Distributed Generation

Wind-PV Hybrid System, Hybrid Energy Storage System (HESS), Adaptive Neural Network (ANN) Control, DC Microgrid, Alternative Fuel Vehicles (AFVs), Mobile Charging Station (MCS) grid-supportive dc machine, offering improved voltage smoothing, inertia response, and ancillary support to weak or heavily loaded grids. Simulation results indicate that the wind-assisted SMPB with adaptive ANN control enhances system stability, reduces transient oscillations, improves renewable energy utilization, and minimizes reliance on the utility grid. This study provides a simulation-based validation of a resilient and efficient solution for future power systems with high AFV integration.

Distributed generation (DG) sources are becoming more important in electrical networks due to the increase of electrical energy demands. However, DG sources can have a profound effect on network power loss. Hence, optimal placement and size of DGs are extremely important. This study presents an efficient heuristic algorithm based on optimal placement and size of multiple DGs within distribution systems in order to reduce power loss. This algorithm is backtracking search algorithm (BSA). Two main DGs, photovoltaic and synchronous compensator, are integrated in two different radial distribution systems (RDS), IEEE 33-bus system and IEEE 69-bus system. To demonstrate the effectiveness of the proposed method, the results obtained by BSA are compared with a genetic algorithm (GA) as well as other results in the literature.

This study provides an overview of distributed generation (DG) and shunt capacitor banks (SCB) integration in radial distribution networks (RDN). The integration of DG and SCB (IDG-SCB) supplies active and reactive power to the network to fulfill the network's increasing power demand, improve power quality, and increase network stability and security. This work also takes into consideration network reconfiguration (N-Rec). The N-Rec is rearranging RDN topology or structure to accomplish particular goals including load balancing, loss decreasing, regulating voltage and enhanced reliability. Reconfiguring the RDN makes sure that no single line is overloaded while others are left unused by dividing loads across its branches. Through load balancing, the system's overall capacity is maximized and the chance of overloading and related losses is decreased. In addition, N-Rec establishes channels for two-way power flow and maximizes the network's ability to manage fluctuating generation patterns; reconfiguration makes it easier to integrate IDG-SCBs into the RDN. The purpose of this work is to conduct a review of IDG-SCB with N-Rec in the last five years. The focus of this survey is on the allocation of DG and SCBs, their sizes both continuous and discrete. The optimization methods are categorized based on whether they are single-objective, multi-objective, or hybrid, and then further classified based on the objective functions applied in the method, constraints subjected to optimization, and the RDN that has been tested in the method. Additionally, a statistical analysis is conducted to analyze the most frequently used methods, objective functions, constraints, and RDNs in the field. Finally, As a case study, an analysis was conducted to propose the results of the most objective function used, i.e. power loss, in the most frequently used RDN, which are IEEE 33 and 69-bus RDN. The analysis considered IDG-SCB with N-Rec. The results show the effectiveness of IDG-SCB with N-Rec in improving power distribution system resilience and efficiency, as evidenced by the significant reduction in power loss of up to 97.95% that can be achieved by its implementation.

In this paper, it is proposed that a two-terminal high voltage direct current (HVDC) be integrated into the power system. Line-commutated converter (LCC)-HVDC is used because of its ability to reduce line losses, which improves overall system efficiency. Shunt capacitors also aid in voltage maintenance by compensating for the reactive power demand. In essence, limiting voltage drops in electrical networks promotes a more efficient power transmission and distribution by lowering resistive losses. In power system investigations, it was discovered that the HVDC link and SCB exist separately. So, for the first time, the backtracking search algorithm (BSA) is used to solve the optimal reactive power flow (ORPF) of a power system with a HVDC link and shunt capacitor banks (SCB). Although BSA simulations on a modified IEEE 30 bus yielded successful results, ABC was also utilized for comparing the outcomes of different methods. Overall, three separate cases of the modified IEEE 30 bus system were examined. When the acquired results are compared to other methods, the suggested algorithm is found to be better at concerning effectiveness as well as performance.

This research endeavors to enhance grid-connected solar photovoltaic systems by refining the methodology used to select suitable geographical photovoltaic sites. The prevalent criterion in existing literature for choosing sites emphasizes proximity to power transmission lines. However, this criterion overlooks essential grid requirements, such as approved bus bars and permissible sizes for new photovoltaic systems. To overcome this limitation, the study introduces a novel criterion. Instead of relying solely on proximity to transmission lines, the proposed approach replaces this with the concept of optimal geographical bus bar locations. Additionally, the generated findings can be fine-tuned through consideration of the specified system size of the grid, thereby improving the identification of geographically suitable sites more effectively. The proposed methodology lies in the incorporation of five distinct approaches. The primary approach employs the Artificial Bee Colony (ABC) algorithm to ascertain the most suitable bus bars and sizes for three Photovoltaic Distributed Generation (PV-DG), with a focus on minimizing power loss within Aden's electric grid. The second approach involves utilizing the Analytic Hierarchy Process-Weighted Linear Combination (AHP-WLC) method in conjunction with Geographic information systems (GIS) to accurately select appropriate PV sites. The third approach is an initial design to eliminate small geolocations that do not align with the PV system's size. Fourthly, the Least Costly Path Model (LCPM) is harnessed to choose a single geolocation. The final approach is dedicated to performing techno-economic analysis. The key findings reveal that the optimal bus bars are located at 30, 46, and 64 in the grid, with a total injected active power of 26 MW. Simultaneously, the identified optimal geolocations cover areas of 0.88, 0.48, and 1.37 km^2 for each bus bar, respectively. In the comparative analysis, the utilization of the proposed criterion involving optimal bus bars results in a larger geographical coverage (97.99 km 2) compared to the traditional criterion (76.28 km 2). Furthermore, the techno-economic evaluation indicates the viability of implementing PV systems in Aden. The authors have a strong conviction that the framework and outcomes of this study will garner significant attention and prove valuable in assisting decision-makers to improve the planning of grid-connected solar photovoltaic systems.

