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This article analyzes the interconnection of ethnic boundary-making and collective responsibility, with reference to the social-humanitarian work of Russia's ethnic minority organizations. Employing data from interviews with minority representatives, it shows that the notion of collective responsibility has played a role in reproducing (symbolic) ethnic boundaries, through the interplay of three factors: ethnic institutions, social networks and power distribution. These processes unfold in a sociopolitical context that places an emphasis on stability and security. Meanwhile, macro-level dynamics (state-endorsed discourses and structural conditions) interact with micro-level processes (minority representatives' experiences and actions) – both impacting upon Russia's nationalities policy. [ABSTRACT FROM AUTHOR]

The high adoption of electric vehicles (EVs) and the rising need for charging power in recent years calls for advancing charging service infrastructures and assessing the readiness of the power system to cope with such infrastructures. This paper proposes a novel model for the integrated operation of dynamic wireless charging (DWC) and power distribution systems offering charging service to in‐motion EVs. The proposed model benefits from a hierarchical design, where DWC controllers capture the traffic flows of in‐motion EVs on different routes and translate them into estimations of charging power requests on power distribution system nodes. The charging power requests are then communicated with a central controller that monitors the distribution system operation by enforcing an optimal power flow model. This controller coordinates the operation of distributed energy resources to leverage charging power delivery to in‐motion EVs and mitigate stress on the distribution system operation. The proposed model is tested on a test distribution system connected to multiple DWC systems in Salt Lake City, and the findings demonstrate its efficiency in quantifying the traffic flow of in‐motion EVs and its translation to charging power requests while highlighting the role of distributed energy resources in alleviating stress on the distribution system operation. [ABSTRACT FROM AUTHOR]

The continuous increase in energy demand strains distribution networks, resulting in heightened power losses and a decline in overall performance. This negatively impacts distribution companies' profits and increases consumer electricity costs. Optimal distributed generation (DG) allocation in distribution networks can mitigate these issues by enhancing power supply capabilities and improving network performance. However, achieving optimal DG allocation is a complex optimization problem that requires advanced mathematical techniques. Nature-inspired (NI) swarm intelligence (SI)-based optimization techniques offer potential solutions by emulating the natural collective behaviors of animals. This paper reviews the application of NI-SI algorithms for optimal DG allocation, specifically focusing on reducing power losses as a key objective function. The review analyzes a significant body of literature demonstrating the effectiveness of NI-SI techniques in addressing power loss challenges in distribution networks. Additionally, future research directions are provided to guide further exploration in this field. [ABSTRACT FROM AUTHOR]

This article discusses a support vector regression-based interval power flow prediction method for distribution networks with distributed generators (DGs) integration. The method aims to address the challenges of uncertain power flow analysis in distribution networks, particularly as the size of the system increases. The proposed method utilizes intervals to describe system uncertainty and employs support vector regression for model training. Simulation results demonstrate that the method exhibits high prediction accuracy, adaptability, and computation efficiency, meeting the requirements for rapid and real-time power flow analysis in distribution networks with DGs integration. The text also discusses the construction of an Interval Power Flow (IPF) prediction model for distribution networks using Support Vector Regression (SVR). The model is a multi-output model that predicts the upper and lower bounds of power flow results, taking into account uncertainties in the distribution system. The model has been shown to have high prediction accuracy and computational efficiency in studies conducted on IEEE 33bw and IEEE 69 cases. The text concludes by stating that the claims expressed in the article are solely those of the authors and do not necessarily represent the views of their affiliated organizations or the publisher. The article titled "Distributionally robust multistage dispatch with discrete recourse of energy storage systems" by Zheng et al. explores the concept of distributionally robust multistage dispatch with discrete recourse of energy storage systems. The authors propose a mathematical model that considers uncertainties in renewable energy generation and electricity demand, and aims to optimize the dispatch of energy storage systems. The study highlights the importance of incorporating uncertainty [Extracted from the article]

With the development of China's national economy and the acceleration of urbanization, the production and life of society are increasingly dependent on power supply. Thus, how to ensure the safety and reliability of electric energy is becoming more and more important. It would cause substantial losses in case of a large‐scale blackout, which is what we need to avoid. The recovery speed of the power outage is one of the most vital core issue, fast recovery of power supply in the distribution network is a significant way of improving emergency disposal efficiency. In order to make full use of the emergency resources and improve the efficiency of distribution network emergency repair, this paper presents a coordinated optimal scheduling of emergency power supply vehicles and emergency repair teams in the case of multiple faults. First, the emergency repair disposal path optimization model is created, with the goal of minimizing emergency disposal time and outage losses. Second, an emergency power supply vehicle scheduling model is established, with the goal of maximizing the economic benefit. And the optimal dispatch model of the emergency power supply vehicle with the highest value of the recovery load is built by using the transfer strategy matrix. Finally, the binary particle swarm optimization (BPSO) algorithm is used to solve the optimization models in the IEEE33 node distribution network model. The results indicates that the optimized scheduling strategy considering the coordinated dispatch of emergency power supply vehicles and emergency repair teams can be obtained, and the validity of the proposed method is verified. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With the increasing penetration of electric vehicles (EVs) in road traffic, the spatial and temporal stochasticity of the travel pattern and charging demand of EVs as a mode of transportation and an electrical load have generated different degrees of congestion impacts on both the power grid and the transportation network. Based on this, this paper proposes a power-transportation-coupled distribution network optimization strategy based on stochastic user traffic equilibrium theory. First, a stochastic user equilibrium-mixed traffic flow allocation model that expresses user non-completely rational path selection behavior is established, and the traffic flow equilibrium solution is obtained using an improved method of successive algorithm and mapped to charging loads. Second, a distribution network power flow optimization model under the coupled power-transportation architecture is established to optimize the operation state of the distribution network by combining distributed resources such as energy storage, demand response loads, wind power, photovoltaic, and gas turbines. [ABSTRACT FROM AUTHOR]

