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Rajendran Pillai, V.R.; Rajasekharan Nair Valsala, R.; Raj, V.; Petra, M.I.; Krishnan Nair, S.K.; Mathew, S. Evolution of Microgrid Technology. Encyclopedia. Available online: (accessed on 21 June 2024).
Rajendran Pillai VR, Rajasekharan Nair Valsala R, Raj V, Petra MI, Krishnan Nair SK, Mathew S. Evolution of Microgrid Technology. Encyclopedia. Available at: Accessed June 21, 2024.
Rajendran Pillai, Vipin Raj, Rohit Rajasekharan Nair Valsala, Veena Raj, Muhammed Iskandar Petra, Satheesh Kumar Krishnan Nair, Sathyajith Mathew. "Evolution of Microgrid Technology" Encyclopedia, (accessed June 21, 2024).
Rajendran Pillai, V.R., Rajasekharan Nair Valsala, R., Raj, V., Petra, M.I., Krishnan Nair, S.K., & Mathew, S. (2023, May 05). Evolution of Microgrid Technology. In Encyclopedia.
Rajendran Pillai, Vipin Raj, et al. "Evolution of Microgrid Technology." Encyclopedia. Web. 05 May, 2023.
Evolution of Microgrid Technology

Microgrids are energy systems that can operate independently or in conjunction with the main electricity grid. There are numerous subdomains of microgrid technology research, each of which focuses on a distinct component of microgrid design, operation, and management. Energy storage, control, power electronics and power quality, renewable energy integration, stability, storage, protection and cybersecurity, regulation and distribution, and economic and business models are some of the major areas of microgrid technology study.

microgrid carbon emissions renewable energy electric vehicles

1. Control Strategies for Microgrids

The largest discovered cluster was examined first in the citation network study. The content of the research articles of the important nodes along the evolutionary path was studied to monitor the evolution, constraints, and future research potential. One of the central nodes focuses in particular on the design and assessment of controllers, which integrates synchronization algorithms to insure a seamless and secure reconnection of the utility and microgrids after the fault is rectified [1][2]. Incorporating an inverter-based microgrid [3] with parallel inverters [4][5][6][7] into a distribution network has made it easier to use distributed generation, storage, and renewable energy sources. It has also improved power quality and reduced losses, increasing the system’s dependability and efficiency. The actual and reactive power requirements for the microgrid must be distributed among the inverters in line with their ratings, and the grid voltage must be controlled in Figure 1. In small grids with substantial nonlinear and unbalanced load proportions, the waveform quality in terms of balance, transient disturbances, and harmonics needs to be actively regulated [8]. Additionally, a method for handling inverters connected in parallel in a standalone AC supply system [9] and control strategies for flexible microgrids composed of parallel connections between numerous line-interactive UPS systems using droop approach [4][10][11][12][13][14][15][16] to prevent vital interactions among UPS units were also discussed [17]. Numerous control techniques based on active power flow [18][19][20][21][22], line impedance [5][16][23][24][25][26][27][28], voltage quality [14][16][22][29][30][31][32][33], stability [12][21][25][26][34][35][36][37][38][39], load sharing [40], frequency quality [30][31][33][37][41], current quality [3][22][32][42], system dynamics (DOF) [43], synchronization [44], reliability [45], operating cost [45][46], hierarchy [26][47][48][49][50], reactive power [16][21][22][36][51][52], communication time delay [50], etc., were reported in the literature for enhancing the overall performance of the microgrid, as shown in Figure 1.
Figure 1. Microgrid control techniques.
This cluster studies microgrid control strategies. The research has focused on designing and testing controllers with synchronization algorithms to allow a smooth and secure reconnection of the utility and microgrids following a fault. Due to multiple distributed energy resources (DERs) and demands, microgrids are hard to manage. Microgrid control and monitoring require communication networks and data management systems, which are prone to failure. The unpredictability of distributed energy resources (DERs) and demand in a microgrid makes system management difficult. An inverter-based microgrid with parallel inverters in a distribution network makes distributed generation, storage, and renewable energy easier and more reliable and efficient. Active power flow, line impedance, voltage quality, stability, load sharing, frequency quality, and reactive power have all been used to improve microgrid performance. To increase microgrid performance and dependability, this research may continue to explore new control mechanisms.

