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Distributed cooperative control strategies for DC microgrids have been rapidly evolving in recent years. However, introducing a cyber layer to enhance robustness, scalability, and reliability also exposes the system to potential cyber‐attacks. The damage inflicted by such attacks on the system performance can be catastrophic, reaching a point where it may devastate the system normal operation. By using model reference adaptive control (MRAC), this article proposes a resilient approach that does not require an accurate model of the system and despite the uncertainties for detecting false data injections into the reference DC voltage and simultaneously mitigating their adverse effects on the system stability and performance. The proposed technique employs an observer to detect possible false data injections in an online manner. By emulating the behavior of an ideal reference model, the MRAC ensures adaptive adjustments of the control parameters over time to mitigate the negative effects of potential attacks effectively and despite non‐idealities such as measurement noise, parameter variations, and environmental changes in DC microgrids, the MRAC effectively manages false data injection attacks. Simulation studies are conducted using diverse scenarios involving a three‐node DC microgrid to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the growth of the load in the electricity networks, sufficient investment in the generation and lines expansion should be made in order to provide the energy needed by consumers with the lowest possible investment and operation costs. This issue is especially important in distribution networks, which are faced with the uncertainties of renewable energy generation and the development of microgrids and related issues. In this article, the planning of generation and lines expansion has been modeled with the aim of minimizing the total costs of microgrids, based on the cooperative approach. For this purpose, a bi‐level model has been developed; on the upper level, microgrids make investment decisions with a cooperative approach, and a constrained stochastic formulation has been developed with considering operational uncertainties on the lower level. Also, in this article, in order to ensure the supply of critical loads in island conditions, the self‐sufficiency index is defined. Three case studies have been considered to ensure the effectiveness of the developed model. In case 1, each microgrid will be able to supply its load only by generating of its units and purchasing from the retail market. In case 2, the possibility of trading with other microgrids in a non‐cooperative approach will also be available to the microgrids operators, and in case 3, microgrids can exchange energy with other microgrids in a cooperative manner. The simulation results showed that due to the possibility of using nearby microgrid resources, the cost of microgrid load supply in case 2 was reduced by 4.84% compared to case 1. Also, this cost in case 3 was reduced by 5.23% and 0.38%, respectively, compared to cases 1 and 2, due to the use of a cooperative manner in microgrid load providing. [ABSTRACT FROM AUTHOR]

To improve the stability of the inverter‐based microgrid (MG), this paper employs a novel data‐driven based method to coordinately adjust control parameters of inverters in a fast local manner. During the design process, an offline eigenvalue based optimization problem that is used to calculate the optimal control parameters under various operating conditions is first constructed. In order to reduce reliance on full system information, a feature selection algorithm is utilized to extract the most relevant local measurements that influence the adjustment of each control parameter. Then, regarding local measurements as input variables and optimal control parameters as output variables, based on northern goshawk optimization (NGO) and long short‐term memory (LSTM) network, a novel deep learning algorithm is proposed to train the local parameter adjustment model (LPAM) by learning the mapping relationship between them. During the application, to guarantee the stability of MG all the time, a security region based shielding mechanism is developed, where the improper control parameter adjustment will be replaced by a safe one. The case study indicates that the proposed algorithm has better mapping accuracy than traditional LSTM neural networks and also faster calculation speed than the traditional offline optimization‐based method. The effectiveness and advantages of the proposed method are demonstrated in a modified 9‐bus MG. [ABSTRACT FROM AUTHOR]

Strong uncertainties of distributed renewable generations, loads and real‐time market prices impose higher requirements on the online scheduling for microgrids. Traditional model‐driven methods exist several limitations due to low solving efficiency, difficulty in handling uncertainties, reliance on accurate prediction information, and inability to leverage accumulated historical data. This paper proposes a data‐driven improved imitation learning based approach for online microgrids optimization. First, a mixed integer linear programming model is established to derive offline optimal decisions within the given scenarios, which serve as expert demonstrations to help construct a sample database for imitation learning. Next, a direct imitation learning model based on eXtreme Gradient Boosting (XGBoost) is established to learn the mapping relationship between the system state and the scheduling decision, and the model training is refined by input clustering and output classification. At the input end, the operation scenarios are clustered to form multiple sub‐feature sets to achieve targeted training for different scenarios. At the output end, a binary variable is added to the label set to realize high‐precision decisions of the action of the energy storage system. Numerical case studies demonstrate the performance advantages of the proposed method. [ABSTRACT FROM AUTHOR]

To address the issues of node interaction power overrun and high carbon emissions that may arise during distributed optimization in multi‐energy parks (MEPs), this paper proposes a distributed low‐carbon and economic operation method for multi‐energy parks based on a cloud platform that considers network transmission capacity.The proposed method achieves maximun profit by designing a two layer collaborative architecture for distributed optimization operations. At the top level, cloud platform services are utilized to build a model for checking network transport capacity and carbon emission quotas, optimizing network node over‐limit inspection. The bottom layer constructs a distributed optimization model for multi‐energy complementation in each park, taking into account the information privacy and individual interests of each multi‐energy park. An improved alternating direction multiplier method (ADMM) is proposed to effectively solve the two‐layer framework. The case studies show that the distributed optimization method under cloud platform services proposed in this paper can achieve maximum revenue for integrated energy service provider while ensuring the safe operation of multi‐energy parks, and reasonably allocate the benefits of collaborative operation among various parks while promoting carbon emission reduction. [ABSTRACT FROM AUTHOR]

Considered as basic structures of next‐generation energy system, environment‐friendly and flexible microgrid (MG) systems are potential solutions to address integration issues of stochastic renewable energy sources. Adaptable energy management approaches provide the possibility to construct effective and various energy interaction. The purpose of this paper is to present a problem‐oriented review of energy management in MG systems. This paper first comprehensively reviews recent research studies on MG, particularly in multi‐microgrid (MMG). Then, this paper proposes a concept of energy utilization model for energy management, which includes a discussion of modern concepts including MG, MMG along with picogrid, nanogrid and virtual power plant. And a synthetic energy management framework including stability, touch, efficiency, evenness, and resilience dimensions is proposed and formulated based on energy utilization mode. Then energy management system is illustrated from the perspectives of system function, management architecture, operation logic and data analysis, and further, a systematic four‐layer hierarchical architecture of management systems for whole MG and MMG systems is proposed. Moreover, key technologies in energy management are summarized and reviewed from the aspects of control, communication, prediction, optimization, and evaluation. Last, eight main prospects on the future trend of energy management in MG and MMG are also presented. [ABSTRACT FROM AUTHOR]

