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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]

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]

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]

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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]

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]

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]

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]

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]

For cooperation among distributed generations in a DC microgrid (MG), distributed control is widely applied. However, the delay in distributed communication will result in steady‐state bias and the risk of instability. This paper proposes a novel distributed control for time‐delayed DC MGs to achieve accurate current proportional sharing and weighted average voltage regulation. Firstly, by utilizing an advanced observer based on the PI consensus algorithm, the steady‐state bias problem is addressed. Then, using the passivity theory, stability analysis is conducted to reveal the principle of system instability caused by communication delay. On this basis, to offset the adverse effects of communication delay on the system stability, scattering transformation is introduced in the observer‐based distributed control. Moreover, considering the potential delay from the measurement stage in real‐life scenarios, the sufficient condition of the system stability is concluded by constructing the Lyapunov–Krasovskii functional. Finally, the performance of the proposed control and conclusions of stability analysis are verified by hardware‐in‐loop tests. [ABSTRACT FROM AUTHOR]

The traditional short circuit ratio index does not consider the impact of energy storage devices (ESDs) and cannot be used for the collaborative optimization of ESDs and renewable energy sources (RESs). Therefore, this paper proposes a novel synergistic capacity short circuit ratio (SCSCR) index, which can reflect the interaction between multiple RESs and ESDs on each bus under different capacities. Based on the proposed SCSCR, a coordinated optimization method of RESs and ESDs is proposed. It ensures system strength while determining the optimal location and capacity proportion of RESs and ESDs. Firstly, the location and capacity proportion of RESs are obtained through optimization method because the system strength is mainly determined by RESs connection points. Secondly, the optimal location and capacity proportion of the ESDs are found under the optimal configuration of the RESs. The proposed optimization method ensures the system strength while obtaining the optimal location and optimal capacity proportion of RESs and ESDs. Finally, the simulation results verify the effectiveness of this method on the IEEE 9 bus and 39 bus systems, providing reference for the site selection and efficient operation of RESs and ESDs. [ABSTRACT FROM AUTHOR]

This article investigated the charge and discharge management structure of electric vehicles (EVs) in intelligent parking lots (IPLs). It seems that with the expansion of renewable energy sources (RESs) as clean energy and investigation of the effects of EVs on the operation and planning of future distribution networks around the way EVs exchange energy with each other and the upstream network operator, RESs such as solar and wind sources, along with hydrogen storage are essential. Therefore, a new stochastic multi‐scenario approach for charge/discharge management of EVs parked in IPL was proposed, in which a newly developed model for the IPL with a hydrogen storage system (HSS) consisting of a fuel cell, an electrolyzer, and a hydrogen storage tank was presented. In the proposed model, the constraints of upstream network and power balance constraints, with the operating constraints related to the IPL, including EVs, renewable energy sources, and the hydrogen storage system, were formulated as the most important objectives of the optimization problem. The optimization algorithm, Competitive Swarm Optimizer (CSO), was formulated for implementation, and its results were compared with those of the PSO and GWO algorithms. The CSO must handle a variety of practical, large‐scale optimization problems. Based on the obtained results, the excess energy purchased from the upstream network or renewable sources was used as hydrogen storage for consumption during peak hours. As expected, the technical constraints and financial goals of the system were met, and the proposed system was evaluated on a 33‐bus network. As the expected benefits will be the most beneficial with the presence of renewable sources, the final profit will be reduced by taking into account uncertainty for charge/discharge management in EVs such as the uncertainty of electric load, market price, wind turbine, and photovoltaic cell sources. Nonetheless, the profit obtained from renewable resources is preferable to losses resulting from the uncertainty of the system, and according to expectation, the performance of the system for managing the optimal charging and discharging of EVs in the IPL will be acceptable with the maximum profit for the grid. [ABSTRACT FROM AUTHOR]

