on the Italian extra-high-voltage/high-voltage (EHV/HV) grid. The choice of this network relies on its representativeness, as CIGs are the majority of new installations in the Italian generation mix. [ABSTRACT FROM AUTHOR]

Trends in the use of renewable energy sources to power buildings do not bypass objects for which maintaining a power supply is critical. This also applies to railway signal boxes. The aim of the research work was to test the multisource power supply system for a railway signal box with power electronic converter systems and a DC bus, built as part of the research project. The assumption for powering the railway signal box building was to use renewable sources, energy storage devices, and a 3 kV DC traction network as the second required power supply grid. Both power grids were connected by power electronic converters, and the power values of the converters were set based on the calculated power balance values using the values measured at the system nodes and the set constraints. The tests primarily tested the response of the power supply system to changes in load power and power generated by the photovoltaic source, as well as the charge level of the energy storage devices. The correctness of the control algorithm's operation was assessed based on the recorded power values in the power supply system nodes. The tests were carried out for 60 scenarios that covered all normal and emergency operating conditions. During the tests, delays in response to changes in the power supplied to the converters and the values of circular power flow between the power grid connections were recorded. The recorded delays ranged from 2 to about 50 s and the circular power flows did not exceed 1500 W. Based on the results of the tests, it was found necessary to improve the power measurement system in the power supply system nodes and to improve the quality of communication and the transmission time of measurement data transmission time. [ABSTRACT FROM AUTHOR]

Predicting the need for modeling and solutions is one of the largest difficulties in the electricity system. The static-constrained solution, which is not always powerful, is provided by the Gradient Method Power Flow (GMPF). Another benefit of using both dynamic and transient restrictions is that GMPF will increase transient stability against faults. The system is observed under contingency situations using the Dynamic Stability for Constrained Gradient Method Power Flow (DSCGMPF). The population optimization technique is the foundation of a recent algorithm called Training Learning Based Optimization (TLBO). The TLBO-based approach for obtaining DSCGMPF is implemented in this work. The total system losses and the cost of the individual generators have been optimized. Analysis of the stability limits under contingency conditions has been conducted as well. To illustrate the suggested approaches, a Standard 3 machine 5-bus system is simulated using the MATLAB 2022B platform. [ABSTRACT FROM AUTHOR]

This paper reviews renewable energy integration with the electrical power grid through the use of advanced solutions at the device and system level, using smart operation with better utilisation of design margins and power flow optimisation with machine learning. This paper first highlights the significance of credible temperature measurements for devices with advanced power flow management, particularly the use of advanced fibre optic sensing technology. The potential to expand renewable energy generation capacity, particularly of existing wind farms, by exploiting thermal design margins is then explored. Dynamic and adaptive optimal power flow models are subsequently reviewed for optimisation of resource utilisation and minimisation of operational risks. This paper suggests that system-level automation of these processes could improve power capacity exploitation and network stability economically and environmentally. Further research is needed to achieve these goals. [ABSTRACT FROM AUTHOR]

As the scale of power systems continue to expand and their structure becomes increasingly complex, it is likely that branch power flow exceedance may occur during the operation of power systems, posing threats to the safe and stable operation of entire systems. This paper addresses the issue of branch flow exceedance in power networks. To enhance the operational efficiency and optimize the adjustment effects, this paper proposes a method for eliminating branch power flow exceedance by improving the particle swarm optimization (PSO) algorithm through the introduction of sensitivity and distribution factors. Firstly, it introduces the basic theory and calculation methods of sensitivity analysis, focusing on deriving the calculation principles of power flow sensitivity and voltage sensitivity, used to predict the responses of power flow at each branch in the power network to power or voltage changes. Subsequently, the paper provides a detailed derivation of the calculation principles for the line outage distribution factor (LODF), which effectively assesses the changes in branch power flow in the power network under specific conditions. Finally, a method for eliminating branch power flow exceedance based on a combination of sensitivity analysis and PSO algorithm is proposed. Through case analysis, it is demonstrated how to use the sensitivity and distribution factor to predict and control the power flow exceedance issues in power systems, verifying the efficiency and practicality of the proposed method for eliminating branch power flow exceedance. The study shows that this method can rapidly and accurately predict and address branch power flow exceedance in power system, thereby enhancing the operational safety of the power system. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) sources play a special role in the operation of active energy networks. The microgrid (MG) is known as a suitable substrate for the development and installation of DGs. However, the future of energy distribution networks will consist of more interconnected and complex MGs, called multi-microgrid (MMG) networks. Therefore, energy management in such an energy system is a major challenge for distribution network operators. This paper presents a new energy management method for the MMG network in the presence of battery storage, renewable sources, and demand response (DR) programs. To show the performance of each connected MG's inefficient utilization of its available generation capacity, an index called unused power capacity (UPC) is defined, which indicates the availability and individual performance of each MG. The uncertainties associated with load and the power output of wind and solar sources are handled by employing the chance-constrained programming (CCP) optimization framework in the MMG energy management model. The proposed CCP ensures the safe operation of the system at the desired confidence level by involving various uncertainties in the problem while optimizing operating costs under Mixed-Integer Linear Programming (MILP). The proposed energy management model is assessed on a sample network concerning DC power flow limitations. The procured power of each MG and power exchanges at the distribution network level are investigated and discussed. [ABSTRACT FROM AUTHOR]

In Korea, the demand for complete underground installation of power distribution equipment installed on roads and green areas is increasing. In addition, KEPCO is making efforts to build a more reliable system for the underground distribution system. To meet these needs, this paper proposes the S-substation. In the S-substation, an RMU, a large power transformer, and an LV-Board (including ATCB and MCCB) are installed within the underground structure. This paper proposes three models to apply the S-substation to the underground distribution system. Power flow analysis is conducted for each model by simulating a variety of loads and DERs, and the frequency fluctuations are also examined under different distribution system events. An economic analysis is also conducted to select the optimal model. The economic analysis focuses on VOLL and construction costs. Based on power flow and economic analysis, one model is selected, and the underground distribution system that the model is applied is presented. [ABSTRACT FROM AUTHOR]

The electrical protection of active distribution networks is crucial for ensuring reliable, safe, and flexible operations. However, protecting these networks presents several challenges due to the emergence of bi-directional power flows, network reconfiguration capabilities, and changes in fault current levels resulting from the integration of inverter-based resources. This paper introduces an innovative protection strategy for active distribution networks, leveraging the principles of distributed coordination and multi-agent systems. The proposed strategy consists of two stages. The first stage involves a fault detection algorithm that relies solely on local measurements, while the second stage uses agent classification to compute the optimal operating time based on a dynamic matrix representation of the fault path, combined with a simplified distributed optimization problem. The coordination process is formulated as a set of linear optimization problems, simplifying the solution. The proposed protection strategy is validated in a real-time simulation environment using a modified CIGRE MV European grid as a case study, considering low-impedance symmetric fault scenarios and topological changes. The results demonstrate that the protection scheme exhibits robust performance, enhancing the adaptability of the protection equipment while ensuring suitable sensitivity and operational speed. [ABSTRACT FROM AUTHOR]