A DC microgrid with renewable energy sources can achieve reduced current ripple, higher efficiency, faster dynamics, high voltage gain, and less operational stress by interfacing with an interleaved boost converter (IBC). The stability of an IBC linked to a DC microgrid supplying a constant power load (CPL) can be imperceptibly guaranteed by a conventional controller. A tightly regulated CPL with nonlinear and negative incremental impedance characteristics will lead to stability issues. Uncertainties such as load and line variations will further affect the stability of the system. A nonlinear passivity-based control algorithm requires more attention than a traditional controller to achieve the stability of power converters. This article explains the Brayton-Moser (BM) passivity-based controller (PBC) for a 2-level interleaved boost converter (IBC) interfaced DC microgrid with CPL. The suggested controller can achieve high signal stability by injecting a series-connected virtual impedance. The stability of the proposed controller has been assessed using the Lyapunov stability approach. A BM passivity-based controller for a 2-level IBC with CPL has been derived and investigated under various operating modes using MATLAB and Simulink. It was also observed that the proposed system achieves at least 2 % improvement in efficiency and 50 % reduction in current ripple. To evaluate the performance of BM Passivity-based controller, a comparative analysis was performed between the suggested controller and the traditional PI controller, which is also included in this paper.

This study investigates the application of the multiobjective grey wolf optimizer (MOGWO) for optimal placement of thyristor-controlled series compensator (TCSC) to minimize power loss in power systems. Two conflicting objectives are considered: (1) minimizing real and reactive power loss, and (2) minimizing real power loss and TCSC capital cost. The Pareto-optimal method is employed to generate the Pareto front for these objectives. The fuzzy set technique is used to identify the optimal trade-off solution, while the technique for order preference by similarity to the ideal solution suggests multiple optimal solutions catering to diverse utility preferences. Simulations on an IEEE 30 bus test system demonstrate the effectiveness of TCSC placement for power loss minimization using MOGWO. The superiority of MOGWO is confirmed by comparing its results with those obtained from a multiobjective particle swarm optimization algorithm. These findings can assist power system utilities in identifying optimal TCSC locations to maximize their performance.

The growing concerns regarding the depletion of fossil fuels, CO2 emissions, and the effects of climate change prompt the usage of plug-in electric vehicles (PHEVs) all over the world in a big way. The increased electrical demand brought on by the charging of electric vehicles puts a burden on the distribution network parameters like energy loss, voltage profile and thermal limits. Recently, renewable energy-based distribution generation (RDGs) units are firmly integrated with the transmission and distribution system networks to lower the carbon footprint generated due to conventional thermal power plants. In addition, Battery Energy Storage Systems (BESS) are used to enhance grid operation and lessen the consequences of the high intermittency nature of RDGs power. In this work, two charging methods of PHEVs are considered: charging electric vehicles at home during night-time and charging electric vehicles at public fast charging stations (PFCS). The uncertain nature of arrival time and trip distance of PHEVs are addressed using probability density functions (PDFs). The 33-bus test system consists of commercial, industrial and residential buses is taken to implement the proposed methodology. In this work, 500 PHEVs are taken into consideration. The aforementioned charging methods produce a 24-h electric demand for PHEVs, which is then placed on the corresponding distribution system buses. The effect of PHEVs on technical distribution system metrics, including voltage profile and energy loss, is investigated. To improve the above metrics, optimal planning of inverter-based non-dispatchable PV units and dispatchable PV-BESS units in the distribution network by the inclusion of PHEVs electric load demand is addressed. The Pareto-based meta-heuristic multi-objective chaotic velocity-based butterfly optimization method (MOCVBOA) is chosen for optimization of desired objectives. The results of the MOCVBOA optimization algorithm are compared with those of the other optimization algorithms, NSGA-II & MOBOA, frequently described in the literature to assess its effectiveness.

