Review of Recent Developments in Microgrid Energy Management Strategies

Abstract

The grid integration of microgrids and the selection of energy management systems (EMS) based on robustness and energy efficiency in terms of generation, storage, and distribution are becoming more challenging with rising electrical power demand. The problems regarding exploring renewable energy resources with efficient and durable energy storage systems demand side management and sustainable solutions to microgrid development to maintain the power system’s stability and security. This article mainly focuses on the overview of the recent developments of microgrid EMS within the control strategies and the implementation challenges of the microgrid. First, it provides energy management strategies for the major microgrid components, including load, generation, and energy storage systems. Then, it presents the different optimization approaches employed for microgrid energy management, such as classical, metaheuristic, and artificial intelligence. Moreover, this article sheds light on the major implementation challenges of microgrids. Overall, this article provides interactive guidelines for researchers to assist them in deciding on their future research.

1. Introduction

Through numerous environmental regulations and efforts, the widespread promotion of green energy and de-carbonization has stimulated research into novel solutions. In this context, complementary technologies, and renewable energy resources (RER) have drawn significant interest worldwide. An increase in the utilization of RER throughout the electricity networks might facilitate their decentralization [1,2,3,4]. A possible method of linking RER to the distribution grids is utilizing the microgrids (MGs) that are made up of an accretion of loads, distributed generation (DG) units, and energy storage systems (ESS) [5]. The MGs provide numerous technological [6,7], social [8,9], economic [10], and environmental [11,12,13] advantages. They assist in bringing electricity to remote areas, lessen blackouts, and boost the system’s energy effectiveness. Additionally, the MGs lowers system losses and offer auxiliary services to maintain the efficient operation of the energy networks. A microgrid is a self-sufficient energy system that serves a discrete geographic footprint, such as a college campus, hospital complex, business center, or neighborhood [14]. A smart MG is a solution that maintains the electricity grid sustainability by assuring an intelligent and dependable power supply [15]. In general, the MG is a single controllable local power network with distributed energy sources (solar Photovoltaics (PV), wind energy, diesel generator, fuel cell, wave energy, etc.), ESS, controllable distributed loads (households, commercial, industrial, etc.), and advanced energy management systems (EMSs) as shown in Figure 1. It can be operated alone or in connection with the utility grid. A few popular definitions are given below:

• The U.S. Department of Energy [16]: “a group of interconnected loads and distributed energy resources within clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid. A microgrid can connect and disconnect from the grid to enable it to operate in both grid-connected or island-mode.”
• International Electrotechnical Commission [17]: “the electrical systems with loads and distributed energy resources at low or medium voltage level. According to IEC 62898-1, microgrids are classified into isolated microgrids and non-isolated microgrids. Isolated microgrids have no electrical connection to larger electric power systems.”
• Energy Networks Australia [18]: “an autonomous or local energy grid, with the control capability to operate separately to the traditional grid.”

Figure 1. Schematic view of a generalized microgrid.

Figure 1. Schematic view of a generalized microgrid.

However, the stand-alone or isolated MG, sometimes called an “island grid,” only operates off the grid and is connected to the wider electric networks. They are usually designed for geographical islands or rural electrification [NO_PRINTED_FORM]. It is worth noting that the grid-connected MG systems are mostly considered in this article.

Due to widespread attention, the overall implementation costs for MGs have been declining for several years. Therefore, the already booming microgrid market is expected to increase more in the near future. It is anticipated that the global MG power capacity will be increased by 3.5 GW in 2020, and the number will be around 20 GW in 2029. However, it is worth noting that the already installed power capacity of MGs in 2020 was around 25 GW [19,20]. Currently, Asia Pacific is the global leader in the MG market. The market is concentrated in Australia, India, Japan, and China. This market primarily focuses on remote MGs, except for Japan, where grid-connected systems are prominent. Although North America is the second-highest MG market holder, this region is ranked number one in the case of grid-connected MGs. In contrast, Europe is considered the leader in reducing greenhouse gas emissions; this region represents only 9% of the MG market share. As an alternative to the MG, they focus on another distributed energy resources platform, virtual power plants, and other alternatives. Recently, a considerable uplifting has been observed in Latin America due to a $14 billion long-term proposed MG project in Puerto Rico (Puerto Rico is considered to be in Latin America, although it is US soil) [20].

However, the MG encounters energy management and coordination challenges due to the integration with intermittent RER, load demand and electricity price uncertainty, and ESS operation schedules. To reduce these issues and enhance MG energy management operation, efficient EMSs are required [21]. The microgrid EMS implements optimal scheduling in line with predefined objectives for optimizing energy costs by performing required functionalities on a supporting platform [22,23,24,25]. It is also essential to balance supply and demand by efficiently managing the internal energy flows and the grid link in real-time. With an effective EMS, the MG provides cascaded benefits for networks, clients, and environments [26,27,28,29]. Therefore, various EMS implementation methods for microgrid applications have been reported [30,31,32,33,34]. Meliani et al. [35] discussed the management techniques reviewed by notable authors for different EMS solution approaches. Sarzosa et al. [36] studied the EMS analysis scheme by identifying the main trends in the database, information gathering, and paper selection, and they summarized classified frameworks and features mainly on resolution techniques. They also proposed an EMS case study where an objective function was defined for cost minimization. Ref. [37] introduced the centralized EMS approaches in Inter-connected Multi-Microgrids to promote the systematic use of distributed energy resources. The EMS in campus microgrids brought a unique concept in reviewing optimization and modeling techniques [38].

A notable amount of articles reviewed energy management strategies for the MG. Most of them focused on the typical control and operation strategies of the MG while developing EMS schemes [39,40,41,42,43,44]. A good number of articles also addressed special issues, including the control strategies for solar PV-based MGs [45], control strategies for individual and community MGs [46], primary control strategies for islanded MGs [47], and power-sharing strategies in the MG [48]. Another popular trend related to the EMS is the optimal sizing of the MGs and their resources. For instance, the architecture, operating modes, and sizing strategies were illustrated by Ouramdane et al. [49]. Refs. [50,51] reviewed optimal sizing strategies of a stand-alone MG, while the optimal sizing of a hybrid MG was reviewed in Ref. [52], and sizing strategies for solar PV-based MGs were presented in Ref. [53]. Okhuegbe et al. [54] critically analyzed the optimal sizing of hybrid energy resources and employed optimization techniques. Refs. [55,56] reviewed the optimal sizing of energy storage systems for MG applications and EMSs. Zhou et al. [57] illustrated a comprehensive overview of typical functionalities and architectures of the multi-microgrid EMS. Besides, other critical issues include flexible energy resources for renewable resources incorporation [55], decision-making strategies [56], types of distributed energy resources [58], energy management challenges [59], and implementation of weather forecasts [60] for both AC and DC microgrids. Refs. [61,62,63] reviewed the energy management strategies for smart homes connected to renewable energy resources and electric vehicles. In the mentioned articles, the authors mainly contributed to reviewing the control strategies, optimal sizing of MG architecture and resources, and energy generation management. However, only a few articles [64,65,66] reviewed the optimal scheduling of resources. To the best of the authors’ knowledge, articles were rarely found that summarized MG energy management strategies, optimization approaches applied to grid-connected systems, and relevant implementation challenges. Considering the mentioned notes, this article reviews and discusses energy management strategies, optimization approaches applied to grid-connected systems, and relevant implementation challenges. The specific contributions and novelties are highlighted in the following points:

