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This paper provides a comprehensive bibliometricanalysis of solid-state circuit breakers, including technological developments and control methods in electric power distribution systems. By compiling and analyzing data from the Scopus database, the most cited papers in the field of protection system mechanisms can be identified. These papers, published in influential journals from various countries, covered different subject areas and case studies. Notably, 77.64 % of the articles focused on technological development and experimental control methods, while 22.35 % were review-based studies. This underscores the increasing interest among researchers in advancing the sustainability and reliability of protection devices in electric power distribution systems. The paper aims to identify and analyze the highly cited published articles on the respective field to provide future research direction on the technological development and control methods towards solid-state circuit breaker protection devices. The review also underlines numerous factors, issues, challenges, and difficulties that next-generation power semiconductor should overcome with in regards to system sustainability. Thus, this analysis will strengthen the scopes and provide context for the development of technology and control in the main protection devices in order to achieve an efficient, reliable, cost-effective, and sustainable power supply.

This work presents a stochastic two-stage mixed-integer nonlinear programming (MINLP) optimization model for the long-term planning of a distribution system (DS) to improve renewable energy integration over a ten-year period. The outer-stage problem simultaneously minimizes the long-term expected planning costs, power losses, and voltage deviations by determining the optimal sizing and placement of renewable energy resources (RESs), such as solar photovoltaic distributed generators (PV-DGS), wind-DGs, and battery energy storage systems (BESSs). In contrast, the inner-stage problem emphasizes the reduction of hourly operational expenses, power losses, and voltage deviations through the identification of optimal scheduling for demand response programs (DRPs) and network reconfiguration (NR). The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized to address the outer-stage optimization problem. Multi-Objective Particle Swarm Optimization (MOPSO) is employed to address the inner-stage issue. In both phases, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is utilized at the conclusion of each iteration to identify the ideal solution from a collection of non-dominated solutions. Monte Carlo simulation (MCS) is utilized to model the system’s unknown factors, including solar radiation, wind speed, load demand, and energy pricing. Subsequently, the backward reduction algorithm (BRA) is employed to streamline the resulting scenarios into a more feasible and representative subset, therefore mitigating excessive computational effort. The suggested model is validated utilizing the IEEE 33-bus DS developed in MATLAB R2023b. Simulation outcomes from various case studies indicate that incorporating optimal DRP and NR scheduling into a hybrid system of RESs and BESSs enhances renewable energy penetration by 17.39% compared to the case utilizing just BESSs. Moreover, the established model, featuring a wind-DG/PV-DG/BESS/DRP/NR configuration, achieves significant improvements in all objective functions, including a 31.14% reduction in total system cost, a 61.67% decrease in power loss, and a 58.11% improvement in voltage deviation, compared to the base case.

Solar photovoltaic (PV) power plants’ performance is severely impacted by multi-level irradiances or partial shading, leading to power losses and voltage instability. Also, partial shading adds further complexity to the maximum power point tracking algorithms by introducing numerous peaks in the power curves, resulting in additional losses. Numerous solutions are presented to deal with shading losses, and dynamic reconfiguration is the most effective; however, higher switch count and complex architecture make it impractical in real-world implementation. Hence, this study proposes a low-complexity architecture based on the grouped string voltage balancing approach. This approach utilizes a voltage balancing converter connected to groups of strings to enhance the power output of the array of PV plants, maintain overall system voltage stability, and eliminate the possibility of multiple peaks formation in the power curves. The effectiveness of the proposed approach is tested under numerous static and dynamic partial shadings and analyzed using power curves, power output, losses, efficiencies, and voltage stability. The validation is done by comparing the proposed approach with conventional and advanced architectures for a 32.5 kW system. The results show that the proposed method requires a 50 % reduced switch count than existing techniques, achieves 99.54 % efficiency, and maintains an average voltage stability of 0.01.

