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Growth in renewable energy systems, direct current (DC) microgrids, and the adoption of electric vehicles (EVs) will substantially increase the demand for bi-directional converters. Precise control mechanisms are essential to ensure optimal performance and better efficiency of these converters. This paper proposes a deep neural network (DNN)-based controller designed to precisely control bi-directional converters for vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) applications. This control technique allows the converter to quickly attain new reference values, enhancing performance and efficiency by significantly reducing the overshoot duration. To train the DNN controller, large synthetic data are used by performing simulations for various sets of conditions, and the results are validated with a hardware setup. The real-time performance of the DNN controller is compared with a conventional proportional–integral (PI)-based controller through simulated results using MATLAB Simulink (version 2023a) and with a real-time setup. The converter attains a new reference of about 975 μ s with the proposed control technique. In contrast, the PI controller takes about 220 ms, which shows that the proposed control technique is far better than the PI controller. [ABSTRACT FROM AUTHOR]

This paper focuses on a water pumping system fed by a hybrid (PV–Wind) generator. The water pumping system uses centrifugal pumps driven by variable speed Induction Motors (IM) controlled by a Field Oriented Control (FOC). The absence of battery storage to decouple sources and power demand is the main originality of the contribution, together with the typical adaptation of the FOC strategy. Furthermore, the absence of battery storage will consequently lead to fixing the system operating point at a steady state which is imposed both by the intermittent renewable energy sources and by the hydraulic load characteristics. The basic idea is then to adapt the system impedance by using the two degrees of freedom offered by the power source inverter in order to control, firstly, the DC bus voltage and, secondly, the rotor flux of the induction machine; the adaptation of the FOC strategy is based on this idea. Simulation results clearly confirmed by experimental investigations show the satisfying performance of the system even with variable powers of the intermittent renewable source. [ABSTRACT FROM AUTHOR]

The paper deals with an optimization problem in an electricity distribution network with different types of distributed generation and a battery storage system in terms of a smart grid concept. The optimization problem considers two objectives, namely, the annual energy losses and the exchange of energy with the higher-level power grid. The decision variables of the problem are the allocation of the different distributed generation units and the battery storage system, the annual power profiles of the controllable distributed generation and the battery storage system, and the power factor profiles of the controllable and noncontrollable distributed generation. All decision variables are solved simultaneously in a single optimization problem. The variable load shapes of the grid consumers and the profiles of the photovoltaic and wind power systems are considered in the study. All data are observed at the annual level with hourly resolution. The problem solving method uses computational intelligence techniques, namely, metaheuristic optimization methods and artificial neural networks. The study proposes a framework for optimizing the decision variables in the planning phase of distributed generation and battery storage, and for controlling the variable power and power factor profiles based on an artificial neural network in the implementation phase. The optimization problem is solved with a power system simulation program and a metaheuristic optimizer in cosimulation synergy. The three cases of distributed generation and battery storage are considered simultaneously. The proposed method is applied to the test grid operator IEEE with 37 buses, and reductions in annual energy losses and energy exchange are obtained in the ranges 34–86% and 41–99%, respectively. [ABSTRACT FROM AUTHOR]

The energy transition hinges on the effective integration of renewable energy sources into the power grid. Islands can provide invaluable insights into the challenges and opportunities of integrating variable renewable energy into the grid due to their relatively small power systems, isolated grids, and diverse availability of renewable energy resources. This paper presents a study on the system benefits and challenges of marine energy integration in insular power systems, focusing on the Orkney Islands as a case study. A microgrid modeling approach that optimizes the mix of renewable sources and energy storage systems for future scenarios considering strategic time horizons (2030, 2040, and 2050) was employed. Results suggest that integrating ocean energies, namely, wave and tidal energy, yields notable benefits compared to traditional renewable energy sources exclusively. These benefits encompass reduced installed capacity, minimized energy storage requirements, lower excess generation, and overall cost-saving. [ABSTRACT FROM AUTHOR]

This paper addresses a crucial omission in the traditional approach to solving the classic economic dispatch problem within microgrids featuring renewable energy sources—the often-neglected frequency disturbances arising from reductions in system inertia. To remedy this, we present an innovative economic dispatch model empowered by nonlinear optimization (NLP), incorporating stringent minimum inertia constraints essential for ensuring system stability over a 24-h horizon. Our approach involves a comprehensive exploration of the intricate relationship between system inertia and frequency stability, culminating in the seamless integration of these inertia constraints into the economic dispatch model. To validate the practicality of our model, we present two distinct scenarios: a base case representing conventional dispatch methodologies and an alternative case that considers the imposition of inertia restrictions. These scenarios are rigorously tested and implemented using the CICGRE TF C6.04 test system. Employing the powerful GAMS platform alongside the NPL model, we successfully solved the dispatch problem. Our results underscore the significance of maintaining system inertia within the 1.54-s threshold proposed by our model, showcasing a tangible reduction in generation costs as a direct outcome of this enhanced approach to economic dispatch. This research advances the understanding of microgrid management and offers a practical solution to enhance system stability and economic efficiency in renewable-energy-powered microgrids. [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]

Power system can utilize the flexibility provided by energy‐intensive enterprises (EIEs) for large‐scale renewable energy penetration. However, system operators cannot obtain the manufacture scheme of EIEs due to business privacy protection. Furthermore, highly regulated electricity prices further restrict the flexible utilization of EIEs. To overcome these issues, a coordinated dispatch framework between power system and EIEs under the intrinsic time of use (TOU) prices is designed in this paper, which requires only several boundary information for interaction. Firstly, the mathematical formulation of EIEs is given based on the inherent features analysis of typical production equipment and the optimal operation scheme of EIEs can be obtained. Then, considering uncertainties of both source and loads, the purchasing energy of EIEs from the network can be adjusted through the designed framework to track renewable energy dynamics and the clear equivalence class method is used to solve fuzzy chance constrained programming. Next, the improved Asymmetric Nash Bargaining (ANB) method is adopted to carry out the distribution of benefits. Finally, a modified IEEE‐30 test system is given to verify the feasibility of the proposed method, which can effectively improve the regional consumption level of renewable energy and ensure interests of all sides. [ABSTRACT FROM AUTHOR]

