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Power transmission using a voltage source converter‐ (VSC‐) based high‐voltage direct current (HVDC) system offers autonomous control of real and reactive power, constant DC voltage polarity, and bidirectional power flow. This helps to realize the multiterminal VSC‐HVDC system and its integration into renewable energy sources to meet the growing power demand. However, there is a risk of higher voltages and currents during a DC line fault. The barrier to the advancements of VSC‐MTDC systems is the nonavailability of commercial, higher‐rated DC circuit breakers. This necessitates research on alternative methods of DC fault‐clearing schemes with available technologies. In this direction, a superconducting fault current limiter (SCFCL) is an alternative option to mitigate the problems encountered in VSC‐MTDC system operation. Because of this, there are not many VSC‐MTDC systems available worldwide. This paper discusses different issues associated with the transient performance of the VSC‐MTDC system. A representative case involving resistive SCFCL for DC line protection is presented. The simulations are carried out in the PSCAD/EMTDC platform. [ABSTRACT FROM AUTHOR]

Recently, renewable energy sources integrated with microgrid (MG) networks have provided safe, secure, and reliable power supply to both utility and industrial purposes. Power quality disturbances (PQDs) seriously affect the performance of MG networks and reduce the lifecycle of numerous sensitive devices in MG networks. Hence, this paper presents a new approach to detect and classify the PQDs using discrete wavelet transform, multiresolution analysis, and optimized‐kernel support vector machine. The obtained unique features from DWT‐MRA are fed to train the well‐known intelligent classifiers. In the optimized‐kernel SVM model, computing power is enhanced for classifying multiple PQ events based on the local density and leave‐one‐out (LOO) algorithm. To get higher separation in feature space, the kernel width of each sample is estimated based on the local density. By using the LOO method, an improved grid search strategy is implemented to get the penalty parameter to achieve satisfactory results. Moreover, a typical MG network is simulated in MATLAB software considering the validation of the proposed technique to address the power quality issues in MG networks, and the results of the proposed method are compared with other conventional ML classifiers. The simulation results confirm that the proposed method is more effective and accurate than other intelligent classifiers. [ABSTRACT FROM AUTHOR]

This paper presents a pioneering exploration into the optimization of Home Energy Management Systems (HEMS) through the novel application of the Bacterial Foraging Metaheuristic Optimization (BFMO) algorithm and Deep Reinforcement Learning (DRL). The study systematically addresses the pressing challenge of enhancing residential energy efficiency, focusing on dynamic appliance scheduling within HEMS. A robust methodology is established, encompassing data collection from smart homes, implementation details of the BFMO algorithm, DRL techniques, and a comprehensive evaluation framework. The unique contribution of this research lies in the effective integration of the BFMO algorithm and DRL to orchestrate energy‐conscious scheduling of home appliances within HEMS. The BFMO algorithm demonstrates its adaptability to fluctuating energy costs and consumption patterns by simulating the foraging behaviour of bacteria. At the same time, DRL enhances the system's ability to learn and optimize scheduling decisions over time, showcasing their combined efficacy in real‐world scenarios. The algorithms' iterative application of chemotaxis, reproduction, elimination‐dispersal, swarming, and learning consistently yields optimized appliance schedules. The main focus of this study resides in the evaluation metrics illustrating the tangible benefits of BFMO and DRL compared to traditional HEMS. Significant reductions in total energy consumption and cost, accompanied by improved peak demand management, exemplify the algorithms' impact. Furthermore, the study delves into enhancing user comfort, integrating renewable energy sources, and the overall robustness of HEMS, all demonstrating the multifaceted advantages of the BFMO and DRL approaches. This research contributes methodologically by introducing and detailing these algorithms and provides a valuable dataset and evaluation metrics for future research in the domain. The findings underscore the immediate and long‐term relevance of optimizing HEMS with BFMO and DRL, catering to researchers, practitioners, and policymakers involved in advancing smart grid technologies and sustainable residential energy management. In summary, this work establishes the BFMO algorithm and DRL as pioneering and versatile tools for energy‐conscious appliance scheduling in HEMS, offering a substantial leap forward in the quest for efficient and sustainable residential energy management. [ABSTRACT FROM AUTHOR]