This paper designs a power controller for power converters using fuzzy logic. The proposed controller will automatically adjust the frequency and voltage when the load changes to improve the power quality of the microgrid. Besides, the controller can realize accurate power sharing among the power converters in the microgrid, thereby suppressing the circulating current between the inverters. Furthermore, to ensure the control system operates stably and accurately during voltage and frequency adjustments, this paper employs a sliding-mode controller rather than a conventional proportional-integral controller. The proposed control method has a voltage deviation from the rated value when the load changes in the range of 1.5 Volts to 2.7 volts, and a frequency deviation from the rated value when the load changes in the range of 0.2 to 0.4 Hz. The accuracy of reactive power division is 100%. The proposed controller is simulated using MATLAB/ Simulink software, and the results obtained from the simulation have verified the effectiveness of the proposed method.

Microgrids are increasingly becoming popular to improve energy access and increase the resiliency of weakly connected rural networks. The economic operation of these microgrids with renewable energy resources is key for maximizing the benefits. The objective of this paper is to implement the economic dispatch of a microgrid using quadratic programming, considering the active and reactive power capability of the renewable energy resources. The open distribution system simulator (OpenDSS) is used for obtaining the load flow of the distribution network, and the converged solution is given as input to MATLAB for optimization. The simulation is carried out for one day with a variation of hourly load, solar PV radiation, and wind in both grid-connected and islanded modes under six different cases. The methodology is implemented on a modified IEEE-13 node test feeder distribution system. The simulation result shows that the microgrid with distributed generation (DG) capable of supplying bo...

One of the major issues in the distribution network (DN) is ensuring that power systems operate optimally in light of the effects of distributed generation (DG). In a broader sense, optimal operation in a power system refers to the most efficient use of all active and reactive power generation and control equipment that adheres to physical and technical constraints. Most studies focused on DG size and location in the DN, using various optimization techniques for loss reduction. But in a practical distribution network, reliable operation is dependent on the demand and power supply at any given moment. Solar DGs provide variable power throughout the day, and loads are similarly variable. It is difficult for the DN to function efficiently and reliably while handling variable loads and DG power supplies. Voltages and power losses are measured as loads change by connecting solar DGs to assess the performance of the DN.

The rising inclusion of renewable energy sources into distribution networks has accelerated the adoption of distributed generation (DG) technologies such as solar photovoltaic (PV) and wind turbines. This paper explores the effect of solar and wind DG integration on voltage profiles, power losses, and economic performance in a practical 41-bus radial distribution system. Using the Power World Simulator (PWS) software, the load flow analysis is performed to evaluate different DG placement strategies and penetration levels using the loss sensitivity factor (LSF) method. The results indicate that optimal placement of solar and wind DGs notably improves voltage stability and effectively reduces both real and reactive power losses in the distribution system. Furthermore, the economic analysis demonstrates annual savings of ₹29.08 lakhs for solar DG and ₹33.40 lakhs for wind DG, with payback periods of approximately 11 years, indicating strong technical and financial feasibility. The findings highlight that strategic DG planning can simultaneously enhance system reliability, efficiency, and economic viability in modern distribution systems.

This study presents an innovative approach to enhance power system efficiency by integrating Static Var Compensator (SVC) devices. Leveraging Adaptive Acceleration Coefficients in Particle Swarm Optimization (PSO) algorithms, specifically Autonomous Particles Groups (APG-PSO) and Nonlinear Dynamic Acceleration Coefficients (NDAC-PSO), the research focuses on optimizing power flow and reducing fuel costs. The IEEE 30-bus system serves as the testing platform, and the results are compared with existing literature benchmarks. The study is implemented using MATPOWER in the MATLAB application, showcasing the practicality and effectiveness of the proposed methodology in achieving improved power system performance.

The paper aims to identify the optimum size and location of photovoltaic distributed generation systems and distribution static synchronous compensators (DSTATCOMs) systems to minimize active power losses in the distribution network and enhance the voltage profile. The methodology employed in this article begins by thoroughly discussing various acceleration algorithms used in Particle Swarm Optimization (PSO) and their variations with each iteration. Subsequently, a range of PSO algorithms, each incorporating different variations of acceleration coefficients was verified to solve the problem of active power losses and voltage improvement. Simulation results attained on Standard IEEE-33 bus radial distribution network prove the efficiency of acceleration coefficients of PSO; it was evaluated and compared with other methods in the literature for improving the voltage profile and reducing active power. Originality. Consists in determining the most effective method among the various acceleration coefficients of PSO in terms of minimizing active power losses and enhancing the voltage profile, within the power system. Furthermore, demonstrates the superiority of the selected method over others for achieving significant improvements in power system efficiency. Practical value of this study lies on its ability to provide practical solutions for the optimal placement and sizing of distributed generation and DSTATCOMs. The proposed optimization method offers tangible benefits for power system operation and control. These findings have practical implications for power system planners, operators, and policymakers, enabling them to make informed decisions on the effective integration of distributed generation and DSTATCOM technologies.

