Comprehensive Review of Hybrid Energy Systems: Challenges, Applications, and Optimization Strategies

Abstract

This paper provides a comprehensive review of hybrid energy systems (HESs), focusing on their challenges, optimization techniques, and control strategies to enhance performance, reliability, and sustainability across various applications, such as microgrids (MGs), commercial buildings, healthcare facilities, and cruise ships. The integration of renewable energy sources (RESs), including solar photovoltaics (PVs), with enabling technologies such as fuel cells (FCs), batteries (BTs), and energy storage systems (ESSs) plays a critical role in improving energy management, reducing emissions, and increasing economic viability. This review highlights advancements in multi-objective optimization techniques, real-time energy management, and sophisticated control strategies that have significantly contributed to reducing fuel consumption, operational costs, and environmental impact. However, key challenges remain, including the scalability of optimization techniques, sensitivity to system parameter variations, and limited incorporation of user behavior, grid dynamics, and life cycle carbon emissions. The review underlines the need for robust, adaptable control strategies capable of accommodating rapidly changing energy environments, as well as the importance of life cycle assessments to ensure the long-term sustainability of RES technologies. Future research directions emphasize the integration of variable RESs, advanced scheduling, and the application of emerging technologies such as artificial intelligence and blockchain to improve system resilience and efficiency. This paper introduces a novel classification framework, distinct from existing taxonomies, addressing gaps in prior reviews by incorporating emerging technologies and focusing on the dynamic nature of energy management in hybrid systems. It also advocates for bridging the gap between theoretical advancements and real-world implementation to promote the development of more sustainable and reliable HESs.

1. Introduction

The integration of hybrid energy systems (HESs) and energy storage systems (ESSs) has attracted significant attention in recent years, driven by the urgent need for sustainable and efficient energy solutions [1]. HESs actively combine multiple energy sources, such as solar photovoltaic (PV) panels, fuel cells (FCs), and batteries (BTs) within a unified framework. This configuration leverages the strengths of each technology while minimizing its individual limitations. For example, solar PV panels generate clean energy during the day, fuel cells provide a reliable backup during periods of low solar availability, and batteries store excess energy for later use. This synergy ensures a balanced supply–demand profile and reduces dependence on external grids. Several studies, including [2], have emphasized the benefits of such integration in enhancing energy efficiency and sustainability.

This article presents a collection of studies that focus on energy management systems (EMSs) and optimization techniques tailored to specific applications, including commercial buildings, cruise ships, healthcare facilities, and microgrids (MGs) [3,4,5,6,7,8,9]. Healthcare facilities, for instance, demand reliable and continuous power to support critical medical equipment, making them ideal candidates for hybrid systems that integrate renewable energy sources (RESs) and storage technologies. Cruise ships, which often operate off-grid for extended durations, also benefit from hybrid systems by ensuring a stable, sustainable, and cost-effective power supply. In the case of MGs, EMS plays a crucial role in optimizing interactions between energy sources and loads. It evaluates variables such as energy production, fuel consumption, emissions, load demand, and system performance under various operational scenarios [10,11,12,13,14]. These systems allocate energy resources efficiently, minimize wastage, and adapt dynamically to fluctuations in energy availability and consumption patterns.

The integration of these technologies provides numerous benefits. HESs significantly reduce fuel consumption by harnessing RESs, thereby lowering greenhouse gas emissions. They also enhance energy self-sufficiency, particularly in remote areas, by optimizing resource allocation and enabling localized energy generation. Moreover, these systems improve the economic viability of energy storage solutions by reducing operational costs and enhancing system reliability [11,15,16,17]. An insightful study by [18] examines the economic profitability of energy communities involving various renewable sources; however, it does not consider the role of energy storage systems. These findings highlight the potential of hybrid systems to meet the growing demand for efficient, resilient, and environmentally sustainable energy solutions.

In response to the challenges of climate change and the depletion of fossil fuel resources, there has been a growing emphasis on the development and implementation of sustainable energy systems [19]. The integration of renewable energy and energy storage technologies has emerged as a promising solution to meet the rising demand for clean and reliable power supply [20]. Hybrid systems, which combine various energy sources such as solar PV, FCs, and conventional generators, offer a viable pathway to achieve sustainable and efficient energy generation [21,22].

The goal of this paper is to present a comprehensive and systematic review of the latest developments in hybrid systems, with a particular focus on their design, optimization, and real-world applications. This review adopts an organized approach to analyze recent literature on hybrid energy systems. The studies are categorized based on the types of energy sources used, such as solar PV, fuel cells, and batteries; the primary operational goals, including cost reduction, emission mitigation, and reliability enhancement; and the main areas of application, such as MGs, healthcare facilities, and commercial buildings. The selection of papers was based on their relevance to hybrid energy systems, clarity in methodology, and overall scientific contribution to the field.

This review builds upon existing literature by introducing a classification framework that organizes current research across diverse HES contexts. It also provides a critical assessment of recent optimization strategies in EMS, with particular attention to multi-objective methods addressing environmental, economic, and operational criteria. By combining structured classification with targeted evaluation, this work aims to support a more integrated understanding of hybrid system design and implementation.

Figure 1 illustrates the generalized architecture of an HES integrated with a utility grid, serving as a conceptual reference for the systems analyzed in this paper. By bridging the gap between theoretical innovation and practical deployment, this review not only consolidates existing knowledge but also offers a roadmap for future research directions in HES and EMS development.

The structure of the paper is as follows. Section 2 presents the problem formulation, outlining key design considerations and providing a comparative analysis of on-grid and off-grid systems. Section 3 provides a comprehensive state-of-the-art literature review, covering challenges in hybrid systems, their applicability across various domains, optimization techniques, and control strategies. Section 4 focuses specifically on optimization approaches used to improve HES’s performance. Section 5 explores grid control strategies with an emphasis on integration, scalability, and practical considerations. Section 6 discusses recent developments and remaining challenges in renewable energy systems (RE systems) and EMSs, emphasizing their role in the transition to sustainable energy solutions. Finally, Section 7 concludes the paper by summarizing the main findings and identifying potential directions for future research.

2. Problem Formulation

This section addresses the challenges in designing energy systems, focusing on system architecture, life cycle analysis (LCA), cost of energy (CoE), and optimal sizing.

It covers case studies and research on integrating multiple energy sources, optimizing energy EMS, and enhancing the efficiency, reliability, and sustainability of energy systems. The subsections discuss key design considerations, comparison of on-grid and off-grid systems, energy resources, control mechanisms, objective functions (OFs), and optimization techniques.

2.1. Design Considerations

2.1.1. Integration of Multiple Energy Sources

Integrating RESs like solar PV and wind with conventional sources such as FC, diesel generators, and the grid presents several challenges, including system compatibility, energy management, and balance of supply and demand. System compatibility issues arise due to the variable outputs of RESs, which can cause voltage fluctuations and harmonic distortions, leading to over-voltage conditions when distributed generation exceeds local consumption [23,24]. Effective energy management involves real-time forecasting and control to optimize production, minimize curtailment, and maintain stability. Balancing supply and demand is particularly challenging due to the intermittency of RESs, which necessitates that storage systems store excess energy and discharge it when needed [25]. Moreover, grid integration of RESs introduces stability concerns, necessitating advanced management systems and possible infrastructure adjustments to handle fluctuations in power generation and consumption.

2.1.2. Sizing and Optimization

Determining the optimal size of the energy generation and storage components is crucial for efficient, reliable, and cost-effective energy systems [26]. Optimization algorithms are needed to balance competing objectives, such as maximizing energy production, minimizing costs, and reducing environmental impact while avoiding energy shortages or blackouts. A known challenge in optimization algorithms is their computational complexity, which can hinder their ability to run in real time, especially for large-scale systems with numerous variables. This limitation complicates implementation in dynamic environments where rapid decision-making is essential for maintaining grid stability and energy reliability, as noted in several studies [27,28,29,30].