The surging electricity demand in Pakistan has led to frequent blackouts, prompting government initiatives to expand power plant capacities and improve the national grid. The government prioritizes integrating large‐scale renewable energy sources, such as wind and solar power, to reduce dependence on conventional power plants. However, the intermittency of renewables leads to forecasting errors, requiring extra power reserves from conventional units, thereby escalating operational costs and CO2 emissions. The country currently utilizes a manual mechanism for power balancing operations, overlooking critical grid constraints of the transmission line loadings. In such conditions, injecting large‐scale power from renewables can lead to significant fluctuations in line power flows, risking transmission line loadings and compromising the system's secure operation. Hence, this paper has developed an automatic generation control (AGC) model for the highly wind‐integrated power system to alleviate line congestions in the network and enhance the economic operation of the system. The study utilizes the Pakistan power system as a case study to simulate the proposed model. The developed real‐time power dispatch strategy for the AGC system considers the constraints of the transmission line to avoid congestion. It integrates wind energy as operating reserves to enhance the economic operation of the system. When managing line congestion, it identifies overloaded bus lines and adjusts power regulation accordingly while compensating for shortfalls by augmenting transmitted power from regional grid stations. However, it maintains a constant dispatch ratio without line overloads, aligned with generation capacities. Additionally, the strategy integrates reserve power from the wind power plant and traditional generating units to further improve economic operations. Simulations have been conducted using PowerFactory software, employing the eight‐bus and five‐machine models to replicate the characteristics of the Pakistan power system. The results demonstrate the effectiveness of the proposed AGC design in mitigating transmission line congestion of power systems that are heavily integrated with wind energy sources while simultaneously ensuring the economic operation of generating units. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources into power systems has increased significantly in recent years. Among various types of renewable energy, the use of wind energy is growing rapidly due to its low operating cost, wide distribution worldwide, and no greenhouse gas emissions. However, power systems integrated with wind energy may face stability and reliability issues due to the intermittent nature of wind power. Therefore, in power systems connected to wind farms, it is usually required to use some compensators such as static synchronous series compensator (SSSC) to increase the system performance under abnormal conditions. On the other hand, for an SSSC to be effective in improving the system performance, it must be equipped with a suitable controller. In this paper, a fuzzy logic controller (FLC) is used for the SSSC because of its advantages over conventional controllers. Extensive research has been conducted in power systems with wind turbines in which SSSC or FLC has been used; however, their simultaneous application in such systems has received less attention. Therefore, this article aims to fill this gap. The proposed method is implemented on two power systems and the simulation results are analyzed. In both systems, the dynamic behavior of three different wind farms is examined. In the first and second wind farms, either a squirrel cage induction generator (SCIG) or doubly-fed induction generator (DFIG) are used, whereas in the third one which is a combined wind farm (CWF), an equal number of SCIG and DFIG are employed. In wind farms with SCIG or DFIG, an SSSC is also utilized. Furthermore, an FLC is employed for the SSSC to improve its efficacy. A proportional integral (PI) controller is also considered for the SSSC, and its results are compared with FLC results. The simulation results confirm the superiority of FLC over PI controller. [ABSTRACT FROM AUTHOR]

Available transfer capability (ATC) is an important measurement index to evaluate the security margin of interconnected power grids and serve as a reference for the transmission right transaction. In modern power systems, ATC is affected by the transmission network topology, renewable power output uncertainty, and load demand uncertainty. Traditional works usually model the power source-load uncertainty by using robust optimization, interval optimization, or chance-constraint optimization, which cannot fully reflect the probabilistic distribution of the daily source-load uncertainty. This paper proposes an ATC assessment methodology based on the typical stochastic scenarios of renewable output and load demand of multiarea power systems. Furthermore, the conditional generative adversarial network (CGAN) algorithm is adopted to generate and select representative scenario sets based on historical raw data, which can fully reflect the usual operating condition of a system with high renewable energy penetration. The scenario set that is fed into the ATC assessment model can fully characterize the impact of source-load uncertainty on daily ATC. Finally, the proposed method is verified by a modified three-area IEEE 9-bus system and a real-world provincial power system. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) play a crucial role in modern power distribution systems, particularly in ensuring reliable and efficient energy supply, integrating renewable energy sources, and enhancing grid resilience. Voltage and frequency stability are paramount for MG operation, necessitating advanced control frameworks to regulate key parameters effectively. This research introduces a multilayer interactive control framework tailored for MGs utilizing distributed energy resources (DERs). The framework comprises primary control layers, integrating internal voltage and current controller loops, and secondary layers employing distributed finite‐time control (DFTC) strategies. Through simulation studies and comparative analyses with traditional proportional‐integral (PI) controllers, the effectiveness of DFTC controllers in reducing initial oscillations and improving stability is demonstrated. Major findings include the superior performance of DFTC controllers in stabilizing voltage and frequency parameters, optimizing power output, and enhancing overall operational efficiency. Additionally, insights into the operational dynamics of MG systems highlight the significance of advanced control strategies in mitigating fluctuations and ensuring system stability. Furthermore, the proposed method demonstrates significant efficacy improvements over conventional approaches. Voltage stability is enhanced with oscillation amplitudes less than 0.01 pu, active power control achieves a stable level of 0.93 pu, and frequency fluctuations are reduced to 0.004 Hz and effectively recovered to 0.002 Hz. These improvements suggest that the proposed method enhances system stability and control precision by approximately 95% compared to conventional methods, as it achieves much tighter control over voltage, active power levels, and frequency fluctuations. [ABSTRACT FROM AUTHOR]

