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Review

Enhancing Microgrid Stability and Energy Management: Techniques, Challenges, and Future Directions

by
Muhammad Umair Safder
1,2,*,
Mohammad J. Sanjari
1,
Ameer Hamza
2,
Rasoul Garmabdari
1,3,
Md. Alamgir Hossain
3 and
Junwei Lu
1,3
1
School of Engineering and Built Environment, Griffith University, Gold Coast, QLD 4222, Australia
2
Department of Electrical and Computer Engineering, Comsats University Islamabad, Lahore Campus, Lahore 54000, Pakistan
3
Queensland Micro- and Nanotechnology Centre, Griffith University, Brisbane, QLD 4111, Australia
*
Author to whom correspondence should be addressed.
Energies 2023, 16(18), 6417; https://doi.org/10.3390/en16186417
Submission received: 16 July 2023 / Revised: 17 August 2023 / Accepted: 30 August 2023 / Published: 5 September 2023
(This article belongs to the Section A1: Smart Grids and Microgrids)
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Abstract

:
Microgrid technology offers a new practical approach to harnessing the benefits of distributed energy resources in grid-connected and island environments. There are several significant advantages associated with this technology, including cost-effectiveness, reliability, safety, and improved energy efficiency. However, the adoption of renewable energy generation and electric vehicles in modern microgrids has led to issues related to stability, energy management, and protection. This paper aims to discuss and analyze the latest techniques developed to address these issues, with an emphasis on microgrid stability and energy management schemes based on both traditional and distinct approaches. A comprehensive analysis of various schemes, potential issues, and challenges is conducted, along with an identification of research gaps and suggestions for future microgrid development. This paper provides an overview of the current state of the field and proposes potential areas of future research.

1. Introduction

Due to environmental and economic concerns associated with the use of fossil fuels, the electric power industry has been working on generating more electricity from renewable energy sources (RESs). RES-based microgrids are becoming increasingly common, and the importance of distributed generators in power system generation is expanding [1]. Microgrids contain low-voltage distribution networks that include DER, reconfigurable loads, storage devices, and modern control techniques. This limits the use of conventional energy generation resources and their environmental impacts such as CO2 emission have encouraged the need for renewable energy [2]. Wind power, solar power, combined heat power, small hydro power, tidal power, geothermal power, and wave power are the renewable energy resources utilized to replace conventional fuel-based power generation units. Due to the restriction on greenhouse gas emissions for a clean environment, batteries and hydrogen fuel cell deployment are proposed for modern microgrids [3]. In order to maximize the benefits of renewable resources, each type has its own challenges. For instance, wind generation relies on wind speed and weather conditions to produce energy, while solar PV power generation is influenced by changing solar irradiation. It has become more complicated to operate and control power systems due to the high penetration and intermittent nature of renewable energy resources. The stability and environmental impacts of RESs compared to traditional power generation sources are discussed in [4].
The integration of renewable energy systems into the power grid requires reconfiguration of the power grid, including control operation, power quality and stability of microgrids, and the interrelated technological challenges. For extracting maximum power, the maximum power point tracking algorithm has been a widespread practice for many years. Different auxiliary algorithms are incorporated to handle the frequency and voltage stability in integrated microgrids [5]. In some cases, when the integration of RESs is significant relative to an unreliable or poorly managed power system, stability and protection issues coexist [6]. Deployment of energy conversion units (power inverters) to integrate the RESs into the power grid highlighted the control and stability issues of the power systems network [7,8,9] that originated the microgrid concept comprising of one or more distributed generation resources (DGRs) along with RESs. Power ratings of DGRs vary from a few Watts to 300 MW, classifying micro, small, medium, and large DGRs [10] and an electric grid consisting of loads, control, and distributed energy resources that act as one unit with defined operating boundaries is referred to as a microgrid [1]. Microgrids can supply both AC and DC depending upon the available generation and application. Solar power, fuel cells, and energy storage units (ESUs) deliver DC power and can form a DC microgrid. For reliable operation of a microgrid, sophisticated control techniques such as droop control and hierarchical control methods are used to handle voltage deviation and constant power flow [11]. On the other hand, by increasing battery–supercapacitor storage it is easy to handle sudden/average power surges. Input voltages can be regulated quickly, energy can be managed effectively, and battery currents can be reduced [12]. The integration of a PV-based DC microgrid with the power network is performed using power inverters that have the ability to provide DC power to the load. One of the most important variables guiding the microgrid in all modes of operation is the balance of power supply and demand. The main grid is required to maintain balance in grid-connected mode. The microgrid must balance the load in island mode by increasing its generating capacity or dispersing the strain. The stable operation of a microgrid is improved through a hierarchical control and energy management system [13]. On the other hand, wind power, micro turbines, and biomass are used to constitute an AC microgrid. The integration and operation of AC microgrid to the power network is supplemented by a multiagent system to enhance the intelligence of microgrid networks [14].