The issue of generation expansion planning in microgrids has become a challenging issue in electricity industry for two reasons: load growth and uncertainties in renewables' generation. Therefore, this issue is considered here. Here, the modelling of generation expansion planning problem has been developed in a network of microgrids in a decentralized manner, considering normal and contingency conditions. On the other hand, in order to further develop the study considered, decentralized generation expansion planning model of microgrids by considering contingency conditions has been addressed in a cooperative approach to minimize total costs. In developed model, investment decisions are made at the higher level and operational constraints has been considered at lower one. Also, case studies are defined in three different scenarios: islanding operation of microgrids as first scenario and peer‐to‐peer trading of microgrids in non‐cooperative and cooperative approaches as second and third scenarios, respectively. The results of simulations have shown that by facilitating the transactions between microgrids, their total costs are reduced. The costs of the whole set of microgrids in the non‐cooperative scenario are reduced by 9.4% compared to the islanding scenario; and the costs are reduced by 7.5% in the cooperative scenario compared to the non‐cooperative scenario. [ABSTRACT FROM AUTHOR]

The rapid fault isolation is the necessity for the proficient operation of the microgrid because it can increase resiliency of the system by restoration of the power distribution system after isolation of the faulty area. In modern power system, many renewable and nonrenewable sources are integrated through different types of converters; therefore, it is too much tedious to develop a protection scheme which can easily detect and isolate faults under such unpredictable faulty conditions. Further, variation in weather conditions and the fault current level during such conditions is not predictable for traditional protection schemes and needs modification. In addition to above difficulties in the microgrid, distinct category of the AC faults makes protection task more difficult when fault resistance is varying due to change in grounding conditions. Motivated by the above challenges in the proposed microgrid, an ensemble of kNN-based protection scheme has been devised in this work to provide robustness to the microgrid. The tasks of the mode detection, fault detection/classification and faulty section identification under varying weather conditions have been considered in grid-connected and islanded modes as a class of the problem. Discrete wavelet transform has been used to pre-process the measured voltage and current signals retrieved from the appropriate bus. To validate the protection scheme, numerous cases of dissimilar operating scenarios have been considered under both of the operating modes. The results in Sect. 6 indicate that protection scheme is efficient and reliable to increase the robustness of the microgrid. [ABSTRACT FROM AUTHOR]

The depletion of fossil fuels has triggered a search for renewable energy. Electrolysis of water to produce hydrogen using solar energy from photovoltaic (PV) is considered one of the most promising ways to generate renewable energy. In this paper, a coordination control strategy is proposed for the DC micro-grid containing PV array, battery, fuel cell and proton exchange membrane (PEM) electrolyzer. For electrolytic cell, the maximum efficiency is obtained by deducing the energy conversion efficiency of PEM electrolyzer. Combined with the storage of hydrogen in hydrogen storage equipment, an adaptive power conversion control approach is proposed. For the battery and supercapacitor (SC), the state of charge (SOC) and over charging and discharging power are considered to avoid the influence of excessive power and over charge and discharge on the device life span. For fuel cells, when the battery and SC cannot meet the power demand, the power supply is carried out with constant power control scheme. The validity and correctness of modeling and control strategies referred in this paper are verified through simulation results based on MATLAB/Simulink software platform. [ABSTRACT FROM AUTHOR]

An optimization method is proposed for the grid-tied unbalanced MG to reduce overall operating costs and voltage deviation. The proposed method consists of battery systems, EV, PV units, DG, and WT units. The proposed technique is the Ladder Spherical Evolution (LSE) Search algorithm, while finding the optimum method that minimizes both the net cost of the microgrid and the VDI while taking into account uncertain parameters is the major goal of the proposed work. The proposed model includes a 3-phase power flow to prevent the adoption of unworkable solutions. The restrictions for a 3-phase unbalanced network (such as DG, PV, WT, and BS units) originate from their technological constraints and the current-related depiction of power flow. At this point, the performance of the proposed technique is better than the existing approaches in MATLAB. The proposed method manages uncertainties, reduces functional costs and the voltage variation index, and offers more dependable and realistic solutions. The total cost of SSA is 480$, CHA is 470$, AOS is 450$, and the proposed is 340$. Thus, the proposed method provides an optimal solution with less computation and low cost than the existing methods. [ABSTRACT FROM AUTHOR]

