Search

This paper studies the fuzzy $ H_\infty $ H ∞ control of nonlinear DC microgrids subject to the dynamic event-triggered mechanism (ETM), aperiodic DoS attacks, noises and mismatching premises. First, using the information of DC microgrid's T-S fuzzy model and aperiodic DoS attacks, a discrete-time resilient dynamic ETM is proposed, which can save constrained system resources, as well as actively exclude attack-induced dropouts and Zeno behaviour. Second, a fuzzy switched system model is built, which provides a unified platform to evaluate effects of all the affecting factors such as the dynamic ETM and DoS attacks. Third, by constructing a piecewise Lyapunov functional, criteria for exponential stability with guaranteed $ H_\infty $ H ∞ performance are obtained, and an event-triggered fuzzy injection current controller is further designed. Simulation results confirm that, in the presence of aperiodic DoS attacks and noises, the proposed controller can stabilise the nonlinear DC microgrids, while the dynamic ETM works well in reducing the triggering rate without dropouts. Tradeoffs can be made between control and communication resources, and the proposed fuzzy controller achieves shorter settling times and smaller overshoots than the robust controller. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid method for controlling bidirectional power sharing among grids and dc microgrids. The interlinking converter (IC) and current-fed dual active bridge (CFDAB) converters connect the grid and dc microgrid. The proposed approach is the joint implementation of golden eagle optimization and radial basis function neural network, hence, named the GEO-RBFNN approach. The bidirectional power distribution control signal analysis significantly diminishes power distribution deviation and circulating current. This power-sharing control system neglects the need for battery backup since the required amount of power is brought from or handled by an AC utility. The dc microgrid incorporates the dc to dc converter, from which boost converters use sources and buckconverters use supply loads. The control of bidirectional power sharing among the microgrids and the grid is attained with the aid of the GEO-RBFNN algorithm. It delivers certain aids, like higher predictability, highly reliable results, high speed, and less complexity. The outcome is investigated on the MATLAB platform. According to the comparison outcome, the proposed method delivers higher performance than the existing system. The proposed method gives higher comparison outcomes than the current methods, which can conquer the associated problems. The proposed method-based settling time is 3.356 ms, and the rise time is 1.555 ms. The efficiency based on the proposed method is 97.54%, which is higher than the existing methods. [ABSTRACT FROM AUTHOR]

The impacts of nonlinearities from the I–V curve cause large oscillation and fluctuation occur in the PV attached DC voltage, BDC, load current, and solar current during low penetration of irradiance. PV system nonlinearities, weak damping, and lack of inertia causes lead to stability issues for DC microgrids. The stability of the system has been shifted to an unstable region during low penetration discussed for traditional controllers. In this paper, dual virtual series and parallel emulation with inertia feedforward controller have been introduced to mitigate fluctuations, initial DC notching, and damping issue for the DC microgrid system. This control method simulates the characteristics of the DC machine's robust and versatile method of DC/DC converter. The crossover frequency of the system has been reduced during low penetration with SPIEC which is not sufficient to prevent the oscillation and notching of the system. The system stability is enhanced through the proposed controller during low penetration analysed by pole-zero plots. During low penetration, the system pole has been shifted to a stable region towards the left side of the s-plane by the proposed controller to the traditional controller. The transient performance like oscillation, overshoot, and DC initial notching is resolved by the feed-forward current controller. However, the steady-state error is resolved by the voltage restoration controller. The proposed controller aims to enhance the power sharing among the energy storage and PV system under constant/variable loads. The oscillation, overshoot, and DC initial notching of the energy storage and PV have been discussed and made fast dynamic response by the proposed controller. [ABSTRACT FROM AUTHOR]

This manuscript proposes a hybrid method for energy management (EM) of a solar photovoltaic (PV) hybrid microgrid (MG) for the residential distribution system (DS). The proposed approach integrates the single candidate optimizer algorithm (SCOA), and gradient boost decision tree algorithm (GBDT), called the SCOA-GBDT algorithm. The main contribution of this manuscript is to (a) effectively achieve battery storage, solar PV, and loads to improve energy savings and lessen the loss of conversion; (b) effectively handle solar photovoltaic, battery storage, and loads to recognize cost-effective power distribution using the proposed technique; and (c) effectively handle weak photovoltaic power prevalent in the DC side of MG by an auxiliary-battery arrangement to preserve energy. The proposed energy management strategy lowers the conversion losses in the residential DS. Here, the dc loads are provided by a solar photovoltaic, the utility-grid provides the AC loads, and an auxiliary-battery bank is considered for storing the energy. Then, the performance of the proposed technique is done in MATLAB software and is compared to different existing approaches. From the simulation outcome, it is concluded that the proposed approach reduces costs and losses compared to the existing approaches. [ABSTRACT FROM AUTHOR]

This paper presents a quasi-resonant, isolated, high-gain, Y-source, DC/DC converter for a DC microgrid. Here, quasi-resonance is obtained by the LC tank during the shoot through period, resulting in natural commutation at the turn-off instant of the MOSFETs. The LC tank is formed by an additional parallel capacitor $({C_p})$ (C p) and transformer leakage inductance. The high voltage gain is obtained by including an isolation transformer with lower turns ratio, coupled inductor and voltage doubler. Here, frequency is modulated to regulate the output voltage with ON period $({T_{on}})$ (T on ) constant. This converter's zero current switching feature allows it to operate at a higher switching frequency and thus enhances the power density and efficiency. This converter's continuous input current feature is an added advantage. The proposed converter inherits all the benefits of traditional impedance source converters. Finally, a 250 W hardware prototype is developed, and its performance is tested in the laboratory. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in incorporating microgrids into existing electrical power networks to reduce reliance on conventional grids. This is further due to the numerous benefits of microgrids, most notably the ability to operate in either autonomous or grid-connected mode, making microgrids a highly versatile structure to incorporate intermittent production and energy storage. However, despite the many benefits, it is difficult to present resilience protection for DC bus bar to ensure the efficient operation of a DC microgrid, as there is a non-zero crossing phenomenon of DC fault current, while zero crossing phenomenon of an AC fault. Additionally, the DC bus bar has both an AC side with rectified DC and a DC line current, creating protection concerns for microgrids. To address these issues, this article proposes a novel fast fault detection scheme for DC microgrids with multiple renewable energy sources, energy storage, and loads. The scheme incorporates current and voltage sensing through current and voltage transformers, along with advanced digital relays, to detect and isolate faults on the DC microgrid. By analyzing rectified AC and DC line data, the scheme employs a differential current-based protection strategy to safeguard the DC bus bar. Validated through comprehensive fault simulations in PSS/SINCAL, the method proves effective in maintaining DC bus voltage stability and enhancing microgrid flexibility. [ABSTRACT FROM AUTHOR]