2.1.3. Life Cycle Analysis (LCA)

Evaluating the environmental impact of energy systems throughout their life cycle, from manufacturing to operation to disposal, is crucial for ensuring sustainability. LCA is a method used to assess the environmental aspects and potential impacts of a product or system over its entire life cycle. It considers all stages, including raw material extraction, production, use, and disposal, thereby providing a comprehensive evaluation of the environmental footprint. The LCA process typically involves four main phases, as follows: goal and scope definition, inventory analysis, impact assessment, and interpretation [31,32]. Key studies [33,34,35] have explored the application of LCA in RE systems, shedding light on how to optimize the life cycle of energy components. By pinpointing the stages with the highest environmental impacts, LCA informs better decision-making in system design and operation, ultimately helping to minimize carbon emissions, resource depletion, and waste generation. This approach is especially useful for comparing the environmental sustainability of different energy systems, guiding the selection of the most eco-friendly options [36].

2.1.4. Cost of Energy (CoE)

The CoE is a critical factor in the design of energy systems, accounting for installation, maintenance, and operational costs. Balancing the high initial investment with long-term economic benefits, such as reduced fuel consumption and increased renewable energy self-consumption, is essential for achieving economic viability. A significant challenge in this regard is the integration of intermittent energy sources like solar or wind, which often require backup systems, thus increasing costs. However, optimizing the CoE involves minimizing the operational costs and reducing dependency on fossil fuels. Advances in system scaling and hybrid energy solutions can lower the CoE by improving overall system efficiency and utilizing economies of scale. In addition, financial support mechanisms such as subsidies and incentives play a crucial role in mitigating initial cost burdens. One study demonstrated that integrating multiple renewable sources into a hybrid system can significantly reduce the CoE by enhancing system reliability and minimizing backup requirements [37]. Another study examined the economic impacts of HESsin developing regions, noting that the integration of solar and wind could lower CoE, especially when backed by supportive policies and incentives [38]. Finally, a study projected that economies of scale and hybrid systems would be key to reducing CoE over time [39].

2.1.5. System Architecture

Designing the architecture of an energy system involves considering components for energy generation, storage, conversion, and control. This process applies not only to systems designed from scratch but also to microgrids integrating existing components, such as RESs (solar, wind), ESSs (batteries, hydrogen), and grid connections [40,41]. The architecture of such systems often focuses on optimizing performance, ensuring efficient energy flow, and enabling scalable and reliable operation [42,43]. Furthermore, when integrating pre-existing systems, challenges related to compatibility, control strategies, and communication protocols need to be addressed to maintain seamless operation and future growth potential [44].

2.2. Comparison and Analysis of ON-GRID and OFF-GRID Systems

Energy systems can be broadly categorized into ON-GRID and OFF-GRID systems, a distinction that is widely discussed in the literature [45]. This classification highlights the distinct characteristics of each system and its respective optimization strategies. ON-GRID systems are typically connected to the national grid, allowing for bidirectional energy flow, while OFF-GRID systems are independent and designed to operate without any grid connection, often incorporating energy storage solutions for reliability and autonomy. This section provides an overview of these systems, their characteristics, and their optimization techniques, as discussed in the literature [46]. It also includes an analysis of their advantages and constraints, as explored in the research [47].

2.2.1. Characteristics of ON-GRID and OFF-GRID Systems

ON-GRID systems: These systems rely on the utility grid for power, which can be drawn when generation is insufficient, and excess energy can be fed back into the grid. They are designed to ensure grid stability with lower energy storage requirements.

OFF-GRID systems: Operating independently, OFF-GRID systems rely on RESs like solar PV, wind turbines, or fuel cells for electricity generation. They typically incorporate storage solutions to ensure a continuous power supply during periods of low generation.

2.2.2. Energy Resources and Control Mechanisms

Energy resources: ON-GRID systems rely primarily on the utility grid but can incorporate RES like solar PV or wind turbines to reduce dependence on the grid [48]. OFF-GRID systems, on the other hand, depend on RES such as solar, wind, or FC as their main sources of energy. Backup generators or batteries may be used in off-grid systems to handle periods of low renewable generation [49].

Control mechanisms: ON-GRID systems utilize smart meters and inverters to regulate the flow of electricity between the system and the grid. Grid operators may use demand response programs to manage electricity consumption during peak hours. OFF-GRID systems require intelligent EMS to optimize energy production, storage, and distribution to ensure a steady electricity supply and efficient energy flow.

2.2.3. Objective Functions and Optimization Techniques

Objective functions (OFs): ON-GRID systems aim to reduce electricity consumption costs [50], increase self-consumption of renewable energy, and ensure grid stability [51]. OFF-GRID systems focus on energy reliability, self-sufficiency, and minimizing dependency on external energy sources [52].

Optimization techniques:

ON-GRID: Optimization methods include demand response algorithms, load management approaches, and economic dispatch models. Demand response algorithms optimize electricity consumption by adjusting demand in response to grid conditions, particularly during peak hours, to reduce electricity costs and maintain grid stability [53]. Load management approaches involve controlling or shifting energy demand to prevent grid overloads and reduce peak demand, which enhances system efficiency and grid reliability [54]. Lastly, economic dispatch models are used to determine the optimal mix of power generation to minimize costs while ensuring system reliability, thus balancing generation from both conventional and renewable sources [55].

OFF-GRID: OFF-GRID systems use optimization techniques such as dynamic programming algorithms (DPAs), genetic algorithms (GAs), particle swarm optimization (PSO), and fuzzy logic (FL) to maximize energy management and storage system sizing. DPAs are used to find optimal solutions by breaking down complex problems into simpler subproblems, often applied in storage sizing and load management [56], but it is also utilized in ON-GRID systems [57,58]. GAs mimic natural selection to optimize system parameters by iterating through possible solutions and selecting the best fit, commonly used for hybrid system design [13]. PSO simulates social behavior to find optimal solutions by adjusting particle positions based on their own and neighbors’ experiences, widely applied in RE system optimization [11]. FL uses linguistic variables and rules to handle uncertainty in decision-making processes for energy management, improving efficiency in systems with unpredictable load and generation patterns [8].

2.2.4. Analysis of ON-GRID and OFF-GRID Systems

ON-GRID Systems:

Strengths: ON-GRID systems offer a reliable power supply and contribute to grid stability [51,52], with reduced dependence on energy storage due to continuous access to the utility grid. They are capable of integrating various energy sources, including renewable technologies, and can deliver significant economic advantages [38,47,53].

Weaknesses: Despite their advantages, ON-GRID systems are dependent on the grid, limiting energy independence [45]. They also have a higher environmental impact due to reliance on conventional power sources and are subject to grid regulations that may affect operational flexibility [12,47].

OFF-GRID systems:

Strengths: OFF-GRID systems provide energy independence and environmental benefits, as they primarily rely on RESs [36,45]. They are ideal for remote applications where grid access is unavailable, and can reduce the need for extensive grid infrastructure [59].

Weaknesses: However, OFF-GRID systems typically require higher initial investment [60] and are more complex to manage, particularly in terms of energy storage [25]. They face challenges in energy storage sizing and integration, and their scalability is often limited due to the need for sophisticated control systems [21,22].

The choice between ON-GRID and OFF-GRID energy systems depends on the specific application, location, and goals. ON-GRID systems offer grid stability but may have environmental drawbacks, while OFF-GRID systems provide environmental benefits and energy independence but require higher upfront costs and more complex management. For an optimal solution, hybrid systems that combine the advantages of both can be considered. A summary of ON-GRID and OFF-GRID systems is presented in

Table 1. Summary of ON-GRID and OFF-GRID systems.