• It critically reviews the energy management strategies of the MG, focusing on load, power generation, and energy storage systems management.
• It provides an insightful analysis of various optimization techniques, including classical, metaheuristic, and artificial intelligence, for the grid-tied MG to reduce operational costs.
• It also highlights MG implementation challenges and recommendations for researchers and policymakers.

The remaining parts of the article are arranged in the following sections: Section 2 presents the research methodology, followed by the literature survey. Section 3 illustrates the microgrid’s different energy management strategies, including generation, load, and storage systems management. Section 4 presents the various optimization approaches employed for microgrid energy management and scheduling. Section 5 discusses MG implementation challenges, including technical, social, economic, security, and regulatory challenges. Finally, the article summarizes the conclusions, with a few recommendations in the Section 7.

2. Research Methodology

This article followed a systematic review to categorize the relevant studies and meta-analyses for the topic under investigation. It deliberately analyzed recent ten-year literature on microgrids’ EMSs, although searches were limited to papers written in English only. The first keywords, such as artificial intelligence, challenges, energy management schemes, computational intelligence, optimization, power systems, renewable energy resources, and smart grid, were searched in Scopus from 2013 to 2022. Figure 2 represents the literature survey results for five keywords: microgrid, smart + microgrid, microgrid + management, microgrid + control + management, and microgrid + energy + management [67]. The search discovered almost 400 documents, and this article picked nearly 250 of them from renowned directories, including Science Direct, Springer, IEEE Explore, Wiley, and Multidisciplinary Digital Publishing Institute (MDPI). In addition, this article also incorporated several documents outside the mentioned directories, due to their significance and relevance to the topic under investigation. Figure 3 shows the bibliometric visualization for the author-supplied keywords of the microgrid EMS [68].

3. Microgrid Energy Management Strategies

In general, the major components of the MG are the load, generation units, and energy storage systems. This section briefly illustrates the management of the mentioned components.

3.1. Load Management

Precise load management of the MG systems has become a critical issue, as the MGs are comprised of different kinds of loads. Therefore, an error-free load management tool is necessary to ensure MG power management efficiency. Maintaining the balance of generation versus load is always a critical issue for any power system operator. Likewise, the MG control system must constantly analyze and prioritize loads to keep this balance. In general, the MG loads can be primarily ramified as critical and non-critical loads, as shown in Figure 4 [69]. Critical loads play crucial roles for a facility and need to be uninterruptible, regardless of the situations encountered. Examples include hospitals, nursing facilities, data centers, etc. On the other hand, a noncritical load can be divided into discretionary loads and emergency load sheds. Discretionary loads can be shed during peak time to reduce the load for a short time. Heating, ventilation, and air conditioning (HVAC) equipment, washers, dryers, water heaters, etc. fall under this category. Emergency shedding loads of residential and commercial buildings are crucial for MG protection to avoid a blackout. To detect the conditions where the non-essential load should be separated from an island, Ref. [70] proposed the idea of an intelligent load shedder module to link in series with non-critical loads. Moran [70] explored methodologies for calculating, prioritizing, and managing loads under all conditions to optimize efficiency. The classification and identification of critical versus non-critical, and active versus inactive loads, along with the maintenance of quantitative data, require some strategies for matching loads to generation. Abdelazeem et al. [71] suggested a hybrid methodology to control energy in residential, commercial, and industrial microgrids, based on demand-side management and multi-agent systems.

Gawande et al. [72] used the concept of a series of electric springs to regulate fluctuations in bus voltage and fault ride-through conditions. This study analyzed power conservation during intermittency in generation by making non-critical loads absorb less power, with stable power absorption by the critical load. Ahmed et al. [73] proposed a neuro-fuzzy load priority list-based integrated EMS by implementing demand-side management while considering the importance of load sections, such as residential, commercial, industrial, and hospital. In modern electrical networks, loads may also engage in energy and generation management. According to a contract between the customer and network owner, any loads should move their usage to off-peak at peak network time. The consumption form refers to the quantity and dimension of requirements, including residential [74,75], commercial [76,77], and industrial [78,79] demands, based on the classification of the connected loads.

Table 1. Load management models reported in the recent literature.

Ref.	Model	Analysis
Kennedy et al. [71]	Intelligent Load Shedder (ILS) module	Detected non-critical load for being from the island
Abdelsalam et al. [72]	Antlion Optimization Algorithm (ALO)	Minimized total energy costs and maximized the load factor
Alahmed et al. [74]	Neuro-fuzzy LPL-based system	Implement demand management based on the importance of the load section
Lee et al. [80]	Two-stage stochastic programming	Forecasts to schedule limits hence improve the availability of power
Gawande et al. [73]	DC series electric spring	Regulated bus voltage fluctuation and fault ride-through condition
Sedighizadeh et al. [81]	Hybrid big bang big crunch algorithm	Investigated stochastic behavior of electric vehicles and other sensitive loads

presents various load management models as reported in the recent literature. It is evident from the discussions that load management is an integral part of efficient EMSs of the microgrid systems.

3.2. Power Generation Management

Distributed resources are the most dominant components of the MG systems, and they require sophisticated power control mechanisms to ensure smooth operation. Rathod et al. [82] presented the contributions of generation management in hybrid MGs, as well as the grid integration of various DGs. The authors recommended using solid oxide fuel cells (SOFC) as an auxiliary unit in the MG because of greater efficiencies. A study presented the generation management problems of MGs integrated with tidal energy in Darwin, Australia [83]. Two different models, namely frequency analysis and the electrical model of the triple combined cycle (TCC)-based SOFC with gas and steam turbines, were proposed by Obara and Miyazaki [84] to deal with power fluctuations due to large-scale PV modules in the MG. They used the swing equation for the SOFC-TCC grid-connected frequency model and analyzed the power response characteristics of the electricity model. Boqtob et al. [85] reviewed generation units’ contribution to managing load demand, considering the demand nature of MG categories. In a similar literary work, a combined droop and communication-based EMS was recommended to regulate DG source voltage and power to optimize the operation points of both grid-connected and stand-alone MGs [86]. Goyal et al. [87] analyzed the techniques of permanent magnet synchronous generator (PMSG)-based wind energy integration. They used a modified droop control based on synchronization to conserve constant frequency, in order to manage power fluctuations of the wind energy system. Omkar et al. [88] demonstrated a fixed-speed PMSG-based total transfer capability system model with power converters to understand how the tidal turbine conversion system integrates with the microgrid. Based on the presented discussion, it is obvious that power management of the distributed generation units is very crucial for MG energy management systems.

