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The primary goal of a power distribution system is to provide nominal voltages and power with minimal losses to meet consumer demands under various load conditions. In the distribution system, power loss and voltage uncertainty are the common challenges. However, these issues can be resolved by integrating distributed generation (DG) units into the distribution network, which improves the overall power quality of the network. If a DG unit with an appropriate size is not inserted at the appropriate location, it might have an adverse impact on the power system's operation. Due to the arbitrary incorporation of DG units, some issues occur such as more fluctuations in voltage, power losses, and instability, which have been observed in power distribution networks (DNs). To address these problems, it is essential to optimize the placement and sizing of DG units to balance voltage variations, reduce power losses, and improve stability. An efficient and reliable strategy is always required for this purpose. Ensuring more stable, safer, and dependable power system operation requires careful examination of the optimal size and location of DG units when integrated into the network. As a result, DG should be integrated with power networks in the most efficient way possible to enhance power dependability, quality, and performance by reducing power losses and improving the voltage profile. In order to improve the performance of the distribution system by using optimal DG integration, there are several optimization techniques to take into consideration. Computational-intelligence-based optimization is one of the best options for finding the optimal solution. In this research work, a computational intelligence approach is proposed to find the appropriate sizes and optimal placements of newly introduced different types of DGs into a network with an optimized multi-objective framework. This framework prioritizes stability, minimizes power losses, and improves voltage profiles. This proposed method is simple, robust, and efficient, and converges faster than conventional techniques, making it a powerful tool of inspiration for efficient optimization. In order to check the validity of the proposed technique standard IEEE 14-bus and 30-bus benchmark test systems are considered, and the performance and feasibility of the proposed framework are analyzed and tested on them. Detailed simulations have been performed in "MATLAB", and the results show that the proposed method enhances the performance of the power system more efficiently as compared to conventional methods. [ABSTRACT FROM AUTHOR]

Regulating voltage and current signals in microgrids (MG) is essential to ensure stability, optimize power quality, support grid integration, enhance operational efficiency, and promote safety within the system. This paper introduces a novel Linear Matrix Inequalities (LMI)-based approach aimed at regulating voltage and current signals within microgrids through the utilization of sliding mode control. The MG under examination in this paper is composed of a voltage source inverter (VSI) for DC to AC voltage conversion, a filter to ensure sinusoidal signal quality, and an array of loads, including those with uncertain characteristics. The objective of this study is to regulate the output voltage and current in a short period of time in the presence of diverse loads. By promptly adjusting voltage and current levels, the microgrid can effectively accommodate fluctuations in demand and maintain optimal performance under changing conditions. The presented controller consists of two parts: a state feedback gain calculated from the LMI and a sliding mode-based controller to maintain system stability. This controller is intended to reject disturbances, track reference signals, and minimize steady-state errors in a limited time. The satisfactory performance of the microgrid will have a significant impact on various parameters, such as frequency, active power, reactive power, and power factor. Simulating the voltage source inventor and presenting numerical results demonstrate the effectiveness of the proposed controller to provide high robustness against uncertainty and nonlinear loads while maintaining system stability.

Under the background of the new power system, it is important to make full use of the distributed generation in the distribution network to achieve carbon peaking and carbon neutrality goals. With the wide access of distributed power sources, there is bi-directional power flow between the transmission and distribution networks. If the transmission and distribution networks still follow the traditional equivalence method for optimal power calculation, accurate results will not be obtained. Therefore, a transmission-distribution-network-coordinated optimal power flow model is established. The node tearing method is used to decouple the coupling variables of the transmission network and distribution networks. On this basis, the iterative form of the model with immobile points is obtained by mathematical derivation. The traditional heterogeneous decomposition algorithm only uses the latest calculation results for iterative computation. Hence, combining with the Anderson acceleration idea in mathematics, the heterogeneous decomposition algorithm based on Anderson acceleration is proposed by using the historical iteration values to optimize the iterations of immobile points. It is demonstrated by numerical experiments that, the established model can fully utilize the initiative of the distribution networks and reduce carbon emissions. The proposed algorithm has high accuracy. In addition, compared with the heterogeneous decomposition algorithm, its convergence performance is significantly improved.

This paper proposes a novel hybrid technique for enhancing power quality (PQ) in distributed generation (DG) systems by deploying a unified power quality conditioner (UPQC). Here, the proposed hybrid method is the joint execution of white shark optimizer (WSO) and recalling-enhanced recurrent neural network (RERNN), called the WSO-RERNN technique. The primary objective of this novel approach is to effectively mitigate voltage sag and reduce voltage harmonics under varying load conditions. It is important to investigate the voltage sag, swell and harmonic distortion of the system to obtain an enhanced PQ of the energy supply. Therefore, this paper shows the brief impact of PQ in DG utilizing the proposed unified PQ conditioner controller. The WSO-RERNN control technique enhances the performance of the UPQC controller by providing the optimal control signal. By then, the efficiency of the proposed approach is done in MATLAB, and the performance is compared with those of existing optimization techniques, including Ant Lion Optimizer (ALO), Grey wolf optimization (GWO) and Salp swarm algorithm (SSA) methods. [ABSTRACT FROM AUTHOR]