One of the most popular renewable energy sources is solar photovoltaic, so it is essential to comprehend the PV model and model it accurately. Many parameters need to be extracted from the photovoltaic to assess the performance of the PV cell. Extracting the unknown parameters from the PV cell is significantly important for the analysis of the fault diagnosis, Maximum power point tracking (MPPT), control, and efficiency of the photo-voltaic system. The unknown parameters are (Photo current, series and shunt resistances, diode ideality factor, reverse saturation current). This algorithm is applied to PV cell (R.T.C France Si.) at the standard (STC) conditions utilizing the Three Diode Model, and applied to different PV modules like PWP201, SM55 at different temperatures and irradiances. This paper presents the Chaotic War Strategy Optimization (CWSO) algorithm to further reduce the error between measured and calculated values. The primary benefit of the proposed approach is its utilization of chaotic maps to calculate and dynamically adjust the internal parameters of the optimization algorithm. The extracted parameters and RMSE are compared to those of other cutting-edge algorithms to discover the most effective one for the parameter estimation issue to assess this algorithm’s efficacy.

The voltage source active power filter (VS-APF) is being significantly improved the dynamic performance in the power distribution networks (PDN). In this paper, the superconducting magnetic energy storage (SMES) is deployed with VS-APF to increase the range of the shunt compensation with reduced DC link voltage. The proposed SMES is characterized by the physical parameter, inductive coil, diodes and insulated gate bipolar transistors (IGBTs). The deep Q- learning (DQL) algorithm is suggested to operate SMES based VS-APF for the elimination of harmonics under different loading scenarios. Apart from this, the other benefits like improvement in power factor (PF), load balancing, potential regulation are attained. The simulation studies obtained from the proposed method demonstrates the correctness of the design and analysis compared to the VS-APF. To show the power quality (PQ) effectiveness, balanced and unbalanced loading are considered for the shunt compensation as per the guidelines imposed by IEEE-519-2017 and IEC-61000-1 grid code by using dSPACE-1104-based experimental study.

This work relates to the reduction of a noninteger commensurate high dimensional system. The essential objective of this article is to come up with an approximating technique to replace the original high dimensional system with a low dimensional model preserving the properties of the original system in its shortened model. Superiority of the proposed technique is exhibited by correlating the reduced model with the models of other current methods. The simulation results are cited to approve that the recommended technique has high efficiency with a closing value of time-domain specifications. Conclusively, more logical comparisons were made between other existing methods. The performance indices are calculated for both original system and reduced model system and presented in the manuscript.

With the maturation of nonlinear systems, considerable endeavors have been made to provide valid and high-speed controllers to supervise superior and more complex systems. Artificial intelligence has been remembered as the head topic among designers in the last decade. One of the popular control techniques is fuzzy logic, which is known to provide a controller that simulates the behavior of an expert operator. On the other hand, due to the necessity of change in human energy sources and the popularity of solar energy, attention to the greatest utilization of this category of green resources has significantly increased. Maximum power point tracking (MPPT) in solar systems is a headed topic, with innovative methods being presented every day despite numerous articles. However, the less discussed topic is the choice of a fuzzy inference system. In this article, the two classes of Mamdani and Sugeno are discussed to introduce the best controller for extracting more power from a solar system by implementing both types and gaining an understanding of their differences. In addition, the influence of the number of input membership functions on the controller performance is investigated. Therefore, two different input membership functions are given to each fuzzy system model. It should be noted that fuzzy system setup has been done by genetic algorithm to respond to the mortal desire to automate various processes, which is a subset of artificial intelligence. Accordingly, four different fuzzy systems have been designed and implemented on a solar system. The results were tested and summarized in various radiations in MATLAB Simulink.

Microgrid (MG) bridges different inverter interfaced distributed energy resources (DERs) to operate and control efficiently within a grid. The multi-master-slave control strategy can provide robust control of inverter interfaced DERs in close proximity. The role of master DERs is significant in synchronising the slave DERs and reducing the effect of single unit failure. However, faulty master DERs and master side communication failure can also suddenly affect the synchronisation of the slave DERs. Unfortunately, previous studies on multi-master-slave MGs have not addressed this issue. So, in this paper, a synch invulnerable multi-master-slave control strategy is proposed by introducing a synch selector with the centralised secondary controller. The synch selector is designed to pick the synchronising references from the first available healthy master units while keeping other masters operational and sending the synchronising references to the slave DERs. It also significantly reduced the dependency on communication lines up to the possible extent. Real-time simulation results are presented to demonstrate the effectiveness of the proposed control strategy. The presented results show that the unhealthy master units seamlessly can be side-stepped, and the next possible healthy master unit can be assigned to send reference signals to the slave DERs, hence, resisting synch vulnerability. The observations also show accurate power-sharing, full utilisation of DERs, and reliable and stable operation of the entire MG.