Community microgrids hold significant promise to address the challenges posed by the growing electrification of transportation and other energy-intensive demands, such as the electrification of heating. This potential is further supported by rigorous scientific research, which highlights their capacity to enhance energy resilience, reliability, and sustainability. In the specific context of Aotearoa New Zealand, community microgrids exhibit the potential to significantly improve energy resilience and self-sufficiency. This article outlines the evidence-based benefits, challenges, and high-potential use cases of community microgrids in Aotearoa New Zealand, drawing on both domestic and international research. [ABSTRACT FROM AUTHOR]

In this paper, demand response for isolated industrial micro-grid is investigated, in which the typical manufacturing assembly process, battery, conventional generator and PV generation are contained. The typical manufacturing assembly process is modelled by fully considering the coupling relationships between tasks and buffers. On the other hand, the uncertainty for PV generation is taken into account, where the data-driven distributionally robust chance constraints are designed and the error between day-ahead forecast and real-time operation is dealt with by the reserve capacity of conventional generator. In addition, the distributionally robust chance constraints and the expectation operator are reformulated as linear constraints, which can be solved by commercial solver directly. Finally, a case study using lithium-ion battery factory is presented to prove the superiority and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Hybrid microgrid (HMG) control strategies reveal several critical research gaps that must be addressed. Current approaches often fall short in efficiency and reliability due to the inherent complexities of transitioning between grid-connected mode (GCM) and islanded mode (IM), resulting in increased losses and operational difficulties. Additionally, managing overloads, especially in IM, poses significant challenges for maintaining stable voltage and frequency regulation. This paper seeks to address these issues by introducing a novel universal control structure (UCS) for HMGs, which includes a frequency compensation unit (FCU) and a three-stage bidirectional AC/DC droop. It presents a streamlined yet robust solution for handling overloads, maintaining power balance, and ensuring precise voltage and frequency regulation while promising smooth maneuver in both GCM and IM. The proposed UCS provides flexible and reliable operation, allowing for the versatile placement of DC and AC sources and loads. This will reduce power transformation stages thereby lowering cost and increasing efficiency of system. Through experimental validation, this study demonstrates the effectiveness of the proposed control in addressing these critical research gaps and advancing the field of HMG control strategies. [ABSTRACT FROM AUTHOR]

Nowadays, intelligent control methods play an important role in the advancement of technology and the human movement towards further evolution. The development of new frameworks for power generation and distribution systems by designing a microgrid structure with economic capabilities is one of these areas of progress. Therefore, this paper introduces a new practical method for controlling the frequency of provisional microgrid and is able to cover the following issues at the same time including (1) It considers the nonlinear model of provisional microgrid which has a hybrid structure (AC and DC) in addition to renewable energy sources; (2) Introduces a method for microgrid frequency control under different operational conditions that performs based on the brain emotional learning; (3) Ensures the operation and applicability of the control method for the provisional microgrid through implementation of FPGA for the first time; (4) Confirms the robustness of the proposed method under severe load changes and generation from renewable sources. So, the effects of wind turbines and solar energy are considered in the simulation scenario and under the influence of various changes in load and system uncertainties, the robustness and efficiency of the proposed method are well demonstrated. [ABSTRACT FROM AUTHOR]

This research proposes an intelligent artificial neural network (ANN) controller designed for a micro-grid featuring solar photovoltaic, wind, and Battery Energy Storage (BES) systems, integrated with a shunt voltage source converter. The primary goal of this proposed method is to minimize Total Harmonic Distortion (THD) and ensure a stable Distribution Line Commutation Voltage (DLCV) amidst load variations, achieving a rapid settling period. The STF not only facilitates phase synchronization but also effectively separates harmonic and fundamental components. This research contributes to advancing micro-grid control strategies, particularly in enhancing stability and reducing distortion during variable operating conditions. Based on an analysis of the four test cases, it is evident that the proposed strategy lowered the THDs to 4.63%, 3.45%, 3.05%, and 4.70% for each test case, as well as the PF to a level that was similar. The proposed controller shows better results compared with other controllers like Sliding Mode Controller (SMC) and the Proportional-Integral Controller (PIC). [ABSTRACT FROM AUTHOR]

This research presents a description of a bidirectional pulse width modulated converter that functions as a rectifier by using the direct power control strategy and space vector modulation technology. In contrast to the voltage-oriented control strategy, this method minimises the number of internal current loops and requires more tuning. While functioning as an inverter, it applies the space vector modulation technique as well. The approach, on the other hand, is not the same as rectifiers since it calculates the duty ratio of the neighbouring voltage vectors in each small triangle that is generated in each sector that has a period of 60 degrees. In both situations, the neutral point potential has been brought back into equilibrium. [ABSTRACT FROM AUTHOR]

A continuous energy supply to the load side is required by modern power systems. This calls for a sound understanding of how to forecast load demand in the present and the future with the least degree of inaccuracy. Typically, a sequential method with two steps—forecasting and optimization—is used to derive judgments from data. For achieving this goal, optimized power flow is focused in this paper through load forecasting, mode selection, and optimization of power forecasting. Firstly, load forecasting is implemented using time series, and economic and weather-related information for the different consumer's load. Then mode selection is implemented using Hidden Markov Model that determines the requested load for grid-connected or RES mode. When composite RES is developed, the percentage of serviced load rises as more renewable energy sources are added. Following the implementation of the consumer load and mode selection, optimization is used to improve the power flow. The empirical findings show enhanced prescriptive performance when compared to answers found in single- and multi-household contexts. Also, we offer insightful information on how explaining performance is described. [ABSTRACT FROM AUTHOR]

