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This paper presents a control strategy for a photovoltaic (PV)-based distributed generator (DG) to enhance the frequency regulation of a microgrid. A microgrid is a small-scale power system that integrates distributed energy sources and interconnected loads, which can operate independently or in parallel with the main grid. However, the high integration of DGs poses stability problems, particularly the frequency regulation under contingencies in the microgrid. For example, the shutdown of one generating unit or the reduction of system inertia owing to the high integration of converter-based DGs will increase the risk of instability of the microgrid and the main grid. Therefore, maintaining and increasing the frequency regulation capability is crucial in microgrid operation. This paper focuses on developing a controller for PV DG based on virtual inertia control combined with the conventional control method of a converter in order to regulate the microgrid frequency under contingency. The proposed control strategy for a PV-based DG is then verified through simulation of the 14-bus microgrid model using MATLAB/Simulink, showing regulation in frequency under island mode operation of the microgrid. In addition, comparisons between the original and proposed control strategy microgrid model are then carried out. [ABSTRACT FROM AUTHOR]

The conventional overcurrent protection is ineffective in isolating faults in microgrids due to the low fault current levels contributed by inverter-interfaced distributed generations (IIDGs). To extend the microgrid protection methods, the distance protection is considered as a common alternative to detect faults. However, the fault-ride-through (FRT) requirements and the high impedance fault (HIF) detection challenge the application of the conventional distance protection. To solve the two problems, a new inverse-time distance (ITD) protection for medium-voltage AC microgrids was proposed in this paper. The primary ITD protection was designed to meet the FRT requirements, and the backup protection was coordinated to isolate faults. The fault resistance and distributed generation infeed effect on the measured impedance were mitigated by a communication-assisted method proposed in this paper. Owing to the shorter communication channel time in distribution feeders and lower communication investments without a synchronous clock, the proposed method can be used to improve the ITD protection performance. An 11 kV AC microgrid was simulated with different fault types in the islanded mode and grid-connected mode. Time domain simulation verified the effectiveness of the proposed ITD protection. [ABSTRACT FROM AUTHOR]

Transformation of the distribution network, integrated with Distributed Energy Resources (DER), into a MicroGrid (MG) is an attractive approach in improving the reliability and resilience of the network. In this paper, by introducing the important and necessary aspects of the MG formation problem, an effective methodology for transformation of a distribution network into the MG is proposed. In the presented methodology, optimal allocation of DERs within MG is obtained by optimization of power losses and voltage stabilization along feeders. Moreover, the short-circuit constraints are also taken into account simultaneously while allocating DERs. The reason for the latter is that the integration of DERs alters the amplitude and direction of the short circuit current, which can cause the overall fault current to exceed the designed fault-level capability of the circuit breakers. This issue may put CBs at risk of failure and degrading MG resilience. In this regard, resilience-oriented evaluation of the MG formation, from the perspective of considering short circuit level and coordination of OCRs, is also performed. Another advantage of short circuit studies is devising complete and economic overcurrent-based protection plan for MG in both operating modes (grid-connected and islanded). It should be pointed out that adopting an appropriate control routine is a key element in the MG formation. Hence, a control strategy, based on Energy Storage System (ESS) utilization, is suggested in this paper. This feature, which is another contribution of the paper, will guarantee seamless transition to island mode and stable operation of the MG. The proposed formulation of the MG formation is solved using a modified multi-objective PSO and the Pareto-optimal set is obtained. However, since the optimal solutions are nondominated to each other, a Fuzzy Satisfaction Method (FSM) is utilized to find the optimal solution among the Pareto-set. Utilization of the proposed methodology leads to the successful transformation of the distribution network into a MG, which not only considers the load flow constraints but also preserves the optimal coordination of the protective relays and resilience of the MG. In addition, seamless transition and stable operation of the MG in island mode is achieved. [ABSTRACT FROM AUTHOR]

In this paper, DR programs are integrated with the unit commitment economic dispatch model for a single day to lower total operating costs for an insular microgrid. The proposed model takes into account the forecasting errors associated with wind, solar, and load demands. A new combined DR program is presented to enhance microgrid operation and financial effectiveness, benefiting microgrid consumers. The price elasticity and consumer profit are the foundation for DR modeling. The optimization problem is developed as mixed‐integer nonlinear programming (MINLP) and solved using GAMS software. For the case study, an insular microgrid consisting of two microturbines, a wind turbine, solar photovoltaic, and battery storage is considered. Optimization is carried out under both with and without the DR program. The outcomes show that by implementing TOU and DLC DR programs, the operating cost is reduced by 13.55% and 9.68%, respectively. While consumers experience a financial loss in TOU‐DR, they earn profit in DLC‐DR. Therefore, a combination of the two, i.e., TOU + DLC DR, is proposed, reducing operating costs by 10.73% while increasing profit for users. The suggested approach benefits the microgrid operator as well as its users, encouraging the construction and operation of insular microgrids in rural or isolated areas. [ABSTRACT FROM AUTHOR]

Driven by the carbon peaking and carbon neutrality target, the large-scale grid-connected of renewable energy such as wind and solar has increased, and the volatility and randomness have posed new challenges to the stability of the power grid frequency. In this case, battery energy storage is a grid auxiliary resource with fast response and adjustable parameters, which can provide frequency support for the grid system in a short period. This paper studies the frequency regulation strategy of large-scale battery energy storage in the power grid system from the perspectives of battery energy storage, battery energy storage station, and battery energy storage system, respectively. First of all, the droop control based on logistic function and the virtual inertia control based on piecewise function are proposed for battery energy storage frequency regulation, which improves the performance of battery energy storage power output effectively. Second, the weighting factor is set according to the current battery charge to achieve the most optimal distribution of frequency regulation power for each battery pack in the battery energy storage station. In the end, a control framework for large-scale battery energy storage systems jointly with thermal power units to participate in system frequency regulation is constructed, and the proposed frequency regulation strategy is studied and analyzed in the EPRI-36 node model. The results of the study show that the proposed battery frequency regulation control strategies can quickly respond to system frequency changes at the beginning of grid system frequency fluctuations, which improves the stability of the new power system frequency including battery energy storage. In addition, this paper also provides a certain reference for the construction of the new power system dispatching that integrates "Generation-Grid-Load-Storage" in the future. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) are becoming more popular globally as a reaction to critical energy concerns. Modern energy management technologies are used to maximize their efficiency while preserving the reliability of the grid. A hybrid energy storage system (HESS) connects to the DC microgrid through the bidirectional converter, allowing energy to be transferred among the battery and supercapacitor (SC). In this paper, a fuzzy logic control (FLC) technique is developed for PV-based DC microgrid systems that use both batteries and SCs. The proposed method uses the unbalanced energy from the battery pack to enhance the overall effectiveness of the HESS. The FLC approach is performed to validate under conditions of variable irradiance using MATLAB Simulink. When sudden changes in irradiance occur, the proposed FLC brings the voltage back to the desired level in terms of transient response like 33 ms settling times and 19% overshoot values. The results exhibit that the proposed method is more efficient in terms of time response, power output, increasing battery life, and ensuring a continuous supply of the PV system. [ABSTRACT FROM AUTHOR]

