Hybrid Renewable Energy Systems for Off-Grid Electrification: A Comprehensive Review of Storage Technologies, Metaheuristic Optimization Approaches and Key Challenges

Abstract

Hybrid Renewable Energy Systems (HRESs) are a practical solution for providing reliable, low-carbon electricity to off-grid and remote communities. This review examines the role of energy storage within HRESs by systematically comparing electrochemical, mechanical, thermal, and hydrogen-based technologies in terms of technical performance, lifecycle cost, operational constraints, and environmental impact. We synthesize findings from implemented off-grid projects across multiple countries to evaluate real-world performance metrics, including renewable fraction, expected energy not supplied (EENS), lifecycle cost, and operation & maintenance burdens. Special attention is given to the emerging role of hydrogen as a long-term and cross-sector energy carrier, addressing its technical, regulatory, and financial barriers to widespread deployment. In addition, the paper reviews real-world implementations of off-grid HRES in various countries, summarizing practical outcomes and lessons for system design and policy. The discussion also includes recent advances in metaheuristic optimization algorithms, which have improved planning efficiency, system reliability, and cost-effectiveness. By combining technological, operational, and policy perspectives, this review identifies current challenges and future directions for developing sustainable, resilient, and economically viable HRES that can accelerate equitable electrification in remote areas. Finally, the review outlines key limitations and future directions, calling for more systematic quantitative studies, long-term field validation of emerging technologies, and the development of intelligent, Artificial Intelligence (AI)-driven energy management systems within broader socio-techno-economic frameworks. Overall, this work offers concise insights to guide researchers and policymakers in advancing the practical deployment of sustainable and resilient HRES.

1. Introduction

1.1. Background

The global transition towards clean energy has brought to the fore the necessity for Hybrid Renewable Energy Systems (HRES) in energizing off-grid and distant communities. In such contexts, photovoltaic (PV) arrays and WTs are prevalent renewable resources due to their availability and environmental advantages [1,2]. However, their inherent intermittency and uncertainty are major barriers to ensuring a stable, high-quality, and cheap electricity supply to rural communities [3]. Figure 1, shows the conceptual architecture of a typical off-grid HRES. Renewable generation sources, WTs and PV arrays, supply power to a common power bus, whose outputs are regulated by maximum power point tracking (MPPT) and pitch/curtailment control. A Battery Energy Storage System (BESS), interfaced through a bidirectional power conversion system, is tasked with supplying important services such as load shifting, state-of-charge (SOC) balancing, frequency stabilization, and emergency backup. A diesel generator is introduced into the system as a backup unit, providing the system with black-start capability and additional support during periods of prolonged renewable energy shortages. The coordination of the entire system is implemented through an Energy Management System (EMS)/Controller that communicates with each subsystem. The EMS manages loads, regulates SOC within the BESS, and transmits control commands to PV and wind units. The EMS via these communication and control loops supplies stability to the system, safe operation, and optimized utilization of renewable resources [4]. As part of global efforts to ensure equitable and sustainable energy access, rural electrification strategies are increasingly focusing on HRES to integrate diverse energy sources. This configuration highlights the complementary role of HRES, in which diesel sets and BESS fill the gaps of intermittent renewables to deliver guaranteed electrification to rural villages. According to the report presented in [5], the number of people who should have access to electricity between 2024 and 2030, taking into account population growth, is shown in Figure 2. At the same time, it reduces reliance on costly and environmentally degrading diesel generation while enhancing resilience and sustainability. Beyond these advantages, operational challenges still face implementation, more specifically, high capital costs, severe environmental impacts on component life, installation and maintenance logistical issues, and recycling and resources supply uncertainties [6]. Consequently, it is crucial to critically examine issues of technological innovation and the deployment of HRES in remote areas. Knowing such issues in more detail can enlighten policymakers, scholars, and business interests to formulate policies that can make HRES more feasible, reliable, and sustainably long-lasting for off-grid applications.

1.2. Related Works

In [7], the authors provided an overview of the role played by electrochemical storage in off-grid power supply schemes, particularly in developing countries where the conventional grids are absent and renewable energy sources are variable. The study briefly addressed system needs, design, technology selection, and test methods, emphasizing that energy management plays a crucial role in ensuring durability. It further highlighted the increasing dominance of lithium-ion technology in off-grid systems after 2015, owing to cost minimization, availability of hardware, and improved appliances. In [8], the authors conducted a comprehensive review of integrating renewable energy zones in low-transmission networks. They emphasized the supremacy of BESS over non- BESS in managing frequency and voltage instability, enhancing power quality, and managing renewable intermittency. Proven BESSs, control methods, and global deployment examples were highlighted to emphasize their core contribution to enhancing grid stability and integrating intermittent renewable energy sources. The authors analyzed in [9], the feasibility of creating renewable energy communities for remote and off-grid sites through hybrid PV and lithium-ion BESS. Using PVGIS irradiation data and load generators, they modeled and optimized system configurations in case studies in Australia, Congo, and Canada. Results showed that PV–BESS can achieve self-sufficiency in Congo and Australia with sufficiently large system sizes.

In contrast, Canada is severely challenged by its long winter seasons and low solar irradiance, necessitating oversized storage or hybrid options, such as biomass. In [10], the Author offered a comprehensive review of recent progress in ESS for grid integration of renewable energy. The paper highlighted the operation and control challenges brought about by intermittent renewables , such as generation uncertainty, voltage stability, and power quality. It reinforced the role of ESS in stabilizing fluctuations and providing ancillary services. Various technologies, including batteries, flywheels, compressed air energy storage (CAES), Pumped hydro storage (PHS), Superconducting magnetic energy storage (SMES), and supercapacitors, were compared in terms of efficiency, response time, and maturity level, concluding that Li-ion batteries are currently market leaders. At the same time, hybrid and long-duration storage remain pivotal to future large-scale renewable integration. In [11], the authors optimized an off-grid HRES for village electrification in Odisha, India, with solar, wind, and water resources. They compared hydrogen and various battery types with a diesel generator under different dispatch strategies. In [12], the authors reviewed the design, control, and optimization of off-grid microgrids with ESS integration. By comparing the storage technologies such as lithium-ion batteries, PHS, and compressed air, they determined ideal capacity configurations that maximize renewable utilization, reduce costs, and improve resilience during extreme conditions. The authors, in [13], developed a generic optimization model to determine the optimum size of PV/wind/BESS for remote villages and applied it to Ras-Shaitan, Egypt. Using the that gray wolf optimizer and cross-validating with Hybrid Optimization of Multiple Electric Renewables (HOMER), particle swarm optimizer, genetic algorithm, and wild horse optimizer, they proved that the gray wolf optimizer provided better convergence and reduced cost of energy by about 17% compared to HOMER. In [14], the authors optimized a stand-alone hybrid power system with PV, WTs, and BESS based on minimizing the net present cost overall while providing a guaranteed supply of electricity. They used an adapted grasshopper optimization algorithm with a load-following strategy. They found that combining PV and BESS was the optimal choice to meet demand at the lowest cost and highest reliability. The study further revealed that changes in interest rates and capital charges for storage units significantly impact the system’s total energy cost and the degree of reliability. In [15], the authors developed an integrated renewable energy system comprising micro-hydro, solar, wind, biomass, biogas, and BESS for rural electrification in a district of Karnataka, India. They minimized system sizing to reduce the total cost of net present value and energy cost by employing a genetic algorithm. They proposed three feasible scenarios with the most suitable zone-based configurations with ideal mixtures of resources, device types, and biomass generator scheduling. In [16], the authors developed a hybrid energy system for a rural area in Saudi Arabia, incorporating solar, wind, BESS, and a diesel generator. The best configuration minimized costs without sacrificing reliability, demonstrating the requirement for storage to minimize diesel consumption. In [17], the authors developed a robust control strategy for power systems in remote areas that can withstand random disturbances, such as bushfires, lightning, and mudslides. Using the Markov Jump System paradigm, they described topology changes as stochastic jumps and designed an output feedback controller for restoring frequency and voltage. Simulation results showed that the collective action of PV panels, WTs, and BESS, using this method, reduced voltage fluctuations, preserved frequency stability, balanced the state of charge of the batteries, and ensured better performance than traditional control methods in promoting resilience. In [18], authors designed an off-grid solar PV system for remote rural villages in Dera Ghazi Khan, Pakistan, to provide sustainable electricity where access to the national electricity grid is low. With PVsyst 7.2, they conducted a techno-economic evaluation for community facilities, demonstrating that off-grid solar with BESS backup is significantly lower in cost compared to expanding transmission lines. This results in a 5% annual savings on energy bills and improved self-reliance. In [19], authors developed a model of joint ESS for off-grid villages, in which adjacent customers share systems. The neighbor configuration reduced operating costs and increased storage capacity compared to single and communal installations. In [20], the authors compared PV -wind hybrid systems and different storage technologies for rural electrification in Cameroon based on actual wind and solar data from three locations. Using the cuckoo search algorithm, they found that the most affordable and most reliable solution across all locations and load levels was a BESS -based PV -wind hybrid system, which is better performing compared to fuel cell hybrid systems, even when these are technically possible. In [21], the authors proposed a multi-approach paradigm for the hybrid energy system design, which is operationally feasible, economically viable, and environmentally sustainable for off-grid communities. A case study on Con Dao Island, Vietnam, revealed that although an off-grid system consisting of PV, wind, batteries, and diesel had the minimum net present cost, the best performance in environmental friendliness and energy cost was achieved by an on-grid system with high penetration of renewables and export capacity. It is important to note that many local communities cannot afford to invest in these technologies. In addition, the costs of maintaining and establishing infrastructure and equipment can also hinder adoption. There is also another important point: the adoption of new technologies requires social and cultural changes. Hence, some local communities are distrustful of new technologies, and there are barriers to their adoption, which are especially evident in communities with deep traditions and resistance to change. To promote technology adoption in local communities and address economic and financial issues, it is necessary to change attitudes and increase trust in new technologies. In [22], the authors proposed a stand-alone PV—BESS configuration for rural areas using a bidirectional Cuk converter on the PV panel. The configuration boosted maximum power point tracking, reduced current oscillations at the panel and BESS terminals, and extended BESS life. The system incorporated an isolated interleaved boost converter, a Cuk bidirectional converter, and a 3-level T-type inverter, and simulations and hardware-in-the-loop testing validated its performance. In [23], a PV -wind hybrid energy system for a Western Australian remote community was assessed by the authors through comparison of different storage technologies in technical and economic perspectives. Their study tested the effect of temporal resolution, storage decisions, PV tracking, and BESS longevity through sensitivity analysis, illustrating the importance of proper system sizing to achieve cost-effectiveness, reliability, and minimized environmental emissions. In [24], the authors analyzed the feasibility of HRES for rural communities based on three scenarios: diesel with batteries, solar with batteries, and solar–diesel- BESS. Findings showed that the PV-diesel generator–BESS configuration was the most economic and viable setup to deliver rural electricity needs at a low cost. In [25], two remote area power supply setups, a PV—BESS and a thermoelectric–battery setup, were evaluated under Melbourne climatic conditions without backup generators. Results showed that although the thermoelectric battery system was 66% more costly to set up than the PV- battery system, it also had the benefits of utilizing both electrical and heat energy for household use. In [26], the authors compared the performance of an off-grid PV -wind–diesel- BESS hybrid system for Kuala Lumpur International Airport Sepang Station, Selangor, Malaysia, using real solar and wind data. Simulations in HOMER showed that the optimal design reduced the net present cost by 29.65% and decreased CO₂ emissions by 16 tons annually compared to conventional generation, demonstrating the techno-economic feasibility of the system for remote sites with similar climates. In [27], authors have taken into account the optimal design of an independent PV-wind-BESS to electrify a remote area in Borj Cedria, Tunisia. They explored system architecture, component modeling, and two best-case designs using evolutionary algorithms, with a focus on the trade-offs between storage system performance (deep discharges and cycles) and overall system cost.