2. Optimization and Management of Microgrid Systems

The following cluster of the citation network regards the different optimization methods for microgrids. “The expert multi-objective AMPSO (Adaptive Modified Particle Swarm Optimization) algorithm [53] has been compared to other evolutionary algorithms such as GA (genetic algorithms) and PSO (Particle Swarm Optimization) in a study of optimization methods for microgrid with renewable energy sources, including tidal energy [54], and a backup Micro-Turbine/Fuel Cell/Battery hybrid power source”. Incorporating cutting-edge distributed energy resources can greatly enhance the performance of power systems, particularly distribution networks. However, it is important to note that excessive use of renewable energy under certain conditions could result in negative effects on the system’s performance. It has been shown through simulations utilising a bi-level operational framework based on the energy band that adding a microgrid to a DN increases the system’s capabilities as a whole. Bi-level cooperation problems were solved using enhanced non-dominated sorting genetic algorithms, which integrate the optimisation of the distribution networks and the profit maximisation of the microgrid [55]. SystemC-AMS-based modelling and simulation frameworks for cyber-physical electrical energy systems (CPEES) were also developed for optimization [56].
To ensure the dependability and stability of a distribution network (DN) with many microgrids, various frameworks for operation management [57][58], market management [59][60], and security and risk management [61][62][63] (as shown in Figure 2) and algorithms for efficient energy [64][65][66][67][68] were also developed. Energy management is shown in Figure 3. Algorithms based on demand-side response [69][70][71][72], voltage stability [73], price elasticity [74], consumer comfort [75][76], multi-carriers [77], distributed generation (DG) [78], customer response [79], operating costs [63][76][79][80][81][82][83][84], hybrid renewable energy sources [85][86][87][88][89], interconnected microgrids [90][91] environmental pollution [92], emissions [93][94], cooperative participation [95], and storage [81][96][97] were found to be the key routes in this network. Reverse power flows, local oscillations, and frequency fluctuations are the key uncertainties [98] of microgrids, which challenge their stability, reliability, and protection. Smart modelling of microgrids using Petri nets (PNs) aids in handling these kinds of encounters, which are extensively employed to illustrate and research the operations of industrial systems in both discrete and continuous-time occurrences. Time-based pricing networks are seen as more reliable options for grid integration strategies in the context of unpredictable loads and renewable energy sources. Electric-vehicle-based microgrids have the potential to utilize energy storage systems, with the vehicles being able to aid the microgrid in satisfying load demands and maintaining voltage and power quality.
Figure 2. Microgrid management.
Figure 3. Microgrid energy management.
However, during periods of high EV usage, the microgrid may experience increased load. Implementing EV power scheduling can address the profit and cost profile as well as support demand-related issues faced by microgrids.
Microgrid optimization and management now includes advanced PSO algorithms, dynamic demand response (DR), hierarchical models, and smart consumer behaviour. Hierarchical decentralized frameworks were created to manage MEMGs and smart consumers. Deep-learning-based forecasters and risk-averse information gap decision theory (IGDT) scheduling risk controlling were applied to predict uncertain parameters. A prediction-based approach for designing dynamic demand response (DR) systems that match smart consumers’ behaviour reduced peak energy and heat costs by 17.5% and 8.78%, respectively [99]. Hierarchical models were constructed for generation scheduling, mobile unit allocation, distribution feeder reconfiguration (DFR), and maintenance crew scheduling to improve DC-MG resilience. Simulations indicated that DFR and proactive interventions cut ENS by 19,124 kWh and 4101 kWh, respectively, when considering load demand, wind speed, and solar radiation uncertainty. Information exchange between microgrids boosted the supply service level to key loads by 48.16%, boosting resilience by 3.47% [100]. Enhanced mixed binary–continuous PSO employed V-shaped QPSO to handle binary variables for six scenarios, taking market price, supply, and demand volatility into account, to tackle unit commitment (UC) problems in microgrids. This quadratic PSO outperformed classical PSO with SPSO and HPSO in several instances [101]. PSO was used to handle dynamic economic load dispatch (DELD) in grid-connected MGs with integrated demand response programs. The two-point estimate method (TPEM) addressed demand, renewable-energy generation, and market price uncertainties. The simulations showed that demand response integration reduced the study microgrid’s running costs by 21.77% [102]. Fast-start distributed generators and fast-responding needs were studied to boost microgrid flexibility and affordability. Fast-responding needs can work with a scenario-based scheduling, but slow-responding demands cannot. If switched off at the DA stage, a slow-start generator cannot be restarted [103]. A bi-level bidding system was created to manage energy exchange between networked microgrids with traditional and smart users. Load demand and renewable generation uncertainty were managed using the CVAR approach. Simulations show that risk-taker scheduling lowers the market-clearing price and increases smart customers’ comfort index [104]. Finally, CCHP MGs with battery-charging stations received an information gap decision theory (IGDT)-based energy management system [105]. Considering the ON/OFF history of power/heat/cooling units, least up-time and least down-time constraints, start-up and shut-down ramp rate limits, and other risk factors, battery energy storage (BES) and thermal energy storage (TES) reduced MG operating expenses by 7.4% and 1.82%, respectively [106]. The battery-charging station (BCS) raised the MG operation cost by 20.19%, and taking into account uncertainties, it increased by 8.22% [107].