One of the critical emerging branches of solar technology is photovoltaic/thermal (PV/T) systems that amalgamate solar collectors and solar photovoltaic panels into a unit to produce heat and electricity from stochastic solar insolation. In sunny countries, the conversion efficiency ( $\eta$ ) reduces due to the elevated temperature of solar cells because solar panels absorb a sizeable portion of solar insolation as heat. The critical function of PV/T is to minimize the temperature of photovoltaic modules and enhance their electricity production with yielded thermal energy used for other applications. Energy and exergy are two essential aspects of examining an energy system. The exergy analysis of such systems is of great concern because it works on the quality of energy. The energy and thermal and electrical efficiencies are enhanced by applying proper cooling media in the PV/T. This brief provides a comprehensive review of the air, fluids, and PCM-based cooling media of the PV/T systems. A thorough review of various recently published research in the heat extraction methodologies of PV/T systems has been incorporated into this study. Based on the rigorous review, future recommendations for the implementation of cooling medias are also included in this study. The vivid tabular analysis of heat extraction methodologies provides a proper guideline for the researchers. This review work will provide a deep insight into the investigated area for the industrialists and researchers working in the field of PV/T technology.

This study describes the best hybrid energy system in terms of emissions, cost, and other factors. All the computations performed by HOMER Pro. A standalone hybrid power system model is suggested in this study. The suggested concept combines diesel generation with PV and Wind energy sources. The National Aeronautics and Space Administration (NASA) provided the data for simulation in HOMER to determine system performance. To reduce dependency on either conventional energy or renewable energy sources, a hybrid renewable energy system may be employed. Studies have shown that the suggested approach and the optimization technique for sizing standalone hybrid power systems both settle really well. The study aim is to optimize the size and expense of a renewable energy system at the chosen location in order to satisfy the electrical demand. The evaluation of the hybrid systems is based on the net present cost (NPC), levelized cost of energy (COE), initial cost, operating cost, and renewable fraction. The results support the use of RES at the chosen location, with the PV-Wind-Diesel generator system emerging as the most cost-effective RES with a COE of 0.2424 $/kWh. The outcomes are in favor of using a hybrid renewable system.

The economical use of available fuel for producing electricity has been a very important challenge for power companies due to the continuously declining supply of fossil fuels. FCJHPDEED (fuel-constrained joint heat and power dynamic economic environmental dispatch) and JHPDEED (joint heat and power dynamic economic environmental dispatch) with DSM (demand-side management) integrating solar PV plants, WTGs (wind turbine generators), and PHS (pumped hydro storage) plants have been presented. Using SPEA 2 (strength Pareto evolutionary algorithm 2) and NSGA-II (non-dominated sorting genetic algorithm-II), FCJHPDEED and JHPDEED have been solved. It is seen that the results obtained without fuel constraints are more optimal than the results obtained with fuel constraints. The joint heat and power dynamic economic dispatch cost obtained with fuel constraints is approximately 2.14% more than the cost obtained without fuel constraints and joint heat and power dynamic emission dispatch, and the emission obtained with fuel constraints is approximately 6.7% more than the emission obtained without fuel constraints.