In power systems, nonlinear loads cause harmonic distortion, adversely affecting sensitive equipment such as induction motors, power electronics, and variable-speed drives. This paper presents a novel control strategy that integrates with existing hierarchical control systems to mitigate voltage imbalances and harmonic disturbances in AC-islanded microgrids. The proposed method utilizes selective harmonic order filtering through multiple second-order generalized integrators (MSOGI) to extract negative, positive, and harmonic order components. The distributed generation (DG) unit control mechanism is designed to immediately correct voltage imbalances and harmonic disruptions, distributing the compensatory load evenly to rectify real and reactive power imbalances and harmonic disturbances. The microgrid's control architecture primarily includes droop controllers for real and reactive power of positive sequences, voltage and current regulation inner control loops, an additional loop for correcting imbalances and harmonics, and secondary controllers to maintain voltage magnitude and frequency at nominal levels, ensuring high-quality voltage at the point of common coupling (PCC). The effectiveness of this approach is demonstrated through simulation results on the MATLAB/Simulink platform, proving its ability to effectively mitigate voltage imbalances and harmonic issues with the total harmonic of voltage reduced to approximately THDv = 0.5% and voltage unbalance factor (VUF) within approximately 0.1%. [ABSTRACT FROM AUTHOR]

Regulating voltage and current signals in microgrids (MG) is essential to ensure stability, optimize power quality, support grid integration, enhance operational efficiency, and promote safety within the system. This paper introduces a novel Linear Matrix Inequalities (LMI)-based approach aimed at regulating voltage and current signals within microgrids through the utilization of sliding mode control. The MG under examination in this paper is composed of a voltage source inverter (VSI) for DC to AC voltage conversion, a filter to ensure sinusoidal signal quality, and an array of loads, including those with uncertain characteristics. The objective of this study is to regulate the output voltage and current in a short period of time in the presence of diverse loads. By promptly adjusting voltage and current levels, the microgrid can effectively accommodate fluctuations in demand and maintain optimal performance under changing conditions. The presented controller consists of two parts: a state feedback gain calculated from the LMI and a sliding mode-based controller to maintain system stability. This controller is intended to reject disturbances, track reference signals, and minimize steady-state errors in a limited time. The satisfactory performance of the microgrid will have a significant impact on various parameters, such as frequency, active power, reactive power, and power factor. Simulating the voltage source inventor and presenting numerical results demonstrate the effectiveness of the proposed controller to provide high robustness against uncertainty and nonlinear loads while maintaining system stability. [ABSTRACT FROM AUTHOR]

This paper focuses on designing a simple yet efficient H∞ controller for regulating the voltage/frequency, output current, and active/reactive power-sharing in a microgrid (MG). The purpose of designing the robust H∞ controller is to regulate the frequency and voltage in face of the uncertainties in the microgrid and load variation. The studied DC MG test bed includes an SMPS (switching-mode power supply) and DC-DC converter, necessary sensor and data acquisition systems. The proposed control scheme is simulated using MATLAB/Simulink and is then verified in real-time using LabVIEW. The control scheme is developed in the LabVIEW software's block diagram panel of the virtual instrumentation interface. From both simulation and LabVIEW implementation, load sharing among the DGs, and voltage and frequency regulation at the nominal values are achieved by employing the proposed H∞ controller. The robustness of the proposed controller is evaluated by comparing it with a robust observer-based controller (Zarei A, Mousavi Y, Mosalanezhad R, Atazadegan MH. Robust voltage control in inverter-interfaced microgrids under plug-and-play functionalities. IEEE Syst J 2020;14:2813–24) under balanced, unbalanced, and non-linear loads. The comparison results envisage that the proposed controller provides improved voltage/frequency regulation and power-sharing during disturbances including the addition of non-linear load to the MG system. [ABSTRACT FROM AUTHOR]

Environmental concerns and the Paris agreements have prompted intensive efforts towards greener and more sustainable transportation. Persistent expansion of electric vehicles (EV) in the transportation sector requires electric vehicle charging stations (EVCSs) to accommodate the increased demand. Offsetting demand and alleviating the resultant electrical grid stress necessitates establishing grid-integrated renewable energy sources (RESs) where these sustainable strategies are accompanied by variable-weather-related obstacles, such as voltage fluctuations, grid instability, and increased energy losses. Strategic positioning of EVCSs and RES as distributed generation (DG) units is crucial for addressing technical issues. While technical constraints have received considerable attention, there is still a gap in the literature with respect to incorporating the additional complex optimization problems and decision-making processes associated with economic viability, social acceptance, and environmental impact. A possible solution is the incorporation of an appropriate multi-criteria decision analysis (MCDA) approach for feasible trade-off solutions. Such methods offer promising possibilities that can ease decision-making and facilitate sustainable solutions. In this context, this paper presents a review of published approaches for optimizing the allocation of renewable energy DG units and EVCSs in active distribution networks (ADNs). Promising published optimization approaches for the strategic allocation of multiple DG units and EVCSs in ADNs have been analyzed and compared. [ABSTRACT FROM AUTHOR]

When False Data Injection (FDI) attacks inject false data into the controllers of a microgrid, it can lead the controllers to make decisions based on inaccurate information, ultimately resulting in system collapse. This study presents a novel approach to dynamically adjust output voltage in Campus Microgrids (CMG). Initially, we use feedback linearization techniques to address the inherent nonlinear complexities in Distributed Generation (DG) dynamics. Moreover, during voltage regulation, controllers and sensors may be susceptible to FDI attacks. To address this, an elastic follower observer is introduced to mitigate the impact of false information injection attacks on voltage coordination, thus ensuring the robustness and stability of the system. Notably, our method reduces dependence on global information and fault boundaries within the communication network, thereby enhancing system elasticity and efficiency. In conclusion, through comprehensive simulation experiments, we evaluate the effectiveness and performance of our proposed strategy. The simulation results validate the success of our approach in voltage regulation and its efficiency in fault management. These findings bear significant implications for the advancement of voltage regulation strategies in distributed generation systems. [ABSTRACT FROM AUTHOR]