This work presents the use of the wavelet transform and computational intelligence techniques to quantify voltage short-duration variation in electric power systems, with respect to time duration and magnitude. The wavelet transform is used to determine the event duration, as well as for obtaining a characteristic curve relating the signal norm as function of the number of cycles for a waveform without disturbance that is used as reference for the calculation of the magnitude of the event. A Generalized Regression Neural Network (GRNN) is used to interpolate not stored points of the characteristic curve. The method is part of a process to automate the post operation signal analysis in electric power systems, and it is used to quantify the voltage short-duration variation magnitude of previously selected signals. The method has been shown efficient, and some results obtained from the application of this method to power system real signals are presented.

In this paper, a multi-objective analytical method to evaluate the impacts of optimal location and sizing of distributed generation is presented. This method is based on an analysis of the exact loss formula and continuous power flow in a radial distribution system. Based on two methods of analysis, power loss and weakest voltage buses and lines are calculated and then the optimal size of distributed generation is determined. After that, by considering the minimum power losses and the maximisation of voltage stability, the proposed index determines and ranks positions to decide the optimal distributed generation location in the system. This method allows us to find the best places and size to connect a number of distributed generation units by optimising the objective functions. The simulation results were obtained using a 33-bus radial distribution system to determine the location and size of the distributed generation units. The results show the effectiveness of voltage profile improvement, loading factor improvement and power loss reduction. Further, the problems of a single objective function and the placement of the distributed generation unit using analytical methods are solved by the proposed approach.

Improving distribution grid reliability is a major challenge for planning and operation of distribution systems having a high share of distributed generators (DGs). The rise of DGs share can lead to unplanned contingencies while on the other hand, they can provide flexibility in supporting grid operations after a contingency event. This paper presents an optimal energy interruption planning approach that dispatches the flexibility from DGs and performs a cost-optimal load shedding of flexible loads. The proposed approach is tested on a synthetic grid, representing typical urban and low voltage feeders in EU with distribution networks modeled in radial and meshed configurations. The study shows that the proposed optimization process can be successfully used to plan resources during contingency event and this can lead to a reduction in energy not supplied and improvement of reliability indices.

Abandoned mine lands across the United States represent one of the most underutilized land resources with significant potential for renewable energy development. These sites, often characterized by degraded soil conditions, limited agricultural viability, and proximity to transmission infrastructure, present a strategic opportunity for large-scale deployment of solar photovoltaics (PV), wind energy systems, and battery energy storage systems (BESS). This paper presents a comprehensive technical performance evaluation of integrating BESS with renewable energy projects deployed on abandoned mine sites in the United States. The study examines system efficiency, grid stability improvement, capacity factor enhancement, intermittency mitigation, and economic performance under varying operational conditions. A comparative analysis between standalone renewable systems and hybrid renewable-storage configurations is conducted using simulated operational datasets and established engineering performance metrics. Findings indicate that integrating BESS significantly enhances dispatchability, improves renewable penetration levels, reduces curtailment losses, and increases overall system reliability. The study further identifies key engineering constraints such as soil instability, legacy contamination risks, and grid interconnection limitations. The results provide a technical foundation for policymakers, energy developers, and environmental planners seeking to repurpose abandoned mine lands for sustainable energy infrastructure.

In multi-agent systems, achieving consensus among autonomous agents is a fundamental problem with wide-ranging applications, from autonomous robotics to distributed sensor networks. However, the real-world deployment of such systems often involves communication links prone to impairments, including packet loss, delays, and network congestion. These communication challenges present formidable obstacles to achieving consensus reliably and efficiently. In this paper, consensus protocols were introduced for network with and without communication impairments and convergence analysis were provided in all the cases. The intricate dynamics of consensus issues in multi-agent-based distributed control under the influence of communication link impairments, connectivity and consensus protocol were established. Undirected communication graphs used to model the topology for agents' connectivity is significant to addressing consensus issues of communicating agents. The paper also discusses the tradeoffs and design considerations in developing consensus strategies resilient to communication failures while optimizing performance. Simulation results show that an isolated agent in a network can achieve consensus only when there is a reference value. It was also established that communication impairments significantly degrade the performance of distributed agents in a network.

This paper aims to develop a multi-criteria technique that originated in Geo-Information Systems (GIS), horizontal radiation data and Light Detection and Ranging (LIDAR) to discover the installation potential of photovoltaic systems in an urban area and assess the output of yearly generation of electricity. In a small area in the center of Baghdad (Iraq), data that were obtained from LIDAR provide a precise representation of the urban environments through the development of a Digital Surface Model (DSM) that was utilized for computing the degree of the roofs of building local inclination and orientation using the impact of shadow for the different elements (other construction and trees). The paper introduces the possibility of distinguishing two forms of roof: for the first one, PV panels were situated parallel with the roofs, while for the second, the optimum position was obtained through the utilization of the structures to mount them. For the second case, the self-shading was considered. Incident radiation on the surface of panels is computed using a geometric technique that depends on horizontal radiation per hour, including direct and diffuse components. Finally, the evaluation of the effective production also includes panels efficiency and various sources of waste, particularly temperature.

Artificial intelligence (AI) has rapidly become a core enabling technology in photovoltaic (PV) power systems, supporting improvements in forecasting accuracy, operational control, fault diagnosis, and system-level energy management. Despite the rapid growth of this field, a comprehensive understanding of its intellectual structure, thematic evolution, and emerging methodological directions remains fragmented. To address this gap, this study develops an integrated bibliometric-thematic analysis framework to systematically map the knowledge structure, research trajectories, and methodological frontiers of AI applications in PV power systems. The analysis is based on 4752 peer-reviewed journal articles indexed in Scopus (2006-2025). It combines performance analysis, co-citation analysis, keyword co-occurrence analysis, and bibliographic coupling to answer five structured research questions. The results demonstrate that PV power forecasting constitutes the central intellectual backbone of AI-based PV research, with the highest citation concentration and the strongest thematic connectivity across clusters. Thematic evolution analysis reveals a clear methodological transition from conventional machine learning models toward hybrid deep learning architectures, uncertainty-aware prediction frameworks, and physicsbased AI integration. Furthermore, emerging research frontiers are characterized by generative learning models, multi-source data fusion strategies, and resilience-oriented fault diagnostics, while critical gaps persist in benchmarking standardization, uncertainty quantification, system-level integration, and largescale industrial deployment. Unlike prior reviews that focus on isolated technical applications, this study provides the first integrated performance analysis and science-mapping synthesis that connects intellectual foundations, thematic evolution, and frontier innovations across the entire AI-based PV ecosystem. The findings offer a structured research roadmap and actionable guidance for researchers, PV plant operators, and policymakers aiming to design intelligent, scalable, and resilient PV energy systems that support the global low-carbon transition.