As the proportion of renewable energy generation systems increases, traditional power generation facilities begin to face challenges, such as reduced output power and having the power turned off. The challenges are causing changes in the structure of the power system. Renewable energy sources, mainly wind and solar energy cannot provide stable inertia and frequency regulation capability. Ultimately, the power system's emergency response capability to face an N-1 is reduced, which leads to a reduction in system stability. Therefore, the application technology of the battery energy storage system is used to support the impact of changes in the new power system structure. This paper designed control technologies based on the WECC second-generation generic model, namely, dynamic regulation, steady regulation, and virtual inertia regulation. The models and control strategies are verified on Taiwan's 2025 power system target conditions, which consider the expected capacities for battery energy storage systems, and renewable energy sources with different load and N-1 fault levels. According to the simulation results, the capabilities of the RoCoF limitation, frequency nadir, frequency recovery, and system oscillation regulation are evaluated in the proposed strategies. Finally, the analysis results can help power operators make informed decisions when selecting and deploying battery energy storage systems. [ABSTRACT FROM AUTHOR]

Ancillary frequency control schemes (e.g., droop control) are used in wind farms to improve frequency regulation in grids with substantial renewable energy penetration; however, droop controllers can have negative impacts on the damping of wind turbine torsional mode, thereby reducing the lifespan of the turbine gearbox. This paper presents a battery energy storage system (BESS) damper to improve the damping of torsional vibrations when using doubly fed induction generators (DFIGs) for frequency regulation in a microgrid. We formulated a linearized model comprising diesel generators, a wind turbine with five-mass drivetrain, and BESS. We also designed a feedforward compensator to deal with phase lag between the BESS damper signal and DFIG torque. The proposed BESS damper was shown to improve the torsional mode damping by moving the eigenvalues for torsional mode leftward to desirable locations on the complex plane. Dynamic simulations performed using MATLAB/SIMULINK demonstrated the efficacy of the proposed BESS torsional mode damping scheme in terms of torsional mode 1 damping performance and frequency response. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the park integrated energy system (PIES) operation, and a robust bilevel optimal dispatch is proposed. Firstly, the robust uncertainty set is constructed based on the K-means++ algorithm to solve the uncertainty of renewable energy sources output in PIES. Then, the bi-level dispatch model is proposed, with the operator as the leader and consumers as the follower. The upper model establishes an electricity-heat-gas integrated energy network, and the lower model considers the demand response of consumers. Optimizing the pricing strategies of energy sources to determine the output of each energy conversion equipment and the demand response plan. Moreover, analyzing the decision-making process of the robust bi-level model and the solution method is given. Finally, case studies show that the proposed dispatch model can increase operator profits and reduce consumers' energy costs. The in-sample and out-of-sample simulations demonstrate that the proposed ellipsoid uncertainty set possesses high compactness, good robustness, and low conservatism. [ABSTRACT FROM AUTHOR]

Several issues of individual microgrids (MGs) such as voltage and frequency fluctuations mainly due to the intermittent nature of renewable energy sources' (RESs) power production can be mitigated by interconnecting multiple MGs and forming a multi-microgrid (MMG) system. MMG systems improve the reliability and resiliency of power systems, increase RESs' utilization, and provide cost-efficient power to the consumers. This paper provides a comprehensive review of the conducted studies in the MMG area summarizing different operational goals and constraints proposed in the literature for efficient operation of MMGs. Besides, different MMG architectures in which the MGs can be interconnected to form an MMG system and their characteristics are discussed. This paper also provides a state-of-the-art review on different control strategies and operation management methodologies for the operation and control of MMGs in centralized, decentralized, distributed, and hierarchical structures. A classification of different sources of uncertainties in an MMG system and proposed uncertainty handling strategies are also presented. Finally, the paper is complemented with a discussion of the main open issues and future research directions of MMG systems. [ABSTRACT FROM AUTHOR]

DC microgrids with a small electrical network of locally available energy resources and distributed loads are becoming popular in real-life applications, especially in remote areas and in some specific urban applications. This, in turn demands a serious insight into the control and reliability enhancement of such systems so that the availability of secure and safe energy can be assured to the users. But the integration of intermittent renewable energy resources in dc microgrids lead to a serious concern regarding the stability of the microgrid bus voltage under continuous change in available source power and the loading conditions. While the microgrid management is still a challenge under steady state operating conditions, it becomes much complex under some emergency events like faults, failure of controllers/sources and overloads etc. This paper proposes a novel reconfigurable coordinated control architecture for a Low Voltage DC (LVDC) microgrid comprises of solar photovoltaic, fuel cell based renewable energy sources supported Energy Storage System (ESS) formed with battery and ultra-capacitor banks. The reliability of the system is ensured in the proposed reconfigurable coordinated control with three layered hierarchy viz. Emergency control layer sandwiched between the supervisory and local control layers. The proposed control strategy is implemented through a new Microgrid Bus Controller (MGBC) strengthened by an "emergency" control layer which handles the responsibility in emergency conditions to coordinate the energy sources, loads and controllers. The MGBC facilitates the fault identification and isolation to ensure the reliable operation of the healthy microgrid sections and speedy fault recovery. The MGBC is dedicated for observing the bus parameters and identify the fault location by monitoring the bus branching currents. Some of the key hardware and simulations results validating the proposed architecture along with the details of the control implementation are presented in this paper. [ABSTRACT FROM AUTHOR]

In recent years, due to the increasing number of renewable energy sources, which are characterised by the stochastic nature of the generated power, interest in energy storage has increased. Commercial installations use simple deterministic methods with low economic efficiency. Hence, there is a need for intelligent algorithms that combine technical and economic aspects. Methods based on computational intelligence (CI) could be a solution. The paper presents an algorithm for optimising power flow in microgrids by using computational intelligence methods. This approach ensures technical and economic efficiency by combining multiple aspects in a single objective function with minimal numerical complexity. It is scalable to any industrial or residential microgrid system. The method uses load and generation forecasts at any time horizon and resolution and the actual specifications of the energy storage systems, ensuring that technological constraints are maintained. The paper presents selected calculation results for a typical residential microgrid supplied with a photovoltaic system. The results of the proposed algorithm are compared with the outcomes provided by a deterministic management system. The computational intelligence method allows the objective function to be adjusted to find the optimal balance of economic and technical effects. Initially, the authors tested the invented algorithm for technical effects, minimising the power exchanged with the distribution system. The application of the algorithm resulted in financial losses, €12.78 for the deterministic algorithm and €8.68 for the algorithm using computational intelligence. Thus, in the next step, a control favouring economic goals was checked using the CI algorithm. The case where charging the storage system from the grid was disabled resulted in a financial benefit of €10.02, whereas when the storage system was allowed to charge from the grid, €437.69. Despite the financial benefits, the application of the algorithm resulted in up to 1560 discharge cycles. Thus, a new unconventional case was considered in which technical and economic objectives were combined, leading to an optimum benefit of €255.17 with 560 discharge cycles per year. Further research of the algorithm will focus on the development of a fitness function coupled to the power system model. [ABSTRACT FROM AUTHOR]