This study investigates the techno-economic impacts analysis of renewable energy-based hybrid energy storage system integrated grid electric vehicles charging station (EVCS) in Malaysia. Focusing on three potential locations namely Pulau Pinang, Johor Bharu and Kuala Terengganu, the research aims to address the increasing electricity demand from the expanding electric vehicle (EV) infrastructure while mitigating grid instability issues caused by sudden load surges, increased electrical losses, and overload of high voltage devices that leads to power quality issues. Using the HOMER Pro platform, the study models and optimises an EVCS configuration-based hybrid energy storage system that incorporates renewable energy sources (RES) such as photovoltaic (PV), wind turbines (WT), lithium-ion (Li-ion) batteries, hydrogen (H2) tank, fuel cell (FC) and electrolysers considering various geographical and meteorological conditions. The hybrid energy storage configuration offers a long-term energy storage solution, surpassing current batteries' capabilities while providing a stable electricity supply for a sustainable EVCS system. The results demonstrated favourable outcomes, with the total Net Present Cost (NPC) ranging from $1.4 million to $3.4 million across all locations, and the Cost of Energy (COE) ranging from $0.03/kWh to $0.16/kWh. These findings suggest that the optimization methodology is adaptable for implementation in diverse locations with different meteorological conditions. This innovation is beneficial for developing renewable-based EVCS infrastructures, which can support economic growth in Malaysia. In addition, the study emphasizes the necessity of advanced control algorithms to manage power quality issues during peak demand and suggests future field trials to validate the system's real-world performance. Additionally, the optimized EVCS-based hybrid energy storage system contributes to reducing carbon dioxide (CO2) emissions, promoting a cleaner environment and an eco-friendly energy ecosystem.

This paper highlights the importance of precise assessments of greenhouse gas (GHG) emissions associated with power generation for effective policy making in environmental sustainability. The current assessment approaches based on historical data or estimated generation using energy models may not accurately reflect the reality of future power systems due to the impact of spatial-temporal and techno-economic characteristics of generation mix and load demands. To address this, the paper presents a comprehensive methodology for accurately quantifying the geographical and temporal variations in GHG emissions associated with generating units’ operation, startup, and shutdown at an hourly resolution. The methodology is based on a detailed electricity model that considers various sources of generation, techno-economic, and spatial-temporal characteristics of system components. The study demonstrates the effectiveness of the methodology in quantifying GHG emissions in the IEEE RTS-GLMC system, with a focus on CO2, N2O, and CH4. The analysis reveals significant variations in GHG emissions among different generation buses and hours of the year, attributed to the high proportion of renewable energy in the generation mix. The paper emphasizes the inadequacy of examining marginal environmental impacts based on GHG emission intensity alone and suggests a more thorough analysis based on total GHG emissions generation. Finally, the paper emphasizes the crucial role of time-varying and marginal assessment techniques in identifying effective strategies for reducing GHG emissions in the electricity sector, including optimizing the operation and capacity of generation units, energy storage systems, and electric vehicles, including their locations.

Abstract Frequency regulation of autonomous microgrids with intermittent renewables is a tedious task, which requires additional support from energy storage systems (ESS). Ideally, the system requires a faster‐acting ESS to arrest the initial frequency deviation and high energy capacity ESS to restore the frequency to its nominal value. Pico hydel energy storage (PHES) is one such high energy capacity ESS developed from the agricultural pumping systems. However, PHES has a slower response and cannot switch rapidly between pumping and generation modes. Hence, this paper incorporates a battery with PHES to achieve a faster dynamic response and absorb high‐frequency power dynamics. Additionally, a frequency regulation strategy with synchronverter control is proposed to mimic synchronous machines, especially to reduce the rate of change of frequency (ROCOF) and frequency nadir. Firstly, a small‐signal modelling approach is proposed, and it was utilized to assess the system stability through eigenvalue analysis. Subsequently, transient simulations were carried out to gauge the performance of the synchronverter strategy with vector control for frequency regulation. As a result, the frequency nadir and ROCOF were substantially reduced with the synchronverter strategy compared to the vector control based on the simulation results.