This study developed a model to compensate for both active and passive power in an electric microgrid with an optimal power flow to allow a detailed analysis of its power flow, which encourages the integration of renewable energy sources (RESs). Mixture integer linear programming (MILP) is proposed, with distributed generation (DG) integrated into the existing bars. It is developed in the GAMS tool to minimize operating costs in the microgrid as an objective function, to guarantee reliability, and quality in the microgrid. The algorithm calculates an optimal solution that meets all requirements for reliability and quality in the microgrid. Finally, a comparison of data between the two systems without and with DG was performed, showing an improvement in voltage profiles, reduced active power losses in the lines, a better power factor (PF), and improved angular behavior; that also adheres to the restrictions set forth at a minimum operating cost. A nine-bar model has been used to model the problem by IEEE standards. [ABSTRACT FROM AUTHOR]

Recently, the transition from conventional to renewable energy sources (RESs), from internal combustion engine vehicles to electric vehicles (EVs), and from the main grid to microgrids (MGs) are essential goals to both reduce greenhouse gas emissions and ensure the stability of power systems. However, the transitions cause new concerns in the grid including technical challenges and financial viability. This study examines the RES-based MG under realistic conditions considering the uncertainty in fleet size of EVs, emergency generator capacity, solar irradiation, and wind speed in island mode. The work aims to provide effective solutions including conventional methods alongside today's trend namely doubly fed electrical generators (DFIG), vehicle-to-grid mechanism (V2G), maximum power point tracking controller (MPPT), voltage source inverter (VSI) with high switching frequency for technical challenges, and virtual synchronous generator (VSG) mechanism for financial viability. The paper provides a guide for resizing the emergency generators capacity depending on system instability. The observations verify that the control mechanisms reinforce the system to remain stable by decreasing the range of frequency fluctuation from 3.1 to <0.05 Hz, the peak point of frequency from 51.8 to 50.05 Hz, and the emergency generator capacity from 0.7605 to 0.3420 MVAr at MATLAB and Simulink. [ABSTRACT FROM AUTHOR]

The cost-effectiveness of the hybrid design of microgrids for determining if a hybrid MG system is necessary is taken into consideration as the study focuses on improving power access in an optimal and economical manner. Both the cost of electricity (COE) and the loss of power supply probability (LPSP) of the hybrid MG design are optimized. The hybrid microgrid design including the photovoltaic (PV)-wind systems with battery storage and diesel generators are modeled and optimized for technical and economic feasibility using MATLAB. The energy management technique is applied between hybrid energy sources and loads. For this hybrid microgrid energy management system, an optimisation approach called spider monkey optimization (SMO) is employed. Using their distinct fission and fusion techniques, spider monkeys always engage in natural foraging behavior. Here, a variety of common constraints related to PV, wind, energy storage elements, inverters, and diesel generators with a variety of cost functions and dimensions are resolved by the proposed optimization method. The SMO optimization method is used to find a utilitarian optimal hybrid energy system configuration with a minimum COE, minimum LPSP, and leading renewable factor to meet the customer load throughout the entire time horizon. [ABSTRACT FROM AUTHOR]

Renewable energy sources are being incorporated into power networks to guarantee dependable and inexpensive electricity for the urban and industrial sectors. Recent research approximates microgrids powered by renewable energy and controlled by smart grids may provide more reliable and efficient energy systems economically. Its natural interaction among renewable energy sources, electric demands, and energy storage devices makes DC microgrid a contemporary electrical grid technology. In this paper, a nonlinear control approach with outstanding precision, robustness, and simplicity of tuning and implementation, is made to deal with the control of the DC microgrid system to bring out the efficacy of the system at higher points. A closed-loop continuous time sliding mode controller is proposed for a boost converter with a cascade filter. DC microgrid system with sliding mode controller is simulated and the analysis has been carried outs with motor load to check the adequacy of the proposed system. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are considered to be the most promising technology upgrades in the coming years to reduce the carbon emission, since the EVs could replace the conventional combustion vehicles as well as mitigate power fluctuations in power grids. This paper focuses on addressing the frequency stability issues in isolated hybrid grids (IHGs) by introducing a fractional order proportional-integral-derivative (FOPID) control for grid-connected power converters. A particle swarm optimization (PSO) technique is adopted to optimize the parameters of the controllers. The advantages of the proposed control method over the conventional proportional-integral-derivative (PID) control, proportional-integral with proportional-derivative (PI-PD) control, and proportional-integral-derivative with filter (PIDF) control schemes are verified by simulation case studies. Besides, a modified virtual rotor concept (MVRC) is proposed in this paper to enhance the inertia of the system. Furthermore, the performance of using EVs to regulate the frequency of IHGs is compared to the superconducting magnetic energy storage (SMES) units, capacitive energy storage (CES) units, and redox flow batteries (RFB) under fixed and variable load conditions. [ABSTRACT FROM AUTHOR]

This paper describes a case study of continuous hydrologic modeling in the Brahmani River basin in India using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC–HMS). The soil moisture algorithm (SMA) was employed as loss method for continuous simulations in the thirteen sub-basins. The sensitivity analysis showed that soil storage, tension storage and soil percolation are most sensitive parameters. The model performance was assessed for calibration period of 15 years (1990–2004) and validation period of 12 years (2005–2016), using statistical and visual evaluation techniques. During the calibration period, the observed and simulated values showed a good fit, with R2 ranging from 0.67 to 0.74, NSE from 0.66 to 0.72, and PBIAS from −8.5 to −9.5%. Similar performances are obtained during the validation period, with R2 ranging from 0.62 to 0.70, NSE from 0.60 to 0.68, and PBIAS from −2.5 to 21.4%. The results of the SMA procedure employed in the HEC-HMS model give valuable information for flood control and water supply management in the Brahmani Basin and similar other regions in Eastern India. [ABSTRACT FROM AUTHOR]