Because of the worldwide trend of microgrid (MG) and renewable energy (RE)-based distributed power generation (DG), advanced power flow control schemes with wide bandgap (WBG) semiconductor technologies to ensure high-level performance of grid-connected MGs is one of the crucial research topics. In grid-connected MGs, a static switch (SS) is commonly used at the point of common coupling (PCC) of two systems. In this paper, the role of SS is replaced by a SiC-based three-phase back-to-back (BTB) inverter system for seamless switching between grid-connected and standalone modes through advanced power flow control schemes. According to scenarios of different grid/load conditions and available DG capacities in an MG, various advanced control functions can be developed for both MG operating modes: bidirectional control of active and reactive power flows, seamless switching between operating modes, improvement of grid power quality (PQ), and voltage stabilization. In this paper, mathematical models of the BTB inverter in a synchronous reference frame (SRF) is first derived, and the required controllers are then designed. For functional testing, two typical cases are simulated and analyzed in a MATLAB/Simulink environment and then verified through 1kVA small-scale hardware implementation with Texas Instruments (TI) digital signal processor (DSP) TMS320LF2812 as the control core. Results show satisfactory performances of power flow control and PQ improvement of MG. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive overview of the microgrid (MG) concept, including its definitions, challenges, advantages, components, structures, communication systems, and control methods, focusing on low-bandwidth (LB), wireless (WL), and wired control approaches. Generally, an MG is a small-scale power grid comprising local/common loads, energy storage devices, and distributed energy resources (DERs), operating in both islanded and grid-tied modes. MGs are instrumental to current and future electricity network development, such as a smart grid, as they can offer numerous benefits, such as enhanced network stability and reliability, increased efficiency, an increased integration of clean and renewable energies into the system, enhanced power quality, and so forth, to the increasingly growing and complicated power systems. By considering several objectives in both islanded and grid-tied modes, the development of efficient control systems for different kinds of MGs has been investigated in recent years. Among these control methods, LB communication (LBcom)-based control methods have attracted much attention due to their low expenses, recent developments, and high stability. This paper aims to shed some light on different aspects, a literature review, and research gaps of MGs, especially in the field of their control layers, concentrating on LBcom-based control methods. [ABSTRACT FROM AUTHOR]

Summary: This paper introduces a unique simulation framework for testing and validating multiple operation issues of grid connected microgrids. The framework is widely adjustable and enables various control actions, for example, load shedding, demand response, power curtailment, and integration of battery energy storage systems (BESS). The microgrid operation framework runs several algorithms, providing data for the optimization of the actors at different time scales. In the first part of the paper, the framework, three predictor algorithms, and the network topology used for the tests are presented in detail. Balancing power requirements of microgrids are dominantly determined by uncertainties of intermittent renewable energy‐based generation and changes in consumption. Beyond the environment development, the aim of the present research is to quantify the temporal change of balancing needs between certain confidence intervals. Accordingly, in the second part of the paper, possible approaches to determine the balancing power needs are presented, including the stochastic approach of the new System Operation Guideline (SO GL). The last part of the paper presents error characteristics of the predictor algorithms and balancing power needs are calculated in the context of the European balancing market terminology. Daily and seasonal temporality of errors is examined. The authors recognized that in transition to a more flexible distribution grid operation, the importance of possible microgrid market value propositions is increasingly appreciated. Future research perspectives include physical demonstration and validation of possible ancillary service strategy planning for microgrids. [ABSTRACT FROM AUTHOR]

Thermostatically controlled loads (TCLs) are regarded as one of the promising resources for suppressing power fluctuations due to renewable energy (RENs). However, due to the great burdens of fully considering each users’ characteristics, it is difficult to achieve unity of the individual optimal decisions and global optimum in demand-side management (DSM). This paper proposes a game-theoretic DSM that can optimize the global power consumption schedule by individual TCL user's optimization. By integrating the prediction of REN outputs into the pricing mechanism, the proposed DSM can guide the users to make their best power consumption schedules along with intermittent REN generations, thus to smooth the tie-line power of microgrids. In this paper, a novel pricing mechanism is firstly developed based on the concave N-person game theory, which is more adaptive and flexible compared with existing game-theoretic DSM. Then, an individual's power consumption optimization and its simplified model are developed considering the constraints of the TCL model and personal preferences. The simplified model can be easily solved and achieve a fast solution in the power consumption game. An implementation framework of the proposed DSM is further developed for practical application. Finally, numerical studies verify the effectiveness of the proposed models and methods. [ABSTRACT FROM AUTHOR]

Summary: Arguably, the most important challenge nowadays in the energy industry is the utilization of intermittent renewable energy sources. On the one hand, renewable sources are the future in terms of sustainability. While on the other hand, several questions remain unanswered in connection with their day‐to‐day operation and system integration. Creating microgrids with local control of the distributed energy resources seems to offer solutions but there is a lack of practical experience. Especially in Europe, where a microgrid with islanding capability is connected to a widespread, synchronously operating grid, it is a complicated task, owing to the control methods. In this paper, the technical possibilities are presented, which are necessary to allow island mode operation of a microgrid. The case study discusses a "living lab" in which several energy generation technologies have been deployed thus it is a good representation of future renewable‐based microgrids. To support the island operation, numerical calculations and simulations are used to determine power and energy needs of necessary flexibility measures. Basis of the calculations is the year‐long data measured on‐site. Sizing and operation modes for energy storage and demand‐side resources and an architectural scheme are presented. Net present value for all technological options are compared to aid the selection of the best option. A sensitivity analysis is also performed to assess how potential change in various parameters of the economical calculation affect the results. It is shown that provision of uninterruptible power supply and thus island operation of the site can be provided within reasonable techno‐economical limits. [ABSTRACT FROM AUTHOR]