A decade ago, Malaysia introduced the Renewable Energy Act 2011. This led to the RE proliferation, especially with the adoption of solar photovoltaics as an alternative energy source for prosumers to generate green energy and reduce their energy costs. Since then, the RE policies have evolved with the introduction of feed-in tariff (FiT) and various version of Net Energy Metering (NEM) schemes. Such initiatives may not be holistic enough to benefit all stakeholders; thus, Malaysia introduced its first pilot P2P energy trading in 2019. However, there was no significant progress to the P2P pilot thereafter, with a Go-to-Market plan. As such, this review proposes a model for P2P energy trading for Malaysia based on several key success factors including the market design, trading mechanism, physical and virtual infrastructure, policy and governance and social. Malaysia’s electricity market structure is also compared to South Korea, Germany, Thailand, United Kingdom, and Singapore (ranked in the top 20 Ease of Getting Electricity by World Bank in 2019) to understand the implication of P2P energy trading adoption. Apart from that, this paper also highlights the key technical and non-technical reviews of the P2P energy trading implementation.

In this article, a new solar PV fed Dynamic Voltage Restorer (DVR) based on Trans-Z-source Inverter (TransZSI) is proposed to improve the power quality of on-grid Photovoltaic (PV) systems. DVR is a power electronic compensator using for injecting the desired voltage to the Point of Common Coupling (PCC) as per the voltage disturbance. In the proposed DVR, in place of traditional VSI, TransZSI with outstanding merits of buck/boost, a broader range of voltage boost gain, fewer passive components, and lower voltage stress, is put forth. For efficient detection, accurate voltage disturbances mitigation, and also lessening the injected voltage harmonics, a hybrid Unit Vector Template with Maximum Constant Boost Control (UVT-MCBC) method is proposed for TransZSI-DVR. The performance of the proposed TransZSI-DVR with UVT-MCBC has been analyzed under severe sag, slight sag with harmonics, swell, and interruption. The comparative studies and simulation results have shown the effectiveness of the proposed TransZSI-DVR, as opposed to traditional ZSI-DVR and VSI-DVR. The TransZSI-DVR in the PV system has mitigated voltage sag/swell/interruption. It has also improved the power quality of both the injected voltage to the PCC and PV system's output voltage.

The efficient and compact design of multilevel inverters (MLI) motivates in various applications such as solar PV and electric vehicles (EV). This paper proposes a 53-Level multilevel inverter topology based on a switched capacitor (SC) approach. The number of levels of MLI is designed based on the cascade connection of the number of SC cells. The SC cells are cascaded for implementing 17 and 33 levels of the output voltage. The proposed structure is straightforward and easy to implement for the higher levels. As the number of active switches is less, the driver circuits are reduced. This reduces the device count, cost, and size of the MLI. The solar panels, along with a perturb and observe (P&O) algorithm, provide a stable DC voltage and is boosted over the DC link voltage using a single input and multi-output converter (SIMO). The proposed inverters are tested experimentally under dynamic load variations with sudden load disturbances. This represents an electric vehicle moving on various road conditions. A detailed comparison is made in terms of switches count, gate driver boards, sources count, the number of diodes and capacitor count, and component count factor. For the 17-level, 33-level, and 53-level MLI, simulation results are verified with experimental results, and total harmonic distortion (THD) is observed to be the same and is lower than 5% which is under IEEE standards. A hardware prototype is implemented in the laboratory and verified experimentally under dynamic load variations, whereas the simulations are done in MATLAB/Simulink.

Ability to organize data spatially while conserving the topological relation between data features makes the Self Organizing Map (SOM) a very useful tool for analysis and visualization of high dimensional data such as a power transformer's Dissolved Gas Analysis (DGA). Past SOM application required large historical data for its training and has limited fault detection sensitivity. In this paper, the effects of input features and data normalization are studied to enhance SOM's clustering. SOM is trained using DGA results extracted from actual faulted transformers. Combination of input features and data normalization methods are tested on SOM before the best SOM is identified. Validation is conducted using several datasets i.e. the IEC Technical Committee 10 database. Compared with past SOM applications, the proposed SOM required lesser training data, improved SOM's sensitivity in incipient fault detection and has good diagnosis accuracy. The proposed SOM is also compared with other AI-based DGA interpretation method i.e. Support Vector Machine (SVM) for benchmarking.