The paper proposes an instantaneous power theory (IPT) based an improved low voltage ride-through (LVRT) strategy for photovoltaic-proton exchange membrane fuel cell (PV-PEMFC) based grid following hybrid microgrid architecture. The concept of the instantaneous power theory-based proportional-integral control (IPT-PIC) mechanism has been introduced to enhance the dynamic response of the grid-tied power electronic medium. Based on the direct quantities measured, the processed IPT-PIC output has been used to regulate the active and reactive current injection during the grid side contingencies. Besides, the instantaneous power theory-based fuzzy current control (IPT-FCC) mechanism has also been proposed and compared with the conventional instantaneous power theory-based approaches and found to be suitable for real field applications, particularly in the hybrid microgrid architecture. The percentage improvement of the LVRT capability has been found in the detailed simulated platform using MATLAB/Simulink 2021a. Furthermore, the response time, computational burden and real-time applicability have been evaluated in the dSPACE DS1103PPC controller board. [ABSTRACT FROM AUTHOR]

DC microgrids are gaining more importance in maritime, aerospace, telecom, and isolated power plants for heightened reliability, efficiency, and control. Yet, designing a protective system for DC microgrids is challenging due to novelty and limited literature. Recent interest emphasizes standalone fault detection and classification, especially through data-driven machine-learning approaches. However, the emphasis remains on progressing state-of-the-art tools for fault diagnosis in DC microgrids. Therefore, this work emphasizes fault detection and classification in a low-voltage standalone DC microgrid using a data-driven machine learning hybrid approach: bagged ensemble learner and cosine k-nearest neighbour (C-kNN) algorithms. The proposed fault detection and classification scheme makes the use of local voltage and current measurements which enhances the admissibility of the proposed scheme. The bagged ensemble learner accurately identifies the faults in the line, whereas the cosine k-nearest neighbor classifies the fault as pole to ground or pole to pole for further corrective actions. A diverse set of test scenarios encompassing faulty and normal conditions has been analyzed and validated by randomizing data inputs. The test model comprising PV, battery source, and loads have been constructed in MATLAB/Simulink environment. The proposed scheme promises accurate fault identification and classification in normal and noisy environments. To establish the robustness of the proposed approach, the outcomes of the fault detection and classification scheme have been compared with the methods reported in the literature. The results indicate that the proposed method outperformed in comparison to existing methods. [ABSTRACT FROM AUTHOR]

This paper presents a robust scheme to detect and isolate faults quickly to prevent significant damage to the DC microgrid. The proposed technique uses the joint framework of Hilbert–Huang transform and empirical mode decomposition for feature extraction and bagging tree classifier to accurately and swiftly identify DC faults, which is challenging due to the limited time available to interrupt them. The intermittency pertaining to PV source and outage of distributed energy resources (DERs) may further complicate the protection task. In this regard, this paper proposes an intelligent scheme for fast fault detection and classification in DC microgrid. The joint framework of Hilbert transform and empirical mode decomposition has been used to calculate discriminatory attributes for characterizing the fault behavior in the signal. The ensemble strategy of efficient bagging tree classifier has been exploited after extensive testing and comparison with other modern approaches in this framework. Compared to other methods, the proposed scheme is more precise and faster which ascertain its efficacy in providing resilient protection to the DC microgrid with immunity to stochastic behavior pertaining to weather intermittency and DER outage. The performance of developed protection technique has also been validated on OPAL-RT digital simulator for authenticating its applicability in field applications. [ABSTRACT FROM AUTHOR]