This paper presents an efficient energy management scheme for an islanded DC microgrid included with vanadium redox flow battery (VRFB) with supercapacitor based hybrid energy storage system for a 3-phase variable load profile. The analysed islanded DC microgrid system is composed with sources such as lithium-ion batteries, fuel cell, supercapacitors, and vanadium redox flow battery (VRFB), along with involved DC/DC (buck, boost, and bidirectional) and DC/DC converters. In this paper, an adaptive proportional plus integral (PI) control based energy management scheme is implemented. The main theme on which the performance of this system is analysed is the state of charge of energy storage devices (VRFB, supercapacitor, and Li-ion battery) and the overall efficiency of system. The voltage spikes due to sudden change in load have been regulated by using supercapacitor and Li-ion battery with VRFB. To minimize the fuel consumption, an adaptive proportional plus integral control strategy is being used. In this paper the modelling and simulations are carried out by using MATLAB/SIMULINK software to demonstrate minimizing the effect of load fluctuations with the help of supercapacitor and batteries by synchronizing it to the DC microgrid. [ABSTRACT FROM AUTHOR]

To improve the voltage and frequency stability and suppress the voltage harmonics of a microgrid, a virtual synchronous generator (VSG) control strategy based on the harmonic current bypass control is proposed in this paper. First, a robust voltage control link is added to the virtual excitation component of the traditional VSG control strategy to reduce the amplitude error of the inverter's driving voltage caused by the reactive power-voltage droop coefficient. A harmonic current bypass control link is then used to adjust the inverter's voltage signal to reduce the total harmonic distortion of the microgrid. Finally, a simulation model of the microgrid is established using the MATLAB/Simulink software, and its stability is analyzed. In addition, the proposed control strategy is also compared with the traditional droop control and traditional VSG control strategies, focusing on the frequency instability and harmonic suppression performance of the microgrid. The numerical simulation results verified the effectiveness and superiority of the improved VSG control strategy proposed in this paper. [ABSTRACT FROM AUTHOR]

The efficiency of on-site consumption of new energy and the economy of dispatching strategy for that in modern microgrids are increasingly concerning, which are closely related to the microgrid control model with source-load uncertainty. To this end, this paper proposes the multiagent hierarchical IQ (λ)-HDQC regulation strategy to realize the source-load-storage-charging collaborative control of the microgrid model with high-permeable new energy. The first layer adopts the IQ (λ) strategy, which avoids the overestimation and underestimation problems of traditional reinforcement learning by the coupled estimation method. The second layer adopts the HDQC allocation strategy, which solves the problem of low utilization of new energy in the proportional allocation method and improves the adaptability of the regulation strategy in the complex stochastic environment. The interaction of the two-layer strategies realizes the source-network-load-storage-charge global dynamic interactive regulation of microgrids. Indicators of energy efficiency are constructed in this paper to measure the simulation results. And the superiority of the proposed strategy is verified through the simulation results of the microgrid system. [ABSTRACT FROM AUTHOR]

In order to optimize the economy and environmental protection of microgrid, this paper establishes a demand response model based on comprehensive satisfaction, combines the advantages of the classical multiobjective particle swarm algorithm and multiobjective firefly algorithm, and proposes a hybrid particle swarm optimization and firefly algorithm (HPSOFA) to solve the joint economic and environmental dispatch problem of microgrid and improve the wind and light consumption capacity. The proposed improved algorithm introduces a random perturbation term and adaptive learning coefficients, and the algorithm is selected by the dominance relationship of individuals, which improves the diversity of populations and increases the possibility of the algorithm to solve the global optimum. The proposed algorithm is used to solve the test functions of different dimensions, and the results show that the Pareto front of HPSOFA has better distribution and accuracy, which verifies the effectiveness of the proposed algorithm. Simulation analysis is carried out using power data from a region of Liaoning Province, China, and experiments conclude that the operating cost and environmental cost of HPSOFA are significantly lower than other algorithms. [ABSTRACT FROM AUTHOR]

Solar photovoltaics are considered the practical solution to energy access and climate change issues, especially in tropical countries that receive relatively more sunlight throughout the year. However, questions arise on the reliability of these systems in providing sufficient supply to meet the users' electricity needs. This paper looks at the reliability of a solar project installed on two rooftops on an off-grid island in Cebu, Philippines, that provides increased electricity access to 11 households. PVSyst and HOMER Pro software analyzed solar PV systems performance and techno-economics. The simulations yielded the annual mean values of reference yield, array yield, final yield, array capture loss, system loss, performance ratio, and capacity factor are 5.66 kWh/m2/day, 3.51 kWh/kWp/day, 3.23 kWh/kWp/day, 2.15, 0.278, 57.10%, and 18.96%, respectively. The peak PV resource of 3.30 kWp/day can supply the 1.66 kWp/day of the consumer's electrical demand. It was concluded that the current installation could supply the electrical load demand of the residents; however, consideration for the potential increase in demand must be in place. While renewable energy sources are relevant in achieving 100% electrification in rural communities, their ability to address the energy demands of the users must be carefully considered in planning and design. [ABSTRACT FROM AUTHOR]

Load forecasting is an integral part of the energy study unit to schedule the generating unit by the load demand. Many studies were conducted on load forecasting based on real power demand; however, very few papers were published on reactive power demand. In this research work, an attempt has been made to predict the requirement of reactive power as a function of demand for real power. Household loads are considered for evaluating the demand for reactive power as a critical load. The attempt has been made based on the data collected from the laboratory experimental setup for one year. The load forecasting requires time series analysis of the data set along with error minimization between predicted values and actual value; therefore, the global flower pollination algorithm along with Holt-Winters' exponential model has been used to predict the reactive power. Autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) models have been used as benchmarking models to evaluate the effectiveness of the model under various conditions. Python-developed model has been used to predict the demand for reactive power, and a MATLAB model has been developed to optimize the cost function. A detailed comparative analysis of the proposed model along with some well-established optimized models such as GA, PSO, and FPA has been presented related to evening peak demand for a microgrid architecture in Conclusion. The analysis includes median values of different quantities such as nMBE, nMAE, nRMSE, and RMSE. Normalized MBE, indicating underestimation and overestimation, is negative for ARIMA but 0.42 for HW-GFPA during validation and 0.43 for the testing data set. Normalized RMSE, measuring the variance between actual and forecasted values, is lowest at 0.803 for proposed HW-GFPA during validation and 0.799 for testing. [ABSTRACT FROM AUTHOR]