3. Microgrid Regulation

The third largest cluster in term of number of papers in the citation network examines microgrid regulation, shown in Figure 4. Multi-agent system (MAS) technology manages a microgrid to optimize energy exchange between producing units, local loads, and the main grid using a classic distributed algorithm based on the symmetrical assignment problem [108]. Using interconnected microgrids and lumped loads, these systems arrange energy resources for island power systems. Energy resource scheduling comprises three phases. First, each microgrid’s internal demand is scheduled. The next step is to search for the best wholesale energy deals coming from a network’s electricity export providers. Finally, each microgrid is rescheduled to match demand from the wholesale energy market simulation and internal needs [109]. Multi-agent systems manage multiple microgrid-distributed energy resources in a two-level configuration. The symmetrical assignment problem with a naive auction algorithm matches energy market buyers and sellers. Market participants include generation, load, auction, grid, and storage organisations [110]. DSC regulates frequency, voltage, and power in microgrids using local unit controllers. Microgrid situations with more distributed generators (DGs) require this technique. Secondary control’s narrow traffic pattern allows local-controller, local-area communication. The voltage restoration separates the voltage and frequency control design by employing a distributed finite-time control strategy to converge all distributed generations (DGs) to the set point in constrained time [111]. Consensus-based distributed frequency control with control input limitations restores frequency [112]. A novel coordinated power controller design architecture optimises scattered generator active power output. Distributed generating systems have DED and CC function modules for each bus. Distributed consensus-theory-based DED calculates each generator’s optimal active power generation references [113]. Distributed cooperative VUC control is used for islanded microgrids. Each distributed generator (DG) may balance sensitive load bus (SLB) voltage. Each local DG’s contribution level (CL) is proposed to demonstrate compensation ability. Each local DG features a supplementary compensation architecture and communication layer [114][115]. Control strategies are necessary for isolated microgrids with inconsistent communication in order to regulate frequency and voltage of each distributed generator (DG) and share active/reactive power. Droop-based secondary and tertiary control strategies are established using iterative learning mechanics. The DGs only need to communicate sporadically and in a low-bandwidth manner with their neighbours, as control inputs are updated at the end of each iteration. Periodic communication can be costly and inefficient in microgrid control [116]. In isolated microgrids, secondary frequency and voltage are managed through event-triggered distributed control.
Figure 4. The evolution of R&D of microgrid regulation.
The proposed control strategies aim to restore frequency and voltage, accurately share active power, and minimize communication among the secondary controllers. Feedback control laws are replaced with estimator outputs, which are only updated during event-triggered times [117].
A new master–slave-organised DC microgrid network control technique with distributed iterative-event triggers and constrained communication capacity can synchronise DER voltages. Low-bandwidth communication networks can optimise load sharing for economic operation [118]. Broadcast gossip distributes peer-to-peer control entirely. To govern voltage and reactive power sharing, DER units need local voltage and current readings from their neighbours. The broadcast gossip communication protocol’s scalability and reliability allow control inputs to precisely share reactive power across each DER by restoring voltage levels at the shared coupling. Distributed controllers replace the central hierarchy in local DERs. Line switches’ peer-to-peer requirements ensure microgrid system stability, allowing DERs to plug-and-play and survive topology changes [119][120]. For DC cyber-physical microgrids, resilient neighbour-based distributed cooperative control involves slow-switching topologies and communication delays. The proposed robust control method can synchronize a DC microgrid’s voltages by achieving optimal load sharing for DERs’ generation cost reductions to improve economic operation at the same layer through a sparse communication network, taking communication delays and slow-switching topologies into account [120][121]. In an isolated AC microgrid, a two-layer distributed control technique may regulate the output power of huge DERs such as PVs and BESSs to achieve self-consistent proportional power sharing with time delay [122][123]. A distributed dynamic event-triggered control rule for each distributed generator handles secondary frequency restoration and active power distribution in an AC microgrid system with constrained varying time delays. Dynamic event-triggered approaches reduce communication costs. Lyapunov function analysis provides stability, active power sharing, and asymptotic frequency restoration. The adequate condition limits time delays [123]. DoS attacks are mitigated using a secondary-control-layer-distributed resilient control technique. 
In conclusion, managing and connecting a variety of systems and components is necessary for microgrids. For regulators, this makes microgrid operation and construction complicated. Regulations governing microgrid connectivity vary by country. Regulators struggle to develop guidelines for utility and microgrid collaboration. System security and dependability must be balanced with innovation and the incorporation of renewable energy sources in microgrid control. Microgrid management makes sure that microgrids integrate well with the main power grid. Regulations for consumer protection, grid management, and interconnection are mentioned. The laws strike a compromise between customers, utilities, and microgrid operators. Future microgrid regulation is anticipated to support the integration of renewable energy, enhance system stability and reliability, and protect consumers. New regulations may be necessary for distributed energy sources, energy storage technologies, and electric automobiles. International standards and harmonized regulations are required to facilitate the development and implementation of microgrids throughout the world due to the rising demand for them and their potential to increase energy security and decrease reliance on conventional grid systems. Lastly, microgrid regulation is expected to support the integration of renewable energy, system dependability, and stakeholder interests.