Adapting the power and energy systems by integrating renewable sources is necessary to address climate change. On the other hand, microgrids are gaining prominence in meeting power and energy requirements, including in remote locations. Consequently, the power system's penetration of renewable energy-based microgrids is increasing. Planning an isolated microgrid necessitates cost-effective capacity sizing of energy sources and storage systems for maintaining continuity in power supply. Considering the variability and uncertainty of photovoltaic (PV), wind energies, and load variations, deciding the optimal size of renewable-rich, isolated microgrids is challenging. Batteries and fuel cells are potential storage solutions for managing variability. However, a more trustworthy sizing approach is necessary to maximize power availability at the lowest possible cost, even in the face of uncertainty. Moreover, providing the microgrid owner with the opportunity to choose from a range of optimal solutions is also essential. Therefore, incorporating the uncertainty handling feature with the help of robust assessment under worst-case scenarios in the multi-objective optimization method can provide a more trustworthy solution. In this connection, a novel algorithm is proposed that instills robustness in the solutions provided by traditional non-dominated sorting genetic algorithm-II (NSGA-II), which can offer multiple break-even solutions. The isolated microgrids with PV, wind as sources, and storage technologies such as Lithium-ion (Li-ion) batteries, fuel cells, and a combination of both, a less explored scenario, have been compared to determine the effective storage option over the long run while considering uncertainties in renewable energy and load variations. The NSGA-II benchmark solutions developed under these uncertainties and variations are used to validate the robustness of the solutions obtained from the proposed robust algorithm. With a good trade-off between the cost and availability aspects, the proposed algorithm is found to be superior in getting maximum availability with minimum cost under uncertainties. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed robust frequency control (DRFC) scheme for industrial applications that can effectively adjust the frequency and regulate the active power-sharing ratio of islanded microgrids (MGs). The proposed method also enhances the H∞ performance and the transient response of the MGs by imposing pole placement constraints in the linear matrix inequality (LMI) framework. The proposed scheme utilizes the information on active power and frequency deviations of each distributed generator (DG) and its neighboring DGs to generate an auxiliary control signal implemented by the secondary control layer. The auxiliary control signal improves the input of the droop controller block at the primary control layer, ensuring both frequency regulation and proportional active power-sharing among the DGs. To show the robustness of this novel feature, an AC-islanded MG is simulated under various load and fault scenarios. The simulation results demonstrate the effectiveness and efficiency of the proposed scheme in stabilizing the frequency and damping ratio of the MG while maintaining an active power-sharing balance. [ABSTRACT FROM AUTHOR]

Although field medical microgrids have been widely studied as an important component of future medical power systems, current sharing control in field medical microgrids under false information injection (FDI) attacks has rarely been researched. Based on this, this paper proposes a distributed fuzzy control method for power sharing in field medical microgrids considering communication networks under FDI attacks. First, the field medical microgrid is modeled as a multi-bus DC microgrid system with power coupling. To provide voltage control and initial current equalization, fractional order PI control is applied. In order to reduce the model complexity, the concept of block modeling is employed to transform the model into a linear heterogeneous multi-agent system. Secondly, a fully distributed current sharing fuzzy control strategy is proposed. It can precisely realize current sharing control and reduce the communication bandwidth. Finally, the proposed control strategy is verified by simulation results. [ABSTRACT FROM AUTHOR]

The life cycle of a microgrid covers all the stages from idea to implementation, through exploitation until the end of its life, with a lifespan of around 25 years. Covering them usually requires several software tools, which can make the integration of results from different stages difficult and may imply costs being hard to estimate from the beginning of a project. This paper proposes a unified platform composed of four modules developed in MATLAB 2022b, designed to assist all the processes a microgrid passes through during its lifetime. This entire platform can be used by a user with low IT knowledge, because it is completed with fill-in-the-blank alone, as a major advantage. The authors detail the architecture, functions and development of the platform, either by highlighting the novel integration of existing MATLAB tools or by developing new ones and designing new user interfaces linked with scripts based on its complex mathematical libraries. By consolidating processes into a single platform, the proposed solution enhances integration, reduces complexity and provides better cost predictability throughout the project's duration. A proof-of-concept for this platform was presented by applying the life-cycle assessment process on a real-case study, a microgrid consisting of a photovoltaic plant, and an office building as the consumer and energy storage units. This platform has also been developed by involving students within summer internships, as a process strengthening the cooperation between industry and academia. Being an open-source application, the platform will be used within the educational process, where the students will have the possibility to add functionalities, improve the graphical representation, create new reports, etc. [ABSTRACT FROM AUTHOR]