Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6, 5089-5111 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: almihatMGM@tut.ac.za Review on recent control system strategies in Microgrid Mohamed G Moh Almihat1*, Josiah L. Munda2 1,2Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa; almihatMGM@tut.ac.za (A.G.M.A.) mundaJL@tut.ac.za (J.L.M.) Abstract: Microgrids (MGs) are integral to the evolving global energy landscape, facilitating the integration of renewable energy sources such as solar and wind while enhancing grid stability and resilience. This review presents a comprehensive analysis of control strategies in MG systems, addressing both conventional and advanced methodologies. We explore traditional control methods, such as droop control and Proportional Integral Derivative (PID) controllers, for their simplicity and scalability, but acknowledge their limitations in handling non-linearities and real-time adaptation. Model Predictive Control (MPC), Adaptive Sliding Mode Control (ASMC), and Artificial Neural Networks (ANN) are some of the more advanced techniques that make systems more flexible, better at managing energy, and stable even when operations change quickly. The review further delves into the role of the Internet of Things (IoT), predictive analytics, and real-time monitoring technologies in MGs, emphasizing their importance in enhancing energy efficiency, ensuring real-time control, and improving system security. The review places emphasis on energy management systems (EMS), which optimize supply and demand balance, reduce uncertainty, and enable seamless integration of distributed energy resources (DERs). The paper also highlights emerging trends such as blockchain, AI-driven controls, and deep learning for MG optimization, security, and scalability. Concluding with future research directions, the paper underscores the need for more robust control frameworks, advanced storage technologies, and enhanced cybersecurity measures, ensuring that MGs continue to play a pivotal role in the transition to a decentralized, low-carbon energy future. Keywords: Advanced control, Conventional control, Distributed energy resources, Energy storage systems, Microgrids. 1. Introduction The global shift to renewable energy is critical due to fossil fuels’ environmental and economic impacts. Growing renewable energy technologies render integrating distributed energy resources (DERs) into power networks especially challenging as governments attempt to reach worldwide climate targets [1], [2]. Microgrids (MGs) are becoming more capable of managing this integration and obtaining distributed, renewable energy systems [3]. A MG is a local power network that can operate either in tandem with the primary grid or on its own. It manufactures, stores, and supplies local users with both non-renewable and renewable DERs [4], [5]. In islanded mode, an MG operates autonomously; in grid-connected mode, it links to the central grid. Especially in places prone to grid instability, outages, and natural disasters, energy networks have to adapt to boost dependability and resilience [6]. Linked to the grid, MGs generate power, balance frequency and voltage, and include renewable energy sources. Should the central grid fail, the MG could rapidly switch to islanded mode, running large loads with its DERs. MGs are ideal for military locations, data centers, and hospitals because their dual operational mode ensures energy stability [7], [8]. Advanced control techniques, along with real-time energy supply and demand EMS, enable the integration of multiple energy sources into a consistent supply chain [9], [10], [11], [12]. MGs use renewable energy to reduce greenhouse gas emissions and decarbonize electricity. Their concentrated character lowers transmission losses and makes energy solutions possible in underdeveloped or rural areas without centralized systems. Predictive analytics, remote control, and 5090 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate real-time monitoring among Internet of Things (IoT) technologies help to increase MG efficiency [12], [13], [14]. The IoT ensures simple interaction among MG components, including DERs, storage systems, and consumers, resulting in improved load control and energy distribution. Renewable energy output is unpredictable and requires real-time supply-demand balance, making integration problematic [4]. To rectify these imbalances, the grid-connected microgrid absorbs excess energy or provides power during low generation. Islanded MGs must fully utilize their distributed energy sources, complicating energy management [5], [15]. The rapid digital transformation of energy systems necessitates strict cybersecurity rules to prevent data breaches, hacker attacks, and harmful control orders that could compromise infrastructure or energy distribution [12], [16], [17]. Currently under development are security techniques for IoT-enabled MGs to safeguard operations and data. There are many types of resilient control schemes that MGs need, such as droop control, proportional-integral-derivative (PID) control, hierarchical control with primary, secondary, and tertiary levels, neural networks, fuzzy logic, and model predictive control (MPC) [18], [19]. To maintain stability, these systems the real-time mentoring and control change operating settings and adapt to MG variations [20]. Effective MG energy management is vital; energy management in a multifarious grid is also vital. Using centralized control systems, conventional grids deliver electricity unidirectionally from big-scale sources to consumers [21], [22]. MGs, particularly those run on renewable energy—expensive and difficult to control—need energy storage systems (ESS). Careful monitoring of storage system size, placement, and operation helps to prevent inefficiencies or overinvestment in storage capacity [6], [23], [24]. MG technology presents yet another difficulty with regard to scalability and standardization. MG systems in larger energy networks must be compatible by standardizing protocols, control systems, and communication channels. It is challenging to integrate DERs and develop a sizable MG in the absence of set procedures [24], [25]. The need to look at, assess, and categorize modern control systems that increase MG efficiency drives this study. MGs are an essential part of the energy transition as renewable energy sources increasingly occupy world energy networks. Still, their effective application calls for tackling many important problems in data administration, real-time monitoring, and control systems. By grouping recent advancements in control methods and including ideas on IoT-based monitoring systems, this paper aims to close the present knowledge gap. The focus here is on examining many conventional and creative control strategies and their implications for MG stability, sustainability, and scalability. This paper is important because of its thorough strategy to grasp and improve MG control systems. Attaining a low-carbon future depends on MGs, which improve system resilience and enable distributed clean energy generation from all around. This study offers vital information to legislators, engineers, and researchers on the operational and technological advancements in MG control, enabling the worldwide large-scale MG deployment. The study emphasizes how IoT technologies are becoming more important in improving monitoring, communication, and control systems, generating opportunities for the development of next-generation smart grids. Future MGs might greatly increase their security and adaptability by including artificial intelligence (AI), cloud computing, and blockchain technologies. This paper presents a thorough study of MG control systems, as well as their application in improving energy distribution, stability, and the integration of renewable energy sources. The paper includes the following contributions: • Traditional methods include droop control and PID controllers, while more advanced methods such as MPC and neural networks require in-depth analysis. • A close study of how IoT technology, via real-time monitoring, data analytics, and decision- making, is transforming MG energy management. • Analysis of EMS that lower uncertainty, improve the use of DERs, and match energy supply with demand, thereby enabling optimal functioning. • Future research should identify the technological obstacles in current MG management techniques and provide recommendations for their resolution, particularly through the integration of AI-driven control systems. 5091 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate This comprehensive review article comprises six sections, each of which focuses on the most recent and important MG control strategies. In the section 2, we look at basic control methods like droop control, PID controllers, and multi-agent systems (MAS). In the section 3, we look at more advanced control methods like MPC, adaptive sliding mode controller, and methods based on AI. Section 4 examines IoT-based monitoring systems, SCADA systems, cloud computing, and smart meters for real- time energy management. Section 5 talks about current trends in MG control, such as the use of blockchain, smart contracts, and deep learning algorithms. It also talks about possible research areas for improving MG performance in both island and grid-connected settings. In section 6, the conclusion summarizes essential findings and offers recommendations for future research, emphasizing technological developments to enhance the efficiency, reliability, and scalability of MGs. 2. Microgrid Control Strategies There are several control strategies in microgrid, the following section discussing different types of microgrid control strategies. Figure 1 shows the different control strategies. Figure 1. Types of control methods in microgrid. 2.1. Conventional Control Methods Although structurally simpler and more often used in past MG installations, traditional control methods cannot handle the increasing complexity of modern energy systems. This section will look at traditional methods, including droop control, PID controllers, and MAS, within the framework of modern MG construction, therefore providing a thorough study of their uses, advantages, and disadvantages [26], [27], [28]. 5092 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate 2.1.1. Droop Control Droop control is the recommended approach for MG load sharing. This is a classic instance of distributed control. Because it does not involve communication across DERs, droop management is ideal for systems with unstable or unrealistic communication infrastructure [29], [30], [31]. It is rather common in island MG voltage source inverters (VSIs) and grid-connected ones. For small to medium-sized MGs, droop control is a cheap and simple solution with few processing resources. Scalability allows for the addition of more DERs, while decentralization reduces the likelihood of a failure point [32], [33], [34]. Maintaining stability is challenging, though, without major modifications or extra control levels. This could lead to inaccurate control of voltage and frequency, slower system responses to load or generation surges, and incompatibility with larger systems [35], [36]. Using virtual impedance approaches to boost droop control’s ability to manage voltage and frequency, or by adding more control layers, researchers have lately tried to improve its performance. Sadly, many times these developments lead to more complexity and less scalability [37]. 2.1.2. Proportional-Integral-Derivative Controllers MG systems widely use PID controllers to manage voltage, frequency, and current. Both grid- connected and stand-alone MGs frequently use PID controllers, which provide a straightforward and efficient method of linear system control [38], [39], [40]. Many industrial applications use PID controllers because of their basic control structure, which allows for constant voltage and current control. Solar and wind power are two examples of non-linear systems that present challenges for renewable energy sources [41], [42], [43], [44]. These systems need particular modifications to guarantee stability and best performance. MGs with many interacting DERs and dynamic loads—the latter of which they could find challenging to control—may also require more complex control strategies [45], [46]. Despite these disadvantages, PID controllers are still a wise choice for systems where the loads and generation are predictable in terms of voltage and current. Current MG applications are coupling PID controllers with adaptive control methods like fuzzy logic controllers to better manage non-linearities and dynamic changes in the system [42], [47]. These hybrid approaches combine the simplicity of PID management with the adaptability of more complex systems, making them fitting for MGs with a large volume of renewable energy [48], [49], [50]. 