4. Stability of Microgrids

Microgrid stability is the major concern addressed in the fourth cluster in the citation network. Intelligent controller systems estimate system variables and adjust to operational changes to outperform the traditional controllers [124]. A mathematical model shows how modest rooftop photovoltaic (PV) power plants affect a larger power system’s economic and performance characteristics. PV electricity generation had a high break-even cost below 10% [125]. Battery storage stabilises PV system power output (Figure 5), but it is expensive and wasteful. This study proposes altering MPPT control parameters to smooth short-term power output changes in PV systems without additional equipment. The proposed measure restricts PV system power output growth by moving the MPPT control operating point to a position where maximum power is not created with current insolation when insolation increases rapidly [126]. PV/battery cuts peak demand by 7% [127]. The benefit of PV and emergency storage when used together is greater than when these two technologies are used separately, and distributed PV and storage may enhance grid security [128]. A new energy-storage-modelling software links wind turbines, solar PV arrays, and variable electrical loads. The model calculates the filling and emptying of the energy store and anticipates power curtailment or unfulfilled demand. This unique modelling strategy outperforms previous methodologies [129]. The output of a PV system is optimized using MPPT, but it converges to a local maximum instead of the maximum of the curve. A two-stage MPPT control is suggested for non-uniform insolation [130]. A Photovoltaic Energy Capacitor System (PV-ECS) power-generating system using solar energy estimation has been described, and energy capacitor systems coupled to power electronics devices can control power [131][132]. Fuzzy logic and PSO are used to optimize the most prevalent proportional–integral (PI)-based frequency controllers in AC microgrid systems [133], and Kriging-based surrogate modelling reduces assessment costs [134]. A microgrid test platform evaluates the performance and robustness of synthesized controllers under disturbances and uncertainties [135]. A marine vessel equipped with a portable islanded microgrid comprising PV, wind turbine, SWE, and ESS employs a fuzzy PD + I load frequency controller (LFC). Electric vehicles manage load frequency in islanded microgrids [136]. Alternative power-balancing technologies are being investigated because battery energy storage systems (BESS) are expensive and degrade quickly. Low-frequency EV BESSs with vehicle-to-grid capability are popular.
Figure 5. The methods of enhancing stability of microgrids.
When employed in V2G scenarios, a novel multi-objective fractional order control approach for EVs optimizes the V2G controller under a variety of operating conditions caused by intermittent renewable energy sources [137]. An efficient two-area interconnected microgrid (ICG), based on renewable energy sources without batteries, uses dish Stirling solar power generation, wind power generation, plug-in hybrid electric vehicles, a diesel engine driven generator, heat pumps, and freezers [138]. For load frequency analysis in hybrid microgrids with wind, micro-hydro, biogas, and biodiesel generators, one study linearizes a medium-sized linear-Fresnel-reflector solar-thermal power unit. The model simulates workable DR approaches for isolated and interconnected modes [139]. Optimization-based FO controller tuning uses GOA, GSA, GA, and PSO [140]. The stability boundary locus (SBL) method finds the FOPI controller’s stable parameters space or fuel-cell microgrid stability boundary curves. The system’s characteristic equation determines the SBL’s stable zones. Electrolysers and fuel cells are sustainable [141][142]. Hydrogen Fuel Cells (HFCs) connected to microgrid control frameworks are studied for their efficiency and environmental friendliness. Power restrictions with high demand or transient events fluctuate HFCs. This study improves virtual synchronous generator (VSG) control for power production systems that combine HFCs and supercapacitors (SCs) [143]. Two-stage photovoltaic power generation has DC-link voltage management difficulties. Using DC-side synchronised active power regulation, two-stage photovoltaic (PV) power generation without energy storage was developed [144].
Microgrid stability study ensures safe and reliable operation. This involves studying how microgrids affect electricity system stability during disruptions and creating new control algorithms to improve voltage and frequency stability. Research uses machine learning and artificial intelligence to improve microgrid performance. Control algorithms, distributed energy resources (DERs), and photovoltaic (PV) and energy storage power output are improved to improve microgrid stability.
Future research on the topic of microgrid stability will focus on enhancing the stability of these systems through the creation of new and improved control algorithms. These algorithms will use machine learning and artificial intelligence to optimize microgrid performance and stability in real time. There will be research undertaken to evaluate the effect of microgrids on the stability of the electrical grid during outages and to regulate the voltage and frequency levels in microgrids to preserve system stability. Microgrid stability is crucial, since these systems are vital to the electrical system’s safety and reliability.