The continuous integration of distributed power into the distribution network has increased the complexity of the distribution network and created challenges in distribution-network reconfiguration. In order to make the distribution network operate in the optimal mode, this paper establishes a multi-objective reconfiguration-optimization model that takes into account active network loss, voltage offset, number of switching actions and distributed power output. For a distribution network with a distributed power supply, it is easy for the traditional binary particle swarm optimization algorithm to fall into a local optimum. In order to improve the convergence speed of the algorithm and avoid premature convergence, this paper adopts an improved binary particle swarm optimization algorithm to solve the problem. The IEEE33 node system is used as an example for simulation verification. The experimental results show that the algorithm improves the convergence speed and global search ability, effectively reduces the system network loss, and greatly improves the voltage level of each node. It improves the stability and economy of distribution-network operation and can effectively solve the problem of multi-objective reconfiguration. [ABSTRACT FROM AUTHOR]

Integrating electric vehicles (EVs) into the coupled power distribution network (PDN) and transportation network (TN) presents substantial challenges. This paper explores three key areas in EV integration: charging/discharging scheduling, charging navigation, and charging station planning. First, the paper discusses the features and importance of EV integrated traffic–power networks. Then, it examines key factors influencing EV strategy, such as user behavior, charging preferences, and battery performance. Next, the study establishes an EV charging and discharging model, with particular emphasis on the complexities introduced by factors such as pricing mechanisms and integration approaches. Furthermore, the charging navigation model and the role of real-time traffic information are discussed. Additionally, the paper highlights the importance of multi-type charging stations and the impact of uncertainty on charging station planning. The paper concludes by identifying significant challenges and potential opportunities for EV integration. Future research should focus on enhancing coupled network modeling, refining user behavior models, developing incentive pricing mechanisms, and advancing autonomous driving and automated charging technologies. Such efforts will be essential for achieving a sustainable and efficient EV ecosystem. [ABSTRACT FROM AUTHOR]

Rotor turn short circuit fault of permanent magnet synchronous machines (PMSMs) in the distribution system has a significant issue caused by unbalance loading. This fault has a significant electromagnetic impact on the rotor winding in relation to the machine's external equipment. As a result, while designing the power system distribution network, the PMSMs rotor winding short circuit current limitation receives majority of attention. For simulation a 3-phase short-circuit model of synchronous generator is developed here by using MATLAB/SIMULINK software. Therefore, a method is used to determine a PMSM's internal winding quantity in the event of a short circuit, as well as its location within the distribution network. The PMSM is energized with a small sinusoidal voltage at standstill situation in this suggested approach to compute the synchronous inductance and winding resistance through the output current. It has been demonstrated that the number of shorted windings may be calculated using the variation of output current caused by an internal winding short circuit event under zero fault resistance condition. Except for intricate machine modelling or investigation of machine internal winding short circuit models comprising several tappings, this useful technique can be used to determine fault intensity for a specific synchronous machine. This paper primarily addressed on fault extremity protection strategies using before-fault and after-fault activities by employing harmonic filters. A three-phase synchronous machine model with a protective system is implemented in the distribution network using MATLAB/SIMULINK is taken into consideration in order to verify the findings. The results presented in this work are vital to a complete solution that includes fault reduction techniques. Harmonics elimination and instant fault mitigation techniques with load unbalancing are analyzed based on effective outcomes to judge the system stability and feasibility of the short circuit current intensity assessment. [ABSTRACT FROM AUTHOR]

The access to clean and drinkable water is becoming one of the major health issues because most natural waters are now polluted in the context of rapid industrialization and urbanization. Moreover, most pollutants such as antibiotics escape conventional wastewater treatments and are thus discharged in ecosystems, requiring advanced techniques for wastewater treatment. Here we review the use of artificial intelligence and machine learning to optimize pharmaceutical wastewater treatment systems, with focus on water quality, disinfection, renewable energy, biological treatment, blockchain technology, machine learning algorithms, big data, cyber-physical systems, and automated smart grid power distribution networks. Artificial intelligence allows for monitoring contaminants, facilitating data analysis, diagnosing water quality, easing autonomous decision-making, and predicting process parameters. We discuss advances in technical reliability, energy resources and wastewater management, cyber-resilience, security functionalities, and robust multidimensional performance of automated platform and distributed consortium, and stabilization of abnormal fluctuations in water quality parameters. [ABSTRACT FROM AUTHOR]

Featured Application: The research presented in this document focuses on the development and application of a cable-to-terminal connection recognition technology based on pose estimation, specifically utilizing the YOLOv8-pose model. This technology is designed to enhance the efficiency and accuracy of automated inspection systems in substations, which are critical nodes in power transmission and distribution networks. The technology is directly applicable in the routine inspection of substations where it can automate the process of detecting and diagnosing the connection status of cables and terminals. This real-time monitoring capability helps in early fault detection and prevention, thereby ensuring the reliability and safety of the power grid. Substations, as critical nodes for power transmission and distribution, play a pivotal role in ensuring the stability and security of the entire power grid. With the ever-increasing demand for electricity and the growing complexity of grid structures, traditional manual inspection methods for substations can no longer meet the requirements for efficient and safe operation and maintenance. The advent of automated inspection systems has brought revolutionary changes to the power industry. These systems utilize advanced sensor technology, image processing techniques, and artificial intelligence algorithms to achieve real-time monitoring and fault diagnosis of substation equipment. Among these, the recognition of cable-to-terminal connection relationships is a key task for automated inspection systems, and its accuracy directly impacts the system's diagnostic capabilities and fault prevention levels. However, traditional methods face numerous limitations when dealing with complex power environments, such as inadequate recognition performance under conditions of significant perspective angles and geometric distortions. This paper proposes a cable-to-terminal connection relationship recognition method based on the YOLOv8-pose model. The YOLOv8-pose model combines object detection and pose estimation techniques, significantly improving detection accuracy and real-time performance in environments with small targets and dense occlusions through optimized feature extraction algorithms and enhanced receptive fields. The model achieves an average inference time of 74 milliseconds on the test set, with an accuracy of 92.8%, a recall rate of 91.5%, and an average precision mean of 90.2%. Experimental results demonstrate that the YOLOv8-pose model performs excellently under different angles and complex backgrounds, accurately identifying the connection relationships between terminals and cables, providing reliable technical support for automated substation inspection systems. This research offers an innovative solution for automated substation inspection systems, with significant application prospects. [ABSTRACT FROM AUTHOR]