Recently, infiltration of distributed energy resources (DERs) is augmented considerably to upsurge network flexibility, better economic indicator, and reduced power loss. But integration of different DERs may cause challenges in power grid. To overwhelmed these challenges and obtain maximum advantage of DERs, virtual power plant's concept has been emerged. Virtual power plants (VPPs) has the capacity to partake in electricity market and rivalry of VPPs to achieve more profit, deregulated multi-operator markets are developed. This paper suggests dynamic optimum power flow (DOPF) for multi-operator VPPs considering demand side management (DSM) and uncertainty of renewable energy sources. VPPs with different proprietorships are interconnected with each other by tie lines. Each VPP has small hydro power plants (SHPPs), solar PV plants (SPVPs), wind turbine generators (WTGs), bioenergy power plant (BPPs), and plug-in electric vehicles (PEVs). VPP 1 comprises IEEE 33-bus system, VPP 2 comprises 15-bus system, and VPP 3 comprises IEEE 69-bus system. Bottlenose dolphin optimizer (BDO), HPSO-TVAC, and GWO have been applied to solve DOPF problem and maximize the net profit of multi-operator VPPs.

Due to the diminution of fossil fuels, the profitable usage of fuel for the generation of power is reducing. It is an extremely vital concern for power generation companies. The fossil fuel-based power plant must operate within its fuel limits and contractual restrictions. Therefore, economic optimization is required to optimize the fuel cost for generation under fuel constraints. To address this issue, in this work, a horse herd optimization algorithm (HOA) is developed to solve multicounty fuel-constrained dynamic economic dispatch with demand-side management incorporating wind turbine generators, solar photovoltaic plants, and pumped hydro storage plants. The efficiency of the suggested HOA algorithm has been revealed on a selected test system. The HOA technique shows better convergence as compared with the other methods. Moreover, the computation time requirement of the HOA technique is also lower than that of other compared methods. From numerical results, it is observed that the consumption of fuel is sufficiently high if constraints are incorporated into the problem. Simulation results are matched up to those obtained from hierarchical particle swarm optimization with time-varying acceleration coefficients, fast convergence evolutionary programming, and differential evolution. After comparison, it is observed that the recommended HOA offers a better-quality solution.

Electric vehicles (EVs) have various advantages over traditional internal combustion engines (ICEs), including reduced carbon emissions, greater energy efficiency, and a lessened reliance on petroleum supplies. The use of EV charging infrastructure and power levels are reviewed in this article. Battery performance is affected by the design of the battery as well as the charger parameters and infrastructure. In this paper, the off-board and on-board charging methods with bidirectional and unidirectional power flow are compared. Hardware restrictions and connectivity concerns are eased with a unidirectional charger. The bidirectional charger enables both battery energy injection back into the grid and the vehicle. Power is constrained by the onboard charger due to its size, weight, and price. Both conductive and inductive onboard chargers are viable. For high current rates, which are not supported by EVs, it is feasible to develop an off-board charger. The time required for charging, amount of power, cost, equipment, location, infrastructure configurations, and other parameters are provided, compared, and reviewed for different power level chargers, such as level-1 (slow), level-2 (semi-fast), and level-3 (fast).

In modern age, the over usage of automobiles using oil based fuel, has resulted in the excessive transportation-related emission as well as caused fuel prices to rise unconditionally around the world.In this situation, the usage of electric vehicles has opened a new and alternative pathway to tackle this global problem, and provide necessary energy conservation along with emission reduction.The target is to determine an optimized hybrid renewable system with the least possible values for the individual component Levelized and Net present costs of energy.Here the work presents an analysis on the establishment of a hybrid power system to run a general village located in Digha (21 0 37.6′ N, 87 0 30.4 ′ E) West Bengal, India.The next goal of reported work is to implement stand-alone renewable system with EV load and optimize the cost of the energy produced, operation and maintenance; consequently, curbing the use of conventional DG sets.The PV solar arrays are utilised as the main sources of the energy used, while the battery storage system is used otherwise.The wind and solar data have been referenced from NASA's meteorological department.The location in Digha consumes 1650 kWh/day with 385.51 kW peak power and a deferrable load of 24.86 kWh/day with 4.62 kW peak load demand.An innovative methodology has been designed to facilitate the determination of electric loads for the isolated location.