The operation of a microgrid can be carried out in a grid-connected mode and an islanded mode. The switching from grid-connected to islanded mode and vice versa depends upon load demand, power system stability, protection, and security of the microgrid. To perform this islanded operation intentionally or automatically, different control algorithms are used to ensure the secure operation and security dynamics of the system [1,15,16]. To meet the challenges of aging power infrastructure, integration of RESs, increased load demand, and reduction in environmental hazards, a reconfigured and improved version of the electric power grid is required [17]. Microgrid technology provides a way to not only meet power-sharing challenges but also monitor current power system networks [16,18]. Smart microgrids provide wide-area monitoring, control, communication infrastructure in power networks, RESs integration, transmission enhancement, and distribution system management along with demand-side management [19,20]. The electric coupling of DGRs to the power grid is represented as a point of common coupling (PCC). There are a variety of interface technologies used for PCC. Coupling transformers along with interconnection relays and generators with power electronic converters are commonly used in PCC techniques [21]. The latter has an advantage with respect to using a specific power inverter as per the grid connectivity requirements to fulfill the power need on the demand side. RES-coupled microgrids through stochastic planning could be useful, especially for dynamic loads such as electric vehicles but can cause stability issues in DC buses [22,23]. Direct coupling of RESs such as wind power or small hydel power may result in reactive power losses and local flicker in the system [24,25]. Research advancements in the use of wind power and solar power integration to the hybrid microgrid with stability and control to satisfy extreme load demand, referring to electric vehicles. Metaheuristic and optimization approaches are discussed in [26,27,28,29] but these approaches cannot fully cover the dynamics of electric vehicle charging that put stress on grid-tied microgrid systems and compromise their voltage stability and resistance.
The use of power electronic converter technology makes the interconnection efficient and effective if a suitable control mechanism is designed for RESs and the power inverter interface [9,30,31]. Voltage source inverters (VSI) are mainly used as interfacing units for RESs, DGRs, and microgrids. Modeling the VSI in different operational modes is discussed in [32] and their various control system has been reviewed in [9]. An integrated microgrid with small hydro power plant PI tertiary control as a speed reference setting to analyze the over excitation of voltage and frequency [33]. A microgrid is controlled in [34] by a fuzzy potential function method based on a small signal stability assessment. Energy source communication and related cost in the microgrid is reduced through event trigger control [35]; however, this study is presented as an ideal case. Real environment and cyber-security can easily affect this control scheme. Challenges related to voltage stability, frequency deviations, and active and reactive power flow for grid-connected as well as islanded modes of microgrids are handled by deploying battery energy storage unit, optimal scheduling, and secondary control, respectively, in [36,37,38,39]. The interfacing technology to the main grid determines the contingencies in the energy sources as well as impacts the distributed system operation [40]. A brief description of microgrid stability issues and potential solutions are listed in Table 1 and Table 2. The importance of rotor angle, voltage, and frequency in stability issues is highlighted in Table 1 for microgrids and conventional grids as both face these challenges. Likewise, Table 2 presents solutions to the issues discussed in Table 1. These tables clarified that choosing a solution as per system needs is important instead of simply using the same techniques and classical methods for every system. Table 2 shows the different stability needs for microgrids and conventional grids.
The challenging aspects of interfaced DGRs are dependent on numerous factors. These factors include power generation unit efficiency, power quality, extreme load demand and supply, topology changes, environmental issues, economic aspects, DGR stability, protection, and effective control of DGRs in microgrids. Microgrids often cause power imbalances whenever they switch from grid-tied to islanded mode. There are several reasons that threaten the reliability and stability of operations. These reasons are bulk EV load, harmonics, lower system inertia, reduced voltage stability, and oscillations associated with power distribution for low frequency. However, in spite of the fact that distributed energy resources such as renewable energy and diesel generation can cope with increasing demand while reducing greenhouse gas emissions and environmental pollution, it is difficult to store and distribute energy without interruption. It is because they require a lot of storage and distribution space. On the other hand, control system design plays an important role in harvesting maximum benefits from RESs and DGRs. Efficient control systems are a never-ending challenge in existing and future power systems. Mainly, the control design is classified as local control and centralized control. Local control is further classified based on information exchange as decentralized and distributed control. Different professional standards (IEEE, IET) are set for microgrid integration and operation. These standards define the operational mode of DGRs (grid-connected and islanded) and power operational requirements such as maximum voltage and frequency deviations and power loss limitations.
In this paper, the extensive literature related to microgrid stability, protection, and energy management issues is presented. The literature is carefully selected in a systematic way that covers the right number of papers from each year and has the most relevant information in it. The selection of these references is presented through a pie chart in Figure 1 which shows a balanced percentage of the literature selection from the last twenty years. The main contributions of this paper include:
  • The paper emphasizes the importance of advanced energy management and stability approaches in modern microgrid systems to tackle stability, power flow, and protection issues arising from the high penetration of renewable energy sources and fast dynamic loads. It provides a comprehensive understanding of the fundamental concepts, challenges, and opportunities related to microgrids, which can guide researchers in developing effective solutions.
  • The paper analyzes and discusses the techniques developed to improve the performance and reliability of modern microgrid systems by addressing stability and energy management issues through both traditional and distinct approaches. It conducts a comprehensive analysis of various schemes, potential issues, and challenges, which can help researchers identify research gaps and suggest potential areas of future research.
Overall, this paper contributes to the field of microgrids by providing insights into the current state of the field, highlighting potential areas of future research, and proposing techniques to address the issues related to stability, energy management, and protection in modern microgrid systems.
The paper is organized as follows: Section 2 presents the basic notations related to microgrids and power systems. In Section 3, energy management schemes for modern microgrids are presented. Section 4 presents the protection of DGR/RES-based modern microgrids. Stability issues of microgrids are discussed in Section 5. A comprehensive discussion related to the literature and future possibilities is presented in Section 6. The paper is concluded in Section 7.