3.3. Storage System Management

The ESS is an indispensable part of the MG systems for storing and dispatching electrical power to smooth the intermittency of the load and RER generation, in order to maintain stability, improve power quality, and ensure system reliability. The ESSs are classified as electrical, mechanical, chemical, electrochemical, and thermal, based on their storage and response characteristics. In the MG, a high-power density ESS ensures the management of transient disturbances, while high-energy density devices provide power for a longer duration. Various energy management strategies of the ESS were demonstrated in Ref. [89] for different conditions of RER output power while meeting the power demands. The battery management system (BMS) is one of the vital components of the microgrid EMS. Puente et al. [90] highlighted the essential subsystems and control parameters based on the security operations center and the charging/discharging of batteries while designing the block diagram and charging algorithm of the BMS. To ensure dynamic power distribution among multiple energy storage packs, a virtual battery algorithm was formulated to evaluate their power characteristics [91]. Conde et al. [92] defined the operational scenarios of BMS control, highlighting the regulatory action of batteries using a power measurement unit for overall system control. Several heuristic techniques, such as accelerated particle swarm optimization (APSO), JAYA, particle swarm optimization (PSO), and linear programming-based interior point algorithms, were utilized for the microgrid EMS to reduce operational costs [93]. In another application, Khan and Singh [94] employed metaheuristic optimization techniques (PSO, GA, and DE) based on several parameters of fitness with average values and the convergence of population to optimize the battery management (charging and discharging mode of batteries) while solving the issues of operational cost minimization.

Table 2. Recent storage system technologies with contributions.

Ref.	Year	ESS Technologies	Contribution/Findings
Abadi et al. [95]	2022	BESS and BESS-SC (Super Capacitor)	Highlighted notable works on microgrid voltage regulation for supplying complex power load; then, implemented the filtration-based MPC to analyze the ESS performance
Amirthalakshmi et al. [96]	2022	Hybrid ESS	Employed the pinch analysis method for comparative analysis of the ESS
Ferahtia et al. [97]	2022	Multiple BESS	Proposed a droop control strategy considering various factors, including the SoC speed balancing method, droop factor, voltage control, and current limit
Penthia et al. [98]	2018	Superconducting Magnetic Energy Storage	Regulated DC (Direct Current) using dynamic evolution control by minimizing power loss and oscillations; reported superior voltage profile analysis across power converter for the PI (Proportional Integral) controller and reduced total harmonic distortion
Saloux and Candanedo [99]	2020	Thermal Energy Storage	Reduction of energy consumption using an optimal control strategy
Yin et al. [100]	2020	Hydrogen Storage System	Reported higher amounts of hydrogen production and significant reductions in power fluctuations; investigated the efficacy during the under-voltage situation
Sedighnejad et al. [101]	2014	Compressed Air ESS	Gained the optimum harvested energy index and achieved better storage efficiency
Vasudevan et al. [102]	2021	Pumped Hydro Storage	Investigated different control strategies of the variable speed pumped hydro storage to elucidate the dominating approach at each level of the control hierarchy
Yang et al. [103]	2021	Supercapacitor Energy Storage	Proposed a robust fractional order controller for SCES to minimize control error and expenses; then, enhanced controller gain using an interactive teaching-learning optimizer

briefly outlines the ESS management strategies for MGs in maintaining power quality and reliability. From the presented analysis, it can be concluded that appropriate ESS management as important for the microgrid EMS as the load and power generation management.

4. Optimization Approaches for Microgrid Energy Management

Many studies have been conducted based on the system’s topologies, architectures, and operating modes [104,105,106]. For instance, the stochastic character of installed RESs can be controlled and optimized by a dependable power supply to customers while maintaining appropriate operating conditions for the storage system, electricity bill, and occupancies. Suggested EMS optimization mechanisms are shown in Figure 5. The remainder of this section contains a brief description of the selected approaches.

4.1. Objective Functions and Constraints

The deployment of EMS optimization techniques outlines the primary target functions, including power quality, dependability, pollution, and costs [107,108,109,110]. The fundamental goal of utilizing economic objective functions, for example, is to reduce the price of power. For cost reduction in MGs, many formulations have been investigated. For example, the cost minimization problem was framed as a dynamic economic load dispatch problem [111]. Jafari et al. [112] suggested an electrical market approach for improving the dependability of islanded multi-MG networks. A techno-economic goal function was used to account for the MG owners’ profit while enhancing the system’s reliability. For the probabilistic modeling of RER and loads, distribution functions were employed, and an electrical market strategy was developed to increase the profit of the MG owners.

However, power quality, particularly power loss, continues to be a significant concern for system dependability. Murty et al. [113], who examined a multi-objective EM in a MG system, enhanced a helpful literature study for multi-objective EM. Techno-economic analysis and energy dispatch were given for independent and grid-connected MG infrastructure with hybrid RER and storage devices. Following the definition of the system’s restrictions and goal functions, appropriate optimization methods are needed to guarantee power flows between the installed RER/storage and the MGs and between the MGs and the utility grid. The remainder of this section is devoted to a review of critical approaches found in the literature. The energy management system is a computerized system comprised of a software platform that provides essential support for the effective generation and transmission of electrical energy [56]. This platform also ensures adequate security of the energy supply with minimal cost.

4.2. Classical Methods

The classical approaches mainly concentrate on optimizing the energy resources and transmission with the mother grid. Still, a lot of work is needed to focus on the battery depth of discharge, greenhouse gas emissions, the privacy of customers, and system reliability. Two well-known classical solution methods of the EMS optimization approaches are (a) linear and nonlinear programming methods and (b) dynamic programming and rule-based methods. Several researchers used these strategies to solve the EMS control approaches. For example, Sukumar et al. [114] presented an EMS combining continuous run mode, power-sharing mode, and on/off mode. While the constant run and power-sharing modes were solved using the linear programming optimization method, the on/off mode was solved using the mixed-integer linear programming method (MILP). In most linear programming approaches, the constraints and objective functions are linear functions with whole-valued and real-valued choice variables. This methodology is frequently used for system analysis and optimization because it is a versatile and powerful tool for tackling big and complicated issues, such as distributed generating and MG systems. Vergara et al. [115] used a non-linear programming method to minimize the cost of a residential three-phase MG. The initial development of the non-linear model was then converted into the MILP. The results showed that the converted method experienced less error and computational time than the nonlinear three-phase optimal power flow formulation. Heymann et al. [116] presented a dynamic programming optimization method. The comparative results showed that this method was more effective in operational cost and computation time than the classical non-linear and MILP methods. Wang et al. [117] presented a Lagrange-programming neural networks (LPNN) approach for the efficient control and administration of MG systems, with the primary goal of lowering the total cost of the MG. This study divided the load into four categories: controlled load, thermal load, price-sensitive load, and critical load, with variable neurons and Lagrange neurons coupled to provide optimal MG operation scheduling.