The ever increasing trend of renewable energy sources (RES) into the power system has increased the uncertainty in the operation and control of power system. The vulnerability of RES towards the unforeseeable variation of meteorological conditions demands additional resources to support. In such instance, energy storage systems (ESS) are inevitable as they are one among the various resources to support RES penetration. However, ESS has limited ability to fulfil all the requirements of a certain application. So, hybridization of multiple ESS to form a composite ESS is a potential solution. While integrating these different ESS, their power sharing control plays a crucial role to exploit the complementary characteristics of each other. Therefore, this article attempts to bring the numerous control strategies proposed in the literature at one place. Various control techniques implemented for HESS are critically reviewed and the notable observations are tabulated for better insights. Furthermore, the control techniques are classified into broad categories and they are briefly discussed with their limitations. From the carried-out analysis, the challenges faced towards the implementation of HESS for standalone and grid connected microgrid systems are presented. Finally, the future directions are laid out for the researchers to carry out the research and implementation of HESS technologies. Overall, this article would serve as a thorough guide on various control techniques implemented for HESS including their features, limitations and real-time applications.

The increase in the penetration of renewable energy sources (RES) has reduced the effective inertia of the power system dominated by synchronous machines. The reduction in inertia has paved the way to the development of inertia emulation techniques, which complements the increased penetration of RES. The synchronverter technology is one among the virtual inertia emulation techniques which resembles close to the synchronous machine. The advancements in control and adaptations of synchronverter for various applications demand a comprehensive yet critical review. Thus, the main objective of this paper is to present a critical review of synchronverter technology which could increase the inertial response of RES. A critical analysis of modifications made to original synchronverter and the stability assessment techniques is presented. The effect of parameter variation on stability and dynamic performance is brought out as a decision matrix by analysing the movement of eigenvalues presented in various works. Towards the end, a synoptic overview is presented to depict the research trend and emphasize the domain which requires further attention. Furthermore, a brief discussion of the research gaps perceived during the phase of review is presented to leverage future research in the field.

This paper suggests a method to place and size the battery energy storage system (BESS) optimally to minimise total system losses in a distribution system. Subsequently, the duck curve phenomenon is taken into consideration while determining the location and sizing. The locations and sizing of BESS were optimised using a metaheuristic algorithm with high exploration and exploitation ability which is known as the Whale Optimisation Algorithm (WOA). Meanwhile, the performance of WOA was validated using other algorithms, i.e., Particle Swarm Optimisation and Firefly Algorithm. The results demonstrated the capability of WOA to determine the optimal BESS location and sizing for all cases, with and without considering the duck curve issue for loss reduction. Besides that, the duck curve issue can be mitigated by appropriately optimising the energy storage system (ESS) to reduce the steep ramp of the duck neck and ducktail and to lift the duck belly. In conclusion, although less loss reduction was achieved as a tradeoff to fulfil the constraint on net load ramp limit, the required BESS sizing was much smaller than the case without those constraints and charging operation, which makes this solution economically viable.

To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted.

This paper proposes a three-phase inverter combined with two LLC resonant tanks series resonant DC-DC boost converter with variable frequency control. The three-phase inverter side of the proposed circuit is connected to identical two-level LLC tanks to ensure balanced resonant currents. The proposed converter requires less switching devices and transformers as compared to the conventional interleaved LLC resonant converter, which competitively offers higher efficiency and reduced size and cost. Furthermore, the proposed converter works above the resonant frequency to achieve zero voltage switching (ZVS) for the entire operating frequency range (42.5kHz s

The automobile companies are focusing on recent technologies such as growing Hydrogen (H2) and Fuel Cell (FC) Vehicular Power Train (VPT) to improve the Tank-To-Wheel (TTW) efficiency. Benefits, the lower cost, `Eco' friendly, zero-emission and high-power capacity, etc. In the power train of fuel cell vehicles, the DC-DC power converters play a vital role to boost the fuel cell stack voltage. Hence, satisfy the demand of the motor and transmission in the vehicles. Several DC-DC converter topologies have proposed for various vehicular applications like fuel cell, battery, and renewable energy fed hybrid vehicles etc. Most cases, the DC-DC power converters are viable and cost-effective solutions for FC-VPT with reduced size and increased efficiency. This article describes the state-of-the-art in unidirectional non-isolated DC-DC Multistage Power Converter (MPC) topologies for FC-VPT application. The paper presented the comprehensive review, comparison of different topologies and stated the suitability for different vehicular applications. This article also discusses the DC-DC MPC applications more specific to the power train of a small vehicle to large vehicles (bus, trucks etc.). Further, the advantages and disadvantages pointed out with the prominent features for converters. Finally, the classification of the DC-DC converters, its challenges, and applications for FC technology is presented in the review article as state-of-the-art in research.