This review provides a comprehensive exploration of distributed generation (DG) in the context of radial distribution systems, encompassing various DG technologies, current advancements, and their integration challenges. It evaluates the impacts of DG on distribution networks, highlighting diverse applications, drivers, and benefits. A significant focus is on the optimal integration of DG, assessing various optimization techniques, algorithms, and objective functions used to address this challenge. Through a detailed analysis of existing literature, this study identifies that no single optimization method stands out as universally superior. Instead, the research concludes that hybrid optimization techniques, particularly those combining heuristic methods such as genetic algorithms with other approaches, offer the most effective and reliable solutions for the complex task of DG integration. These hybrid methods leverage the strengths and mitigate the weaknesses of individual techniques, providing more robust and dependable outcomes. This conclusion underscores the need for innovative, multi-faceted approaches to optimize DG integration in evolving distribution systems. [ABSTRACT FROM AUTHOR]

Summary: Grid‐tied photovoltaic system (GTPS) are widely favored due to their inherent benefits. The interface of parallel power electronic converters in these systems is rapidly increasing. The inverter in GTPS has a very important role in power conversion, transfer, and control. However, their challenging working conditions contribute to susceptibility to power switching device failures. Traditional fault diagnosis and tolerant approaches exhibit limitations in control efficiency, model dependency, and slow recovery from fault. This study proposes a multi‐agent twin delayed deep deterministic policy gradient (MATD3PG) configuration for intelligent parallel inverter control, fault diagnosis, and fault‐tolerant operation in GTPS structure. By leveraging the multi‐agent reinforcement learning (RL) framework, an optimal control of the parallel inverter can be achieved, encompassing fault‐tolerant operation using MATLAB Simulink/PLECS. The major advantage the given technique offers is that it carries out optimum fault‐tolerant operation without causing the system derating. Experimental findings demonstrate that the proposed fault‐tolerant model based on RL traditional methods is able to ensure continuous supply to the connected loads even during fault events. Further, the transition time from fault occurrence to recovery is found to be 6 ms, which is quite less compared with the fault‐tolerant techniques presented in literature. Through real‐time fault diagnosis‐based results, the proposed approach ensures precise tracking of reference currents, quicker response times, uninterrupted supply, and smooth transition to the post‐fault operation mode. [ABSTRACT FROM AUTHOR]

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Distributed generation resources have played a major role in strengthening the performance of the distribution network (DN). Strategically placing and sizing these resources can relieve the stress on distribution line, fostering a more reliable and sustainable energy supply for a greener tomorrow. The literature outlines various optimization techniques, each with its pros and cons based on the methodology used. Some of these techniques are unable to find out the size and placement of DGs that can supply both active and reactive power, while others have a low rate of convergence and the probability of falling into local optima. This research presents a hybrid technique (AT-GSA) based on an analytical and grid search algorithm that can solve the aforementioned problems. Here the goal is to minimize power losses in the DN while enhancing the DN's voltage profile and voltage stability index (VSI). IEEE 33-bus and IEEE 69-bus systems are being used to validate the proposed technique. The findings indicate that by injecting real and reactive power, the IEEE 33-bus and IEEE 69-bus systems experienced a reduction in losses of 88.26% and 93.21%, respectively, compared to base losses. The average absolute error (AAE) of the voltages shows a substantial decrease, declining from 0.0561 and 0.0268, respectively, to 0.0062 and 0.0069. The VSI for each DN has also shown a substantial increase in comparison to the reference case. [ABSTRACT FROM AUTHOR]

In this paper, a Modified Genetic Algorithm (MGA) combined with ETAP (Electrical Transient Analyzer Program), is proposed to optimize the placement and sizing of Distributed Generation (DG) units while considering multiple objectives, such as improving voltage profiles, reducing line losses, and managing short-circuit levels. These factors are critical for maintaining power quality, system reliability, and overall stability. The MGA approach is validated through simulations using the IEEE 33-bus distribution system, modeled in both MATLAB and ETAP. The results show significant improvements in voltage stability, loss reduction, and short-circuit level management, enhancing energy management and operational efficiency. A comparative analysis with alternative algorithms, including the Bat Algorithm (BA), Bacterial Foraging Optimization Algorithm (BFOA), and Artificial Immune System (AIS), demonstrates that MGA achieves superior convergence speed and accuracy, making it highly effective for DG placement and power system performance optimization.

The growing demand for energy, driven by rapid economic development, necessitates higher electricity consumption. However, conventional energy systems relying on fossil fuels present environmental challenges, prompting a shift towards renewable energy sources. In Kosovo, coal-fired power plants dominate electricity production, highlighting the need for cleaner alternatives. Worldwide efforts are underway to increase the efficiency of photovoltaic systems using sustainable materials, essential for ecological and human health. Solar and wind energy are emerging as sustainable alternatives to traditional fossil fuels. However, global concerns about energy security and environmental sustainability are driving countries to prioritize renewable energy development. In Kosovo, the integration of renewable energy sources, such as wind and solar energy, is progressing rapidly. However, challenges such as voltage stability and power losses need to be addressed. Distributed generation offers a solution by increasing energy reliability and reducing greenhouse gas emissions. Further research is needed to assess the technical, economic, and environmental implications of integrating renewable resources into Kosovo's energy system, focusing on power quality, system reliability, and voltage stability. The research focused on the eastern region of the country, operating at the 110 kV substation level. Challenges in energy quality arise due to the lack of 400 kV supply and the continuous increase in energy consumption, especially in the Gjilan area. This paper investigated integrating renewable energy, especially wind and solar sources, into the medium- and long-term plans at the Gjilan 5 substation to enhance energy quality in the area. Successful integration requires detailed analysis of energy flows, considering the impact of photovoltaics (PVs) on distribution system operation and stability. To simulate and analyze the effects of renewables on the transmission system, voltage profile, and power losses, a case study was conducted using ETAP software. The simulation results present a comparison between scenarios before and after integrating renewable systems to improve energy quality in the identified area.