2.1.3. Multi-Agent Systems New advances in MG control have led to the development of MAS, in which different entities (like DERs) work together to achieve a common goal (like saving energy or keeping the system stable). Every agent can communicate with each other to coordinate their activities and form adjustment based on local data. Improved communication and coordination across DERs, scalability, and autonomous control are other advantages of MG management systems, such as MAS [51], [52], [53]. It enhances the system and helps to lessen the need for centralized control. For big or complex MGs, careful planning and implementation are necessary. MAS relies on strong communication networks; therefore, unstable or high-latency connections can pose issues. Poor agent coordination can cause conflicts that compromise system goals, causing instability or inefficiency [54], [55]. Despite these constraints, the MAS makes the MG work better. MGs, particularly in renewable energy systems, require distributed MAS control. New AI and machine learning technologies have improved MAS’s capacity and allowed agents to learn from their failures, improving decision-making. Complex design and execution are MAS’s major barriers to acceptance [52], [56]. 2.2. Advanced Control Techniques Modern energy networks, especially those with high renewable energy penetration, are becoming more complex. This requires more advanced control tactics. MG operations can start with typical control tactics. These solutions use AI and machine learning to make MGs more shock-resistant and efficient [57], [58]. 5093 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate 2.2.1. Model Predictive Control The complex control approach MPC maximizes control actions based on system model projections of future states. Non-linear systems, such as MGs, are ideal for MPC because they use renewable energy. Through future state prediction and forecast error reduction, MPC optimizes high-variability systems like renewable energy generation. Due to their non-linear system regulation, MGs can use several energy sources and loads [59], [60], [61]. By updating models with real-time data, MPC increases real-time application performance. MGs, or large-scale systems with low processing power, may struggle to run due to their high demand. A successful MPC application in real-time systems necessitates knowledge of mathematical methodologies and system operations [62], [63]. Cloud and edge computing have reduced the computational cost of MPC in real-time systems. Moving computational activities to the cloud allows MGs to benefit from MPC’s optimal control without expensive on-site hardware. Adding machine learning to MPC’s prediction models might improve their accuracy and performance [64], [65]. 2.2.2. Adaptive Sliding Mode Control Adaptive Sliding Mode Control (ASMC) works well for systems with many shocks or uncertainty. ASMC rapidly adapts the control approach as the system grows, providing excellent uncertainty and disturbance resistance. ASMC’s reliable control technology works well with fluctuating systems, such as renewable energy [66], [67]. Rapid dynamic responsiveness is ideal for MGs, which must respond quickly to load or generation variations. However, sensitive sensors or communication networks may compromise its usefulness. ASMC’s complex control theories require a thorough understanding of the system under control, making them difficult to apply, especially in large systems [68]. Combining ASMC with Kalman filters or another noise-reducing method may help reduce measurement noise. Thanks to advances in sensor technology and communication networks, ASMC’s data is more accurate, enhancing performance [67], [69], [70]. 2.2.3. Artificial Neural Networks Artificial neural networks (ANNs) can learn and improve their control technique. Complex, non- linear systems, such as MGs, rely on renewable energy and are excellent for ANNs. ANN benefits include self-learning, adaptation to difficult nonlinear systems, and superior decision-making [34]. small or newly built MGs may struggle to gather the massive data needed for these models. Due to their computational expense, ANNs are difficult to integrate into real-time systems with limited processing [71], [72], [73]. Understanding ANN decision-making makes it difficult to identify and modify control measures. Modern big data analytics and machine learning have substantially increased the availability of enormous quantities needed to train ANNs. Scientists also study hybrid models that combine MPC or ASMC with ANNs to increase real-time system performance [73], [74], [75]. 2.2.4. Fuzzy Logic Control Fuzzy logic control (FLC) is appropriate for uncertain or volatile systems. The fuzzy logic approach for making decisions with inadequate or confusing inputs is ideal for renewable energy-dependent MGs [42], [76]. FLC is adaptable and regulates enormous amounts of uncertainty, including renewable energy penetration, which improves MG stability and performance. Systems can include mathematical models even when they are scarce, because they do not require perfect models. FLC is slower than other advanced control methods’ dynamic reactions. Large or complex systems are also difficult to implement due to human rule-setting [77], [78]. Finally, FLC scaling can be difficult in systems with several interacting DERs or fluctuating demands. Combining FLC with predictive control systems or machine learning algorithms helps researchers enhance its dynamic responsiveness. These hybrid techniques help MG systems of all sizes and complexity by combining the adaptability of FLC with the accuracy and speed of modern methods [79], [80]. 5094 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate 2.3. Difference between Conventional Control Methods and Advanced Techniques This section discusses conventional control methods and advanced techniques for load-sharing in MGs. Traditionally employed for low-voltage systems, droop control suffers with precision and can be destabilised under strong harmonic loads. Though basic, PID controllers may not operate as best they could with renewable energy sources because of their great variance [30], [35], [45]. Although they demand strong communication links, MAS are great for distributed systems. Although MPC is known for its predictive accuracy but has real-time computational demands that restrict its scalability for big systems, advanced solutions include [42], [43], [47]. Though they need more processing resources and have slower dynamic reactions, fuzzy logic and neural networks provide flexibility and adaptability for MG control [34], [42]. Focussing on stability and scalability as more renewable energy sources are combined, Table 1 offers a whole perspective of control strategies for MG systems. Table 1. An overview on different control strategies. S/N Control method Type Advantages Disadvantages Ref . 1. Droop control Conventional • Easy to implement without communication. • Modular and scalable for different MG sizes. • Poor voltage- frequency regulation, especially in low voltage systems. • Slow dynamic response. [29], [30], [31], [32], [34], [81], [82], [83] 2. Proportional- integral- derivative Conventional • Commonly used for stability in voltage and current regulation. • Suitable for grid- connected MGs. • Requires precise tuning. • Can struggle with non- linear systems or renewable energy fluctuations. [19], [28], [38], [44] 3. Multi-agent systems Conventional • Agents can operate autonomously. • Useful for decentralized control, improving communication and coordination. • Complex to design and implement. • Performance relies heavily on communication network reliability. [48], [83], [84] 4. Proportional- resonant controllers Conventional • Effective in reducing steady-state error. • Suitable for AC systems. • Requires careful tuning. • Can have poor harmonic performance. [85], [86] 5. Model predictive control Advanced • Optimal control by predicting future states and minimizing forecast error. • Suitable for nonlinear systems and real-time control. • High computational power required. • Difficult to implement in large-scale systems. [10], [11], [60], [61], [62], [63], [83], [87], [88] 6. Fuzzy logic control Advanced • Flexible and robust under varying system conditions. • Handles nonlinearities well. • Slower dynamic response. • Requires extensive rule-setting for complex systems. [42], [76] 5095 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate 7. Adaptive sliding mode control Advanced • High robustness in handling disturbances and uncertainties. • Fast dynamic response. • Sensitive to measurement noise and requires complex control laws. [66], [67], [82] 8. Artificial neural networks Advanced • Capable of learning from data and improving control over time. • Suitable for highly complex and dynamic systems. • Requires large datasets for training. • High computational cost and can be difficult to interpret and tune. [34], [71], [72] 9. Reinforcement learning Advanced • Can learn optimal control strategies through interaction with the environment. • Suitable for nonlinear and complex systems. • Requires significant time for training and computational power. • May not perform well in highly volatile environments. [56], [71], [89] 10. Virtual impedance control Advanced • Improves power- sharing and voltage regulation, particularly under nonlinear loads. • Not guaranteed to regulate voltage under all conditions. • Complex implementation with prior knowledge of system parameters. [34] 2.4. Impact of Microgrid Control Strategies on Future Microgrid Developments and the Global Economy MGs are playing an increasingly important role in the ever-changing energy landscape, especially in improving the stability, resilience, and transition to greener energy sources [89]. MGs are smaller- scale systems that connect renewable energy sources like solar PV, wind turbines, and battery storage to the larger power grid or can function autonomously (in islanded mode) in the event that the larger grid is down [87], [90], [91]. Among the areas that MG control systems will impact going forward are energy resilience, environmental sustainability, and economic growth [92]. This paper delves deeper into how control strategies in MGs influence the evolution of new technologies and their global economic consequences. MG control solutions in areas prone to grid instability or natural disasters define the energy system’s resilience [92]. Using real-time supply and demand balancing, these systems maintain a constant energy supply even in isolation. They guarantee that, during a power outage, other important medical equipment, including life-support systems, gets first attention [93]. The Brooklyn-Queens Demand Management project in New York City clearly highlights the need for strong MG control mechanisms [94], [95], [96]. By optimizing renewable energy sources and minimizing the use of fossil fuels, MG management systems contribute to efforts to combat climate change and subsequently reduce greenhouse gas emissions [81], [97]. These techniques also have an impact on finances; MG management systems increase energy efficiency, cut peak demand, and save money by best using intermittent renewable energy sources like wind and solar while maintaining grid stability. Local electricity generation during times of high energy costs or peak demand helps to reduce dependency on costly main grid imports [98], [99], [100]. MGs have a significant impact on global economies as more nations opt for decarbonized energy systems and reduce their consumption of fossil fuels. MGs will help to meet net-zero emissions targets by providing energy independence to otherwise underdeveloped or underprivileged communities, lowering electricity costs, and boosting employment prospects in the design, installation, and maintenance sectors [25], [94], [101]. Smart grids and distributed energy markets greatly influence MG management techniques. Conventional, centralized power networks transport electricity from far- 5096 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate off generators to far-off users. MGs’ advanced control technologies enable distributed energy markets and peer-to-peer energy trading by democratizing control of energy and thereby reducing reliance on utilities [102], [103], [104]. This 2017 Puerto Rico case highlights the crucial role of MG control technologies in rebuilding power networks following Hurricane Maria. These MGs, including solar and wind sources in the island’s electricity mix, enable Puerto Rico to grow more sustainably [105], [106]. 3. Energy Management Systems 3.1. The Role of EMS in Balancing Power Supply and Demand Since they enable to maximize the balance of power supply and demand, EMS are vital parts of MG operations. The increasing integration of RES into MGs—such as solar, wind, and ESS—is changing the function of EMS in controlling variability and guaranteeing consistent energy supply. Effective control of energy generation, consumption, and storage guarantees with a well-designed EMS decreased operating costs and increased energy efficiency [107], [108]. Modern MGs suffer from the intermittent character of renewable energy sources, namely solar and wind, since they provide different production depending on changing weather conditions. Real-time supply and demand synchronization relies on an EMS, which also helps to prevent power imbalances that can cause system inefficiencies or failures [109], [110]. A solar-powered MG might create extra electricity during periods of intense sunlight, which the EMS either exports to the main grid or stores in batteries. On the other hand, during periods of low generation, the EMS meets demand using stored energy [111], [112]. This dynamically alters MG energy flow to ensure a consistent power supply, regardless of changes in renewable output. Demand response systems enable MGs to regulate or decrease their power consumption during high demand periods, thereby averting grid overload and excessive energy consumption [113], [114]. By means of smart appliances, HVAC systems, and lighting control, the EMS can provide real-time load control. During peak times, this saves energy; during off-peak hours, it uses less expensive electricity [115]. 