2. Basic Notations, Preliminaries, and Concepts

This section details the acronyms and basic definitions associated with microgrids and power system networks. A complete list of frequently used acronyms in the paper is presented in Table 3.

3. Energy Management Systems

Energy management systems (EMSs) have become an essential tool for managing energy in distributed energy systems (DESs) to meet load demand. With the increasing adoption of renewable energy sources and the emergence of new technologies such as energy storage systems, microgrids, and demand response, there is a growing need to optimize the management of energy resources. EMSs provide a solution to this problem by enabling real-time monitoring and control of energy flows, which allows for the efficient integration of DERs into the grid.
An energy management system, also known as a microgrid central control (MGCC) unit, is crucial for coordinating a microgrid’s components and ensuring a secure, reliable, and cost-effective operation. The control approach should represent the microgrid as a single entity and utilize a hierarchical control approach with primary, secondary, and tertiary levels, as depicted in Figure 2. The primary level ensures power sharing and controls terminal voltage, while the secondary and tertiary levels optimize microgrid operation. The primary and secondary control levels can be implemented in a decentralized manner to increase the reliability of a microgrid. It is important to have a centralized tertiary control level for each microgrid to coordinate it with other microgrids and the grid utility as well as provide optimal energy management of DG units in the presence of a high penetration of RES [1]. Traditional control approaches incur heavy computational burdens and costs, but advanced approaches allow for decentralized implementation, enhancing microgrid reliability during a single point of failure.
Power flow fluctuations occur in power systems due to the interconnection of RESs/DGRs in the power systems that cause voltage and frequency stability issues [1]. Microgrids are subject to uncertain operating conditions due to the high levels of penetration of intermittent generation. In this context, managing the uncertainty of renewable energy generation and meeting power demand is one of the main challenges [68,69,70,71,72]. An optimal energy management system is necessary for the power system to manage the uncertainties from RESs on the demand and supply sides [73]. In order to ensure an economical, sustainable, and reliable operation of the MG, an energy management system (EMS) is required to manage both the supply and demand sides of the energy equation. Numerous benefits have been demonstrated for EMS systems, including energy savings, energy balance, reduced greenhouse gas emissions, energy balance, and customer participation, reactive power support, frequency regulation, and reduction in reliability and loss costs. Several energy management systems, optimal energy management, and scheduling algorithms are presented in [74,75,76]. Different nature-inspired optimization algorithms are deployed for the energy management of a PV-based residential microgrid such as the firefly algorithm along with forecasting schemes, but these kinds of approaches require high-efficiency computing systems otherwise their benefits cannot be fully harvested [77]. In most cases, these algorithms are deployed in simulation only rather than hardware. For residential systems, simple but effective approaches should be used to fulfill the purpose of domestic energy management. Most of the proposed algorithms were designed according to the energy mix, weather conditions, optimal cost of operation and maintenance along with demand-side preferences [78]. Energy management algorithms are designed to reduce power flow fluctuations by optimizing the system-level objectives. To achieve that optimization goal, an offline and centralized energy management scheme is presented that uses the inner power flow level to keep bus voltages and system frequencies within the desired range. While keeping different operational constraints within the limits, this scheme also ensures power balance through the outer optimization level [79].
The control design and related energy management techniques such as Monte-Carlo simulations, modified particle swarm optimization algorithms, prosumer-based energy management, robust optimization, and stochastic programming are used to reduce power flow fluctuations caused by load side variations and security reasons in [68,69,70,80]. Maintaining and securing modern power systems is essential and depends on renewable energy resources and their dynamics. An encrypted object is compared with the dynamic characteristics of the system using a 4D chaotic system together with a test image in [81] against potential cyber-attacks and maintenance purposes. Another two-dimensional logistic complex-mapping algorithm presented in [82] is capable of resisting common attacks because of the complex encoded data. Apart from multi-objective distributed control techniques, data encryption algorithms are also popular in microgrids because they are more reliable and accurate. Most of these central controller-based techniques are mono-objective and the objective function is only used to improve the life of the power system, or in some cases for the reduction in the operational cost of a microgrid. Central and distributive control have their benefits but one thing that should be kept in mind is that if the central controller collapses the whole system will collapse. On the contrary, this is not the case with distributive control. To reduce greenhouse gas emissions by shifting loads from fossil fuel-based generators to renewable energy generators through demand-side management with dynamic pricing a smart grid-based greenhouse energy management control is presented in [83]. A hybrid energy management approach utilizes artificial intelligence models specifically a neural network used with a nonlinear MPC controller to predict the battery state of charge, to substitute the process model. AI-based techniques work great with predictive control, but the high computational requirement is the only drawback of these techniques which ultimately increases the overall cost of the system. In [84], considering the uncertainty in the energy management of RESs, the importance of optimal use of battery energy is presented. In [69], a real-time operation considering battery degradation cost is discussed. A comparison between real-time and scheduled energy management systems is demonstrated in [70], with the conclusion of emphasizing real-time operation. Moreover, mixed-integer linear-programming-based iterative algorithms are applied for scheduling optimization in place of complex mathematical models that are required by an MPC method to maximize the profits from microgrid operation is presented in [85,86].
For the successful and reliable operation of microgrids, power quality evaluation is also important. It can be achieved through energy management or independent distributive control for RESs. A multivariate Gaussian distributed covariance scheme is used to merge the independence and correlation of the evaluation indicators of microgrids to evaluate power quality [87]. Like most scheduling methods, this technique also uses a cost-based function. However, other constraints such as real power operating limits and up/down time for RESs could be included to make the optimization useful. To avoid power flow fluctuations due to RESs in the power systems, the concept of the microgrid with DC RES-based microgrid with energy storage is introduced. Moreover, it is also used to avoid DC over voltages due to motors (PMSG) during line faults and slow-changing power surges in the system. The microgrid can switch to the islanded mode of operation if power flow fluctuation approaches its limits; therefore, an RES-based islanded microgrid is controlled through adaptive EMSs using the transmission line constraints as control parameters in [88]. However, this kind of system is particularly not implementable in real life because it needs multi-objective and multi-constraint EMSs. Another operational management scheme is introduced to reduce the bidirectional congestions in different RESs along with the RES capacity optimization in microgrids [89,90] but the privacy of the end user is not considered in this scheme.
Several control strategies are developed for the converter of energy storage units and several control schemes are developed for microgrid technology to cope with the problem of voltage and frequency deviations [91,92]. Modern microgrids always need state-of-the-art power electronics converters and advanced control technologies since they are adopting more RESs, storage systems, and dynamic loads like EVs. The power electronics sector is also working to develop cutting-edge inverters with great controllability and flexibility to meet contemporary demands. A three-port converter system is presented [9] for storage and wireless power transfer systems. Several DC-AC-DC converters with resonant capacitors are included in the proposed converter system. An inductor and four switches are used to share a common location so that the converter can perform as a DC-DC converter for storage systems and as a DC-AC converter for wireless power systems at the same time. This scheme is particularly useful for single-source microgrid systems, but it is not appropriate for microgrids with multiple sources of power. Other back-to-back converters are utilized to interconnect voltage source converter-based autonomous power microgrids [30]. Only by selecting appropriate parameter ranges can MGs be operated consistently in different situations. However, microgrids can be destabilized by parameters such as synchronization PLL and DC link voltage, according to the sensitivity analysis these parameters must be considered in the main control system.
Integrating DERs with power electronic converters is essential for their reliability and efficiency. There are three different types of faults that can occur in converters, which include sensor, actuator, and system faults. In the presence of uncertainties and dynamic load disturbances, filter banks are used to estimate the power spectrum of the power electronic converter signal [31]. However, the small signal stability in not considered which is crucial for reliable operation of microgrids.
A power electronic inverter control is developed in [93,94] to synchronize frequency and voltage through an EMS by reducing the vulnerability voltage and frequency deviations to a tolerable range in the power network. In [95], opposition learning is used to optimize the scheduling of distributed generators in a microgrid through kho kho optimization. It is also used to achieve global optimization in a way that minimizes the cost and emission of harmful gases. However, this technique is not well practiced and has its limitations in dealing with the dynamic responses of modern microgrids. In [96], a combined cooling, heating, and power microgrid is proposed with an integrated energy management and trading model that maximizes energy efficiency and reliability while minimizing cost. The trade between diesel generators and microgrids is based on the available power and load demand. To solve this trade-based energy management the authors used integer linear programming through branch and bound algorithm. This paper does not emphasize the importance of renewable energy resources such as solar and battery energy storage systems. Most of the modern microgrid system is based on solar and BESS which is entirely ignored in this paper. Another energy management scheme for multimicrogrids is presented in [97], in which the author proposed EMSs for cost minimization through day-ahead scheduling. They also presented the maximization of the customer comfort index. A mixed-integer linear programming framework is chosen for the EMS. This paper presents a very good idea but the only drawback is realization. It is very difficult to realize the presented method because the dynamic study and relation between local electricity providers and customer regulation are neglected. The switching algorithm and plan are not possible practically because they need a huge survey to obtain data from customer needs. In [98], a central controller-based energy management scheme is presented which emphasizes the stochastic optimization of the power, heat, and hydrogen-based microgrid system. Constraints of the EMS are defined based on the power demand and heat exchange between the power sources of the microgrid. The proposed method is very strict in terms of flexibility. Any new addition of source and load changes will ruin the optimization strategy. Across a wide range of systems and applications, energy management algorithms improve energy efficiency, maximize energy utilization, and reduce energy consumption. An algorithm’s universality refers to its ability to be applied and effective in different contexts and scenarios, based on their fundamental features and principles. The following factors contribute to the universality of energy management algorithms such as optimization technique, system modeling, adaptability, scalability, interoperability, decentralization, real-time performance, robustness, and reliability. A generalized energy management algorithm and alternate solutions for reviewed energy management schemes used for microgrids are presented in Figure 3 and Table 4, respectively.