Distributed generation (DG) has gained prominence in distribution systems in recent decades due to its capability to reduce carbon footprint, improve power quality, voltage support and maintain a sustainable system. Although DG has numerous benefits, unplanned DG integration can adversely impact the power system parameters and the distribution company's (Discom) income. This work addresses optimal site and sizing planning for renewable and dispatchable DG, considering long-term economic and technical benefits. A multi-objective (MO) problem is formulated to reduce the cost associated with DG units, their operation and maintenance (OM), fuel, emission, and operational objectives. The impacts of hourly variation in the power generation of renewable DG units and load demand profiles are investigated through case studies. Annual load growth and load modeling are included in the study to have a pragmatic model. An improved particle swarm optimization (IPSO) technique having chaotically varying inertia weight is used to determine the optimal solution. Profit and payback period (PP) calculations for the purchase and sale of electricity on the contract price for the project duration are discussed in detail. The proposed approach has been demonstrated to be effective through simulation studies conducted on the modified IEEE-33 bus and the real Italian distribution system. [ABSTRACT FROM AUTHOR]

The growing demand for energy, driven by rapid economic development, necessitates higher electricity consumption. However, conventional energy systems relying on fossil fuels present environmental challenges, prompting a shift towards renewable energy sources. In Kosovo, coal-fired power plants dominate electricity production, highlighting the need for cleaner alternatives. Worldwide efforts are underway to increase the efficiency of photovoltaic systems using sustainable materials, essential for ecological and human health. Solar and wind energy are emerging as sustainable alternatives to traditional fossil fuels. However, global concerns about energy security and environmental sustainability are driving countries to prioritize renewable energy development. In Kosovo, the integration of renewable energy sources, such as wind and solar energy, is progressing rapidly. However, challenges such as voltage stability and power losses need to be addressed. Distributed generation offers a solution by increasing energy reliability and reducing greenhouse gas emissions. Further research is needed to assess the technical, economic, and environmental implications of integrating renewable resources into Kosovo's energy system, focusing on power quality, system reliability, and voltage stability. The research focused on the eastern region of the country, operating at the 110 kV substation level. Challenges in energy quality arise due to the lack of 400 kV supply and the continuous increase in energy consumption, especially in the Gjilan area. This paper investigated integrating renewable energy, especially wind and solar sources, into the medium- and long-term plans at the Gjilan 5 substation to enhance energy quality in the area. Successful integration requires detailed analysis of energy flows, considering the impact of photovoltaics (PVs) on distribution system operation and stability. To simulate and analyze the effects of renewables on the transmission system, voltage profile, and power losses, a case study was conducted using ETAP software. The simulation results present a comparison between scenarios before and after integrating renewable systems to improve energy quality in the identified area. [ABSTRACT FROM AUTHOR]

Numerous studies have introduced a variety of synchronization techniques that involve many structures. However, since the majority of synchronization methods influence or increase the system states from a stability perspective, these methods necessitate retuning the controller parameters, reconstructing a converter controller, or employing a different control construction. This paper proposes a novel, yet simple and straightforward, method for implementing a synchronization technique concept based on the conventional synchronization method known as the "dark lamp" process. Since the proposed method is based on logic functions, there is no additional state in the entire system of the model, which is the cornerstone of this paper. The proposed method is validated and studied in a time–domain simulation environment, specifically a PSCAD/EMTDC. The proposed new synchronization technique in this paper overcomes the discussed synchronization techniques in the literature by dispensing with the need either to use a phase-locked loop (PLL) or to retune the controller parameters and, furthermore, does not increase the system state equation. Meanwhile the proposed techniques in the literature increase the system state equation by at least two states, which affects the entire system performance and stability. The results of this paper show promising and important outcomes that will further the study of the synchronization technique. [ABSTRACT FROM AUTHOR]

This article discusses the optimal placement of distributed generation (DG) in power distribution systems and the evaluation of losses and voltage using machine learning algorithms. The proposed algorithm accurately predicts DG positions using Support Vector Machine, Random Forest, and Radial Neural Networks. The research highlights the effectiveness of these algorithms in enhancing system performance and providing valuable insights for decision-making in the field of distributed generation. The proposed algorithm effectively addresses voltage imbalance, line loading, and power losses in the distribution system, offering advantages over other algorithms. The performance of the algorithm is evaluated using metrics such as R-squared and Mean Absolute Percentage Error, and it consistently outperforms existing methods in terms of prediction accuracy. The findings suggest that the proposed model can effectively address the challenge of predicting distribution systems. The given text is a list of references to various research papers and articles related to distributed generation in power systems, providing valuable insights for researchers and library patrons interested in understanding and exploring the field. [Extracted from the article]

In the modern era, the demand for an interconnected power system, which can integrate electric vehicles and renewable energy sources is increasing day by day. The primary goal of this demand is to make a sustainable and green power source, which can be fulfilled by utilizing Electric vehicles and renewable energy sources. However, there are some demerits of this technology, its lesser system inertia and hence it is not sufficient to respond to the required load capabilities. Further, the approach of electric vehicles with moving batteries can enable higher performance and resolve the issue. In this current analysis, the vehicles to grid idea (V2G) technique has been discussed, which can react as automatic generation control (AGC) in a three-area deregulated environment including hydro, thermal, and gas turbine units. In this type of power grid, all the sources such as Solar, wind, geothermal, and DEG power is also incorporated. To grip various ambiguities, the current study proposed a cascade combination of Interval type-2 fuzzy and Fractional Order Proportional-integral-derivative (FOPIDN) controllers. Further, the current work proposed a modified quasi-opposition Arithmetic Optimization Algorithm (QOAOA) to tune the scaling factor and membership function of an interval type-2 fuzzy FOPIDN controller. To discuss the significance of the anticipated controller, the estimated outputs have been compared with previously reported controllers and optimization techniques. The effect of constant and variations in DG, the penetration level of (PEVs) in various operating modes, subjected to step and random load disturbances have been focused on. Finally, to validate the outcomes of the proposed controller, a real-time (RT) hardware-in-the-loop (HIL) simulation has been adopted by using OPAL-RT. [ABSTRACT FROM AUTHOR]