In this paper, a novel flexible AC transmission system (FACTS) device named generalized optimal unified power flow controller (GOUPFC) is introduced to control the power flows in multi transmission lines and to regulate the voltages and angles at the load buses. The detailed power injection modeling of GOUPFC is presented in this paper. The optimal location of GOUPFC is determined based on line collapse proximity indicator (LCPI). A multi-objective function is framed in terms of average voltage deviation (AVDI), real power loss (Ploss) and average line collapse proximity indicator (LCPIavg) to test the effectiveness of the proposed device. The simulation studies are performed on standard IEEE 57-bus test system under single line contingency and considering various renewable energy source (RES) penetrations. The control parameters of GOUPFC are optimized by using whale optimization (WO-BAT) algorithm, by hybridizing WOA and BAT algorithms, and the superiority of WO-BAT is observed in minimizing the proposed objective function and enhancing the voltage profile. [ABSTRACT FROM AUTHOR]

Microgrid (MG) systems are growing at a rapid pace since they can accommodate the high amount of renewable energy. Since the MG consists of small distributed generators (DG) with volatile characteristics, an efficient energy management system is the main requisite in MG. In this paper, a chaotic sine cosine algorithm with crossover operator (CSCAC) is proposed for the day-ahead MG optimal energy scheduling problem. CSCAC includes a novel non-linear transition parameter based on the chaos system which can help the algorithm escape from local optima. A chaotic search operator is proposed to enhance the local search ability. Furthermore, a crossover operator is devised to combine the advantages of different search strategies and achieve a comparatively better balance of exploration and exploitation. First, the effectiveness of CSCAC is validated on several benchmark functions. Then, it is applied to the day-ahead energy scheduling in a MG with three wind power plants, two photovoltaic power plants and a combined heat and power plant (CHP). Furthermore, it is implemented in two more cases considering the uncertainty and stochastic nature of the renewable power sources. Experimental results demonstrate the superiority of CSCAC over other comparative algorithms in the optimal MG energy management problem. [ABSTRACT FROM AUTHOR]

It is difficult to adapt the traditional section margin distribution method to the power system with a high proportion of renewable energy generation (REG). This paper proposes a section margin allocation method of REG clusters considering the randomness of source and load power. Firstly, the probability density function of the predicted REG power error is estimated, the probability that REG stations meet the output command after the section margin distribution is calculated, and the output realization probability of each REG station is discussed. The typical operation mode set of the REG cluster is then obtained by clustering the REG stations according to the operation history, and the load rise space of the REG cluster under each typical operation mode is calculated. Considering the randomness of REG power and its load power, the section margin is allocated to each REG station in the REG cluster so as to ensure that each REG station reaches the highest output probability. On the premise of ensuring the safe and stable operation of the grid, this method facilitates the management of REG clusters and the accommodation capacity of the power system for REG. Simulations of the power system of IEEE 39 nodes verify the rationality and validity of the section margin allocation method. [ABSTRACT FROM AUTHOR]

Due to prevailing uncertainties of renewable energy and time coupling constraints of energy storage (ES), robustness and nonanticipativity of scheduling results directly influence the operational feasibility of regional microgrids. To this end, this paper proposes a new multistage generation scheduling method for regional microgrids with renewables and ES, which can simultaneously guarantee the robustness and nonanticipativity of scheduling solutions. First, a feasibility proposition is established based on the backward derivation thoughts, which addresses the feasibility requirements of microgrids against uncertainties of renewables and loads. Second, based on this feasibility proposition, a scenario-based multistage robust scheduling model with robust and nonanticipative constraints is presented, in which the uncertainties of transaction prices, renewables, and loads are simulated via representative scenarios to guarantee the economic performance of scheduling results. Third, with on/off decisions of thermal units as well as safe ranges of ES energy levels and thermal outputs derived from the scheduling model, a real-time rolling economic dispatch model is established to determine active and reactive power as well as nodal phase voltage values. Numerical tests implemented on a real regional microgrid illustrate efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

The world energy system is slowly turning toward renewable energy resources. The concepts of distributed generation and microgrids are into implementation but still in the deliberation phase. The benefits of a small power system are its cost, easy installation, integration of renewable with non-renewable energy sources, and switching between on-grid/off-grid modes. Renewable energybased microgrids have the main challenges of maintaining their frequency-voltage characteristics and the system becomes more complex when they are interconnected. These sources being intermittent in nature need to be supported by other resources like diesel/biogas-based system such that at time of small variation in load or natural sources (wind/solar), the power requirement is met through the support provided by diesel/biogas-based systems. Also, the controller should be fast enough to minimize the changes such that the system reaches a steady state. In this paper, renewable-based rural microgrids consisting of Wind, Solar, and Biogas are modeled and interconnected through the Low voltage DC line. Control approach has been developed in such a way that whenever there is a disturbance in the system due to an increase/decrease in load or input to the renewable energy sources the biogas-based system of individual microgrid increases/decreases its generation to support the system requirement. Noextra power is drawn through the interconnecting network. Modeling of the system and its dynamic Study has been carried out in Matlab/Simulink. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a new robust distributed approach for the energy management of rural multi‐microgrids communities in Chile. These rural communities are often geographically isolated and have a poor connection to the main electricity grid. Operating them as individual microgrids allows the community to connect to the grid or behave as a stand‐alone system (islanded). Connecting and coordinating the operation of the microgrids can improve the system's robustness to energy and demand uncertainty, as well as reducing the dependency on the main grid. However, this coordination can be challenging because the communication between the different microgrids can sometimes deteriorate, moreover, both load and local generation profiles are challenging to predict. For these reasons, in this paper, a new robust distributed hierarchical Energy Management System (EMS) for coordination of multiple microgrids (multi‐microgrids) is introduced, which uses robust distributed Model Predictive Control (MPC). Distributed optimisation permits improving reliability in the presence of poor or lack of communications between microgrids. To consider the uncertainty associated with renewable energy generation and load consumption, fuzzy prediction interval models are included in the MPC design. The proposed method is compared with robust centralised MPC and deterministic distributed MPC to assess its performance. Simulation results are presented using data representative of a rural community in coastal Chile applied to the robust MPC design. The outcomes of this work demonstrate that the proposed robust distributed EMS can improve the power supply, apply peak shaving, and can successfully operate when there is a loss of communication between microgrids. The use of decreasing fuzzy intervals allows an accurate prediction of both load and renewable energy generation, making the system robust to uncertainty in the available resources. Moreover, results show that in absence of communication, the power supply obtained under different scenarios with the distributed EMS is better than the equivalent centralised EMS. [ABSTRACT FROM AUTHOR]