ABSTRACTRenewable generation plays an important part in today's power system. With the inclusion of the inverter interfaced renewable energy sources (RESs) into a microgrid, the total system inertia decreases and it leads to increased frequency deviation in presence of a disturbance. This paper proposes a cascaded Integral Minus Tilt Integral Derivative with Filter (I–TDN)‐Proportional‐Integral (PI), [(I‐TDN)‐PI] controller for frequency stabilization of a hybrid microgrid in presence of electric vehicles (EV). The microgrid model includes reheated thermal power plant with high degree of non‐linear system such as inverter based RESs like photovoltaic and wind generation systems. A diesel engine generator is incorporated for load frequency control during perturbation in the system frequency. Virtual inertia controller (VIC) with inverter based energy storage system (ESS) is commonly used to improve system inertia and frequency stability of the microgrid. In addition to the ESS, this paper proposes inclusion of the EVs in this VIC. The optimal gains of the proposed cascaded I‐TDN‐PI controller are determined using Mountain Gazelle Optimizer (MGO), a modern metaheuristic optimization algorithm. Sensitivity of the proposed controller is investigated in presence of system nonlinearities, load perturbations, time delay, system parameter variation and RES power fluctuations. The simulation results justify the robustness of the proposed control structure for frequency stabilization of the microgrid. [ABSTRACT FROM AUTHOR]

ABSTRACT Since the microgrid lacks inertia compared to the conventional grid with synchronous generators, the microgrid is unable to address the frequency change issues resulting from the integration of large‐scale distributed generation. Due to the ability to provide virtual inertia, generalized droop control (GDC) and virtual synchronous generator (VSG) control are considered effective solutions for improving frequency regulation. However, in response to external frequency disturbances, the grid‐connected inverters may experience a significant transient active power overshoot caused by GDC and VSG. In this paper, the GDC is used as the fundamental control architecture, and then the small signal models of the GDC and VSG are compared and analyzed under various disturbances. A reduced‐order method for the GDC model is proposed to simplify the analysis of GDC. Additionally, GDC adaptive inertia (GDCAI) and adaptive inertia for operation mode switching (AIOMS) are proposed to mitigate frequency fluctuations and improve active power response. The effectiveness of the two control strategies is verified by MATLAB/Simulink simulation and StarSim hardware‐in‐the‐loop (HIL) experiment. [ABSTRACT FROM AUTHOR]

In the multi type load information of hybrid microgrids, data loss or incompleteness may occur due to network congestion, signal interference, equipment failures, and other reasons. Especially with the continuous generation of new load data, gradually incorporating these new data into the existing aggregation process to achieve continuous updating and optimization of load information. Therefore, this article proposes a digital twin model construction method for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints. The Leida criterion and cubic exponential smoothing method are used to preprocess various load data of hybrid microgrids, remove abnormal data, reduce data fluctuations, and make the data more interpretable. Establish integrity constraints for multiple load data of hybrid microgrids and extract load characteristics of hybrid microgrids. Based on these, establish a digital twin model for the incremental aggregation of multiple load information in a hybrid microgrid, and solve the model using an improved K-means algorithm to achieve continuous updating and optimization of load information. The experimental results show that the data sharing delay of this method is 0.12 s, the load is basically consistent with the actual value, and the relative error of the load data is 4%. [ABSTRACT FROM AUTHOR]

In the context of the continuous development of new energy vehicles, an increasing number of electric vehicles (EVs) are being integrated into microgrids, which impacts the operation of microgrids. It is necessary to analyze the emission scheduling of microgrids connected with EVs to ensure the smooth and reliable operation of microgrids with EV integration. This paper aims to realize optimal microgrid scheduling. This article took the case of EV connection with microgrids through orderly charging and discharging. Firstly, mathematical models for each output unit in the microgrid were established. Then, aiming to minimize both the operational cost and pollution emission treatment cost of the microgrid, an emission dispatch optimization model was developed. The whale optimization algorithm (WOA) was chosen as the solving algorithm, and an improved WOA (IWOA) was obtained by optimizing the original WOA in terms of population initialization and position updating parameters. An example analysis revealed that the IWOA demonstrated superior optimization performance compared to other optimization algorithms. In the scenario of orderly charging and discharging, the EVs discharged during peak hours and charged during off-peak hours to balance the microgrid load. The operating cost obtained through the IWOA was 967.25 yuan, and the pollution emission treatment cost was 241.52 yuan, resulting in a total cost of 1208.77 yuan. These results confirm the reliability of the proposed IWOA in solving the model and its applicability in optimizing microgrids with EV integration. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper introduces a novel hybrid approach, termed ZOA‐SNN, for fault detection and identification in converter‐interfaced microgrids. By integrating the Zebra Optimization Algorithm (ZOA) with Spiking Neural Network (SNN) technology, the proposed method provides a comprehensive solution suitable for both grid‐connected and autonomous microgrid operation scenarios. The technique effectively isolates faults in the microgrid while maintaining operation continuity, particularly in islanded conditions. When operating in grid‐connected mode, distributed generators (DGs) provide electricity as needed. When the grid is not available, power sharing amongst DGs is controlled by voltage angle droop control. By isolating malfunctioning portions, the proposed protection system reduces load shedding, while DG control guarantees smooth islanding and resynchronization. Evaluation on the MATLAB platform demonstrates the superior performance of the proposed technique compared to existing algorithms such as Augmented Lagrangian Particle Swarm Optimization (ALPSO), Graph Convolutional Network (GCN), and Buffalo Optimization (BO). With an accuracy, recall, precision, and F1‐score reaching 98.5%, 99.2%, 99.1%, and 99.1%, respectively, the ZOA‐SNN approach excels in fault detection and classification. Additionally, it significantly reduces computation times for parameter calculation, enhancing efficiency in microgrid control systems. These results highlight the innovation and advantages of the ZOA‐SNN approach in enhancing the reliability and efficiency of fault detection systems in microgrid environments. [ABSTRACT FROM AUTHOR]