Power systems protection has become more vital in recent years to ensure stability, reliability, security, and power quality due to the exponential growth of grid-connected photovoltaic (GCPV) systems. As a result, several nations have set new grid codes for the grid integration of PV plant installations to overcome these concerns. Investigating Fault Ride Through (FRT) capacity is one of the primary criteria for grid codes. For 3-phase GCPV systems, fault detection and classification approaches are proposed in this study. Firstly, different faults that occurred in the GCPV system are categorized and compared, with the critical and analytical evaluation of grid codes, particularly FRT requirements such as Low Voltage Ride Through (LVRT) and High Voltage Ride Through (HVRT) for different nations. Further, a detailed classification and a comparison of the existing FRT techniques are presented for better control methods based on system complexity, detection accuracy, and other evolutionary criteria. To ensure smooth grid operation, accurate fault detection and condition monitoring of the systems are required. This paper discusses a machine learning (ML) based technique for detecting faults in 3-phase GCPV systems. Multi-peak phenomena caused by FRT capabilities and anti-islanding detection are the main problems experienced while integrating a PV system into the local grid. The fault classification technique is developed using weighted K-nearest neighbor (WKNN) and fine Gaussian support vector machine (FGSVM) based ML approaches utilizing Wavelet Transform. The proposed ML-based findings show that the fault detection algorithm-based classification accuracy has significantly improved.

Plug-in Electric Vehicles (PEVs) have gained popularity in recent years due to their environmentally friendly attributes. However, integrating PEVs into distribution systems poses novel challenges and opportunities. This research paper undertakes a comprehensive assessment of the repercussions of PEVs and distributed generation (DG) integration on the distribution system’s reliability, power loss, and voltage stability. In order to alleviate peak loads, select PEVs are deployed in Vehicle-to-Grid (V2G) mode. The integration of DG units, particularly solar Photovoltaic (PV) and wind turbine (WT) systems, is explored to mitigate the potential strain of PEV charging on the distribution system. The uncertainty of renewable DG and PEV charging-discharging, along with time-varying residential and commercial loads, are included in the analysis. Power interruption duration and frequency-based reliability index are considered in this work. The combined effects of PEVs’ Grid-to-Vehicle (G2V) and V2G mode and DG integration are comprehensively investigated, and the impact assessment is conducted on the modified IEEE 33 bus distribution system to ensure the practicality and applicability of the research findings.

This paper proposes a new real-time distributed voltage control (RTDVC) scheme for medium and low voltage (MV&LV) distribution networks (DNs) based on the analytical target cascading (ATC) method. In this scheme, photovoltaic (PV) units at the LV level, on-load tap changer (OLTC) and distributed generation (DG) clusters at the MV level are optimally coordinated to regulate the bus voltages in MV&LV DNs to be close to the nominal value while minimizing the number of operations of OLTC and smoothing the reactive power outputs of DGs. Based on the ATC method, the large-scale strongly coupled voltage control problem of MV&LV DNs is decomposed into several subproblems while guaranteeing the optimality of the primal voltage optimization problem. The decomposed subproblems are iteratively solved in parallel in the MV and LV controllers, which achieves optimal voltage control in a distributed manner and effectively reduces the computation burden. An MV&LV test system with two 20 kV feeders and 39 0.4 kV LV grids was simulated to demonstrate the control performance of the proposed distributed voltage control scheme.

When False Data Injection (FDI) attacks inject false data into the controllers of a microgrid, it can lead the controllers to make decisions based on inaccurate information, ultimately resulting in system collapse. This study presents a novel approach to dynamically adjust output voltage in Campus Microgrids (CMG). Initially, we use feedback linearization techniques to address the inherent nonlinear complexities in Distributed Generation (DG) dynamics. Moreover, during voltage regulation, controllers and sensors may be susceptible to FDI attacks. To address this, an elastic follower observer is introduced to mitigate the impact of false information injection attacks on voltage coordination, thus ensuring the robustness and stability of the system. Notably, our method reduces dependence on global information and fault boundaries within the communication network, thereby enhancing system elasticity and efficiency. In conclusion, through comprehensive simulation experiments, we evaluate the effectiveness and performance of our proposed strategy. The simulation results validate the success of our approach in voltage regulation and its efficiency in fault management. These findings bear significant implications for the advancement of voltage regulation strategies in distributed generation systems.

In this study, the effects of agent-based modeling on deregulated electricity markets using AMES (Artificial Market Environment Simulator) have been investigated. It is shown how the Independent System Operator (ISO) intervenes to control market dynamics and maintain supply-demand balance through locational marginal pricing (LMP). This study uses a model called Agent-Based Model (ABM) to simulate generation companies (GenCo’s) and load-serving entities (LSEs) in order to enhance market efficiency and stability. The methodology encompasses both static and dynamic testing of GenCo’s, integrating learning algorithms to optimize bidding strategies. The results showed that allowing generators to learn from previous experiences enabled them to make better offers in real-time, thus leading to higher profits. Furthermore, it shows that where DG units are located in the power system as well as their size can significantly raise generator earnings. Finally, when generators have the ability for learning, their profits increased considerably under various scenarios compared with those examples where learning cannot be applied, which demonstrates how helpful strategic learning could be for market participants. Moreover, it looks into the consequences of power system faults such as line faults; it demonstrates that distributed generation can mitigate the impacts of power system interruptions and uphold load flow in order to enhance overall market resilience. It draws attention to the fact that agent-based models are a powerful analytical technique for assessing and enhancing deregulated electricity markets. Additionally, political and operational choices obtained from dynamic and strategic modeling approaches like these will be good for electrical power systems.