Characteristic	ON-GRID	OFF-GRID
Grid connection	Yes [45]	No [45]
Power supply	Dependent on grid [45]	Independent of grid [45]
Energy storage needs	Lower [47]	Higher [47]
Integration of multiple energy sources	Yes [21,22]	Yes [21,22]
Economic benefits	Potential revenue from net metering and feed-in tariffs [38]	No revenue from selling excess energy [38]
Energy independence	Limited [45]	Complete [45]
Environmental benefits	Varies depending on energy sources used [36]	Typically higher [36]
Remote applications	Suitable [59]	Ideal [59]
Grid infrastructure costs	Higher [60]	-
Initial investment	Lower [60]	Higher [60]
Grid support	Available [45]	Limited [45]

.

3. State-of-the-Art Literature Review

This section provides a comprehensive overview of the current state of research on HESs. It synthesizes key studies and emerging trends, offering insights into the challenges, applications, optimization techniques, and control strategies associated with these systems. The literature is organized into distinct subsections that address specific aspects of HESs, including grid integration, energy storage, resource management, cost scalability, and recent advancements in optimization algorithms and control strategies.

The article selection process, illustrated in the form of a flowchart, began with the identification of 360 records through Google Scholar, ScienceDirect, and Scopus. After removing duplicates and irrelevant entries, 310 records were screened based on their titles and abstracts. Of these, 180 were excluded due to irrelevance, language barriers, or accessibility issues. The remaining 130 full-text articles were assessed for eligibility, resulting in the exclusion of 70 studies for reasons such as outdated content or insufficient detail. Ultimately, 60 studies were included in this review. The complete selection process is depicted in Figure 2.

3.1. Challenges in HES

HESs face several technical and operational challenges that must be addressed to improve their effectiveness, reliability, and scalability.

3.1.1. Grid Integration and Stability

Integrating RESs like solar, wind, and hydropower into existing grids presents challenges such as voltage and frequency instability due to the intermittency of power generation. Zhang et al. [8] and Ghenai et al. [12] emphasized the complexities in balancing supply and demand, frequency regulation, and preventing grid blackouts. Real-time data and forecasting tools are crucial but limited by computational constraints. In hybrid microgrids, stability relies on coordinated control layers, as discussed by Jain et al. [61], who reviewed hierarchical control methods: primary, secondary, and tertiary layers. The primary layer regulates current or voltage; the secondary control optimizes power sharing and corrects errors; and the tertiary control manages power flow and energy coordination with the grid.

However, hybrid AC-DC microgrids require advanced coordination between AC and DC buses, especially for power-sharing and managing distributed energy storage systems. Further research is needed on scalability, extreme conditions, and cost-effectiveness of these control strategies. Jain et al. [61] highlighted that the inclusion of multiple interlinking converters (ILCs) enhances power-sharing in hybrid microgrids, but this may lead to system stability and reliability issues. These challenges can include circulating currents and increased costs. The article also emphasized that the reliability of communication infrastructure in microgrids, especially for distributed control systems, is a critical factor in ensuring the overall stability and performance of the system. This is consistent with findings in recent studies [62,63,64]. that emphasize the importance of communication reliability for the effective functioning of microgrids.

Recent studies have expanded the integration of hybrid RE systems beyond traditional configurations. Notably, floating PV-hydro hybrids have emerged as an innovative spatial synergy for renewable energy integration, especially in regions with limited land availability. These systems combine floating PV arrays with hydroelectric power, leveraging the complementary nature of solar and hydropower [65].

3.1.2. Energy Storage and Backup System Challenges

ESSs are essential for ensuring the reliability of HES, particularly under fluctuating generation and load conditions. However, they face multiple operational challenges beyond cost and degradation, such as limitations tied to the state of charge (SoC). For instance, batteries cannot absorb energy when fully charged or deliver power when deeply discharged, and their charge/discharge rates are constrained by C-rate and voltage dynamics [11,14]. Additionally, energy storage is not limited to batteries; technologies like supercapacitors [66,67,68] and flywheels [69,70,71,72] also support stability and power quality in microgrids. Rodriguez et al. [73] proposed using historical outage data to optimize fuel cell backup sizing for low-voltage buildings, offering a cost-effective solution in specific regions. However, comparing this with other backup systems could provide more reliable, scalable, and cost-efficient alternatives.

3.1.3. Resource Variability, Forecasting, and Optimized Management

Hybrid systems depend on complex algorithms to manage energy availability from renewable sources, but the inherent variability of these resources makes accurate forecasting challenging. Studies like those by Ferahtia et al. [74] and Vivas et al. [75] have explored forecasting models, but discrepancies between forecasted and actual power generation often lead to inefficiencies, exacerbated by inconsistent weather patterns and changes in demand. Lazos et al. [17] also showed that predictive weather data improve energy optimization and cost savings in commercial buildings by enhancing HVAC management. However, their study could benefit from a comparison of forecasting techniques, such as machine learning models, and more practical case studies. Additionally, Cingoz et al. [76] proposed an optimized resource management strategy for microgrids with PV systems and FC to improve energy efficiency through better load matching. Still, it does not address the economic feasibility, long-term operational costs, or scalability of integrating and maintaining multiple energy sources.

3.1.4. Cost, Scalability, and Energy Scheduling

Many studies focus on optimizing hybrid systems under idealized conditions, such as constant load profiles and optimal weather, which rarely reflect real-world applications. Zia et al. [77] highlighted the challenge of scaling hybrid systems for larger populations and urban environments, especially with the complexity of integrating various energy sources like solar, wind, and hydrogen. In some coastal or island scenarios, additional renewable sources such as tidal turbines are also considered as part of hybrid configurations. Furthermore, second-life battery applications have emerged as a promising solution for improving cost-effectiveness and sustainability in large-scale systems, as discussed in recent studies on battery re-purposing [78]. Additionally, Wang et al. [79] explored energy scheduling for hybrid FC-PV-battery systems using stochastic dynamic programming, optimizing scheduling to manage erratic demand and variable renewable energy production. However, these studies’ reliance on idealized assumptions limits their applicability to practical, large-scale systems, where dynamic variables and uncertainties are more prevalent.

3.1.5. Socio-Technical Challenges

Addressing socio-technical challenges, such as user behavior and community-based energy sharing, is crucial to the success of HESs. User behavior modeling, as outlined in [80], plays a pivotal role in understanding how individuals’ energy consumption patterns influence system performance. Additionally, recent research on community-based energy sharing in [81] offers a bridge between theory and practice, ensuring the equitable distribution of energy in decentralized systems.

3.2. Applicability of Hybrid Energy Systems

Hybrid energy systems can be applied to various sectors, each with unique requirements. Their applicability depends on the technological solutions selected, economic constraints, and environmental factors.

3.2.1. Residential and Commercial Applications

In residential and commercial settings, hybrid systems are seen as a viable means to reduce energy bills, increase energy independence, and promote environmental sustainability. Zhang et al. [8] and Vivas et al. [75] presented case studies where hybrid systems, including solar PV and battery storage, have been implemented successfully in residential areas, showing significant potential for energy savings. However, the high initial costs associated with installing these systems and the need for long-term maintenance remain barriers to widespread adoption. Moreover, systems must be sized appropriately to meet varying loads, which requires a detailed analysis of local energy consumption patterns.