This paper proposes a load shedding model for the island microgrid based on the ranking of loads and the power stability index (PSI). Loads are ranked based on the improved analytic hierarchy process (AHP) algorithm. Real-time measurement systems have the function of collecting data for very important, important, and less-important loads at each bus load. From this data, the improved AHP method is applied to rank the loads. The advantage of this method is that the subjectivity is eliminated and not depending on the expertise of the system operator when implementing the traditional AHP method. Besides, the minimum amount of load shedding power is calculated, taking into account both primary and secondary control methods. The objective is to minimize the impact on power consumers and ensure that the frequency returns to an acceptable range. In addition, when implementing load shedding, voltage quality, and stability are considered. The PSI serves as a crucial parameter for assessing the voltage stability of microgrid buses. This index is combined with load ranking weights to obtain combined weights for the load shedding plan. Consequently, the proposed load shedding plan prioritizes minimizing damage to customers, improving voltage quality and stability, and ensuring frequency is within permissible limits. The 16-bus microgrid system is applied to compare with traditional methods and to prove the efficiency of the suggested technique. [ABSTRACT FROM AUTHOR]

In recent years, a sharp increase in integration of renewable energy sources (RESs) in power system network has been observed. High penetration of RES interfaced with power electronics converters-inverters with reduced or no inherent inertia compromises modern power system's overall stability. Due to low inertia, voltage and frequency deviations far off the allowable threshold occur. To overcome this challenge, an adaptive inertia control strategy based on optimization technique is proposed. The improved particle swarm optimization (PSO) and genetic algorithm (GA) optimization techniques-based PID controller has been used to generate the appropriate virtual inertia coefficient for effective emulation of inertia in the presence of energy storage system. The conventional PSO suffers local optima stagnation, resulting in premature convergence during searching process in order to achieve global and local position. To address this issue, the velocity update equation was modified on inertia weight (w) using an additional exponential term with linear decreasing inertia weight PSO (LDIW-PSO). In this paper, exponential power is taken strategically instead of squaring it in order to reduce the number of iterations for faster convergence. Finally, a microgrid based on wind and solar energy is simulated using MATLAB/Simulink where three cases, 2% disturbance, 3% disturbance, and 4% disturbance, have been considered. Here, the evaluation of proposed system is carried out based on four main performance indices (ITAE, IAE, ISE, and ITSE). Furthermore, validation was done through hardware prototype to get experimental results in real time. The results from MATLAB simulation and experimental setup are in sync. [ABSTRACT FROM AUTHOR]

With the increase in renewable energy, improving the utilization rate of renewable energy is of great practical significance. The microgrid has been proved effective in addressing this issue. As a flexible load, electric vehicles are connected to the grid on a large scale, which will have an impact on the grid. In order to solve this problem, this paper proposes a microgrid energy management model for electric vehicle charging stations, which takes into account the economics of microgrid operation and the stability of grid operation. Subsequently, this paper proposes an evolutionary multiobjective optimization algorithm to deal with constraints. Finally, this paper verifies the effectiveness of the proposed model and algorithm through experiments. [ABSTRACT FROM AUTHOR]

In this paper, the optimal operation of microgrids (MGs) with thermal blocks, distributed generations (DGs), storage systems, and responsive loads is presented to achieve optimal scheduling of active, reactive, and thermal power of the mentioned elements in the day-ahead (DA) reactive power and energy market environment. The thermal block has a combined heat and power (CHP) system, a boiler, and thermally responsive loads. This scheme minimizes the difference between the total operating costs of the MG and power sources and the total revenue gained from the sale of energy and reactive power of the mentioned elements in the markets located in the MG. It is constrained by the AC power flow equations, network operation constraints, and the operating model of these elements. Furthermore, this scheme is subject to the uncertainties of energy price, load, and renewable power. In this paper, to access the optimal resistant solution against the maximum prediction error associated with the mentioned uncertainties, a robust model based on information gap decision theory (IGDT) is used. Finally, by implementing the proposed scheme on a 119-bus radial MG, the obtained numerical results confirm the ability of the scheme to simultaneously improve the economic and operational situation of the MG. The proposed scheme succeeded in improving energy cost, energy loss, voltage drop, and power factor of the distribution substation by roughly 101%, 44%, 41%, and 16% compared to power flow studies, even in the worst-case scenario caused by uncertainties. [ABSTRACT FROM AUTHOR]

This paper proposes the design of a controller using the artificial neural network (ANN) for a solar photovoltaic (PV)-fed cascaded multilevel inverter (CMLI) to enhance the power quality. The objective of this presented ANN controller is to obtain a maximum output voltage with no filter components. This paper also investigates and eliminates the voltage harmonics that occurred in a solar-fed cascaded 3-stage inverter using various techniques such as pulse width modulation (PWM), digital logic control (DLC), fuzzy logic controller (FLC), and ANN, and the results are compared. Based on the results, the proposed ANN-based controller efficiently reduces harmonics and improves the power quality. This is achieved by solving the harmonic equations and thereby changing the switching angles of each semiconductor to a minimum value. The ANN is trained by a dataset consisting of varying input voltage and switching angles. The simulation was performed using MATLAB/Simulink for different types of controllers like PWM, DLC, FLC, and ANN for the 3-stage inverter. The simulated results are compared with the results obtained from a 3 kWp photovoltaic plant connected to the CMLI. Finally, on the basis of performance analysis, it was confirmed that the ANN-based controller effectively eliminates harmonics and improves the power quality. [ABSTRACT FROM AUTHOR]

In order to realize rapid service-oriented construction of resources and efficient supply-demand matching in avionics systems, an ontology-based resource modeling and matching framework in avionics systems is proposed. First, this paper constructs a hierarchical resource organization framework based on virtual resource pool, covering dispersed and heterogeneous avionics resources and shielding the differences and diversity of resources; next, introduces ontology technology into resource modeling and encapsulates resources as services using the idea of "resource as a service," facilitating the access and control of resources; in addition, proposes a matching framework which takes into account the energy consumption, in which it firstly tries to find a list of resources which can meet task requirements by a stepwise intelligent matching strategy based on semantic similarity, and the resources in the list will then be sorted according to the energy consumption. Finally, a radar detection case is taken to demonstrate that the proposed model and method are feasible and effective. [ABSTRACT FROM AUTHOR]

DC microgrids have been quite popular in recent times. The operational challenges like control and energy management of the renewable-driven standalone DC microgrids have been an interest of research. This paper presents a bidirectional quasi Z-source DC-DC converter (BQZSDC). This converter topology has been developed based on a conventional buck-boost type bidirectional converter, and it interfaces the storage system and the common DC bus. The challenge, however, lies in effectively managing the uncertain renewable energy sources and the storage system and catering for the loads simultaneously. An effective control strategy is needed for that energy management and to achieve various microgrid objectives. This paper deals with one such effective control strategy implemented for BQZSDC. That is, the fixed-frequency double-integral sliding mode control (FF-DISMC) controls the converter to regulate the DC bus voltage and battery current. A detailed analysis of the controller is conducted, and its performance is evaluated for both charging (buck) and discharging (boost) modes. Simulations have been performed in MATLAB, showing that the controller performs satisfactorily in achieving the objectives of voltage regulation and battery current regulation. Finally, the performance of the proposed controller is validated with the hardware setup. [ABSTRACT FROM AUTHOR]