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This paper investigates the techno-economic analysis process for designing renewable energy systems for El-Fasher City, Sudan. First, a solar photovoltaic system for residential use in El-Fasher City is designed using the System Advisor Model (SAM), a robust tool for analyzing renewable energy projects. The study then explores the application of SAM in designing a community-scale wind turbine system at the same site. While the combination of wind power and solar photovoltaics has the potential to enhance the community's sustainability and resilience by creating a more diverse and reliable renewable energy mix, this study analyzes both systems separately. The findings contribute to the development of sustainable energy practices by providing a systematic approach to planning renewable energy systems tailored to specific locations. The results aim to educate policymakers, academics, and practitioners, encouraging the adoption of efficient and effective renewable energy solutions.

The rapid deployment of distributed energy resources (DERs) especially solar photovoltaic (PV) inverters and electric vehicle (EV) charging points into residential distribution systems has given rise to power quality problems in the form of harmonic distortion. This paper critically reviews the theoretical basis, source characterization, modelling and mitigation approaches to harmonic distortion in residential microgrids. Based on an analysis of fifty recent publications, the paper assesses the efficacy of passive harmonic filters (PHFs), active power filters (APFs) and hybrid solutions vis-à-vis international standards, primarily IEEE 519-2022. Main conclusions confirm that unchecked harmonic current injection from 100 kW EV fast-chargers and pulse-widthmodulated (PWM) solar inverters can result in a total harmonic distortion (THD) exceeding the 5.0 % voltage distortion limit, with subsequent transformer derating, increased I²R losses and accelerated insulation ageing. The review further demonstrates that singletuned passive shunt filters, sized using the Electrical Transient Analyzer Program (ETAP), offer a cost-effective and standards compliant mitigation route for the residential sector where active filter economics are prohibitive. Research gaps, including the lack of integrated PV-EV harmonic characterization at the point of common coupling (PCC) in low volt residential systems, are highlighted with recommendations for future research.

In Myanmar, as the main power generation is hydro power generation. the utility cannot supply sufficient power to customers during the dry season. Besides interruptions occur frequently due to aging system and lack of prospered protection. Therefore, reliability is an urgent issue in Myanmar. As a result of unbalance between generation and load, the distribution system is getting poor voltage profile, instability and high power losses in high load condition. According to network characteristics, the failure of a component always leads to consequence interruption in a radial distribution system.  Therefore, it is a must consideration to mitigate these challenges to enhance the system reliability. There are many techniques to solve the reliability problems such as reclosers, switching devices (manual and automated switches), system reconfiguration, feeder re-conducting and integration of distributed generation (DG). In this paper, system reliability assessment is evaluated in detail w...

Climate change and environmental issue is a common concern for all of us. On the other hand, energy is essential for our daily life and how to get secure, affordable and environmental sustainable energy becomes global issue. Distributed generation(DG) is one of the solution to mitigate environmental impact and to integrate as a part of the smart grid. Solar photovoltaic can be used abundantly as the small multidistributed generation system to improve system performance in many ways. According to the previous researches, it is clear that DG can make system performance better. However, choosing location and sizing is a must considerable issue for integration of DG in the distribution network. In some research, the location and size was determined only depended on the amount of loss reduction. In our research, three factors such as reliability index, voltage stability index and loss reduction are going to evaluate to figure out the location and size of the DGs. Using forward-backward sweep method in MATLAB, power loss condition is analysed. Then, loss reduction level is check by installation DG in each bus and the most effective buses or the buses at which the highest amount of loss DG can reduce are assigned as locations for DG. And then the voltage stability index (VSI) is calculated and one of the nominate buses which has highest VSI index is chosen as the designated bus for allocation. In this way, the optimal location of DG can be assigned with the consideration of losses and VSI index. In another way, the location of DG can be determined with the consideration of reliability index. From the concept of reliability, the most effective or optimal location is the bus at which the reliability is the highest. In this research, the location and size of DGs were considered based on combination of three indices such as VSI, loss and reliability index.

This paper presents results from a study on the impact of smart grid solutions, which includes development of a generic framework for power system analyses. The study has been performed as one of several independent studies, part of a national governmental task on smart grid in Sweden. A large amount of weather data, along with electricity consumption and wind power generation data, have been analyzed. Achieved results from these initial analyses can be used as reference material and have also been used within case studies presented. The proposed framework is flexible and numerous combinations of scenarios are possible to define. Integration of wind and solar power, analyses of transfer limits using static or dynamic rating and energy storage can be considered as well as weather effects. Results show how power systems can handle more electricity consumption and generation. The study shows that Smart Grid solutions are beneficial for resource efficient electricity grids. Moreover, di...