3.2.2. Healthcare and Critical Infrastructure

Hybrid systems have proven useful in ensuring a continuous power supply to healthcare facilities, which require high levels of energy reliability. Several studies have examined hybrid systems for supporting critical infrastructure [9,82,83,84], highlighting their potential to enhance energy resilience. Among these, Izadi et al. [9] stand out for implementing a PV and hydrogen-based system to power COVID-19 hospital wards, providing grid-independent operation. This study is particularly notable due to the integration of hydrogen technology, which, while more advanced and complex, offers long-term storage and a lower carbon footprint. Hydrogen systems are well-documented for their potential to lower carbon emissions, especially when compared to conventional fossil-fuel-based power sources, resulting in a smaller overall carbon footprint [85]. However, such systems face technical challenges related to cost, control complexity, and seamless integration with other backup solutions.

3.2.3. Remote and Islanded Microgrids

Hybrid systems are increasingly being used to power remote and islanded microgrids, where access to the main grid is limited or unavailable. For example, studies by Zia et al. [77], Ghenai et al. [10], and Haddad et al. [86] discussed the use of wind, solar, and battery storage in off-grid communities, where these systems provide a reliable and sustainable energy solution. In such cases, hybrid systems can be designed to match local energy production and demand, improving local energy access. However, challenges such as maintaining energy reliability and reducing the dependence on diesel generators for backup remain significant obstacles to the widespread adoption of these systems. Furthermore, several additional studies [71,87,88,89,90,91] have explored innovative approaches to integrating RESs and storage systems in isolated regions, demonstrating successful implementations across various remote locations. Despite these advancements, challenges such as ensuring energy reliability and reducing reliance on diesel generators for backup power remain significant barriers to the widespread adoption of these systems.

3.2.4. Transportation and Maritime Applications

HESs are increasingly being adopted in maritime transport to address the sector’s high emissions and fuel consumption, particularly in port areas where environmental impact is most critical [92,93]. Ghenai et al. [7] discussed the use of PV and fuel cell systems in ships to reduce operational costs and emissions. Additional studies have explored battery and renewable energy integration in maritime vessels and port operations, demonstrating potential for improved energy efficiency and sustainability [94,95,96,97]. While these technologies offer promising results, challenges remain in terms of high implementation costs and meeting the large energy demands of ships.

3.3. Optimization Techniques and EMS for Hybrid Energy Systems

Optimization plays a vital role in the efficient operation and control of HESs. In particular, the EMS acts as the operational core, using various optimization methods to make real-time decisions regarding power generation, storage utilization, and energy dispatch. This subsection reviews the key optimization strategies implemented within EMS frameworks, with a focus on fuzzy logic control (FLC), metaheuristics, and hybrid optimization methods, setting the stage for a broader synthesis in Section 4.

3.3.1. Fuzzy Logic Control (FLC)

Zadeh [98] introduced fuzzy logic as a mathematical framework to model imprecise reasoning, which laid the foundation for FLC. Researchers have widely adopted FLC to manage energy flow in hybrid systems, particularly those involving PV and battery components. By applying fuzzy rules that mimic human decision-making, FLC effectively handles system uncertainties and nonlinear behavior. Zhang et al. [8], Fathy et al. [99], Rewea et al. [100], and Vivas et al. [75] demonstrated that FLC optimizes energy generation, storage, and consumption, enabling systems to maintain efficient performance even under variable conditions. However, tuning membership functions and fuzzy rules remains a major challenge, as it demands extensive computational effort and experimentation, especially in systems with multiple energy sources.

3.3.2. Metaheuristic Optimization Algorithms

Metaheuristics such as GA and PSO have been widely adopted to optimize the sizing and control parameters of HES. Maheri et al. [13] and Samy et al. [101] have applied GA and PSO to minimize system costs while improving efficiency and reliability. These techniques provide solutions to multi-objective problems involving conflicting goals (e.g., cost, reliability, emissions). However, these methods require large computational resources and can be sensitive to the selection of algorithm parameters, making them challenging to implement in real-time systems.

3.3.3. Hybrid Optimization Techniques

The integration of different optimization techniques, such as combining GA with PSO or using fuzzy-based approaches alongside evolutionary algorithms, offers the potential to address the shortcomings of individual methods. Krim et al. [14] and Khezri et al. [102] have demonstrated the effectiveness of hybrid optimization in balancing multiple objectives, such as minimizing both operational costs and environmental impact. These hybrid approaches can be particularly useful in large-scale systems where multiple variables must be optimized simultaneously.

Qamar et al. [6] proposed a hybrid particle swarm optimization–modified weight watcher optimization (PSO-MWWO) technique for energy management in hydrogen-based microgrids. Their approach focuses on optimizing cost reduction, fuel savings, and total harmonic distortion (THD) minimization. While this method improves the performance of traditional algorithms, it faces challenges, including high computational cost and a lack of real-time validation. This research highlights the benefits and limitations of hybrid optimization techniques in microgrid applications.

3.3.4. Advanced EMS Architectures and Strategies

EMS architectures have evolved to incorporate a variety of optimization and control strategies. These include the following:

Adaptive and Novel Algorithm-Based EMS:

Adaptive control and intelligent optimization techniques enhance EMS performance by adjusting control parameters in real-time. Fathy et al. [99] demonstrated adaptive controllers that respond to fluctuations in hybrid PV-wind systems, though these are limited by high computational requirements. To address such limitations, heuristic algorithms such as the manta ray foraging optimization algorithm (MRFOA) [15] have been introduced, allowing real-time EMS tuning in grid-connected PV systems for maximized energy output. Similarly, gray wolf optimization (GWO) has gained popularity due to its fast convergence and global search capability [103,104]. GWO-based EMS implementations have been used to optimize power flow and stabilize hybrid systems. While these algorithms show promising results, further research is needed for enhancing system resilience and ensuring real-time feasibility in extreme scenarios.

Transactive energy control (TEC):

TEC represents a decentralized EMS strategy, where energy transactions are guided by market-based signals and user-defined preferences. It enables prosumers (producer-consumers) to autonomously manage their generation, storage, and consumption through peer-to-peer energy trading and dynamic pricing mechanisms [105]. Alizadeh et al. [106] proposed a distributed TEC model to coordinate prosumers using decentralized negotiation, improving communication and scalability. Stephant et al. [107] employed game theory and the alternating direction method of multipliers (ADMM) for flexible control in energy communities. Despite these advancements, TEC implementation in large-scale systems requires further study, particularly regarding blockchain integration, cybersecurity, and regulatory constraints.

Microgrid EMS architectures with storage integration:

Modern EMS architectures often integrate diverse distributed energy resources (DERs), including PV systems, batteries, and hydrogen units. For instance, Zhang et al. [88] proposed an EMS for an islanded AC microgrid that coordinated a PV array, lithium-ion battery, hydrogen storage, and an FC. The EMS dynamically balances load and renewable availability, optimizing performance based on energy storage states. However, economic feasibility and the impacts of renewable fluctuations were not fully addressed. Meliani et al. [108] explored EMS integration of fluctuating DERs, improving efficiency through multi-objective control algorithms. Their work underscores the need for practical implementation and low-cost, real-time control hardware.

Blockchain-based EMS:

Blockchain technology is emerging as a tool for enhancing EMS architectures by enabling peer-to-peer energy trading within MGs. Recent research [109] showed that blockchain can facilitate decentralized energy transactions, providing transparency, security, and automation. By integrating blockchain into EMS architectures, microgrids can offer flexible energy trading solutions among local users, optimizing energy use and promoting grid stability.

Fuel cell and hydrogen integration within EMS:

FCs enhance system reliability by providing dispatchable power in HES. EMS strategies must handle complex interactions between FCs, BTs, and renewables while optimizing hydrogen production and consumption. Zia et al. [16] and Ghenai et al. [10] modeled EMS frameworks incorporating fuel cells, highlighting challenges in fuel cell efficiency, cost, and maintenance. Effective control strategies are necessary to ensure the sustainable integration of hydrogen technologies within EMS frameworks.