Although HRES have been extensively studied, the existing body of literature remains fragmented across three dimensions. First, storage technologies are often evaluated in isolation, and no comprehensive assessment has yet determined which types of storage are most effective for off-grid systems, nor how the complementary roles of short- and long-duration storage can be integrated to enhance sustainability and reduce costs. Second, system architectures are typically examined at either the AC or DC level, with limited reviews addressing hybrid AC/DC configurations that are increasingly prevalent in practice. Third, metaheuristic optimization strategies are usually tested in individual studies. However, there is still a lack of comparative perspectives that directly link algorithmic performance to planning outcomes, such as cost reduction, reliability indices, and resilience in off-grid contexts.

1.3. Research Gaps and Contributions

Based on prior review articles on HRES for remote area electrification, there are evident gaps within the field that are aimed to be addressed by the present review paper. One of the most evident gaps in earlier research is the lack of comprehensive and comparative analyses of different ESS. In all of the previous reviews, energy storage has been discussed in an isolated manner, and economic and technical aspects of these systems’ interaction and interrelationship have not been studied in detail. This article provides a comprehensive review of different types of ESS, along with a comparison of their technical and economic characteristics. Additionally, this paper seeks to address another critical gap: the scarcity of qualitative assessments on the utilization of emerging technologies, such as hydrogen, as a significant energy source. Although hydrogen has enormous potential in various applications, including and the provision of electricity to isolated region energy storage its use in large markets faces numerous obstacles. These range from technical obstacles to economic and regulatory obstacles. This review paper contributes to enhanced understanding of the potential and hindrances to the development of hydrogen technology in the context of HRES by investigating such hindrances and highlighting major challenges toward hydrogen becoming a traditional source of energy. Moreover, in most of the previous research, the real-world practical applications of HRESs in other countries, particularly in off-grid areas, were not widely investigated. This paper presents practical applications of these systems through an investigation of real-world projects in different countries that have incorporated HRES into off-grid areas, thereby demonstrating their practical utilization in real-world environments.

Unlike previous studies, this review paper explores the application of metaheuristic optimization algorithms in multi-dimensional optimization and optimal resource utilization within off-grid integrated systems. In addition to introducing the algorithms, the real-world application of each algorithm to address specific design and optimization problems of HRESs in off-grid systems is explained with real-life examples from various projects.

1.4. Methodology

A systematic review of literature was performed to search and synthesize recent quality literature on HRES, ESS, and metaheuristic optimization algorithms. The review aimed to achieve a comprehensive understanding of the latest research and refer to the most important trends and developments in these connected domains. The search process was followed as per the PRISMA guidelines to maintain transparency and reproducibility. Literature was retrieved from four major science databases, Scopus, Web of Science, IEEE Xplore, and ScienceDirect, for the period 2000–2025, which corresponds to the most prolific research and development period in the area. The search terms applied were “HRES”, “off-grid electrification,” “energy storage,” “metaheuristic optimization,” and “energy management system.”

Only peer-reviewed English-language journal articles were included to guarantee the reliability and quality of the documents reviewed. Non-hybrid setups, non-off-grid, and non-technical optimization and storage-centric studies were removed after title and abstract screening. 850 records were found in the databases. Duplicate removal resulted in 620 papers remaining to be reviewed at the title and abstract level. Based on the inclusion criteria and pertinence, 280 studies were removed, and 340 full-text papers were thoroughly checked. Following the eligibility check, 194 papers were also removed due to their failure to meet the methodological or thematic criteria. In the end, 146 pertinent studies were incorporated into the final synthesis to compile a comprehensive and balanced state-of-the-art review. The overall selection process is delineated according to the PRISMA flow, presented in Figure 3.

1.5. Paper Structure

Section 2 introduces storage systems and their role in off-grid systems. Section 3 is dedicated to introducing the structure of topologies, challenges, and some recent innovations applied in the field of off-grid systems. Section 4 introduces the types of metaheuristic algorithms and the works that use these algorithms. Section 5 is dedicated to introducing the discussion and future directions, and finally, Section 6 is devoted to the conclusion.

2. ESS Technologies and Their Applications in Off-Grid Systems

Figure 4, provides a comprehensive classification of ESS into five broad categories: chemical, electrical, electrochemical, mechanical, and thermal [28,29,30]. Such categorization reveals the diversity of storage technologies and provides a clear platform for describing their roles in modern energy systems. In the chemical category, hydrogen storage systems are among the means of supporting long-term ESS and even flexibility for cross-sector use, such as power generation, space heating, and mobility [31]. Electrical storage is ideally used for voltage regulation, loss reduction, and peak shaving [32]. The electrochemical family, such as lithium-ion and lead-acid batteries, is the linchpin of off-grid systems due to its efficiency, sufficient energy density, and continuously decreasing costs. The mechanical storage systems comprise CAES, flywheels, and PHS. The latter are primarily utilized in high-capacity or geographically favorable sites, where they provide stable and long-term solutions for balancing supply and demand. Finally, thermal storage systems are classified into three types: latent heat, sensible heat, and thermochemical storage. Although their primary applications are in heating and cooling, they can be ancillary in hybrid energy systems where they augment electrical storage. In general, Figure 4, provides a consistent overview of the ESS and is used for further analysis, for example, the percentage composition of each technology in off-grid systems displayed in Figure 4. This breakdown emphasizes that the choice of storage technology is heavily context-sensitive, considering factors such as size, storage duration, cost, and system requirements, and that no single technology can fulfill all needs on its own. Instead, effective system design often relies on the combined use of multiple types of storage.

Table 1. Technical specifications of different types of storage systems [8,29,33,34,35,36,37,38,39,40].

Storage Type	Life (Cycle)	Efficiency	Capital Costs	Capacity	Specific Energy	Rating	Energy Density
Compressed Air Energy Storage	30,000	40–60%	400–800 $/kW	>100 MW	10–50 Wh/kg	>100 MW	0.5–20 kWh/m3
Flywheel ESS	>100,000	80–95%	294–2880 $/kW	0.1–20 MW	10–50 Wh/kg	1.655 MW	20–80 Wh/L
Pumped ESS	10,000–30,000	70–80%	5–100 $/kW	0.3–30 MW	0.3–30 Wh/kg	>100 MW	0.3 W h kg−1
Flow Batteries	<13,000	60–70%	600–1500 $/kW	<12 MW	15–300 Wh/kg	<100 MW	15–600 Wh/L
Lithium-ion Batteries	1000–10,000	80–90%	1200–4000 $/kW	0.25–25 MW	100–300 Wh/kg	<2 MW	70–250 W.h/kg
Lead-Acid Batteries	100–2000	60–90%	218–3644 $/kW	0.25–25 MW	25–50 Wh/kg	<50 MW	25–90 KWh/m3
Super Capacitor	>1,000,000	90–95%	140–560 $/kW	0.3 MW	1–1000 Wh/kg	<10 MW	0.5–5 Wh/kg
Superconducting magnets	10,000	95%	140–636 $/kW	0.1–10 MW	0.5–5 Wh/kg	10–100 MW	0.5–5 Wh/kg
Sodium–Sulfur (NaS) Batteries	2500–5000	75–86%	980–2800 $/kW	0.05–34 MW	150–240 Wh/kg	0.5–50 MW	150–240 (Wh/kg)

, presents the technical specifications of different types of storage systems. In addition,

Table 2. Technical specifications of different types of thermal ESS [41,42,43,44,45,46].