5. Microgrid—Energy Storage

Energy storage (Figure 6) is a major issue in microgrid construction and is discussed in the fifth cluster of the citation network. Thermal energy stores use thermal inputs and outputs to connect to the system, while electrical energy stores use electrical inputs and outputs, and the efficiency and reliability of these electrical energy systems (EES) need to be monitored consistently [145]. Electrical energy is stored in flywheels, pumped hydro, compressed air, and electrochemical devices. Ice storage, custom thermal storage medium, and phase transition materials use material, sensible, and latent heat capacities [146]. All solar electric systems need batteries. Their efficiencies and lifespans affect PV system performance and economics. Batteries made specifically for photovoltaic systems need to have a high cycle stability and a very low discharge rate. An algorithm for large battery storage systems measures electrolyte-specific gravity and voltage at a predetermined temperature to establish a battery’s state of charge [147]. The life cycle costs of a rural energy-storage electromechanical flywheel battery and a lead-acid-battery storage system were compared. Flywheels were cheaper than lead batteries over time. Based on the foregoing, small-scale flywheel energy storage could boost rural electricity in sub-Saharan Africa. Electromechanical flywheel battery storage reduces lead-acid-battery disposal environmental impacts. Examining the separation of Cd and Ni from Ni-Cd batteries using an aqueous two-phase system (ATPS) made of water, copolymer L35, and Li2SO4 is crucial when taking environmental considerations into account. 
Figure 6. Microgrid energy storage Systems.
Wind and hydro solutions fulfil local electricity needs, reducing the reliance on fossil fuels and imports compared to the wind–light complementary pumped storage power system. This system employs solar, wind, and complementary energy sources to generate clean electricity and store backup power [148][149][150]. It has the potential to grow, with a low-cost wind–hydropower system being analysed from both an investment perspective (to maximize returns) and a system perspective (to increase renewable energy penetration and reduce costs). Genetic algorithms are used to optimize the system [151]. To address the issue of resource volatility causing electricity demand to exceed generation, pumped storage can replace batteries in wind–solar hybrid power systems [152]. The combination of wind, solar, and hybrid technologies creates and stores electricity at low cost [153]. In Cameroon, hybrid systems combining pico-hydro and photovoltaic with biogas generators reduce electricity costs [154]. Deep-cycle batteries are superior for standalone renewable power systems, but pumped storage with a battery bank is 55% cheaper than deep-cycle batteries and more economical with a hydraulic controller. Increasing storage autonomy and capacity would make pumped storage more cost-competitive. Pumped storage with batteries is the best option for reliability, energy efficiency, and technology implementation [155]. Microwave-induced CO2 gasification of carbon compounds could store energy. Charcoal used the least energy in a study that studied f=materials. Activated carbon and charcoal reacted well with CO2, while anthracite and coke did not. Charcoal was the most energy-efficient, especially at high volumetric hourly space velocities (VHSVs). The multimode microwave oven was more energy-efficient than the single-mode oven. Initial studies demonstrated that this method could reach energy efficiency of roughly 50% at laboratory scale. To compete with energy storage technologies, these performances can be improved. 