Today, the Internal Combustion Engine (ICE) is gradually being replaced by electric motors, which results in higher efficiency and low emission of greenhouse gases. The electric vehicle either works wholly or partially on electrical energy generated from batteries and ultra-capacitors. The battery or ultra-capacitor is either charged from the AC supply connected to a grid line in a plug-in electric vehicle or from ICE in a hybrid electric vehicle. Alternatively, the battery charges from the traction motor by regenerative braking. In the reverse direction, the energy from the battery or ultra-capacitor is injected into the AC grid line in the plug-in electric vehicle. Power electronic converters play a vital role in the conversion process from grid line to traction motor and in the reverse direction. In this paper, the role of power electronics converters in an electric vehicle is elaborated. The bidirectional DC-DC converter plays a vital role in the power conversion process of electric vehicles. The existing bidirectional DC-DC converter topologies are discussed with a comprehensive review, comparison, and application. Additionally, the advancement in power electronics converters to improve the efficiency and reliability of the vehicular system is elaborated.

A partial shading condition is an environmental phenomenon that causes multiple peaks in Photovoltaic (PV) characteristics. Introducing robust and reliable Maximum Power Point Tracking technique is essential in PV systems to extract the Global Maximum Power Point (GMPP) irrespective of the environmental conditions. Therefore in this manuscript, a novel optimization algorithm is implemented for MPPT. The developed technique named Chaotic Flower Pollination Algorithm (C-FPA) merges the chaos maps (Logistic, sine, and tent maps) to tune the basic algorithm parameters adaptively. The effectiveness of the introduced variants is proved using several patterns of partial shading condition. Moreover, these variants are certified for tracking the GMPP in case of dynamic and sudden variation in the irradiance conditions. Several statistical analysis is carried out to evaluate the performance of the proposed variants in comparison with the standard version of the Flower Pollination Algorithm (FPA). The significant outcome clarifies that combining the chaos maps with FPA improves the dependability and stability of the FPA and offers higher tracking efficiency with a reduction of tracking time by 50% when compared to FPA. Moreover, the proposed C-FPA provides a better dynamic response, especially with the tent chaos map.

This paper introduces a novel control system with maximum power point tracker (MPPT) for the photovoltaic system with grid integration. Hybrid adaptive neuro-fuzzy inference system (ANFIS) and artificial bee colony (ABC) algorithm employed to optimize the membership function. Hence, for minimizing the root mean square error (RMSE), this controls the SEPIC-based MPPT algorithm to achieve rapid PV power tracking. The system performance is improved by fuzzy logic control (FLC), which generates the switching signal to the power switches of the inverter. A dSPACE (DS1104) control board employed for experimental validation of MPPT and inverter control strategies. The novelty of the proposed hybrid MPPT controller is the optimal tuning of ANFIS membership function with the ABC algorithm and been neither discussed before for PV power applications. The experimental responses completely validate the reliability of the PV grid integration with anti-islanding protection. The recentness of this research work is PV MPPT functioning using the hybrid ANFIS-ABC-based algorithm, been not described practically by any researchers in the past works.

In a conventional energy storage system in a grid-connected solar power stations, solar power is transferred to the grid through a PV-Inverter, and the battery is charged and discharged through a bi-directional converter. In this paper, a novel grid energy storage system for large-scale PV systems is discussed. With the proposed configuration, the battery charging and discharging are carried out through an AC voltage regulator which is connected in series to the line. For this system, cascaded H-bridge (CHB)-based PV-Inverter which is suitable for a high power application is selected. In case of failure in one H-Bridge of a CHB inverter, it is difficult to integrate solar inverter with the grid as the voltages of inverter and grid are not matched. Fault tolerant operation of the CHB-based PV-Inverter can also be achieved through the proposed configuration. In this paper, basic operation and control of a voltage regulator, application of the voltage regulator in grid energy storage systems, fault tolerant operation of a CHB inverter through the voltage regulator are presented. To validate the performance of the controls proposed, Real-time simulations are carried out by interfacing the simulated power circuit with the real controller card with the help of an Opal-RT make real-time simulator. Performance of the proposed system is analyzed through presented results.