4. Protection of DGR/RES-Based Modern Microgrids

The variability and intermittency of RESs require the development of appropriate systems and standards for the protection of DGR/RESs. The protection system ensures that the DGR/RESs and the grid are protected from potential faults and that the DGR/RESs can safely and securely operate in the grid. The development and implementation of these systems and standards in Table 5 are critical for the successful integration of DGR/RESs into the grid, enabling a more sustainable and resilient power system. The protection of power systems, especially for distributed generation units, is a challenging task. The protection relay should be intelligent enough to identify the fault or no-fault condition at all levels of load.
The use of a fuse (auto-reclosing) makes the protection mechanism more effective. Auto-reclosing uses a relay operation that opens with a minimum delay to protect overhead medium-voltage DGRs [47]. The scheme to generate a tripping signal for a circuit breaker operation is based on current, i.e., the higher the current, the faster the tripping time; this scheme is referred to as inverse time overcurrent protection [107]. Induction machines, especially in a motor operation, make up a high load that increases the fault current. The use of a power electronic inverter interface minimizes the fault current in the distribution system [108]. The fault current minimization in inverter-interfaced induction machines depends on inverter control algorithms [109]. Efficient protection may be applied to the distributed generation system by changing protection settings and changing the time step (additional time step) in protection coordination. The addition of another circuit breaker and the use of communication infrastructure (relay communication/protection component communication) in the system also provide protection [108]. The bus bar protection scheme uses a blocking scheme in circuit breaker operation (delay in circuit breaker) to protect the bus bar. At the fault occurrence (fault current), the circuit breaker detects the fault and opens (delay in circuit breaker with respect to circuit breaker in outgoing DGRs). The operation is performed for both grid-side and generator side faults [110]. Table 5 and Figure 4 depict the analysis of different protection methods and proposed schemes used for DG-based microgrids.
Table 5. Overview of different protection schemes for DG-based microgrids.
Table 5. Overview of different protection schemes for DG-based microgrids.
ReferencesProtection MechanismLimitation
[111]Machine-learning-based fast-tripping protection scheme is implemented by using traveling waves for fault location and action in microgrids. Fault location algorithm trains the Gaussian process through Parseval curves of the conductor that is formulated for microgrids.
The hypothesis of this work and simulations are supported by Sandia National Laboratories, the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy.		Machine learning required an intensive data set. For a sensitive protection system, the data which is used here to train the machine learning algorithm is not sufficient and its formulated indirectly which can malfunction the protection scheme.
[112,113]Rule-based adaptive protection scheme combined with ANN-SVM diagnosis model was used to locate the fault and change the network topology to update the protection settings. For protection setting calculation to build a data set, Ohm-based protection is chosen.The proposed scheme needs information on network parameters to train the ANN model. Dynamic changes from DERs in the network are not considered which can alter the protection settings on which the model is trained.
[114,115]Dynamic models for power grid and protection systems that can simulate different cascading failure mechanisms compared to existing quasi-steady state (QSS) models are presented. Multiple distributed generation resources could be included for a better protection mechanism.
[116]Inductance-based protection through local measurements from the topology of the microgrid.Weak system in terms of communication.
[117]ANN-based microgrid protection through identification and detection of fault location.Protection operating time can be inaccurate.
[118]Sensor-based protection through measured data for fault detectionIntermittent nature of RESs is ignored.
[119]Deep belief network through machine learning approach for asymmetrical fault detection.Load-based faults are difficult to train in this method.
[109]AI-based protection through artificial neural network prediction method.The dynamic nature of RESs makes it difficult to directly predict the event.
[120]Machine-learning-based protection through local measurements. Microgrid stability is ignored.
[121]A traveling wave (TW)-based protection scheme to localize the fault. Communication malfunction is ignored.
[122]A multilayer perceptron (MLP) neural network is used for error determination. This algorithm is based on dividing the existing distribution network into multiple zones, each of which can operate in an isolated mode.Complex systems required highspeed computing resources
[107]An adaptive central protection method is presented in a way to handle the changing settings of protection in the presence of DG units, disconnecting associated DG units in the event of a fault. Balancing different DG automation, using residual current limiters, intelligent transformers, and adaptive protection. It is a centralized protection scheme. Any problem in the central zone can collapse the entire system.
[107,108,109,110,120,121]AGC systems combining BES/SMES, wind turbines, FACTS devices, and PV with AGC techniques based on digital, self-tuning control, adaptive, VSS systems, and intelligent/soft computing control are recommended.Complex and high-computing-requirement-based systems have too many components to control in protection control.
[107,121]A multiagent system-based adaptive protection and control algorithm is created for the DG controller and relays to reduce the impact of fault current. A type of centralized protection mechanism can collapse the entire system if control is malfunctioning.
In a distributed generation, if the large generator experiences the fault, the fault current is generally high enough to trigger the protection mechanism, but this protection system observed fails to trip for the bus bar fault in the same system. For a distributed generation operation, generator protection is an important task. Different standards are set according to power system operation and region. The generalized protection scheme includes voltage, frequency, and power during faults and these faults are usually shunt faults masked as abnormal operation for grid-connected and islanded modes of operation [110]. Different protection mechanisms for island detection of distributed generation are developed such as rate of change of frequency, vector surge, under/over voltage, and frequency protection [119], small disturbance signal for voltage/current control mechanism, power line carrier for the grid, fault estimation, active frequency drift, and reactive power balance [123].
Figure 5 represents an overview of the basic protection algorithm for the synchronous machine in distributed generation. The protection algorithm is applied based on load flow and short circuit analysis. The fault current produced by the synchronous machine unit is comparatively long-lasting. Initially, it can be as much as six times the generator full-load current, and when the generator field fails, it can gradually decline over a period of seconds below the full-load current. In addition, the voltage on the generator is significantly reduced when there is a failure, which may be utilized to help in fault detection. Based on the current contribution and voltage depression, many protection techniques are used to protect the system. Based on the load flow analysis, these current and voltage fluctuations in the system are identified as faults. If faults such as over-current faults, phase-to-phase faults, and phase-to-ground faults exist, the following protection operations may be applied for the system safety, i.e., circuit breaker operation, fuse operation, auto-reclosing mechanism, sectionalize operation (cutting off faulted segments in the distribution line), and blocking scheme with circuit breaker operation for grid or generator fault. On the other hand, the algorithm will activate the advanced protection scheme for any other faults. After the protection is operated, the algorithm imposes a check status of the fault. If the faults are cleared immediately by the protection measures, then the algorithm will end with no faults. If not, the protection schemes will run again to clear the faults.