3.2. Impacts of Energy Management Systems on the Global Economy EMS are changing worldwide consumption, storage, and energy generation. Their significant influence on the global economy spans industrial production, urban development, integration of renewable energy sources, and rural electrification, among other sectors. More energy-efficient energy consumption made possible by EMS technologies lowers running costs, boosts productivity, and stimulates long-term economic development by means of their foundation [116], [117]. EMS enable cost savings and efficiency increases in companies where power consumption makes up a significant share of running expenses, driving the worldwide shift to energy-efficient technologies. By automating manufacturing schedules to match low electricity prices, EMS technologies lower running expenditures and energy waste. They also assist building managers in reducing energy consumption by controlling HVAC systems, lighting, and other appliances [118], [119], [120]. Smart city initiatives increasingly employ EMS to regulate public transport systems, street lights, and other city-wide infrastructure in large metropolitan areas. EMS can provide security and energy independence to locations that rely on imported energy or malfunctioning power systems [121], [122]. By incorporating EMS into off-grid MGs to provide power to remote sites disconnected from the central infrastructure, developing countries can reduce their dependency on imported fossil fuels and increase the resilience of their energy systems. Given that energy availability is exactly proportionate to economic stability and output, this development in energy security has major financial consequences [123], [124]. The increased need for EMS technologies in a wide spectrum of sectors, including engineering, maintenance, software development, and energy system integration, is stimulating job growth. As specialized labor to design, install, and maintain smart grids, intelligent transportation systems, and renewable energy projects expands, engineers, technicians, and data scientists are finding opportunities [125]. EMS has a significant economic impact on regions that are heavily involved in renewable energy infrastructure or are undergoing energy transitions. In nations including the United States, Germany, and China, green jobs sectors have grown dramatically. By encouraging a more energy-efficient 5097 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate economy, EMS helps companies flourish in once unstable areas of power, therefore eradicating energy shortages in developing nations [126], [127]. 3.3. Comparative Analysis of Centralized, Decentralized, and Distributed EMS Schemes Three types of energy management exist in MGs: centralized, distributed, and decentralized systems, each with benefits and drawbacks. Optimizing performance and guaranteeing system resilience depend on an awareness of the features of these systems, since they fit the MG’s special requirements and design. Within an MG—that is, generation, storage, and load balancing—a centralized EMS is one in which one controller manages all DERs [128], [129], [130]. This kind of technology is commonly used in grid-connected MGs or situations requiring coordination among numerous DERs. It simplifies control and coordinated operation, resulting in better energy flow. However, the central controller’s decision-making process and sensitivity to a single point of failure still limit the MG’s general adaptability. Notwithstanding their shortcomings, sophisticated systems with varying DERs depend on a coherent decision-making platform centralized EMS provides [131], [132]. In a distributed EMS system, each DER is under independent supervision, while individuals or groups of them handle their own generation, storage, and load balancing. This approach increases defect tolerance and flexibility, allowing any DER to react quickly to local load or generation changes [133], [134], [135]. To guarantee that every DER runs in harmony, increasingly complex communication protocols and algorithms required. Moreover, the absence of a central controller could lead to coordination problems, therefore compromising system equilibrium and efficiency. In general, decentralized EMS offers both benefits and drawbacks [135], [136]. A decentralized EMS is a hybrid system leveraging the finest aspects of centralized and distributed systems. Under conjunction with a higher-level supervisory controller, local controllers in a EMS are in charge of managing particular DERs or groups of DERs. Thus, local controllers can make decisions while still optimizing the system overall [137], [138]. The main features of EMS systems are scalability and robustness, which let one easily include new DERs and provide redundancy. Their maximization of local and system energy flows comes from combining distributed management with central monitoring [138], [139], [140]. Robust networks are necessary to transmit data and coordinate actions between supervisory controllers and local ones, resulting in significant communication needs. Because of their hybrid character, EMS systems are more expensive and difficult to establish and operate than either centralized or scattered ones. Decentralized EMS systems have advantages and drawbacks, really depending on the situation [141], [142]. 3.4. Challenges in Integrating Renewable Energy Resources and Maintaining System Stability As the use of renewable energy sources increases in MGs, new challenges arise in ensuring system stability. Solar and wind power, among other renewable energy sources, can produce quite different outputs depending on the temperature and other external factors. Although renewable energy is crucial in reducing carbon emissions and promoting sustainability, MG voltage and frequency stability can be difficult to reach [143], [144]. Because renewable energy sources are weather-sensitive, there is a demand-supply mismatch covering wind and solar energies. EMS is critical in reducing the effects of these imbalances because it constantly monitors power generation and consumption. ESS allows EMS to store additional energy during high renewable output and release it during low output, preserving power availability even in low renewable generating conditions. MGs need voltage and frequency stability to work [145], [146], [147]. Demand-side management lowers system strain and avoids major voltage or frequency aberrations; automated generation control (AGC) allows real-time generator output changes; and voltage control maintains MG voltage levels. These methods optimize MGs, improving power transmission and protecting sensitive equipment [148], [149]. 3.5. Balanced Optimization Techniques and Practical Challenges EMS optimization solutions aim to balance cost control, energy efficiency, and system dependability. MGs that rely heavily on renewable energy may struggle to reach equilibrium. MGs use multi-objective optimization to balance many goals. One needs sophisticated algorithms to evaluate 5098 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate multiple goal trade-offs to find the optimum compromise [150], [151]. EMS also employs stochastic programming to manage input unpredictability like demand swings and renewable energy generation. EMS can optimize system performance in various conditions using this capability. Depending on computational complexity, data availability, and scalability, some optimization tactics may be tough [152], [153]. Many optimization methods are too computationally intensive for real-time applications. Understanding system conditions in rural or underdeveloped areas may be difficult. As MGs develop in scope and complexity, scaling optimization methodologies become harder to use [154], [155], [156]. 4. Monitoring and IoT Technologies Control is one area where IoT technology has rapidly changed energy system monitoring and management. The IoT can help MGs manage their DERs by collecting real-time energy output, consumption, and system performance data. MGs, especially those with a high renewable energy mix, need dependable and stable IoT monitoring solutions [157], [158], [159], [160]. Smart meters, cloud computing, SCADA, and IoT applications are covered in this section. The section also addresses emerging cybersecurity and MG data management challenges. 4.1. IoT-Based Monitoring Systems and Their Application in Microgrids IoT monitoring systems allow real-time, detailed MG activity monitoring via linked devices, sensors, and communication networks. These systems process and analyze data from load centers, inverters, batteries, wind turbines, and solar panels to optimize MG performance, energy economy, and system resilience [154], [159], [160]. MGs generally use IoT monitoring systems to collect and analyze real-time data from system health, storage, consumption, and electricity production. To avoid downtime, operators can employ predictive maintenance to identify potential issues before they become system failures. IoT devices assist decision-making, energy consumption optimization, waste reduction, and energy distribution by analyzing the MG in different conditions [161], [162]. IoT monitoring improves energy efficiency and demand-side control in MGs. IoT devices employ demand-side management and energy-saving solutions with accurate consumption data. IoT thermostats can control heating and cooling depending on occupancy and outside temperature data, while smart lighting systems operating when a room is empty and automatically switch off or on the lights [163]. Systems for IoT-based lighting, HVAC, and equipment optimization consider operational demands, energy costs, and demand projections, thereby benefiting both industrial and commercial buildings. This optimization helps to stabilize the system and save energy expenditures by lowering demand peaks. MGs largely rely on DERs, which the IoT could assist to link: solar panels, wind turbines, and energy storage devices [164], [165]. By closely monitoring these resources in real time and providing comments on how best to distribute the energy to the EMS, IoT technologies ensure their running efficiency. Sensors connected to the IoT monitor the power production of every panel in solar-powered MGs and highlight any decrease in efficiency caused by dirt, shade, or malfunctioning equipment. IoT solutions allow wind-powered MGs to maximize energy acquisition by detecting wind speed, turbine performance, and changing blade angle [166], [167], [168]. 4.2. SCADA Systems, Cloud Computing, and Smart Meters Many key technologies improve the dependability, scalability, and efficiency of MG monitoring systems that rely on the IoT. Among such technologies are cloud computing, smart meters, and SCADA systems. SCADA systems are absolutely vital in industrial applications if MGs are to monitor and control energy output, storage, and consumption in real-time [169], [170], [171]. A MG control system is made up of sensors and transducers that read information from different parts of the system, RTUs that talk to the sensors, PLCs that use the readings to decide how to run the system, and HMIs that let operators see how the system is doing [172], [173], [174]. When IoT technology and SCADA systems cooperate to give operators finer-grained control and real-time data viewing, better MG management is feasible. SCADA systems can automate MG control in two ways: automatically dispatching energy from storage when needed, and inverter setpoints to ensure voltage stability [173], [175]. 5099 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate MG management is seeing increasing use of cloud computing, which lets one store, process, and analyze vast volumes of data generated by IoT-based monitoring systems. Through cloud platforms, MG operators may get advanced analytics, machine learning algorithms, and predictive models—all of which would be expensive or challenging to apply on-site [176], [177], [178]. MGs run on IoT monitoring systems, and smart meters are absolutely vital in these systems since they give real-time data on energy consumption in homes, companies, and buildings. Their two-way communication skills between the MG and the end-user enable demand-side management, dynamic pricing, and better billing accuracy [171], [179]. Smart meters track, among other things, power quality indicators, peak demand times, and hourly or minute-by-minute consumption statistics. The central EMS or SCADA system receives this data, which aids in load management, enhances energy distribution, and ensures efficient distribution. MGs are able to run more effectively, and users can make better judgments on their energy use thanks to smart meters that give operators and consumers real-time data [175], [180], [181], [182]. 