ABSTRACT To enhance the power quality of microgrid inverters and reduce the influence of changes in inductance parameters and external disturbances on the direct power control of the inverter system, a terminal sliding mode control strategy with a variable exponential power reaching law has been proposed. The designed new reaching law comprises a variable exponential term and an enhanced power term. The variable exponential term contains an arctangent function, and the power term coefficient is replaced by a function about the sliding mode. Therefore, the convergence rate can be adaptively adjusted based on different stages of the system, ensuring a faster rate of convergence throughout the process of approaching the sliding mode surface. To weaken the effects of changes in inductance parameters, a power disturbance observer is designed to estimate the internal disturbances induced by the filtered inductance in the system. Subsequently, a sliding mode control law containing disturbance observations is derived. Moreover, a variable exponential terminal sliding surface is designed to adjust the convergence rate of system errors on the sliding surface in stages, thereby enhancing the control performance of the system. The simulation results show that the new reaching law has faster convergence rate and better dynamic performance. The convergence speed of the system error can be accelerated by the designed variable exponential terminal sliding surface. The sliding mode control strategy with the variable exponential power reaching law is applicable to the power control system of three‐phase inverters in microgrids, thereby significantly enhancing the dynamic performance and robustness of the system. [ABSTRACT FROM AUTHOR]

Increasing the use of renewable energy in microgrids (MGs) offers environmental and economic benefits. However, the unpredictable and intermittent nature of available resources poses challenges for optimal MG scheduling. Hybrid AC–DC microgrids provide a solution, seamlessly integrating renewables while reducing energy losses and improving power grid reliability. Additionally, incentive‐based demand response programs promote flexible energy consumption, further mitigating the variability of renewable generation and enhancing grid stability. This paper investigates the challenges and potential of high renewable penetration in hybrid AC–DC MGs, analysing the role of demand response programs in system optimization. The microgrid's energy management is modelled using MILP, while a Stackelberg game represents the demand response program. These models are integrated to optimize energy management and demand response jointly. Simulations demonstrate the cost‐saving benefits of this integrated framework, achieved through coordinated flexible resource scheduling and incentive‐based demand response programming. [ABSTRACT FROM AUTHOR]

The document discusses the application of optimization principles in the planning and operation of active distribution networks (ADNs) to handle distributed generation resources like renewables, battery energy storage systems (BESSs), and electric vehicles (EVs). Various optimization techniques are used to address challenges such as voltage regulation, loss minimization, demand response, and renewable energy integration, aiming to develop efficient, flexible, and sustainable networks transitioning towards smart grids. The document also highlights the importance of multi-objective optimization approaches in balancing conflicting objectives like cost, reliability, and sustainability, making objective functions essential tools for modern energy distribution systems. [Extracted from the article]

With the proposed "double carbon" target for the power system, large-scale distributed energy access poses a major challenge to the way the distribution grid operates. The rural distribution network (DN) will transform into a new local power system primarily driven by distributed renewable energy sources and energy storage, while also being interconnected with the larger power grid. The development of the rural DN will heavily rely on the construction and efficient planning of the microgrid (MG) within the agricultural park. Based on this, this paper proposes a two-stage optimal scheduling model and solution strategy for the microgrid distribution network with multi-source agricultural load aggregation. First, in the first stage, considering the flexible agricultural load and the market time-of-use electricity price, the economic optimization is realized by optimizing the operation of the schedulable resources of the park. The linear model in this stage is solved by the Lingo algorithm with fast solution speed and high accuracy. In the second stage, the power interaction between the MG and the DN in the agricultural park is considered. By optimising the output of the reactive power compensation device, the operating state of the DN is optimised. At this stage, the non-linear and convex optimization problems are solved by the particle swarm optimization algorithm. Finally, the example analysis shows that the proposed method can effectively improve the feasible region of safe operation of the distribution network in rural areas and improve the operating income of a multi-source agricultural load aggregation agricultural park. [ABSTRACT FROM AUTHOR]

Interleaved boost converters (IBCs) are commonly used as interface converters for DC microgrids (MGs) due to their high efficiency and low output ripple. However, the MGs system can easily become unstable due to the negative impedance characteristics of constant power load (CPL) and rapid power fluctuations. This paper proposes a fixed-time backstepping sliding-mode controller (FTBSMC) aimed at stabilizing the MGs system and achieving fixed-time tracking of the DC bus voltage. Firstly, the fixed-time disturbance observer (FxTDO) estimates the load disturbance at a fixed time, which improves the fast disturbance resistance of the system. Then, based on the dis-turbance estimation, the FTBSMC is designed, which combines the fast dynamic response of the sliding-mode control with the global stability of the backstepping control, avoiding the singularity problem of the conventional sliding-mode control. In addition, a first-order nonlinear filter is employed to avoid the direct differentiation of conventional backstepping control and at the same time to ensure global fixed-time stability. The fixed-time convergence of the proposed FTBSMC is rigorously demonstrated by using Lyapunov stability analysis. Finally, the FTBSMC proposed is verified by simulation and experiment in terms of faster dynamic response and stronger robustness. [ABSTRACT FROM AUTHOR]