In the current digital era, organizations are constantly facing an array of security threats that can compromise sensitive information and interfere with their operations. For them to properly safeguard their assets, companies need to implement powerful security monitoring systems. One example is Wazuh, an open-source security information and event management (SIEM) system that provides end-to-end visibility into an organization's security posture. The focus of this project is to install and set up Wazuh, including the installation of agents on different endpoints, such as Windows and Linux. Through log gathering and forwarding to the Wazuh server, we can guarantee that important security incidents are recorded and analyzed. The integration of log sources, such as pfSense firewalls, is an essential part of this process and helps us to better identify possible security threats. Also, the project will entail the establishment and personalization of rules and decoders in Wazuh to adapt its detection to the particular requirements of our company. This will allow us to better detect and react to security incidents by correlating events and triggering timely alerts. In addition to incident detection and response, Wazuh will also be used for monitoring compliance, ensuring our organization complies with applicable security standards and regulations. With the ability to produce compliance reports and observe system configurations, we are able to stay in a robust security stance and avoid risks. The significance of this project is underscored by the burgeoning number of cybersecurity incidents. As per CERT-In (Indian Computer Emergency Response Team), more than 1.2 million cybersecurity incidents occurred in India during 2023 alone, such as targeted attacks on government, healthcare, and financial institutions. The scenario turned worse in 2024 with cybercrime complaints crossing 25 lakh and estimated losses crossing Rs 20,000 crore. Through this project, we hope to create an overarching security monitoring system that not only increases our ability to respond to incidents, but also assists with compliance. In the end, we hope to help create a safer organizational environment in the wake of these increasing threats.

This paper examines the application of energy storage to enhance the smallsignal stability of an electrical power system with renewable power generation. In particular, a linearized model of the system and an LMI-based control design method are used to achieve frequency and voltage regulation subject to small perturbations to mechanical power inputs to synchronous generators in the system. The controller design is validated using the following simulation studies: (1) a single machine connected to an infinite bus (SMIB) (2) a two-machine (synchronous generator and doubly-fed induction generator) connected to an infinite bus. Simulation results show that the proposed controller can simultaneously achieve frequency and voltage regulation.

Digital financial platforms are rapidly evolving toward intelligent,
multimodal ecosystems where user interactions are no longer confined to
traditional graphical interfaces but span voice commands, biometric
verification, document imaging, and conversational AI agents. In such
environments, conventional quality engineering approaches primarily
focused on deterministic UI flows and text-based validations fail to capture
the dynamic, probabilistic, and context-dependent nature of multimodal
interactions. This paper addresses this gap by proposing a Multimodal
Quality Engineering (MQE) framework that systematically integrates voice
intelligence (including speech recognition accuracy, intent detection, and
acoustic variability handling) with image-based validation (such as OCR
reliability, facial recognition integrity, and document authenticity checks)
into unified, end-to-end testing pipelines. The framework leverages
advancements in multimodal learning architectures, including cross-modal
representation models that align audio, visual, and textual data into shared
semantic spaces, enabling more coherent validation of user journeys across
modalities. Additionally, it incorporates AI-driven testing strategies such
as synthetic data generation, adversarial testing, and continuous learning-
based validation to assess not only functional correctness but also
robustness under noisy inputs, latency constraints in real-time financial
transactions, and overall user experience consistency. By synthesizing
developments from multimodal artificial intelligence, modern software
testing paradigms, and financial technology systems, the proposed MQE
approach provides a scalable, interpretable, and automation-friendly
quality assurance model that can adapt to the increasing complexity and
regulatory sensitivity of next-generation digital financial platforms.

India is home to a diverse population that lives in many different locations, the great majority of which are located in rural areas. Unfortunately, the power in these outlying communities is often unreliable, which has a detrimental impact on the residents' daily lives. To solve this problem and improve the power situation, plugging in renewable power sources, such as solar power, is increasingly needed. By reducing Aggregating Technical and Commercial (AT&C) losses and improving energy quality, solar power production paves the way for smart grid and microgrid adoption in rural areas. Changes in energy consumption and distribution patterns in rural areas may result from the move to smart grids. Solar energy has a lot of untapped potential in India, but the country's typical solar business models face real economic hurdles. Thoroughly resolving these challenges is crucial for ensuring the successful uptake and long-term viability of solar electricity in India's rural regions. This article only focuses on the problems and delays with photovoltaic business strategies that are available at India's secondary distribution level.

This paper presents a comprehensive analysis of load expansion planning through the integration of photovoltaic systems at the Bir Alganame 220/30 kV substation. An electrical planning system serves as a crucial tool for designing electrical systems that effectively meet evolving energy demands. The study investigates the impact of load expansion on hybrid photovoltaic system design, focusing on system reliability and total project cost as primary design objectives. The research methodology encompasses a detailed load flow study conducted in 2024, coupled with load forecasting for 5 and 10year horizons at an 8% load increase rate. The investigation includes analyzing the optimal parameters of the PV system integration with the existing grid to ensure proper functionality. Additionally, the study addresses the probabilistic scenario set of load demand and natural characteristics of renewable energy, which is crucial for power system planning studies. The research emphasizes the importance of proper transmission system network development to reliably and securely evacuate the generated power to meet future domestic load demand. Through simulation and analysis, this study demonstrates an integrated approach to improving power system performance while considering geographical and environmental factors affecting PV output. The findings provide valuable insights into optimal electrical network planning procedures, incorporating both long and short-term perspectives through PV system integration.