Characteristic	Latent Heat Storage Systems	Sensible Heat ESS	Thermochemical Storage Systems
Storage capacity (kWh/m3)	50–150	10–25	120–250
Energy density	Moderate	Minimal	Maximum
Energy transport	Short distance	Short distance	Theoretically unlimited
Operating Temperature (°C)	Up to 900	Up to 1400	Up to 1600
Technology	Moderate	Simple	Complex
Specific energy (W hth/kg)	50–150	10–50	120–250
Storage density (GJ/m3)	0.2–0.5	0.2	0.4–3
Common Applications	Solar thermal power plants, industrial waste heat	Residential and industrial heating	High-temperature solar power plants, Heating, Ventilation, and Air Conditioning systems
Capital Costs-Power ($/kW)	6000–15,000	3400–4500	1000–3000
Capital Costs-Energy ($/kWh)	10–50	0.1–10	8–100
Storage Efficiency	60–98%	50–90%	>90%
Need to maintain	Low	Low	Moderate
Sensitivity to temperature and environmental conditions	Low	Moderate	High

and Table 3, respectively, show the technical specifications of thermal storage devices and hydrogen storages. The capital cost values ($/kW) are based on data from the United States.

Thermal storage systems (such as latent, sensible, and thermochemical storage) typically have greater advantages over batteries in terms of reliability [47]. These types of storage systems are more durable and less affected by capacity loss over time due to the use of physical processes such as phase change or temperature change. However, batteries may lose efficiency over time due to the effects of charge and discharge cycles. Additionally, thermal storage systems are also economically advantageous due to their lower maintenance requirements and lower costs. It is important to note that thermal storage devices must be discharged exactly when the thermal load is needed; otherwise, its energy will not be usable [48].

Table 3. Technical specifications of the hydrogen storage system [41,49].

Technical Specifications	Value
Power Range (MW)	0.3–50
Capital cost-Power based ($/kW)	340–1144
Capital cost-Energy based ($/kWh)	2–17
Discharge Time	From sec to 24 h
Response time	<1 s
Energy Density (Wh/kg)	1200–1400
Efficiency (%)	30–50
Life Years	20–30
Cycling Capacity	20,000

Table 3. Technical specifications of the hydrogen storage system [41,49].

Technical Specifications	Value
Power Range (MW)	0.3–50
Capital cost-Power based ($/kW)	340–1144
Capital cost-Energy based ($/kWh)	2–17
Discharge Time	From sec to 24 h
Response time	<1 s
Energy Density (Wh/kg)	1200–1400
Efficiency (%)	30–50
Life Years	20–30
Cycling Capacity	20,000

Using hydrogen as a mass-market energy source faces a number of serious barriers that bar its extensive deployment. All these challenges cross technical, economic, and regulatory arenas. Let us encapsulate some of the most critical challenges:

• Excessive Capital Expenditure for Hydrogen Infrastructure

Capital expenditure on infrastructure for the production, storage, transport, and distribution of hydrogen is high, constituting a barrier to large-scale deployment [50]. Green hydrogen production technologies from renewable sources are costly. Therefore, despite the huge potential, green hydrogen production remains in the early stages of development [51].

Water splitters or electrolyzers generating hydrogen and oxygen are expensive equipment, although their prices should decline with improving technology and economies of scale [52].

Storage and Distribution: Hydrogen storage, particularly in liquid or compressed forms, requires highly specialized equipment due to its low energy density and highly flammable nature [53,54]. Building transportation infrastructure (pipelines, storage facilities, and fueling stations) adds another cost.

Scaling up manufacture: The cost of scaling up green hydrogen production facilities to global demand is a key challenge [55]. Renewable energy projects on a large scale, such as wind farms or solar parks, are required to provide the electricity required for electrolysis, with their own investment challenges.

Such high excessive capital expenditure requirements mean governments or private investors have to commit in the long term, and the lack of short-term profitability deters prospective investors.

• Regulatory Challenges

Regulation is the second significant challenge facing hydrogen deployment [56]. The regulatory landscape for hydrogen remains in development across most regions, with the possibility of inconsistency or a lack of regulation hindering momentum. The most important regulatory problems are:

Safety Regulations: Hydrogen is a highly flammable gas, and its safety procedures should be well defined, especially in its production, storage, and transport [57]. Random safety regulations in different places or countries can make it difficult for companies operating globally.

Permitting and Approvals: Permitting for hydrogen production plants or infrastructure development, such as pipelines or refueling stations, can be time-consuming and complex [58]. Local authorities may lack the skill to deal with the specific challenges posed by hydrogen technologies.

Support and Incentives: The majority of countries have yet to develop policies friendly to hydrogen adoption. Australia’s push to lead the global hydrogen industry faces major regulatory challenges that could hinder growth and investment [59]. The Department of Climate Change, Energy, the Environment and Water stresses that clear and consistent regulations are crucial to attract investors [59]. Reliable and transparent incentives in the form of subsidies for hydrogen production or tax credits for green hydrogen would make it possible for hydrogen to be competitive with other forms of energy. Without policy support, hydrogen markets cannot be established.

Carbon Pricing and Emission Standards: As clean as hydrogen is, however, the carbon intensity of its production can be highly variable depending on production technology. Governments might need to put in place mechanisms to ensure that hydrogen production aligns with emission reduction targets, and this might involve tough standards for “green” vs. [60] “blue” hydrogen (the latter being produced via carbon capture).

• Shortage of Skilled Workforce

The hydrogen sector is a highly specialized one, and few employees possess the skills needed to design, build, and maintain hydrogen technologies. This shortage in expertise can slow down development and also affect the efficiency of deployment.

Lack of Training Programs: As hydrogen technologies are still developing versus traditional energy systems, trained personnel working in hydrogen production, storage, and distribution are limited [61]. Universities and technical schools are gradually coming on board, yet training and education programs trail technology development efforts.

Cross-Disciplinary Knowledge: Hydrogen is an interdisciplinary field that draws on chemical, mechanical, and electrical engineering and renewable energy systems knowledge [62]. Workers need to have a higher-level understanding of the said disciplines with specialized knowledge in hydrogen systems, which is not easy to come by.

Scaling Workforce Development: Even in the places where sufficient investment is brought to bear on training programs, scaling the workforce to meet global demand can be challenging. The majority of regions are vying against other high-tech industries for access to the same talent pool, further exacerbating the shortage.

Solutions and Moving Forward: In an effort to overcome these challenges, both public and private efforts will be needed:

Funding and Investment: Governments can offer subsidies, tax incentives, and grants to reduce the cost of hydrogen infrastructure development [63]. Private investment in R&D can drive technological development to reduce capital expenditure in the long term.

Clear Regulatory Frameworks: Governments can speed up the development of hydrogen-specific regulation, including safety directives and infrastructure standards. For example, the industry in China is in the policy-making stage [64]. Therefore, government policies have promoted the development of the hydrogen industry and encouraged provincial governments to formulate policies and incentives [64]. An integrated global regulatory framework can enable it to become easier for companies to ramp up operations geographically.

Workforce Development: Governments and schools have a responsibility to work in concert to develop programs and curricula directed at hydrogen technologies.

Collaboration between industry and academia can bridge important skills gaps and employ a pool of skilled people [65]. Once these challenges are resolved, the transition to a hydrogen-focused energy economy can become more feasible and scalable, opening the door for accelerated adoption in the years to come [65].

Table 4. Greenhouse gas emissions in different ESS [66].

Storage Type	Greenhouse Gas Emissions (kg CO2eq/MJ Fuel)
Hydrogen Storage	0.003–0.3
Lead acid batteries	77–110
Lithium-ion batteries	20–82
Flow batteries	15–93
Pump storage	1–6
Fly wheels	3.5–159
Sodium–Sulfur (NaS) Batteries	715–784 *
Sensible Heat ESS	0.91–30
Latent Heat Storage Systems	20
Thermochemical Storage Systems	19
Superconducting Magnets	416
CAES	Varies from 2 to 107 depending on the type
Super Capacitor	5.56–8.33

* Greenhouse gas emissions in NaS batteries are higher due to energy-intensive processes required for material extraction, high-temperature operation, and battery manufacturing, all of which are heavily dependent on fossil fuels.

examines the impact of different types of ESS on greenhouse gas emissions.

Among the most compelling challenges of utilizing energy systems in remote and off-grid regions is selecting the ideal technology for ESS. The fluctuation of renewable sources such as solar and wind power requires storage to deliver a constant and trustworthy energy supply. In this respect, four big groups of storage technologies, electrochemical, hydrogen-based, mechanical, and thermal, have been seriously considered.

Electrochemical storage (batteries): Lithium-ion [67] and lead-acid batteries [68] are by a wide margin the most prevalent off-grid microgrid solutions. They are highly efficient, have rapid response, modular, and convenient to install, making them well-suited to small- to medium-sized systems. Their most outstanding liabilities are the relatively high up-front costs, especially lithium-ion and environmental conditions sensitivity.

Hydrogen storage: The combination of electrolyzers, hydrogen storage vessels, and fuel cells enables long-term storage, particularly valuable in matching the seasonal fluctuation of renewable sources [69]. These also enable the possibility of cogeneration, where electricity and heat are produced. The capital expense and safety aspects associated with storing and handling hydrogen remain hindrances to widespread use in isolated areas.

Mechanical storage: PHS [70] and CAES [71] are well-established technologies for large installations. PHS, indeed, is a low-cost, long-lasting storage technology with efficiencies over 70%. Nevertheless, PHS and CAES require suitable geographical or geological conditions, so they are not as appropriate for small, isolated communities.

Thermal storage: Heat stored in water tanks or phase-change materials is a cheap and simple solution, particularly when combined with solar thermal systems or diesel generators [72]. Although they are primarily limited to space heating and air conditioning usage and not to generating electricity, they still have the potential to serve a proper secondary function within hybrid off-grid systems.