In the off-grid photovoltaic DC microgrid, traditional droop control encounters challenges in effectively adjusting the droop coefficient in response to varying power fluctuation frequencies, which can be influenced by factors such as line impedance. This paper introduces a novel Multi-strategy Harris Hawk Optimization Algorithm (MHHO) that integrates variable universe fuzzy control theory with droop control to develop an adaptive variable universe fuzzy droop control strategy. The algorithm employs Fuch mapping to evenly distribute the initial population across the solution space and incorporates logarithmic spiral and improved adaptive weight strategies during both the exploration and exploitation phases, enhancing its ability to escape local optima. A comparative analysis against five classical meta-heuristic algorithms on the CEC2017 benchmarks demonstrates the superior performance of the proposed algorithm. Ultimately, the adaptive variable universe fuzzy droop control based on MHHO dynamically optimizes the droop coefficient to mitigate the negative impact of internal system factors and achieve a balanced power distribution between the battery and super-capacitor in the DC microgrid. Through MATLAB/Simulink simulations, it is demonstrated that the proposed adaptive variable universe fuzzy droop control strategy based on MHHO can limit the fluctuation range of bus voltage within ±0.75%, enhance the robustness and stability of the system, and optimize the charge and discharge performance of the energy storage unit. [ABSTRACT FROM AUTHOR]

Uncertainty is an important subject in optimization problems due to the unpredictable nature of real variables in the power system area, which can condition the solution's accuracy. The effective modelling of stochastic variables can contribute to the reduction in losses in the system under evaluation and facilitate the implementation of an effective response in advance. To model uncertainty variables, the most extended technique is the scenario generation (SG) method. This method evaluates possible combinations of complete curves. Classical scenario generation methods are founded on probability distributions or robust stochastic optimization. This paper proposes a novel approach for constructing scenarios using the Ant Colony Optimization (ACO) algorithm, referred to as ACO-SG. This methodology does not require a previous statistical study of uncertainty data to generate new scenarios. A historical dataset and the desired number of scenarios are the inputs inserted into the algorithm. In the case study, the algorithm used historical data from the Savona Campus Smart Polygeneration Microgrid of the University of Genoa. The approach was applied to generate scenarios of photovoltaic generation and building consumption. The low values of the Euclidean distance were used in order to check the validity of the scenarios. Moreover, the error deviation of the scenarios generated with the goal of daily power were 1.77% and 0.144% for the cases of PV generation and building consumption, respectively. The different results for both cases are explained by the characteristics of the specific cases. Despite these different results, both were significantly low, which indicates the capability of the algorithm to generate any kind of feature within curves and its adaptability to any case of SG. [ABSTRACT FROM AUTHOR]

The publication addresses the dynamic state challenges encountered during development of a dual active bridge (DAB) converter within DC microgrid systems. The conventional startup method is identified as instigating a cascade of unfavorable outcomes, encompassing elevated starting current, transformer current asymmetry, DC voltage distortions, EMI and heightened thermal stress on semiconductor components. Additionally, it necessitates precise calibration of magnetic components and diodes. A proposed remedy to these issues is introduced, involving a control method based on an additional phase shift to modulate the current of the primary H bridge. This novel control methodology is posited as a means to mitigate the aforementioned undesirable effects associated with traditional converter initiation techniques. The research also delves into considerations of a proper design procedure for the converter. Emphasis is placed on integrating the novel control methodology into the design framework in order to effectively address challenges arising during transient states. Validation of the proposed solution is substantiated through a series of laboratory tests, the results of which are comprehensively presented in the article. These tests affirm the efficiency of the system when incorporating the novel control methodology, thereby substantiating its practical utility in mitigating the issues identified during the initiation phase of the DAB converter in DC microgrid systems. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an adaptive nonlinear control scheme to ensure the stability of a boost converter in a DC microgrid that supplies a constant power load (CPL) and a resistive load. The proposed controller comprises a passivity‐based control (PBC) and a Nonlinear Disturbance Observer‐Based Robust Control (NDOBRC). The PBC guarantees the overall stability of the proposed controller, whereas the NDOBRC compensates for various sources of disturbances and uncertainties in the system, such as CPL power. The large‐signal stability of the PBC is established using the Lyapunov function. Simulation and experimental results demonstrate the effectiveness of the proposed controller in terms of superior performance compared to the PI method. [ABSTRACT FROM AUTHOR]

The advancement of renewable energy technology has been significantly aided by solar photovoltaics (PV). Since solar PV is a weather‐dependent source, it cannot be dispatched. To ensure that the solar PV system can harvest the maximum amount of electricity for the available irradiance level, maximum power point tracking (MPPT) algorithms are used. For standalone low‐voltage DC (LVDC) microgrids to utilize the energy storage system as efficiently as possible, maximum power extraction is essential. The sensed PV voltage and current are essential for these MPPT algorithms to ensure that the maximum power point of the panel is captured. This work proposes an effective fault‐tolerant control (FTC) scheme for the solar PV subsystem in the LVDC microgrid that can seamlessly extract the maximum power despite the PV voltage sensor being faulty. The proposed FTC scheme uses a sliding mode observer (SMO)‐based method to detect and isolate PV voltage sensor faults in the standalone LVDC microgrid. The efficacy of the proposed FTC is assessed in a range of circumstances involving load disturbance, irradiance change, and various sensor fault scenarios. The performance of the proposed FTC is validated using experimental analysis on the LVDC microgrid testbed and MATLAB simulations. Given a faulty PV voltage sensor, at a given operating condition of the microgrid, the proposed FTC scheme is successful in reducing the additional power burden on the battery storage by at least two times. Consequently, the additional discharge in terms of SoC is also seen to be decreased by at least 9%. The proposed FTC technique outperforms the popular MPPT approaches for solar PV in terms of PV voltage sensor fault tolerance in the microgrid. [ABSTRACT FROM AUTHOR]

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

This paper investigates the stability issue in direct current microgrid (DC MGs) due to linear and nonlinear constant power load (CPL). The deterioration can be damped out by inserting virtual resistances to minimize the impact of negative resistance of the CPL. However, large virtual resistances caused low stability region. This paper proposed a dual series virtual impedance with fuzzy logic-based voltage and current feed-forward controller. The dual series virtual impedance and fuzzy logic (FL) are used for improvement of transient behavior and steady-state error. However, the crossover frequency increased by inserting CPL is minimized but not much improved by virtual impedance controller. A fuzzy logic-based voltage and current feedforward controller moves from instable to stable region. With FL based current feedforward controller, the crossover frequency has been minimized from 454 rad/sec to 23.8 rad/sec for 5 kW load. The feedforward current can not only improve the transient response but also mitigate the crossover frequency for small-signal modelling stability of microgrids. The comparative stability and transient performance have been demonstrated for variation of CPL (5–9 kW) and source. The hardware prototypes and simulation analysis are used to validate the proposed stable operation criteria of the DC microgrid. [ABSTRACT FROM AUTHOR]