4.3. Cybersecurity Concerns and Data Management in IoT-Based Microgrid Systems MGs running IoT technology have many advantages, but they also bring fresh problems, especially with relation to data management and cybersecurity. Cyberattacks and data breaches are becoming more common as MGs grow ever more dependent on digital communication networks [183], [184]. MGs are particularly vulnerable to cyberattacks, including data tampering, denial of service (DoS) attacks, and unlawful access, as they depend on IoT devices and cloud computing. For consumers and operators, these hazards could lead to financial losses, power interruptions, and damage to machinery [184], [185]. Moreover, MG systems are based on the IoT. Ensuring safe storage and quick access to the massive volumes of data produced by IoT devices—some of which may be private or proprietary—depends on finding effective storage solutions. Data integrity also determines the MG’s dependability. Smart meter data could reveal personal information about energy consumption; consequently, privacy protection is rather important [186], [187]. MG operators are looking to distributed data management technologies—including blockchain—to solve these challenges. Blockchain technology could monitor energy transfers in an immutable way to ensure that all data is safe and verifiable [188], [189]. Data encryption, strong authentication systems, regular software upgrades, firewalls, intrusion detection systems, and blockchain integration constitute a multi-layered security strategy operators should follow that helps to reduce the effect of these hazards and better control their data [190]. Implementing cybersecurity policies assists MG operators in ensuring safe data storage, transportation, and processing, as well as defending their systems from intrusions. Adhering to these rules will safeguard future data and ensure the safety of their systems [191]. 5. Future Directions Innovations in control systems, energy management software, and IoT-based monitoring solutions are enabling mass MG industry growth in the next decades. MGs are becoming more resilient and efficient as new control and management trends, including blockchain technology, smart contracts, and deep learning algorithms, find application. Depending on developments in blockchain, AI, machine learning, and the IoT, MG research and development will likely evolve in the next few years [192], [193], [194]. Thanks in part to these technologies, MGs hold promise in fields such as autonomy, efficiency, resilience, and financial viability. These are among the most amazing recent developments in MG control, management, and technological integration. Among these developments are deep learning algorithms as a tool for efficiency improvement, smart contracts as a tool for MG automation, and blockchain’s growing relevance in the energy sector [195], [196], [197]. These developments could enable distributed energy trading and peer-to-peer energy markets, thereby changing the global energy picture and making MGs more logical and scalable [198]. 5100 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate 5.1. Emerging Trends in Microgrid Control One area where blockchain technology is finding fresh uses is MGs, originally meant for safe financial transactions in digital currencies like Bitcoin. MGs means of direct links between energy producers and customers, MGs replaces the need for human interactions in utilities or centralized energy markets. Given its distributed character, blockchain presents a fantastic substitute for enabling this kind of energy exchange [194], [195], [199]. Blockchain ensures responsibility and transparency by creating a safe, unchangeable record for every energy transaction. Smart contracts, built on blockchain technology, automate energy trading based on predefined parameters, allowing the use of MGs. Smart contracts enable customers and MG creators to automatically negotiate energy contracts. The blockchain encodes these contracts, which initiate automatic execution upon meeting specific criteria [200], [201], [202]. Three areas smart contracts can enhance MG operations are automation, trust, and openness. Smart contracts also have the potential to reduce costs. Two peer-to-peer energy markets are experimenting with smart contracts based on blockchain technology: Power Ledger in Australia and Brooklyn MG in New York [203], [204]. AI and machine learning algorithms are transforming MG management by enabling predictive maintenance, automatic energy flow optimization, and real-time decision-making that process the data analysis of enormous quantities gathered by DERs, ESS, and IoT devices, AI-driven systems which can improve the general MG stability, dependability, and efficiency [160], [202], [203], [204]. Deep learning algorithms are among the most significant applications of AI in MG management because they can precisely estimate future energy demand, improving energy resource allocation, lowering the need for costly peaking power plants, and reducing energy waste. AI ensures the most economical and least expensive charging and discharging of batteries by optimizing the operation of energy storage systems [205], [206]. MGs enable distributed energy markets—where buyers and sellers of energy do not require centralized utilities—through blockchain technology. These markets let individuals, businesses, and other entities trade the additional power their distributed resources generate [207], [208]. Blockchain technology could alleviate issues with integrating large amounts of renewable energy into traditional centralized networks and hasten the global change to distributed energy systems and renewable energy. Hybrid control systems that combine distributed and centralized control enable MGs to be better controlled [209], [210]. 5.2. Potential Research Areas for Optimizing Microgrid Performance Their continuous improvement is offering several fresh directions of research on MG efficiency, resilience, and scalability. This section emphasizes many significant study domains that will influence MG evolution going forward. MGs have inherent volatility, which renders integrating large amounts of renewable energy challenging [211], [212]. Future research should aim to identify control systems that can withstand significant swings in energy generation while still maintaining system stability. In this sense, one can use adaptive control strategies, advanced energy storage integration, and predictive algorithms for energy generation and demand forecasting [213]. Next-generation energy storage systems are absolutely essential for MGs, particularly for those running renewable energy sources that are not always consistent. The current technology falls short in three areas: cost, lifetime, and scalability; these relate to lithium-ion batteries [213], [214]. Future research should concentrate on developing more affordable, longer-lasting, and more efficient storage solutions without compromising quality. Future research subjects include hydrogen storage, solid-state batteries, and flow batteries, which are all feasible. As the number of MGs rises, coordination and communication among them become increasingly important. Important research topics include cooperation control strategies, blockchain-enabled energy trading, and standard interoperability development. MGs’ growing complexity and interconnectedness call for AI’s usage in predictive maintenance and system optimization [215], [216], [217]. AI-powered algorithms can analyze data from sensors, IoT devices, and SCADA systems to optimize the MG and project operation when repairs are required. Predictive maintenance systems using machine learning look at sensor-generated data to ascertain when parts are most likely to break. Optimization algorithms track data in real-time and 5101 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate change the system’s operation so as to maximize the efficiency of the MG’s energy resource use. A self- healing network can keep running well once it finds and isolates a problem [169], [217], [218], [219]. 6. Conclusion MG technology will greatly assist the world in transitioning to more robust and environmentally sustainable energy sources. Along with future directions in this field, this paper reviews recent developments in MG control approaches, EMS, and monitoring technologies based on the IoT. As they grow, MGs will help greatly with the integration of renewable energy sources, energy efficiency improvement, and critical infrastructure resilience enhancement. MG control systems are emerging to make use of innovative ideas, including ASMC, model predictive control, and ANN, in response to the challenges presented by the widespread use of renewable energy sources. EMS allow MG stability, integration of distributed energy resources, and power supply-and-demand balancing. Although there are other EMS systems available with different advantages and drawbacks, distributed EMS is fast becoming the preferred approach because of its scalability and flexibility. Monitoring technologies based on the IoT provide real-time data on energy generation, consumption, and system performance, therefore creating new cybersecurity and data management challenges. Emerging technologies— distributed energy markets, automated system optimization, smart contracts, AI, and machine learning—are ready to change MG operations. 6.1. Recommendations for Future Research As the energy scene changes, MGs will become more and more important in giving communities dependable, cheap, and ecological energy. If MGs overcome the challenges described in this article and continue to develop in key sectors, they will be very important for the future of the planet’s energy system. To fully realize the potential of MGs in the global energy transition, future research should focus on the following areas: • We are developing control systems that can uphold system stability by enduring fluctuations in demand and the increasing share of renewable energy sources. • Improved energy storage technologies will help MG energy storage systems be more scalable, cost-effective, and efficient. • We are enhancing cooperative control and communication among MGs so they may cooperate to maximize energy flows and raise system resilience. • AI-driven algorithms for predictive maintenance and system optimization enable MGs to operate consistently with less downtime and improved energy economy. 6.2. Research Contributions The efforts significantly advance the knowledge and practice of IoT technology, EMS integration, and MG control methodologies. The primary contributions of this study are summarized as follows: • One of the key outputs of this work is the careful analysis of both conventional and innovative control techniques in MGs. Classifying the applications into two main categories—conventional methods and advanced techniques—the paper provides a thorough evaluation of their uses, advantages, and constraints. Conventional techniques consist of PID controllers, droop control, and multi-agent systems. Advanced methods include AI-driven systems, ASMC, and model predictive control. Particularly in places with significant penetration of renewable energy, researchers and engineers striving to improve MG control systems will find this to be a valuable resource. • Crucially, research on MG management and control systems including blockchain, smart contracts, and deep learning algorithms must include blockchain, smart contracts, and deep learning algorithms. Through an analysis of how these technologies might open the path for P2P energy trading and distributed energy markets, this paper adds value to the sector by improving system security, transparency, and efficiency. As scientists look at hybrid control systems that 5102 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate might combine scattered decision-making with centralized optimization, MGs are growing more scalable and durable. • This paper emphasizes the importance of monitoring systems based on the IoT so that MGs can gather data in real time and maximize their performance. Results reveal that, together with IoT devices, cloud computing and supervisory control and data acquisition (SCADA) systems improve system stability and energy economy. We also address important issues like cybersecurity and data management, and provide strategies to prevent such threats. • This paper identifies key areas for future research, such as advanced energy storage technologies, predictive maintenance driven by AI, and resilient control strategies, so laying out a plan for engineers and researchers to address the always shifting issues of MG optimization and integration in the dynamic energy market. • Academics and business will greatly value this study because it lays the groundwork for future advancements in MG control, management, and technological integration. Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] F. Nawaz, E. Pashajavid, Y. Fan, and M. Batool, “A Comprehensive Review of the State-of-the-Art of Secondary Control Strategies for Microgrids,” IEEE Access, vol. 11, pp. 102444–102459, 2023, doi: 10.1109/ACCESS.2023.3316016. [2] D. Jain and D. Saxena, “Comprehensive review on control schemes and stability investigation of hybrid AC-DC microgrid,” Electric Power Systems Research, vol. 218, p. 109182, May 2023, doi: 10.1016/j.epsr.2023.109182. [3] N. P. Srinivas and S. Modi, “A Comprehensive Review of Microgrids, Control Strategies, and Microgrid Protection Schemes,” ECS Transactions, vol. 107, pp. 13345–13370, Apr. 2022, doi: 10.1149/10701.13345ecst. [4] A. J. Albarakati et al., “Microgrid energy management and monitoring systems: A comprehensive review,” Front. Energy Res., vol. 10, Dec. 2022, doi: 10.3389/fenrg.2022.1097858. [5] S. Ishaq, I. Khan, S. Rahman, T. Hussain, A. Iqbal, and R. M. Elavarasan, “A review on recent developments in control and optimization of micro grids,” Energy Reports, vol. 8, pp. 4085–4103, Nov. 2022, doi: 10.1016/j.egyr.2022.01.080. [6] J. Singh, S. Singh, K. Verma, A. Iqbal, and B. Kumar, “Recent control techniques and management of AC microgrids: A critical review on issues, strategies, and future trends,” International Transactions on Electrical Energy Systems, vol. 31, no. 12, pp. 1–39, Jul. 2021, doi: 10.1002/2050-7038.13035. [7] M. Uddin, H. Mo, D. Dong, S. Elsawah, J. Zhu, and J. M. Guerrero, “Microgrids: A review, outstanding issues and future trends,” Energy Strategy Reviews, vol. 49, p. 101127, Sep. 2023, doi: 10.1016/j.esr.2023.101127. [8] A. Akter et al., “A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation,” Energy Strategy Reviews, vol. 51, p. 101298, Jan. 2024, doi: 10.1016/j.esr.2024.101298. [9] Z. Ullah, S. Wang, G. Wu, M. Xiao, J. Lai, and M. R. Elkadeem, “Advanced energy management strategy for microgrid using real-time monitoring interface,” Journal of Energy Storage, vol. 52, p. 104814, Aug. 2022, doi: 10.1016/j.est.2022.104814. [10] T. Falope, L. Lao, D. Hanak, and D. Huo, “Hybrid energy system integration and management for solar energy: A review,” Energy Conversion and Management: X, vol. 21, p. 100527, Jan. 2024, doi: 10.1016/j.ecmx.2024.100527. [11] A. R. Singh, R. S. Kumar, M. Bajaj, C. B. Khadse, and I. Zaitsev, “Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources,” Sci Rep, vol. 14, no. 1, p. 19207, Aug. 2024, doi: 10.1038/s41598-024-70336-3. [12] R. Elazab, A. A. Dahab, M. A. Adma, and H. A. Hassan, “Reviewing the frontier: modeling and energy management strategies for sustainable 100% renewable microgrids,” Discov Appl Sci, vol. 6, no. 4, p. 168, Mar. 2024, doi: 10.1007/s42452-024-05820-6. [13] L. Chen et al., “Green building practices to integrate renewable energy in the construction sector: a review,” Environ Chem Lett, vol. 22, no. 2, pp. 751–784, Apr. 2024, doi: 10.1007/s10311-023-01675-2. [14] M. J. B. Kabeyi and O. A. Olanrewaju, “Smart grid technologies and application in the sustainable energy transition: a review,” International Journal of Sustainable Energy, vol. 42, no. 1, pp. 685–758, Dec. 2023, doi: 10.1080/14786451.2023.2222298. [15] M. M. Islam, M. Nagrial, J. Rizk, and A. Hellany, “General Aspects, Islanding Detection, and Energy Management in Microgrids: A Review,” Sustainability, vol. 13, no. 16, Art. no. 16, Jan. 2021, doi: 10.3390/su13169301. [16] M. Khalid, “Smart grids and renewable energy systems: Perspectives and grid integration challenges,” Energy Strategy Reviews, vol. 51, p. 101299, Jan. 2024, doi: 10.1016/j.esr.2024.101299. https://creativecommons.org/licenses/by/4.0/ 5103 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate [17] Y. Lv, “Transitioning to sustainable energy: opportunities, challenges, and the potential of blockchain technology,” Front. Energy Res., vol. 11, Sep. 2023, doi: 10.3389/fenrg.2023.1258044. [18] H. Fan, W. Yu, and S. Xia, “Review of Control Strategies for DC Nano-Grid,” Front. Energy Res., vol. 9, Mar. 2021, doi: 10.3389/fenrg.2021.644926. [19] D. K. Mishra, P. K. Ray, L. Li, J. Zhang, M. J. Hossain, and A. Mohanty, “Resilient control based frequency regulation scheme of isolated microgrids considering cyber attack and parameter uncertainties,” Applied Energy, vol. 306, p. 118054, Jan. 2022, doi: 10.1016/j.apenergy.2021.118054. [20] M. Najafzadeh, R. Ahmadiahangar, O. Husev, I. Roasto, T. Jalakas, and A. Blinov, “Recent Contributions, Future Prospects and Limitations of Interlinking Converter Control in Hybrid AC/DC Microgrids,” IEEE Access, vol. 9, pp. 7960–7984, 2021, doi: 10.1109/ACCESS.2020.3049023. [21] V. Khare and P. Chaturvedi, “Design, control, reliability, economic and energy management of microgrid: A review,” e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 5, p. 100239, Sep. 2023, doi: 10.1016/j.prime.2023.100239. [22] N. Salehi, H. Martínez-García, G. Velasco-Quesada, and J. M. Guerrero, “A Comprehensive Review of Control Strategies and Optimization Methods for Individual and Community Microgrids,” IEEE Access, vol. 10, pp. 15935– 15955, 2022, doi: 10.1109/ACCESS.2022.3142810. [23] N. Naseri, I. Aboudrar, S. El Hani, N. Ait-Ahmed, S. Motahhir, and M. Machmoum, “Energy Transition and Resilient Control for Enhancing Power Availability in Microgrids Based on North African Countries: A Review,” Applied Sciences, vol. 14, no. 14, Art. no. 14, Jan. 2024, doi: 10.3390/app14146121. [24] S. Choudhury, “A comprehensive review on issues, investigations, control and protection trends, technical challenges and future directions for Microgrid technology,” International Transactions on Electrical Energy Systems, vol. 30, no. 9, p. e12446, 2020, doi: 10.1002/2050-7038.12446. [25] S. Choudhury, “A comprehensive review on issues, investigations, control and protection trends, technical challenges and future directions for Microgrid technology,” International Transactions on Electrical Energy Systems, vol. 30, no. 9, p. e12446, 2020, doi: 10.1002/2050-7038.12446. [26] I. F. Davoudkhani, P. Zare, S. J. S. Shenava, A. Y. Abdelaziz, M. Bajaj, and M. B. Tuka, “Maiden application of mountaineering team-based optimization algorithm optimized 1PD-PI controller for load frequency control in islanded microgrid with renewable energy sources,” Sci Rep, vol. 14, no. 1, p. 22851, Oct. 2024, doi: 10.1038/s41598- 024-74051-x. [27] A. X. R. Irudayaraj et al., “Decentralized frequency control of restructured energy system using hybrid intelligent algorithm and non-linear fractional order proportional integral derivative controller,” IET Renewable Power Generation, vol. 17, no. 8, pp. 2009–2037, 2023, doi: 10.1049/rpg2.12746. [28] I. F. Davoudkhani, P. Zare, A. Y. Abdelaziz, M. Bajaj, and M. B. Tuka, “Robust load-frequency control of islanded urban microgrid using 1PD-3DOF-PID controller including mobile EV energy storage,” Sci Rep, vol. 14, no. 1, p. 13962, Jun. 2024, doi: 10.1038/s41598-024-64794-y. [29] P.-H. Huang, P.-C. Liu, W. Xiao, and M. s Moursi, “A Novel Droop-Based Average Voltage Sharing Control Strategy for DC Microgrids,” Smart Grid, IEEE Transactions on, vol. 6, no. 3, pp. 1096–1106, May 2015, doi: 10.1109/TSG.2014.2357179. [30] A. Rashwan, A. Mikhaylov, T. Senjyu, M. Eslami, A. M. Hemeida, and D. S. M. Osheba, “Modified Droop Control for Microgrid Power-Sharing Stability Improvement,” Sustainability, vol. 15, no. 14, Art. no. 14, Jan. 2023, doi: 10.3390/su151411220. [31] U. B. Tayab, M. A. B. Roslan, L. J. Hwai, and M. Kashif, “A review of droop control techniques for microgrid,” Renewable and Sustainable Energy Reviews, vol. 76, no. C, pp. 717–727, 2017, Accessed: Oct. 02, 2024. [Online]. Available: https://ideas.repec.org//a/eee/rensus/v76y2017icp717-727.html [32] T. S. Tran, D. T. Nguyen, and G. Fujita, “The Analysis of Technical Trend in Islanding Operation, Harmonic Distortion, Stabilizing Frequency, and Voltage of Islanded Entities,” Resources, vol. 8, no. 1, Art. no. 1, Mar. 2019, doi: 10.3390/resources8010014. [33] M. Abbasi, E. Abbasi, L. Li, R. P. Aguilera, D. Lu, and F. Wang, “Review on the Microgrid Concept, Structures, Components, Communication Systems, and Control Methods,” Energies, vol. 16, no. 1, Art. no. 1, Jan. 2023, doi: 10.3390/en16010484. [34] A. M. Jasim, B. H. Jasim, V. Bureš, and P. Mikulecký, “A New Decentralized Robust Secondary Control for Smart Islanded Microgrids,” Sensors (Basel), vol. 22, no. 22, p. 8709, Nov. 2022, doi: 10.3390/s22228709. [35] L. Radaelli and S. Martinez, “Frequency Stability Analysis of a Low Inertia Power System with Interactions among Power Electronics Interfaced Generators with Frequency Response Capabilities,” Applied Sciences, vol. 12, no. 21, Art. no. 21, Jan. 2022, doi: 10.3390/app122111126. [36] F. Ullah et al., “A comprehensive review of wind power integration and energy storage technologies for modern grid frequency regulation,” Heliyon, vol. 10, no. 9, p. e30466, May 2024, doi: 10.1016/j.heliyon.2024.e30466. [37] I. A. Khan, H. Mokhlis, N. N. Mansor, H. A. Illias, L. Jamilatul Awalin, and L. Wang, “New trends and future directions in load frequency control and flexible power system: A comprehensive review,” Alexandria Engineering Journal, vol. 71, pp. 263–308, May 2023, doi: 10.1016/j.aej.2023.03.040. [38] I. F. Davoudkhani, P. Zare, A. Y. Abdelaziz, M. Bajaj, and M. B. Tuka, “Robust load-frequency control of islanded urban microgrid using 1PD-3DOF-PID controller including mobile EV energy storage,” Sci Rep, vol. 14, no. 1, p. 13962, Jun. 2024, doi: 10.1038/s41598-024-64794-y. 5104 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate [39] F. Kamal and B. Chowdhury, “Model predictive control and optimization of networked microgrids,” International Journal of Electrical Power & Energy Systems, vol. 138, p. 107804, Jun. 2022, doi: 10.1016/j.ijepes.2021.107804. [40] M. S. Bakare, A. Abdulkarim, A. N. Shuaibu, and M. M. Muhamad, “Energy management controllers: strategies, coordination, and applications,” Energy Informatics, vol. 7, no. 1, p. 57, Jul. 2024, doi: 10.1186/s42162-024-00357-9. [41] Z. A. Arfeen et al., “Insights and trends of optimal voltage-frequency control DG-based inverter for autonomous microgrid: State-of-the-art review,” International Transactions on Electrical Energy Systems, vol. 30, no. 10, p. e12555, 2020, doi: 10.1002/2050-7038.12555. [42] M. Hermassi et al., “Design of Vector Control Strategies Based on Fuzzy Gain Scheduling PID Controllers for a Grid-Connected Wind Energy Conversion System: Hardware FPGA-in-the-Loop Verification,” Electronics, vol. 12, no. 6, Art. no. 6, Jan. 2023, doi: 10.3390/electronics12061419. [43] M. Das, M. Catalkaya, O. E. Akay, and E. K. Akpinar, “Impacts of use PID control and artificial intelligence methods for solar air heater energy performance,” Journal of Building Engineering, vol. 65, p. 105809, Apr. 2023, doi: 10.1016/j.jobe.2022.105809. [44] M. A. E. Mohamed, K. Jagatheesan, and B. Anand, “Modern PID/FOPID controllers for frequency regulation of interconnected power system by considering different cost functions,” Sci Rep, vol. 13, no. 1, p. 14084, Aug. 2023, doi: 10.1038/s41598-023-41024-5. [45] H. Shukla and M. Raju, “Application of COOT algorithm optimized PID plus D2 controller for combined control of frequency and voltage considering renewable energy sources,” e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 6, p. 100269, Dec. 2023, doi: 10.1016/j.prime.2023.100269. [46] A. Latif, L. Khan, S. Agha, S. Mumtaz, and J. Iqbal, “Nonlinear control of two-stage single-phase standalone photovoltaic system,” PLoS One, vol. 19, no. 2, p. e0297612, Feb. 2024, doi: 10.1371/journal.pone.0297612. [47] S. Jamal, J. Pasupuleti, and J. Ekanayake, “A rule-based energy management system for hybrid renewable energy sources with battery bank optimized by genetic algorithm optimization,” Sci Rep, vol. 14, no. 1, p. 4865, Feb. 2024, doi: 10.1038/s41598-024-54333-0. [48] I. Alotaibi, M. A. Abido, M. Khalid, and A. V. Savkin, “A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources,” Energies, vol. 13, no. 23, Art. no. 23, Jan. 2020, doi: 10.3390/en13236269. [49] S. S. Ali and B. J. Choi, “State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review,” Electronics, vol. 9, no. 6, Art. no. 6, Jun. 2020, doi: 10.3390/electronics9061030. [50] M. Saadati Toularoud, M. Khoshhal Rudposhti, S. Bagheri, and A. H. Salemi, “Enhancing Microgrid Voltage and Frequency Stability through Multilayer Interactive Control Framework,” International Transactions on Electrical Energy Systems, vol. 2024, no. 1, p. 4933861, 2024, doi: 10.1155/2024/4933861. [51] M. Hasan et al., “A critical review on control mechanisms, supporting measures, and monitoring systems of microgrids considering large scale integration of renewable energy sources,” Energy Reports, vol. 10, pp. 4582–4603, Nov. 2023, doi: 10.1016/j.egyr.2023.11.025. [52] I. Ahmed et al., “Review on microgrids design and monitoring approaches for sustainable green energy networks,” Sci Rep, vol. 13, no. 1, p. 21663, Dec. 2023, doi: 10.1038/s41598-023-48985-7. [53] A. J. Albarakati et al., “Microgrid energy management and monitoring