More complicated problems that can be discretized and sequenced are solved using dynamic programming approaches. The investigated issues are generally divided into subproblems that are addressed optimally, and the acquired answers are then overlaid to produce an optimum solution for the original problem [118]. A rule-based solution approach is utilized for grid-connected and islanded modes of the MG [119]. As a result, rule-based approaches are commonly used to implement the EM system, since they do not require any future data profiles to decide, making them more suited to real-time applications. Bukar et al. [120], for instance, provided a rule-based EMS in which a rule-based algorithm was utilized to implement RER use priority and control the power flow of the suggested MG components. A nature-inspired optimization method was employed to optimize the MG system’s operations with respect to long-term capacity planning. The proposed objective function’s primary purpose was to reduce the cost of energy in MG systems and the chance of power supply failure. Rule-based techniques for controlling and optimizing energy flow in MG systems have been presented in other papers. Merabet et al. [121] devised a control method to ensure power compatibility with the EMS for various resources in the MG. The hybrid system in the MG was experimentally validated using a real-time control system. The findings indicated that the suggested technique kept the MG subsystems running smoothly under various power-generating and consumption situations. Luu et al. [122] investigated a method for constructing the optimal EM for a MG-connected system that considered the cost of energy trading with the main grid, as well as the cost of battery aging. The authors employed a dynamic programming technique to reduce the system’s cash flow while optimizing the power supply from the main grid. Unlike traditional techniques, dynamic programming algorithms may be thought of as mathematical optimization methods that can break down a complex issue into smaller sub-problems that can then be addressed recursively. They can make the best judgments. However, they come at a high cost in processing, making them challenging to implement in embedded devices.

Table 3. Review of some recent literature on classical techniques for microgrid energy management.

Ref.	Method	Contributions	Application	Key Findings
Moazeni and Khazaei [123]	MILP	Minimizing the daily cost of energy	Water-energy microgrid system	Water sector energy demand was decreased by maintaining a cleaner supply to the energy sector
Vitale et al. [124]	Dynamic programming	Development of a fast reduced-order sub-model	Islanded and grid-tied microgrids	Achieved reduced payback period through the proper capacity exploration
Pedro et al. [125]	MINLP	Modeled an unbalanced three-phase electrical distribution system with droop control	Islanded microgrids	Reduced average maintenance load and cost curtailments
Balderrama et al. [126]	Linear programming	Identified an open-source modeling framework to bridge the gap between field practices and two-stage stochastic modeling approaches	Community microgrid	Recommended robust and optimal system configurations with a minimal impact on the final costs for the community
Iqbal et al. [127]	Non-linear programming	Modelling of a peer-to-peer energy-sharing strategy	Community microgrid	Minimized overall device errors (~25%) compared to traditional sharing mechanisms
Liu et al. [128]	Stochastic programming	Development of a multi-period investment planning scheme	Islanded microgrid	Achieved better economic and synergetic performances compared to the traditional model.
Restrepo et al. [129]	Optimization- and rule-based EMS	Development, implementation, and commissioning of different EMS strategies for testbed microgrid	Canadian Renewable Energy Laboratory	Yielded better overall performance over the rule-based EMS using similar communication links while maintaining stability.
Bukar et al. [130]	Rule-based	Development of a rule-based energy management scheme based on queuing theory.	Long-term capacity planning for MG	The optimization problem minimizes energy costs and maximizes system reliability.
Almada et al. [120]	Rule-based	Management and control of a microgrid with distributed energy resources under standalone operation, grid connection, and transition between the aforementioned operating modes	AC microgrid	MG control and management techniques work well under all operating conditions
Rippia et al. [119]	Rule-based	Enhancing the energy management of a grid-connected microgrid comprised of renewable energy sources, loads, and ESS	Grid-connected microgrids	The rule-based method performed better than the MILP while ensuring nearly no performance loss by offering a sizable decrease in computation time

demonstrates the review of classical techniques for the microgrid EMS in recently published articles.

4.3. Heuristic and Metaheuristic Approaches

Heuristic and metaheuristic methods are frequently used in the literature to solve complex and non-differentiable optimization problems from various engineering fields, including transportation, communication, power systems, product distribution, and microgrid energy management [131]. Among many approaches, the genetic algorithm and particle swarm optimization methods are two popular meta-heuristic methods to solve the EMS of the MG, due to their parallel computational ability. Chalise et al. [132] formulated a multi-objective EMS concentrating on a remote MG’s economic load dispatch and battery degradation cost. This work considered day-ahead scheduling using a genetic algorithm and real-time operation using a rule-based approach. A PSO-based optimal EMS for both islanded and grid-connected modes of the MG has been proposed in [133]. The objective functions for the islanded and grid-connected modes were to minimize the operational and maintenance costs and maximize the energy trading profit with the primary grid. The results show that this technique provided a better solution than the genetic algorithm in terms of the global optimum solution and the time of computation. Apart from these two well-known solution approaches, the genetic algorithm and PSO methods of the EMS, there are other approaches, such as differential evolution [134], gray wolf optimization (GWO) [135], ant colony optimization (ACO) [136], etc.

Paperi et al. [137] presented a heuristic technique for determining the best functioning and EMS of the MG system. The research topic was framed as a single-objective optimization problem, only focusing on cost reduction. Khan et al. [138] presented a metaheuristic-based system by combining the Harmony search algorithm with improved differential evolution. During peak periods, several knapsacks were utilized to ensure that power consumption was below a predetermined threshold value—the suggested system beat existing metaheuristic approaches in terms of cost and peak-to-average ratio. Ref. [139] suggested a genetic algorithm-based optimum EMS system for a grid-connected MG system that considered electricity price, power usage, and RER generation uncertainty. The multi-period gravitational search method solves a deterministic EM issue [140]. Aghajani et al. [141] employed a multi-objective PSO method while addressing the EMS of the MG system. Wei et al. [142] created a standalone modular microgrid model to shorten the feasible economic dispatch regions, formulate an optimization model, and define optimum microgrid system operating strategies. An improved genetic algorithm was proposed to investigate this problem. The employed strategy was capable of solving the EMS problems with many constraints and produced a high-quality solution. Prasant and Joseph [143] developed a methodology for evaluating the techno-economic and environmental efficiency of supplying uninterrupted electricity to a microgrid composed of seven components—wind turbines, solar PV, lead-acid batteries, fuel cells, biodiesel generators, electrolyzes, and small-scale hydrogen tanks—in Tucson, Lubbock, and Dickinson, TX, USA. They measured the configurations with the lowest levelized cost of energy (LCOE) using the genetic algorithm.