The implementation of low-voltage-ride-through (LVRT) requirements for distributed generation (DG) sources in medium voltage (MV) distribution networks introduce challenges for the existing protection systems. These challenges stem from coordination issues between line protection systems (fuses and reclosers) when the DG may continue to operate in line with LVRT requirements during faults in the network. To address this challenge, we propose an enhanced voltage protection module (EVPM) for DG interconnection protection relays (IPR). EVPM employs a space phasor model (SPM) of three-phase voltages at the point of common coupling (PCC) to detect faults in the distribution network and the consequent recloser operation. This allows for prompt isolation of the DG from the faulty network in coordination with the reclosing relays, by over-riding the LVRT requirement. This reduces the time required for reclosing and ensures recloser fuse coordination without changing the settings of the existing protection systems based on DG penetration levels. To assess the EVPM performance under different scenarios, transient simulation studies are carried out in three case study networks. The results validate the efficacy of the proposed EVPM.

The setting work of backup protection using steady-state current is tedious, and mismatches occasionally occur due to the increased proportion of distributed generations (DGs) connected to the power grid. Thus, there is a practical need to study a backup protection technology that does not require step-by-step setting and can be adaptively coordinated. This paper proposes an action sequence adaptive to fault positions that uses only positive sequence fault component (PSFC) voltage. Considering the influence of DGs, the unified time dial setting can be obtained by selecting specific points. The protection performance is improved by using the adjacent upstream and down-stream protections to meet the coordination time interval in the case of metallic faults at the near- and far-ends of the line. Finally, the expression and implementation scheme for inverse-time backup protection (ITBP) based on the unified characteristic equation is given. Simulation results show that this scheme can adapt to DG penetration scenarios and can realize the adaptive coordination of multi-level relays.

The primary goal of a power distribution system is to provide nominal voltages and power with minimal losses to meet consumer demands under various load conditions. In the distribution system, power loss and voltage uncertainty are the common challenges. However, these issues can be resolved by integrating distributed generation (DG) units into the distribution network, which improves the overall power quality of the network. If a DG unit with an appropriate size is not inserted at the appropriate location, it might have an adverse impact on the power system’s operation. Due to the arbitrary incorporation of DG units, some issues occur such as more fluctuations in voltage, power losses, and instability, which have been observed in power distribution networks (DNs). To address these problems, it is essential to optimize the placement and sizing of DG units to balance voltage variations, reduce power losses, and improve stability. An efficient and reliable strategy is always required for this purpose. Ensuring more stable, safer, and dependable power system operation requires careful examination of the optimal size and location of DG units when integrated into the network. As a result, DG should be integrated with power networks in the most efficient way possible to enhance power dependability, quality, and performance by reducing power losses and improving the voltage profile. In order to improve the performance of the distribution system by using optimal DG integration, there are several optimization techniques to take into consideration. Computational-intelligence-based optimization is one of the best options for finding the optimal solution. In this research work, a computational intelligence approach is proposed to find the appropriate sizes and optimal placements of newly introduced different types of DGs into a network with an optimized multi-objective framework. This framework prioritizes stability, minimizes power losses, and improves voltage profiles. This proposed method is simple, robust, and efficient, and converges faster than conventional techniques, making it a powerful tool of inspiration for efficient optimization. In order to check the validity of the proposed technique standard IEEE 14-bus and 30-bus benchmark test systems are considered, and the performance and feasibility of the proposed framework are analyzed and tested on them. Detailed simulations have been performed in “MATLAB”, and the results show that the proposed method enhances the performance of the power system more efficiently as compared to conventional methods.

We provide a strategy for minimizing losses and redistributing loads in distribution systems while emergency repairs are being made. The proposed approach takes advantage of the preexisting, network-accessible, and Power Companies' Adoption of Residential Energy Storage Batteries devices. Batteries are expected to be used increasingly often to deal with a few of the growing challenges with renewable, among them the infamous duck curve difficulty, as renewable energy sources that are widely dispersed, like photovoltaic (PV) and wind turbines, become more popular. The proposed approach may be implemented using signals in reaction to demand. To demonstrate its value, we provide a method for concurrent simulation for designing and analyzing strategies for optimizing a distribution that benefits from the synergy-connected smart grid, intelligent structures, and decentralized battery systems to reduce overall energy consumption and costs while enhancing power management. The suggested method is created and verified inside of the Smart Builds co-simulation environment. From what we can tell from simulations, energy storage devices provide interim relief for line-outage-affected distribution networks. [ABSTRACT FROM AUTHOR]