This paper investigates the fault ride through (FRT) capability improvement of a doubly fed induction generator (DFIG)-based wind turbine using a dynamic voltage restorer (DVR). Series compensation of terminal voltage during fault conditions using DVR is carried out by injecting voltage at the point of common coupling to the grid voltage to maintain constant DFIG stator voltage. However, the control of the DVR is crucial in order to improve the FRT capability in the DFIG-based wind turbines. The combined feed-forward and feedback (CFFFB)-based voltage control of the DVR verifies good transient and steady-state responses. The improvement in performance of the DVR using CFFFB control compared with the conventional feed-forward control is observed in terms of voltage sag mitigation capability, active and reactive power support without tripping, dc-link voltage balancing, and fault current control. The advantage of utilizing this combined control is verified through MATLAB/Simulink-based simulation results using a 1.5-MW grid connected DFIG-based wind turbine. The results show good transient and steady-state response and good reactive power support during both balanced and unbalanced fault conditions.

Under the present electricity tariff structure in Malaysia, electricity billing on a monthly basis for commercial and industrial consumers includes the net consumption charges together with maximum demand (MD) charges. The use of batteries in combination with photovoltaic (PV) systems is projected to become a viable solution for energy management, in terms of peak load shaving. Based on the latest studies, maximum demand (MD) reduction can be accomplished via a solar PV-battery system based on a few measures such as load pattern, techno-economic traits, and electricity scheme. Based on these measures, the Maximum Demand Reduction (MDRed) Model is developed as an optimization tool for the solar PV-battery system. This paper shows that energy savings on net consumption and maximum demand can be maximized via optimal sizing of the solar PV-battery system using the MATLAB genetic algorithm (GA) tool. GA optimization results revealed that the optimal sizing of solar PV-battery system gives monthly energy savings of up to 20% of net consumption via solar PV self-consumption, 3% of maximum demand (MD) via MD shaving and 2% of surplus power supplied to grid via net energy metering (NEM) in regards to Malaysian electricity tariff scheme and cost of the overall system.

This research work explains the practical realization of hybrid solar wind-based standalone power system with maximum power point tracker (MPPT) to produce electrical power in rural places (residential applications). The wind inspired Ant Colony Optimization (ACO)-based MPPT algorithm is employed for the purpose of fast and accurate tracking power from wind energy system. Fuzzy Logic Control (FLC) inverter controlling strategy is adopted in this presented work compared to classical proportional-integral (PI) control. Moreover, single Cuk converter is operated as impedance power adapter to execute MPPT functioning. Here, ACO-based MPPT has been implemented with no voltage and current extra circuit requirement compared to existing evolutionary algorithms single cuk converter is employed to improve conversion efficiency of converter by maximizing power stages. DC-link voltage can be regulated by placing Cuk converter Permanent Magnet Synchronous Generator (PMSG) linked rectifier and inverter. The proposed MPPT method is responsible for rapid battery charging and gives power dispersion of battery for hybrid PV-Wind system. ACO-based MPPT provides seven times faster convergence compared to the particle swarm optimization (PSO) algorithm for achievement of maximum power point (MPP) and tracking efficiency. Satisfactory practical results have been realized using the dSPACE (DS1104) platform that justify the superiority of proposed algorithms designed under various operating situations.

A photovoltaic system is highly susceptible to partial shading. Based on the functionality of a photovoltaic system that relies on solar irradiance to generate electrical power, it is tacitly assumed that the maximum power of a partially shaded photovoltaic system always decreases as the shading heaviness increases. However, the literature has reported that this might not be the case. The maximum power of a partially shaded photovoltaic system under a fixed configuration and partial shading pattern can be highly insusceptible to shading heaviness when a certain critical point is met. This paper presents an investigation of the impact of partial shading and the critical point that reduce the susceptibility of shading heaviness. Photovoltaic string formed by series-connected photovoltaic modules is used in this research. The investigation of the P-V characteristic curve under different numbers of shaded modules and shading heaviness suggests that the photovoltaic string becomes insusceptible to shading heaviness when the shaded modules irradiance reaches a certain critical point. The critical point can vary based on the number of the shaded modules. The formulated equation in this research contributes to determining the critical point for different photovoltaic string sizes and numbers of shaded modules in the photovoltaic string.