systems: A comprehensive review,” Front. Energy Res., vol. 10, Dec. 2022, doi: 10.3389/fenrg.2022.1097858. [54] J. A. Rodriguez-Gil et al., “Energy management system in networked microgrids: an overview,” Energy Syst, Jul. 2024, doi: 10.1007/s12667-024-00676-6. [55] D. B. Aeggegn, G. N. Nyakoe, and C. Wekesa, “A state of the art review on energy management techniques and optimal sizing of DERs in grid-connected multi-microgrids,” Cogent Engineering, vol. 11, no. 1, p. 2340306, Dec. 2024, doi: 10.1080/23311916.2024.2340306. [56] Y. Zahraoui et al., “AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review,” Sustainability, vol. 16, no. 12, Art. no. 12, Jan. 2024, doi: 10.3390/su16124959. [57] N. E. Benti, M. D. Chaka, and A. G. Semie, “Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects,” Sustainability, vol. 15, no. 9, Art. no. 9, Jan. 2023, doi: 10.3390/su15097087. [58] A. Bennagi, O. AlHousrya, D. T. Cotfas, and P. A. Cotfas, “Comprehensive study of the artificial intelligence applied in renewable energy,” Energy Strategy Reviews, vol. 54, p. 101446, Jul. 2024, doi: 10.1016/j.esr.2024.101446. [59] A. Castelletti et al., “Model Predictive Control of water resources systems: A review and research agenda,” Annual Reviews in Control, vol. 55, pp. 442–465, Jan. 2023, doi: 10.1016/j.arcontrol.2023.03.013. [60] P. Lu, N. Zhang, L. Ye, E. Du, and C. Kang, “Advances in model predictive control for large-scale wind power integration in power systems,” Advances in Applied Energy, vol. 14, p. 100177, Jul. 2024, doi: 10.1016/j.adapen.2024.100177. [61] M. Schwenzer, M. Ay, T. Bergs, and D. Abel, “Review on model predictive control: an engineering perspective,” Int J Adv Manuf Technol, vol. 117, no. 5, pp. 1327–1349, Nov. 2021, doi: 10.1007/s00170-021-07682-3. [62] M. Cavus, Y. F. Ugurluoglu, H. Ayan, A. Allahham, K. Adhikari, and D. Giaouris, “Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids,” Applied Sciences, vol. 13, no. 21, Art. no. 21, Jan. 2023, doi: 10.3390/app132111744. [63] K. V. Konneh, O. B. Adewuyi, M. E. Lotfy, Y. Sun, and T. Senjyu, “Application Strategies of Model Predictive Control for the Design and Operations of Renewable Energy-Based Microgrid: A Survey,” Electronics, vol. 11, no. 4, Art. no. 4, Jan. 2022, doi: 10.3390/electronics11040554. 5105 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate [64] M. Aghahadi, A. Bosisio, M. Merlo, A. Berizzi, A. Pegoiani, and S. Forciniti, “Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges,” Applied Sciences, vol. 14, no. 11, Art. no. 11, Jan. 2024, doi: 10.3390/app14114528. [65] H. M. Hussein et al., “State-of-the-Art Electric Vehicle Modeling: Architectures, Control, and Regulations,” Electronics, vol. 13, no. 17, Art. no. 17, Jan. 2024, doi: 10.3390/electronics13173578. [66] T. Zhang, X. Li, H. Gai, Y. Zhu, and X. Cheng, “Integrated Controller Design and Application for CNC Machine Tool Servo Systems Based on Model Reference Adaptive Control and Adaptive Sliding Mode Control,” Sensors (Basel), vol. 23, no. 24, p. 9755, Dec. 2023, doi: 10.3390/s23249755. [67] Q. Hong, Y. Shi, and Z. Chen, “Adaptive Sliding Mode Control Based on Disturbance Observer for Placement Pressure Control System,” Symmetry, vol. 12, no. 6, Art. no. 6, Jun. 2020, doi: 10.3390/sym12061057. [68] T.-C. Kao, M. S. Sadabadi, and G. Hennequin, “Optimal anticipatory control as a theory of motor preparation: A thalamo-cortical circuit model,” Neuron, vol. 109, no. 9, pp. 1567-1581.e12, May 2021, doi: 10.1016/j.neuron.2021.03.009. [69] S. Park, M.-S. Gil, H. Im, and Y.-S. Moon, “Measurement Noise Recommendation for Efficient Kalman Filtering over a Large Amount of Sensor Data,” Sensors (Basel), vol. 19, no. 5, p. 1168, Mar. 2019, doi: 10.3390/s19051168. [70] A. Chhabra, J. R. Venepally, and D. Kim, “Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise Situations,” Sensors, vol. 21, no. 24, Art. no. 24, Jan. 2021, doi: 10.3390/s21248304. [71] S. Tufail, H. Riggs, M. Tariq, and A. I. Sarwat, “Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms,” Electronics, vol. 12, no. 8, Art. no. 8, Jan. 2023, doi: 10.3390/electronics12081789. [72] C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electron Markets, vol. 31, no. 3, pp. 685–695, Sep. 2021, doi: 10.1007/s12525-021-00475-2. [73] C. E. Lawson et al., “Machine learning for metabolic engineering: A review,” Metabolic Engineering, vol. 63, pp. 34–60, Jan. 2021, doi: 10.1016/j.ymben.2020.10.005. [74] L. K. Vora, A. D. Gholap, K. Jetha, R. R. S. Thakur, H. K. Solanki, and V. P. Chavda, “Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design,” Pharmaceutics, vol. 15, no. 7, p. 1916, Jul. 2023, doi: 10.3390/pharmaceutics15071916. [75] L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” Journal of Big Data, vol. 8, no. 1, p. 53, Mar. 2021, doi: 10.1186/s40537-021-00444-8. [76] H. K. Shaker, H. E. Keshta, M. A. Mosa, and A. A. Ali, “Adaptive nonlinear controllers based approach to improve the frequency control of multi islanded interconnected microgrids,” Energy Reports, vol. 9, pp. 5230–5245, Dec. 2023, doi: 10.1016/j.egyr.2023.04.007. [77] M. Abdelateef Mostafa, E. A. El-Hay, and M. M. ELkholy, “Recent Trends in Wind Energy Conversion System with Grid Integration Based on Soft Computing Methods: Comprehensive Review, Comparisons and Insights,” Arch Computat Methods Eng, vol. 30, no. 3, pp. 1439–1478, Apr. 2023, doi: 10.1007/s11831-022-09842-4. [78] L. P. Wagner, L. M. Reinpold, M. Kilthau, and A. Fay, “A systematic review of modeling approaches for flexible energy resources,” Renewable and Sustainable Energy Reviews, vol. 184, no. C, 2023, Accessed: Oct. 02, 2024. [Online]. Available: https://ideas.repec.org//a/eee/rensus/v184y2023ics1364032123003982.html [79] P. Pandiyan, S. Saravanan, K. Usha, R. Kannadasan, M. H. Alsharif, and M.-K. Kim, “Technological advancements toward smart energy management in smart cities,” Energy Reports, vol. 10, pp. 648–677, Nov. 2023, doi: 10.1016/j.egyr.2023.07.021. [80] E. Hosseini et al., “Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018–2023),” Energy Strategy Reviews, vol. 53, p. 101409, May 2024, doi: 10.1016/j.esr.2024.101409. [81] R. Jain and A. Arya, “A Comprehensive Review on Micro Grid Operation, Challenges and Control Strategies,” in Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems, in e-Energy ‘15. New York, NY, USA: Association for Computing Machinery, Jul. 2015, pp. 295–300. doi: 10.1145/2768510.2768514. [82] L. S. N. D. and M. R., “Review on advanced control techniques for microgrids,” Energy Reports, vol. 10, pp. 3054– 3072, Nov. 2023, doi: 10.1016/j.egyr.2023.09.162. [83] K. M. Bhargavi, N. S. Jayalakshmi, D. N. Gaonkar, A. Shrivastava, and V. K. Jadoun, “A Comprehensive Review on Control Techniques for Power Management of Isolated DC Microgrid System Operation,” IEEE Access, vol. 9, pp. 32196–32228, 2021, doi: 10.1109/ACCESS.2021.3060504. [84] Z. H. A. Al-Tameemi, T. T. Lie, G. Foo, and F. Blaabjerg, “Control Strategies of DC Microgrids Cluster: A Comprehensive Review,” Energies, vol. 14, no. 22, Art. no. 22, Jan. 2021, doi: 10.3390/en14227569. [85] S. Somkun, “Unbalanced synchronous reference frame control of singe-phase stand-alone inverter,” International Journal of Electrical Power & Energy Systems, vol. 107, pp. 332–343, May 2019, doi: 10.1016/j.ijepes.2018.12.011. [86] G. M. Pelz, S. A. O. da Silva, and L. P. Sampaio, “Comparative analysis involving PI and state-feedback multi- resonant controllers applied to the grid voltage disturbances rejection of a unified power quality conditioner,” International Journal of Electrical Power & Energy Systems, vol. 115, p. 105481, Feb. 2020, doi: 10.1016/j.ijepes.2019.105481. [87] M. I. Saleem, S. Saha, U. Izhar, and L. Ang, “Optimized energy management of a solar battery microgrid: An economic approach towards voltage stability,” Journal of Energy Storage, vol. 90, p. 111876, Jun. 2024, doi: 10.1016/j.est.2024.111876. [88] J. Won and Y.-S. Ko, “Proportional–Resonant Controller Based on Virtual Impedance for Harmonic Suppression of ESS-UPS System,” IEEE Access, vol. 11, pp. 102991–103000, 2023, doi: 10.1109/ACCESS.2023.3317707. 5106 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate [89] S. Li, A. Oshnoei, F. Blaabjerg, and A. Anvari-Moghaddam, “Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods,” Sustainability, vol. 15, no. 11, Art. no. 11, Jan. 2023, doi: 10.3390/su15118952. [90] M. M. Iqbal, S. Kumar, C. Lal, and C. Kumar, “Energy management system for a small-scale microgrid,” Journal of Electrical Systems and Information Technology, vol. 9, no. 1, p. 5, Mar. 2022, doi: 10.1186/s43067-022-00046-1. [91] A. K. Erenoğlu, İ. Şengör, O. Erdinç, A. Taşcıkaraoğlu, and J. P. S. Catalão, “Optimal energy management system for microgrids considering energy storage, demand response and renewable power generation,” International Journal of Electrical Power & Energy Systems, vol. 136, p. 107714, Mar. 2022, doi: 10.1016/j.ijepes.2021.107714. [92] N. R. Deevela, T. C. Kandpal, and B. Singh, “A review of renewable energy based power supply options for telecom towers,” Environ Dev Sustain, vol. 26, no. 2, pp. 2897–2964, Feb. 2024, doi: 10.1007/s10668-023-02917-7. [93] S. Mehta and P. Basak, “A comprehensive review on control techniques for stability improvement in microgrids,” International Transactions on Electrical Energy Systems, vol. 31, no. 4, p. e12822, 2021, doi: 10.1002/2050-7038.12822. [94] S. Shahzad, M. A. Abbasi, H. Ali, M. Iqbal, R. Munir, and H. Kilic, “Possibilities, Challenges, and Future Opportunities of Microgrids: A Review,” Sustainability, vol. 15, no. 8, Art. no. 8, Jan. 2023, doi: 10.3390/su15086366. [95] S. C. Doumen, D. S. Boff, S. E. Widergren, and J. K. Kok, “Taming the wild edge of smart grid – Lessons from transactive energy market deployments,” The Electricity Journal, vol. 36, no. 2, p. 107253, Mar. 2023, doi: 10.1016/j.tej.2023.107253. [96] A. Hirsch, Y. Parag, and J. Guerrero, “Microgrids: A review of technologies, key drivers, and outstanding issues,” Renewable and Sustainable Energy Reviews, vol. 90, pp. 402–411, Jul. 2018, doi: 10.1016/j.rser.2018.03.040. [97] M. J. B. Kabeyi and O. A. Olanrewaju, “Sustainable Energy Transition for Renewable and Low Carbon Grid Electricity Generation and Supply,” Front. Energy Res., vol. 9, Mar. 2022, doi: 10.3389/fenrg.2021.743114. [98] K. Obaideen et al., “On the contribution of solar energy to sustainable developments goals: Case study on Mohammed bin Rashid Al Maktoum Solar Park,” International Journal of Thermofluids, vol. 12, p. 100123, Nov. 2021, doi: 10.1016/j.ijft.2021.100123. [99] F. Wang et al., “Technologies and perspectives for achieving carbon neutrality,” The Innovation, vol. 2, no. 4, p. 100180, Nov. 2021, doi: 10.1016/j.xinn.2021.100180. [100] L. Ahmethodzic and M. Music, “Comprehensive review of trends in microgrid control,” Renewable Energy Focus, vol. 38, pp. 84–96, Sep. 2021, doi: 10.1016/j.ref.2021.07.003. [101] S. T. Onifade, S. Erdoğan, and A. A. Alola, “The role of alternative energy and globalization in decarbonization prospects of the oil-producing African economies,” Environ Sci Pollut Res, vol. 30, no. 20, pp. 58128–58141, Apr. 2023, doi: 10.1007/s11356-023-26581-6. [102] A. Kumar et al., “State-of-the-art review on energy sharing and trading of resilient multi microgrids,” iScience, vol. 27, no. 4, p. 109549, Apr. 2024, doi: 10.1016/j.isci.2024.109549. [103] M. S. Bakare, A. Abdulkarim, M. Zeeshan, and A. N. Shuaibu, “A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction,” Energy Informatics, vol. 6, no. 1, p. 4, Mar. 2023, doi: 10.1186/s42162-023-00262-7. [104] M. Wolsink, “Conceptualizations of smart grids –anomalous and contradictory expert paradigms in transitions of the electricity system,” Energy Research & Social Science, vol. 109, p. 103392, Mar. 2024, doi: 10.1016/j.erss.2023.103392. [105] R. B. Duffey, “Power Restoration Prediction Following Extreme Events and Disasters,” Int J Disaster Risk Sci, vol. 10, no. 1, pp. 134–148, Mar. 2019, doi: 10.1007/s13753-018-0189-2. [106] M. O. Román et al., “Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria,” PLoS One, vol. 14, no. 6, p. e0218883, Jun. 2019, doi: 10.1371/journal.pone.0218883. [107] S. Ferahtia, A. Houari, T. Cioara, M. Bouznit, H. Rezk, and A. Djerioui, “Recent advances on energy management and control of direct current microgrid for smart cities and industry: A Survey,” Applied Energy, vol. 368, p. 123501, Aug. 2024, doi: 10.1016/j.apenergy.2024.123501. [108] A. Kumar et al., “An effective energy management system for intensified grid-connected microgrids,” Energy Strategy Reviews, vol. 50, p. 101222, Nov. 2023, doi: 10.1016/j.esr.2023.101222. [109] E. Hernández-Mayoral et al., “A Comprehensive Review on Power-Quality Issues, Optimization Techniques, and Control Strategies of Microgrid Based on Renewable Energy Sources,” Sustainability, vol. 15, no. 12, Art. no. 12, Jan. 2023, doi: 10.3390/su15129847. [110] M. H. Alsharif, A. Jahid, R. Kannadasan, and M.