This subsection has outlined key optimization methods and control strategies within EMS. A broader comparison of these and other approaches, including artificial intelligence (AI) and iterative techniques, is provided in Section 4, highlighting their respective strengths and limitations in optimizing HES.

Table 2. Summary of the reviewed papers with a focus on their objectives, optimization methods, and achievements.

Research Article	PV	FC	WT/TT	ESS	Diesel Generator	ON-GRID/OFF-GRID	Single/Multi OF	Optimization Method	Design Constraints/Objectives	Desired Outcomes	Limitations/Weaknesses
[6]	✓	✓	WT	✓		ON-GRID	Single	PSO-MWWO hybrid optimization algorithm	C1. Voltage and frequency regulation C2. Power balance C3. RES uncertainty C4. Component constraints (e.g., SoC, capacity)	1. Minimize cost and fuel consumption 2. Improve power quality (THD reduction) 3. Optimize HESS sizing and operation	1. High computational complexity 2. Limited scalability assessment
[7]	✓	✓			✓	OFF-GRID	Single	Search space	C1. Power system components capacity O1. Dispatch strategy (load following or cycle charging)	1. Performance 2. Life cost of electricity	Limited scalability to larger systems; assumes static load profiles.
[8]	✓			✓		ON-GRID		Fuzzy logic	C1. The rules of the electricity bill O1. Operating mode of different time slots (peak, off-peak, and off days)	1. To reduce: a. Electricity bill b. CO2 emissions of commercial building 2. CoE	Limited representation of: a. Dynamic load demand b. renewable energy variability.
[9]	✓	✓		✓		ON-GRID	Multi	NNGA	O1. Lowest cost rate O2. Minimum CO2 emissions O3. Least power from the grid O4. Highest O2 production	Optimum sizing of: a. PV panels b. Electrolyzer c. Fuel-cell	1. Computational complexity 2. Potential overfitting in NNGA
[10]	✓	✓		✓	✓	OFF-GRID	Single	Search space	C1. Distributed power generation system capacities O1. CoE O2. Net present cost	1. Low CoE 2. Meets the daily and annual AC primary load of the building 3. Low greenhouse gas emissions	1. Assumes idealized conditions 2. Lacks robustness to demand variation
[11]	✓			✓		Both	Single	PSO	O1. Total output energy of PV and Battery ESS O2. Capital cost O3. Replacement cost O4. Operation cost O5. Maintenance cost C1. Total output load	1. Minimum cost 2. Optimum PV and Battery ESS size	PSO can converge prematurely in complex multi-modal problems
[12]	✓	✓				ON-GRID	Single	Search Space	C1. Power system components capacity C2. Daily power load requirement for the building	1. To increase the penetration of renewable energy 2. Low-levelized CoE 3. Low CO2 emissions	Simplistic modeling of grid interaction and component dynamics
[13]	✓	✓	WT	✓	✓	Both	Both	GA and NSGA-II	O1. Feed-in and profit O2. Penetration and CO2 Emission O3. Present Value of TLSC	Optimum size and configuration: a. Number of WTs b. Turbine rotor radius c. Total size of the PV panels d. Number of batteries e. Nominal power of the diesel generator, FC, and electrolyzer.	Computational time increases with system size and objectives
[14]	✓			✓		ON-GRID	Single	Fuzzy logic	O1. Identify the parameters of the MF of FL-EMS O2. Optimize the sizing of BT	Decrease the levelized CoE	Focuses only on BT optimization; limited insights for broader systems.
[15]	✓					ON-GRID	Single	MRFO	O1.The gain of the PI controller for the DC-DC and DC-AC converters	1. To achieve smooth power quality 2. Improve performance of the grid-connected PV system	Applicability limited to specific PV systems; lacks real-world testing
[16]	✓		TT	✓	✓	OFF-GRID	Single	Primal-dual interior point algorithm in Python 3.8 using the GEKKO package	O1. Operating cost of DG O2. LCOEs of PV and tidal marine turbine systems O3. Battery degradation cost O4. Load shedding cost	To reduce operating and maintenance costs	High reliance on accurate modeling of tidal and PV resources
[74]	✓	✓	WT	✓	Gas Turbine	ON-GRID	Single	MPA	C1. The provided power must be equal to the load power C2. DG unit capacity O1. DGs operating cost	1. To enhance the performance 2. Reduces the daily operating cost	1. High computational demand 2. Weather dependency for WT systems
[75]		✓		✓		ON-GRID	Multi	Fuzzy logic	C1. Power balance C2. Energy forecast C3. SoC C4. HESS degradation and efficiency	1. Ensuring power balance 2. Conservative use of HESS 3. O&M cost reduction 4. Bus voltage control 5. Reduction of energy losses	1. Dependence on precise forecasting 2. Degradation model accuracy
[77]	✓		✓	✓		OFF-GRID DC	Single	MILP	C1. Power from each RES is within the allowable thresholds as forecasted. C2. SoC C3. Total power demand C4. Power is balanced C5. Losses in the system	1. Minimizing operation cost 2. Supply and demand balance 3. Battery lifetime improvement 4. Maximum utilization of RESs	1. High dependency on accurate forecasts 2. Simplified assumptions about RES availability
[79]	✓	✓		✓		OFF-GRID	Single	Rule-based and SDP	C1. Magnitude and rate of the FC and battery operation C2. Characteristics of the PV C3. Uncertain PV output into account in the actual system operation scheduling	1. Optimal EMS 2. Ensure the robustness and effectiveness of the SDP algorithm	Lack of scalability for larger and more complex systems
[81]	✓			✓		ON-GRID	Single	MILP-based techno-economic model Static and dynamic energy sharing modeling	C1. No remuneration for excess energy to users C2. SOC limits, charge/discharge limits	1. Increase self-consumption of community PV generation 2. Reduce electricity costs 3. Assess static vs. dynamic sharing schemes	1. Relies on static load and generation profiles. 2. The optimization method (LP) may not handle nonlinear system dynamics effectively.
[99]	✓	✓		✓		ON-GRID	Single	Optimal adaptive FLC-EMS with eight different optimizers (PSO, SSA, AOA, MPA, AEO, EO, PO, and TSA)	C1. SoC C2. The net power O1. Fuzzy MFs are optimized using one of the proposed algorithms to enhance the EMS performance	To enhance the system’s power saving	Limited comparison with non-fuzzy optimization approaches
[101]	✓	✓	WT			ON-GRID	Single	Hybrid firefly/harmony search algorithm (HFA/HS) and PSO	C1. Loss of power supply probability (LPSP)	1. Optimal size of PVs, WTs, hydrogen tanks, FCs, and electrolyzers 2. Minimizing the total net present cost with a specific quantity of LPSP	1. Limited scalability 2. Reliance on heuristic tuning parameters.

provides a comprehensive summary of the key studies reviewed in this section. It focuses on their objectives, optimization methods, achievements, and limitations, offering a clear comparison of the various HESs discussed. This overview serves as a foundation for identifying the existing research gaps, which will be addressed in the subsequent section.

Figure 3 presents a matrix-style representation summarizing the key objectives and optimization methods used in the reviewed studies. Each cell contains the reference numbers corresponding to studies that address a specific combination of objective and optimization techniques. It visually highlights the prevalence of goals such as CoE reduction, emission minimization, and system performance, along with commonly applied techniques like FL, GA, and PSO. This graphical overview helps identify patterns and trends, showing how recent research increasingly integrates multiple objectives and advanced optimization approaches to enhance HES performance.

As summarized in Table 2, the reviewed studies highlight various approaches to optimizing HESs, their respective achievements, and the limitations encountered. The comparison of these methods sheds light on the current state of research, offering valuable insights into areas that require further exploration. The research gaps arising from the limitations identified in these studies will be discussed in the following section, providing direction for future work in this field.