As the penetration ratio of wind power in active distribution networks continues to increase, the system exhibits some characteristics such as randomness and volatility. Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control. Based on the spatio-temporal features of Numerical Weather Prediction (NWP) data, it proposes the WVMD_DSN (Whale Optimization Algorithm, Variational Mode Decomposition, Dual Stream Network) model. The model first applies Pearson correlation coefficient (PCC) to choose some NWP features with strong correlation to wind power to form the feature set. Then, it decomposes the feature set using Variational Mode Decomposition (VMD) to eliminate the nonstationarity and obtains Intrinsic Mode Functions (IMFs). Here Whale Optimization Algorithm (WOA) is applied to optimise the key parameters of VMD, namely the number of mode components K and penalty factor a. Finally, incorporating attention mechanism (AM), Squeeze-Excitation Network (SENet), and Bidirectional Gated Recurrent Unit (BiGRU), it constructs the dual-stream network (DSN) for short-term wind power prediction. Comparative experiments demonstrate that the WVMD_DSN model outperforms existing baseline algorithms and exhibits good generalization performance. [ABSTRACT FROM AUTHOR]

A new method for the generation of axial power distributions using artificial neural network (ANN) technique and real-time measured planar radial peaking factor, Fxy (hereinafter 'Live Fxy') method for digital core protection system of pressurized water reactors is presented. ANN with Simulated Annealing (SA) technique to find global optimum solution was used to calculate core average axial power distribution. In Live Fxy method, axial node-wise Fxys were calculated by multiplying pin/box factors to the measured assembly powers inferred from in-core detector signals without calculating detailed 3D power distributions. The hot pin power distribution for DNBR and LPD was then obtained by multiplying the core average axial power distribution and Fxys at axial nodes. The validation of the method was performed for various core conditions (e.g. core power levels, control rod positions, etc.) of Korean OPR1000 power plant. The result showed a decrease in RMS errors by 2.2% ~ 7.0% (3.6% on average), and the minimum thermal margins for DNBR and LPD were increased by 6.4% and 15.6%, respectively. The application of the method would improve the accuracy of axial power distribution and thermal margin, contributing to the operability and safety of digital core protection system. [ABSTRACT FROM AUTHOR]

The penetration of distributed renewable energy sources degrades the protection of microgrids, which leads to incorrect data flow in the energy systems. It is critical to detect faults, types of defects and location of faults in order to improve the protection and security of microgrids. To cater this issue in hybrid renewable energy system, a novel fault detection scheme is adopted using artificial intelligence. The renewable energy based microgrid system is implemented in the IEEE 13 bus power network to obtain the normal and faulty voltage and current data.. The system is simulated using MatLab/Simulink platform. From the time series data, the features are decomposed using empirical wavelet transform (EWT). First, EWT evaluates the frequency components in the signal, then calculates the bounds and gets the basis of the oscillating components. The obtained samples are classified using a Hybrid Convolutional Recurrent Neural Network (HCRNN) and optimized by the Pelican Optimization Algorithm. The 11 types of faults are identified along with the location of fault in the system is obtained. The results are compared with the existing methods and found that the proposed method has improved the fault sample detection accuracy by 1.56%. [ABSTRACT FROM AUTHOR]

On the basis of predecessors' coordination optimization of active and reactive power in distribution network, For the necessity of the optimal operation in the distribution network, part of power generated from photovoltaic (PV) cannot be sold to users, and cannot enjoy subsidies. Similarly, the network loss in the power transmission will also bring a certain economic loss. This paper comprehensively considers the economic loss caused by the network loss and PV abandon of the distribution system, and establishes a model to minimize the economic loss. To solve this problem efficiently, the method of DistFlow equation and mixed integer second order cone programming (MISOCP) is used to solve the problem, in this method, the original mixed integer nonlinear programming non-convex problem is transformed into a convex problem, which makes the optimization problem easy to solve. The modified IEEE 33 and IEEE 69 distribution networks are tested by the above method. The optimized results are able to meet the target and have very small relaxation gaps, and the voltage level is also optimized. This coordinated optimization approach helps to optimize the economic operation for active distribution networks with PVs. [ABSTRACT FROM AUTHOR]

When a severe fault occurs in the distribution network, all or parts of it may be disconnected from the upstream network. Partitioning of these islanded areas is a solution to supplying the affected loads. Due to the variable nature of loads and renewable distributed generation (DG), the static model of partitioning with a fixed nature during island operation cannot be suitable. Therefore, in this article, considering the variable nature of loads and renewable distributed generation, a dynamic model is presented for the island partitioning to restore more valuable loads, which is suitable for quick decision‐making in emergencies. Also, a method for deciding on the mode of charging and discharging storage systems in emergencies is proposed. This model considers time limitation, uncontrollable DGs, controllable DGs and their control, controllability, and priority of loads, tie‐switches, storage systems, simultaneous faults, different situations of unintentional islanding of the distribution network, position of switches, and variable nature of loads and distributed generations. So, it is more comprehensive than the previous methods. Applying the proposed model to the modified IEEE 69‐bus system with controllable and uncontrollable generation and storage systems assuming different scenarios shows the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Natural disasters would destroy power grids and lead to blackouts. To enhance resilience of distribution systems, the sequential load restoration strategy can be adopted to restore outage portions using a sequence of control actions, such as switch on/off, load pickup, distributed energy resource dispatch etc. However, the traditional strategy may be unable to restore the distribution system in extreme weather events due to random sequential contingencies during the restoration process. To address this issue, this paper proposes a distributionally robust sequential load restoration strategy to determine restoration actions. Firstly, a novel multi‐time period and multi‐zone contingency occurrence uncertainty set is constructed to model spatial and temporal nature of sequential line contingencies caused by natural disasters. Then, a distributionally robust load restoration model considering uncertain line contingency probability distribution is formulated to maximize the expected restored load amount with respect to the worst‐case line contingency probability distribution. Case studies were carried out on the modified IEEE 123‐node system. Simulation results show that the proposed distributionally robust sequential load restoration strategy can produce a more resilient load restoration strategy against random sequential contingencies. Moreover, as compared with the conventional robust restoration strategy, the proposed strategy yields a less conservative restoration solution. [ABSTRACT FROM AUTHOR]