Table 4. Recent literature on meta-heuristic techniques for microgrid energy management.

Ref.	Algorithm	Contributions	Application	Key Findings
Quazi et al. [144]	NSWOA	Hybridization of WOA with a non-dominated sorting technique	Islanded microgrid	Achieved optimal solutions with lower computational expenses compared to other reported algorithms
Leonori et al. [145]	GA	Investigation of strategies to synthesize rule-based fuzzy inference systems	Demand response services	Reduced the system complexity and maximized profit generation by 10% compared to the referenced solution
Hussein et al. [146]	SFOA	Formulation of the multi-objective problem for controller parameter tuning	Inverter based microgrid	Enhancement of system performance and flexibility compared to particle swarm optimization
Almadhor et al. [147]	BAPSO	Determination of optimal locations and sizes for the solar generation systems	PV-based microgrid	Reduced transmission power loss and achieved faster convergence with less computational burden
Singh and Gope [148]	GWO	Optimization problem formulation for load frequency control	Two-area multi-microgrid	Achieved superior performance with the GWO-tuned controller over the cuckoo search algorithm-tuned controller
Roslan et al. [149]	LSA	Development of an optimal power scheduling strategy	Scheduling controller	Savings of 62.5% in overall costs and 61.98% in carbon dioxide emission reduction
Shafiullah et al. [135]	GWO	Formulation of the multi-objective problem considering ESS degradation cost	Community microgrid	Generation of quality solutions with a competitive computational effort
Suman et al. [150]	PSO-GWO	Formulation of the optimal planning problem	Rural microgrid	Obtained a reduced average cost of electricity by meeting a good portion of the load demand
Soham and Kamal [151]	CE-DE	Optimization problem formulation for minimization of running costs and reduction of pollution.	Benchmarked microgrids	Avoided pre-mature convergence and generated competitive solutions
Perol et al. [77]	Evolutionary algorithms	Development of control strategies for power and energy management	Low voltage microgrids	Reduced operation costs and energy losses, thus improving overall efficiency
Hossain et al. [152]	Modified PSO	Proposed an optimal battery control strategy for real-time management	Grid-tied microgrids	Achieved a 12% reduction in operation costs for a time horizon of 96 h
Kavitha et al. [153]	MPO	Solved supply-demand problems to minimize production costs	Islanded and grid-tied microgrids	Generated around 8% higher profit with better optimization ability and faster convergence
Tomin et al. [154]	Monte-Carlo tree search algorithm	Developed a unified approach for optimal energy and benefits management	Community microgrids	Improved supplied energy quality and reduced the levelized cost of energy index from 20% to 40%

summarizes the meta-heuristic methods for the microgrid EMS. It was evident from the presented analysis that the various meta-heuristic approaches showed satisfactory performances in achieving optimal solutions (minimal costs or maximum profits) while solving microgrid EMS problems with various constraints and uncertainties. Besides, in most of the cases the authors reported better or competitive efficacy for their employed algorithms compared to others. However, it is challenging to come up with solid conclusions about the superiority of any specific algorithm, as they all should provide similar results ideally due to their stochastic nature. Besides, their efficacy also depends on the proper selection of the hyper-parameters.

4.4. Artificial Intelligence Methods

Artificial neural networks are an example of a method that is created artificially. They are stochastic approaches that may be utilized to address optimization issues for systems that contain random variables. The fluctuating nature of RER in MG systems is caused by meteorological conditions, which impact electricity generation. Solanki et al. [155] presented a mathematical approach for intelligent load control in a stand-alone MG system. Neural networks were utilized to simulate the loads examined, and a predictive control was applied to manage the energy, based on expected load fluctuation. The EMS based on artificial intelligence primarily concentrated on Fuzzy logic and neural networks [156], as well as multi-agent systems. A fuzzy logic-based EMS with a battery and hydrogen energy storage system for a microgrid has been proposed [157]. The authors claimed that this solution could respond well to required load demands and meet the established technical and economic criteria. Wang et al. [118] formulated a neural network-based EMS for the MG. The objective function is to reduce the overall fuel, operation, maintenance, and emission cost of the generation units. Ghorbani et al. [158] proposed a multi-agent-based EMS approach, where consumers, storage units, generation units, and the grid are considered agents for a grid-connected MG. In this work, the objective function was to reduce power imbalance costs. Results showed that the time required to take the decision was better for the decentralized approach than that of the centralized approach. Among the other known artificial intelligence solution techniques, game theory, the Markov decision process, and theadaptive intelligence technique are remarkable.

Neural networks are primarily used to regulate, optimize, and detect system characteristics in online and offline applications. Given their capacity to address the system’s stability through self-learning and prediction skills, neural networks, unlike prior techniques, can handle issues with nonlinear data in large-scale MG systems [159,160,161]. Despite the solutions’ efficacy, intelligent energy management in smart MG systems still needs real-time and predictive control methodologies.

Table 5. Review of artificial intelligence methods related to microgrid energy management.

Ref.	Technique	Contributions	Application	Key Findings
Dong et al. [162]	Fuzzy logic	Developed day-ahead fuzzy rules for real-time energy management under various operational uncertainty	Multi-energy microgrid	Exhibited superior performance compared to the online rule-based and meta-heuristic optimization-based offline scheduling schemes
Zehra et el. [163]	Fuzzy logic	Proposed the control strategies for renewable energy resources and battery storage systems	DC microgrid	Achieved better controllability compared to the sliding and integral sliding mode controllers
Singh and Lather [164]	HBSANN	Addressed demand-generation disparity for effective power-sharing between various ESS.	Low-voltage DC microgrid	Exhibited lower voltage overshoot and settling time compared to conventional strategy
Nakabi and Toivanen [165]	Reinforcement learning	Outlined various flexible resources for coordination with priority	Microgrid	Exhibited the highest model performance and convergence to near-optimal policies
Tan and Chen [166]	NN	Designed a multi-objective optimization model for multiple microgrid systems	Multiple microgrids	Achieved a 36.86% lower forecasting error; obtained better pareto solutions and faster convergence
Tayab et al. [167]	HHO-FNN	Developed a hybrid approach for short-term load forecasting	Queensland electric market	Reduced the mean absolute percentage error ranging from 33.30% to 60.76%
Priyadarshini et al. [168]	EE-RRVFLN	Proposed a maximum power point tracking model for multiple PV-integrated MG under partial shading conditions and load uncertainty.	PV-BESS integrated microgrid	Established the superiority of the employed approach over the conventional and random vector functional link neural networks

illustrates various artificial intelligence methods related to microgrid EMSs.