3.4. Synthesis and Research Gaps

Despite significant advancements in HESs, several research gaps remain:

3.4.1. Real-World Testing and Long-Term Performance

Much of the literature is based on simulations or small-scale case studies, which limits the applicability of the findings to large-scale, real-world systems. More research is needed to conduct long-term performance evaluations, defined here as multi-year assessments (typically 3–5 years), and real-world testing, particularly for residential, commercial, and healthcare applications. This extended evaluation horizon provides a more comprehensive understanding of system durability, economic feasibility, and environmental impact, aligning with recent studies such as [110,111].

3.4.2. Hybrid Optimization Algorithms

While multi-objective optimization techniques have shown promise, more research is needed to refine hybrid optimization approaches that can handle complex, real-time decision-making in large-scale systems. Further work on reducing the computational burden of these algorithms is essential for their practical implementation.

3.4.3. Energy Storage Technologies

Research into new energy storage technologies, such as solid-state batteries, hydrogen storage, and pumped hydro storage, could greatly improve the scalability and cost-effectiveness of hybrid systems. Additionally, addressing the degradation and lifespan issues of existing storage technologies is critical for ensuring their long-term viability.

3.4.4. Advanced Control Strategies

Adaptive and FLC strategies are useful for managing hybrid systems, but more work is needed to develop scalable, robust control systems that can handle the complexity of large hybrid systems with multiple energy sources and storage options.

4. Optimization Techniques for Hybrid Energy Systems

Optimization techniques play a pivotal role in the design, sizing, and operational control of HESs, facilitating the efficient utilization of energy resources while meeting objectives such as cost minimization, emissions reduction, and improved reliability. This section offers a structured synthesis of major optimization strategies, broadly categorized into AI-based methods and Iterative techniques.

Although Section 3.3 concentrates on the practical implementation of optimization algorithms within EMS, particularly in real-time control and decision making, this section provides a deeper examination of the foundational optimization approaches themselves. It evaluates their theoretical foundations, comparative advantages, and limitations in optimization tasks, including component sizing, forecasting, and control coordination. By highlighting the suitability of each technique under varying conditions, such as system scale, complexity, and component interactions, this section aims to guide both researchers and practitioners in selecting the most appropriate optimization methods tailored to their specific hybrid system configurations and performance goals.

4.1. Artificial Intelligence Techniques

AI-based methods, including GA, PSO, reinforcement learning (RL), and others, have become increasingly popular for optimizing HESs due to their ability to handle complex, nonlinear, and dynamic systems. These methods are particularly effective when real-time data and adaptive learning are required, such as in energy scheduling and load management in systems with variable renewable generation. AI techniques excel in optimizing multiple objectives, like reducing operational costs and emissions, but they can suffer from high computational demands, sensitivity to initial conditions, and, in some cases, difficulty in scaling to very large systems.

4.1.1. Genetic Algorithm (GA)

The GA is a search heuristic inspired by the process of natural selection. It is commonly used for solving multi-objective problems such as minimizing fuel consumption and emissions in HESs. GA operates by generating a population of potential solutions and iteratively evolving them through selection, crossover, and mutation to find optimal solutions [112]. In the context of HESs, GA has been employed for system design and optimization, especially in scenarios involving multiple conflicting objectives such as cost, emissions, and reliability [13]. For instance, GA was used in the optimal sizing and design of a hybrid solar-wind-battery system for a remote island, balancing economic and technical constraints [113]. However, one limitation of GA is its computational cost when dealing with large search spaces or highly constrained systems.

4.1.2. Harmony Search (HS)

HS is an optimization algorithm inspired by the process of musical improvisation. It generates new solutions by mimicking the process of musicians searching for a harmonious sound. This method is valued for its simplicity and fast convergence, making it ideal for smaller systems with minimal parameter tuning. However, it often converges prematurely to suboptimal solutions in complex landscapes, limiting its use in large-scale systems. Samy et al. [101] applied HS to optimize HESs in remote areas, achieving notable cost reductions, though premature convergence remains a concern.

4.1.3. Particle Swarm Optimization (PSO)

PSO is inspired by the social behavior of birds flocking or fish schooling. It is effective for optimization problems where derivative information is unavailable. PSO updates the position of particles (potential solutions) based on their own experiences and the experiences of their neighbors. In HESs, PSO is used for optimizing parameters like energy costs and renewable energy penetration. Despite its adaptability, PSO is sensitive to the stochastic nature of its results and can lead to longer computation times [11].

4.1.4. Reinforcement Learning (RL)

RL is a type of machine learning in which an agent learns to make decisions through trial and error by interacting with its environment. In the context of HESs, RL is particularly useful for tasks such as energy scheduling and balancing grid demand with renewable generation. However, RL techniques are often computationally intensive and require large datasets for effective training, which can pose challenges in real-time applications or in systems with limited data availability [114]. Once trained, RL models can significantly enhance system performance by providing adaptive and data-driven strategies. This makes RL particularly valuable for managing dynamic ESSs, where conditions fluctuate rapidly and decisions must be robust to uncertainty. For example, Pinthurat et al. [115] demonstrated the application of RL for real-time ESS optimization, where a policy learned during the design phase is deployed to improve energy storage decisions in dynamic scenarios. Thus, while RL may not be ideal for real-time learning due to its computational demands, it remains a powerful tool for offline design optimization and policy development, offering high adaptability and long-term performance benefits in complex hybrid systems.

4.1.5. Simulated Annealing (SA)

SA is a stochastic optimization technique known for its robustness in solving nonlinear and multimodal problems. It statistically guarantees convergence to a global optimum under appropriate cooling schedules and sufficient iterations [116]. In the context of HESs, SA has been effectively employed during the design phase for tasks such as optimizing energy management strategies under various demand scenarios, allowing for the development of control policies that balance cost and reliability [117]. While these policies may later guide real-time decisions, the optimization process itself occurs offline. One of the main limitations of SA is its sensitivity to the choice of initial parameters and temperature schedules, which can affect convergence quality and computational efficiency.

4.2. Iterative Methods

Iterative methods focus on refining solutions by minimizing or maximizing an objective function while adhering to system constraints. These methods are particularly useful when incremental adjustments are needed in response to fluctuating energy demands or system performance. Unlike AI techniques, iterative methods tend to be less computationally demanding and are better suited for systems with well-defined optimization objectives and constraints [118,119].

4.2.1. Conjugate Gradient (CG) Method

The CG method is efficient for large-scale optimization problems, particularly those involving distributed generation or complex networks in hybrid systems. It offers faster convergence compared to gradient-based methods like gradient descent, making it highly effective for optimizing power flow in hybrid microgrids [120]. However, its applicability is limited when dealing with non-differentiable functions or highly complex optimization landscapes.

4.2.2. Gradient Descent (GD)

GD is widely used for continuous, differentiable objective functions, and its simplicity makes it easy to implement in energy dispatch problems. It works well for optimizing systems with fewer variables but can struggle with complex, high-dimensional systems due to its tendency to converge slowly and become stuck in local minima [121]. This makes it less suitable for highly dynamic or nonlinear systems without careful tuning.

4.2.3. Newton–Raphson (N-R) Method

The N-R method is a faster converging optimization technique compared to gradient descent, particularly for smooth, continuous functions. It has been applied effectively to power flow problems in hybrid microgrids [122]. However, it requires accurate second-order derivatives and complex implementation, which can make it less scalable and more challenging to apply in large-scale or nonlinear systems.