Distribution lines are an important component of a power system. Lightning disasters have serious adverse effects on the reliability of the power supply in distribution networks. In response to the current lack of research on lightning protection in distribution networks under continuous lightning strikes, we built a transient simulation model and calculated the lightning withstanding level and lightning outage rate of distribution lines under continuous lightning strikes. In addition, the impact of different factors on the lightning withstanding level and lightning outage rate of distribution lines under continuous lightning strikes was calculated for different lightning protection strategies. Finally, differentiated lightning protection strategies based on the lightning outage rate calculation were proposed. [ABSTRACT FROM AUTHOR]

Energy information is vulnerable to malicious denial of service (DoS) attacks due to the diversity and openness of the smart grid environment. In order to cope with the above challenges, this paper first proposes to adopt Markov hopping model to describe the random packet loss of measurement due to DoS attacks. Then, based on Holt two‐parameter exponential smoothing and untraced Kalman filtering techniques, a one‐step predictive value compensation method for measurement data loss is proposed, and an improved dynamic untraced particle filtering algorithm based on data fusion compensation strategy is designed. Finally, an IEEE‐30 bus system is used to simulate the proposed dynamic state estimation method, which proves that the proposed method can effectively resist DoS attack. [ABSTRACT FROM AUTHOR]

With the continuous development of new power systems, the intelligence of distribution networks has been increasingly enhanced. However, network security issues, especially distributed denial-of-service (DDoS) attacks, pose a significant threat to the safe operation of distribution networks. This paper proposes a novel DDoS attack defense mechanism based on software-defined network (SDN) architecture, combining Rényi entropy and multi-level convolutional neural networks, and performs fine-grained analysis and screening of traffic data according to the amount of calculation to improve the accuracy of attack detection and response speed. Experimental verification shows that the proposed method excels in various metrics such as accuracy, precision, recall, and F1-score. It demonstrates significant advantages in dealing with different intensities of DDoS attacks, effectively enhancing the network security of user-side devices in power distribution networks. [ABSTRACT FROM AUTHOR]

The penetration of renewable energy resources for distribution networks can significantly impact the security of power supply systems. To improve the penetration efficiency, a hierarchical optimization planning for a distributed generation (DG) network using the improved sand cat swarm optimization (ISCSO) is proposed to determine the optimal location and capacity of DG into the distribution network. Firstly, the sensor is used to collect data, and the ISCSO algorithm is used to construct a complex nonlinear DG planning problem, thus reducing the impact of DG uncertainty. Second, a DG hierarchical planning model is established to select the optimal location and capacity of DG access to the distribution network. Finally, in the classical test system, the effectiveness of the proposed model is verified by setting up multiple cases. The test results confirm that the total annual cost and power loss of the DG system can be reduced by 13.1 and 40.5%, respectively. [ABSTRACT FROM AUTHOR]

In today's era of rapid technological advancement, the emergence of drone technology and intelligent power systems has brought tremendous convenience to society. However, the challenges associated with drone image recognition and intelligent grid device fault detection are becoming increasingly significant. In practical applications, the rapid and accurate identification of drone images and the timely detection of faults in intelligent grid devices are crucial for ensuring aviation safety and the stable operation of power systems. This article aims to integrate Transformer models, transfer learning, and generative adversarial networks to enhance the accuracy and efficiency of drone image recognition and intelligent grid device fault detection.In the methodology section, we first employ the Transformer model, a deep learning model based on self-attention mechanisms that has demonstrated excellent performance in handling image sequences, capturing complex spatial relationships in images. To address limited data issues, we introduce transfer learning, accelerating the learning process in the target domain by training the model on a source domain. To further enhance the model's robustness and generalization capability, we incorporate generative adversarial networks to generate more representative training samples.In the experimental section, we validate our model using a large dataset of real drone images and intelligent grid device fault data. Our model shows significant improvements in metrics such as specificity, accuracy, recall, and F1-score. Specifically, in the experimental data, we observe a notable advantage of our model over traditional methods in both drone image recognition and intelligent grid device fault detection. Particularly in the detection of intelligent grid device faults, our model successfully captures subtle fault features, achieving an accuracy of over 90%, an improvement of more than 17% compared to traditional methods, and an outstanding F1-score of around 91%.In summary, this article achieves a significant improvement in the fields of drone image recognition and intelligent grid device fault detection by cleverly integrating Transformer models, transfer learning, and generative adversarial networks. Our approach not only holds broad theoretical application prospects but also receives robust support from experimental data, providing strong support for research and applications in related fields. [ABSTRACT FROM AUTHOR]

The reliability of the power supply for 5G base stations (BSs) is increasing. A large amount of BS backup energy storage (BES) remains underutilized. This study establishes a double-layer optimization distribution network (DN) considering BS clusters. An energy consumption characteristics and scheduling ability model of the BSs was established to address the differences in the characteristics of different traffic flows. A double-tier planning model for BS-joining grid market ancillary services is proposed. The upper-layer model addresses optimal tidal flow problems in DNs to minimize integrated operating costs, while the lower-layer model focuses on BES economic optimization. The double-layer model changes into a single-layer linear model using the Karush-Kuhn-Tucker (KKT) condition and the Big M method. Simulation validation using the IEEE-33 node DN proves that this approach can reduce DN operating costs, regulate voltage fluctuations, and guarantee economical and safe DN operation. [ABSTRACT FROM AUTHOR]

Introduction: Accurate prediction of line losses in distribution networks is crucial for optimizing power system planning and network restructuring, as these losses significantly impact grid operation quality. This paper proposes a novel approach that combines advanced feature selection techniques with Stacking ensemble learning to enhance the effectiveness of distribution network loss analysis and assessment. Methods: Utilizing data from 44 substations over an 18-month period, we integrated a Stacking ensemble learning model with multiple feature selection methods, including correlation coefficient, maximum information coefficient, and tree-based techniques. These methods were employed to identify the key predictors of power loss in the distribution network. Results: The proposed model achieved a Mean Absolute Percentage Error (MAPE) of 3.78% and a Root Mean Square Error (RMSE) of 1.53, demonstrating a substantial improvement over traditional linear regression-based prediction methods. The analysis revealed that historical line loss and line active power were the most influential predictive variables, while the inclusion of time-related features further refined the model's performance. Discussion: This study highlights the efficacy of combining multiple feature selection methods with Stacking ensemble learning for predicting power loss in 10 kV distribution networks. The enhanced accuracy and reliability of the proposed model offer valuable insights for electrical engineering applications, potentially contributing to more efficient and sustainable energy distribution systems. Future research could explore the applicability of this approach to other distribution network voltage levels and investigate the incorporation of additional environmental and network-specific factors to further improve power loss prediction. [ABSTRACT FROM AUTHOR]