Remote communities can improve energy security and living circumstances by using renewable energy, especially solar and wind power linked with microgrid technology. Solar, wind, and energy storage allow isolated communities to generate sustainable electricity at cheaper costs than fuel. Renewable energy technologies benefit isolated microgrids. In the best instance, PV panels reduce LCOE by 19% compared to diesel engine systems. Using biomass as the major energy source reduces costs by twice as much, and gasifier-based and ORC-based systems create roughly 95% renewable electricity. However, usage of biomass in huge amounts will increase environmental pollution. By combining conventional power with local renewable energy in a remote place, the hybrid microgrid energy system is controlled accurately. The concept shows pumped storage of hydroelectricity’s efficacy in irrigation and power restitution. Fuel savings and CO2 reduction are demonstrated by the proposed technology [156][157][158]. Hybrid microgrid architecture using an Equilibrium Optimizer (EO) is proposed. The microgrid system is designed using EO because it quickly finds the best option. EO selects the best system design to reduce cost, increase stability, and cover load in varied climates. PV, WT, battery, and diesel generator constitute a microgrid. This study minimises net present cost (NPC) while preserving reliability, availability, and renewable percentage [159][160]. For optimal convergence, efficacy tools are needed for microgrid design. Stochastic metaheuristic algorithms solve complex problems best. The Gradient Artificial Hummingbird Algorithm (GAHA) reduces microgrid system energy cost (EC) by combining a gradient-based optimizer (GBO) with Artificial Hummingbird Algorithm (AIHA) [161][162][163]. The hybrid Harris Hawks Optimizer Arithmetic Optimization Algorithm (HHHOAOA) is a new metaheuristic algorithm for sizing and designing autonomous microgrids, which increases solution variety during optimization to improve solution accuracy [164]. EV-load scheduling reduces standalone microgrid costs, and the artificial hummingbird algorithm outperforms mainstream metaheuristics in off-grid microgrid size. Load demand affects off-grid microgrid cost more than meteorological data. Battery storage reduces overbuilt and excessive curtailment hazards [165].
Energy storage technologies are too expensive for microgrids to justify. Energy storage for isolated or low-income microgrids may be found difficult. Integrating storage devices into microgrids is difficult because they must match the microgrid’s load and generation profile. This includes coordinating storage-system charging and discharging with microgrid generators and loads. Energy storage devices need regular replacement and maintenance. Remote or inaccessible microgrids may struggle with this. Energy storage is essential to a microgrid, but its cost, technical challenges, safety concerns, durability concerns, and regulations make it difficult to adopt. Energy storage research tries to overcome these challenges. To improve microgrid stability and reliability, energy storage solutions are being integrated. Alternative power-balancing methods are being studied because BESS are expensive and degrade quickly. Vehicle-to-grid low-frequency electric vehicle battery energy storage devices are common. Machine learning and AI are improving control algorithms and microgrid stability. Researchers are using energy-storage-modelling software to link wind turbines, solar PV arrays, and variable electrical loads to optimize microgrid efficiency.
The creation of new energy storage business models is another aspect of the future potential of energy storage in microgrids. New business models that use energy storage as a resource that generates income are emerging as the cost of energy storage continues to fall. Energy storage, for instance, can be utilised to offer grid services such as frequency management or participation in the capacity market, bringing in more money for microgrid operators. Last but not least, integrating energy storage with EV charging infrastructure is a part of the future scope of energy storage in microgrids. Energy storage can be crucial in regulating this demand and lessening the burden on the grid as more EVs are deployed and the need for charging infrastructure grows.