4.5. Other Methods

Proactive control is one of the most intriguing EMS methods. This technique is based on a mixed-integer optimal control issue, which can be expressed as a mixed-integer nonlinear programming problem [123]. According to the literature, the notion of proactive control for EM in MG systems is seldom applied. For control-based prediction judgments, the notion is particularly appealing. Proactive control can be enhanced in future investigations for EM in MG systems, thanks to the advancement of information and communication technologies, particularly microcontrollers. In addition, the approach can improve the system’s performance in existing system disruptions. Amirioun et al. [169] provided a MG proactive control method for dealing with the adverse effects of severe windstorms. When the anticipated windstorm alerts arrived, the method discovered a conservative MG schedule with the fewest susceptible branches operating while the whole load was serviced. The cautious timetable guaranteed that the MG functioned normally before the windstorm, while decreasing the MG’s susceptibility when the event arrived. This technique benefited from generation rescheduling, network reconfiguration, parameter tuning of the droop-controlled units, and conservation voltage regulation. Panteli et al. [170] talked about unified resilience evaluation, the operational enhancement method, and a technique for evaluating the impact of severe weather conditions. Another study by Amin et al. [171] combined BESS and PV systems under a hierarchical transactive EM method to lower customer power costs. A cost-benefit analysis technique that integrated PV units with battery storage systems was created for proactive residences. The control algorithm managed the battery’s charge/discharge cycle based on a cost-benefit analysis of real-time energy rates and battery costs, providing users with a precise estimate of their investment returns and annual savings. When a proactive system is handled utilizing predictive techniques, the performance of this method may be improved. Reactive Feedback Control and Model Predictive Control were compared with respect to energy used, energy error, and management effort for a specific data center by Rahmani et al. [172]. The research suggested a data center model-based feedback control method to improve service quality, energy consumption, and managerial effort. Moreover, the combination of different approaches mentioned earlier was also used in a few cases for energy management in microgrid systems [173,174,175,176].

5. Microgrids Implementation Challenges

The increasing demand for distributed power generation brings attention to MGs [177]. The ideal method for utilizing MGs is by making their challenges or limitations adaptive [33]. This section reviews the challenges of microgrids and epitomizes existing and proposed solution techniques to overcome the flexible challenges in microgrids. Figure 6 depicts the summarized view of the microgrid’s implementation challenges.

5.1. Economic Issues

Many complexities will arise regarding potential revenue and multiple users for indicating the cost and benefits in an MG. Among them, standby charges and exit fees [178] are the major issues that need to be resolved for developing microgrids. Kraemer et al. [179] identified some optimization problems with economic perspectives, such as the energy market, generation information, etc. However, some factors regarding the levelized cost need to be reduced by utilizing government subsidies and initializing energy programs [180]. The rising fuel costs for diesel generators is another significant barrier that needs to be addressed with care. Bellido et al. [181] made a vital remark to decimate the Am Steinweg MG’s EMS cost by making grid integration feasible. But management control systems need to be economically sustainable by supporting energy regulations [182].

5.2. Social Obstacles

As the MGs introduce complexity to the market, they may cause negative impacts on society due to the regulatory gaps that customers might not understand and be impacted by. Consumers need to be aware of the smart grid’s beneficiary role in low carbon energy access, which is a greater obstacle to its strategic development, as well as ensure active participation in demand response management to become prosumers [183]. The lack of supportive regularities and awareness among customers hinders effective microgrid systems [184]. In multi-level architecture, the coordination of several small-scale MGs is very challenging, due to the conflicts of the owner’s demands [185]. Therefore, appropriate frameworks should be developed to overcome the mentioned challenges.

5.3. Engineering Challenges

The development of microgrid infrastructure requires several factors to be ensured for overall system management, which is discussed in brief in the following parts of this subsection.

5.3.1. Technical Issues

Protection: MG setup must be designed with sufficient sequenced protection equipment [186]. The protection of load, distributed generators, and lines must be ensured when establishing an MG [187]. Due to multiple DG units, short circuit current level variation in grid-connected microgrids creates some issues. In the islanded mode, the total short circuit current capacity differs from the grid-connected condition, leading to the failure of the protection relay [188]. Thus, protection for a dual mode that protects both grid-connected and islanded modes is prioritized for safe operation. Shanmugapriya et al. [189] have pointed out several protection challenges of DC microgrids, such as grounding, power instability, etc., of which the fault intervention of circuit breakers is the most crucial, since no zero-crossing current exists for DC faults. Some protection devices are recommended from the summarized flowcharts used in Sandia National Laboratories, where digital microprocessor relays exhibit better fault detection and monitoring performance following the standard IEEE C37.90-2005 [190]. Moreover, solid-state circuit breakers with different categories are the most suitable because of their high current carrying capacity and switching speeds with no arcing. Altaf et al. [191] have reviewed the protection schemes in which solutions are provided for each fault event. In another review work, the protection schemes were identical for each microgrid mode (islanded, grid-connected, and hybrid) to analyze the test features for DG units and loads [192].

Stability: In an MG system, maintaining the system stability with static converter-based RER is one of the main challenges [193]. A microgrid system faces three main issues related to stability: angular instability caused by lower system inertia, voltage instability due to lower power distribution support, and lower frequency oscillation triggered by changing energy sharing ratios between the DGs [182,194]. Eskandari and Savkin [195] have recommended a sliding mode controller as a sustainable solution to MG-transient stability issues. Furthermore, while addressing the cyber security concerns about the stability of the DC microgrid, Leng et al. [196] have designed a flowchart for an adaptive stabilization mechanism to annihilate cyber-attack elements from the microgrid.

Operation/Control: The absence of standard maintenance procedures is another prime factor that affects MG sustainability [197]. Swift and exact voltage and frequency control are essential requirements for weak low-voltage network-based microgrids to operate in islanded mode [198]. The continuous operation of the plant raises the microgrid system’s maintenance requirements [199]. A model predictive control approach has been used to improve power, cost, and production along with the presence of uncertainties [200]. The Newton method allows the use of full state-space models, including the complete representation of the control systems. This results in an outstanding, straightforward alternative for harmonic stability and power quality analysis [201].