In this study, a stochastic multi-objective structure for optimization of the intelligent electric parking lots (EPLs) is implemented in the distribution network for minimizing the power losses annual costs, power purchased from the main grid, unsupplied energy of subscribers, cost of vehicles to the grid as well as minimizing the network voltage deviations considering battery degradation cost (BDC) and network load uncertainty (NLUn). In this research, the unscented transformation method (UTM) is used for NLUn modeling and this method is easily applicable and has a low computational cost. An improved meta-heuristic algorithm named improved fire hawks optimization (IFHO) is utilized for decision variables finding defined as the site and size of the EPLs in the distribution network. The conventional fire hawks optimization (FHO) algorithm is inspired by the fire hawks foraging behavior and in this research, the Taylor-based neighborhood technique (TBNT) is used to reduce the dependency and the possibility of becoming trapped in local optimal. To evaluate the proposed methodology, the simulations are implemented in three scenarios (1) EPLs optimization without BDC and NLUn based-UTM, (2) EPLs optimization with BDC and without NLUn, and (3) EPLs optimization with BDC and NLUn. The results of the third scenario considering BDC and NLUn showed that the EPLs optimization integrated with a multi-objective framework by finding the EPL's optimal size and capacity in the network via the IFHO has reduced the annual losses, voltage deviations, ENS cost, and substation cost by 21.06%, 12.15%, 70.82%, and 39.10%, respectively compared to the base distribution network. Additionally, the results demonstrated that incorporating the BDC and NLUn, the annual losses, voltage oscillations, ENS cost, grid cost, and EPLs have increased in comparison with the EPLs optimization without BDC and NLUn based-UTM. In addition, the TBNT based-IFHO superiority has been confirmed in different scenarios by achieving better values of the objectives and also obtaining the convergence process with lower convergence tolerance and higher convergence accuracy. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to address the optimal coordination of directional overcurrent relays (DOCRs) in modern power distribution networks. By utilizing the different optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO), pattern search (PS), grey wolf optimization (GWO), the study aims to tackle the inherent complexity and nonlinearity of the relay coordination problem effectively. GWO stands out due to its ability to handle highly nonlinear optimization problems by leveraging the social behavior and hunting mechanisms of grey wolves and its ability to quickly converge to near-optimal solutions make it a popular choice. This unique feature enables the algorithm to explore the solution space more efficiently by repositioning solutions around each other, thereby facilitating better exploitation of the solution space. The effectiveness of the proposed GWO algorithm is evaluated using fault data generated from various test systems ranging from small-scale 8-bus networks to large 15-bus systems. The results demonstrate several key advantages, reduced operating time, robust coordination, and reduced coordination interval. Compared to other optimization algorithms, the GWO algorithm achieves a reduced coordination interval between primary and backup relay pairs. This optimization contributes to faster and more precise fault detection and isolation within the network in comparison to other techniques. Overall, the findings highlight the superior performance and robustness of the GWO algorithm in addressing the optimal coordination challenges of DOCRs in modern power distribution networks thereby enhancing the efficiency and reliability of protection systems in complex electrical grids. [ABSTRACT FROM AUTHOR]

Fuel cell electric vehicles (FCEVs) hold great promise for achieving sustainable and environmentally friendly transportation. However, their widespread adoption depends on the efficient development of the hydrogen supply chain (HSC) required for refueling these vehicles. Given the interdependence between the HSC and power distribution networks (PDNs), this paper presents an innovative model to jointly plan the HSC and reinforce PDNs. In this new model, the inherent uncertainties related to wind energy generation, load demand, and FCEV consumption are modeled. Furthermore, coordinating the planning of hydrogen storage and battery energy storage systems is incorporated. The results underscore the critical importance of simultaneous planning for the HSC and PDN reinforcement. Moreover, the findings reveal that hydrogen storage alone can provide sufficient energy arbitrage, leading to a 9.6% reduction in total costs compared to the scenario where storage is disregarded. [Display omitted] • A novel planning model for hydrogen supply chains and power distribution networks. • Both hydrogen storage and battery energy storage are included. • Uncertainties related to wind generation and loads are modeled. • Model results show that simultaneous planning cuts costs by 25.9%. • Hydrogen storage alone reduces costs by 9.6%. [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]

With the development of the power system, power users begin to use their own power supply in order to improve the power economy, but this also leads to the occurrence of the risk of self-provided power supply. The actual distribution network has few samples of power supply risk and it is difficult to identify the power supply risk by using conventional deep learning methods. In order to achieve high accuracy of self-provided power supply risk identification with small samples, this paper proposes a combination of transfer learning, convolutional block attention module (CBAM), and convolutional neural network (CNN) to identify the risk of self-provided power supply in an active distribution network. Firstly, in order to be able to further identify whether or not a risk will be caused based on completing the identification of the faulty line, we propose that it is necessary to identify whether or not the captive power supply on the faulty line is in operation. Second, in order to achieve high-precision identification and high-efficiency feature extraction, we propose to embed the CBAM into a CNN to form a CBAM-CNN model, so as to achieve high-efficiency feature extraction and high-precision risk identification. Finally, the use of transfer learning is proposed to solve the problem of low risk identification accuracy due to the small number of actual fault samples. Simulation experiments show that compared with other methods, the proposed method has the highest recognition accuracy and the best effect, and the risk recognition accuracy of active distribution network backup power is high in the case of fewer samples. [ABSTRACT FROM AUTHOR]