Advance Protection Scheme

Microgrids may be set up in a variety of ways depending on their load requirements, faults, maintenance needs, and dynamic changes in DG penetration. In order to prevent unwanted tripping, increase system accuracy, and hasten the reaction of the protection system to unforeseen changes in the grid, modern Al-based protection schemes are crucial.
Machine learning and artificial neural network-based protection methods to detect the fault location and forecast the asymmetrical faults can easily be managed by these methods [109,117,118,119,120]. In certain circumstances, it is difficult to fit in and impossible to adjust to these changes. Only a difficult protection settings update procedure can address these issues, which may not be feasible if MGs are linked to RESs. The distributed generation network can be protected using a multiagent-based protection scheme. Protection components such as relays and circuit breakers are referred to as agent nodes, and the communication infrastructure is referred to as edges [107]. A graph-theoretic distributed cooperative control approach is deployed to synchronize the protection operation [107,108]. In addition, voltage, frequency, and power measurements are shared within the communication network to alert the protection mechanism if a fault is detected in the network. A multiagent protection scheme is presented to detect the faults by measuring the voltage and current to trip only those feeders that are faulted. Moreover, this scheme also communicates these faulty events to the central controller of the microgrid to warn nearby protective agents [110]. This scheme requires operational testing in different environments for practical purposes.

5. Stability of Microgrids

Microgrids face a number of stability issues that can negatively impact their performance and the quality of power they deliver. These issues include frequency stability, voltage stability, and transient stability, among others. The significance of studying the stability issues of microgrids lies in the fact that understanding and mitigating these issues is crucial for ensuring the safe and reliable operation of microgrids. By addressing stability issues, we can improve the performance of microgrids, enhance their ability to integrate renewable energy sources, and ensure their long-term viability as a sustainable energy solution.
The integration of DGRs and RES-based microgrids in the power grid has resulted in several transitions in the power system. These transitions include generation units, transmission lines, load mechanisms, communication infrastructure, monitoring and data processing, and controller designs. With the huge penetration of distributed generation units and RESs, the power system inertia is compromised and causing stability issues. Different approaches for system inertia [124] are proposed such as virtual inertia support to suppress the fast changes in frequency and DC voltages to enhance the system stability. Some researchers used precise hierarchical control to manage the active and reactive power along with scheduling of primary and secondary reserves to ensure system stability. For these kinds of approaches, controller design needs precision and very fine-tuning [125,126,127]. The integration of DGRs in the power systems has caused several impacts and these impacts vary based on the DGRs type, interconnection methodology, and operation. These impacts of DGRs in the power systems are referred to as network stability. Stability in terms of voltage and frequency synchronization in the network are the key issues [39]. Conventional power plants are connected through transmission or sub-transmission channels for bidirectional power transfer according to the IEEE standard 1547–2018 for interconnection and interoperability of DERs with associated power system interfaces [128,129,130,131]. DGRs are connected via distribution channels and power is delivered to the consumer directly [132,133]. The distributed generation is correctly regulated to compensate for harmonics and voltage imbalance in the microgrid. It is assumed that this instability may occur due to overloading of the system caused by bulk dynamic load, power quality issues, unbalanced compensation for critical buses, and incorrect protection operation promoting blackouts or brownouts [134,135,136,137,138,139]. The problem of stability in the power systems is addressed by dividing the stability issues into different layers. The division is based on the primary layer (consumer) and the secondary layer (generation). The primary layer deals with the power quality, reliability, and consumer end price [106,140,141,142]. The secondary layer represents voltage stability, synchronization in frequency, and protection of the power system. Figure 6 shows the classification of stability issues in the power systems.
Achieving stability in the secondary layer will automatically ensure the stability of the primary layer. This section covers the voltage and frequency instability issues and their impacts on the power system, the proposed solutions, and future directions.

5.1. Stability Issues in the Primary and Secondary Layer

Power quality issues refer to variations in voltage, current, and frequency in the system during operation. The variations in the power system cause an unbalance in current and voltage and result in harmonics [134,143], and these harmonics are the main culprits for an unstable system. These variations may lead to disconnection of the generation unit or premature failure [144,145]. For blackouts and related power quality variations, understanding the different mechanisms for cascading failure through a dynamic simulation model of both power networks and protection systems is presented. This is because no single mechanism can capture all the aspects of cascading failure resulting in blackouts. The secondary voltage control system has a selective harmonic compensator to reduce harmonics brought on by voltage distortion in [113,114]. Another case is islanded operation caused by voltage harmonics from the DGRs. In the secondary voltage control system, a selective harmonic compensator was introduced to reduce harmonics brought by voltage distortion [133]. Integration of RESs/DGRs in the power systems causes various stability issues in the power network. The stability issues/variations in the voltage (drop, overvoltage) and frequency deviations in the power system are caused due to load [146]. An increase/decrease in load may cause power loss/excess. Power restoration operations such as motor slip readjustment, transformer tap changing, and voltage regulation operation are applied to control stability issues [147,148]. In some cases, to overcome the stability issues and load balance anomalies on the distribution side, a virtual operator module that simulates actions and mitigation measures of grid operators is used. For example, an OSER simulator is used to investigate the planning aspects of power systems such as transmission and distribution, management of interconnections, voltage, reactive power, generation, and load balances are taken into account for this planning tool [149,150,151,152]. The implementation of those techniques restores load but increases stress in the power network due to increased reactive power consumption. The increase in reactive power consumption further increases the voltage and frequency instability [152,153,154,155]. The author in [156] discusses the voltage and frequency instability due to load dynamics attempting to restore reactive power consumption beyond generation unit/transmission network capabilities. Active and reactive power flow in inductive reactance of transmission networks in power systems causes significant voltage drops and frequency deviations [157]. Voltage and frequency instability due to generator field, generator armature currents, time overload capability limits of generators, and generators operating with limited reactive capability are discussed in [157]. The stability of voltage is disturbed due to increasing disturbances in the reactive power demand beyond the sustainable capabilities of the power network [157,158,159]. The self-excitations of synchronous machines, shunt capacitor compensation, loss of generation units, or heavily loaded lines cause voltage collapse and frequency synchronization problems in the power system. It is emphasized that renewable energy integration introduced the frequency stability issue as a major challenge in the operation of microgrids and this instability promotes the low system inertia. Considering the load inertial contribution, the synchronous inertial response to a frequency disturbance is estimated. Then non-synchronous inertia is estimated by separating aggregate synchronous generator and load inertial responses from overall estimated system inertia. It is expected that inertial controllers are selected using this technique by selecting proportional gains and time constants that are appropriate for the stable operation of microgrids [160]. Capacitive behaviors of transmission networks and under excitation limiters cause voltage and frequency instability in power buses. Voltage instability in HVDC links due to long-distance operations along with control strategies in HVDC links causing stress due to active and reactive power are discussed in [161,162].