6. Microgrid Protection

Microgrids can combine numerous distributed renewable energy sources with distribution networks, but creating a sufficient protection mechanism is a hurdle for microgrid installation, as shown in Figure 7 and addressed in the respective cluster of the citation network. Digital relays and a communication network were suggested to defend the microgrid system instead of conventional methods, which are inefficient [166]. Distributed generators (DGs) can improve power system dependability and quality through intentional islanding or microgrid operation. Controlling and protecting voltage and frequency are the biggest challenges for microgrids. Protection plans for lines and DGs during islanded operation, control methods for inverter-based DGs to manage voltage and frequency [167], and microprocessor-controlled relay-based protection methods for low-voltage microgrids were also created [168][169]. Microprocessor-controlled relays also protected looping or meshed microgrids [170]. Intelligent protection methods such as wavelet transforms and decision trees [171] can effectively protect the microgrid from problematic circumstances related to such substantial operational condition swings. A superimposed reactive energy protection strategy uses directional features and a threshold to identify the microgrid’s problematic phase and section. The Hilbert transform calculates superimposed reactive energy (SRE) and sequence components of superimposed current in this protection technique. This protects looping and radial microgrids against solid and high-impedance faults [172].
Figure 7. Various methods and algorithms used for Microgrid Protection.
Apart from this, a protection approach for microgrids employing interval type-2 fuzzy logic [173], using two separate fuzzy systems to detect, classify, and locate microgrid faults while taking into account the numerous uncertainties involved with faults, was also discussed. “After a single-phase tripping event, these fuzzy systems employ the phase angle between superimposed modal voltage and modal current to identify the fault direction, helping to secure the microgrid and acting as a fall back in case the primary protection fails” [174]. PV-integrated microgrids use power electronic converters due to photovoltaic (PV) source operation’s intermittent load demand. A convolutional neural network (ConvNet)-based protection technique was used to distinguish between PV system inverter failures and distribution line symmetrical and asymmetric faults, as well as to detect, classify, and identify the defective section [175]. Most fault detection methods use a communication mechanism to convey information between protection units, which could compromise the entire protection system given the vulnerability of power electronic converters in DC microgrids. In contrast, an equivalent inductance-based fault detection system employing a simplified fault current equation was found to be more effective at performing the task [176]. Despite DC microgrids’ many advantages and features, protecting them is difficult due to factors including photovoltaic (PV) systems’ self-limited current, wind energy systems’ long time constants, and communication systems’ dependence, among others. A protection system for DC microgrids was also developed that uses the rate of power (dP) and rate of voltage (dV) and maps them as a dP-dV profile [177] suitable for all RES and energy storage systems, regardless of the DC microgrid’s power rating and design. In addition to this, to guarantee the adaptability of DC microgrids to system reconfiguration and weather sporadicity, an ensemble-classifier-based protection technique has also been published [178], where the ensemble-based method is insensitive to individual classifier bias and dataset dimension/size [179]. A fault detection/classification, mode detection, and section identification approach based on Discrete Wavelet Transform (DWT) and Extreme Learning Machine (ELM) has been developed for grid-connected and island-mode microgrids to tolerate nonlinear wind-speed fluctuation. 
Microgrids frequently consist of a variety of generators, storage devices, and loads, making it challenging to ensure that all components can communicate and work together efficiently. The remote control and monitoring of microgrids raises concerns regarding cybersecurity and the possibility of malicious attacks on the system. To safeguard data exchanges between microgrid components and control systems, secure communication protocols will be necessary. This will prevent hackers from intercepting and modifying data as well as interfering with communications between microgrid components. In the future, cyber-physical systems (CPS) will play an increasingly vital role in microgrid operations. CPS merge physical and cyber technologies to build a system that is more efficient and resilient. Nonetheless, safeguarding CPS will be essential to ensuring that they are not susceptible to assault. Critical to minimising the impact of any cyberattack on the microgrid will be the development of an incident response strategy that includes methods for detecting, responding to, and recovering from cyberattacks. This research will likely focus on developing intelligent protection systems that can adapt to changing conditions and respond in real time to protect the microgrid against disturbances. However, like any power system, microgrids are susceptible to a variety of risks that must be managed to ensure their safe and efficient operation. These risks include equipment failure, cybersecurity threats, natural disasters, and regulatory compliance. To mitigate risks associated with microgrid operations, operators can adopt diverse strategies, including but not limited to regular equipment maintenance, cybersecurity measures, disaster preparedness plans, and compliance monitoring. Adequate risk management is crucial for ensuring the dependability, safety, and financial sustainability of microgrid systems. The application of advanced analytics and machine-learning methodologies for the detection and anticipation of potential hazards represents a promising avenue for the advancement of microgrid risk management. The utilization of various techniques can facilitate the detection of potential hazards prior to their manifestation through the assessment of extensive amounts of information from diverse origins, thereby enabling preventive management and alleviation of risks.
The integration of cybersecurity protocols within the framework of microgrid risk management represents an exciting prospect for future exploration. The employment of digital technologies and the Internet of Things (IoT) in microgrids has led to an increase in cybersecurity risks, posing a significant threat to the operation and management of these systems. The integration of cybersecurity measures into the risk management framework is imperative to ensure the protection of microgrids from potential online threats. The future risk management scope of microgrids includes the advancement of novel frameworks and techniques for managing risks. As microgrids continue to evolve and diversify, it will be necessary to create novel risk management frameworks and methodologies that are tailored to the specific risks associated with different types of microgrids, including off-grid or islanded microgrids. Future microgrid risk management will include energy management, grid integration, and renewable energy integration. The establishment of a comprehensive approach to microgrid management can ensure optimal performance and risk mitigation. This can be achieved by integrating risk management with other aspects of microgrid operation and management. The study of risk management in microgrids has emerged as a significant field of investigation, owing to the escalating implementation of microgrids on a global scale. Current research involves risk assessment frameworks, risk scenario modelling, and risk management tactics. Despite advancements, research on renewable energy integration, climate change implications, and cost-effective and dependable risk management methods is still lacking. Further investigation is imperative in these domains to guarantee secure and enduring functioning and endorse the extensive implementation of microgrids as a fundamental element of the energy shift.