Overall, for scattered villages and far-flung communities, electrochemical batteries are the most promising and common type of storage since they are versatile and can be easily deployed. For island cultures with highly volatile renewable supplies, hydrogen-based systems can provide meaningful complementary services. Mechanical storage systems such as PHS are only feasible in case of good geography, and thermal storage offers a niche but cost-effective solution for local applications of heating and cooling. No single storage technology fits all cases; the choice depends on site conditions, cost, and end-use. Figure 5, illustrates the percentages of the three main ESS technologies used in off-grid applications, as extracted from reference [28]. As is apparent, electrochemical storage is at the forefront with approximately 29.43% of the total percentage. This category primarily includes lithium-ion and lead-acid batteries, whose dominance is attributable to their high efficiency, quick response, continuous cost reduction, and scalability. In off-grid systems where the load is directly supplied by renewable energies like solar PV and wind, such storage devices are required to stabilize the grid and balance production fluctuations. Of course, it is important to note that car batteries can also play a role in off-grid systems. Mechanical and electrical storage technologies account for 14.21% and 5.07%, respectively [28]. Mechanical storage in the form of PHS and flywheels has typically been employed where geographical conditions are favorable or where short-term stability is demanded, with high reliability and long lifetimes. Chemical storage alternatives such as hydrogen and synthetic fuels, on the other hand, possess long-term storage capabilities and can be utilized not only for electricity demand but also for heating and transportation, explaining their high percentage in off-grid systems. The nearly equal division between mechanical and chemical storage reflects the dual requirement for short-term and long-term storage capacities in isolated systems. Electrical storage technologies, with 5.07 %, include supercapacitors and superconducting magnetic ESS. They serve their main role in instantaneous power, voltage, and frequency stabilization, but with a limited energy capacity, they are rather complementary than leading solutions. Finally, thermal storage systems have a very small share in off-grid applications.

As the world transitions to renewable energy sources to mitigate climate change and reduce dependence on fossil fuels, it becomes increasingly necessary to address the intermittent nature of renewable electricity generation, such as solar and wind. Among the key challenges facing the integration of renewable energies is the mismatch between energy production and consumption, which constantly leads to fluctuations in demand and supply, where the issue of uncertainty and its robust management is at stake [73]. ESS technologies are key to addressing this issue by accumulating excess energy produced during periods of high generation and making it available when demand exceeds supply. They enable the more efficient utilization of renewable energy, enhance grid stability, and provide backup power during low generation. Below is a review of various ESS, their features, applications, advantages, and limitations [74]. Both short-duration storage devices, which address short-term variations, and long-duration storage systems, designed to retain energy for extended durations to supply stable and firm energy, are covered [75].

• Short-Duration ESS

This system is designed to manage rapid energy fluctuations over short periods (hours or days) by storing energy temporarily and quickly discharging it when needed [76]. Technologies include lithium-ion batteries, Nickel-Cadmium batteries, and supercapacitors, which offer fast discharge capabilities. These systems are mainly used for daily load regulation and grid support during peak demand periods [77]. While offering fast response times and high flexibility for residential, commercial, and industrial applications, they come with challenges such as high costs and a limited lifespan due to battery degradation over time.

• Long-Duration ESS

Long-duration storage is used for storing energy over extended periods (days, weeks, or even months), ideal for when energy production exceeds consumption [78]. Key technologies include thermal ESS (storing energy as heat in materials like molten salts [79]), hydrogen storage (converting energy into hydrogen via electrolysis), and PHS (using excess energy to pump water to higher elevations for later electricity generation). These systems are typically used for seasonal ESS and backup power during low production periods. They offer long-term storage with high energy retention but are costly to install and less efficient in energy conversion compared to short-duration systems.

• Cooperative Short- and Long-Duration Storage

Combining both short- and long-duration ESS optimizes performance by addressing both short-term and long-term fluctuations in energy supply. This hybrid approach combines batteries (short-duration) with thermal ESS and hydrogen storage (long-duration). It is used to manage both short-term and long-term fluctuations in HRES and to supply energy during peak and low-generation periods. The main advantages include reduced energy waste and increased system reliability. However, the system’s complex design and higher installation and maintenance costs pose challenges.

3. Topological Configurations and Key Challenges of Off-Grid Systems

HRES implementations generally follow three types of topologies, as shown in Figure 6 [75]. Below are the different types of coupled topologies introduced as shown in this figure:

3.1. DC-Coupled Topology

In a DC-coupled configuration, all primary direct-current sources and storage devices—such as PV arrays, battery banks, supercapacitors, and hydrogen ESS are connected directly to a common DC bus through individual DC/DC converters. These converters perform maximum-power-point tracking (MPPT) for PV modules, regulate charging and discharging of storage devices, and maintain bus voltage stability. Integrating hydrogen production, power generation, and Artificial Intelligence (AI) in smart grids enhances sustainability, resilience, and efficiency [80]. Any loads or generators that inherently produce AC power are first rectified through AC/DC converters before interfacing with the DC bus. This architecture minimizes conversion stages for DC sources, which can improve efficiency and reduce control complexity on the DC side.

3.2. AC-Coupled Topology

In an AC-coupled configuration, the system centers around an AC bus [81]. Rotating technologies such as WTs or hydrokinetic turbines natively produce AC power, but the voltage and frequency they produce vary with wind or water speed [82]. To synchronize with the system’s AC bus frequency and maintain power quality, these sources are typically connected through back-to-back AC/DC–DC/AC converters (often called grid-forming [83,84] or grid-following inverters [83,85]). These power electronic interfaces allow frequency regulation, reactive power support, and islanded operation, which are critical in off-grid environments. DC sources (like PV) and DC storage must be inverted to AC before being tied to the bus, which can increase conversion losses but simplifies interconnection of standard AC loads.

3.3. Hybrid AC/DC-Coupled Topology

The hybrid AC/DC topology combines the benefits of both approaches and is now the most widely adopted architecture for off-grid HRESs [86]. In this arrangement, AC and DC buses are maintained separately, and each energy source or storage unit is connected to the bus that matches its native current type (e.g., PV and batteries to the DC bus, wind and diesel generators to the AC bus). A bidirectional interface converter links the AC and DC buses, enabling controlled power exchange between them [87]. This structure allows DC loads to be supplied directly from the DC bus with fewer conversion stages, while still supporting conventional AC loads and AC generation sources. It also provides greater operational flexibility, scalability, and resilience by isolating disturbances on one bus from the other.

In

Table 5. Comparison of some key characteristics for three different topologies [88].

	AC Coupled	DC Coupled	Hybrid
Ideal for	Homes connected to the grid	Off-grid homes	Off-grid homes or those needing backup power
Reliability	Average reliability due to reliance on multiple components	Higher reliability due to direct connection to batteries	High reliability, but system complexity can affect maintenance
Installation	Relatively simple	More complicated	More complicated
Durability	Average	Above average	Above average
Efficiency	Less effective in energy storage	More effective in energy storage	Most efficient in energy storage
Backup power	Not intended for backup power	Not intended for backup power	Designed for backup power
Cost	Least expensive	More costly	Most costly

, a comprehensive review has been conducted of the three introduced topologies.

The hybrid AC/DC microgrid systems have also been recognized for their flexibility in reconciling the use of renewable and conventional means of energy to produce power in diverse geographical locations, especially off-grid or remote areas. The flexibility stems from the fact that the AC/DC configuration can efficiently handle both AC and DC components in the same system, thus being capable of easily accommodating different energy sources like PV panels, WT, DG, and ESS (batteries). Several quantitative studies confirm the fact that hybrid AC/DC microgrids are more flexible than other power generation configurations.

For instance, a case study of Egypt’s El Kharga Oasis revealed that the most effective hybrid AC/DC system configuration, which included 21 kW of solar PV, 20 kW of WTs, 15 kW of diesel generators, and 76 kW of batteries, recorded a renewable fraction of 90.1% with a cost of energy of 0.10 $/kWh and a lowest rate of CO₂ emissions of 7.8% compared to standalone diesel generators [89]. This design demonstrates how the hybrid AC/DC system integrates multiple renewable energy sources with diesel backup power to ensure a stable and continuous power supply, even in areas with diverse environmental conditions, such as the desert environment of El Kharga Oasis.

Based on the classification presented in

Table 6. Summary of advantages, disadvantages, and suitability of different topologies (DC, AC, and hybrid AC/DC) for off-grid HRES [91].

Topology	Advantages	Disadvantages	Suitability in Off-Grid/Remote Areas
DC-Coupled	• Suitable for long distance transmission unlike AC-Coupled. • Simple design and easier maintenance. • Stable DC bus for PV and batteries.	• Most loads are AC: Requires inverters. • Concerns regarding voltage compatibility and potential corrosion issues with the DC electrodes. • Limited frequency regulation capability.	Best for solar + BESS dominant systems (small villages, telecom stations, lighting systems) with primarily DC loads.
AC-Coupled	• High reliability Natural interface with rotating machines (wind, micro-hydro, diesel). • Compatible with common AC household and industrial loads. • Mature inverter technologies for grid-forming control.	• DC sources (PV, batteries) must be inverted: Conversion losses. •Unsuitable for long distance transportation • Frequency and voltage stability harder without main grid.	Suitable for wind/hydro-rich regions or communities with conventional AC appliances. Often used in rural villages or islands.
Hybrid AC/DC-Coupled	• Combines strengths of both AC and DC buses. • Direct connection of each source/storage to its native bus. • Flexible power exchange via interface converter. • High resilience.	• Higher initial cost. • More complex design and control. • Requires skilled operation.	Most popular choice for modern off-grid microgrids in remote areas with diverse resources (PV, WT, diesel generators, storages) and mixed AC/DC loads.

, the implementation of off-grid HRESs can generally be organized into three distinct topological configurations: DC-coupled, AC-coupled, and hybrid AC/DC-coupled [90]. Each topology presents unique operational advantages, technical challenges, and suitability for deployment in remote areas.