With the advancement in technology and population growth, the motivation of integrating utility grid with the microgrid through power electronic converters increases. This paper presents the control of power electronic converter connected at the grid side. This converter regulates the power flow between renewable energy system (RES), load and grid in addition to the compensation of harmonic and reactive power. The reference currents are generated to decide the power flow from RES to the load and to suppress the harmonic and reactive power in the grid making grid current sinusoidal. In this paper, the reference current generation techniques like instantaneous real-reactive (p-q) method and extension p-q method are implemented and compared. Linear Quadratic Regulator (LQR) is used to track the reference current. The control law of LQR is designed in a simple way using simplified equivalent circuit of a three-phase grid integrated renewable energy system. The system is tested experimentally under balanced and unbalanced conditions in two different modes such as insufficient renewable power and excess renewable power. The supremacy of extension p-q method over instantaneous p-q method is proved by getting low total harmonic distortion and better power factor under balanced and unbalanced conditions experimentally. [ABSTRACT FROM AUTHOR]

Incorporating PEM electrolyzers (PEMElz) into microgrid systems presents a promising avenue for utilizing surplus renewable energy to produce hydrogen while meeting the system operation requirements. The accurate emulation of the behavior of PEMElz in such systems requires a detailed representation of their electrochemical and thermal characteristics. Yet modeling these characteristics optimally poses a challenge due to their inherent complexity, characterized by multivariate, non-linear, and multi-modal characteristics. To address this challenge, this paper proposes an advanced approach to model PEMElz by introducing general forms for the integrated electrochemical and thermal model based on a set of optimally identified parameters. The introduced general forms are generated using an n-dimensional polynomial fitting process for a set of optimally identified parameters, which are determined using the Partial Reinforcement Optimizer (PRO) under various temperature and pressure levels. These general forms provide a reliable PEMElz model to adapt to any temperature and pressure operating conditions. The proposed approach's effectiveness in capturing the static characteristics of PEMElz is verified by analyzing polarization and thermal curves across multiple operating conditions. Furthermore, the proposed general forms are implemented in Matlab/Simulink to emulate the PEMElz behavior when it is incorporated into a microgrid dominated by high penetration levels of variable generation resources such as solar photovoltaic (PV). This demonstration highlights the direct relevance and tangible impact of the proposed general-formulated model based on the optimal identified parameters approach in various operating scenarios. • Identified optimal parameters for a combined electrochemical and thermal PEMElz. • Tailored partial reinforcement optimizer (PRO) for PEMElz parameter identification. • Deriving general forms for the PEMElz model parameters. • Investigated proposed PEMElz in microgrid system. • The general forms are a computationally effective tool reflecting PEMElz performance. [ABSTRACT FROM AUTHOR]

The article discusses advancements in smart energy systems aimed at reducing carbon emissions and improving energy efficiency through innovative planning, operation, and equipment. Topics discussed include distribution network and grid planning, energy storage and control strategies, and renewable energy integration and forecasting.

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A renewable energy-based DC micro grid system supports the DC power flow for domestic and industrial purposes. The DC voltage drawn from the source is to be adjusted by using the power electronic converters. So, the controlling of converter came into existence. In this paper the Flyback converter is modelled to get the rated voltage regulation with the desired output voltage with proper duty cycle at the converter. The nonlinear system is converted into the linear one by using the lie derivative control technique. In this technique the relative values of current loop and voltage loop is linearized to get the desired output voltage at the flyback converter. Thus as compared to PI controller the proposed controller flows good for the transient response of the power system. The boost converter designed in the existing has modelled with the low output range but in the Flyback converter the ability to achieve the voltage regulation in DC micro grid with variable inductance and capacitance is defined. Simulation result shows the modelled flyback converter flows good and accurate as compared to the PI controller with the settling time difference of 85s of voltage dynamics in the DC micro grid system. The steady state and transient response of the system is evaluated by comparing the simulation of PI and the proposed controller used in the real time power transmission system to reduce the power loss at receiving side. [ABSTRACT FROM AUTHOR]

This paper proposes a novel configuration for a multiport boost converter (MPBC) with a single inductor (SI), accounting for equivalent series resistances (ESRs) and minimizing input switching stress. The MPBC performance is evaluated and compared with other established topologies. The proposed MPBC interfaces two unidirectional input DC power ports and a rechargeable port for an energy storage element (ESE) with two output ports. The design integrates two renewable sources with the ESE as a third source. One output is for higher voltage, linked to a single‐phase inverter for AC loads. The other output is for lower DC voltage, used for DC loads. The configuration can be adjusted based on requirements. This converter has numerous applications in renewable energy systems, electric vehicles, and agriculture. The steady‐state and small signal modeling of MPBC has been done to derive the mathematical expressions for analyzing stability, stresses (both voltage and current), and performance considering ESRs. A 240 W, MPBC is fabricated along with improved switching strategies using DSP TMS320F28379D. Experimental and simulation results are compared to show the effectiveness of proposed scheme on stability, stresses, and efficient power transfer. Output power is regulated effectively by sharing the input power thereby reducing voltage stress on switches. [ABSTRACT FROM AUTHOR]