Harmonics: Harmonics in the microgrids hinder the electrical network’s stable operation by threatening the safety of the appliances, including sources, loads, and energy storage systems [194]. Due to the operation of power converters, harmonics rise, and this needs to be tackled with care for the safe and secure operation of the MG systems [182]. A single-tuned Passive Harmonic Filter has been proposed to mitigate harmonics from the grid and renewable energy sources [202].

5.3.2. Reliability

Power system instability brought on by MG energy management raises the possibility of significant events like blackouts. Recent research has concentrated on a variety of topics, including the management of variable RER in MGs under reliability constraints [203], the reliable energy management of hybrid RER-based MGs with unit commitment and energy storage [204,205], stability analysis according to power load characteristics [206], and reliability assessments of the microgrid EM that take cyberattack localization and tolerant control into account [207]. Reliability research of EM considers the unavailability of DGs and mobile generation, since MGs are typically located close to the distribution side, where two main phases of load demand, as well as peak and off-peak hours, are under investigation. The distribution side of the MG architecture is being explored with the ultimate objective of increasing the architecture’s resilience through continuous power delivery to meet demand.

5.3.3. Management

A MG management system involves tasks involving appropriate design, standard maintenance, sufficient local expertise, monitoring systems, project supervision, etc. [200]. All these tasks must be coordinated appropriately to implement a successful renewable-based MG to ensure its project duration and standards.

5.3.4. Design Factors

A magazine article on MG solutions demonstrated several challenges to be addressed while designing a robust MG. Anti-islanding tests must be passed by creating a control system with an interconnection switch. Cybersecurity is one of the key considerations when selecting an open standard communication system, in which certification from the accreditation board is necessary and allows for smarter apparatus and automation systems.

5.4. Security Challenge of AMI

With the fast-growing development of smart grids, technology such as advanced metering infrastructure (AMI) used in these systems has led to further advancement. The technological challenges related to fast data acquisition can be tackled quickly with the deployment of AMI [208]. Thus, if attackers analyze the data, they can achieve alarmingly high precision “Consumer profiling,” and be able to know, for instance, the number of people living in the building, type of appliances, advanced metering infrastructure based on smart meters in smart grid protection capacities, occupancy periods, and alarming systems. Profiling allows attackers to detect consumer actions without sophisticated algorithms or computer-aided software. The AMI security issues will typically emerge from three distinct aspects: end-user safety, cyber-attack network resilience, and power theft [209]. For instance, Ref. [210] demonstrated that the AMI systems (intelligent electronic devices and phasor measurement units) were at risk for data manipulation while undergoing performance evaluation of the grid-tied MG. J. Murrill et al. [211] demonstrated the ability to classify the usage of significant devices in a customer’s home by analyzing accumulated energy consumption data from a smart meter. Dorri et al. [212] introduced a blockchain-based system that allows energy prosumers to negotiate distributed energy prices and exchange energy. The authors [213] conducted an innovative metering network security study containing over one million smart meters, over 100 data collectors, and two meter data management systems. They demonstrated the importance of systematically identifying each target—particularly the vital data collector—and its components and functionality. Potential attacks and their direct impacts provided a security review of a broad metering network.

The scheme suggested in Ref. [214] can ensure higher safety and performance. Shrestha et al. [215] proposed an AMI-based secure operation approach for complex networks, such as the smart grid. A safe and multi-recipient scheme was proposed to solve security issues with remote downlink control commands in Ref. [216]. Researchers also investigated a deeper understanding of cyber-security priorities, criteria, and future research developments in the smart grid [217]. Li et al. [218] presented the importance of the Internet of Things (IoT) and a cyber-secure distributed strategic energy plan that provides distributed decision-making intelligence to networked microgrids while safeguarding their specific mandates for optimal functioning. Kimani et al. [219] addressed the implementation of the IoT as a smart grid-enabling technology, with a detailed survey of key security concerns and challenges. They also described major challenges and future solutions in IoT-based grid networks. Authors built attack trees to evaluate the paths a cyber attacker could take to attack a local metering network [220]. The security overview of the different schemes is discussed in Table 6, by considering certain safety criteria. Besides, an insight into how effective the schemes are relative to each other is also presented. Those parameter schemes, forward and backward security, authentication, confidentiality, integrity, key freshness, key sharing, and key generation are considered. The processes of key refreshment, key sharing, and key generation are performed through multiple steps involving contact between various AMI entities. It is essential to provide optimized overhead communication for key refreshment, key sharing, and key generation for time-critical scenario processes. Main refreshing depends on how users enter or leave the disaster recovery (DR) project. The group key is refreshed during the update, i.e., when a user enters or quits the DR project. The forward confidentiality applies to the fact that new users will not access previously used hidden keys and messages involved in a DR project. In comparison, backward confidentiality means that users who exit a DR project cannot access future hidden keys. Regarding group key management, forward secrecy relates that the expelled members would be unaware of the new community switch. Backward confidentiality in group key management ensures that new members cannot gain information about previous group keys. Preserving confidentiality forwards and backwards should be assured if users involved in DR projects are not reliable and have the privilege to join the DR project or leave it at any time.

Islam et al. [221] optimized the attack signal to maximize gains from legal participants while preserving supply-demand equilibrium in the local energy market. Additionally, block-chain-enabled microgrids can contribute to a grid that is more affordable, resilient, environmentally friendly, and has low transmission loss [222]. The benefits and drawbacks of integrating blockchain with microgrids should be thoroughly investigated. In addition, other distributed ledger methods like Tangle or Hashgraph might be safer for managing information and energy trade [223].

Table 6. Security analysis of the smart metering system in recent literature.

Authors	Scheme	Forward and Backward Security	Authentication	Confidentiality	Integrity	Key Freshness	Key Sharing	Key Generation
Nabeel et al. [224]	SEES	√	√	√	√	√	$\times$	√
Benmalek et al. [225]	Mk-AMI	√	$\times$	$\times$	$\times$	√	$\times$	√
Yu et al. [226]	KMSS-IC	√	√	√	√	$\times$	$\times$	√
Benmalek et al. [218]	ESKAMI	√	$\times$	√	√	√	$\times$	√
Wan et al. [227]	SKM	√	√	√	√	√	$\times$	√
Benmalek et al. [228]	SAMI	√	√	√	√	√	√	√
Odelu et al. [229]	-	√	√	√	$\times$	√	√	√
Gope [219]	PMAKE	$\times$	√	√	√	$\times$	√	√

Table 6. Security analysis of the smart metering system in recent literature.