The increasing concerns about greenhouse gas emissions have made it necessary to incorporate environmental constraints in the operation of power systems. The CO2 emission-constrained short-term unit commitment problem (CSCUCP) is a multiobjective optimization problem that involves minimizing both the cost of operation and the CO2 emissions. This paper proposes an integer-coded shuffled frog-leaping algorithm (SFLA) to minimize both total CO2 emissions and operating costs for the unit commitment problem (UCP) over a one-day scheduling period. The SFLA is inspired by the natural food-searching behavior of frogs. The proposed method aims to determine the optimal start-up and shut-down times for generating units to meet fluctuating loads while minimizing operating costs and CO2 emissions. The method takes into account fuel costs, start-up and shut-down costs, and maintenance costs while satisfying various constraints. The study uses the IEEE 39 bus with a 10-unit test system, and the results are related to conventional methods. The proposed method consistently produces lower CO2 emissions and total operating costs compared to the existing methods. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources into modern electric grids has led to a rise in uncertain factors that must be managed to maintain voltage security during reactive power optimization (RPO). Traditional deterministic RPO methods fail to account for these uncertainties, which can result in power grid security issues such as voltage violations. To address these challenges, this paper proposes a data-driven interval-based reactive power optimization method (IRPOM). The IRPOM represents the uncertainties associated with renewable power generation and load demands as intervals within the RPO problem formulation. The proposed method uses an improved particle swarm optimization algorithm to solve the RPO problem. In each iteration, the uncertain power flow is solved using the optimizing-scenarios method- (OSM-) based interval power flow (IPF) algorithm. This approach calculates the real power losses and checks whether state quantities, including voltage, power flow, and generator output, exceed their limits. Furthermore, a data-driven modeling approach is introduced to reduce the conservativeness of the IRPOM solutions. The effectiveness of the proposed method is demonstrated through detailed computational analysis on a modified IEEE 30-bus system. The results show that the proposed approach ensures economic efficiency while maintaining a low bus voltage threshold crossing probability close to zero. [ABSTRACT FROM AUTHOR]

The global demand of electrical power has increased enormously due to various reasons such as fast-changing and challenging technologies, climatic change, economic growth, and lifestyle of mankind. Due to global warming issues and the depletion of conventional sources of power generation, the utilization of renewable energy sources has drastically increased. So, many challenges exist in integrating the microgrid with AC grid and load. The alternative arm converter (AAC) is among the most innovative converter topologies used in high voltage direct current (HVDC) applications. This research work presents a new control strategy to generate pulses to trigger the switches in the AAC in a proper sequence to obtain a smooth output waveform. The AAC output is controlled by implementing various controllers such as the proportional-integral-derivative (PID) controller, fractional order PID (FOPID) controller, and FOPID controller tuned by metaheuristic algorithm-bacterial foraging optimization technique (BFOT). Also, a comparative analysis is performed based on the spectral analysis of the output voltage obtained. In comparison to other controllers, the FOPID controller optimized by the bacterial foraging optimization technique (BFOT) produced the least total harmonic distortion (THD) of the AAC output voltage. In addition, this paper also discusses about the performance and analysis on the design of an energy management system (EMS) to optimally utilize the energy sources such as PV system, wind system, and battery based on their availability which feed the AAC. The energy management system controls the entire integrated system in association with an integrated CUK-SEPIC converter to fulfil the load demand at the point of common coupling, either from the microgrid or the AC grid. A nine-level AAC model is designed integrating the microgrid, grid, and industrial loads with an energy management system using MATLAB/Simulink. The performance parameters of the entire model are analysed at every stage to provide stabilized output to meet the load demand. [ABSTRACT FROM AUTHOR]

Considering the increasing penetration of renewable energy sources (RESs) into power grids, adopting efficient energy management strategies is vital to mitigate the uncertainty issues resulting from the intermittent nature of their output power. This paper aims to resolve the energy management problem by presenting an adaptive robust optimization (ARO) model in which uncertainties associated with solar and wind powers, consumer demand, and electricity prices are considered. The proposed model comprises three stages, one master and two subproblems, using both linearized and convexified power flow equations. Meanwhile, a new optimization-based bound tightening (OBBT) method is also presented to strengthen the relaxation. The proposed solution method solves the microgrid (MG) operation management problem with high accuracy due to considering the convexification method in a reasonable computational time and reducing operational costs compared to conventional models. The numerical results indicate the benefits of the proposed ARO and solution approach over traditional methods. [ABSTRACT FROM AUTHOR]

In synchronous machines, electromechanical swinging can be damped by parametric control of the excitation current. This is possible only in case the excitation time constant is much smaller than the mechanical constant of the machine. The method described in this paper is effective for damping oscillations caused by oscillations in the grid frequency, grid voltage, and mechanical torque fluctuations. The method is based on the Lyapunov stability theory and demonstrated on a real synchronous machine. This machine operates as a noninterruptible backup power system. The original power fluctuations were up to 50% of the nominal power of the machine. With the described control, a sevenfold increase in the damping of fluctuations caused by grid frequency variations has been achieved. [ABSTRACT FROM AUTHOR]

In order to manage the electricity consumption information of microgrid users, the reliability of electricity information collection is studied in this paper. The normal communication between the acquisition terminal and the smart meter is a key factor affecting the accurate collection of power information; it is the basis for ensuring the operation of the microgrid as well. In order to improve the reliability of the low power line communication between the acquisition terminal and smart meters, this article first uses the static networking method to layer the smart meters and select relays from them and then select the optimal communication path based on integrating communication quality and relay forwarding number dynamically, which could avoid the signal conflict problem caused by simultaneous communication. Finally, by analyzing the influence of the time-varying power line channel on the smart meter communication, a method based on the integrated communication quality and the relay number to consider the time variability of low power line communication is proposed. Choosing the optimal path of the smart meters when the communication path is abnormal can not only establish a new communication path for communication in time, but also avoid communication failures caused by the time-varying channel. Through MATLAB simulation, the time-varying dynamic network of the power line channel is introduced in this paper, which improves the reliability of the smart meter communication and has certain guiding significance for the actual smart meter network construction in microgrid. [ABSTRACT FROM AUTHOR]