To optimize radial distribution systems, this study suggests the utilization of the Grey Wolf Optimizer (GWO), a hybrid metaheuristic optimization method, combined with the Sine Cosine method (SCA). The primary objective of this work is to enhance the distribution system by determining the most efficient network reconfiguration, sizing, and placement of various distributed energy sources in distribution system. The energy sources considered include capacitors, solar cells, wind turbines, biomass-based distributed generation units, and battery storage units. To achieve this goal, the proposed strategy incorporates the power loss sensitivity technique, which assists in identifying suitable candidate buses and accelerates the resolution process. Moreover, the model considers fluctuations in solar irradiance and wind speed using Weibull and Beta probability distribution functions, compensating for the intermittent nature of renewable energy sources and the variability in demand. To address power fluctuations, voltage surges, significant losses, and inadequate voltage stability challenges, battery energy storage, diesel generators, and dispatchable biomass DGs are employed to mitigate variability and enhance supply continuity. The proposed approach is evaluated and validated by comparing it to existing optimization strategies using IEEE 69-bus and 84-bus RDSs. The results demonstrate that the suggested technique achieves faster convergence to near-optimal solutions. The proposed methodology yields a significant reduction of up to 80% in power losses in the 69-bus system and a 35% reduction in the 84-bus system, signifying higher performance than existing methods. [ABSTRACT FROM AUTHOR]

This study aims to peak power shaving and reduce the cost of energy by using improved energy management system (EMS) in a microgrid. This study has three scenarios. In the first scenario, the EV charging station and the wind turbine operate standalone. In the second scenario, a microgrid structure including an energy storage system (ESS) and an EMS is established. The threshold algorithm has been used for the energy management. In the last scenario, fuzzy logic-based EMS is used for energy management. The impact of EV charge station and wind turbine on the grid and the cost of energy are analyzed for three scenarios. The results show that the use of microgrid-connected ESS significantly reduces the cost of energy and creates a more balanced load for the grid side. It is also proved that the EMS affects the energy losses. It is revealed that when the proposed fuzzy logic-based EMS is used, the energy cost is reduced by 78% and the peak power is reduced by 70%. Thus, it is seen that the proposed EMS successfully reduces both the cost of energy and the negative impact of EVs on the grid by balancing grid side load. [ABSTRACT FROM AUTHOR]

Today the integration of renewable energy production technologies into power systems brings a new challenge in terms of optimal usage of renewable. It is known that due to the discontinuous nature of renewable energy, the challenge can be associated with the presence of unwanted voltage fluctuations and power losses in power grids. This study performs optimization of the calculation of hosting capacity to determine the maximum amount of renewable energy that can be further expanded. This can be possible by energy storage deployment on the same power grids. The battery storages are expected to have power injection and absorption associated with discharging and charging of energy storages, respectively, to solve voltage fluctuations and power disturbances. Thus, to enhance the probability of distributed generation penetration, it is necessary to have optimal allocation and size during the renewable energy penetration in low-voltage distribution networks. Global optimization as a genetic algorithm (GA) method is used during hosting capacity assessment. The results show that using GA-based optimization, total cost and loss can be reduced by 38.8 and 73%, respectively. The practical results are verified by the IEEE-33 bus system. [ABSTRACT FROM AUTHOR]

This research article considers a microgrid (MG) with various distributed generation sources (DGs) such as microturbine (MT), fuel cell (FC), wind turbine (WT), and solar cell (PV) along with the battery storage system. This article aims to simultaneously minimize operational costs and emissions for a daily schedule, while considering uncertainties such as load demand, market prices, and electricity generated by different renewable energy sources (RESs). This article proposes the slime mould algorithm (SMA) to solve this complex optimization problem. Initially, SMA is tested for three scenarios considering the minimization of operational cost and emission, respectively, as two single objectives. In Scenario A, a reduction of 1.95% in operational cost and 4.57% in emissions is observed compared to the Grasshopper Optimization Algorithm (GOA). In scenarios B and C, SMA reduces operational cost by 0.78% and 2.65%, respectively, while also reducing emissions by 6.27% and 0.28%, respectively, when compared to GOA. The results obtained by other algorithms are worse than those obtained by GOA. This study also showed that SMA effectively considers multi-objective functions by using the weighted sum method and the fuzzy decision-maker to approach the Pareto-optimal solution, achieving a good tradeoff between operational cost and emissions in each scenario. [ABSTRACT FROM AUTHOR]

Two parameters, voltage and frequency, are required for smooth delivery of quality power to consumer. Therefore, in this paper enhancement of frequency and voltage stability of multi microgrid system with a proposed hybrid fuzzy logic controller with two-degree of freedom tilt-integral derivative with filter (2DOFTIDFFUZZY) are analyzed. A novel algorithm known as artificial electric field algorithm (AEFA) technique is employed to obtain optimal controller parameters. Firstly, a two-area system widely used in literature is considered for proper comparison. Efficacy of 2DOFTIDFFUZZY controller is confirmed by comparing it with different controllers for the same system. Further, a multi microgrid system is considered for unified voltage-frequency control. Automatic Voltage Regulator (AVR) effect states a substantial improvement in system dynamics with the proposed controller. Under different unfavorable conditions like source and load variation, dynamic system performances are studied to enlighten robustness of proposed controller. Simulation results elucidate that proposed 2DOFTIDFFUZZY controller optimized with AEFA technique can achieve better system stability in case of any vulnerability. [ABSTRACT FROM AUTHOR]

Renewable energy, sometimes called 'clean energy,' comes from natural processes or constantly renewed sources. For instance, wind and sunlight continue to move and shine, even though their availability depends on time and weather. In recent years, the high penetration of renewable energy sources, especially wind energy, has created new problems in power networks. One of the paramount concerns that we, as renewable energy specialists, must address is the inherent variability and unpredictability associated with the power generation of wind turbines. Using wind energy in microgrids with lower power and voltage levels can make them much more effective because wind energy is naturally variable. This issue specifies the need to perform a possible analysis of micro-grids that use wind energy to generate power. In this paper, the small signal stability of micro-grids will be studied under the influence of production uncertainty from wind energy. For this purpose, the method based on Gram-Charlier expansion is used as a possible numerical analysis method. Also, a method specific to micro-grids will be used to obtain the equations of the system's state, which provides excellent flexibility for modelling new components. [ABSTRACT FROM AUTHOR]