This study examines the optimal sizing of an off-grid hybrid system comprising solar photovoltaic (PV), rice husk-based biomass, and lead-acid battery for meeting the electric demand of a rural community. Considering a selected remote village in Bangladesh as a case study, the proposed optimized system is primarily compared with the diesel generator and the micro gas turbine (MGT)-based options in techno-economic and environmental terms. The potential social benefits, such as the employment creation and the improvement in the human development index in the locality, have been investigated in this study. Moreover, the impacts of operational greenhouse gas emissions on the human health damage and the surrounding ecosystem have been examined. Additionally, an exergy analysis of the hybrid system and the components has been carried out. Results indicate that in addition to being the environmentally preferable option, the proposed PV/biomass/battery system offers a lower cost of energy of 0.314 $/kWh compared to the MGT-based system (0.377 $/kWh). Although the diesel-based system offers a marginally better economy (9.55% less energy cost), it comes with the expense of probable damages to human health and the ecosystem worth of $15,211 and $6,608, respectively, making biomass the best option with no such damages. Exergy analysis reveals higher loss from PV than biomass and 13.09% system exergy efficiency. The assessment of the social indicators testifies to the potential of promoting the human development index from its current value and the formation of 1.41 jobs to as high as 15.15 full-time permanent jobs with the installation of hybrid systems in the community.

This paper provides a comprehensive review of the research work related to Reliability Assessment Methodologies for grid-connected photovoltaic (PV) systems performed in recent literature. Solar power is emerging as the fast growing source of energy in the world as a result of rising environmental concerns regarding the hazards of climatic change linked with the production of electricity using fossil fuels. Although PV systems can support small businesses and households on their own, many people prefer a grid-connected PV system (PVS) because of the net profit it provides. Grid-integrated PV system, however, comes with many reliability issues. Evaluating the reliability of grid-integrated photovoltaic system is thus an important area of research. The article presents a critical survey of the state-of-art technologies for assessing the reliability of a PV system. Issues related to the reliability of the grid-integrated PVS are spotted along with the solution techniques. Reliability indices for analyzing the PVS performance are also discussed.

The increased integration of renewable energies (REs) raised the uncertainties of power systems and has changed the approach to dealing with power system challenges. Hence, the uncertain nature of all the power system variables needs to be considered while dealing with the optimal planning and operation of modern power systems. This paper presents a probabilistic optimal active and reactive power dispatch (POARPD) based on the point estimate method (PEM), considering the uncertainties associated with load variation and wind power generation. In the POARPD, the deterministic optimal active and reactive power dispatch (OARPD) is performed in two stages, which gives a deterministic two-stage OARPD (TSOARPD). The objectives of TSOARPD are the operating cost (OC) minimization in stage 1 and voltage stability (VS) maximization in stage 2, whereas the VS is improved by maximizing the system’s reactive power reserve (RPR). In this paper, instead of using multiobjective optimal power flow, this TSOARPD is used to give more importance to VS when the system is substantially loaded. The POARPD problem is solved using PEM for modified IEEE-9 bus and standard IEEE-30 bus test systems by considering the correlation between the loads. The results are compared with Monte Carlo simulation (MCS). While solving POARPD, the voltage-dependent load model is used to account for the real-time voltage dependency of power system loads. This paper discusses the detailed procedure of solving POARPD by considering correlation and the increased nonlinearities by giving more importance to VS when the system is heavily loaded.

In this paper, the optimal allocation problem of the Electric Vehicles Parking Lots and DG in micro-grid is studied as a novel optimization problem. In the proposed problem, the different factors including the technical and the economic issues are considered for achieving a realistic solution. In addition, FACTS devices have been added to the network by considering the costs of installation, repair and maintenance, and their effecton the technical and economic parameters of the network has been investigated. Also, how to optimally charge and discharge storage devices and how to optimally inject active or reactive power by charging stations to improve energy management in the network has been studied. In order to evaluate the impact and reduce economic costs, the optimal number of distributed renewable energy, storage and FACTS equipment along with the optimal number of parking lots with Existence of different number of electric vehicles in the survey and microgrid network has been designed using a two-stage optimization method. The method is simulated on the standard IEEE 33 bus. The results show that the optimal power factor of parking lots can be determined during the unloading of vehicles in such a way that the technical parameter of the network is improved.