Induction motors are the most common type of motor in power systems, constituting approximately 70–90% of the dynamic loads, making them significant contributors to system dynamics. In transient voltage stability analysis, dynamic equivalent models are commonly used to simplify the representation of a group of induction motors. This paper presents a method for the grouping and aggregation of induction motors at a common bus. Firstly, grouping rules are provided for clustering induction motors into several subgroups based on the mechanical principles of rotor force and motion, and aggregation rules are provided for aggregating a motor subgroup into a single-unit model based on the relationship between voltage drop and power transmission in distribution networks. Secondly, guided by the grouping rules, high-speed remaining electromagnetic torque and low-speed remaining electromagnetic torque are defined as new clustering indicators, and an adaptive K-means clustering method using silhouette coefficient verification is introduced to obtain the optimal motor subgroups. Thirdly, guided by the aggregation rules, a dynamic equivalent method is further introduced to obtain the equivalent single-unit model from a motor subgroup. Lastly, a transient voltage stability simulation in a typical distribution network is presented to illustrate that the proposed clustering and equivalent methods are more reasonable, accurate, and effective than traditional methods, as the obtained model has better dynamic characteristics and can more accurately reproduce the process of voltage collapse. [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]

Power distribution networks should strive for reliable delivery of energy. In this paper, we support this endeavor by addressing the Maintenance Resources Allocation Problem (MRAP). This problem consists of scheduling preventive maintenance plans on the equipment of distribution networks for a planning horizon, seeking the best trade-offs between system reliability and maintenance budgets. We propose a novel integer linear programming (ILP) formulation to effectively model and solve the MRAP for a single distribution network. The formulation also enables flexibility to suit new developments, such as different reliability metrics and smart-grid innovations. Then we develop a straightforward ILP formulation to address the MRAP for several distribution networks which takes the advantages of exchanging maintenance information between local agents and upper management. Using a general-purpose ILP solver, we performed computational experiments to assess the performance of the proposed approaches. Optimal maintenance trade-offs were achieved with the new formulations for real-scale distribution networks within short running times. To the best of our knowledge, this is the first time that the MRAP is optimally solved using ILP, for single or multiple distribution networks. [ABSTRACT FROM AUTHOR]

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Voltage stacking is a technique in which multiple integrated circuits are stacked in series between the supply voltage instead of in parallel, thus improving the energy efficiency of the power distribution network. Unfortunately, voltage stacking presents stability challenges for integrated circuits within the stack. A first-order model to quantify variability, stability, and power metrics for an array of voltage-stacked asynchronous integrated circuits is presented. Voltage variability and power consumption are accounted for and discussed. Limitations of the model are identified outside of the nominal behavior. The number of columns in the architecture, chip leakage, and supply voltage are shown to be the key contributors to the stability, performance, and energy efficiency of a system of voltage-stacked asynchronous processors. A higher leakage to active power ratio, though usually avoided by chip designers, is shown to improve stability and be key in designing stacks without external balancing. Outputs of the model enable system and chip designers to evaluate first-order trade-offs in energy efficiency, performance, and system cost. These fundamental data allow designers to make informed design and optimization trade-offs between asynchronous voltage-stacked architectures and the integrated circuits used therein. Analysis of this model shows that various voltage-stacked configurations, such as one with a 48 V supply using 100 rows and 11 columns, can be designed with less than 10% voltage variation per chip, mitigating the need for external voltage balancing. [ABSTRACT FROM AUTHOR]

This paper presents an integrated coil design method for inductive power-transfer (IPT) systems. Because a medium-voltage direct current (MVDC) distribution network transmits power at relatively high voltages (typically in the tens of kV), accurate fault diagnosis using high-performance sensors is crucial to improve the safety of MVDC distribution networks. With the increasing power consumption of high-performance sensors, conventional power supplies using optical converters with 5 W-class output characteristics face limitations in achieving the rated output power. Therefore, this paper proposes a safe and reliable power supply method using the principle of IPT to securely maintain the insulation distance between the distribution network and the current sensor-supply line. A 100 W prototype IPT system is investigated, and its feasibility is validated by comparing its performance with conventional optical converters. [ABSTRACT FROM AUTHOR]

This article examines the impact of meteorological conditions represented by ambient temperature, ambient humidity, wind speed, and daily precipitation sum on the reliability of low-voltage cable lines. Cable line reliability is crucial to the stability and safety of power systems. Failure of cable lines can lead to power outages. This can cause serious economic and social consequences, as well as threaten human safety, especially in the public sector and critical infrastructure. In addition, any interruption of cable lines generates costs related to repairs, operational losses, and possible contractual penalties. This is why it is so important to investigate the causes of power equipment failures. Many power system failures are caused by weather factors. The main purpose of this article is to quantify the actual impact of weather conditions on the performance and reliability of power equipment in distribution networks. Reliability indicators (failure rate, failure duration, restoration rate, and failure coefficient) for low-voltage cable lines were calculated as a function of weather conditions. Empirical values of the indicators were determined based on many years of observations of power lines operating in the Polish power system. An analysis of the conformity of their empirical distribution with the assumed theoretical model was also conducted. By quantifying the impact of specific weather factors on the operation of power equipment, it becomes possible to identify the ranges in which failures are most likely. [ABSTRACT FROM AUTHOR]

Most conventional power systems adopt radial distribution network wherein multiple loads are connected across the distribution transformer. As the number of loads increases, it results in poor voltage profile at the distant receiving end reducing power delivery. This issue worsens with the largescale influx of electric vehicles and power converter-fed loads, which draw constant power irrespective of supply voltage. Such loads exhibit negative incremental resistance behavior and also have a dynamic response which affects the network in a manner different from constant impedance loads. This paper compares the effects of constant power and constant impedance loads by modeling adjustable converter dynamics for constant power loads. It analyzes line currents, load voltages and power transmitted in a four-load radial test system with optional distributed sources. Results show poorer voltage profile and the effect of power converter dynamics in constant power loads compared to conventional loads. Adding distributed sources improves voltage profile considerably, and transmission losses are reduced. Steady state analysis is then extended to an IEEE 31-bus 23 kV distribution test system with similar results. Transmission losses are computed along different branches, and the influence of loads and sources are analyzed. The outcomes of the analysis can be used in arrival of loss allocation in a system where peer to peer energy sharing is envisaged. [ABSTRACT FROM AUTHOR]

The article examines strategies for managing voltage issues in distribution networks integrated with distributed energy resources, particularly photovoltaic (PV) systems. Topics include the challenges of voltage regulation in active distribution networks (ADNs) due to high proportions of distributed energy resources, the role of distributed energy storage and flexible loads in emergency voltage control, and the proposed composite sensitivity analysis for prioritizing voltage control measures.