-K. Kim, “Unleashing the potential of sixth generation (6G) wireless networks in smart energy grid management: A comprehensive review,” Energy Reports, vol. 11, pp. 1376–1398, Jun. 2024, doi: 10.1016/j.egyr.2024.01.011. [111] M. Hajiaghapour-Moghimi, E. Hajipour, K. Azimi Hosseini, M. Vakilian, and M. Lehtonen, “Cryptocurrency mining as a novel virtual energy storage system in islanded and grid-connected microgrids,” International Journal of Electrical Power & Energy Systems, vol. 158, p. 109915, Jul. 2024, doi: 10.1016/j.ijepes.2024.109915. [112] T. Afolabi and H. Farzaneh, “Optimal Design and Operation of an Off-Grid Hybrid Renewable Energy System in Nigeria’s Rural Residential Area, Using Fuzzy Logic and Optimization Techniques,” Sustainability, vol. 15, no. 4, Art. no. 4, Jan. 2023, doi: 10.3390/su15043862. [113] E. T. Sayed et al., “Renewable Energy and Energy Storage Systems,” Energies, vol. 16, no. 3, Art. no. 3, Jan. 2023, doi: 10.3390/en16031415. 5107 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate [114] M. F. M. Zublie, M. Hasanuzzaman, and N. A. Rahim, “Energy Efficiency and Feasibility Analysis of Solar Power Generation Using Hybrid System of an Educational Institution in Malaysia,” International Journal of Photoenergy, vol. 2023, no. 1, p. 1673512, 2023, doi: 10.1155/2023/1673512. [115] A. Mateen, M. Wasim, A. Ahad, T. Ashfaq, M. Iqbal, and A. Ali, “Smart energy management system for minimizing electricity cost and peak to average ratio in residential areas with hybrid genetic flower pollination algorithm,” Alexandria Engineering Journal, vol. 77, pp. 593–611, Aug. 2023, doi: 10.1016/j.aej.2023.06.053. [116] M. A. Bhuiyan, Q. Zhang, V. Khare, A. Mikhaylov, G. Pinter, and X. Huang, “Renewable Energy Consumption and Economic Growth Nexus—A Systematic Literature Review,” Front. Environ. Sci., vol. 10, Apr. 2022, doi: 10.3389/fenvs.2022.878394. [117] S. A. H. Zaidi, R. U. Ashraf, I. Khan, and M. Li, “Impact of natural resource depletion on energy intensity: Moderating role of globalization, financial inclusion and trade,” Resources Policy, vol. 94, p. 105112, Jul. 2024, doi: 10.1016/j.resourpol.2024.105112. [118] M. Amir et al., “Energy storage technologies: An integrated survey of developments, global economical/environmental effects, optimal scheduling model, and sustainable adaption policies,” Journal of Energy Storage, vol. 72, p. 108694, Nov. 2023, doi: 10.1016/j.est.2023.108694. [119] S. Gennitsaris et al., “Energy Efficiency Management in Small and Medium-Sized Enterprises: Current Situation, Case Studies and Best Practices,” Sustainability, vol. 15, no. 4, Art. no. 4, Jan. 2023, doi: 10.3390/su15043727. [120] D.-S. Lee and C.-C. Cheng, “Energy savings by energy management systems: A review,” Renewable and Sustainable Energy Reviews, vol. 56, pp. 760–777, Apr. 2016, doi: 10.1016/j.rser.2015.11.067. [121] A. Hassebo and M. Tealab, “Global Models of Smart Cities and Potential IoT Applications: A Review,” IoT, vol. 4, no. 3, Art. no. 3, Sep. 2023, doi: 10.3390/iot4030017. [122] M. G. M. Almihat, M. T. E. Kahn, K. Aboalez, and A. M. Almaktoof, “Energy and Sustainable Development in Smart Cities: An Overview,” Smart Cities, vol. 5, no. 4, Art. no. 4, Dec. 2022, doi: 10.3390/smartcities5040071. [123] M. G. Gebreslassie et al., “Delivering an off-grid transition to sustainable energy in Ethiopia and Mozambique,” Energy, Sustainability and Society, vol. 12, no. 1, p. 23, May 2022, doi: 10.1186/s13705-022-00348-2. [124] V. R. Rajendran Pillai, R. Rajasekharan Nair Valsala, V. Raj, M. I. Petra, S. K. Krishnan Nair, and S. Mathew, “Exploring the Potential of Microgrids in the Effective Utilisation of Renewable Energy: A Comprehensive Analysis of Evolving Themes and Future Priorities Using Main Path Analysis,” Designs, vol. 7, no. 3, Art. no. 3, Jun. 2023, doi: 10.3390/designs7030058. [125] S. Dawn et al., “Integration of Renewable Energy in Microgrids and Smart Grids in Deregulated Power Systems: A Comparative Exploration,” Advanced Energy and Sustainability Research, vol. n/a, no. n/a, p. 2400088, 2024, doi: 10.1002/aesr.202400088. [126] K. Kappner, P. Letmathe, and P. Weidinger, “Causes and effects of the German energy transition in the context of environmental, societal, political, technological, and economic developments,” Energy, Sustainability and Society, vol. 13, no. 1, p. 28, Aug. 2023, doi: 10.1186/s13705-023-00407-2. [127] Z. Yu and X. Guo, “Influencing factors of green energy transition: The role of economic policy uncertainty, technology innovation, and ecological governance in China,” Front. Environ. Sci., vol. 10, Feb. 2023, doi: 10.3389/fenvs.2022.1058967. [128] D. Espín-Sarzosa, R. Palma-Behnke, and O. Núñez-Mata, “Energy Management Systems for Microgrids: Main Existing Trends in Centralized Control Architectures,” Energies, vol. 13, no. 3, Art. no. 3, Jan. 2020, doi: 10.3390/en13030547. [129] I. L. Machele, A. J. Onumanyi, A. M. Abu-Mahfouz, and A. M. Kurien, “Interconnected Smart Transactive Microgrids—A Survey on Trading, Energy Management Systems, and Optimisation Approaches,” Journal of Sensor and Actuator Networks, vol. 13, no. 2, Art. no. 2, Apr. 2024, doi: 10.3390/jsan13020020. [130] M. I. Abdelwanis and M. I. Elmezain, “A comprehensive review of hybrid AC/DC networks: insights into system planning, energy management, control, and protection,” Neural Comput & Applic, vol. 36, no. 29, pp. 17961–17977, Oct. 2024, doi: 10.1007/s00521-024-10264-5. [131] T. T. Mai et al., “An overview of grid-edge control with the digital transformation,” Electr Eng, vol. 103, no. 4, pp. 1989–2007, Aug. 2021, doi: 10.1007/s00202-020-01209-x. [132] R. G. Allwyn, A. Al-Hinai, and V. Margaret, “A comprehensive review on energy management strategy of microgrids,” Energy Reports, vol. 9, pp. 5565–5591, Dec. 2023, doi: 10.1016/j.egyr.2023.04.360. [133] K. M. R. Pothireddy and S. Vuddanti, “Alternating direction method of multipliers based distributed energy scheduling of grid connected microgrid by considering the demand response,” Discov Appl Sci, vol. 6, no. 7, p. 343, Jun. 2024, doi: 10.1007/s42452-024-05975-2. [134] A. El Zerk, M. Ouassaid, and Y. Zidani, “Decentralised strategy for energy management of collaborative microgrids using multi-agent system,” IET Smart Grid, vol. 5, no. 6, pp. 440–462, 2022, doi: 10.1049/stg2.12077. [135] K. Twaisan and N. Barışçı, “Integrated Distributed Energy Resources (DER) and Microgrids: Modeling and Optimization of DERs,” Electronics, vol. 11, no. 18, Art. no. 18, Jan. 2022, doi: 10.3390/electronics11182816. [136] R. S. Tulabing, B. C. Mitchell, and G. A. Covic, “Localized management of distributed flexible energy resources,” International Journal of Electrical Power & Energy Systems, vol. 157, p. 109790, Jun. 2024, doi: 10.1016/j.ijepes.2024.109790. [137] S. Panda et al., “An Insight into the Integration of Distributed Energy Resources and Energy Storage Systems with Smart Distribution Networks Using Demand-Side Management,” Applied Sciences, vol. 12, no. 17, Art. no. 17, Jan. 2022, doi: 10.3390/app12178914. 5108 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate [138] R. Seshu Kumar, L. Phani Raghav, D. Koteswara Raju, and A. R. Singh, “Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids,” Applied Energy, vol. 301, p. 117466, Nov. 2021, doi: 10.1016/j.apenergy.2021.117466. [139] X. Xing, L. Xie, and H. Meng, “Cooperative energy management optimization based on distributed MPC in grid- connected microgrids community,” International Journal of Electrical Power & Energy Systems, vol. 107, pp. 186–199, May 2019, doi: 10.1016/j.ijepes.2018.11.027. [140] E. J. Smith, D. A. Robinson, and S. Elphick, “DER Control and Management Strategies for Distribution Networks: A Review of Current Practices and Future Directions,” Energies, vol. 17, no. 11, Art. no. 11, Jan. 2024, doi: 10.3390/en17112636. [141] S. Ahmad, M. Shafiullah, C. B. Ahmed, and M. Alowaifeer, “A Review of Microgrid Energy Management and Control Strategies,” IEEE Access, vol. 11, pp. 21729–21757, 2023, doi: 10.1109/ACCESS.2023.3248511. [142] M. H. Elkholy et al., “A resilient and intelligent multi-objective energy management for a hydrogen-battery hybrid energy storage system based on MFO technique,” Renewable Energy, vol. 222, p. 119768, Feb. 2024, doi: 10.1016/j.renene.2023.119768. [143] D. O. Obada et al., “A review of renewable energy resources in Nigeria for climate change mitigation,” Case Studies in Chemical and Environmental Engineering, vol. 9, p. 100669, Jun. 2024, doi: 10.1016/j.cscee.2024.100669. [144] K. K. Jaiswal et al., “Renewable and sustainable clean energy development and impact on social, economic, and environmental health,” Energy Nexus, vol. 7, p. 100118, Sep. 2022, doi: 10.1016/j.nexus.2022.100118. [145] J. Sinopoli, “Chapter 12 - Facility Management Systems,” in Smart Building Systems for Architects, Owners and Builders, J. Sinopoli, Ed., Boston: Butterworth-Heinemann, 2010, pp. 129–137. doi: 10.1016/B978-1-85617-653-8.00012-0. [146] Y. Lv et al., “Review on influence factors and prevention control technologies of lithium-ion battery energy storage safety,” Journal of Energy Storage, vol. 72, p. 108389, Nov. 2023, doi: 10.1016/j.est.2023.108389. [147] G. Liu, L. Qu, R. Zeng, and F. Gao, “12 - Energy Internet in China,” in The Energy Internet, W. Su and A. Q. Huang, Eds., Woodhead Publishing, 2019, pp. 265–282. doi: 10.1016/B978-0-08-102207-8.00012-6. [148] K. Ullah, A. Basit, Z. Ullah, S. Aslam, and H. Herodotou, “Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview,” Energies, vol. 14, no. 9, Art. no. 9, Jan. 2021, doi: 10.3390/en14092376. [149] P. Kurukuri, M. R. Mohamed, P. Srinivasarao, Y. Arya, P. K. Leung, and J. K. K. Dokala, “A state-of-the-art review on modern and future developments of AGC/LFC of conventional and renewable energy-based power systems,” Renewable Energy Focus, vol. 43, no. 2, Sep. 2022, doi: 10.1016/j.ref.2022.09.006. [150] J. Montano, J. P. Guzmán-Rodríguez, J. M. Palomeque, and D. González-Montoya, “Comparison of different optimization techniques applied to optimal operation of energy storage systems in standalone and grid-connected direct current microgrids,” Journal of Energy Storage, vol. 96, p. 112708, Aug. 2024, doi: 10.1016/j.est.2024.112708. [151] J. Zhang, “Energy Management System: The Engine for Sustainable Development and Resource Optimization,” Highlights in Science, Engineering and Technology, vol. 76, pp. 618–624, Dec. 2023, doi: 10.54097/cvfd9m83. [152] M. R. Khan, Z. M. Haider, F. H. Malik, F. M. Almasoudi, K. S. S. Alatawi, and M. S. Bhutta, “A Comprehensive Review of Microgrid Energy Management Strategies Considering Electric Vehicles, Energy Storage Systems, and AI Techniques,” Processes, vol. 12, no. 2, Art. no. 2, Feb. 2024, doi: 10.3390/pr12020270. [153] A. Cabrera-Tobar, A. Massi Pavan, G. Petrone, and G. Spagnuolo, “A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids,” Energies, vol. 15, no. 23, Art. no. 23, Jan. 2022, doi: 10.3390/en15239114. [154] L. Fei, M. Shahzad, F. Abbas, H. A. Muqeet, M. M. Hussain, and L. Bin, “Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes,” Sensors (Basel), vol. 22, no. 19, p. 7448, Sep. 2022, doi: 10.3390/s22197448. [155] A. G. Gad, “Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review,” Arch Computat Methods Eng, vol. 29, no. 5, pp. 2531–2561, Aug. 2022, doi: 10.1007/s11831-021-09694-4. [156] P. K. Mandal, “A review of classical methods and Nature-Inspired Algorithms (NIAs) for optimization problems,” Results in Control and Optimization, vol. 13, p. 100315, Dec. 2023, doi: 10.1016/j.rico.2023.100315. [157] C. Queiroz, A. Mahmood, and Z. Tari, “SCADASim – a framework for building SCADA simulations,” IEEE Trans. Smart Grid, vol. 2, no. 4, pp. 589–597, Dec. 2011, doi: 10.1109/TSG.2011.2162432. [158] M. Pau et al., “A cloud-based smart metering infrastructure for distribution grid services and automation,” Sustainable Energy, Grids and Networks, vol. 15, pp. 14–25, Sep. 2018, doi: 10.1016/j.segan.2017.08.001. [159] S. Kumar, P. Tiwari, and M. Zymbler, “Internet of Things is a revolutionary approach for future technology enhancement: a review,” Journal of Big Data, vol. 6, no. 1, p. 111, Dec. 2019, doi: 10.1186/s40537-019-0268-2. [160] D. Voumick, P. Deb, and M. M. Khan, “Operation and Control of Microgrids Using IoT (Internet of Things),” Journal of Software Engineering and Applications, vol. 14, no. 8, Art. no. 8, Aug. 2021, doi: 10.4236/jsea.2021.148025. [161] A. Ucar, M. Karakose, and N. Kırımça, “Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends,” Applied Sciences, vol. 14, no. 2, Art. no. 2, Jan. 2024, doi: 10.3390/app14020898. [162] D. Zhong, Z. Xia, Y. Zhu, and J. Duan, “Overview of predictive maintenance based on digital twin technology,” Heliyon, vol. 9, no. 4, p. e14534, Apr. 2023, doi: 10.1016/j.heliyon.2023.e14534. [163] S. Elkateb, A. Métwalli, A. Shendy, and A. E. B. Abu-Elanien, “Machine learning and IoT – Based predictive maintenance approach for industrial applications,” Alexandria Engineering Journal, vol. 88, pp. 298–309, Feb. 2024, doi: 10.1016/j.aej.2023.12.065. 