In modern generation expansion planning of power systems, installing grid‐connected renewable energy systems is preferred than thermal units due to their low generation cost and environmental pollution. However, the expanded power system must have ability to resist against any outages influenced on the frequency response of the system. So, several frequency‐constrained expansion planning models are extracted to provide a reliable infrastructure to manage the frequency behavior. The main distinction of our model with others is considering the failure of grid‐connected renewables in the expansion planning models. Furthermore, due to the lack of information about the uncertainty of malfunctions, a distributionally robust optimization approach is applied to the problem under several ambiguity radiuses. The results of implementing the proposed method on the IEEE RTS96 case show that increasing the penetration of malfunctioned units can lead to more investment on the thermal units to prevent frequency violation under any outage in the system. With increase of the Kullback–Leibler divergence from zero (stochastic) to 3 (robust), the cost of the robust model is increased about 0.02%. The model is designed for the deregulated market to increase the competition of market through maximizing their benefit and line congestion management with local marginal pricing techniques. [ABSTRACT FROM AUTHOR]

The Distributed Renewable Energy Sources (DRESs) integrate hybrid microgrid and prosumer activities that constitute a dynamic system characterized by unknown network parameters. The dynamic system faces challenges, such as intermittent power supply due to low inertia, renewable intermittence, plug‐and‐play prosumer activities, network topology variations, and a lack of constraint handling. These complexities pose significant issues in designing effective control for frequency regulation and consensus‐based economic load dispatch (ELD) within DRES to meet varying load demands. To address the above challenges, this research employs a machine learning‐based distributed multiagent consensus design that offers a rapid and robust approach, mitigating the limitations associated with the Distributed Average Integral (DAI) control design. The proposed multiagent scheme empowers the successful implementation of ELD and frequency regulation, accommodating the intermittent DRES, diverse network topologies, and the dynamic plug‐and‐play activities of prosumers. Moreover, an optimization‐based DAI tuning model is introduced to overcome tuning limitations. Intelligent renewable energy agents are trained through machine learning‐based regression models that use root mean square error metrics for performance evaluations. The intelligent agents employ DAI control to overcome inherent limitations. The effectiveness of the machine learning‐based DAI is thoroughly evaluated using the DRES‐based IEEE 14‐bus hybrid microgrid system. The quantitative results prove its efficacy in addressing the complex challenges of integrated microgrid dynamics. [ABSTRACT FROM AUTHOR]

DC microgrids are getting more attention because majority of the renewable energy sources generate DC output voltage and also modern gadgets require DC voltage for its operation. In this work, high gain SEPIC (HGSC) topology is derived from switched inductor voltage boosting cell (SIVBC). The HGSC converter provides continuous source current due to SIVBC and high conversion ratio and achieves maximum efficiency of 97.88% when compared with the existing SEPIC topology. The operating modes, conversion ratio expression, power loss distribution, voltage drop, current stress of the semiconductor devices, and efficiency are also analysed. In DC microgrids, the HGSC intends to track the peak power from solar PV array. An incremental conductance algorithm is employed to track the peak power of the solar PV modules. The power flow in the microgrid system is analysed by employing synchronous reference frame theory‐based current controller. In order to validate the theoretical concepts of the HGSC converter, the hardware model is developed for the load rating of 1,000 W/380 V output voltage. [ABSTRACT FROM AUTHOR]

This work presents a novel architecture for the 31‐level asymmetrical DC voltage source configured diode switched multilevel inverter, which has a single phase and fewer components. Using asymmetric DC sources and an H‐bridge, the proposed topology generates a maximum output voltage of 31 levels. This 31‐level topology is suitable for both renewable energy source conversion (RES) and electric vehicle (EV) applications. This topology requires fewer total components, lower cost, and smaller size. Along with the numerous benefits of MLIs, reliability issues are critical due to the larger number of devices required to minimize THD. Several characteristics, such as total standing voltage (TSV), reliability, cost function (CF), efficiency, and overall power losses, are investigated for the developed 31‐level MLIs. The TSV and CF of the proposed MLI are critical factors in demonstrating that the proposed topology is cost‐effective when compared to other recent topologies. Many parameters are thoroughly compared and tabulated, as well as represented graphically. The suggested MLI has lower TSV and component demand. Total harmonic distortion complies with IEEE specifications. The reliability aspects were also calculated and validated. The proposed MLI is controlled by a fuzzy logis controller (FLC) to achieve efficient results. The topology is simulated in MATLAB/Simulink software under a variety of conditions and dynamic load changes, and a prototype with a dSPACE controller is also implemented. [ABSTRACT FROM AUTHOR]