The operation envelope of distribution networks can obtain the independent p-q controllable range of each active node, providing an effective means to address the issues of different ownership and control objectives between distribution networks and distributed energy resources (DERs). Existing research mainly focuses on deterministic operation envelopes, neglecting the operational status of the system. To ensure the maximization of the envelope operation domain and the feasibility of decomposition, this paper proposes a modified hyperellipsoidal dynamic operation envelopes (MHDOEs) method for distribution networks based on adjustable "Degree of Squareness" hyperellipsoids. Firstly, an improved convex inner approximation method is applied to the non-convex and nonlinear model of traditional distribution networks to obtain a convex solution space strictly contained within the original feasible region of the system, ensuring the feasibility of flexible operation domain decomposition. Secondly, the embedding of the adjustable "Degree of Squareness" maximum hyperellipsoid is used to obtain the total p-q operation domain of the distribution network, facilitating the overall planning of the distribution network. Furthermore, the calculation of the maximum inscribed hyperrectangle of the hyperellipsoid is performed to achieve p-q decoupled operation among the active nodes of the distribution network. Subsequently, a correction coefficient is introduced to penalize "unknown states" during the operation domain calculation process, effectively enhancing the adaptability of the proposed method to complex stochastic scenarios. Finally, Monte Carlo methods are employed to construct various stochastic scenarios for the IEEE 33-node and IEEE 69-node systems, verifying the accuracy and decomposition feasibility of the obtained p-q operation domains. [ABSTRACT FROM AUTHOR]

Due to their compact structure and low laying cost, three-core power cables are widely used for power distribution networks. The three-phases of such cables are distributed symmetrically with a 120° shift to each other. Phase current is an important parameter to reflect the operation state of the power system and three-core cable. Three-core symmetrical power cables use a common shield, leading to magnetic field cancelation outside the cable during steady operation. Thus, traditional magnetic-based current transformers cannot measure the phase current on three-core cable non-invasively. In order to measure the phase current more conveniently, a phase current measurement method for three-core cables based on a magnetic sensor is proposed in this paper. Nonlinear equations of a phase current and the magnetic field of a measuring point are constructed. The calculated magnetic field distribution of the three-core cable is verified using a finite element simulation. The effectiveness of the measurement method is further validated through experiments. This proposed method is able to conveniently detect the phase current of three-core power cables, which can help cable maintenance. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive review of the primary distribution design of an advanced network control system, emphasizing its evolution from initial requirements to practical applications. The system solves complex problems of power management by combining real-time data analysis, intelligent decision making for resource allocation, rapid fault correction, remote monitoring and complex optimization methods, all aimed at ensuring stable and safe operation of the power grid. Its performance is geared towards fast response, efficient data processing and synchronous processing tasks, ensuring smooth operation even under heavy workloads. Security is enhanced through strict protocols, encryption methods, and controlled access systems. The system is divided into four layers-data collection, communication, decision-making and application management-using innovative tools such as Kalman filters and deep Q networks. The research showcases the integrated network command system's prowess, achieving an average response time of 0.27 s, 98.5% dispatching accuracy, and 83.2% resource utilization, evidencing exceptional performance. It excels under various tests, including managing high loads with minimal accuracy loss, rapidly adapting to changes with a hydro model response time of 0.22 s, efficiently integrating renewables at 78.0% efficiency, and proving resilient in peak hours, affirming its capability to bolster grid efficiency, reliability, and integration of renewable energy resources. By outlining these specific achievements, this case study not only illustrates the complex design of the system, but also highlights its great potential for improving grid resilience and efficiency, attracting a wide audience interested in the future of energy management. [ABSTRACT FROM AUTHOR]

This study presents a major advance in grid management: the development and deployment of an integrated network command system for the main distribution network. The system integrates cutting-edge information technology, including modules such as command issuance, intelligent routing, security assurance and in-depth data analysis, opening a new era of refined and intelligent power grid management. The research focuses on the application of core technologies such as information communication technology, distributed control system, artificial intelligence and big data analysis, and strengthens the system operation foundation. The chapter on system architecture details the innovative integration of DDQN algorithm and attention mechanism, and carefully constructs intelligent scheduling engine and status monitoring and early warning system, which significantly improves real-time response, decision optimization and active security defense capabilities. Simulation experiments and actual case analysis verify the effectiveness of the system, specifically, the response time is reduced by 75.7%(from 2.1 s to 0.51 s in the traditional system), the data processing speed is still maintained at a high level under high load (100,000 data processing rate is 300/s), and the system stability is as high as 99.97%. The new system also achieved a high degree of automation, reducing annual operation and maintenance costs by 20%, and increasing user satisfaction to 90%, an increase of 28.6% over the previous period. These improvements not only optimize power quality and grid efficiency, but also further confirm that the fault response time is reduced by 30% and the user outage time is reduced by 25%. Therefore, this study not only highlights the innovation of the proposed system, but also demonstrates its significant contribution to accelerating the modernization of power grid management and ensuring safe operation with empirical data. [ABSTRACT FROM AUTHOR]

Accurate powerline classification from LiDAR point clouds is essential for efficient monitoring and management of power distribution networks. Currently, the classification is being done through manual labeling or some semi-automatic methods which are time-consuming and resource intensive. In this study, we explore three deep learning architectures, namely KPConv, PointCNN, and RandLA-Net, for powerline segmentation in aerial and mobile LiDAR datasets. We utilize manually labeled aerial and mobile LiDAR datasets from Surry in Canada and Kerala in India, respectively. Impressive results are obtained, demonstrating high powerline classification accuracy and precision. KPConv outperformed the other architectures, achieving over 98% accuracy for ALS data and surpassing 94% for MLS data. Additionally, we investigate the impact of ground removal on powerline extraction. The results highlight a significant improvement in powerline classification accuracy, with an approximate 3–4% enhancement observed, particularly in the test sets with a higher density of powerline points. Removing ground points effectively reduces noise and clutter in LiDAR data, leading to improved segmentation outcomes. Furthermore, ground removal proves beneficial in classifying wires in challenging scenarios, such as areas with nearby objects or branch and loose wires. KPConv demonstrates superior performance in powerline classification, capturing intricate powerline details even in scenes with varying point densities. PointCNN shows limitations, especially in wire point classification near other objects. RandLA-Net exhibits higher speed but slightly lower accuracy compared to KPConv. The findings contribute to advancing powerline classification and provide valuable insights for more efficient and automated management of power distribution networks. [ABSTRACT FROM AUTHOR]