In order to improve the operation optimization effect of the microgrid system, this paper combines the intelligent system model to construct the operation optimization model of the microgrid system. Moreover, this paper conducts a quantitative calculation and research on the maximum compensation capacity of the active power filter device and proposes a harmonic power adaptive compensation control strategy in the case of the limited output capacity of the APF device. In addition, this paper adopts the method of extracting the harmonic current command in the synchronous coordinate system based on the band-pass filter composed of SOGI and combines the RDFT algorithm to determine the number of harmonics that can be compensated and the degree of compensation for the harmonic number. Finally, this paper combines simulation experiments and comprehensive evaluation to study and verify that the operation optimization effect of the microgrid system based on the intelligent system model is better and can effectively improve the operation effect of the microgrid system. [ABSTRACT FROM AUTHOR]

As a key means to elevate low-carbon energy transformation in China, multi-energy microgrid accelerates the construction of new power systems. In order to scientifically evaluate the benefits of multi-energy microgrids, we proposed a benefit evaluation index system from the dimensions of economy, reliability, low carbonization, and intelligence. Considering the relationship between the evaluation indicators, this paper innovatively proposed a multi-energy microgrid benefit evaluation model based on AHP-VWT-MEEM. In addition, this paper selected different multi-energy microgrid demonstration projects in the China Southern Power Grid Company Limited's region for example analysis. In the example analysis, through the comparison of the comprehensive benefits of the three projects, the comprehensive benefit of the project 3 is the best. On this basis, from the specific configuration plan of Project 3, it can be concluded that the configuration of larger capacity thermal storage can realize the "thermal decoupling" of cogeneration, thereby improving the flexible adjustment capability of the demonstration area, promoting the consumption of renewable energy, and obtaining more considerable comprehensive benefits. At the same time, it is pointed out that the comprehensive benefit evaluation result can be improved by appropriately reducing the investment cost of project 3. [ABSTRACT FROM AUTHOR]

Nowadays, the power demand is increasing day by day due to the growth of the population and industries. The conventional power plant alone is incompetent to meet the consumer demand due to environmental concerns. In this present situation, the essential thing is to be find an alternate way to meet the consumer demand. In present days most of the developed countries concentrate to develop alternative resources and invest huge money for its research and development activities. Most renewable energy sources are naturally friendly sources such as wind, solar, fuel cell, and hydro/water sources. The results of power generation using renewable energy sources only depend on the availability of the resources. The availability of renewable energy sources throughout the day is variable due to fluctuations in the natural resources. This research work discusses two major renewable energy power generating sources: photovoltaic (PV) cell and fuel cell. Both of them provide foundations for power generation, so they are very popular because of their impressive performance mechanisms. The mentioned renewable energy-based power generating systems are static devices, so the power losses are generally ignorable as compared to line losses in the main grid. The PV and fuel cell (FC) power systems need a controller for maximum power generation during fluctuations in the input resources. Based on the investigation report, an algorithm is proposed for an advanced maximum power point tracking (MPPT) controller. This paper proposes a deep neural network- (DNN-) based MPPT algorithm, which has been simulated using MATLAB both for PV and for FC. The main purpose behind this paper has been to develop the latest DNN controller for improving the output power quality that is generated using a hybrid PV and fuel cell system. After developing and simulating the proposed system, we performed the analysis in different possible operating conditions. Finally, we evaluated the simulation outcomes based on IEEE 1547 and 519 standards to prove the system's effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient energy management system (EMS) for industrial microgrids (MGs). Many industries deploy large pumps for their processes. Oftentimes, such pumps are operated during hours of peak electricity prices. A lot of industries use a mix of captive generation and imported utility electricity to meet their energy requirements. The MG considered in this paper includes diesel generators, battery energy storage systems, renewable energy sources, flexible loads, and interruptible loads. Pump loads found in shipyard dry docks are modelled as exemplar flexible industrial loads. The proposed EMS has a two-stage architecture. An optimal MG scheduling problem including pump scheduling and curtailment of interruptible loads (ILs) is formulated and solved in the first stage. An optimal power flow problem is solved in the second stage to verify the feasibility of the MG schedule with the network constraints. An iterative procedure is used to coordinate the two EMS stages. Multiple case studies are used to demonstrate the utility of the proposed EMS. The case studies highlight the efficacy of load management strategies such as pump scheduling and curtailment of ILs in reducing the total electricity cost of the MG. [ABSTRACT FROM AUTHOR]

In order to improve the transient stability of frequency in a small stand-alone microgrid (SSM), this paper takes a SSM composed of a direct-drive permanent magnet synchronous generator (D-PMSG) and a micro gas turbine (MGT) as the background and uses wind turbine generator (WTG) virtual inertia (VI) to participate in the primary (short-term) system frequency regulation. First of all, this paper constructs a grid-connected model composed of a WTG and a MGT, analyzes the WTG virtual inertia frequency regulation mechanism, and explains the principle of proportional-differentiation (PD) virtual inertia control (VIC) and its shortcomings. Secondly, the paper introduces the structure principle of n-order active disturbance rejection control (ADRC) and deduces the design process of second-order ADRC-VIC. Finally, through the simulation and experimental verification, comparing the frequency perturbation of without-VIC, PD-VIC, and ADRC-VIC, it is concluded that PD-VIC and ADRC-VIC both can use the WTG virtual inertia to participate in the primary frequency regulation. The frequency regulation effect of ADRC-VIC is better than PD-VIC, ADRC-VIC can extend the rotor speed recovery time and avoid overshoot, and its frequency fluctuation amplitude and settling time are obviously improved, and ADRC-VIC can effectively avoid the overshoot phenomenon of the MGT output power. [ABSTRACT FROM AUTHOR]

The traditional microgrid electricity transactions face a low security, poor economic benefits, and weak stability. To solve these problems, this paper proposes a multimicrogrid cross-chain transaction model based on quantum blockchain. Specifically, a bidding strategy was developed for the noncooperative dynamic game of aggregator-multimicrogrid alliance, aiming to balance and optimize the benefits of all parties and effectively enhance the consumption rate of electric energy. To improve the transaction efficiency between aggregators, a consensus mechanism was designed for multimicrogrid cross-chain communication, realizing consistent self-adaptation to cross-chain information. In addition, the quantum threshold signature technology was adopted to ensure the reliability of transaction data and create an unconditional secure communication environment. Case analysis shows that our model proposed in this paper not only ensures the security and stability of transactions but also enhances the economic benefits, providing theoretical support and decision support for the optimization of multimicronetwork cross-chain transaction model. [ABSTRACT FROM AUTHOR]