Table 7. Key challenges of rural energy systems in developing countries and their implications for HRES design.

Challenge	Root Causes	Indicators	Implications for HRES Design	Implications for HRES Design	Possible Interventions
Inconsistent and Unstable Demand	Rapid population growth, seasonal variation, productive load uptake	Load factor, demand forecast error, variability index	Requires short- and mid-term storage, modular units, demand response	Requires short- and mid-term storage, modular units, demand response	Time-of-use tariffs, demand shifting, modular capacity addition
Insecure Energy Supply	Supply-demand mismatch, frequent failures, weak grids	SAIDI/SAIFI, LOLP/EUE, voltage fluctuation index	Need for diversified generation mix (PV, wind, distributed generation, batteries), resilient microgrid control	Need for diversified generation mix (PV, WT, distributed generation, batteries), resilient microgrid control	Hybrid AC/DC architectures, grid-forming inverters, reserve margins
Poor Power Sector Performance and O&M	Limited skilled staff, poor spare part logistics, inadequate budgets	MTBF/MTTR, share of available spares, O&M budget ratio	Design for modularity, standardization, and ease of repair	Design for modularity, standardization, and ease of repair	O&M contracts with SLA, local training, critical spare part warehousing
Unpaid Bills and Revenue Risks	Poverty, low trust in service quality, weak tariff systems	Collection rate, arrears ratio, disconnection/reconnection frequency	Financial sustainability constraints on HRES sizing	Financial sustainability constraints on HRES sizing	Prepaid/smart metering, progressive tariffs, community ownership models
Dependence on Traditional Biofuels	Limited electricity/gas access, low affordability of alternatives	Share of biomass/kerosene in household energy use	Need for clean cooking and heating integration in HRES	Need for clean cooking and heating integration in HRES	Clean cooking solutions, hybrid solar–biomass, targeted subsidies
Social and Economic Structural Deficiencies	Urban-rural divide, illiteracy, gender inequality, poor planning	Education enrollment, gender participation rates, energy poverty index	Community acceptance, participatory design required	Community acceptance, participatory design required	Community capacity building, participatory governance, skills training
Infrastructure and Accessibility Constraints	Remote geographies, poor roads, limited transport	Transport time/cost for fuel and spare parts	Need for low-maintenance, modular, and lightweight solutions	Need for low-maintenance, modular, and lightweight solutions	Standardization, lightweight technologies, strategic stockpiles
Data Scarcity and Seasonal Uncertainty	Sparse measurement, poor forecasting, migration dynamics	Confidence intervals for load/resources, seasonal load patterns	Requires robust/stochastic optimization and flexible system design	Requires robust/stochastic optimization and flexible system design	Smart metering, monitoring campaigns, scenario planning
Climate and Environmental Risks	Floods, storms, dust, unsustainable biomass harvesting	Climate risk maps, event-related failure rates	Hardening of infrastructure, resource diversification	Hardening of infrastructure, resource diversification	Elevation, anchoring, dust-proofing, sustainability standards
Regulatory and Policy Gaps	Unclear tariffs, land rights, licensing delays	Permit time, tariff fluctuation index	High project risk and cost of capital	High project risk and cost of capital	Clear tariff frameworks, Public–Private Partnership models, Memorandum of understanding with local governments
Integration of Productive Loads	Need for income-generating energy uses	Share of productive loads, utilization rate of equipment	System design must match productive load needs	System design must match productive load needs	productive use of energy programs, microfinance for equipment

, provides a structured comparison of these topologies, building upon the foundational insights highlighted in Table 6.

Rural energy systems in developing countries face multifaceted challenges that extend beyond mere generation deficits. These include fluctuating demand, weak institutional and financial frameworks, limited Operation and maintenance (O&M) capacity, and climate-related risks. For effective HRES design, these challenges must be translated into measurable indicators and mapped to technical and policy interventions. The enhanced version of Table 7, summarizes these key challenges together with their underlying causes, measurable indicators, implications for HRES design, and potential interventions.

In order to situate the research landscape of HRES for rural and off-grid applications, a diverse set of studies focused on various design, optimization, and assessment aspects of HRES have been conducted. As presented in

Table 8. Summary of key contributions in HRES for rural and off-grid applications across diverse contexts.

References	Contribution
[92]	This research primarily focuses on formulating and optimizing under multiple objectives. This hybrid power system combines PV and wind energy with a dual storage configuration comprising lithium-ion batteries and flywheels aimed at delivering dependable off-grid electricity access to rural and remote communities in Makueni County, Kenya.
[93]	The main contribution of this study lies in providing a comprehensive and multi-level review of HRES for off-grid applications, examining system configurations and energy planning frameworks from the village to the state level. By emphasizing reliability-based models as a key measure of system performance, the study highlights their ability to significantly mitigate the uncertainties associated with renewable resources.
[94]	The main contribution of this research is the formulation and optimization of a grid-independent hybrid wind/PV/biodiesel/BESS, along with the proposal of a novel hybrid algorithm to solve the associated sizing problem. Unlike most prior studies that focus solely on wind–PV combinations, this work extends the scope by integrating biodiesel and BESS, offering a rare and comprehensive analysis for stand-alone applications in Iran.
[95]	The main contribution of this study is the optimization and techno-economic evaluation of HRES for remote Arctic communities currently reliant on diesel generation. By integrating PV panels and WTs with existing diesel infrastructure, the research employs a genetic algorithm-based optimization framework that accounts for wind farm wake effects to design cost-effective system configurations.
[96]	The central achievement of this work lies in designing and optimizing a hybrid microgrid tailored for Kanur village, India, which integrates PV, WTs, ESS, inverters, diesel generators, and micro gas turbines to deliver an affordable and dependable electricity supply.
[97]	The main contribution of this study is the design and techno-economic assessment of renewable energy-based hybrid microgrids to address electricity shortages in rural southern Bangladesh. Using survey data from Ruma, Bandarban, the research develops both on-grid and off-grid configurations, simulated with HOMER Pro and PVsyst.
[98]	The main contribution of this study is the application of a Two-Stage Stochastic Programming framework to optimize HRES for remote regions under uncertainty in renewable generation. Using an ε-constraint approach, the methodology simultaneously minimizes Total Annualized Costs and CO2 emissions, with a case study on the island of Pantelleria.
[99]	The main contribution of this study is the comprehensive evaluation of multiple HRES configurations for supplying electricity to a remote island settlement, integrating technical, economic, environmental, and social dimensions.
[100]	The main contribution of this study is the development and comprehensive assessment of advanced HRES configurations for a remote island community, integrating WTs, PV panels, fuel cells, diesel generators, batteries, converters, electrolyzers, and hydrogen storage units.
[101]	The main contribution of this study is a comprehensive techno-economic and environmental evaluation of six HRES configurations combining renewable and conventional sources for rural electrification in Kunder Char, Bangladesh. Using modeling and sensitivity analysis, the researchers studied the trade-offs between cost, sustainability, and efficiency, and also provided a practical framework for selecting optimal solutions in remote areas.
[102]	The main contribution of this study is the introduction of a modified metaheuristic method, the Hybrid golden search algorithm, for long-term planning and optimization of off-grid HRES. The proposed approach focuses on minimizing annual net costs while enhancing supply reliability, demonstrating superior performance compared to conventional optimization techniques.
[103]	The main contribution of this study is the techno-economic and environmental assessment of HRES for rural electrification on Malawali Island, Malaysia. By comparing multiple configurations of PV, wind, BESS, and diesel generation, the research identifies cost-effective and lower-emission alternatives to diesel-only supply. The proposed framework provides practical insights into designing sustainable off-grid energy solutions for remote island communities.
[104]	The main contribution of this study is the development of an improved dung beetle optimization algorithm that enhances global search capability through convergence functions and spiral path modeling. The proposed method demonstrates superior performance on benchmark tests and is applied to the optimization of off-grid HRES.
[105]	The key contribution of this research is a detailed evaluation of four HRESconfigurations designed to supply sustainable electricity to Henry Island, India, through the integration of PV, WTs, biogas-based generators, BESS, and power converters.
[106]	The principal contribution of this work is the design of an optimized off-grid HRESfor rural communities in Egypt, combining PV, biomass-based generators, and BESSto enhance both economic viability and supply reliability. In addition, the study introduces an improved educational competition optimizer, incorporating a local escape operator and Gaussian distribution mechanisms, which has been validated on benchmark problems and demonstrated superior optimization capability.
[107]	The main contribution of this study is the development of a long-term economic optimization model for hybrid solar–wind–diesel microgrids in remote areas, ensuring operational reliability while accounting for land-use constraints. The Python-based model, implemented with constrained nonlinear optimization methods (Sequential Least Squares Programming and Constrained Optimization BY Linear Approximations), minimizes total system costs, including capital investment, operation, maintenance, and fuel by adjusting renewable capacities and diesel generation. This framework provides an effective tool for designing cost-efficient and sustainable microgrids under spatial limitations and rising fuel costs.
[108]	The main contribution of this study is the development of a hybrid PV–diesel–BESS to power translational sprinkler irrigation machines in remote and water-scarce areas of northwest China. An optimization model minimizing life cycle cost, subject to power reliability and CO2 constraints, is solved using a modified PSO algorithm enhanced with Chebyshev chaotic mapping. The proposed approach demonstrates superior performance over conventional algorithms and provides a reliable, cost-effective framework for powering irrigation systems in off-grid agricultural applications
[109]	The main contribution of this study is the optimized design of a hybrid energy system for remote rural communities in Eastern India, integrating solar, biomass, diesel, and BESS through the HOMER tool. By evaluating multiple configurations and conducting sensitivity analysis, the research identifies cost-effective and environmentally beneficial system architectures, demonstrating the significant role of solar energy in improving economic returns and reducing emissions.
[110]	The key contribution of this work is the development of a new day-ahead energy management strategy for isolated off-grid power systems that integrates demand-side response programs and high penetration hybrid renewable resources. By implementing a probabilistic fuzzy inference model of consumer responses to price-based incentives, the proposed approach increases system reliability and demand-side flexibility.
[111]	The main contribution of this study is the comprehensive assessment of Tsushima Island’s energy system, identifying renewable resource potentials and proposing two transition pathways toward 100% renewable energy. By addressing challenges of fossil fuel dependence, high costs, and limited data, the research demonstrates the feasibility of achieving carbon neutrality in remote islands and offers a replicable model for similar regions worldwide.
[112]	The main contribution of this paper is the development of a hybrid optimization model for the design and analysis of a grid-connected HRES (PV-biomass) for rural areas, which helps reduce energy costs, decrease grid dependence, and promote sustainable development and decarbonization in both the energy and transportation sectors.
[113]	The novelty of this reference lies in its analysis of a HRESusing solid waste to produce electricity, emphasizing the challenges and opportunities afforded by intermittent renewable energy resources. The study employs a techno-economic framework to achieve a cost-effective and reliable energy solution for a rural Indian community with a fully renewable energy supply and a 100% renewable energy fraction designed in the proposed architecture.
[114]	This paper’s primary contribution is showcasing four optimization algorithms (Tree Physiology Optimization, Invasive Weed Optimization, Biogeography-Based Optimization, and Seagull Optimization Algorithm) to design a cost-effective and sustainable HRESfor rural electrification. This paper’s primary contribution is showcasing four optimization algorithms (Tree Physiology Optimization, Invasive Weed Optimization, Biogeography-Based Optimization, and Seagull Optimization Algorithm) to design a cost-effective and sustainable HRESfor rural electrification.
[2]	The main contribution of this reference is the evaluation of a HRES for rural electrification in Somalia, integrating PV, WT, diesel generator, and BESS. The study demonstrates that this configuration provides the most cost-effective solution, reducing greenhouse gas emissions and offering a reliable and sustainable energy source with high renewable penetration.