Distribution systems can form islands when faults occur. Each island represents a subsection with variable boundaries subject to the location of fault(s) in the system. A subsection with variable boundaries is referred to as an island in this paper. For operation in autonomous mode, it is imperative to detect the island configurations and stabilize these subsections. This paper presents a novel scheme for the detection of island boundaries and stabilizing the system during autonomous operation. In the first stage, a boundary detection method is proposed to detect the configuration of the island. In the second stage, a dynamic voltage sensitivity factor (DVSF) is proposed to assess the dynamic performance of the system. In the third stage, a wide area load shedding program is adopted based on DVSF to shed the load in weak bus-bars and stabilize the system. The proposed scheme is validated and tested on a generic 18-bus system using a combination of EMTDC/PSCAD and MATLAB software’s.

Under the current energy sector framework of electricity tariff in Malaysia, commercial and industrial customers are required to pay the maximum demand (MD) charge apart from the net consumption charges every month. The maximum demand charge will contribute up to 20% of the electricity bill, and will hence result in commercial and industrial customers focussing on alternative energy supply to minimize the billing cost. This paper aims to review the technical assessment methods of a grid-connected solar photovoltaic (PV)—battery storage system—with respect to maximum demand shaving. An effective battery storage system can provide the extra energy needed during the peak energy consumption periods, as well as when renewable energy (RE) sources go offline. Based on the reviews, maximum demand shaving with good Return-of-Investment (ROI) can be achieved by considering the actual load profile, technical, and economic aspects of the solar PV-battery system and the Malaysian electricity tariff for commercial and industrial customers.

To mitigate the microgrid instability despite the presence of dense Constant Power Load (CPL) loads in the system, a number of compensation techniques have already been gone through extensive research, proposed, and implemented around the world. In this paper, a storage based load side compensation technique is used to enhance stability of microgrids. Besides adopting this technique here, Sliding Mode Controller (SMC) and Lyapunov Redesign Controller (LRC), two of the most prominent nonlinear control techniques, are individually implemented to control microgrid system stability with desired robustness. CPL power is then varied to compare robustness of these two control techniques. This investigation revealed the better performance of the LRC system compared to SMC to retain stability in microgrid with dense CPL load. All the necessary results are simulated in Matlab/Simulink platform for authentic verification. Reasons behind inferior SMC performance and ways to mitigate that are also discussed. Finally, the effectiveness of SMC and LRC systems to attain stability in real microgrids is verified by numerical analysis.

The introduction of electric vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric vehicles and the power grid has spurred the emergence of a smart grid technology, denoted as vehicle-to grid-technology. Vehicle-to-grid technology manages the energy exchange between a large fleet of electric vehicles and the power grid to accomplish shared advantages for the vehicle owners and the power utility. This paper presents an optimal scheduling of vehicle-to-grid using the genetic algorithm to minimize the power grid load variance. This is achieved by allowing electric vehicles charging (grid-to-vehicle) whenever the actual power grid loading is lower than the target loading, while conducting electric vehicle discharging (vehicle-to-grid) whenever the actual power grid loading is higher than the target loading. The vehicle-to-grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power grid load and capability of the grid-connected electric vehicles. Hence, the performance of the proposed algorithm under various target load and electric vehicles’ state of charge selections were analysed. The effectiveness of the vehicle-to-grid scheduling to implement the appropriate peak load shaving and load levelling services for the grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric vehicle historical data.

In this article a new distinct winding scheme is articulated to utilize three phase induction machines for multifunctional operation. Because of their rugged construction and reduced maintenance induction machines are very popular and well-accepted for agricultural as well as industrial purposes. The proposed winding scheme is used in a three phase induction machine to utilize the machine for multifunctional operation. It can be used as a three-phase induction motor, welding transformer and phase converter. The proposed machine design also works as a single phase induction motor at the same time it works as a three-phase to single phase converter. This new design does not need any kind of special arrangement and can be constructed with small modifications to any standard three-phase induction motor. This modified induction machine is thoroughly tested to determine its efficiency and other parameters and also hardware implementation results are provided in the article, which validate the design and construction.