The active distribution network (ADN) can obtain measurement data, estimate system states, and control distributed energy resources (DERs) and flexible loads to ensure voltage stability. However, the ADN is more vulnerable to cyber attacks due to the recent wave of digitization and automation efforts. In this article, false data injection (FDI) attacks are focused on and they are classified into two types, that is, type I attacks on measurement data and type II attacks on control commands. After studying the impact of these two FDI attacks on the ADN, a new threat is revealed called coordinated FDI attack, which can maximize the voltage deviation by coordinating type I and type II FDI attacks. From the attacker's perspective, the scheme of CFDI is proposed and an algorithm is developed to find the optimal attack strategy. The feasibility of CFDI attacks has been validated on a smart distribution testbed. Moreover, simulation results on an ADN benchmark have demonstrated that CFDI attacks could cause remarkable voltage deviation that may deteriorate the stability of the distribution network. Moreover, the impact of CFDI attacks is higher than pure type I or type II attacks. To mitigate the threat, some countermeasures against CFDI attacks are also proposed. [ABSTRACT FROM AUTHOR]

Reactive power optimisation can effectively reduce active power loss and improve voltage quality, which is a great significance for power system planning. When the reactive power optimisation problem is solved by Harris Hawk optimisation (HHO) algorithm, there are slow convergence and falling into local optima. This is caused by the multiple random parameters in HHO's exploration phase. To solve this problem, the Improved Logistic Chaotic mapping, Sine and Cosine Algorithm (SCA), the dynamic adaptive inertia weights and greedy strategy are introduced; the aim is to speed up convergence, reduce blind spots and improve the search capability. The improved algorithm was tested on the classical 23 benchmark functions; Wilcoxon's signed-rank test and Friedman test were tested, the results show that the improved algorithm can obtain better performance. The improved algorithm is applied to the reactive power optimisation problem in distribution networks with distributed generators (DG). When the reactive power optimisation problem is solved by the improved algorithms HHO, WOA, CSO, CS and PSO, respectively, the improved algorithm can obtain the lowest active power loss. Compared with no optimisation, active power loss is reduced by 33.19%. Finally, the node voltage quality ensures the safe operation of the system. [ABSTRACT FROM AUTHOR]

Minimization of power losses in distribution network (DN) is one of the vast areas of research, where researchers are performing different techniques and methods to reduce the losses. In recent times, network reconfiguration (Nr) has gained more significance in DN to reduce the losses due to its ease of implementation. Most of the work carried on Nr has usually assumed only single hour load model, which didn't vary with time. But in practical DN, load varies during the day. The tie line switching configuration obtained with single hour load model (which doesn't vary with time) is implemented in practical DN, it induces high power losses, reduces the reliability, loadability and voltage profile of the network. Most of the researchers have used single hour load model with Nr and not considered variation in load with Nr. In this study, variation in load is considered with twenty-four hour load. An investigation has been performed to find the losses obtained with variation in load for twenty-four hours. However, changing the topological of the network without disturbing the radiality is a complex non differentiable optimization problem. Nr implemented with twenty-four hour load has more complex computation as compared with single hour load. A quantum-inspired evolutionary algorithm, i.e., adaptive quantum-inspired evolutionary algorithm (AQiEA), is used to solve this complex optimization problem. In this study, two test cases are created in which the first case, uses four different scenarios to find the effect of power losses incurred in the system after opening the switches on single hour load and twenty-four hour load model. An effort has been made for optimal tie line switching configuration for twenty-four hour load instead of single hour load. In addition, an attempt has been made to maximize the economic benefits in DN with Nr on twenty-four hour load. Switching operation cost is considered to study the effect of frequent Nr on the life span of switches. Second case is used to test the efficacy of the proposed algorithm as compared with other techniques. The performance of AQiEA is tested on two IEEE standard test bus systems. Wilcoxon signed-rank test is also used to demonstrate the effectiveness of AQiEA. [ABSTRACT FROM AUTHOR]

In developing countries, electrical power distribution networks are often inadequate, particularly in small health centers. As a result, the electrical energy supplied by the grid is frequently interrupted. The productivity and quality of service delivered by these health centers to the people who live in these areas are severely affected by this issue. This issue can be resolved by incorporating battery storage systems along with renewable energy sources into the distribution system. The direct delivery of energy to customers is greatly aided by these renewable energy supplies. Partially, the grid supports such a system on a limited scale to guarantee the continuity of the energy supply. This study tried to resolve the problem due to these frequent power outages and its economic expenditures. To address the illustrated challenges, we tried to renovate the diesel generator with a solar and battery energy supply. The PVsyst software shows the average global solar radiation in the selected zone is5.84kmh/m2/day$$ 5.84\ \mathrm{kmh}/{\mathrm{m}}^2/\mathrm{day} $$. The annual energy demand for Gedeo health centers in 2023 is 3.32 MWH and the proposed PV‐battery hybrid system has a 10.95 MWH capacity. Moreover, when we utilize a diesel generator the Capital cost (CC), Net present cost (NPC), levelized cost of energy (LCOE), and payback period are 12 452.25$, 13 369.12$, 0.1$, and 10.7 years respectively. The economic assessment result of the proposed system is 4083$, 4727$, 0.059$, and 3.8 years consecutively. In southern Ethiopia, the annual emission from diesel generators alone, excluding the emission from the vehicles is close to 692 tons. Consequently, from the empirical economic assessment the installed solar energy is 90% more beneficial than the existing system. [ABSTRACT FROM AUTHOR]