, research spans from methodological contributions to optimization algorithms to techno-economic analyses of “real-world” case studies in various geographic contexts.

Figure 7, shows the radar chart of the types of combined energy systems collected from the literature. Compared to other options, the most significant share of renewable energy is attributed to PV and WT technologies due to the ease of access to natural resources and lower installation and maintenance requirements. Biomass and diesel have the lowest share due to technical and environmental challenges. BESS, fuel cells, and hydrogen storage systems have a moderate share due to high investment costs and the need for more complex infrastructure compared to renewable systems.

There have been several studies [92,94,96,109], ref. [109] largely viewing the HRES design and optimization and considering hybrid system configurations incorporating numerous resources (i.e., PV, wind, diesel, biomass, batteries, and hydrogen storage) to deliver and manage an energy supply reliably and cost-effectively for rural or islanded regions. At the same time, other contributions [93,99,101] have published systematic reviews and multi-level assessments that have provided various angles towards planning frameworks, performance models, and trade-offs between cost, sustainability, and security. Application-specific studies have also increased in number, focusing on needs within defined contexts such as remote Arctic communities [95], southern Bangladesh [97,101] and villages in Egypt [106], while island contexts have included Pantelleria [98], Malawali [103], Henry Island [105], and Tsushima [111]. These were studies that note the application of HRESs can be modified in approaches to unique geographic, socio-economic, and climatic circumstances. Furthermore, a developing line of research has presented new and improved optimization methods, such as utilizing the hybrid golden search algorithm [102], improving dung beetle optimization [104], or enhanced competition optimizers [106], to illustrate the diversity and advancements.

In evaluating HRES performance, a wide range of indicators is used to measure various aspects of system performance. Below, we will review only some of the basic indicators that have the most significant impact on the evaluation of these systems [128]:

• Expected Energy Not Supplied (EENS)

Definition 1.

The amount of energy that the system fails to supply due to resource intermittency or system outages [129]. EENS quantifies the risk of energy shortage in renewable-based systems. In rural off-grid areas, even small amounts of unmet demand can critically affect agriculture, irrigation, or essential services.

Parameters:

$L$: Average annual load (kW).

$D$: Duration of unavailability (s).

$$EENS={\displaystyle \frac{L.D}{3600}}$$

(1)

2.

Energy Index Ratio (EIR)

Definition 2.

The ratio of delivered energy to the total annual energy demand [128]. Different combinations of renewable sources (e.g., adding wind or energy plantations) are assessed based on their ability to increase EIR in remote village electrification.

Parameters:

$E_0$: Total annual energy demand (kWh).

$EIR$: 1: All demand is supplied (fully reliable).

$EIR$: 0.8: Only 80% of demand is supplied; 20% remains unmet.

$$EIR=1-{\displaystyle \frac{EENS}{E_0}}$$

(2)

3.

Customer Interruption Cost (CIC)

Definition 3.

The economic cost incurred by consumers due to unmet energy demand. Rural communities also suffer hidden costs from power outages, including crop loss and halted irrigation. CIC incorporates these costs into the system evaluation, making it a more realistic indicator of sustainability [128].

Parameters:

$IEAR$: Interrupted Energy Assessment Rate (Rs/kWh), representing the average value of lost energy.

$$CIC=EENS.IEAR$$

(3)

4.

Total System Cost

Definition 4.

The total cost of providing energy from multiple sources to different end-use sectors. Serves as the economic optimization objective. In combination with EIR and CIC, this equation ensures that the selected renewable mix is both cost-effective and reliable [128].

Parameters:

$C_{i,j}$: Unit cost of energy from source i for end-use j.

$R_{i,j}$: Amount of energy supplied from source i to end-use j.

$$C\_Total=\sum (C_{i,j}.R_{i,j})$$

(4)

The purpose of

Table 9. Hybrid energy system type in off-grid conditions in several sample countries and factors influencing its changes.

Reference	Country	Load Type	Hybrid Energy Systems	Variability
[130]	Algeria	Farm	PV/WT/Hydrogen Storage	Solar radiation and wind speed, Discount rate, hybrid power systems capital
[131]	Turkey	Residential, commercial, industrial	PV/Diesel Generator/Microturbine/Electrical Storage	Solar radiation, Inflation rate, Discount rate, Grid purchase and sale price, Fuel price
[132]	India	Residential, commercial, industrial	PV/WT/Diesel Generator/Microturbine/Electrical Storage	Discount rate, Fuel price, hybrid power systems capital, Solar radiation and wind speed
[133]	India	village	PV/WT/Electrical Storage	Solar radiation and wind speed, hybrid power systems capital
[134]	Philippines	Rural health units	PV/Diesel Generator/Electrical Storage	Inflation rate, Fuel price
[135]	Nigeria	Village	PV/WT/Diesel Generator/Electrical Storage	Grid purchase and sale price, Fuel price

, is to demonstrate the diversity of hybrid energy systems and the factors that influence the design and performance of these systems, especially in areas that require sustainable, grid-independent energy solutions.

Turkey is the most efficient country in this table due to its comprehensive and balanced approach towards hybrid power systems in off-grid settings. Turkey has successfully utilized a combination of PV, WT, diesel generators, microturbines, and electrical ESS. This combination of power sources offers more flexibility to meet the energy needs in residential, commercial, and industrial applications. Unlike most countries, such as India and the Philippines, which focus on limited alternatives like solar PV and wind energy, Turkey’s diverse energy portfolio provides greater sensitivity to shifting economic and environmental conditions. One of the key reasons for Turkey’s success is its planning, which is based on a range of economic and environmental variables. Besides solar irradiation and wind speed, Turkey also includes inflation rates, discount rates, fuel prices, and grid energy purchase prices. With this integrated approach, Turkey can better predict energy prices and system performance under various conditions, making it more sensitive to economic fluctuations and changes in the energy market. Furthermore, Türkiye’s hybrid power systems are designed to support various types of loads, including residential, commercial, and industrial ones. The extensive coverage makes the systems more adaptable and poised to supply energy in different sectors, thereby increasing convenience. Compared to countries like Algeria and India, which focus specifically on certain types of loads, such as rural or agricultural loads, the applicability of their systems could be limited.

Turkey has also demonstrated tremendous resilience against energy volatility brought about by climate change. With the integration of solar PV, wind, and storage installations, Turkey has mastered the ability to better withstand volatility in energy supply due to weather variations. This resilience with respect to climatic variations places Turkey in an advantageous position. At the same time, countries used to a single or two means of energy production might be in danger if the resources run out. Finally, economic management plays a crucial role in Turkey’s achievement. The country has managed economic factors, including system capital prices, energy purchase prices, and fuel prices, in a way that makes hybrid energy systems both technically and economically viable. This focus on both technical aspects and economics has helped Turkey install hybrid energy systems that are sustainable and cost-effective in the long run. Overall, Turkey’s success in embracing hybrid energy systems is owing to its holistic strategy with respect to a wide range of environmental, economic, and technical considerations. By seeking to balance diversified sources of energy with sound economic planning, Turkey has optimized its energy solutions and performed well under off-grid conditions, demonstrating sustainability and responsiveness to the changing availability of resources and economic conditions.

Table 10. Technical specifications and cost parameters of energy sources.

Technical Specifications	PV	WT	Diesel Generator	BESS
Operation & Maintenance Costs	$5/kW/year	$500/kW	$0.030/kW/h	$5/year
Lifespan	20–30 Year	20–25 Year	10–15 Year	5–15 Year
Capital cost	480 $/kW	8000 $/kW	500 $/kW	280 $/kW
Efficiency	15–20%	35–45%	30–40%	85–95%
Power capacity	1 kWp	10 kW	1 kW	1 kWh

presents the technical specifications and cost parameters for WT, PV, BESS, and diesel generators [2]. The capital cost and operation & maintenance costs values are based on data from the United States.