5.2. Voltage Stability

Various control strategies are proposed for the stability of the power network with reference to voltage stability. Using the control strategies for the production, absorption, and flow of reactive power in the network, voltage instability can be avoided [163]. A generalized framework including a backstepping method through centralized control to track the reference or nominal values for voltage stability in the MG consisting of RESs/DGRs is presented in [164,165,166]. Error regarding nominal values is calculated and the controller is designed to minimize the voltage error. The control schemes for voltage stability in the power systems are divided based on the implementation structure where centralized control is used for stability (voltage, frequency, power) [17,150,167]. Integration of DGRs/RESs in the power systems and the formation of MG have encouraged control and power engineers to develop new control techniques for MG operation challenges. MGCC control techniques are developed for the stability and synchronization of the power system [168]. MGCC provides control schemes for efficient coordination of DGRs/RESs to serve loads (critical and non-critical). It also provides protection strategies in case of faults in the network. MGCC strategies improve energy balance, efficiency, and cost benefits. PSO control scheme is developed in [103,169] for the flexible operations of power systems concerning stable voltage and synchronized frequency. A voltage compensator strategy for individual DGR/RES is proposed in [160] to avoid voltage sags in the network. The disadvantages of MGCC are excessive cost and network congestion. Failure of a single point of control in MG and the power system may cause reliability and efficiency issues [39]. Distributed control strategies are developed to resolve MG stability issues as well as ensure reliability and efficiency in power systems [104,133]. A comprehensive analysis of distributed control techniques in MG-based power systems is presented. Model predictive control, adaptive control, H∞ control, PI control, and event-triggered control schemes are developed to ensure voltage stability in the MG-based power system [104,105]. Consensus and averaging control techniques are developed in [17,39] to resolve PG’s voltage stability issues.
Distributed control strategies are reliable, but the communication infrastructure response is slow in distributed control as compared to MGCC. The time scale analysis for the stability of MG-based power systems is presented in [39]. To avoid dependence on communication infrastructure a communication-free stability paradigm is developed, referred to as decentralized control [40,55,170]. A wash-out filter-based decentralized control is presented in [39]. Using the final value theorem, the error in voltage and frequency with reference to nominal values is calculated, and implementing wash out of the filter (cascade high pass and low pass) converges voltage and frequency to nominal values. A centralized secondary control with a communication interface, a distributed control with a low data rate, and a decentralized secondary control with a communication-free architecture are used to meet the operational constraints of the microgrid to optimize performance and address potential issues like clock drifts and cyber-security threats [39]. A state variable estimation-based decentralized control scheme for voltage stability is discussed in [161]. Voltage stability based on local signal/local variables in decentralized control using LQR control is presented in [167]. A detailed analysis with experimental validation of voltage stability and frequency synchronization is presented in [39]. Achieving voltage stability in the power network ensures power quality, protection, and reliability in the system, thus ensuring primary layer control. Voltage stability is also linked with the frequency stability in the power network. A brief overview of frequency synchronization issues in power MG interfaced power systems is presented in the next section [55,143]. Active and reactive power control in grid-connected as well as islanded mode DGRs in MG to avoid voltage fluctuations is developed in [171]. Voltage fluctuations in the island mode are avoided using a shunt active power filter in the system. It improves power quality by controlling reactive power variations and voltage fluctuations [160]. Back-to-back power electronic inverter-interfaced MG control is proposed in [172] to avoid the effects of nonlinear and unbalanced loads causing voltage instability in the network.

5.3. Frequency Stability

The main objective of frequency control in power systems is to synchronize the frequency of DGRs/RESs to a common value (system operating frequency). In conventional power systems with synchronous machines, frequency synchronization is achieved due to mechanical rotational speed and electrical frequency coupling [41,42,48]. RESs/DGRs do not support such regulation techniques and cause frequency synchronization issues in power networks. Frequency deviations in the MG interfaced power systems occur due to increased penetration of DGRs/RESs in the system, load variations, increase in power demand, and voltage fluctuations. AGC-based strategies are among the earliest works in the frequency regulation of power systems. A detailed review of AGC strategies is presented in [173]. In [174], centralized and distributed control strategies are discussed for frequency regulation in the bus with neighboring systems (load, generation unit) using linear swing equations. Frequency regulation using AGC and using generator constraints is presented in [157]. For frequency stability, a realistic approach for estimating the non-synchronous inertial response is proposed which allows the operator to select the best inertial response of the system for stable operation. A proportional integral and distributed proportional controllers are developed for frequency synchronization in the power network [175]. Stability conditions with voltage variations for droop control using port Hamiltonian modeling are discussed in [176]. Gradient-based algorithms are proposed in [177]. These algorithms solve the optimal frequency regulation using the optimal power flow model. Several power flow strategies for frequency synchronization are discussed in [178,179]. Detailed analysis of frequency deviations due to voltage fluctuations (drops, sags, collapse, and overvoltage) is discussed in [160]. For frequency and voltage stability, distributed and decentralized control strategies are chosen in [56,170].

5.4. Issues at the Transmission and Distribution Levels

Large penetration of DGRs/RESs in the power systems affects the transmission and distribution lines. Large RESs such as wind parks and solar PV generations are a long distance from the consumer end [180,181]. The power transfer for such a large distance requires building new transmission lines, thus increasing the cost of transmission systems. Cost analysis and transmission line attributes are presented in [129,161]. An increase in high load variations with DGRs/RESs may cause stress in the transmission lines and result in the disconnection of the unit(s). A review of long-distance transmission challenges of power in the electrical network is presented in [182]. To avoid transmission challenges MG concept emerged. It is located near the load area, i.e., at the distribution level. The introduction of MG in the power systems reduced the overloading, power loss, and system instability in the transmission lines. A detailed analysis of challenges at the power system distribution level is discussed in [104]. With the emergence of MG in the power systems, new challenges in the distribution system also emerged. Along with the voltage and frequency stability, optimal power flow and secure microgrid operation are new challenges. Effects of poor power flow from DGRs at the distribution level may result in short circuits, power losses, voltage transients, frequency deviations, congestion in the system branches, power quality, reliability, and protection [182,183]. In a microgrid, energy is generated, stored, and managed independently or in coordination with the main power grid by integrating distributed energy resources (DERs). Energy from these networks is distributed to specific geographic areas, such as buildings, campuses, or communities as small-scale networks. Renewable and convention energy resources have their own challenges when integrated with microgrids. These challenges are, but are not limited to, interconnection and integration, control and management, energy storage, and power management as depicted in Figure 7. These challenges can be countered using state-of-the-art control schemes in primary, secondary, and tertiary control levels for voltage, frequency, and energy management, respectively.

5.5. Issues to Secure a Cyber-Physical System

In order to prevent a wide range of cyber-attacks from attacking the microgrid, modern architecture and a number of advanced protection processes are needed to make the system more secure [184]. With the evolution of power systems architecture concerning design and control, contemporary issues may arise. As a cyber-physical system, the system security in terms of cyber-attacks such as service denial and incorrect data injection is exposed. Detection of attack nodes, as well as control strategies for efficient system operation and protection, is an encouraging research problem [185]. Advanced protection mechanisms, such as a blockchain-based method, must be implemented to cope with cyber-security attacks [186,187]. To ensure the secure, reliable, and economic operation of bulk power systems, cyber-security, monitoring, and protection controls are crucial. Architectural framework with fault and attack resilient control algorithm used as an essential building block for a secure power grid [188]. In some cases, protection operation is observed through an energy management scheme that incorporates the communication system between connected distribution generation and the power grid [189,190]. Any delay from the communication channel can put the power system at risk of cyber-attack. It can be wrong data injected into the grid virtually that causes the protection to operate with any actual fault. To address this kind of scenario, synchronized phasor measurement along with delay monitoring control is implemented to monitor the system [191]. Enhancing cyber-security to mitigate multiple non-simultaneous cyber-attacks. This strategy can be useful in terms of control design and possible contingencies. Problems in frequency and voltage stability could be due to time-varying delays caused by cyber-attacks [192]. Delays in communication and plug-and-play DGRs need to be further addressed. Multi-MG architecture, cyber-security control, and stability are new paradigms in power systems. It is important to rethink the objectives and methods commonly used in information and communication technology applications. A modern power grid presents several challenges to existing security approaches, including inapplicability, nonviability, insufficiency of scalability, incompatibility, or simply inadequacy.