The limitations of centralized optimization methods in managing power distribution systems operations motivate distributed control and optimization algorithms. However, the existing distributed optimization algorithms are inefficient in managing fast varying phenomena, resulting from highly variable distributed energy resources (DERs). Related online distributed control methods are equally limited in their applications. They require thousands of time-steps to track the network-level optimal solutions, resulting in slow performance. We have previously developed an online distributed controller that leverages the system's radial topology to achieve network-level optimal solutions within a few time steps. However, it requires solving a nodelevel nonlinear programming problem at each time step. This paper analyzes the solution space for the node-level optimization problem and derives the analytical closed-form solutions for the decision variables. The theoretical analysis of the node-level optimization problem and obtained closed-form optimal solutions eliminate the need for embedded optimization solvers at each distributed agent and significantly reduce the computational time and optimization costs.

The limitations of centralized optimization methods in managing power distribution systems operations motivate distributed control and optimization algorithms. However, the existing distributed optimization algorithms are inefficient in managing fast varying phenomena, resulting from highly variable distributed energy resources (DERs). Related online distributed control methods are equally limited in their applications. They require thousands of time-steps to track the network-level optimal solutions, resulting in slow performance. We have previously developed an online distributed controller that leverages the system's radial topology to achieve network-level optimal solutions within a few time steps. However, it requires solving a nodelevel nonlinear programming problem at each time step. This paper analyzes the solution space for the node-level optimization problem and derives the analytical closed-form solutions for the decision variables. The theoretical analysis of the node-level optimization problem and obtained closed-form optimal solutions eliminate the need for embedded optimization solvers at each distributed agent and significantly reduce the computational time and optimization costs.

The rapid growth of electric vehicles (EVs) necessitates strategic planning of Electric Vehicle Charging Stations (EVCSs) to ensure grid stability and operator profitability. This paper proposes a comprehensive framework that addresses both objectives. The IEEE 24-bus system is investigated, and performance indices are evaluated through contingency-based outage analysis using Newton-Raphson load flow method. Thirteen high-capacity EVCSs are then sequentially integrated at selected load buses. The optimal power flow (OPF) is solved using the primal-dual interior point method after each placement to assess impacts on voltage profiles and power losses, thereby guiding integration decisions. Post-integration contingency analysis is conducted to uncover new vulnerability scenarios. To enhance grid resilience, optimal placement of the EVs near the most affected lines is examined, demonstrating their effectiveness in improving system performance. In addition, the role of the EVs as distributed power sources in mitigating peak demand is explored. Locational marginal prices (LMPs) are further analyzed to determine profitable EV penetration strategies at high-LMP buses. The proposed framework ensures both secure grid operation and profitable EVCS deployment. Overall, this study offers a systematic approach to EVCS planning and integration, providing actionable insights for developing resilient and economically viable smart grids.

Effective energy management in solar-wind hybrid electric vehicle (EV) systems is complicated by variable renewable supply, unpredictable EV demand, and changing grid pricing. Conventional forecasting and allocation techniques frequently struggle with handling real-time fluctuations, leading to excessive energy usage. This study presents a hybrid framework combining Long Short-Term Memory (LSTM) and Reinforcement Learning (RL) that integrates accurate shortterm energy generation forecasts with adaptive decision-making for optimal energy management. The LSTM module forecasts solar and wind generation utilising multivariate time-series data, encompassing meteorological and system characteristics, while the RL agent allocates energy dynamically among electric vehicles, batteries, and the grid. Simulation findings exhibit enhanced performance compared to baseline approaches, attaining 98.3% accuracy, 97.9% precision, 98.1% recall, 98.0% F1-score, a root mean square error (RMSE) of 1.9, and a R² of 0.99. Comparative analyses utilising Random Forest, independent LSTM, Deep Q-Network, and Support Vector Regression validate that the proposed framework enhances prediction accuracy, energy efficiency, and durability under variable settings. This study presents a scalable, real-time, and dependable solution for renewable energy management in electric vehicles, surpassing current methodologies and delivering actionable information for the sustainable implementation of smart grids.

Fuel cell systems are enhancing due to interest to supply electricity in remote areas as well as distributed power generation especially during the peak loads. Fuel cell generation becomes popular due to cleanliness, portability and suitability for electricity and heat generation. This paper presents the modeling of a fuel cell power plant (FCPP) in terms of fuel cell, dc-dc converter, dc-ac inverter and power system parameters, to interface with loads or grid. The control strategy of this model is hysteresis current controller (HCC). HCC is used to generate inverter switch pulse for the ac grid voltage. MATLAB/SIMULINK is used to validate the modeling and simulation of fuel cell generation and power conditioning unit. The proposed control strategy makes the distribution generation system work properly when the voltage disturbance occurs in the distribution system.

The usage of electric vehicles (EV) increased in recent years as the vehicles design and performances are nearly similar to petrol vehicles. The main source of energy for EV is taken from the grid for charging. So, the penetration of EVs in alternating current (AC) grid creates more power quality issues like voltage sag, swell and harmonics in the current. This energy can also be produced from the renewable energy resources like photo voltaic (PV) power generation. This PV energy can also be used as direct current (DC) grid. The electric vehicle chargers which come with intelligent grid operation is considered as load in this paper. This paper is an attempt to discuss the penetration of EVs in AC/DC hybrid micro grid which causes the power quality problems, and the power quality problem is mitigated by using the unified power quality conditioner (UPQC). The results are analyzed for three cases and four scenarios which is based on the function of UPQC and the action of smart charger in grid connected as well as autonomous mode operation of the AC/DC micro grid when the load is considered as dynamic load. The simulation is carried out in MATLAB2017b environment.