Copyright of Scientia Iranica. Transaction D, Computer Science & Engineering & Electrical Engineering is the property of Scientia Iranica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper addresses the challenge of integrating multiple energy sources into a single-domain microgrid, commonly found in urban buildings, while also providing a platform for energy management. A Lyapunov stability analysis of a simple boost converter was used as a basis for designing the dual control loop of the grid. The versatility of the developed control structure allows for the incorporation of an arbitrary number of sources hence achieving scalability. Next, the energy in the microgrid was separated into exogenous energy and actuator energy. This yielded a description of the system that quantified the condition of stability independent of the decision made by a would-be energy management system. This, in turns, liberates the process of designing an optimized energy management system from stability concerns. The acquired theoretical findings were then translated to a simulation model, where multiple components of the grid were simulated under a typical scenario of operation. Once the simulation phase was concluded, a prototype of the designed grid was constructed to emulate the theoretical results. The prototype exhibited promising performance, matching the simulation predictions to a reasonable degree. [ABSTRACT FROM AUTHOR]

DC distribution systems have garnered interest in recent years due to their advantages over AC distribution systems, such as their high power conversion efficiency and lack of harmonic issues. An isolated DC-DC converter with low-input noise, high efficiency, and high-power density is required for DC microgrids within DC distribution systems. Although existing DC to DC converters have high efficiency and high power density, they still have input noise problems due to pulsating input currents. Thus, a large input filter should be inserted, which increases the cost and degrades the power density. In this study, a novel three-level double LLC resonant converter with zero-input-current ripple is presented for DC microgrid application with a high-voltage DC bus. The input-current ripple of the proposed converter theoretically decreases to zero without adding large input filters. Moreover, the voltage stress on each main switch is only half of the input voltage when using the modified three-level structure, which enables the use of low-voltage-rated power switches for high-voltage input applications. In addition, all the primary switches and secondary diodes are softly switched over a wide input voltage range. The experimental results of the prototype are presented to verify the performance of the proposed converter. [ABSTRACT FROM AUTHOR]

At present, the safe operation of integrated energy systems is significantly affected by the considerable uncertainty inherent to wind and photovoltaic power generation. Based on this, this paper proposes an optimal scheduling model for integrated electricity, heat, and hydrogen-based energy systems on distributed robust optimization (DRO). Firstly, a combined heat and power microgrid system considering hydrogen energy systems was constructed based on the thermoelectric cogeneration characteristics of fuel cells and electrolyzers. Then, a data-driven two-stage distribution robust optimization scheduling model is built by combining typical historical data of wind power output, photovoltaic power output, and load. The results show that the distributed robust method reduces the running cost by 6% compared to the deterministic method. The proposed method and model are capable of meeting the demand for thermoelectric loads within the microgrid in a more cost-effective manner, thereby achieving stable and independent operation of the system. [ABSTRACT FROM AUTHOR]

Distributed renewable energy supply (RES) is a new pattern for the transformation of power grids. As a characteristic case of RES, microgrids have an advantage in convenient operation. However, the energy management of microgrids remains as a major concern. With the emergence of the decentralized paradigm, blockchain potentially provides a reliable energy data metering and payment for the whole life cycle of energy management. In particular, the demand response (DR) in the microgrid can stimulate demanders to spontaneously manage their load consumption and maintain the balance of the energy trading market. To achieve optimal DR, a dynamic pricing strategy under the blockchain and game‐theoretic approach is proposed. First, the blockchain‐based architecture is applied to ensure the reliability of energy data and lay a foundation of binding agreements for games. Then, the pricing mechanism under the cooperative game is formulated to optimize DR. Moreover, to help resolve the optimal response quantity and reduce the supply punishment of the RES providers, the DR requires accurate forecasting of the energy generation and consumption profiles. Therefore, an ensemble method Long Short‐Term Gate Support (LSTGS) is designed to forecast the RES and load power for intelligent agent to make decision on effective energy scheduling and DR. Taking the classic distributed energy context as a case study, we demonstrate the effectiveness of our approach and show that it can achieve DR profits maximized and improve the stability of the energy‐trading market. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Park microgrids, valued for their efficiency and flexibility, require privacy-conscious energy management to ensure a trusted scheduling and trading environment. This paper, focusing on park microgrids with shared energy storage, designs an energy management strategy that comprehensively considers shared energy storage, scheduling transparency, and privacy security. First, a blockchain-based energy management platform is established, forming an energy dispatch consensus committee to execute decentralized scheduling management and decision-making. Next, an optimized energy scheduling smart contract for park microgrids is designed, considering Time-of-Use (ToU) pricing and storage arbitrage to formulate the day-ahead electricity purchase and sales plans as well as the shared energy storage operation plans. Then, a privacy protection strategy based on the Shamir secret sharing scheme is proposed, effectively preventing data leakage during blockchain interactions. Finally, through case analysis, the superiority of the proposed method in microgrid optimized scheduling, data tamper-resistance, and privacy protection is demonstrated. [ABSTRACT FROM AUTHOR]

A novel robust adaptive technique is proposed to estimate the instantaneous power system frequency using a momentum-learning-control-rate-based fractional-order least mean squares approach with enhanced Harris hawks optimization. The adaptive estimation comprises two modules, where the first part involves the design of the momentum-learning-control-term-based fractional-order least mean squares algorithm and second part focuses on parameter tuning of the algorithm through enhanced Harris hawks optimization incorporating chaotic mapping and opposition-based learning. This integration yields a robust and automated adaptive algorithm for frequency estimation with superior performance compared to traditional transform-based techniques, particularly in the presence of noise. The proposed method excels in scenarios where the estimator should manage multiple variables, including step size, fractional-order step constants, and momentum learning control terms. Moreover, it facilitates accurate power frequency estimation for real signals in multiarea power systems or microgrids. To validate the efficacy of the algorithm, computer-simulated data representing step and ramp changes in the frequency were processed. Additionally, the algorithm was tested with signals derived from a multiple-control-area, multisource renewable-based power system. Detailed comparative results were obtained and verified through MATLAB simulations and real-time experimental setup, demonstrating the superior performance of the adaptive model. [ABSTRACT FROM AUTHOR]