6. Discussion and Future Work

The analysis of various distributed generation resources (DGRs)-based microgrid systems reveals that stability and protection are vulnerable to faults related to dynamic load, e.g., bulk EV charging and the intermittent nature of RESs.
The development of a microgrid architecture with centralized, distributed, or decentralized control for power generation (DC, AC) is an ongoing research challenge. This may involve the implementation of new interconnection devices, AC-DC lines, and communication infrastructure. The detection and isolation of faulty units and lines, system security, and protection against faults with respect to the new architecture may lead to new research opportunities in the field of power systems.
Energy management is another critical research area for modern hybrid microgrid systems. The demand for EMS units is increasing due to the substantial number of RESs and EVs entering the system. Efficient power utilization can only be achieved through EMS without investing heavily in new energy resources and storage units. Machine learning and AI are new horizons that need to be explored to design EMSs for hybrid microgrid systems. AI applications are becoming increasingly important in almost every industry these days. Extensive research is being conducted on various AI methodologies, although in most cases, AI-based solutions have not yet been demonstrated in real-world scenarios.
Autonomous market trading using AI-based strategies is another open research area. Future research is likely to focus on improving microgrid performance in the following areas: better methods for energy management, more effective fault detection and isolation, and the development of more robust and efficient interconnection devices and communication infrastructure.
  • A modern microgrid is characterized by the integration of distributed energy resources, a battery storage system, and controllable loads in a power distribution network. To accommodate these challenges, it is necessary to redesign a conventional energy management scheme through AI so that it can cope with the resiliency and reliability needs of microgrids effectively.
  • Performance deterioration in multisource contemporary microgrid operations raises issues despite lowering costs and computing time. To solve these issues and improve the operation of the microgrid, research in state-of-the-art power electronics converter design and intelligent energy management systems are needed.
  • Predictions made by AI are based on previous data and show a remarkable ability to evaluate large datasets and extract insightful information without being bound by pre-existing models. This feature gives AI a significant edge, making it an appealing option for the future.
  • Distributed generation in an islanding mode of operation using inverter-based control approaches and AC microgrid control using inverter-interfaced generation are required to address the challenges in the power systems.
  • The stability considerations of the dynamic nature of bulk EV load have not been thoroughly studied. Therefore, dynamic studies are crucial for analyzing various aspects of new research activities.
  • Research in protection devices and AI-based advanced protection algorithms along with cyber protection of the cyber-physical power system will improve power networks’ protection.
  • Adopting the islanding detection feature with control strategies like load shedding and islanding control will improve the overall efficiency and dependability of the power system, it can also be used to examine the potential security vulnerabilities and attacks in terms of cyber-security.
  • DGRs such as pump storage and hydra power have attracted minimal attention. These kinds of generations should be taken into consideration due to their benefits to the environment, cost, and efficiency.

7. Conclusions

This paper provides a broad perspective on the need for advanced energy management and stability approaches in modern microgrid systems. With the increased penetration of renewable energy sources in microgrids, stability, and protection issues arise, which can be addressed through advanced energy management and AI-based modern protection schemes. However, the analysis shows that in real systems, AI-based EMSs and protection schemes require a strong computational system to operate effectively. On the other hand, inverter technology and control architecture can provide a solution to stability issues through improved inverter design and robust control. The use of control architectures for inverter-interfaced microgrids can address stability issues in both grid-connected and island-mode distributed generation systems. A state-of-the-art energy management scheme and machine-learning-based protection algorithms can prevent microgrid vulnerability to stability and protection issues, ensuring their reliable operation. It is hoped that this paper will provide a comprehensive understanding of the fundamental concepts, challenges, and opportunities related to microgrids and assist researchers in developing effective solutions to address the issues related to stability, energy management, and protection in modern microgrid systems.