This article studies the effect of new optimization techniques with the aim of finding the best solution using a fuzzy approach for operating an independent microgrid. The power flow technique is used to model and solve the problem of optimal active and reactive power control of distributed generation units (DGs) in an island microgrid (MG), while taking into account the improvement of the voltage profile and the voltage stability index by regulating the droop characteristics of DG sources. The suggested algorithms, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Harmony Search (HS), Hybrid HS-GA, and Teaching-Learning Based Optimization, solve the optimization issue (TLBO). The numerical results of optimization algorithms are compared in terms of best solution, number of iterations, and convergence rate. Regarding multi-objective nature of the problem, the non-dominated sorting method is used to obtain the Pareto front for all algorithms. Finally, the best solution is selected from Pareto solutions using proposed fuzzy approach. Simulation results on the standard IEEE 33-bus network in MATLAB show that the TLBO algorithm has higher efficiency, and faster convergence rate compared to other methods in this study. [ABSTRACT FROM AUTHOR]

With the penetration of DGs in power systems, some restrictions such as economic dispatch in non-dispatchable sources, their control technology and voltage/frequency stability may be challenging. In conventional power systems, the transient stability has been obtained by high-inertia of upstream network in all conditions; however, in modern power grids, the micro grids (MGs) which could operate in both islanded mode and grid-connected mode are taken into account to overcome this issue. AC or DC types of DGs usually connect to the loads through power electronic converters, in which for researchers, this issue has been interesting and applicable to continue. When loads have been changed, the frequency has influenced correspondingly and must not exceed the permissible range. Therefore in this paper, this intention has been studied using conventional droop controller and intelligent sliding surface approach (ISSA) to evaluate the DGs effects on load-frequency control. While two or more areas (as some individual MGs) connect to each other via a transmission line, the voltage/frequency stability and power balancing constrained may be changed, which the ISSA is hired to track the frequency changes. To examine this problem, in this paper there are three MGs operate separately to show if the load may not supply, they should connect to adjacent block. If this important concern has not considered, it may cause global black-out. The simulation results obtained by ISSA compared with droop controller method represent the superiority of proposed technique to achieve best power sharing. [ABSTRACT FROM AUTHOR]

This paper proposes an emended Harris hawk optimizer (EHHO) to solve the mixed energy generation scheduling problem with available resources from hydrothermal, pumped storage hydro, solar, and wind units while minimizing the operating cost, utilizing the available water, and satisfying the other operational constraints. Direct heuristics are applied to solve the constraints imposed on the generation scheduling problem. An optimistic binary optimizer is utilized to commit solar and wind units so that solar and wind units share a certain percentage of power demand to meet the uncertainty of renewable power. The hunting behavior of the hawks for the desired prey inspires the Harris hawk optimizer (HHO) because of its flexibility, sturdiness, and scalability features. Despite HHO giving premature convergence as it tends to trap into the local optima while solving complex non-linear optimization problems. Hence, the local search technique (LST) is employed with the HHO to overcome it. LST also empowers the exploitation phase of HHO. Two practical electric power test systems are successfully solved using the proposed EHHO. On implementing the proposed EHHO, with the penetration of pumped storage hydro units, savings in the cost of thermal units range from 3.90 to 11.44%. Further, with the penetration of wind and solar power, the savings in operating costs of thermal units have increased from 12.15 to 20.19%. The main idea and contribution of the proposed work is the thermal fuel cost reduction using renewable energy sources by utilizing EHHO. The convergence curves and whisker box plots demonstrate the EHHO's robustness while solving the electric power test systems. [ABSTRACT FROM AUTHOR]

Renewable energy resources have increasingly been integrated into electric power grids. Moreover, new challenges constantly arise, showing the need for modernization in monitoring, control, and protection. Transmission and distribution systems have explored the use of phasor measurement units (PMUs) in an attempt to meet these requirements. The decreasing cost of hardware components of PMUs, similar to what occurred with digital relays, is an important factor in terms of their worldwide use in different applications. Low-cost variations of PMUs focused on distribution systems, such as micro- and D-PMUs, are growing in number. Thus, this survey article presents significant and recent research conducted in the field of power systems using PMUs. Firstly, it briefly describes the characteristics of PMUs concerning hardware and phasor estimation algorithms. Afterward, recent studies related to transmission and distribution system protection based on PMUs are presented, such as fault detection and location, stability, and wide-area protection. Finally, the article also provided important guidelines, which certainly can be useful for future research in the years to come. [ABSTRACT FROM AUTHOR]

Integrated energy system (IES) has become significant in the global energy revolution because of its unique advantage of promoting clean energy use and improving energy efficiency. However, its low-carbon operational optimization and capacity allocation challenges have sharply increased because, as its scale continuously grows, its inherent strongly coupled multi energy flow and high nonlinearity characteristics have become more prominent. This study proposes a distributed low-carbon operational optimization mechanism for an IES using integrated demand response (IDR) and ladder carbon trading (LCT). First, a low-carbon IES was modeled as a coupled model between energy operators (EO) that contain combined cooling, heating, and power systems (CCHP) and flexible users (FU) based on the Stackelberg game. Second, the operational optimization model was formulated as a source-load synergistic operation scheduling mechanism based on IDR and the LCT. Finally, the strategy of each stakeholder was obtained using a modified distributed algorithm of genetic algorithm nested quadratic programming (GA-QP). The effectiveness of the proposed model was verified through a case study. The economic benefits for each stakeholder improved, as confirmed through the experimental results. The energy cost of the FU decreased by 4.42%, and the revenue from energy sales of the EO increased by 11.9%; renewable energy was prioritized for consumption, with a consumption rate of 100%; the total output of the core devices for energy cascade utilization increased by 15.6%, significantly improving energy supply efficiency; the total carbon emissions decreased by 8.64%; and power and heat loss rates were 0% and 0.00059%, respectively. Highlights With the rapid development of renewable energy, The integrated energy system relies on its inherent operational advantages of high flexibility, high toughness, and multi energy coupling cascade utilization, demonstrates unique advantages in serving as a clean and renewable energy carrier and adapting to the development of distributed energy The energy cost of flexible users has decreased by 4.42%, and the revenue from energy sales of energy operators has increased by 11.9%; Renewable energy is prioritized for consumption, with a consumption rate of 100%; The total output of the core devices for energy cascade utilization has increased by 15.6%, significantly improving energy supply efficiency; The total carbon emissions decreased by 8.64%, both power and heat loss rates are 0%, the error rate of energy supply is 0.00059% Based on the above research, this paper is established a distributed low-carbon operation optimization model for Integrated energy system coupled the EO that contain CCHP and the FU based on Stackelberg game In view of the gradual deepening of the coupling degree of the IES, the increasingly obvious characteristics of multi-agent, the difficulty of centralized scheduling to protect the interests of each independent entity, and the inadequacy of the system's energy cascade utilization advantages, this paper establishes a bilateral distributed operation optimization model based on Game theory In response to the insufficient utilization of the advantages of multi energy coupling in existing research on IES, insufficient attention paid to the interaction between supply and demand on both sides of the system, and insufficient enthusiasm of users as independent entities to participate in system operation scheduling, this article adopts a integrated demand response as the load side operation scheduling strategy [ABSTRACT FROM AUTHOR]