Table 11. Recommended HRES configurations derived from climatic parameters (irradiation, wind speed, and temperature) across selected regions [131,136].

Country	City	Specifications			Recommended HRES Configuration
		Irradiation (kWh/m2/day)	Average Wind Speed (m/s)	Average Temperature (°C)
Turkey	Samsun	3.60	4.40	12.4	PV + WT + BESS
	Bursa	4.30	4.27	14.8	PV + WT + BESS
	Antalya	4.60	3.73	15.6	PV + BESS
	Sanliurfa	5.30	5.53	19.8	PV + WT + Hydrogen Storage
Finland	Turku	2.98	3.29	6.09	PV + BESS + Hydrogen Storage
	Helsinki	2.90	4.25	6.95	WT + Hydrogen Storage
	Oulu	2.63	4.88	3.56	WT + Hydrogen Storage
	Tampere	2.30	3.26	5.29	PV + BESS + Hydrogen Storage
Denmark	Aalborg	2.69	4.80	9.17	WT + BESS
	Esbjerg	2.95	5.06	9.58	WT + Hydrogen Storage
	Torshavn	1.94	7.23	7.33	WT + Hydrogen Storage
Sweden	Stockholm	2.85	3.11	7.34	PV + Battery + Hydrogen Storage
	Gothenburg	2.76	4.23	9.47	WT + BESS
	Trondheim	2.31	2.32	5.33	PV + BESS + Hydrogen Storage

, provides the lead climatic parameters and the resulting suggested HRES configurations for the selected cities of Turkey, Finland, Denmark, and Sweden. Based on solar irradiation, wind speed, and mean temperature, the table recommends each city’s most suitable combination of PV, WT, and storage technology.

Warmer and high-irradiation locations such as Antalya accommodate a PV + BESS configuration as the most suitable, with solar input being the dominant, and the short-term storage satisfactorily addresses the daily fluctuation. For other urban areas in the north, such as Helsinki and Oulu, with lower solar input but stronger wind regimes, WT + Hydrogen Storage systems are required to ensure long-term sustainability over extended low-sunlight periods. Locations with an equal balance of resources, such as Aalborg and Bursa, benefit from system stability and everyday energy continuity with PV + WT + BESSconfigurations.

Table 11, indicates that solar-based systems with short-term storage are advantageous in warm climates, while cold, windy climates need hybrid setups with hydrogen for seasonal energy balancing and greater system resilience.

Seasonal variation in energy demand, particularly for off-grid locations such as vacation homes, poses a significant challenge to the design of renewable energy systems [137]. The application of hybrid systems with renewable energy sources and storage systems is an effective method of overcoming these challenges. Based on recent studies, PV, wind, fuel cell, and diesel generator hybrids were determined to be optimal in addressing seasonal energy demand variations. These systems use solar and wind energy during high-production seasons (e.g., summer) and stored energy or backup diesel generators during low-production seasons (e.g., winter). These hybrid systems ensure a stable and continuous energy supply, even during periods of low renewable energy generation, through the use of ESS, including hydrogen and batteries, for the regulation of energy during periods of low demand. In addition, the use of energy storage in the form of BESS or hydrogen storages helps enhance the flexibility of such systems to adapt to seasonal variations. BESS, in particular are more economically viable than hydrogen-based storage in most instances, with cost-effective solutions for off-grid power systems. The ability to store excess energy generated by windy and sunny periods allows the systems to meet energy demands during the off-season, even when wind and solar energy resources are in abundance. In reference [138], various case studies are tested, and hybrid systems that combine (PV, WT, Fuel Cell, and diesel generator) offer the most effective solution to address seasonal variations in energy demand for off-grid vacation homes and similar applications.

The performance of HRES is highly dependent on local climatic indicators such as solar irradiance, wind speed, and ambient temperature, which all vary seasonally and regionally. To quantitatively assess these effects, this section summarizes selected studies from different geographic and climate zones. These studies characteristically provide measurable ranges of environmental parameters along with their respective outcomes on techno-economic and reliability performance of HRES.

HRES can reduce grid dependence and enhance system reliability. Their performance is highly sensitive to environmental variability, however. In reference [139], Alkhafa et al. conducted a thorough performance analysis of a PV–WT-battery hybrid system under variable operating conditions using a dynamic Python-based model. The study integrated real-world datasets of solar irradiance, wind speed, temperature, and residential load of coastal Bangladesh. The authors confirmed that solar power topped generation (≈62%) in favorable conditions, while variable weather increased Loss of Load Probability from 0.8% to 12.4% and raised LCOE by 64% [139]. The results emphasize the importance of battery capacity and wind uncertainty in cost and reliability, emphasizing the need for optimal storage and predictive control to ensure stable long-term operation.

Furqan Asghar et al. in [140] discussed PV–Deisel generator–BESS and PV–WT– Deisel generator-BESSs under Saudi Arabia’s five typical climatic regions, i.e., eastern, central, northern, western, and southern regions, to estimate the impact of solar irradiance, wind speed, and temperature on efficiency, cost, and emissions of the system. The simulation results showed widespread spatial variability: the northern region performed best in the lowest levelized cost of electricity (≈0.10–0.12 USD/kWh), the highest proportion of renewable power (up to 97%), and the lowest CO₂ emissions (≈0.78 t yr⁻¹). Western and southern regions had relatively higher LCOE (≈0.13–0.14 USD/kWh) and emissions (≈2.6 t yr⁻¹) due to lesser solar and wind capacity and higher ambient temperatures [140]. These findings highlight the importance of local climatic conditions in dictating the layout and economic feasibility of hybrid energy systems for telecommunications applications in different geographic areas.

Reference [141] aims to analyze regional feasibility and performance of solar-green hydrogen hybrid energy systems in relation to different climatic conditions. Its case studies include six diverse zones in Pakistan Rawalpindi, Bahawalpur, Quetta, Gilgit, DG Khan, and Karachi representing different levels of solar irradiance and economic conditions. According to a multi-criteria decision analysis methodology, the study integrates techno-economic and environmental factors, which are utilized in optimizing PV array component sizes, electrolyzers, and hydrogen storage units for supplying uninterrupted 1 MW power supply. The findings indicate that those areas with higher solar irradiance, such as Bahawalpur and DG Khan, yield lowest LCOE (≈0.10 USD/kWh) and highest CO₂ abatement capability (up to 170,000 tons in 25 years) [141]. Low-irradiance and cold regions such as Gilgit exhibit higher capital expenses and operating costs. Generally, the reference emphasizes the strategic significance of solar-green hydrogen hybrid energy systems in enhancing energy resilience, carbon neutrality, and sustainable development in sun-rich developing nations.

Duan et al. in work [142], proposed a new HRES sizing scheme using PV, WT, and fuel cell technologies with hydrogen-based energy storage. The general aim of the study was to minimize total system cost, maximize reliability, and manage seasonal variation through a three-dimensional multi-objective optimization model with total cost of hybrid system operation, power provision shortfall index, and hours provision shortfall index. The case study involved Beijing, China, where actual and forecasted meteorological and load data were contrasted across four seasons (autumn, spring, summer, and winter). In an attempt to enhance the predictive accuracy, a hybrid forecasting model was designed by integrating an enhanced weighted average algorithm with an artificial neural network. The findings demonstrated that the PV/WT/fuel cell configuration exhibited higher cost-effectiveness and reliability compared to PV/fuel cell and WT/fuel cell options. According to the proposed hybrid forecasting and optimization platform, the total cost of operation has been reduced by up to 10.62% [142]. At the same time, forecast accuracy has been improved with significantly lower mean absolute percentage error compared to conventional artificial neural network models. The study concluded that incorporating precise data forecasting and seasonal analysis into HRES design significantly enhances system efficiency, reliability, and economic performance.

In reference [143], the authors emphasize the central role of climatic variability and regional differentiation for the optimum design of Rural HRES. The study’s primary objective is to analyze the impact of spatial differences in solar irradiance, wind speed, hydrological resources, and biomass potential on technology selection, capacity planning, and system operation strategies. Four rural counties in China with varied climatic conditions were selected as case studies: Honghe, Shenzhen, Lankao, and Yiling. In the first part, a multi-criteria decision-making approach was developed to evaluate regional renewable energy potential from resource availability, economic feasibility, and carbon reduction potential. The results showed that the optimal configuration of Rural HRES depends highly on local geographic and climatic conditions. Specifically, the “WT + PV + Biomass Power” scenario offered the minimum system cost (50.71 million yuan), and the “WT + PV + Hydropower” scenario had the maximum exergy efficiency (79.32%) [143]. Furthermore, the study highlighted that neglecting seasonal and climatic variability reduces system efficiency and increases reliance on external grid power. The proposed bi-objective optimization framework, which includes economic and exergy efficiency objectives, provides a climate-resilient and balanced planning strategy for rural energy systems. The results show that region-specific and climate-sensitive planning is paramount to providing sustainable, affordable, and reliable HRESfor rural areas.

Table 12. Quantitative summary of HRES performance under different climatic and regional conditions.