In building a smarter and more flexible low‐carbon smart grid system, alternating current (AC) microgrids using virtual synchronous generator (VSG) technology are viewed as a key link in integrating distributed renewable energy access into the main grid. Given that renewable energy sources (such as solar, hydroenergy, and wind) do not have sufficient capacity for reactive power when not available, AC microgrids face challenges in maintaining stable operation. In order to overcome this difficulty, it is hoped that digging deeper and applying more system information can significantly improve the overall performance of the microgrid. This paper proposes a novel method based on macroscopic state dynamic modeling. This method expands the understanding of the inherent rational control mechanism within the microgrid, enabling the overall control objective of the microgrid to be expressed in a more abstract and direct manner. Additionally, by implementing additional convergence constraint conditions on the macroscopic state dynamics, such as based on some optimality criteria, a set of macroscopic state controllers can be obtained to meet specific performance indicators. Theoretical analysis combined with simulation validation demonstrate the effectiveness of this macroscopic state based control strategy. It proves that when meeting the predefined design requirements, the designed controller can enhance the transient response of microgrids in practical applications, thus supporting higher rate of renewable energy access and promoting the development of the smart grid. [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]

For a proposed multi-source all-renewable microgrid in Nigeria's Middlebelt region, this paper presents a multi-temporal approach to the investigation of the uncertainty in the potential of renewable energy resources. The wind, solar, and hydropower resources for a proposed multisource all-renewable off-grid community microgrid are considered using an array of probabilistic techniques. The peculiar variances in the location's climate throughout the year make the more common method of annual models of renewable resources unsuitable for power system planning. Consequently, a more granular model of its renewable resources over time is needed. Therefore, for the chosen location, for each renewable resource, a composite multitemporal maximum-likelihood estimation-based (MLE) probabilistic model for characterization is developed. A total of 39 probabilistic models are developed. Up to 40% improvement in the accuracy of the statistical measures for renewable resource uncertainty was observed. Multi-temporal approach provides more accurate information for power system planning over time than the conventional approach of single aggregate models, especially for hydropower, which is strongly affected by the relatively sporadic occurrence of rainfall. The study shows that solar energy is promising, hydropower potential is seasonal and complementary, and wind potential is low at the location considered in this study. [ABSTRACT FROM AUTHOR]

Rural electrification represents a great worry for developing nations. Isolated microgrids establishment is the brightest solution for this problem. Optimization strategies for mathematical simulation and modelling are accomplished in this research to size an isolated residential microgrid in Egypt consists of wind generators (WG), photovoltaic (PV), battery energy storage system (BESS) and Biomass generator. This paper's novelty relies on the techno-economic performance comparison of three sizing strategies of isolated microgrid based on renewable sources, considering the sources' uncertainty. Also, the environmental and economic effects of using rice straw in biomass gasification are introduced to handle the problem of dark clouds from its open-air burning. Three categories with nine combinations of renewable energy sources (RES) are introduced in this paper to select the ideal option. Multi-objective genetic algorithm (MOGA) optimized the systems under both deterministic and stochastic conditions, taking into consideration the uncertainties of solar radiation and wind speed using Monte-Carlo techniques. The obtained results are compared with the results of epsilon multiobjective genetic algorithm ("-MOGA). Simulation outcomes show that PV with BESS is the most economical option for microgrid sizing with a total system cost of $ 4.04 million. Besides, Scenario 6's hybrid system provides an appropriate ecological-reliable configuration, especially when considering sources' uncertainties. [ABSTRACT FROM AUTHOR]

It presents an energy management strategy for a thermal-gas-electricity MG, employing Mixed-Integer Linear Programming as the underlying methodology. Specifically, there is a continuing need for investigations into energy management techniques that address uncertainty in renewable-based MGs. Lastly, M. Espitia-Ibarra et al. [[9]] introduce a linear-programming methodology for optimal energy management in MGs, notable for its efficient computational performance, making it suitable for online applications. [Extracted from the article]

Microgrid (MG) is not only undergoing rapid upsurge and an effective combination of distributed generations (DGs)-based renewable energy sources, but it also fulfills a significant function in upgrading grid infrastructure. It is necessary for DGs, during and after any disturbances in the voltage, to stay connected in order to enhance the voltage, most importantly guaranteeing the stability of the power system. In this present context, the behavior of the distributed generations control systems has a significant impact on the MG performance. This paper investigates the effect of different control schemes of the inverter-based DG unit on the stable operation of MG after the occurrence of faults that lead to islanding events. In this paper, an appropriate control strategy is proposed to enhance the fault ride-through of MG. Within the scope of the study, the MG consists of a conventional diesel generator and an inverter-based DG unit. The MG also contains several load types; RLC load as a static load and induction motor as a dynamic load. The MATLAB Simulink is used to model the MG and its inverter control schemes. In order to assure MG stability, the variations of the voltage and frequency are possessed as key references. Results indicate that the control strategies of the inverter have a strong influence on the stability of MG. Moreover, the MG configuration with the proposed FRT resists fault effects for longer durations and this enhances the stability. [ABSTRACT FROM AUTHOR]

This paper proposes a noncooperative game-based planning method for microgrid (MG) considering the interconnection between developing and developed privately-owned clustered MGs on an island. A noncooperative game with imperfect information is designed to regulate the decision-making in planning and operating strategies for rational MG owners. The optimization models for both developing and developed MGs are presented. Different levels of maturity of MG development are modeled by diverse objective functions and constraints, while the data privacy of privately-owned MGs is preserved through imperfect information and prior beliefs. To tackle the seasonality of renewable energy and load on an island, hydrogen storage is considered in the developing MG on the island. Diagonalization and realization are incorporated to develop an effective solving methodology in searching the mixed strategy equilibrium for each MG. The effectiveness of the proposed method is verified using comprehensive case studies. [ABSTRACT FROM AUTHOR]

Hierarchical controls, consisting of primary, secondary, and tertiary layers to realize different functionalities at different time scales, have been applied for microgrids to achieve the stable and economic operations. However, in islanded microgrids with pure renewables (i.e., wind, solar, and battery storage systems (BSS) only), the three layers shall interact rather tightly and frequently in order to effectively leverage limited capabilities of available resources against disturbances. To this end, this paper discusses a more responsive control framework, including the distributed schemes for individual primary, secondary, and tertiary control layers as well as the real-time closed-loop scheme for a prompt coordination of the three layers. Thus, the proposed control framework could promote real-time interaction of resources in islanded microgrids with limited communication and computation burdens. Based on this, an enhanced control module design for BSSs of different configurations is further discussed to optimally coordinate their power and energy states with improved operational stability and economics. The steady-state analysis confirms the compatibility of the designed power and energy goals in tracking optimal power and energy equilibria simultaneously, and a customized Lyapunov candidate function is used to prove the asymptotic stability of the overall control framework. The hardware-in-the-loop simulation validates efficacy of the designed control framework in promptly mitigating disturbances and effectively achieving convergence to the optimal economic equilibria. [ABSTRACT FROM AUTHOR]