Author Contributions

M.U.S.: Conceptualization, Methodology, Validation, Investigation, and Writing—original draft preparation. A.H.: Writing and Review. M.J.S., R.G., M.A.H. and J.L.: Review and Editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data will be available upon request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. A pie chart for the detailed literature data callout.
Figure 1. A pie chart for the detailed literature data callout.
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Figure 2. Energy management hierarchical system.
Figure 2. Energy management hierarchical system.
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Figure 3. Generalized energy management algorithm.
Figure 3. Generalized energy management algorithm.
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Figure 4. Protection methods used for microgrid systems.
Figure 4. Protection methods used for microgrid systems.
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Figure 5. Basic protection algorithm for power systems with DGRs.
Figure 5. Basic protection algorithm for power systems with DGRs.
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Figure 6. Classification of stability issues in power systems.
Figure 6. Classification of stability issues in power systems.
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Figure 7. Microgrid model and challenges.
Figure 7. Microgrid model and challenges.
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Table 1. Characterization of stability issues.
Table 1. Characterization of stability issues.
InstabilityMicrogrid InstabilityConventional/Bulk Power Systems Instability
Rotor angleThe use of well-tuned regulators and governors synchronizing torque and damping problems do not occur in microgrids [41].
Low inertia due to high penetration of RESs, poor tuning of synchronous machines, exciters, and governors [42,43].		Increase in rotor angle instability in power systems due to lack of synchronizing torque in local plant/inter-area mode [41,44].
Short circuits in transmission lines cause rotor angle excursions [41]. Increase in rotor oscillations due to sufficient damping torque in local plant/inter-area mode [45].
VoltageVoltage drops due to current distribution networks in microgrids [41]. Voltage instability due to limits in DGRs (change in terminals, reactive power injections,) and sensitivity of load power consumption [46]. Voltage drops due to penetration of induction machines (motor stalls causing voltage stability) [41,46]. Voltage ripples are caused by capacitors in VSIs used in the interface of generation units [41]. Slow dynamic response with the sluggish tuning of parameters by secondary controllers causes voltage stability. Poor active power sharing and active power supply are other reasons [47].Loss of synchronism in the machine causes a rapid drop in voltage [48]. Increase in reactive power consumption in high voltage networks [42,48].
Increase in system disturbances, e.g., limitations in the capability of transmission network for power transfer, self-excitations of synchronous machines, circuit contingencies, and system load increase [48]. Classical methods of controller bear poor efficiency because of higher data analysis and computational burden. A drop in bus voltages is due to the capacitive behavior of the network [47,48].
FrequencyPoor active power sharing and active power supply [41]. Limits in DGRs (RES penetration, change in microgrid configurations) [49]. Tuning of VSIs and VSIs inner current and voltage control loops in inverter-interfaced microgrids cause frequency deviations [41]. Strong coupling between voltage and frequency in microgrids causes frequency deviations (R/X ratios, voltage sensitivity due to load increase) [49].Poor coordination of protection equipment and controllers causes frequency deviations [48].
Unregenerated islanding causes frequency decay due to insufficient under-frequency load shedding [42].
Generation unit (turbine overspeed control) control causes frequency deviations [49,50].
Table 2. Characterization of stability solutions.
Table 2. Characterization of stability solutions.
StabilityMicrogrid StabilityConventional/Bulk Power Systems Stability
Rotor angleVirtual inertia controller with bandwidth compensator for dynamics and stable operation of the system [44]. Day-ahead scheduling optimization techniques to overcome the risk of frequency violation. Fine-tuning of voltage regulators, governors, and synchronous machines to avoid oscillations [41,43].Control design/stability conditions for synchronizing torque and damping torque to avoid aperiodic oscillations and oscillation instability [51].
VoltageExamination of disturbances such as short circuits, switching of DGRs from grid mode to island mode and vice versa, unintentional islanding, and fault analysis [41,52,53,54,55,56].Design of controller/stability conditions to avoid voltage stability issues [52,53,54,55,56].Examination of response (linear/nonlinear) of a power system to analyze the performance of motors, generator field current limiters, transformer tap changers, etc. [49,57].
Design of controller/stability conditions to avoid voltage stability issues [58,59,60].
FrequencyControl/stability design for low inertia power systems/RESs [61]. Increase in generation units/energy storage devices to recover from large generation unit outages [62]. Voltage control to avoid R/X ratios of microgrid DGRs and change in voltage terminals of DGRs [63].
Design of controller/stability conditions to avoid frequency stability issues [64].		Coordination of protection equipment and control [48]. Load sharing and power sharing [49]. Design of controller/stability conditions to avoid frequency stability issues [63,64,65,66,67,68].
Table 3. Acronyms and abbreviations.
Table 3. Acronyms and abbreviations.
AcronymsDefinitionAcronymsDefinition
PGPower gridLVLow voltage
PSSPower system stabilityHVHigh voltage
RESsRenewable energy resourcesMPPTMaximum power point tracking
DGRsDistributed generation resourcesSMSynchronous machine
ESUsEnergy storage unitsIMInduction machine
PCCPoint of common couplingCBCircuit breaker
VSIVoltage source invertersMGMicrogrid
RDGURenewable distributed generation unitsMGCCMicrogrid centralized control
PSOParticle swarm optimizationAGCAutomatic generation control
EMSEnergy management schemeMPCModel predictive control
Table 4. Energy management approaches and techniques used for microgrids.
Table 4. Energy management approaches and techniques used for microgrids.
Proposed Control ApproachesContingenciesAlternate Approaches
Cost optimization through stochastic modeling using nature-inspired algorithms such as ant colony optimization [99].Wind, natural gas turbinesOnly wind forecasting model is designed and set as a base factor for EMS, modern microgrid systems should opt for distributed control with solar and BESS through neural network or machine learning algorithms.
Cost and emission optimization through metaheuristic honey badger algorithm. For fitness function summation of fuel cost, startup cost, and shut down cost is used along with load balance as problem constraint [100].PV, wind, battery, fuel cellIt is a complete software-based idea with multiple operating scenarios which is extremely hard to realize in an actual system. Central controller-based EMS should opt for such multisource microgrid because it contains almost all the energy sources which are hard to handle separately in real time so a central controller-based EMS strategy should opt for a real feasible solution.
A cloud-based P2P scheme is used for commercial microgrid energy management. Bill estimation through leveraging agents is used to formulate the main function of decentralized EMS. DERs energy sharing is used for bill estimation for consumers and boosts microgrid revenue through numerical analysis [101].PV and windThe proposed method used multiple techniques to handle multiple problems in a single microgrid which is a burden to central computing unit. By doing so the trade between microgrid revenue and microgrid efficient operation will be violated. Distributed control with real-time forecasting models can solve this problem without burdening the computing unit.
PSO-based cost optimization is achieved through a virtual energy storage system idea to accomplish source load energy optimization.
Objective function of EMS is based on the difference between input and operational cost for virtual energy storage units [102].		WindOnly wind is used to claim the proposed work related to virtual energy storage unit. However, any system with PV or BESS does not need a virtual energy storage unit for cost optimization. Moreover, PSO and other metaheuristic algorithms do guarantee the optimal solution.
For systems having only wind source, a simple central controller-based forecasting model works effectively for cost optimization
Particle swarm optimization (PSO) for EMS [68,69,70,80].PV, wind, battery loadPSO is unable to ensure an optimal solution but mixed-integer linear and nonlinear programming can be used for similar outcomes.
Power management is conducted through distributed control [86,97].PV, wind, battery, loadFor multiple RES-based systems, the optimization problem can be conducted through linear/nonlinear programming or AI-based methods. In such cases, the technique can be selected based on accuracy or fast response requirements.
Fuzzy logic control for EMS [85,103].PV and loadAI-based algorithm has similar computation requirement but ensures the optimal solution.
Model predictive control (MPC) through energy management scheme (EMS) [74,104,105].PV and windFor optimal power flow, distributive and adaptive control can be used for the optimization of the system for less complexity and the same result.
Stochastic MPC scheme [75,105].PV and windThis technique can be more useful if used with an elevated level of economic optimization.
EMS for optimal power flow and robust optimization [80,106].PV and windMulti-stage scheduling can be used to obtain robustness that will be more in line with practical.
Convex programming and model predictive control for EMS [76,104].PV, wind, load, batteryMachine learning and adaptive control techniques do not require as much computation efficiency and a complex optimization process.
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Safder, M.U.; Sanjari, M.J.; Hamza, A.; Garmabdari, R.; Hossain, M.A.; Lu, J. Enhancing Microgrid Stability and Energy Management: Techniques, Challenges, and Future Directions. Energies 2023, 16, 6417. https://doi.org/10.3390/en16186417

AMA Style

Safder MU, Sanjari MJ, Hamza A, Garmabdari R, Hossain MA, Lu J. Enhancing Microgrid Stability and Energy Management: Techniques, Challenges, and Future Directions. Energies. 2023; 16(18):6417. https://doi.org/10.3390/en16186417

Chicago/Turabian Style

Safder, Muhammad Umair, Mohammad J. Sanjari, Ameer Hamza, Rasoul Garmabdari, Md. Alamgir Hossain, and Junwei Lu. 2023. "Enhancing Microgrid Stability and Energy Management: Techniques, Challenges, and Future Directions" Energies 16, no. 18: 6417. https://doi.org/10.3390/en16186417

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