In this article, a dual third-order generalized integrator-based phase-locked loop (DTOGI-PLL) and an additional degree of freedom-based frequency locked loop (ADF-FLL) control approaches are utilized on a voltage source converter (VSC) to control an isolated microgrid system under different operating conditions. The proposed adaptive approaches provide a fast dynamic response with a precise estimation of frequency, phase angle, and fundamental load current component compared to that of the TOGI-PLL and least mean square (LMS) algorithms. An ADF-FLL allows closed-loop poles to perform more optimally, resulting in faster dynamic performance without compromising filtering capability. Moreover, the proposed adaptive approaches with VSC provide power support, load leveling, harmonic eliminations, and enhance the isolated microgrid's overall power quality during dynamics in load and energy sources. A solar PV (SPV) array with perturb & observe (P&O) MPPT control is interfaced at the DC link of a bidirectional controller (BDC) to draw maximum solar power. Through dc-link voltage control, the BDC with energy storage system (ESS) ensures the appropriate power flow and power equilibrium between energy sources and load. The Simulation and test results show the dynamic performance of the isolated microgrid is examined under different operational scenarios. [ABSTRACT FROM AUTHOR]

This article proposes a novel approach for the optimal coordination of adaptive directional overcurrent relay (ADOCR) for modernized active distribution system (ADS) with microgrid (MG) functions. The goal is to keep the coordinating circumstances while reducing the protective relay operation time, as much as possible. With the proposed round-off mixed-integer particle swarm optimization (ROMI-PSO), the setting parameters are not limited to only relay time multiplier setting (TMS) and pickup current setting (PS) but can include the discrete parameter as relay characteristic curve setting (CS). In the proposed framework, The CS can be both IEC and IEEE standard curves in the proposed framework. Four buses radial system and IEC-benchmark MG system were used in a number of simulation experiments. Comparing the suggested methodology to other current methods revealed that it is more effective at determining the minimum relay operating times and ensuring the proper operation of protections under various fault allocations and operating circumstances. [ABSTRACT FROM AUTHOR]

In order to reduce the system instability caused by credit risk in microgrid transactions in the blockchain, we propose a smart contract microgrid transaction model considering reputation value. Considering the instability caused by credit risk, the reputation factor is introduced to ensure the secure and stable operation of the microgrid energy trading system. The effectiveness of the scheme is verified by comparing traditional electricity trading and the trading with the introduced credit value scheme through simulation experiments. [ABSTRACT FROM AUTHOR]

For achieving more appropriate Total Cost of Electricity (TCE), this work discusses a novel approach for optimizing the design of a micro grid generation system. The studied system consists of Wind-PV generation supported by Lead-Acid storage batteries. Taking into account the in-depth modeling of each element of the system, the optimization task is solved based on Multi-Verse Optimization (MVO) algorithm. This combination is essentially elaborated to meet consumer needs, mitigate high current inrush and ensure continuity of service all day in all conditions. For the first time, we treated the electrical quantities statistically at the level of the BESS capacities and the obtained results are converted into statistical models by using the Monte Carlo Simulation (MCS). The Obtained results show that the best configuration is chosen by MVO approach in accordance with a high penetration of all component of the system. To advance toward the key question of this study, different economic analyzes are elaborated and furthermore the lowest value of the TCE is reached. As results, it can be seen that a significant value of cost saving is well achieved compared to many other approaches used. [ABSTRACT FROM AUTHOR]

Compared to traditional fuel vehicles, electric vehicles (EVs) can use energy efficiently without polluting the environment, which is why environmental experts are pushing the switch to EVs. When a large number of EVs are connected to the present power grid, negative impacts to the power system dynamics may happen. Implantation of demand response (DR) programming may avoid unplanned demand peaks, and support grid voltage. The objective of this article is to minimize the cost of charging the EVs while also utilizing their battery storage capacities to support grid voltage. Price-based DR programming is used to control the EVs' charging. A study of an 8-bus distribution system with three EVs connected during charging/discharging periods, and different scenarios are simulated and compared with the grid response without any EV. New distribution tariffs have been developed to encourage small electricity users toward peak load restriction. The proposed scheduling method is valuable for the utility to estimate the capability of different control strategies for EV charging. It can also be extended to more complicated systems with large number of EVs and hence applied to a real network. MATLAB environment is used to test the suggested control scheme's robustness and efficacy. [ABSTRACT FROM AUTHOR]

Floating photovoltaic (FPV) systems have generated much interest in the literature, due to the potential synergies with existing hydropower reservoirs. This is of particular relevance to Aotearoa New Zealand, where the national grid's heavy reliance on hydro resources has left the country prone to energy shortages during dry years. This paper investigates the potential implications of a 1-gigawatt peak fleet of distributed FPV systems on seven hydro schemes around the country. The results show that an FPV fleet in Aotearoa New Zealand could provide additional electricity generation when the controlled hydropower storage is low. A larger, wider rollout would allow for the reservoirs to replenish after the country's unique winter peak in electricity demand. [ABSTRACT FROM AUTHOR]

This paper deals with three-phase wind-solar-photovoltaic microgrid (WPV-MG), which is capable of performing in grid-tied and island modes of operation under steady state and in DSTATCOM (Distribution Static Compensator) mode, utility-interactive and disconnecting modes under dynamic state. This microgrid (MG) uses a single VSI (Voltage Source Inverter) with reduced power converters and switches. At the DC bus of VSI, a battery bank is used to provide load leveling either at less renewable generation or at light loads. The control of VSI is also able to maintain power balance among various units and provides PQ (Power Quality) improvement in the MG under local nonlinear loads. During no renewable power generation, it works as a DSTATCOM to improve PQ and provides power factor correction and reduces voltage harmonics in PCC (Common Coupling Point) under local linear/nonlinear loads. During abnormal grid conditions, it provides power backup to local consumers under island operation. For this three-phase WPV-MG, a laboratory prototype is developed and its performance under different operations is validated through test results. [ABSTRACT FROM AUTHOR]