The transformation of power system networks is slowly taking shape, the advent of interruptive technological platforms dealing with digitalization and real‐time trading of power has gained attention based on incorporation of more renewables into the grid. The stochastic nature of renewables pauses security of supply challenges and other related stability concerns, and for this reason efficient methods are investigated in this review to build an understanding of microgrid energy management system (MG‐EMS) and distribution‐based energy management strategies aimed at transforming the conventional grid network into smart grid network. In essence, propagating a technological shift to microgrids which have proven to be ideal distribution networks for residential and commercial loads, have become indispensable in handling distributed energy resources (DER), such as solar, PV, wind, battery energy storage systems (BESS) and small‐scale microgrids, for example in case of excess supply, energy storage system (ESS) has been formulated as a solution to curb excess supply and can offer ancillary services to the grid network. Within the perspective of electricity generation and distribution, microgrid control methodologies, distribution network (DN) management approaches and incumbent optimization strategies used to coordinate and manage grid‐level uncertainties are investigated. In addition, this study proposes distributionally robust optimization (DRO), to manage and mitigate risks associated to shortage or oversupply of power from RESs. [ABSTRACT FROM AUTHOR]

This research addresses the challenge of accurate load forecasting in cluster microgrids, where distributed energy systems interlink to operate seamlessly. As renewable energy sources become more widespread, ensuring a consistent and reliable power supply in the face of variable weather conditions is a significant challenge for power providers. The variability in energy consumption patterns, influenced by human behavior and environmental conditions, further complicates load prediction. The inherent instability of solar and wind energies adds complexity to forecasting load demand accurately. This paper suggests a solution in addressing some challenges by proposing a Modified Temporal Convolutional Feed Forward Network (MTCFN) for load forecasting in cluster microgrids. The Fire Hawk Optimization algorithm is employed to determine optimal configurations, addressing the intricacies of this complex optimization problem. Data collected from the Microgrid Market Share and Forecast 2024–2032 report, the efficiency of the proposed approach is evaluated through metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and R-squared. The RMSE, MSE, MAE, MAPE, and R-squared values of the MTCFN are 0.4%, 1.5%, 0.6%, 6.8%, and 0.8%, respectively. The optimization algorithm's effectiveness is cross-validated through rigorous testing, training, and validation processes, revealing that the FFNN model based on the Fire Hawk Optimization algorithm yields superior load forecasting results. This research contributes to the advancement of signal, image, and video processing in the context of resilient and accurate energy management in cluster microgrids. [ABSTRACT FROM AUTHOR]

Unlike the inverters in the traditional alternating‐current (AC) microgrid, those in a microgrid with series microsource inverters (SMSI‐MG) are connected to the power grid after being cascaded. The authors of this study first divide the control sections according to the degree of grid voltage dips and formulate a coordinated scheme to suppress fluctuations in the output powers of the SMSI‐MG. For the section in which the degree of unbalanced grid voltage dips is relatively low, a current‐limiting strategy that reduces the output power of the SMSI‐MG through the coordinated control of the generator subunits (CCGU) is proposed. More active power can be provided by the SMSI‐MG when the proposed strategy is used, than in the strategy that is based on changing the reference power, and the output reactive power of the SMSI‐MG can be automatically changed with the degrees of dip and unbalance of the grid voltage. The Light Gradient‐Boosting Machine (LightGBM) is used to establish a mapping relationship between the parameters characterizing overcurrent and the reduction quantity in output active power of the SMSI‐MG to implement the CCGU‐based current‐limiting strategy. The complex collaborative control is simplified to improve the low‐voltage ride through (LVRT) capability of the SMSI‐MG. [ABSTRACT FROM AUTHOR]

Renewable energy sources have been widely installed and operated in power systems, particularly in microgrids in the form of distributed generation units. This issue requires efficient energy management tools which take into account the inherent uncertainties of such energy resources. Thus, this paper presents a stochastic framework aimed at scheduling the renewable energy-based and thermal units in a coordinated way. The generation units comprise fuel cell units with proton exchange membrane known as PEMFC-CHP producing heat and power, concurrently. Moreover, the uncertainties arising from wind and solar power as well as market prices are characterized by deploying scenario-based optimization. The mentioned framework considers storing hydrogen and the model is presented within a stochastic mixed-integer nonlinear programming (MINLP) framework. The resulting problem is simulated on a modified 33-bus distribution network and tackled using the modified marine predators algorithm (MMPA)algorithm. The obtained results indicate that the revenue increases by more than 5% compared to other optimization algorithms. Furthermore, taking into account CHP will increase the total profit of the system by more than 15%. • Proposing optimal management of smart parking considering upscale electricity price with Hydrogen Storage Systems (HSS). • Proposing fuel cell units with proton exchange membrane which generate heat and power simultaneously (PEMFC-CHP). • Optimal coordinated scheduling of renewable energy resources in micro-grids improve objective function. • Suggesting the strategy of storing hydrogen is also considered for PEMFC-CHP units. [ABSTRACT FROM AUTHOR]

Extreme weather causes an increase in power system failure. Previous studies on system resilience have often overlooked the user-side of a microgrid. This study proposes composite resilience indices (RI) based on the power supply of a large-scale manufacturing campus microgrid to quantify its ability to withstand extreme events. The proposed RI consider load priority, minimum supply load, total energy supplied, and the performance recovery-to-degradation slope ratio in an islanding microgrid. Accordingly, this study presents a three-stage resilience optimal dispatch and reconfiguration strategy, including energy-level scheduling, grid-level reconfiguration, and dynamic-level verification. A multi-objective optimization approach is used for energy scheduling, followed by system reconfiguration via DIgSILENT system modeling to meet the grid code and maximize load supply. Value at Risk methods are used to verify microgrid stability and load shedding requirements, supported by the virtual synchronous generator control of the energy storage system. The test results from a practical large-scale manufacturing campus microgrid validate the proposed approaches for enhancing system resilience considering load values. [ABSTRACT FROM AUTHOR]