Multiple microgrids can be interconnected to form a networked-microgrid (NMG) system. In this paper, a data-driven decentralized economic frequency control method is proposed for isolated NMG systems. Based on multi-agent deep reinforcement learning (MA-DRL) framework, each DRL agent controls the generator and energy storage system (ESS) in each microgrid of the NMG. For offline training stage, the optimal control strategy is learned by soft actor-critic (SAC) algorithm with a global reward, which aims to restore system frequency while considering economy. Besides, to satisfy system constraints and avoid high learning costs, a safety model scheme is designed and trained to support each DRL agent. Since the agents are trained in the centralized learning process at offline stage, they are able to coordinate in a decentralized manner for online application, which only requires local information to generate optimal control actions. Also, the trained safety model can be applied for online stage to monitor and guide online actions. Finally, numerical tests are conducted to demonstrate the feasibility and effectiveness of the proposed method under the variation of renewable generation and load demand. [ABSTRACT FROM AUTHOR]

Featured Application: This paper is concerned with a multi-criteria technical and economical allocation procedure of microgrids in smart cities. Microgrids have the potential to provide reliable electricity to key components of a smart city's critical infrastructure after a disaster, hence boosting the microgrid power system's resilience. Policymakers and electrical grid operators are increasingly concerned about the appropriate configuration and location of microgrids to sustain post-disaster critical infrastructure operations in smart cities. In this context, this paper presents a novel method for the microgrid allocation problem that considers several technical and economic infrastructure factors such as critical infrastructure components, geospatial positioning of infrastructures, power requirements, and microgrid cost. In particular, the geographic allocation of a microgrid is presented as an optimization problem to optimize a weighted combination of the relative importance of nodes across all key infrastructures and the associated costs. Furthermore, the simulation results of the formulated optimization problem are compared with a modified version of the heuristic method based on the critical node identification of an interdependent infrastructure for positioning microgrids in terms of the resilience of multiple smart critical infrastructures. Numerical results using infrastructure in the city of Pittsburgh in the USA are given as a practical case study to illustrate the methodology and trade-offs. The proposed method provides an effective method for localizing renewable energy resources based on infrastructural requirements. [ABSTRACT FROM AUTHOR]

Networked microgrids that integrate the hydrogen fueling stations (HFSs) with the on-site renewable energy sources (RES), power-to-hydrogen (P2H) facilities, and hydrogen storage could help decarbonize the energy and transportation sectors. In this paper, to support the hydrogen-based networked microgrids planning subject to multiple uncertainties (e.g., RES generation, electric loads, and the refueling demands of hydrogen vehicles), we propose a two-stage stochastic formulation with mixed integer conic program (MICP) recourse decisions. Our formulation involves the holistic investment and operation modeling to optimally site and configure the microgrids with HFSs. The MICP problems appearing in the second-stage capture the nonlinear power flow of networked microgrids system with binary decisions on storage charging/discharging status and energy transactions (including the trading of electricity, hydrogen, and carbon credits to recover the capital expenditures). To handle the computational challenges associated with the stochastic program with MICP recourse, an augmented Benders decomposition algorithm (ABD) is developed. Numerical studies on 33- and 47-bus exemplary networks demonstrate the economics viability of electricity-hydrogen coordination on microgrids level, as well as the benefits of stochastic modeling. Also, our augmented algorithm significantly outperforms existing methods, e.g., the progressive hedging algorithm (PHA) and the direct use of a professional MIP solver, which has largely improved the solution quality and reduced the computation time by orders of magnitude. Note to Practitioners—This paper proposes an optimal planning model for electricity-hydrogen microgrids with the renewable hydrogen production, storage, and refueling infrastructures. Our planning model is extended under a two-stage stochastic framework to address the multi-energy-sector uncertainties, e.g., RES generation, electric loads, and the refueling demands of hydrogen vehicles. The first-stage problem is to optimize the siting and sizing plan of microgrids. Then, in the second-stage problem, the coordinated scheduling of electricity and hydrogen supply systems is modeled as second-order conic programs (SOCPs) to accurately capture the power flow representation under stochastic scenarios. Also, the logical constraints with binary variables are introduced to describe the energy transactions and storage operations, which results in an MICP recourse structure. Note that the stochastic MICP formulation could be very challenging to compute even with a moderate number of scenarios. One challenge certainly comes from integer variables that cause the problem nonconvex. Another challenge follows from the fact that the strong duality of SOCPs might not hold in general. To mitigate those two challenges, we prove that the continuous relaxation of our recourse problem has strong duality, and make use of that continuous relaxation and other enhancements to design an augmented decomposition algorithm. As revealed by our numerical tests, the proposed decomposition method outperforms PHA in both the solution quality and computational efficiency. Comparing to the PHA, our ABD method often achieves tighter bounds with trivial optimality gaps. Also, it could reduce the computation time by orders of magnitude. With the help of advanced analytical tool, the proposed planning framework can be readily implemented in real-world applications. [ABSTRACT FROM AUTHOR]

Two interconnected AC microgrids are proposed based on three renewable energy sources (RESs): wind, solar, and biogas. The wind turbine drives a permanent magnet synchronous generator (PMSG). A solar photovoltaic system (SPVS) with an appropriate inverter was incorporated. The biogas genset (BG) consists of a biogas engine coupled with a synchronous generator. Two interconnected AC microgrids, M1 and M2, were considered for study in this work. The microgrid M2 is connected to a diesel engine (DE) characterized by a continuous power supply. The distribution power loss of the interconnected AC microgrids comprises in line loss. The M1 and M2 losses are modeled as an objective function (OF). The power quality enhancement of the interconnected microgrids will be achieved by minimizing this OF. This research also created a unique frequency control method called virtual inertia control (VIC), which stabilizes the microgrid frequency using an optimal controller. In this paper, the following five controllers are studied: a proportional integral controller (PI), a fractional order PI controller (FOPI), a fuzzy PI controller (FPI), a fuzzy fractional order PI controller (FFOPI), and a VIC based on FFOPI controller. The five controllers were tuned using particle swarm optimization (PSO) to minimize the (OF). The main contribution of this paper is the comprehensive study of the performance of interconnected AC microgrids under step load disturbances, step changes in wind/solar input power, and eventually grid following/forming contingencies as well as the virtual inertia control of renewable energy resources used in the structure of the microgrids. [ABSTRACT FROM AUTHOR]