The efficient and secure data sharing mechanism can support the microgrid to achieve more accurate business control, while the current data processing methods have the problems of large computing overhead and low data sharing security. Aiming at the current problems, this paper proposes a microgrid data sharing method based on blockchain technology based on the processing mode of cloud-edge-terminal architecture. Firstly, the elliptic curve encryption algorithm is used on the edge side to encrypt the data collected by the terminal equipment reliably, so as to improve the security and efficiency of microgrid key management. Then, in the cloud, the Reputation-Evaluation Practical Byzantine Fault Tolerant mechanism (REPBFT) based on smart contract and reputation evaluation can effectively manage the data sharing of edge computing devices, avoid the waste of network computing resources, and further improve the efficiency of microgrid data sharing. The simulation results show that when the number of edge devices reaches 25, the calculation and communication overhead of the proposed method are 63.46 ms and 2.66 KB, respectively, and when the processing data reaches 1024 KB, the security of the microgrid system is still 95%, which can realize safe and reliable data sharing and interaction, and can stably support the optimal operation of the microgrid. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of timely transmission, rapid update, and large magnitude of microgrid data, based on the large data samples generated by microgrid operation, a fault diagnosis and analysis method of microgrid systems supported by big data is proposed in this paper. The multisource joint feature vectors of microgrid are extracted using Wavelet transform, Rayleigh entropy, and big data technology, which combine short-circuit current and voltage. The extracted feature dataset is clustered and segmented to realize deep data mining. Combining BP neural network and big data, the fault diagnosis of microgrid is realized. The simulation results show that the BP neural network algorithm based on big data support can accurately identify the type and phase of internal faults in microgrid, which is more suitable for extracting the temporal characteristics of information and spatiotemporal correlation of data to realize the prediction of big data and solve the core problems in the analysis of big data of microgrid faults, and the accuracy is as high as 96.8%. [ABSTRACT FROM AUTHOR]

With the fast growing of new energy technologies, the proportion of distributed renewable energy sources dominated by wind and light energy in the microgrid continues to increase. However, the uncertainty and randomness of energy itself bring challenges to the stable operation of the power system. Microgrid load forecasting with high accuracy is the key means to handle the above problems. It can provide help for power grid dispatching and decision-making, optimize resource allocation, reduce operation cost, and ensure system safety. In this paper, a load-forecasting algorithm for microgrid based on improved long short-term memory neural network (LSTM) is proposed. Firstly, the criticality analysis of load influencing factors is carried out, and the clustering classification and weight calculation are completed. Then, the input data is preprocessed to ensure the quality of database. Secondly, the LSTM gets improved from three aspects: multilayer convolution channel, lookahead optimizer, and AM weight. And a complete forecasting model is designed to accomplish the load forecasting. Finally, based on the data of a local microgrid in Zhejiang Province, China, simulation experiments are conducted. The results are quantitatively compared with other forecasting algorithms to verify the accuracy and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid multiobjective algorithm, namely, the modified bald eagle search Algorithm (MBES), integrated with the grasshopper optimization algorithm, is proposed to solve the unit commitment (UC) problem. We consider a standard 10-unit power system with two wind farms, two photovoltaic farms, and flexible loads for optimization purposes. The UC problem is tackled under uncertainties related to demand and renewable generation capacities. To account for these uncertainties, probability density functions (PDFs) are assigned to the sources of uncertainty, and Monte Carlo simulation (MCS) is employed to select several scenarios with specific probability coefficients. Additionally, two innovative objective functions based on operation cost and emissions are introduced, with each scenario weighted based on its occurrence probability. To assess the performance of the proposed MOGOA-MBES algorithm, simulations are conducted across three scenarios with varying conditions, and the results are compared against those obtained from several multiobjective algorithms. Our findings, supported by optimization results and the S-metric index, demonstrate that the proposed MOGOA-MBES algorithm outperforms other algorithms in terms of reducing operation cost and emissions. Furthermore, the simulation results reveal that uncertainties lead to an increase in cost and emissions, whereas the inclusion of flexible loads and their participation in the UC program can effectively mitigate cost and emission levels. [ABSTRACT FROM AUTHOR]

In recent years, low-voltage direct current (LVDC) microgrids are becoming more attractive because they represent a solution to integrate renewable sources, storage, and electronic loads bringing some advantages in comparison with traditional AC grids. However, the protection of such a network involves many challenges, especially in the case of LVDC microgrids with more than one feeder and multiple energy sources. Indeed, the traditional protection breakers used for an AC grid cannot isolate the faults and protect the components of a DC grid, while the use of solid-state circuit breakers increases energy losses. This paper deals with the analysis and design of the protection schemes for LVDC microgrids through the combination of mechanical circuit breakers and hybrid circuit breakers. This solution has the advantage of energy loss reduction but introduces further issues due to the slow transition times of the mechanical circuit breakers. Thus, a completely decentralized control system capable of overcoming the fast fault-clearing time, cost-effectiveness, and selectivity issues is designed to protect from pole-to-pole faults. The proposed control strategy is compared with a centralized protection scheme available in the literature through numerical simulation. The two algorithms show similar performances, with a mean voltage dip duration of less than 30 ms and a maximum voltage dip duration of about 100 ms in the most severe fault condition, but the proposed solution is more reliable and flexible since it does not depend on the communication system. [ABSTRACT FROM AUTHOR]

The paper expresses the problem of flexibility pricing in energy hubs (EHs) that are in connection with electricity, heat, and gas networks considering of uncertain energy generation sources. Scheme includes a bilevel formulation. Its upper-level states for modeling of the flexibility services are provided by various resources within the EH. The problem considers maximization of the expected profit of these resources in the flexibility market. The problem constraints include the flexibility model of flexible resources such as storage devices, responsive loads, and controllable distributed generations (DGs). The flexibility model of resources relies on their active and heat power. The lower-level problem calculates energy and flexibility prices and formulates the flexible operation of energy resources considering EHs. Here, constraints include optimal power flow equations in the energy networks; operation model of EHs with power sources, storage devices, and different responsive loads; and flexibility limits of EHs. Also, a linear approximation model is adopted in the suggested design using conventional linearization techniques. Next, the Karush–Kuhn–Tucker (KKT) method is used to derive a single-level model for the problem. The scheme adopts scenario-based stochastic programming (SBSP) so that uncertainties of renewable power, energy price, load, and energy consumption of mobile storage devices are properly modeled. Finally, the results validate the suggested design's potential in modifying and enhancing the operation, flexibility, and economic situation of energy networks and EHs. [ABSTRACT FROM AUTHOR]

The operation of power systems needs uninterrupted and precise wide-area real-time monitoring for secure and automated control of power networks, therefore, there is a requirement of robust state estimation (SE) by the smart control centre. An iterative free static state estimation (IFSSE) scheme is developed using the linear measurement model (LMM) in this paper for microgrids (MGs) integrating distributed generation energy resources (DGERs). The developed scheme of IFSSE utilizes M-estimator (ME) concept, which is combined with the linear static state estimation (SSE) approach in order to provide robust estimation. The developed IFSSE scheme is iteration-free due to the implementation LMM that is achieved from placing the microphasor measurement units (μPMUs) and separate bad data processing (BDP) is not required for this proposed scheme. The proposed scheme's performance has been verified by employing it on modified IEEE-14 bus test systems (BTS) integrating DGERs and modified IEEE 30 BTS integrating DGERs then comparison has been done by taking two cases, i.e., linear SSE without BDP and linear SSE with BDP. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low success rate, delay, and high communication cost in distance English teaching resource sharing, this paper puts forward a method of distance English teaching resource sharing based on Internet O2O mode. Based on the model of distance English teaching resource sharing, this paper designs four processes: query, reply, resource substitution, and resource sharing optimization. Experimental results show that the proposed method can achieve high success rate of resource sharing, low latency, communication cost, and high transmission efficiency. Therefore, it is an effective method. [ABSTRACT FROM AUTHOR]