4.3. Synthesis of Optimization Techniques for HESs

Optimizing HESs requires selecting the appropriate method based on the complexity of the system, the nature of the objective functions, and the desired outcomes. Various techniques, ranging from AI-based methods to classical iterative approaches, offer unique advantages and face specific challenges in their implementation. This synthesis highlights the strengths, limitations, and applicability of different optimization methods, providing a comprehensive overview for researchers and practitioners.

Table 3. Synthesis of optimization techniques for HESs.

Optimization Method	Advantages	Disadvantages	Challenges Addressed	Applicability	Optimization Strategy	Cited Articles
Genetic Algorithm (GA)	Global search capability Effective for multi-objective problems	High computational cost Stagnation at local minima	Nonlinear systems Multi-objective optimization	HES design PV-battery optimization	Evolutionary process through selection, crossover, mutation	[9,13,14]
Harmony search (HS)	Simple implementation Fast convergence	Premature convergence Limited scalability	Small to medium-sized systems	Remote area microgrids Cost minimization	Musical improvisation-inspired optimization technique	[101]
Particle swarm optimization (PSO)	Fast convergence No derivative requirement	Result variability Susceptible to local optima	Dynamic energy management Real-time optimization	PV-H2-Grid systems Load demand response	Swarm-based optimization using cognitive and social behavior	[11,102]
Reinforcement learning (RL)	Adaptive learning Effective for dynamic environments	High data requirements Computationally intensive	Real-time energy scheduling Dynamic load balancing	Smart grids Energy trading	Policy optimization through trial-and-error interaction	[114,115]
Simulated annealing (SA)	Escapes local optima Suitable for nonlinear systems	Slow convergence Sensitive to temperature schedules	Discrete decision-making Cost vs. reliability trade-off	Microgrid optimization Network reconfiguration	Probabilistic global search with temperature control	[117]
Conjugate gradient (CG)	Fast for large-scale systems Efficient convergence	Limited to differentiable functions	Power flow optimization Distributed generation	Large-scale hybrid systems	Gradient-based iterative optimization	[120]
Gradient descent (GD)	Simple implementation Effective for small systems	Slow convergence Trapped in local minima	Continuous optimization Convex problems	Energy dispatch Resource allocation	Gradient-based iterative improvement	[121]
Newton–Raphson (N-R)	Rapid convergence Accurate for smooth functions	Complex implementation Requires second-order derivatives	Power flow analysis Voltage stability	Hybrid microgrid power optimization	Iterative root-finding and system analysis	[122]

presents a comparative analysis of prominent optimization techniques used in HESs, facilitating the selection of the most suitable method for specific system configurations and operational goals.

5. Grid Control Strategies: Integration, Scalability, and Practical Insights

Grid control strategies are central to ensuring the efficient and reliable operation of modern power grids, particularly when integrating HESs. These systems, which combine RESs (such as PV and wind), energy storage, and grid interaction, require sophisticated control techniques to optimize energy flow, address challenges related to intermittency, and ensure grid stability [123]. This section synthesizes the various control strategies used across different hybrid systems, such as PV-battery, PV-hydrogen-grid, and others, and discusses how these strategies address common challenges, their trade-offs, and effectiveness in real-world applications. Additionally, it identifies gaps and areas for further improvement in grid control strategies.

5.1. Integration of Hybrid System with Grid Control Strategies

Integrating modern hybrid systems into existing grid infrastructures presents significant challenges, particularly in ensuring that new technologies interface seamlessly with legacy systems. Many existing grid components, including both hardware and software, were not originally designed to accommodate RE systems and advanced energy storage solutions. Therefore, effective integration requires the development of control strategies that minimize disruption while accommodating new energy technologies.

To achieve this, modular designs and adaptive algorithms are crucial. These strategies allow for scalable and flexible integration, supporting the increasing penetration of renewable energy and the need for dynamic energy storage management. Real-time data analytics, advanced grid sensors, and energy storage systems are essential tools for monitoring, controlling, and optimizing energy flows, thereby ensuring smooth operation during the integration process [124]. While grid integration challenges are also discussed in Section 3.1.1, here the emphasis is on the broader systems-level approach required for integrating complex hybrid systems into existing infrastructure.

5.2. Scalability of Control Systems

Scalability is a key consideration for grid control systems, as HESs often need to expand over time to accommodate the increasing energy demands or to incorporate new technologies. As energy systems become more complex, control strategies must be adaptable to handle an increasing number of distributed energy resources, storage systems, and variable loads.

Techniques such as modular design and flexible architectures allow for the scalable deployment of control systems. In the case of PV-battery systems, scalability can be achieved by adding more battery storage units or PV panels. For PV-hydrogen-grid systems, scalability involves not just increasing renewable generation but also expanding hydrogen production capacity and storage. Adaptive algorithms and predictive analytics further enhance scalability by enabling systems to adjust in real-time to changing grid conditions and optimizing energy flows [125,126].

One of the key challenges with scalability is maintaining system responsiveness as the complexity of the grid increases. Ensuring that control systems can adapt without sacrificing performance or efficiency remains a significant area for improvement.

5.3. Control Techniques and Their Application Across Hybrid Systems

Control techniques vary based on the hybrid system configuration and the specific challenges posed by each system. The following is a synthesis of common control techniques and their applicability to various hybrid systems, such as PV-battery, PV-hydrogen-grid, and others:

5.3.1. Load Balancing

Load balancing is essential for ensuring that energy demand and supply are effectively matched, preventing overloads and optimizing resource utilization. In systems like PV-battery, load balancing ensures that excess renewable energy is stored during high-output periods and used when demand exceeds renewable generation. In PV-hydrogen-grid systems, load balancing becomes more complex as it involves managing not just electricity but also hydrogen production and storage. Advanced load balancing techniques, such as demand response programs, have been used effectively to adjust energy usage during peak periods and maximize the efficiency of renewable energy generation [89].

Trade-offs in load balancing involve optimizing the use of renewable energy without overloading energy storage systems or compromising grid stability. In PV-hydrogen-grid systems, there may be additional trade-offs between storing excess electricity in batteries versus converting it to hydrogen, with each method having implications for system cost, efficiency, and long-term sustainability [123].

5.3.2. Demand Response

Demand response involves adjusting electricity consumption based on grid conditions, incentivizing consumers to shift their usage during peak demand times or when renewable generation is low. This is particularly effective in systems with high penetration of variable RESs, such as PV and wind. In PV-battery systems, demand response can optimize battery usage by encouraging consumers to use more electricity when excess solar power is available, thus reducing reliance on the grid.

In PV-hydrogen-grid systems, demand response can also be integrated with hydrogen production systems, where excess renewable energy is used to produce hydrogen during off-peak hours. However, the effectiveness of demand response in these systems depends on consumer behavior, making it crucial to have well-designed incentive programs and real-time feedback mechanisms [127].

5.3.3. Distributed Energy Resource Management

In hybrid systems, managing DERs such as PV panels, WTs, BTs, and hydrogen systems is critical to grid efficiency. Distributed energy resource management (DERM) techniques optimize the coordination of these resources, ensuring that energy production aligns with demand. This is especially important in systems like PV-battery or PV-hydrogen-grid, where the variability of renewable energy generation requires dynamic adjustments to the energy mix [128].

DERM strategies include optimizing battery storage, hydrogen production, and grid energy exchange to maximize efficiency and minimize reliance on non-renewable sources. However, a significant challenge is coordinating multiple DERs in real-time, particularly as grid complexity grows. Control systems must balance the often competing demands of storing energy, meeting immediate consumption needs, and feeding energy back into the grid [124].

5.4. Grid Control Enhancement: Predictive Maintenance and Security

To ensure the reliability and security of hybrid systems, it is essential to integrate predictive maintenance and robust cybersecurity measures into the overall grid control strategy. These elements enhance system resilience by proactively addressing potential risks and vulnerabilities that could disrupt grid operations.