7. Microgrid and EV Charging

The primary theme that emerged in the next cluster relates to the modelling, optimising, controlling, and maintenance procedures for microgrid EV charging stations and their storage (Figure 8). Microgrids with PV and energy storage systems (ESS) for charging stations have been developed, since the number of electric cars (EV) in cities has expanded quickly [180]. Traditional sizing methods cannot examine large-scale situations using nonlinear optimization models to ensure design economy and dependability.
Figure 8. Microgrid and electric vehicle charging.
Many physical–economic model (PEM) and data-driven model (DDM) techniques have been developed to manage nonlinear battery degradation and optimal power distribution under varied EV charging profiles [181]. A concise tree-based machine learning (ML) model that was tested on a public dataset of data from domestic EV charge points and trained on each charge station based on the behaviour of its users revealed that the forecasting error can be reduced by up to 4 times, which in turn results in a progress of up to 50% in a combined aging–quality of service metric [182]. Microgrids based on PV panels put on rooftops or car parking shades, electrochemical stationary storage, EV charging stations, and public grid connection reduce power grid overload and increase renewable energy [183][184]. PV-powered electric vehicle charging stations with V2G power management were also included [185]. Real-time mixed-integer linear programming problems were created to minimize energy expenditure while taking into account EV arrival and departure [186].
The futures of electric vehicle (EV) charging and microgrids are deeply linked, since both play a vital part in the transition to a cleaner, more sustainable energy system. Integration of microgrids with EV charging infrastructure has the potential to minimize reliance on fossil fuels by allowing EV owners to charge their vehicles using renewable energy generated locally. It also helps balance energy demand and supply, reducing strain on grid infrastructure and encouraging the use of renewable energy sources for EV charging. As the market for electric vehicles (EVs) continues to expand, advancements in microgrids and EV charging will be required to facilitate this transition to a more sustainable energy system. By offering a localised supply of renewable energy and grid services, microgrids can play a significant role in the smart charging of electric cars (EVs). Smart charging is the process of optimising EV charging based on variables including power costs, the availability of renewable energy sources, and grid stability.
Microgrids can facilitate smart charging by combining EV charging stations with renewable-energy generators such as solar or wind turbines. Both the carbon footprint of EV charging and the expense of maintaining charging stations can be decreased as a result. Moreover, microgrids can act as platforms for the application of sophisticated algorithms and management techniques for EV smart charging. These algorithms can optimise EV charging schedules and lower charging costs for EV users by utilising real-time information on power pricing, the availability of renewable energy sources, and system conditions.


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