5109 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate [164] L. Fei, M. Shahzad, F. Abbas, H. A. Muqeet, M. M. Hussain, and L. Bin, “Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes,” Sensors, vol. 22, no. 19, Art. no. 19, Jan. 2022, doi: 10.3390/s22197448. [165] B. E. Sedhom, M. M. El-Saadawi, M. S. El Moursi, Mohamed. A. Hassan, and A. A. Eladl, “IoT-based optimal demand side management and control scheme for smart microgrid,” International Journal of Electrical Power & Energy Systems, vol. 127, p. 106674, May 2021, doi: 10.1016/j.ijepes.2020.106674. [166] U. ur Rehman, P. Faria, L. Gomes, and Z. Vale, “Future of energy management systems in smart cities: A systematic literature review,” Sustainable Cities and Society, vol. 96, p. 104720, Sep. 2023, doi: 10.1016/j.scs.2023.104720. [167] C. P. Ohanu, S. A. Rufai, and U. C. Oluchi, “A comprehensive review of recent developments in smart grid through renewable energy resources integration,” Heliyon, vol. 10, no. 3, p. e25705, Feb. 2024, doi: 10.1016/j.heliyon.2024.e25705. [168] N. Iksan, P. Purwanto, and H. Sutanto, “Real-Time Monitoring of Photovoltaic Systems and Control of Electricity Supply for Smart Micro Grid-PV using IoT,” TEM Journal, vol. 13, no. 1, pp. 514–523, Feb. 2024, doi: 10.18421/TEM131-53. [169] T. Baker et al., “A secure fog-based platform for SCADA-based IoT critical infrastructure,” Software: Practice and Experience, vol. 50, no. 5, Art. no. 5, May 2020, Accessed: Oct. 02, 2024. [Online]. Available: https://onlinelibrary.wiley.com/doi/full/10.1002/spe.2688 [170] M. Sheba, D.-E. Mansour, and N. Abbasy, “A new low‐cost and low‐power industrial internet of things infrastructure for effective integration of distributed and isolated systems with smart grids,” IET Generation, Transmission & Distribution, vol. 17, no. 20, pp. 1–20, Aug. 2023, doi: 10.1049/gtd2.12951. [171] M. Mehmood et al., “Edge Computing for IoT-Enabled Smart Grid,” Security and Communication Networks, vol. 2021, no. 5, pp. 1–16, Jul. 2021, doi: 10.1155/2021/5524025. [172] R. Chataut, A. Phoummalayvane, and R. Akl, “Unleashing the Power of IoT: A Comprehensive Review of IoT Applications and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0,” Sensors (Basel, Switzerland), vol. 23, no. 16, Aug. 2023, doi: 10.3390/s23167194. [173] M. Elsisi, K. Mahmoud, M. Lehtonen, and M. M. F. Darwish, “Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters,” Sensors, vol. 21, no. 2, Art. no. 2, Jan. 2021, doi: 10.3390/s21020487. [174] Md. M. H. Sifat et al., “Towards electric digital twin grid: Technology and framework review,” Energy and AI, vol. 11, p. 100213, Jan. 2023, doi: 10.1016/j.egyai.2022.100213. [175] N. Tariq, M. Asim, and F. A. Khan, “Securing SCADA-based Critical Infrastructures: Challenges and Open Issues,” Procedia Computer Science, vol. 155, pp. 612–617, Jan. 2019, doi: 10.1016/j.procs.2019.08.086. [176] I. Essamlali, H. Nhaila, and M. El Khaili, “Advances in machine learning and IoT for water quality monitoring: A comprehensive review,” Heliyon, vol. 10, no. 6, p. e27920, Mar. 2024, doi: 10.1016/j.heliyon.2024.e27920. [177] M. R. Islam, K. Oliullah, M. M. Kabir, M. Alom, and M. F. Mridha, “Machine learning enabled IoT system for soil nutrients monitoring and crop recommendation,” Journal of Agriculture and Food Research, vol. 14, p. 100880, Dec. 2023, doi: 10.1016/j.jafr.2023.100880. [178] M. Pathak, K. N. Mishra, and S. P. Singh, “Securing data and preserving privacy in cloud IoT-based technologies an analysis of assessing threats and developing effective safeguard,” Artif Intell Rev, vol. 57, no. 10, p. 269, Aug. 2024, doi: 10.1007/s10462-024-10908-x. [179] D. L. S. Mendes, R. A. L. Rabelo, A. F. S. Veloso, J. J. P. C. Rodrigues, and J. V. dos Reis Junior, “An adaptive data compression mechanism for smart meters considering a demand side management scenario,” Journal of Cleaner Production, vol. 255, p. 120190, May 2020, doi: 10.1016/j.jclepro.2020.120190. [180] Z. Chen, A. M. Amani, X. Yu, and M. Jalili, “Control and Optimisation of Power Grids Using Smart Meter Data: A Review,” Sensors, vol. 23, no. 4, Art. no. 4, Jan. 2023, doi: 10.3390/s23042118. [181] X. Li, H. Zhao, Y. Feng, J. Li, Y. Zhao, and X. Wang, “Research on key technologies of high energy efficiency and low power consumption of new data acquisition equipment of power Internet of Things based on artificial intelligence,” International Journal of Thermofluids, vol. 21, p. 100575, Feb. 2024, doi: 10.1016/j.ijft.2024.100575. [182] T. Knayer and N. Kryvinska, “An analysis of smart meter technologies for efficient energy management in households and organizations,” Energy Reports, vol. 8, no. 2, pp. 4022–4040, Nov. 2022, doi: 10.1016/j.egyr.2022.03.041. [183] M. A. Obaidat, S. Obeidat, J. Holst, A. Al Hayajneh, and J. Brown, “A Comprehensive and Systematic Survey on the Internet of Things: Security and Privacy Challenges, Security Frameworks, Enabling Technologies, Threats, Vulnerabilities and Countermeasures,” Computers, vol. 9, no. 2, Art. no. 2, Jun. 2020, doi: 10.3390/computers9020044. [184] T. Mazhar et al., “Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence,” Brain Sci, vol. 13, no. 4, p. 683, Apr. 2023, doi: 10.3390/brainsci13040683. [185] A. Djenna, S. Harous, and D. E. Saidouni, “Internet of Things Meet Internet of Threats: New Concern Cyber Security Issues of Critical Cyber Infrastructure,” Applied Sciences, vol. 11, no. 10, Art. no. 10, Jan. 2021, doi: 10.3390/app11104580. [186] M. Alexandru, A. Lavric, A. Petrariu, and V. Popa, “Massive Data Storage Solution for IoT Devices Using Blockchain Technologies,” Sensors, vol. 23, no. 3, p. 1570, Feb. 2023, doi: 10.3390/s23031570. [187] L. Tawalbeh, F. Muheidat, M. Tawalbeh, and M. Quwaider, “IoT Privacy and Security: Challenges and Solutions,” Applied Sciences, vol. 10, no. 12, Art. no. 12, Jan. 2020, doi: 10.3390/app10124102. [188] S. S. Uddin et al., “Next-generation blockchain enabled smart grid: Conceptual framework, key technologies and industry practices review,” Energy and AI, vol. 12, p. 100228, Apr. 2023, doi: 10.1016/j.egyai.2022.100228. 5110 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate [189] E. Barceló, K. Dimić-Mišić, M. Imani, V. Spasojević Brkić, M. Hummel, and P. Gane, “Regulatory Paradigm and Challenge for Blockchain Integration of Decentralized Systems: Example—Renewable Energy Grids,” Sustainability, vol. 15, no. 3, Art. no. 3, Jan. 2023, doi: 10.3390/su15032571. [190] F. Casino, T. K. Dasaklis, and C. Patsakis, “A systematic literature review of blockchain-based applications: Current status, classification and open issues,” Telematics and Informatics, vol. 36, pp. 55–81, Mar. 2019, doi: 10.1016/j.tele.2018.11.006. [191] W. Weixiong, “The role of blockchain technology in advancing sustainable energy with security settlement: enhancing security and efficiency in China’s security market,” Front. Energy Res., vol. 11, Sep. 2023, doi: 10.3389/fenrg.2023.1271752. [192] P. Koukaras et al., “Integrating Blockchain in Smart Grids for Enhanced Demand Response: Challenges, Strategies, and Future Directions,” Energies, vol. 17, no. 5, Art. no. 5, Jan. 2024, doi: 10.3390/en17051007. [193] M. Adnan, I. Ahmed, S. Iqbal, M. R. Fazal, S. J. Siddiqi, and M. Tariq, “Exploring the convergence of Metaverse, Blockchain, Artificial Intelligence, and digital twin for pioneering the digitization in the envision smart grid 3.0,” Computers and Electrical Engineering, p. 109709, Sep. 2024, doi: 10.1016/j.compeleceng.2024.109709. [194] B. Appasani et al., “Blockchain-Enabled Smart Grid Applications: Architecture, Challenges, and Solutions,” Sustainability, vol. 14, no. 14, Art. no. 14, Jan. 2022, doi: 10.3390/su14148801. [195] S. Kayikci and T. M. Khoshgoftaar, “Blockchain meets machine learning: a survey,” Journal of Big Data, vol. 11, no. 1, p. 9, Jan. 2024, doi: 10.1186/s40537-023-00852-y. [196] L. Pinto-Coelho, “How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications,” Bioengineering (Basel), vol. 10, no. 12, p. 1435, Dec. 2023, doi: 10.3390/bioengineering10121435. [197] M. Li, Y. Jiang, Y. Zhang, and H. Zhu, “Medical image analysis using deep learning algorithms,” Front Public Health, vol. 11, p. 1273253, Nov. 2023, doi: 10.3389/fpubh.2023.1273253. [198] M. M. Taye, “Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions,” Computers, vol. 12, no. 5, Art. no. 5, May 2023, doi: 10.3390/computers12050091. [199] A. Raza, L. Jingzhao, M. Adnan, and I. Ahmad, “Optimal load forecasting and scheduling strategies for smart homes peer-to-peer energy networks: A comprehensive survey with critical simulation analysis,” Results in Engineering, vol. 22, p. 102188, Jun. 2024, doi: 10.1016/j.rineng.2024.102188. [200] K. Y. Yap, H. Chin, and J. Klemeš, “Blockchain technology for distributed generation: A review of current development, challenges and future prospect,” Renewable and Sustainable Energy Reviews, vol. 175, no. 2, p. 113170, Apr. 2023, doi: 10.1016/j.rser.2023.113170. [201] Y. Baashar, G. Alkawsi, A. A. Alkahtani, W. Hashim, R. A. Razali, and S. K. Tiong, “Toward Blockchain Technology in the Energy Environment,” Sustainability, vol. 13, no. 16, Art. no. 16, Jan. 2021, doi: 10.3390/su13169008. [202] P. Vionis and T. Kotsilieris, “The Potential of Blockchain Technology and Smart Contracts in the Energy Sector: A Review,” Applied Sciences, vol. 14, no. 1, Art. no. 1, Jan. 2024, doi: 10.3390/app14010253. [203] A. Korkmaz, E. Kılıç, M. Türkay, Ö. Çakmak, T. Arslan, and U. Erdoğan, “A Blockchain Based P2P Energy Trading Solution for Smart Grids,” Mar. 2021, doi: 10.13140/RG.2.2.30192.58882. [204] X. Zhang, Y. Sheng, and Z. Liu, “Using expertise as an intermediary: Unleashing the power of blockchain technology to drive future sustainable management using hidden champions,” Heliyon, vol. 10, no. 1, p. e23807, Jan. 2024, doi: 10.1016/j.heliyon.2023.e23807. [205] P. Vionis and T. Kotsilieris, “The Potential of Blockchain Technology and Smart Contracts in the Energy Sector: A Review,” Applied Sciences, vol. 14, no. 1, Art. no. 1, Jan. 2024, doi: 10.3390/app14010253. [206] G. Vieira and J. Zhang, “Peer-to-peer energy trading in a microgrid leveraged by smart contracts,” Renewable and Sustainable Energy Reviews, vol. 143, p. 110900, Jun. 2021, doi: 10.1016/j.rser.2021.110900. [207] L. Wang, Z. Wang, Z. Li, M. Yang, and X. Cheng, “Distributed optimization for network-constrained peer-to-peer energy trading among multiple microgrids under uncertainty,” International Journal of Electrical Power & Energy Systems, vol. 149, p. 109065, Jul. 2023, doi: 10.1016/j.ijepes.2023.109065. [208] A. Umar, D. Kumar, and T. Ghose, “Blockchain-based decentralized energy intra-trading with battery storage flexibility in a community microgrid system,” Applied Energy, vol. 322, p. 119544, Sep. 2022, doi: 10.1016/j.apenergy.2022.119544. [209] W. Xu, J. Li, M. Dehghani, and M. GhasemiGarpachi, “Blockchain-based secure energy policy and management of renewable-based smart microgrids,” Sustainable Cities and Society, vol. 72, p. 103010, Sep. 2021, doi: 10.1016/j.scs.2021.103010. [210] H. Taherdoost, “Blockchain Integration and Its Impact on Renewable Energy,” Computers, vol. 13, no. 4, Art. no. 4, Apr. 2024, doi: 10.3390/computers13040107. [211] J. Zhang et al., “Advances and Applications of 4D-Printed High-Strength Shape Memory Polymers,” Additive Manufacturing Frontiers, vol. 3, no. 1, p. 200115, Mar. 2024, doi: 10.1016/j.amf.2024.200115. [212] A.-C. Băroiu and A. Bâra, “A Descriptive-Predictive–Prescriptive Framework for the Social-Media–Cryptocurrencies Relationship,” Electronics, vol. 13, no. 7, Art. no. 7, Jan. 2024, doi: 10.3390/electronics13071277. [213] A. Emrani, Y. Achour, M. J. Sanjari, and A. Berrada, “Adaptive energy management strategy for optimal integration of wind/PV system with hybrid gravity/battery energy storage using forecast models,” Journal of Energy Storage, vol. 96, p. 112613, Aug. 2024, doi: 10.1016/j.est.2024.112613. 5111 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5089-5111, 2024 DOI: 10.55214/25768484.v8i6.3116 © 2024 by the authors; licensee Learning Gate [214] S. Brandi, A. Gallo, and A. Capozzoli, “A predictive and adaptive control strategy to optimize the management of integrated energy systems in buildings,” Energy Reports, vol. 8, pp. 1550–1567, Nov. 2022, doi: 10.1016/j.egyr.2021.12.058. [215] H. Muhsen, A. Allahham, A. Al-Halhouli, M. Al-Mahmodi, A. Alkhraibat, and M. Hamdan, “Business Model of Peer- to-Peer Energy Trading: A Review of Literature,” Sustainability, vol. 14, no. 3, Art. no. 3, Jan. 2022, doi: 10.3390/su14031616. [216] R. Vinuesa et al., “The role of artificial intelligence in achieving the Sustainable Development Goals,” Nat Commun, vol. 11, no. 1, p. 233, Jan. 2020, doi: 10.1038/s41467-019-14108-y. [217] M. Parhamfar, I. Sadeghkhani, and A. M. Adeli, “Towards the net zero carbon future: A review of blockchain-enabled peer-to-peer carbon trading,” Energy Science & Engineering, vol. 12, no. 3, pp. 1242–1264, 2024, doi: 10.1002/ese3.1697. [218] S. Elkateb, A. Métwalli, A. Shendy, and A. Abu-Elanien, “Machine learning and IoT – Based predictive maintenance approach for industrial applications,” Alexandria Engineering Journal, vol. 88, pp. 298–309, Feb. 2024, doi: 10.1016/j.aej.2023.12.065. [219] Z. M. Çınar, A. Abdussalam Nuhu, Q. Zeeshan, O. Korhan, M. Asmael, and B. Safaei, “Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0,” Sustainability, vol. 12, no. 19, Art. no. 19, Jan. 2020, doi: 10.3390/su12198211.