High growth of the population and developed industries, on the one hand, increased the demanded energy considerably. Growth of the fossil fuel-based power generation, on the other hand, has high environmental effects. Accordingly, renewable energy sources are mainly interested in the recent years as clean resources. These sources also can be economical cases in long term. Nevertheless, their intermittent feature is a main drawback. Integration of these resources with the rest of the grid can increase their advantages and tackle their main drawbacks. In the present work, a novel programming method is proposed for energy management and optimization of operational expenses over a grid-connected microgrid (MG). The considered MG contains various renewable resources. Some uncertainties should be considered in the integration of renewable resources with the grid, which can affect the system performance. Main uncertain parameters are studied in this paper such as predicted values for green power sources, maximum capacity for fuel cells (FCs), energy storage sources (ESSs), maximum capacity for transmission line, and also hourly loads, achieving a higher efficiency. This paper utilized fuzzy idea for presentation of mentioned uncertain parameters in the MG. Moreover, this work employed a 3-stage optimization algorithm for obtaining the best programming for the MG by considering the uncertain parameters. This paper also used a practical test system to validate the proposed method through minimizing the total cost. Additionally, a sensitivity analysis is performed to study the effects of oscillations of all components in the MG and their associated uncertainties over the obtained results. [ABSTRACT FROM AUTHOR]

This paper aims to investigate energy management of the hybrid AC/DC microgrid with the high penetration of distributed energy resources (DERs), such as electrical vehicles, heat pumps, and photovoltaics. In the previous studies, energy management of the hybrid microgrid is usually carried out by the system operator in a centralized manner, which suffers from the compromise of privacy information protection and the risk of single-point failure. Therefore, this paper proposes a distributed energy management scheme of the hybrid microgrid using the projection function-based alternating direction method of multipliers (P-ADMM), which allows each subgrid, i.e., AC subgrid and DC subgrid, to make day-ahead schedules independently with information exchanges while obtaining the optimal energy management solution. The energy management problem of the hybrid microgrid is formulated as a mixed-integer quadratic programming (MIQP) model, considering DER and energy storage system operation constraints, system operation constraints, and converter operation constraints. Then, the MIQP model is decomposed and distributed into smaller-scale QP models between subgrids using the P-ADMM algorithm, which can handle binary variables through projection functions. The numerical results conducted on the hybrid microgrid demonstrate that the proposed distributed scheme can effectively achieve optimal energy management for the hybrid AC/DC microgrid in a distributed manner. [ABSTRACT FROM AUTHOR]

In recent years, renewable resources have widely been used to provide necessary energy due to the increasing fossil fuel prices, environmental pollution concerns, and the necessity to meet the growth in energy demands. The output of renewable resources especially solar and wind energies is associated with meteorological parameters, so their reliability creates many challenges for the energy sector. The consumption peak of the gas network is taken into account to adjust the frequency of the microgrid (MG). Both gas network load and electric load distributions are adjusted at the same time. In a multicarrier network, the frequency is regulated in a nonlinear manner. Meanwhile, new necessary loads for production and electric vehicles have imposed new loads on the power network; if proper management is not performed to respond to these new loads, the increase of network frequency deviations may lead the network to fail and even break down. In this paper, a network of various sources including the wind turbine, solar panel, storage (battery and flywheel), electric vehicle (EV), diesel generator (DG) electric power generation, and multicarrier energy hub (MCEH) with combined heat and power (CHP) was designed to examine vehicle-to-grid (V2G) electric vehicles. The ANFIS adaptive fuzzy control method was used to provide a fine-tuning frequency of the network. A comparison between the suggested approach and a fuzzy controller system was carried out to examine the superiority of the introduced approach to the frequency control. The simulations were obtained using MATLAB/SIMULINK software. The simulation outcomes indicated that the SMART controller can achieve good efficiency in frequency regulation and reliable output power in the examined microgrid. Further comparison in terms of effective (RMS) values and maximum frequency deviation indicates the superior performance of the proposed method over the fuzzy method. [ABSTRACT FROM AUTHOR]

Due to the strong uncertainties of renewable energy and load demands, the new type of power systems is facing severe challenges in terms of generation control and load dispatch. Considering the uncertainties of the penetrated renewable energy and diversified load demands, this paper proposes a random fuzzy power flow analysis method (RFPF) to exactly depict the impact on the various flows of power systems. In the RFPF, the random fuzzy models of wind turbine generators, photovoltaic generators, and load demands are established utilizing the stochastic probability functions and fuzzy interval to reveal the uncertainties with high precision. Afterwards, the random fuzzy mean value is developed as an index of accuracy, and the fuzzy number of the output variables is extracted under the 95% confidence level. Furthermore, the proposed RFPF model is executed by applying a three-point estimate method (3PE) to figure out the corresponding power flow of the power system, costing less computation burden compared with the Monte Carlo simulation. Simulation studies conducted on the IEEE-33 system verifies the accuracy of the RFPF and the efficiency of the 3PE. [ABSTRACT FROM AUTHOR]

In a conventional AC distribution system, the conservation voltage reduction (CVR) strategy is widely employed to lower down the voltage of specific load to lower the power consumption. The wide applicability of demand side management (DSM) using CVR in a stand-alone microgrid through VSI-based energy sources is a thrust area, that is, not examined yet and needs to be explored. The fast dynamics and flexible control are the characteristics of the voltage-current droop method which further increases the inertia of the voltage source inverter. For utilizing these advantages of this droop method, it is required to determine the accurate droop gain to properly coordinate the distribution of power among DGs. In this paper, the voltage-current droop method is utilized to carry out the function of DSM, and a modified droop computation method for voltage-current droop is formulated to determine the impedance from initial of the DG point to the downstream end for multifeeder network. As most of the droop control techniques emulate the conventional power grid such as Q-V droop control which reduces voltage with the increase of reactive power, the research prospect is very high in devising the new droop computation method for voltage-current droop for accurate control of power. In addition to it, the work is extended to apply the benefits of voltage-current droop to execute DSM strategy in standalone MG. Moreover, the capability of the proposed estimation of droop parameter is implemented on a standalone 5-bus single-feeder multi-DGs network, and furthermore, the scheme is applied to IEEE-9 bus multifeeder multi-DGs network to show the applicability of the proposed scheme. The simulation results produced from MATLAB/Simulink are compared with the decentralized power-based droop method and conventional voltage-current droop technique to analyse the performance of the devised scheme. [ABSTRACT FROM AUTHOR]