This paper investigates the coordination control of large-scale systems (LSSs) with unknown external disturbances. The disturbances exist in the channels from the controllers to the actuators. To compensate the impact of disturbances, an extended observer using the output states is constructed, and an observer-based event-triggered controller is designed to save communication resources. Moreover, based on the event-triggering strategy, a controller using dynamic information estimation is designed, and Zeno behavior is further excluded. Finally, a DC micro-grid model is used for simulations to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This article presents the optimal short-term scheduling of an integrated heat and power microgrid (MG) considering load uncertainty. The MG comprises combined heat and power (CHP) units, a small thermal power plant, a small boiler, an electrical energy storage system and a thermal storage tank, and can further engage in electrical energy transactions with the upstream network regarded as the day-ahead market. Given the uncertain nature of the electrical and thermal load, a novel scenario-based robust optimization framework is developed to overcome the problem uncertainties. The effectiveness of the proposed scenario-based robust scheme against conventional deterministic and stochastic scheduling methods is verified through numerical simulation on a prototype MG. The results suggest that the proposed approach can effectively forecast the uncertainties in MG load and provide the operator with more realistic results to handle the consuming load uncertainty. [ABSTRACT FROM AUTHOR]

Load frequency management (LFM) work has become more difficult due to hybrid power systems. These power systems are responsible for increased power quality issues, exacerbating control problems. Frequency changes and power quality issues are the central concerns caused by these modern power systems. Frequency deviations occur mainly due to variations in generation, consumption, or both, and one of the critical reasons is the low inertia of the modern renewable penetrated hybrid power systems. So, to handle all these issues related to frequency and many more, the need for the best controller arises, which can easily handle any nonlinearities. This article comprehensively analyzes various load frequency control (LFC) architectures in various power system configurations. It focuses on the frequency management features of various power systems, including microgrids that use renewable energy and electric vehicles, with a complete understanding of current development states. Different controllers and tactics underwent comparative investigations. The detailed tabular analysis of frequency control architectures provides a deep insight into the LFC assessment of various power system configurations. The importance of better controllers for LFC planning in complicated power system domains is also emphasized in this work. [ABSTRACT FROM AUTHOR]

Microgrid load flow studies have recently attracted a lot of attention due to the introduction of independent microgrids with decentralized sources. These microgrids can be operated in grid-connected mode as well as in islanded mode. But the islanded mode operation of microgrids has some inherent limitations that cannot be addressed by conventional load flow approaches. Recent research has suggested some modifications to the traditional methods by treating system frequency as a variable parameter and leaving out the concept of slack bus. In this respect, a novel load flow method has been presented for microgrids that operate as droop-regulated distributed generations (DGs) employing distributed slack bus. The Jacobian matrix has been modified, which leads to improved convergence. Further, a comparison of the modified decoupled Newton Raphson and the modified Newton Raphson load flow techniques has been presented. Several case studies for 6-bus and 38-bus test systems, demonstrate the feasibility of the proposed load flow approach. [ABSTRACT FROM AUTHOR]

In this paper a new non-isolated converter with ultra-high voltage gain is presented. The proposed converter utilises coupled inductors, lift circuits and interleaved technique to convert low-level DC voltages to high-level DC voltages without high turn ratios or large duty cycles. Winding-cross-coupled inductors are employed to raise the voltage ratio. The voltage and current stress of semiconductors are reduced, which causes low conduction losses and improves the efficiency. The way the clamp circuit is used limits not only the spike voltages but also effect on the conversion ratio of the proposed converter and make it larger. The main features of this converter are high voltage gain, low voltage stress of semiconductors, low reverse recovery of diodes, low input current ripple, low conduction loss and high efficiency. A prototype of the proposed circuit is constructed in laboratory at 100 W converting 20 V to 450 V. The experimental results are compared with theoretical analysis which verifies them. [ABSTRACT FROM AUTHOR]

Runoff and sediment movement induced by storms have serious economic, environmental, and social impacts around the world. Hydrological models have become useful decision support tools for watershed management. The development of process-based distributed models is complex as it involves a range of disciplines and spans multiple spatial and temporal scales. In this study, a process-based and mesh-distributed model, suitable for both event-based and continuous simulations, is developed. The watershed is conceptualized in three distinct zones: a surface overland region, an unsaturated subsurface zone, and a groundwater zone. Overland flow is governed by the 2D diffusive wave equation with an optional 1D channel network solver; water in the unsaturated zone is modelled through mass conservation and vertical dominance of the processes; and saturated groundwater flow is governed by the 2D Dupuit equation. Soil erosion and transport are governed by the multi-size, non-equilibrium equations incorporating soil entrainment, transport, and deposition. The model is driven by meteorological input, land use and soil type properties. The numerical approach is a generalization of existing models to overcome some current modelling shortcomings. New contributions of the model are highlighted and model verification and validation are reported against benchmark cases. The model is then applied to the Cache Creek watershed in California, USA. Satisfactory agreement with the measured data is obtained with both the runoff flow rate and sediment load. [ABSTRACT FROM AUTHOR]

This paper focuses on an artificial neural network-based fog computing approach for remote end fault super vision in microgrid systems. The work presented here considers an Artificial Neural Network-based adaptive Line-to-Ground (L-G) and Line-to-Line (L-L) fault monitoring system for a standard IEEE 14 bus microgrid. Discrete Wavelet Transformation (DWT)-based study of statistical parameters of the currents going out from the various generator buses is carried out both in healthy and faulty situations. Faults (L-G and L-L) are created at different load buses and also an algorithm is proposed for detecting fault location. The rule set proposed here is unaffected by variations in fault resistance, making it very much suitable for ground fault monitoring. It provided satisfactory results when examined with various unknown cases. A fog computing layer helps in fast execution and fewer data storage requirements in the cloud. This assessment may be expanded for other kinds of faults in a microgrid system. [ABSTRACT FROM AUTHOR]