Transient stability analysis is conducted to determine the ability of the electric power system in maintaining the operating stability after a major disturbance. The disturbance can be trigger an impact on the stability of the rotor angle, voltage, and system frequency which can cause loss of synchronization. In this paper, the impact of the interconnection of the Tombolo-Pao mini hydro power plant (MHPP) on the stability of the system was analyzed by several scenarios to determine the behavior of system parameters in a 20 kV system interconnection network. This research is an implementation of regulatory provisions relating to the study of the connection to the PLN distribution network through by regulator. Based on the result of simulation study, transient stability of generators at Tombolo Pao power plant about 0.1 second, will not occur with network configuration according to modeling activation of anti-islanding protection of Tombolo Pao Power Plant which is set by 2 second. The simulation results show that the location of the disturbance in the electric power system has been influenced by the behavior of the power plant (synchronous generator) which can lead to the instability of the micro-hydro connected to the micro-grid system 20 kV.

This paper proposes a two-stage decision-making tool to assess the impacts of energy storage systems (ESSs) and offshore wind farms (OSW) integration in the power grid. To quantify the potential impacts, various key performance indicators (KPIs) are incorporated. These KPIs gage the environmental, technical, and economical attributes of the integrated system. The proposed framework uses a unit commitment (UC) mixed-integer linear programming (MILP) model. Two case studies (one for New Jersey and one for New York) are examined with clean power transition targets. The uncertainties of the net-load and power generation from renewable energy sources (RESs) are characterized by Gaussian Process Regression (GPR) models. These models are fine-tuned using the base forecasts generated using our in-house load forecasting tool and the National Renewable Energy Laboratory's (NREL) publicly available generation calculator. The results show that ESS installations almost always improve the performance of the grid, regardless of the location and configuration. Furthermore, the unequally distributed ESS installations show better impacts than standalone centralized ESSs; and the mixed ESS technologies outperform single-type ESS deployments. INDEX TERMS Clean energy transition, net-zero energy system, 100% renewable energy, sustainability, offshore wind (OSW), energy storage system (ESS), planning and operation optimization.

For all industrial and distribution sites, the lagging power factor of electrical loads is a common problem. In the early days, it was corrected manually by adding the capacitor banks of certain values in parallel. Automatic power factor correction (APFC) using a capacitor bank helps to make a power factor that is close to unity. It consists of a microcontroller that processes the value of the power factor to enable the system and monitor the power factor if it falls below (0.77) from the specified level. This paper presents the automatic correction of the power factor by adding the capacitors banks automatically of the desired value in a three-phase system in the form of binary coding (0-7). The main purpose of this system is to maintain the power factor as close as to unity, for the experimental case, it is set to (0.93) which helps to decreases the losses and ultimately increase the efficiency of the system.

DC microgrids (DCMGs), typically envisioned with high bandwidth communications, may well expand their application range by considering additional autonomous control strategies, especially to increase resiliency in contingency situation. These strategies are based on the droop control methods, enabling voltage regulation and proportional load sharing based only on local measurements. Control challenges arise when coordinating the output of multiple DCMGs, each composed of several distributed energy resources through bus-to-bus transactional converters. This paper proposes an autonomous control strategy for transactional converters when multiple DCMGs are connected through a common bus. This control strategy seeks to match the external bus voltage with the internal bus voltage, balancing power. Three case scenarios are considered: standalone operation of each DCMG, excess generation on one DCMG, and generation deficit in one DCMG. Results using Sandia National Laboratories' Secure Scalable Microgrid Simulink library, and models developed in MATLAB are compared. These results demonstrated that the proposed control strategy supported the advantage of seamless operation with and without local generationload mismatches and expanded the range of autonomous operation between DCMGs.

In this paper, the chaotic particle swarm optimization (CPSO) algorithm is combined with MATPOWER toolbox and used as an optimization tool for attaining solving the optimal reactive power dispatch (RPD) problem, by finding the optimal adjustment of reactive power control variables like a voltage of generator buses (VG), capacitor banks (QC) and transformer taps (Tap) while satisfying some of equality and inequality constraints at the same time. CPSO and Simple PSO algorithms will be checked in a large system such as IEEE node-118. CPSO and Simple PSO algorithms have been implemented and simulated in the MATLAB program, version (R2013b/mfile). Then compassion these results with the results obtained in the other algorithms in the literature like the comprehensive learning particle swarm optimization (CLPSO) algorithm. The simulation results confirm that the CPSO algorithm has high efficiency and ability in terms of decrease real power losses (𝑃 𝐿𝑜𝑠𝑠), and improve voltage profile compared with the obtained by using the simple (PSO) algorithm and (CLPSO) at light load.

The increasing penetration of nonlinear power-electronic loads in urban distribution networks, such as variable-frequency drives, electric vehicle charging stations, and inverter-based photovoltaic systems, has significantly degraded power quality. In the studied 33-bus Port Harcourt feeder, uncompensated peak loading produced end-bus voltages as low as 0.903 p.u., violating the ±5% regulatory limit, while 5th and 7th harmonic currents reached 10% and 5% of the fundamental, exceeding IEEE 519 limits. This study proposes a surrogate-accelerated Smell Agent Optimization (SAO) framework for the optimal placement and sizing of multiple shunt Active Power Filters (OPSMAPF) in radial distribution systems. SAO, a bio-inspired metaheuristic based on olfactory gradient navigation, is integrated with a neural-network surrogate model (128/64 ReLU neurons) trained using 5,000 Latin-hypercube Simulink samples to enable rapid multi-objective evaluation. The surrogate reduces simulation demand by 80%, allowing near-real-time optimization. Three Pareto solutions are obtained. Solution A (cost-focused) improves voltages above 0.948 p.u. and reduces harmonics to 6% and 3%. Solution B (balanced) achieves voltages of 0.955-1.001 p.u. with THD of 5.2%. Solution C (performance-focused) produces nearly flat voltage profiles (0.978-1.000 p.u.) with THD reduced to 3.1%. System losses decrease from 245.6 kW to 162.0 kW (34% reduction). Comparisons with Genetic Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer show that SAO provides better Pareto performance, faster convergence, and lower losses with about 47% fewer fitness evaluations. The method is validated on IEEE 33-bus and Port Harcourt feeders under multiple load conditions, demonstrating its suitability for practical utility planning.