This research proposes the application of fractional‐order sliding mode control (FOSMC) at the primary controller level to improve the stability of an islanded microgrid by adjusting its voltage and frequency. The control strategies used in the microgrid are performed in two levels (primary and secondary) in the islanded mode. Practically, most previous studies have worked to improve the primary controller. Droop control is one of the most commonly used methods at the primary level and is adopted in this study as well. The sliding mode control (SMC) strategy is normally used to control linear equations. Thus, the non‐linear microgrid equations were transformed into some linear ones using the input‐output feedback linearization technique. Further, a fractional sliding surface was acquainted. The sliding surface and FOSMC were designed to reject system uncertainties and organize the voltage and frequency. Design parameters were chosen using the Lyapunov stability theorem. The validation of the proposed method using Simulink‐MATLAB confirms its effectiveness in enhancing level power sharing, regulating frequency, and maintaining voltage stability across the system. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities and remote areas. Integrating diverse renewable energy sources into the grid has further emphasized the need for effective management and sophisticated control strategies. This review explores the crucial role of control strategies in optimizing MG operations and ensuring efficient utilization of distributed energy resources, storage systems, networks, and loads. To maximize energy source utilization and overall system performance, various control strategies are implemented, including demand response, energy storage management, data management, and generation‐load management. Employing artificial intelligence (AI) and optimization techniques further enhances these strategies, leading to improved energy management and performance in MGs. The review delves into the control strategies and their architectures, and highlights the significant contributions of AI and emerging technologies in advancing MG energy management. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper addresses the challenge of integrating dispatchable and nondispatchable distributed generators (DGs) in cascaded‐type direct current (DC) microgrids and proposes a communication‐free master–slave control strategy. In the proposed method, all DGs are treated as voltage sources. A master dispatchable DG is primarily responsible for maintaining a constant DC voltage at the point of common coupling (PCC). The rest slave nondispatchable DGs adjust their output power based on a front maximum power point tracking (MPPT) controller. This communication‐free approach enhances system reliability and ease of implementation. Moreover, it ensures load voltage stability, providing a high‐quality power supply for users. Additionally, it enables nondispatchable DGs to achieve MPPT‐based output power and enables power curtailment operation. Subsequently, a small‐signal stability analysis confirms the efficacy of the proposed strategy. Finally, simulation and experiment results further validate its efficacy. [ABSTRACT FROM AUTHOR]

ABSTRACT The autonomous economic control for the islanded AC/DC interconnected microgrid has various modes due to the restriction of the feasible regions of inner and inter‐microgrid resources. To properly handle the cooperative relationship between resources with different regulation characteristics and realize the frequency, voltage stability, and economic operation of the AC/DC interconnected microgrid, a distributed control strategy considering the regulation boundary of the consensus variable is proposed. Firstly, the influence of the variable feasible regions on the consensus control objective is analyzed, and the fundamental principle of the consensus control algorithm considering the variable regulation boundary is expounded. Secondly, the trilevel control strategy consisting of the local equipment control and inner and inter‐microgrid cooperative control is constructed. The local equipment control adopts droop control, and the cooperative control adopts consensus control to realize the elimination of the frequency and voltage deviation and the economic dispatch of active power within and between microgrids. Finally, the AC/DC interconnected microgrid model is built based on PSCAD/EMTDC, and the simulation results verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This study focuses on improving power system grid performance and efficiency through the integration of distributed energy resources (DERs). The study proposes an artificial intelligence (AI) based effective approach for economic dispatch and load management for three linked microgrids (MGs) that operate in both grid-connected and autonomous modes. A day-ahead scheduling method is suggested to calculate the optimal set points for various energy sources in MGs considering various system constraints for safe operation. In addition, a load management approach that shifts the controllable loads from one interval to another is applied to reduce the operating cost of MG. To handle the optimization challenges of energy scheduling and load shifting such complexity and non-linearity, an advanced meta-heuristic method known as the one-to-one based optimizer (OOBO) is used. Overall, the paper proposes a viable and efficient methodology for economical distribution in linked microgrids, which takes advantage of renewable energy resources and incorporates scheduling optimization via the OOBO algorithm. The proposed energy management strategy enhances the system performance, increases energy efficiency, and reduces the daily operational cost by 1.6% for grid connected mode and by 0.47% for islanded operation mode. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy sources (RESs) and power electronics devices has led to a continuous decline in power system stability. Due to the instability of grid-following converters (GFLCs) in weak grids, the grid-forming converters (GFMCs) have gained widespread attention featuring their flexible frequency and voltage regulation capabilities, as well as the satisfactory grid-supporting services, such as inertia and damping, et al. Notably, the risk of wideband oscillations in modern power grids is increasingly exacerbated by the reduced number of synchronous generators (SGs). Thus, the wideband oscillation suppression method based on adaptive active power droop control of GFMCs is presented in this paper. First, the stability of the hybrid grid-forming and grid-following system is obtained according to the improved short circuit ratio (ISCR), where the GFMC is in parallel at the point of common coupling (PCC) of the GFLC. Then, an adaptive adjustment strategy of the active power droop control is proposed to enhance the oscillation suppression capability across the full frequency range, thereby mitigating the wideband oscillation caused by phase-locked loop (PLL) synchronization in the GFLCs. Additionally, a first-order inertia control unit is added to the active and reactive power droop controllers to mitigate frequency and voltage variations as well as suppress potential mid-to-high frequency resonance. Finally, the wideband oscillation suppression strategy is validated by the simulation and experimental results. [ABSTRACT FROM AUTHOR]