Several advantages have been achieved in distribution systems by adding Distributed Generation (DG). These benefits comprise minimizing power losses and improving the voltage profiles. Such benefits can be enhanced if DGs are optimally sized. This paper presents a DG in the distribution scheme to increase node voltages and reduce the power loss of radial distribution systems by using the Modified Shuffled Frog Leaping Algorithm (MSFLA) technique. The core objective is to decrease active-power losses of the system while keeping the voltage profiles within the defined limits of the system. This paper optimally allocates and sizes three types of DGs in various standard radial test systems and a practical radial feeder. Single and multiple DGs are considered for the proposed work by including some special cases. The standard test systems on which proposed work is performed are 33-bus and 69-bus radial feeders. A practical 43-bus radial feeder of factory area Faisalabad has also been taken under consideration for analysis. The Forward–Backward Sweep (FBS) scheme is selected for the load flow analysis, whereas MSFLA is chosen for finding of optimum size(s) and site(s) of DG(s). Various types of DGs results are retained separately and collectively. We have reduced the losses up to 85.14% and 96.1% for the 33-bus system and 69-bus respectively. The results will depict a significant improvement, both in the minimization of losses and also in improving the voltage profile. The results of multi DGs placement will be much more promising than those achieved after placing them independently. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) significantly improves power system reliability and voltage profile, and reduces power losses. These benefits can be maximized by optimizing the DG utilization, which greatly depends on adequate number, size, and placement of DG. This study proposes a high-convergence optimization technique for optimal DG placement in distribution networks, aiming to reduce power losses and improve voltage profile and voltage stability index. The technique is tested on a 7-bus system before being verified on the IEEE 33-bus system. After validation, it is tested on a 71-bus distribution network (DN). The estimated ideal size for a single DG is used as a limit for determining the optimal placement and size of multiple DG units. The results show that there is a loss reduction of 70.09%, 61.43%, and 71.15% for the 7-bus, IEEE 33-bus, and 71-bus systems, respectively, from their base case loss. The average absolute error of voltage of the aforementioned buses also shows similar enhancement, decreasing from 0.0144, 0.0561, and 0.0527, to 0.0058, 0.0209, and 0.0184, respectively. The voltage stability index of each DN has increased significantly in comparison to the base case. The comparison demonstrates that the proposed technique can provide better results for any size of distribution network. [ABSTRACT FROM AUTHOR]

This research article presents a comprehensive investigation into the design, optimization, and performance analysis of a hybrid stand-alone microgrid for an industrial facility in Iraq at coordinates 36.51 and 43.99. The system consists of photovoltaic (PV) modules, inverters, a battery energy storage system (BESS), a generator, and AC loads. Leveraging the capabilities of PVsyst version 7.3.1, HOMER Pro version 3.14.2 and SAM version 2022.11.21 software tools, this study assesses the feasibility and functionality of the hybrid stand-alone microgrid. In this study, PVsyst software is used for detailed designing and analysis of a PV plant, and the PVsyst design file is then used in HOMER Pro software to optimize and design the proposed hybrid stand-alone microgrid, and for detailed performance analysis SAM software is employed. This paper also investigates the impact of ground clearance and ground albedo on the annual generation of bifacial PV modules at various tilt angles. Key findings include a promising normalized production rate of 4.53 kWh/kWp/day with a performance ratio of 0.815 and annual energy production estimates of 84.31 MWh (P50), 79.57 MWh (P90), and 78.24 MWh (P95) for monofacial PV modules, highlighting the system's potential for renewable energy generation. Notably, this research demonstrates the hybrid stand-alone microgrid's capacity to significantly reduce CO2 emissions, saving approximately 1811.6 tons over a 30-year period, thus contributing to sustainability and environmental conservation goals. Additionally, this study reveals operational challenges during the winter months, necessitating generator support to meet load demands. The successful installation and experimental validation of the hybrid stand-alone microgrid underscore its practical viability and its role in advancing clean energy solutions. This research provides valuable insights into hybrid stand-alone microgrid design, emphasizing its importance in ensuring reliable power supply and environmental stewardship. [ABSTRACT FROM AUTHOR]

The motivation of this paper is to present an approach that relies on the league championship algorithm (LCA) to determine the most effective number, placement, and size of distributed generations (DG) within distribution systems. The primary objective of this approach is to minimize the losses of the power system, as well as to enhance the voltage profiles and stability index of the voltage. The optimal location of the DG units is determined through the use of the Loss Sensitivity Factor (LSF), while the optimal size of the DG units is found through the use of LCA. The IEEE 33-bus and 69-bus radial distribution systems were both tested to validate the proposed method, and the results obtained from LCA were compared to those of other methods found in the literature. The simulated results have shown that the LCA method proposed in this paper is highly effective and performs exceptionally well when addressing the problem of optimal location and sizing of DG units in radial networks.

Summary: Recently, the exponential decay of traditional petroleum and coal-based reserves with the ever-rising energy demand has led to the need for alternate energy sources. Distributed Generation (DG) based on renewable energy sources serves as a viable alternative solution for researchers to counter the issue of rising load. Hence the use of renewable energy sources is of utmost priority. Renewable non-traditional energy resources also have the advantage of being an unlimited energy source and climate-friendly in nature. In this context, distributed micro-generating units come into the picture, popularly known as MicroGrid (MG). The MGs are small-scale resources generating electrical energy and connected to the main utility through power electronic converters. The main drawbacks associated with MGs are their association with converters and switching devices, which leads to the injection of disturbances in the power system. Due to the tremendous use of MGs in modern power systems, the inherent intermittent nature of the renewable sources increased infiltration of nonlinear loads, and the distributed nature of micro-generating units, huge power quality (PQ) issues are observed hazardous to both generation and supply ends. PQ issues could be either the reactive power compensation or the generation of harmonics that hamper the normal functioning of the electrical energy system. To maintain healthy transmission and distribution of electrical power, these issues must be taken care of utmost priority. Because of customer satisfaction, utilities have adopted many profitable schemes and power quality improvement methods. In this regard, around 350 recent review articles have been comprehensively surveyed, and a detailed discussion about regulation of power quality issues using the filters, controllers, Flexible AC Transmission Systems (FACTS), optimization techniques (OTs), and machine learning tools with modern and advanced control techniques have been highlighted in this review article. A clear idea regarding power quality and its enhancement in MG has been presented. The technical and economic aspects have also been projected in brief. In addition, an in-depth study covering all challenges has been cited, and possible solutions in the future for hassle-free PQ improvement have been suggested in detail. It is believed that this review article would serve as an efficient background for researchers, academicians, electrical engineers, industrialists, and manufacturers working towards the hassle-free operation of MGs by efficient PQ issues mitigation.