Owing to the current environmental concerns, the RESs have become popular as the microgrid structures for power generation. However, due to capricious weather and loading conditions, the generated power and thereby the microgrid frequency get adversely affected. This instant study puts forth the control strategy for the mitigation of frequency excursions, arising out of step load disturbance, in AC microgrid through adaptive network fuzzy inference system (ANFIS) scheduled fractional ordered proportional-integral-derivative (PID) control optimally tuned with a multiverse optimizer. The control strategy proposition is compared against multi-verse optimized PID and fractional order PI controls. Furthermore, the study investigates as to how EV affects in stabilizing the system frequency in the backdrop of a load disturbance. For a more realistic assessment, the proposition is assessed in the face of system nonlinearities and random load perturbations also to establish its robust and stable behavior. The results prove the efficacy of the multi-verse optimized ANFIS scheduled fractional ordered PID controller. Simulations are executed using MATLAB® software. The results are also validated by experimental studies employing a hardware-in-loop configuration on the OPAL-RT real-time simulator. [ABSTRACT FROM AUTHOR]

This study proposes a cooperative fuzzy optimal scheduling method for multi-energy hubs considering carbon trading. The traditional integrated energy system has difficulty integrating system economy and environment, as well as allocating benefits to multiple subjects. To address the problem, a multi-energy hub cooperative fuzzy optimization dispatching model, considering carbon trading, was developed to promote low carbon emissions and high renewable energy penetration. The model optimizes the dispatch of the integrated energy system while reducing CO2 emissions, also reducing the impact of renewable energy output uncertainty. Moreover, decision-makers can adjust the risk tolerance of the system according to their own risk appetite. To ensure a fair method of benefit distribution to achieve overall optimum through cooperation, this study introduced and examined three methods of secondary benefit distribution in cooperative games: Shapley value method, Kernel method, and Equal DP (Disruption Propensity) method. Finally, the model's economic, environmental protection, and stability were verified by an example of cooperative optimal scheduling and collaborative residual benefit secondary allocation of three energy hubs. Moreover it was found that in the secondary allocation,the Equal DP method was more stable and acceptable to all subjects compared to the other two methods. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable sources, the cogeneration integrated energy system (CIES) is much more efficient and flexible to achieve various types of energy supply for remote loads. Due to uncertainties in both sources and loads, the CIES should be carefully planned and optimised to keep it working at the suitable operation condition. Therefore, this paper proposes a multi-objective capacity allocation optimisation method for the islanded CIES, which is composed of renewable energy generation unit, electric-thermal conversion unit, electric storage, thermal storage and various loads. Firstly, three optimisation objectives are selected as typical indexes, including economic cost, solar energy utilisation, and life cycle carbon emission. An improved radar chart evaluation (RCE) solution is subsequently utilised for calculating a fitness function and building the multi-objective optimisation model. Besides, several compulsory constraints are supplemented to ensure the overall CIES performance satisfy energy supply requirement. As a mature and widely accepted algorithm, a genetic algorithm is chosen as an example to obtain the optimal capacity allocation scheme. Case study of an islanded CIES is provided on the basis of real data from the remote region of northwestern China, which successfully verifies the effectiveness of the proposed optimal capacity allocation method. [ABSTRACT FROM AUTHOR]

DC micro-grids are gaining more attention and becoming more predominant due to its robustness and ease of control. To confront the stability issues of the DC Micro-grid system, a model free intellectual Control approach is proposed and recognized with a deep Q-learning controller to influence the functions of DC–DC boost converter. This self-reliant model of deep Q-learning controller establish a suitable communication link among the agent-environment by applying the reward/penalty algorithm for attaining the stable output voltage with its training statistics. Upon learning the state of agent, an optimal proposal is defined by the controller to attain the stabilized voltage at the output for all possible operating scenarios. The simulation and experimental studies validate the performance of the controller with higher accuracy, reduced overshoot, and faster settling time upon the transient states. [ABSTRACT FROM AUTHOR]

The non-linearly deterministic fault current characteristics with low current magnitude of high impedance fault (HIF) have thrown an inevitable challenge to the microgrid protection system. Moreover, differentiating HIF from non-fault events is also a challenging task under different operating scenarios of the microgrid. In order to address this, a simple and fast protection algorithm, based on Empirical Mode Decomposition (EMD) in conjugation with Teager-Kaiser Energy Operator (TKEO) is proposed in this article. This scheme process the current signals retrieved from both ends of the line through EMD, and subsequently, the current signal energy is extracted by TKEO. The incidence of a fault is affirmed by measuring the energy difference signal and estimation of entropy. By employing the second layer algorithm using entropy analysis, the non-fault disturbances, such as capacitor and load switching, temporary overloading, and DG outage, have been effectively distinguished from HIFs. The proposed technique is tested for grid-tied and autonomous microgrid operation modes with several HIF and non-fault cases. It is inferred that the proposed technique can detect and differentiate HIFs from other system perturbations seamlessly in less time. Compared to other methods, this scheme has procured excellent accuracy and reliability, and is also not affected by the dynamic current behavior of the microgrid. The PSCAD-EMTDC software is used to build and simulate the test system, and the MATLAB programming interface is utilized to develop and test the suggested method. [ABSTRACT FROM AUTHOR]

The unpredictability of solar and wind energy sources affects contemporary power networks and adds frequency variations. An appropriate intelligent controller is necessary to balance electricity between generation and demand. As a result, in this research, we present a sine-cosine adaptive improved equilibrium optimization (SCaIEO) method tuned to Adaptive Type 2 Fuzzy PID Controller (AT2FPID) for frequency management of cutting-edge power systems. The efficiency of the SCaIEO approach is assessed by comparing it to the original equilibrium optimization (EO) and other comparable algorithms for the test function. Moreover, engineering applications of the SCaIEO technique are carried out by constructing an AT2FPID controller to manage the frequency of power systems that include renewable energy and dispersed sources. First, we show that SCaIEO outperforms EO, Particle Swarm Optimization, Genetic Algorithm, Moth Flame Optimization, and Gravitational Search Algorithm in the PID controller (Gravity Search Algorithm). The AT2FPID is next evaluated, and the SCaIEO AT2FPID controller's dominance is proven by equating the outcome to the original, PID Type 1 Fuzzy PID, Type 2 Fuzzy PID controller. [ABSTRACT FROM AUTHOR]