Constructing isolated industrial microgrids with wind power is beneficial for improving the economic benefits of high-energy-consuming production, such as the electrolytic aluminum industry. Due to the specialized structure of industrial microgrids and the unique characteristics of the electrolytic aluminum load (EAL), the common emergency frequency control methods do not apply to the specific operational requirements of isolated industrial microgrids. Since EALs have huge regulating capacities and fast responses, this paper proposes a coordinated emergency frequency control scheme to deal with power disturbances in isolated industrial microgrids. The coordinated frequency control model of an industrial microgrid considering demand-side participation is derived. With the help of output regulation theory, a practical, feasible coordinated frequency controller is designed by introducing frequency deviation and power disturbance as feedback control signals. The proposed control scheme achieves reserve power distribution between the generation and demand sides. The microgrid frequency can be maintained within a permitted range in the presence of large power imbalances. The simulation results conducted in an actual isolated industrial microgrid validate the effectiveness and dynamic performance of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Microgrids have emerged as a crucial focus in power engineering and sustainable energy research, with utility-scale microgrids playing a significant role in both developed and developing countries like the Philippines. This study presents a comprehensive framework for utility-scale microgrid planning, emphasizing the sustainable integration of renewable energy resources to the distribution grid. The framework addresses the operational modes of grid-connected and islanded microgrids, emphasizing the seamless transition between these modes to ensure a continuous power supply. By leveraging local distributed energy resources, the microgrid aims to reduce dependence on the main transmission grid while enhancing resilience and reliability. The proposed planning framework not only eases the economic burden of constructing renewable energy sources but also aids distribution utilities in maximizing local resources to achieve sustainable energy goals. Through a detailed network analysis and modeling, the framework provides a robust foundation for optimizing the energy mix and enhancing the overall system performance. This research contributes to advancing microgrid technology as a key driver towards achieving UN Sustainable Development Goals, particularly in promoting clean and affordable energy access. [ABSTRACT FROM AUTHOR]

When microgrids operate autonomously, they must curtail the surplus of renewable energy sources (RES) while minimising reliance on gas. However, when interconnected, microgrids can collaboratively minimise RES curtailment and gas consumption due to the ability of exchanging power. This paper presents a centralised controller and energy management of multiple standalone AC microgrids interconnected to a common AC bus using back-to-back converters. Each microgrid consists of RES, a battery, a gas-powered auxiliary unit, and a load. The battery's state of charge (SOC) is controlled and is used in the AC bus frequency to indicate whether the microgrid has a surplus or shortage of power. High-level global droop control exchanges power between the microgrids. The optimisation problem for this interconnected system is modelled cooperatively to determine the optimal dispatch solution that minimises the energy cost from the auxiliary unit. The optimal dispatch is solved in three cases using the Nelder–Mead simplex algorithm under different settings: one-variable optimisation, three-variable optimisation with the standard droop equation, and three-variable optimisation with a modified droop equation. The optimised performance results are compared with those of the non-optimised benchmark to determine the percentage of optimal performance. The simulation results show that the total energy cost from the auxiliary unit is minimised by 8.98%. [ABSTRACT FROM AUTHOR]

This paper presents the design and validation of a novel adaptive islanding detection method (AIDM) for a hybrid AC/DC microgrid network using a combination of Artificial Intelligence (AI) and Signal Processing (SP) approaches. The proposed AIDM is aimed to detect and discriminate between the different fault/disturbance conditions that result in islanding and/or non-islanding conditions in a hybrid microgrid. For the islanding and non-islanding conditions detection by the AIDM, firstly, fault/disturbance signals are obtained from a test microgrid. Secondly, these signals are decomposed using Dual-Tree Complex Wavelet Transform. Thirdly, a Synthetic Minority Oversampling Technique (SMOTE) is applied for data preprocessing to increase the accuracy of the classifier. Finally, an artificial neural network (ANN) is used as the classifier for training and testing the proposed AIDM for different microgrid configurations and event scenarios. The proposed method is tested with different data categories from three different microgrid test systems with different scenarios. All modeling and simulations are executed in MATLAB Simulink Version 2023a. Results indicate that the proposed scheme could detect and discriminate between islanding and non-islanding conditions accurately in terms of dependability, precision, and accuracy. An average accuracy of 99–100% could be achieved when tested and validated with microgrid networks adapted from IEEE 13-bus systems. [ABSTRACT FROM AUTHOR]

This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller's reference input, such as the voltage and current at the MPP. Feedback controls employ linear PI schemes or nonlinear, intricate techniques. Here, the converter controller is an IDGC that is improved by directly measuring the converter duty cycle and PWM index in a single DG PV-based MG. It introduces a fast-learning Extreme-Learning Machine (ELM) using the Moore–Penrose pseudo-inverse technique and online sequential ridge methods for robust control reference (CR) estimation. This approach ensures the stability of the microgrid during PV uncertainties and various operational conditions. The internal DG control approach improves the stability of the microgrid during a three-phase fault at the load bus, partial shading, irradiance changes, islanding operations, and load changes. The model is designed and simulated on the MATLAB/SIMULINK platform, and some of the results are validated on a hardware-in-the-loop (HIL) platform. [ABSTRACT FROM AUTHOR]

The construction costs and operational challenges of rural microgrids have garnered widespread attention. This study focuses on grid-connected rural microgrids incorporating wind, solar, hydro, and storage systems, and proposes a two-tier optimization configuration model that considers both enterprise costs and user satisfaction. The upper-tier model aims to minimize enterprise costs, covering construction, operation and maintenance, as well as penalties for a curtailment of wind, solar, and hydro power. The lower-tier model evaluates power reliability and cost-effectiveness to maximize user satisfaction. Using the particle swarm optimization algorithm, this study analyzes a case in Yudaokou, Hebei Province, and proposes three optimization schemes: minimizing enterprise costs, maximizing user satisfaction, and a compromise between the two. The optimal scheme, which employs 17 photovoltaic panels, 12 wind turbines, and 15 energy storage units, achieved a user satisfaction score of 0.90. This two-tier planning model provides practical insights for the rational configuration of rural microgrids and reveals the nonlinear relationship between costs and user experience. [ABSTRACT FROM AUTHOR]