In the context of integrating Renewable Energy Sources, Microgrid (MG) development is pivotal, particularly as a foundational technology for Smart-Grid evolution. Despite advancements in control techniques, challenges persist in ensuring system stability and accurate power sharing across diverse operational conditions and load types. The objective of this research is to control numerous paralleled inverters-based distributed generators (DGs) that contribute to power sharing in an island MG. The proposed methodology involves developing an innovative small-signal model for islanding MGs that incorporate virtual impedances. Subsequently, optimization algorithms based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are proposed and compared for designing the virtual impedances. These algorithms analyze all potential operating points, aiming to minimize reactive power mismatches while maximizing MG stability. The suggested objective function facilitates the simultaneous achievement of these objectives. The proposed approaches were tested using MATLAB-Simulink software, and the comparison of the results between conventional approach and the proposed optimal approaches shows significant improvement in terms of the dynamic response during load changes, such as a decrease in response time by up to 20%, a reduction in overshoot percentage by approximately 15%, and a settling time improvement of nearly 25%. These quantified improvements highlight the effectiveness of the GA and PSO methods in minimizing the reactive power-sharing error while optimizing MG performance and stability. [ABSTRACT FROM AUTHOR]

The widespread integration of renewable energy sources (RESs) into power systems using power electronics-based interface devices has led to a substantial rise in power quality (PQ) issues. There is an immediate requirement for effective monitoring, detection, and classification of power quality disturbances (PQDs) that is needed to take remedial measures and design planning of the system architecture. This study presents a hybrid approach with an objective for the feature extraction and classification of PQDs. The proposed hybrid approach is comprised of an optimized tunable-Q wavelet transform (OTQWT) for the feature extraction and incremental support vector machine (ISVM). A four-stage approach is suggested for the PQ detection and classification in this study. In the first stage, the various data are retrieved both in the form of synthetic data by mathematical formulations and real-time data with prototype design setup. In the second stage, regardless of the specified wavelet function, the PQD signals are decomposed into low-pass and high-pass sub-bands using the tunable-Q wavelet transform (TQWT). However, the utilization of default decomposition parameters to address nonstationary PQ signals may lead to information loss and reduced performance of the system. To avoid this limitation, an OTQWT as an enhanced technique to TQWT based on an Adaptive Particle Swarm Optimization (APSO) is suggested. A modified objective function based on the mean square error (MSE) is used to improve the decomposition process. In the third stage, an efficient classifier is suggested based on the ISVM. Lastly, to test and evaluate the performance of the proposed approach, twelve types of PQDs including noise and multiple occurrences are considered. The comparative analysis with other popular methods reflects the better performance of the proposed approach and justifies its use for PQ detection and classification purposes in real-time​ conditions. [ABSTRACT FROM AUTHOR]

Due to their inherent ability and environmentally friendly nature, renewable energy sources are the only real option for producing pollution-free energy in the modern era. Solar energy is one of the best possibilities in this family for supplying civilization with the power and energy it needs. Researchers can efficiently boost a PV panel's efficiency by using the maximum power point tracking (MPPT) approach to extract the most power from the panel and send it to the load. The authors of this study examined and surveyed the sequential advancement of solar PV cell research from one decade to the next, and they elaborated on the upcoming trends and behaviours. Many maximum power point tracking algorithms (MPPTs) that are employed in photovoltaic systems (PVSs) that function under both uniform and partial shade situations are structurally summarized in this work. Well-written descriptions of the features of photovoltaic modules are followed by a variety of effective control strategies, including both AI-based and traditional controllers. In addition, appropriate knowledge of the various controllers is essential when the PV system is exposed to partial shade, keeping in mind the different control systems' classifications in this situation. A thorough analysis of several soft computing-based techniques is also included, as well as many classical controller-based PV systems. First, well-developed traditional MPPT methods are used, followed by artificial intelligence-based MPPT approaches. Later, a thorough comparison of the various MPPT-controlling approaches is established. For PV systems operating under partial shade conditions (PSCs), the advantages and disadvantages of the various MPPT techniques are outlined, contrasted, and assessed. Future research directions for MPPT are also being investigated. A collection of several datasets pertaining to various control processes that were gleaned from various research articles has also been presented. Researchers working on PV-based MPPT and those working in the sectors of renewable energy production and environmentally sustainable development would be very interested in the findings of this review study. [ABSTRACT FROM AUTHOR]