In the near future, the notion of integrating distributed energy resources (DERs) to build a microgrid will be extremely important. The DERs comprise several technologies, such as diesel engines, micro turbines, fuel cells, photovoltaic, small wind turbines, etc. The coordinated operation and control of DER together with controllable loads and storage devices, such as flywheels, energy capacitors and batteries, are central to the concept of microgrid. Microgrids can operate interconnected to the main distribution grid, or in an islanded mode. This paper reviews the studies on microgrid technologies. The modeling and optimization methodologies of DERs are also presented and discussed in this paper along with system control approaches for DERs and microgrids. The review findings indicate that the use of multimodal indicators that take into consideration the financial, technological, ecological, and social elements of microgrids increased the community's and stakeholders' reaction capability. The microgrid structure under consideration comprises several types of combined heat power devices, boilers, and various types of DERs, including FC units, distributed generators, and MTs. Moreover, compared to grid-connected mode, the microgrid's total operation cost is significantly higher in isolated mode. [ABSTRACT FROM AUTHOR]

In recent years, the microgrid system (MGS) has become an important method for the consumption of renewable energy sources (RES). In the actual operation process, the uncertainties of RES add to the complexity of the MGS operation. Furthermore, the MGS is often operated by multiple subsystem operators. The benefit distribution among subsystem operators is an important factor affecting the overall stable operation of the MGS. In order to resist the interference of the above factors, a two-stage optimization method is proposed in this paper, which includes a bi-level robust optimization (BRO) model in the overall scheduling stage of the MGS and a nucleolus-based cooperative game (NCG) model in the internal cost allocation stage among the subsystem operators. The simulations demonstrated the following outcomes: (1) the P2G device can reduce the operating cost of the MGS by converting electricity into natural gas when the electricity price is low; (2) the two-stage optimization method can ensure the stable operation of the MGS by resisting the disturbance of uncertain wind power outputs in the overall scheduling stage and realizing a reasonable cost allocation among the subsystem operators in the internal cost allocation stage. [ABSTRACT FROM AUTHOR]

With the significantly rising demand for alternate energy resources like renewable sources, the power demands are fulfilled at reduced expenses, and increased efficiency. Despite these alternate generation techniques, there are still considerable challenges in controlling and working Microgrids (MG's) configured in DC and AC. Hybrid Micro-Grids (HMGs) are developed particularly for overcoming the effective power regulation difficulties encountered in standalone MGs by combining the advantages of individual AC and DC configurations. Since microgrids are equipped with power electronic interfaces, unlike the conventional system, maintaining voltage stability, frequency stability, and optimal power management is challenging and requires a different approach. This paper attempts to analyze various stability issues in existing systems and the control methods employed in prior works for a better understanding and management of HMG systems. It also presents a comparative study of related works in terms of their control techniques and achieved performance. The paper further discusses various research challenges prevailing in this field thereby providing an insight into the development of better control methods and techniques for resolving various stability issues in HMG for better management and supporting future applications. [ABSTRACT FROM AUTHOR]

Recent advancements in renewable generation resources and their vast implementation in power sectors have posed serious challenges regarding their operation, protection, and control. Maintaining operating frequency at its nominal value and reducing tie-line power deviations represent crucial factors for these advancements due to continuous reduction of power system inertia. In this paper, a new modified load frequency controller (LFC) method is proposed based on fractional calculus combinations. The tilt fractional-order integral-derivative with fractional-filter (TFOIDFF) is proposed in this paper for LFC applications. The proposed TFOIDFF controller combines the benefits of tilt, FOPID, and fractional filter regulators. Furthermore, a new application is introduced based on the recently presented artificial hummingbird optimizer algorithm (AHA) for simultaneous optimization of the proposed TFOIDFF parameters in the studied two-area power grids. The contribution of electric vehicle (EVs) is considered in the centralized control strategy using the proposed TFOIDFF controller. The performance of the proposed TFOIDFF controller has been compared with the existing tilt with filter, PID with filter, FOPID with filter and hybrid fractional-order with filter LFCs from the literature. Moreover, the AHA optimizer results are compared with the featured LFC optimization algorithms in the literature. The proposed TFOIDFF and AHA optimizer are validated against renewable energy fluctuations, load stepping, generation/loading uncertainty, and power-grid parameter uncertainty. The AHA optimizer is compared with the widely-used optimizers in the literature, including the PSO, ABC, BOA, and AEO optimizers at the IAE, ISE, ITAE, and ITSE objectives. For instance, the proposed AHA method has a minimized IAE after 34 iterations of 0.03178 compared to 0.03896 with PSO, 0.04548 with AEO, 0.04812 with BOA, and 0.05483 with ABC optimizer. Therefore, fast and better minimization of objective functions are achieved using the proposed AHA method. [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]

This paper introduces an enhancement of the protection and operation of the Future Renewable Electric Energy Delivery and Management (FREEDM) system. It uses the solid-state transformers to connect the residential A.C. and D.C. microgrids to the distribution system and fault isolation devices for faulty line isolation. In this paper, a current differential protection scheme has been proposed to detect faults in the FREEDM-based microgrid network. This method is based on the current measurement at the two-line terminals using phasor measurement units to ensure data synchronization and minimize the measuring error. Also, a communication scheme that is based on the Internet of things technology and Wi-Fi is constructed for data monitoring and interlinking between the relays, transducers, and the fault isolation devices in the two-terminals lines. A hypothetical FREEDM system has been used for the verification and testing of the proposed method. Different fault types at different locations and fault resistances have been applied to prove the effectiveness of the proposed protection method in detecting the fault condition. The performance of the proposed method is investigated using the security, dependability, and accuracy indices. A prototype of the FREEDM system is designed, implemented, and tested using the Proteus software simulator and in the laboratory. The results prove the efficiency of the proposed protection method in detecting and isolating the fault conditions in a fast, reliable, and accurate manner. Moreover, the protection scheme achieved high accuracy for all faults, equal to 98.825%. [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]