5.4.1. Predictive Maintenance

Predictive maintenance relies on data analytics to forecast when equipment is likely to fail, allowing for proactive maintenance and minimizing downtime. This is particularly important in hybrid systems, where the integration of RESs and energy storage adds new layers of complexity. Predictive maintenance techniques help ensure that grid components such as batteries, inverters, and hydrogen production units remain operational, thus reducing the risk of system failure [129].

In PV-battery systems, predictive maintenance can optimize battery life by forecasting usage patterns and scheduling maintenance before issues arise. In PV-hydrogen-grid systems, this technique can help ensure the efficient operation of both electricity and hydrogen subsystems, reducing operational disruptions and improving system resilience.

5.4.2. Cybersecurity Measures

With the increasing digitalization of grid systems, cybersecurity becomes a major concern. Hybrid systems that rely heavily on Internet of Things (IoT) devices, data analytics, and remote control mechanisms are vulnerable to cyber threats that could disrupt grid operations. Robust cybersecurity measures, including encryption, real-time monitoring, and intrusion detection, are essential to protect against cyberattacks and safeguard grid stability [130].

A notable example is the 2015 cyberattack on Ukraine’s power grid, which caused widespread outages and highlighted the severe consequences of exploiting energy system vulnerabilities [131]. While such incidents targeted national infrastructure, microgrid-based HESs share similar vulnerabilities. Review studies report common threats such as denial-of-service (DoS), data spoofing, and unauthorized access within microgrid environments [132].

As hybrid systems become more interconnected and automated, ensuring the security of both hardware and software components is critical. Security vulnerabilities in the communication infrastructure can undermine the effectiveness of control strategies, potentially leading to widespread outages or system failures.

5.5. Practical Implementation Insights

Case studies provide valuable insights into the practical implementation of grid control strategies. For instance, research has highlighted the importance of regulatory compliance, stakeholder engagement, and community acceptance in the success of integrating RESs into the grid systems. These factors play a significant role in overcoming resistance to new technologies and ensuring smooth transitions to more sustainable energy systems [133].

A thorough exploration of integration with existing infrastructure and scalability is crucial for advancing grid control strategies. Technologies such as advanced grid sensors, real-time data analytics, and energy storage systems are key enablers in this regard. These technologies allow grid operators to monitor, control, and optimize the flow of energy more efficiently, improving grid stability and enabling the seamless integration of renewable energy. In particular, energy storage systems can mitigate intermittency issues from renewable sources like wind and solar, while real-time data analytics can help forecast energy demand and optimize resource allocation. By bridging theoretical concepts with practical applications, stakeholders can make informed decisions to enhance grid resilience and sustainability [134].

Future research should focus on exploring emerging technologies such as AI, blockchain, and IoT in grid control. AI can be applied to predict grid performance and optimize load distribution, while blockchain can provide secure, decentralized platforms for managing energy transactions and enhancing grid transparency. IoT devices can facilitate the integration of distributed energy resources by providing real-time data on grid conditions. Additionally, regulatory frameworks—such as the European Union’s Clean Energy for All Europeans package and California’s Renewable Energy Standard—are shaping grid management policies and influencing the adoption of these technologies [135,136]. Comparative studies across different regulatory environments can provide valuable insights into policy implications and best practices for grid management, ensuring a balance between technological advancements and regulatory compliance [137].

6. Advancements and Challenges in Renewable Energy Systems and EMS: Toward Sustainable Energy Solutions

The current state of research on RE systems and EMS demonstrates significant progress toward sustainable solutions. Strengths include an interdisciplinary approach, diverse applications, and the utilization of advanced optimization techniques. These advancements highlight the potential for reducing fuel consumption, emissions, and environmental impact. However, several limitations remain that must be addressed to make the research more impactful and applicable to real-world scenarios.

A major area of focus has been RE system-based MGs, especially those integrating photovoltaic systems and batteries. Recent studies have expanded into innovative areas such as DC MGs, which offer improved efficiency and flexibility. While many optimization techniques have been applied to improve the energy and cost efficiency of these systems, a substantial number still rely on single Objective Functions. Multi-objective optimization, though gaining traction, faces challenges in balancing conflicting goals such as minimizing costs, reducing CO₂ emissions, and optimizing energy storage. Furthermore, the scope of optimization is often limited to operational phases, neglecting broader considerations such as system life cycle or long-term scalability.

Despite advancements, comprehensive control strategies for managing the integration of various RES within microgrids remain underdeveloped. Many studies propose optimization solutions without addressing how these strategies can be implemented in dynamic environments with variable energy demand and supply. Accurate load forecasting, which incorporates real-time data, weather predictions, and energy market trends, is vital for improving reliability and efficiency. However, challenges persist in developing forecasting models that are both precise and adaptable to diverse scenarios.

Another overlooked aspect is the environmental footprint of RES technologies, particularly the embodied CO₂ emissions associated with manufacturing and deploying components like solar panels, wind turbines, and batteries. Most research focuses on the operational benefits of RES while neglecting their full life cycle impacts. Incorporating life cycle assessments would provide a more comprehensive evaluation of their sustainability, addressing a critical gap in current research.

Future studies must also consider scalability and real-world validation. Developing solutions that are adaptable to different regulatory environments and grid conditions is essential. Comparative studies across countries with varying policies and market dynamics could offer valuable insights into best practices and the challenges of widespread RES adoption. Furthermore, advancements in emerging technologies like AI and blockchain should be leveraged to enhance grid control and energy management, with a focus on improving system resilience and operational efficiency.

To make the research more practical and impactful, collaborations with industry partners could facilitate the collection of long-term operational data for validating performance under real-world conditions. Additionally, economic analyses that include cost–benefit assessments and payback periods would provide stakeholders with a clearer understanding of the financial viability of proposed solutions. Addressing societal and behavioral factors is also crucial for ensuring the acceptance and effective implementation of RES technologies.

In conclusion, while the advancements in RE systems and EMSs are commendable, addressing the limitations outlined in this section will be crucial for achieving truly sustainable energy solutions. By focusing on comprehensive control strategies, accurate forecasting, life cycle sustainability, scalability, and real-world validation, future research can pave the way for more resilient, efficient, and environmentally conscious energy systems.

7. Conclusions

This paper has provided a comprehensive review of HES, focusing on the optimization techniques and control strategies that enhance their performance, reliability, and sustainability across various applications, including MGs, commercial buildings, healthcare facilities, and cruise ships. The evaluation of the current body of research has highlighted significant advancements in multi-objective optimization, real-time energy management, and advanced control strategies. These efforts have contributed to improving the efficiency of hybrid systems integrating RESs, storage, and grid interactions while addressing various operational challenges in these specific applications.

Despite the notable progress, several key limitations persist. Many optimization techniques face challenges related to scalability and sensitivity to variations in system parameters. Furthermore, there is a lack of integration of user behavior, grid dynamics, and life cycle carbon emissions in system design and operation, which limits the development of truly sustainable energy solutions. Control strategies, although effective in many cases, often lack the flexibility required to adapt to rapidly changing energy conditions in dynamic environments such as healthcare facilities or cruise ships, where reliability is especially critical.

The review of the literature underscores the need for further research into robust, adaptable optimization techniques and control strategies that can better accommodate the dynamic nature of HESs across a range of applications. It also highlights the importance of addressing environmental impacts through life cycle analysis and considering real-world operational constraints in the development of future systems.

In conclusion, while considerable progress has been made in the research on HESs, there remains a need for continued refinement of existing approaches. Bridging the gap between theoretical advancements and practical, scalable solutions will be crucial for meeting future energy demands sustainably, reliably, and efficiently, particularly in applications like MGs, commercial buildings, healthcare facilities, and cruise ships.