Microgrids with distributed generation (DG) are rapidly coming into distribution networks to supply load demand in order to preserve ecological balance and reduce greenhouse gas emissions. Power electronic advancements are making renewables dispatchable to loads while turning passive networks to active with bidirectional power flow. The IEEE-1547-2018 regulations enforced certain standards on microgrids, including the ability to detect unintended failures, island the microgrid in less than 2 seconds, and feed connected loads while maintaining voltage, frequency, and power quality. There are numerous islanding techniques accessible, including passive, active, hybrid, and communication techniques. The active techniques degrade power quality due to injection of deviations, passive type leaves larger nondetection zone (NDZ), and communication type are costly. The hybrid type combines both passive and active methods. To get away from all these issues, a passive technique is formulated in this paper, which measures the differential phase angle of voltage and current at DG output, to detect islanding. This approach reliably identifies islanding in 20 ms at nearly zero NDZ. This approach is also stable during transient conditions such as load switching and throwing off. There is also no power quality issue because there are no injections during testing. This method is tested in MATLAB/Simulink and evaluated using the differential frequency technique to get performance indices in accordance with UL-1741 testing standards. [ABSTRACT FROM AUTHOR]

This paper develops a distributed fixed-time quadratic coordinated control strategy for isolated DC microgrids with time delays. The proposed control scheme based on the droop control strategy is intended to realize bus voltage recovery and current balance distribution under time delays. We first model the isolated DC microgrids as a single integrator system with input delays by the Artstein's transform. The current balance and voltage recovery are achieved by using fixed time control, which does not depend on the initial state of the system. Based on the Lyapunov functional method, the stability analysis criterion of the system is derived. In order to verify the effectiveness of the proposed control method, the DC microgrid system is simulated in Matlab/Simulink. [ABSTRACT FROM AUTHOR]

Isolated microgrids (MGs) face challenges in performance stability and active/reactive power sharing as a result of frequency/voltage deviations and mismatched line impedance issues. In this paper, a consensus-based multiagent system (MAS) is proposed as a solution to restore voltage/frequency deviations and enable true power sharing. The invention of an Intelligent Distributed Secondary Control Scheme (IDSCS) can efficiently achieve hoped-for outcomes. The proposed IDSCS features estimation and compensation sublayers. For the estimation sublayer, discrete dynamic consensus algorithm-based state estimators are presented to collect average information of frequency, voltage, and reactive power. Each DG is viewed as an agent sharing information with its immediate neighbors through a sparse cyber communication network. In the compensation sublayer, online tuned proportional integral (PI) controllers using artificial neural networks (ANNs) are proposed as an intelligent voltage and frequency compensators. This combination uses the simplicity of the PI controller mathematical formula and ANN's ability to deal with parameter variations and nonlinearity. Due to the global nature of the frequency parameter, the active power-sharing compensator is unnecessary. For compensating reactive power deviations, ANNs-based reactive power controllers are proposed. Furthermore, at the primary control level, the proposed strategy employs discrete-time proportional resonant (PR) controllers in a stationary reference frame, eliminating the need for any α β / d q or d q / α β transformations. Distributed implementation of the proposed method guarantees system scalability without MG topology or demand pattern expertise. The control scheme was validated using hypothetical MAS in MATLAB Simulink platform. The simulation findings indicate the proposed MG system can effectively distribute power among the DGs while maintaining voltage and frequency stable. [ABSTRACT FROM AUTHOR]

Microgrids are considered to be smart power grids that can integrate Distributed Energy Resources (DERs) in the main grid cleanly and reliably. Due to the random and unpredictable nature of Renewable Energy Sources (RESs) and electricity demand, designing a control system for microgrid energy management is a complex task. In addition, the policies of microgrid agents are changing over time to improve their expected profits. Therefore, the problem is stochastic and the policies of the agents are not stationary and deterministic. This paper proposes a fully decentralized multiagent Energy Management System (EMS) for microgrids using the reinforcement learning and stochastic game. The microgrid agents, comprising customers, and DERs are considered as intelligent and autonomous decision makers. The proposed method solves a distributed optimization problem for each self-interested decision maker. Interactions between the decision makers and the environment during the learning phase lead the system to converge to the optimal equilibrium point in which the benefits of all the agents are maximized. Simulation studies using a real dataset demonstrate the effectiveness of the proposed method for the hourly energy management of microgrids. [ABSTRACT FROM AUTHOR]

Nowadays, consumption of different energy carriers is increasing due to the division of community activities in various sectors such as residential, commercial, and industrial. The energy hub concept is used to meet the demands of different energy carriers in these sections. In this paper, a new method for energy management of a microgrid is presented in an intelligent network based on three types of commercial, residential, and industrial energy hubs. In the energy hubs, a wide range of components, including renewable and nonrenewable generation units, converters, storage devices, intelligent parking lots consisting of electric vehicles, P2G units, and cogeneration units, are used to supply electrical, heating, cooling, and natural gas energies. Some parameters like renewable generations, energy demands, and arriving and departure time of electric vehicles are considered to be uncertain, and a relevant method is applied to provide a near-realistic profile for them. In addition, a demand response paradigm has also been proposed for three types of electrical, heating, and cooling demands at the hub output side. Therefore, in this method, a mixed integer linear programming model is proposed based on benefit and reduction of emission caused by the activity of gas-burning units for short-term planning and obtaining an optimal solution for generation and sending loads in a distribution network containing energy hubs. To evaluate the performance of the proposed modeling and structure, the presented approach is applied on a modified 33-bus IEEE test network. According to the results of the energy management model, it is possible to significantly increase benefits and also obtain a smoother consumption pattern in consumption with financial incentives. [ABSTRACT FROM AUTHOR]

Rwanda is an East African Community (EAC) nation with rapid and remarkable past development in different sectors and still with the ambitious targets and plans to be achieved in the coming years ahead. The government plans universal electricity access by 2024 with 52% grid connection and 48% off-grid connections. In the transport sector, the concept of electric vehicles has been initiated and started in order to contribute to the UN Paris agreement and decrease the reliance of the transport sector on gaseous fuels which are one source of air pollutants leading to climate change, premature deaths, and morbidity associated with poor air quality. With higher electricity demand than the generation of the Rwandan power grid, different energy strategies are being developed with the overall objective to achieve the targeted universal energy access. In order to overcome the aforementioned issue, this paper proposes an integration of solar PV microgrids for the satisfaction of electric vehicle (EV) technology in Rwanda. Using HOMER Grid software, a managed EV charging station is simulated to a grid connected solar PV microgrid with storage in order to assess the economic impact. The results show that the proposed technology can lower the levelized cost (LCOE) of electricity by 139.7%. This study can contribute to further research developments in either different perspectives related to the integration of distributed energy resources (DERs) with electric vehicles or studies related to affordable and environment-energy systems. [ABSTRACT FROM AUTHOR]