As the modern power system continues to grow in size, complexity, and uncertainty, traditional methods may occasionally prove insufficient in addressing the associated challenges. The improper location of distributed generation varies the voltage profile, increases losses and compromises network capacity. Machine learning algorithms predict accurate site positions, and network reconfiguration improves the capacity of the power system. The proposed algorithm is a hybrid of machine learning and deep learning algorithms. It cascades Support Vector Machine as the main model and uses Random Forest and Radial Neural Networks as classification algorithms for accurately predicting DG position. The non-linearity characteristics of the DG problem are directly mapped to the proposed algorithms. The proposed algorithm is employed on familiar test setups like the IEEE 33-bus and 69-bus distribution systems using MATLAB R2017 as simulation software. The R-squared (R2) values for all parameters yield a value of 1, while the MAPE values are minimal for the proposed cascaded algorithm in contrast to other algorithms of LSTM, CNN, RNN and DQL.

The traditional power system is facing significant transformations due to the integration of emerging technologies, renewable energy sources (RES), and storage devices. This review focuses on the shift from centralized to decentralized control, enhancing flexibility for stakeholders, and the challenges it entails. The paper identifies the problem of limited adaptability in traditional power systems, which restricts stakeholder flexibility and renewable energy source integration. To address this, the paper proposes a transition to a decentralized system. It explores the effects of power system privatization and restructuring, fostering a competitive market across generation, transmission, and distribution levels. It discusses how integrating distributed generations (DGs) and demand-side management (DSM) with ICT protocols can enhance power system control and management efficiency and reliability. The review delves into the challenges of deregulated electricity market (DEM), especially integrating new generation sources and promoting prosumer participation. It proposes leveraging DSM to manage supply–demand variability and support renewable generation integration in distribution sectors. It also discusses the necessity for renewable power producers to develop effective market bidding strategies. The review concludes with key findings and future research directions, providing an overview of the evolving energy market’s trajectory. It aims to inform sustainable and efficient power system discourse and future policy and decision-making.

The rise in popularity of electric vehicles (EVs) is attributable to their economical maintenance, excellent performance, and environmentally-friendly nature due to zero carbon emissions. Nevertheless, the increased utilization of EVs poses challenges for the distribution system’s efficiency. The strategic placement of electric vehicle charging stations (EVCS) is crucial in maintaining the reliability of the radial distribution system (RDS). The improper allocation of EVCS can result in degradation and affect the distribution system. To overcome this issue, a potential solution involves integrating the charging stations with the RDS by utilizing distribution static compensators (DSTATCOMs) and distributed generation (DG) to mitigate the adverse effects of EVCS on the RDS. The appropriate sizing of DG/DSTATCOM depends on variations in load stages, as it impacts the stability of the RDS. Additionally, the uncertainty of distribution loads can lead to an underestimation of power within the system, posing a primary challenge. In this proposed work, two studies were examined: (i) DG and DSTATCOM allocation considering load uncertainty without EVCS impact, and (ii) DG and DSTATCOM allocation considering load uncertainty with EVCS impact. To address this multi-objective problem, an objective function was developed to reduce real power loss while adhering to system equality and inequality constraints. To tackle the challenge, the researchers used the bald eagle search algorithm (BESA), a revolutionary metaheuristic optimization methodology. The efficacy of the proposed approach was validated using two test systems: a 34-bus system and a 118-bus system. The results obtained from these test cases demonstrate that the BESA-based solution is highly exact in reducing real power loss, increasing bus voltage, and enhancing system stability with a significantly high convergence rate. Hence, the proposed approach presents a promising solution for optimizing RDS with multiple objectives.

Today, the use of renewable energy is increasing day by day due to its advantages, to solve existing challenges such as the increase in power demand. Microgrids (MGs) which have AC, DC, and DC/AC types, have received much attention due to their many advantages. MGs can be a suitable solution for supplying power to remote and sensitive areas and they can also increase the reliability of the system. Like all systems, MGs need a reliable control system to provide proper operation. There are many control methods such as robust control and adaptive control and control structures can be divided into two types: centralized and decentralized. This paper provides an overview of different decentralized control methods for MGs, based on recently published research. The methods used in each study are fully described, along with their results. Also, several research questions have been suggested for future research that can be used.

The development of energy management tools for next-generation Distributed Energy Resources (DER) based power plants, such as photovoltaic, energy storage units, and wind, helps power systems be more flexible. Microgrids are entities that coordinate DERs in a persistently more decentralized fashion, hence decreasing the operational burden on the main grid and permitting them to give their full benefits. A new power framework has emerged due to the integration of DERs-based microgrids into the conventional power system. With the rapid advancement of microgrid technology, more emphasis has been placed on maintaining the microgrids' long-term economic feasibility while ensuring security and stability. The objective of this research is to provide a multi-objective economic operation technique for microgrids containing air-conditioning clusters (ACC) taking demand response into account. A dynamic price mechanism is proposed, accurately reflecting the system's actual operational status. For economic dispatch, flexible loads and air conditioners are considered demand response resources. Then, a consumer-profit model and an AC operating cost model are developed, with a set of pragmatic constraints of consumer comfort. The generation model is then designed to reduce the generation cost. Finally, a microgrid simulation platform is developed in MATLAB/Simulink, and a case is designed to evaluate the proposed method's performance. The findings show that consumer profit increases by 69.2% while ACC operational costs decrease by 18.2%. Moreover, generation costs are reduced without sacrificing customer satisfaction.