The concept of the Energy Internet represents a paradigmatic transformation of traditional centralized power systems into decentralized, intelligent networks characterized by bidirectional energy flows and distributed decision-making. At its core, this paradigm envisions energy systems functioning analogously to digital information networks, where distributed energy resources (DERs) interact through real-time communication protocols and autonomous market mechanisms. However, unlike digital networks, electrical systems are governed by immutable physical laws, introducing a layer of complexity that fundamentally constrains decentralized market architectures.

The satisfaction of electricity customers and environmental constraints imposed have made the trend towards renewable energies making them more essential due to their advantages as reducing power losses and ameliorating system’s voltage profiles and reliability. This article addresses the optimal location and size of multiple distributed generations (DGs) based on solar photovoltaic panels (PV) connected to electrical distribution network (EDN) using the various proposed hybrid particle swarm optimization (PSO) algorithms based on chaotic maps and adaptive acceleration coefficients. These algorithms are implemented to optimally allocate the DGs based PV (PV-DG) into EDN by minimizing the multi-objective function (MOF), which is represented as the sum of three technical parameters of the total active power loss (TAPL), total voltage deviation (TVD), and total operation time (TOT) of overcurrent relays (OCRs). The effectiveness of the proposed PSO algorithms were validated on both standards IEEE 33-bus, and 69 bus. The optimal integrating of PV-DGs into EDNs reduced the TAPL percentage by 56.94 % for the IEEE 33-bus and by 61.17 % for the IEEE 69-bus test system, enhanced the voltage profiles while minimizing the TVD by 37.35 % and by 32.27 % for two EDNs, respectively.

Cloud-native systems, characterized by microservices, continuous
deployment, and distributed architectures, present significant challenges
for ensuring software quality due to their inherent dynamism, scalability
requirements, and loosely coupled service interactions. Traditional testing
strategies largely designed for monolithic or static systems struggle to scale
effectively in such environments, often resulting in redundant test
execution, delayed feedback cycles, and insufficient coverage of critical
system paths. This paper introduces a deep learning-driven quality
intelligence framework for adaptive test coverage optimization in cloud-
native environments, aiming to transform testing from a reactive process
into a proactive, self-improving system. By integrating deep neural
networks for predictive defect analysis, reinforcement learning for
adaptive test prioritization, and coverage-guided testing techniques for
efficient exploration of execution paths, the proposed approach enables
continuous learning from system feedback and runtime telemetry. It
dynamically adjusts testing strategies based on historical outcomes, system
behavior, and risk profiles, thereby improving both efficiency and
effectiveness. Furthermore, the framework leverages observability data
and CI/CD pipeline integration to support real-time decision-making and
automated test orchestration across distributed services. The study
synthesizes foundational work in software testing (2000–2010), advances
in AI-driven testing (2015–2023), and modern cloud-native engineering
practices to propose a unified, adaptive testing paradigm. Experimental
insights drawn from prior research and empirical studies indicate that such
intelligent, feedback-driven approaches can significantly enhance defect
detection rates, optimize resource utilization, improve test coverage, and
increase overall system resilience in complex, large-scale software
ecosystems.

Modern distributed microservices architectures introduce significant complexity in debugging and maintaining system reliability due to their highly dynamic, loosely coupled, and heterogeneous nature, where failures often propagate across multiple services in non-obvious ways. Traditional root cause analysis (RCA) techniques, which rely heavily on manual inspection, static rules, or simple statistical correlations, struggle to cope with high-dimensional telemetry data, dynamic service interactions, and cascading failures that evolve in real time. This paper presents an AI-augmented approach to RCA that leverages deep learning models, causal inference techniques, and distributed tracing to automate failure detection and diagnosis with greater precision and speed. By integrating multi-modal observability data logs capturing discrete events, metrics representing time-series system behavior, and traces detailing endto-end request flows into intelligent, unified pipelines, the proposed framework enables holistic system visibility and context-aware analysis. Advanced models such as recurrent neural networks, autoencoders, and graph neural networks are employed to detect anomalies, learn service dependencies, and infer causal relationships, thereby improving fault localization accuracy. Furthermore, the approach aligns with modern AIOps practices by enabling continuous learning, adaptive thresholding, and automated remediation recommendations, ultimately reducing mean time to detection (MTTD) and mean time to resolution (MTTR). By synthesizing foundational statistical methods, contemporary observability frameworks, and recent advances in graph-based deep learning, this study establishes a scalable and robust RCA paradigm tailored for the demands of cloud-native systems and next-generation quality engineering workflows.

In the whole world and especially in Morocco, the electric power sector faces significant challenges and the demand for energy is increasing as fossil fuel sources are disappearing. Moreover, the high cost of construction of large production plants and the obligation to reduce greenhouse gas emissions are among the factors pushing the energy sector to integrate distributed generators DGs based on renewable energies into power grids. However, the integration of these generators increased the values of short-circuit currents in the network, which poses a real threat to the existing protection coordination systems in the distribution network. The aim of this article is to bring together in a single platform all available research addressing the issue of protection coordination in the presence of DGs in the distribution network, in order to help researchers identify future scope. This paper presents a review of the impact of distributed generators on the protection coordination of distribution networks. The solutions proposed in the literature, to mitigate the negative impact of DGs, have been investigated in detail, along with the limitations of these proposed techniques.