Authors	Scheme	Forward and Backward Security	Authentication	Confidentiality	Integrity	Key Freshness	Key Sharing	Key Generation
Nabeel et al. [224]	SEES	√	√	√	√	√	$\times$	√
Benmalek et al. [225]	Mk-AMI	√	$\times$	$\times$	$\times$	√	$\times$	√
Yu et al. [226]	KMSS-IC	√	√	√	√	$\times$	$\times$	√
Benmalek et al. [218]	ESKAMI	√	$\times$	√	√	√	$\times$	√
Wan et al. [227]	SKM	√	√	√	√	√	$\times$	√
Benmalek et al. [228]	SAMI	√	√	√	√	√	√	√
Odelu et al. [229]	-	√	√	√	$\times$	√	√	√
Gope [219]	PMAKE	$\times$	√	√	√	$\times$	√	√

5.5. Regulatory and Policy Issues

Most power-consuming nations are looking at alternative energy sources, such as renewable energy, to reduce conventional fossil fuels and the expenses connected with using them. However, due to policy considerations and its experimental nature, renewable energy lacks a well-acknowledged structure for implementation. As a result, several policies have been implemented to promote renewable energy and distributed energy resources technologies [230]. Since the late 1970s oil and gas crises, the United States has established several energy regulations, such as the IEEE standard 1547-Family, introduced around 2005 [231]. This regulation is critical to energy sustainability and electricity quality. In addition, these policies provide economic incentives, such as exclusion from loss penalties of distribution and transmission, as well as exemption from the climate change assessment. More initiatives in the United States have centered on R&D programs, grants, software and tools, and financing assistance to encourage sustainability initiatives [232].

In the European Union (EU), MG regulation has several issues regarding security, client and energy supplier management, legality, limits, and connections with the primary grid [233]. The EU announced a strategy to reduce fuel use by 20% by 2020 to create a sustainable and steady energy supply. The EU unveiled its 2030 vision in 2014, intending to boost the adoption of renewable energy technology by up to 27% and with respect to greenhouse gas emissions lowering from 40% to 95% by 2050 [234]. The EU introduced the IEC TS 62257-9-2 standard in 2016, followed by the IEC TS 62898-1/2/3 in 2018 and the PD IEC TS 62898-2 in 2020.

Using the policy network theory for the Renewables Portfolio Standard conception in the USA, Pentagon communication mapping among stakeholders had outlined the overall procedure in which the Action Plan helped define legislation policies [235]. The Renewable Energy Policy in the UAE, with a vision to expand energy contribution to 7% by the World Expo 2020, had encountered several challenges; among them, the need for a joint task force between the federal government and Sharjah Electricity and Water Authority is especially remarkable.

China’s tariff strategy is intended to encourage the exploration of renewable energy [236]. By offering privileged pricing, this tariff strategy can supply a continual buy price to the power seller of the grid business with a suitable market rivalry. To promote the use of renewable energy, China enacted various policies, regulations, and programs, including the national climate change program in 2002, renewable energy legislation revisions in 2009, and a favorable tax strategy for renewable energy in 2015 [237]. South Africa’s current policy frameworks are entirely built towards grid electrification, which is skewed and acts against the viability of microgrid use [238]. South Africa’s energy sectors, like those of other African nations, are generally regulated by prioritizing, rules, legislation, and monopolistic laws, which regrettably favor grid electrification while limiting rural electrification. Many countries are beginning to evaluate these rules and legislation to bring to private sector engagement and other major energy actors [183].

In South-East Asian regions, while studying Thailand’s microgrid policy perspective, the key drivers to develop microgrid policies have been identified, in which the objectives of electricity access with other essential infrastructures need to be fulfilled to achieve the United Nations Sustainable Development goal of clean energy [239]. The renewable energy policies of Malaysia, which adopted Economic Planning Unit and the Implementation and Coordination Unit, have been revised to promote the utilization of Palm oil under the National Biofuel Policy, and the action plans are defined to enhance renewable energy capacity to 5.5% [240]. On the other hand, MGs in Singapore experience regulatory barriers to solving the issues of market liberalization, energy share, and ownership under the Electricity Act, as well as microgrid interconnection for licensing under section 2006b of the Energy Market Authority [241]. Microgrid electricity market operators in Australia have realized the drawbacks of existing regulatory regimes unsuitable for complex DERs, and analyzed two distinct regulatory reforms for this purpose.

6. Drawbacks and Recommendations

Since the paper focuses on strategies and the optimization of EMS, some drawbacks regarding the literature review have been identified to make room for improvement for further research on EMS strategies. These drawbacks are described in detail:

• Feasibility studies of EMS regarding grid integration processes need to be addressed. In addition, studies on the enhancement of existing energy management infrastructures and their limitations to grid-connected and off-grid microgrids are also required for the optimization process.
• Marketing research and supply-chain management need to be addressed to draw the attention of investors regarding the features of the EMS framework. Ref. [242] identified the problem regarding ROI (Return on Investment) for MG owners.
• More review works are necessary to realize the complications behind the underdeveloped EMS framework in least-developed and developing countries. In this case, MG policies and task forces need to be outlined to attract investors for microgrid EMS development.
• Guidelines are required for testing and commissioning reports of the EMS in specialized laboratory environments before their deployment for industrial users.

This review work might be beneficial for those who are involved in the research and development work of the microgrid EMS. It is suggested to follow the energy management strategies to upgrade the DGs. Energy emissions from DGs responsible for global warming could be surpassed in future research contributions following the optimization approaches discussed in this review. It is also possible to implement an advanced computation system for the distribution networks in MGs utilizing the optimization approaches suggested in this paper.

7. Conclusions

The MG is one of the hot research topics for power system researchers that includes several aspects, such as energy management and scheduling, controller design, and protection schemes. Thus, research publications have been growing exponentially and skyrocketing in recent years. In line with the trends, the microgrid EMS gained attention, due to various complexities ranging from the integration of intermittent RER, to uncertain load demand and continuous changes in electricity prices. This review article investigated the different strategies employed for energy management in microgrids. First, it discussed various strategies for load demand, energy generation, and energy storage systems management. Then, optimization approaches such as classical, metaheuristic, and artificial intelligence-based techniques employed for EM and resource scheduling were reviewed. The metaheuristic optimization approaches were found to be more efficient than the classical approaches in solving complex optimization EMS formulations, and in achieving competitive solutions. Finally, various challenges, including economic, social, technical, security, and regulatory, which hampered the microgrid implementation were also discussed. The article also provided suggestions for overcoming the challenges related to MG implementation and EMS. It was evident from the observations that further research, development, and innovation projects should be conducted to realize the complete potential of the technology for enabling more RER integration and reducing greenhouse gas emissions. Besides, the comparative analysis between the simulation findings with the experimental outcomes can then be compared.