Ref.	Geographic Region/Country	HRES Configuration	Climatic Indicators Considered	Key Quantitative Results	Main Findings
[144]	Coastal Bangladesh	PV–WT–BESS	Solar irradiance, wind speed, ambient temperature	Solar generation share about 62%; Loss of Load Probability increased from 0.8% to 12.4% under variable weather; Levelized Cost of Electricity increased by 64%	Weather variability has a strong effect on system cost and reliability; adequate battery capacity and predictive control are essential for stable operation.
[145]	Five regions in Saudi Arabia (north, south, east, west, central)	PV–Diesel–BESS and PV–WT–Diesel Generator–BESS	Solar irradiance, wind speed, ambient temperature	Levelized Cost of Electricity ranged from 0.10 to 0.14 USD per kWh; CO2 emissions ranged from 0.78 to 2.6 tons per year; renewable energy shares up to 97%	Northern region showed the lowest cost and emissions; western and southern regions showed higher cost and lower efficiency due to weaker solar and wind resources and higher temperatures.
[146]	Six cities in Pakistan (Bahawalpur, DG Khan, Gilgit, Rawalpindi, Quetta, Karachi)	PV–Electrolyzer–Hydrogen storage	Solar irradiance, ambient temperature	Levelized Cost of Electricity about 0.10 USD per kWh; CO2 reduction up to 170,000 tons over 25 years	Regions with higher solar irradiance achieved the lowest cost and highest carbon reduction; cold and low-irradiance regions had higher investment and operational costs.
[147]	Beijing, China	PV–Wind–Fuel cell (hydrogen-based storage)	Seasonal temperature, solar irradiance, load data	Total operating cost reduced by 10.62%; forecast accuracy significantly improved with lower mean absolute percentage error	Integrating precise forecasting and seasonal analysis improves overall cost-effectiveness, reliability, and efficiency of the hybrid system.
[148]	Four rural counties in China (Honghe, Shenzhen, Lankao, Yiling)	Wind + PV + Biomass or Wind + PV + Hydropower	Solar irradiance, wind speed, hydrology, biomass potential	Minimum total system cost was 50.71 million yuan; maximum exergy efficiency reached 79.32%	Optimal HRES configuration depends strongly on local climatic and resource conditions; climate-sensitive planning improves system sustainability and reliability.

summarizes the content presented by the above studies.

4. Metaheuristic Optimization Approaches for Off-Grid HRES

Metaheuristic optimization approaches can be used for off-grid renewable energy system planning. Some of these algorithms are reviewed below:

4.1. Red Panda Optimization (RPO)

RPO is a novel metaheuristic inspired by the foraging behavior and adaptation mechanisms of the red panda [144]. RPO encompasses two disparate phases: exploration, with a wide scan of the search space to avoid premature convergence, and exploitation, with iterative refining of promising solutions in order to achieve the global optimum. Its local optima-breaking ability, fast convergence rate, and tolerance in the solution of nonlinear and constrained optimization problems set RPO apart.

A recent study introduced a novel planning model for off-grid RES with hydrogen storage, bio-waste facilities, and smart EV charging [145]. The main contribution of this study is to coordinate the electricity and hydrogen flows at the same time in a single planning model, which has not been the emphasis of the majority of the previous studies. In addition, the system is systematically incorporated with a bio-waste component that, in addition to producing renewable energy, reduces environmental pollution through organic waste conversion [145]. Another valuable addition is the use of a smart charging mechanism for EV, allowing charging schedules to be harmonized with system operation and thereby maximizing economic performance. In the interest of being reliable in real-world scenarios, the study applies scenario-based stochastic optimization to model uncertainties of load, renewable generation, and EV use, as well as the RPO algorithm as the solver for overcoming the nonlinear complexity of the problem [145].

The case study was conducted using data for Finland’s Espoo, where an isolated load profile of a society having both electrical and hydrogen demand was considered. The systems were compared for different configurations: (i) base system with wind, PV, and hydrogen storage; (ii) incorporation of bio-waste unit; (iii) inclusion of EV with regular charging; (iv) inclusion of EV with smart charging; and (v) overall model taking into account hydrogen load. The comparative study highlighted several key findings. First, the introduction of a bio-waste unit reduced installation cost annually by 17.8% compared to the base case, highlighting its twofold economic and environmental advantage. Second, smart charging for EV reduced planning costs by 7.8% compared to conventional charging, demonstrating the efficiency of demand-side flexibility within off-grid systems. Third, the presence of hydrogen storage improved economic performance by approximately 7.9%, a more substantial benefit than that of CAES (2.1%), and simultaneously enabled hydrogen supply to exclusive end-users. Finally, as hydrogen loads were modeled explicitly, system planning cost increased overall, yet system design turned more comprehensive and more durable [145].

4.2. Particle Swarm Optimization (PSO)

The PSO algorithm is inspired by the collective behavior of flocks of birds and schools of fish [146]. The particle is a potential solution that updates its position depending on its best experience and the global best experience of the swarm. PSO is easy to implement and converges fast in early iterations. It is, however, prone to premature convergence in complex, high-dimensional problems and requires proper parameter tuning.

The PSO algorithm was utilized as the model for optimizing freestanding renewables with hydrogen storage in a study published recently [147]. Its main innovations are as follows: firstly, unlike most of the earlier studies that assumed constant efficiencies for fuel cells and electrolyzers, careful electrochemical modeling was used to determine part-load performance curves, thereby enabling a more realistic assessment of hydrogen-based component lifetime and cost. Second, BESS and hydrogen equipment lifetime were modeled dynamically based on annual operation profiles rather than static assumptions, thereby improving the accuracy of the Levelized Cost of Energy (LCOE) calculation. Third, the study suggested hybridization of ESS technologies, specifically, a BESS –hydrogen hybrid, as the optimal solution to prevent over dimensioning of PV arrays or BESS banks and reduce system costs. The case study was conducted for Ginostra village on the Stromboli Island (Italy). This entirely off-grid location conventionally operates with diesel generators, with an estimated electricity cost of around 0.86 €/kWh [147]. In the optimization achieved using PSO, it was shown that the integration of solar PV, lithium-ion batteries, and a hydrogen storage system was capable of reducing the LCOE to around 0.51 €/kWh [147]. Key findings state that (1) lithium-ion batteries are more efficient, self-discharge less, and possess a greater lifetime than lead-acid batteries, (2) alkaline electrolyzers are more inexpensive and longer-lived than PEM electrolyzers, and (3) hybrid storage systems (BESS + hydrogen) offer the cheapest and most reliable off-grid community solution with an assembly of the short-term versatility of batteries and long-term storage ability of hydrogen. In conclusion, this research confirmed that PSO is a powerful and efficient optimization technique for determining the optimal sizing of components of renewable energy systems [148]. Its application enables the production of sustainable, stand-alone, and cost-efficient power systems that can replace diesel-based power generation in remote areas while reducing greenhouse gases and local emissions at the same time.

4.3. Whale Optimization Algorithm (WOA)

The bubble-net prey-pursuit behavior of humpback whales inspires the WOA [149,150]. It operates on three mechanisms: prey encircling, spiral update, and random search. The combination of the three mechanisms gives a good trade-off between global and local search. WOA has good exploration ability to search complex landscapes and solve nonlinear optimization problems. Its limitation is slower convergence in the later stages and sensitivity to the number of dimensions.

Work [151], addressed the design of a hybrid system consisting of PV panels, biowaste units, and a fuel cell with hydrogen storage for power supply in off-grid communities. The major innovations are three-fold: (i) integration of PV and biowaste production with hydrogen storage in a manner such that economic as well as reliability constraints are satisfied in tandem, (ii) introducing reliability indices in open form such as the Loss of Power Supply Probability (LPSP), Loss of Load Expectation (LOLE), and Loss of Energy Expectation (LOEE) into the optimization, and (iii) employing the Whale Optimization Algorithm (WOA) that yields faster convergence and higher accuracy than conventional techniques such as PSO. A case study was implemented in Ardabil city, Iran, for various scenarios, including biowaste–fuel cell system design, PV–biowaste–fuel cell system design, assessment of equipment availability (PV, biowaste, and inverter), and analysis of the impact of increased fuel cell investment costs [151]. The results confirmed that the whole PV–biowaste–fuel cell system with hydrogen storage operated at optimal conditions with a Total Net Present Cost (TNPC) of around $2.82 million, a Cost of Energy (COE) of $0.5238/kWh, and a very low LPSP of 0.0029, reflecting high reliability [151]. In comparison to PSO, WOA exhibited cost and reliability performance that were vastly similar but outperformed the latter when it came to the rate of convergence as well as accuracy in determining the optimal system configuration. Scenario analysis further indicated that lowering component availability increased TNPC and worsened reliability indicators. At the same time, the elevated investment costs of the fuel cell reduced its contribution to production, improved the reliance on PV, raised the total cost, and reduced overall reliability. In summary, the study confirmed that WOA is an effective tool for the optimal design of hybrid PV–biowaste–fuel cell systems, offering more stable and economical solutions to remote off-grid locations than existing optimization methods [150].

4.4. Harmony Search Algorithm (HSA)

HSA is based on how musicians improvise to reach a particular harmony. All candidate solutions are stored in a memory structure. When a new solution is required, it can be generated by recalling the selected memory, making minor adjustments, or creating random alternatives. HSA has several advantages, including its intuitive nature, which does not require the use of derivatives, and its capability to solve both discrete and continuous problems. The main disadvantage of HSA, like other meta-heuristic algorithms, is that its rate of convergence is moderate compared to newer algorithms.

Researchers in [152] have conducted valuable research on the design and optimization of a hybrid system consisting of PV panels, WTs, a diesel generator, and BESS to power a remote off-grid area in Rafsanjan, Iran. The case study revealed that relying solely on diesel generation is both costly and environmentally harmful, producing over 64,000 kg of CO₂ annually. Comparative analysis of system configurations showed that the wind/diesel/BESS was the most cost-effective solution, with a total annual cost of about $15,673. In contrast, other configurations, such as PV/diesel/BESS or PV/wind/diesel/BESS, resulted in higher costs. Furthermore, HSA consistently outperformed the discrete simulated annealing algorithm in terms of accuracy and stability of the optimization results. Overall, the findings demonstrate that integrating renewables with diesel and BESS provides an economical and reliable option for off-grid electrification, and HSA serves as an efficient tool for determining the optimal system configuration.

4.5. Ant Lion Optimizer (ALO)

The ALO emulates the hunting behavior of antlions, utilizing both antlions and ants in the process of selecting a new candidate solution [153]. In this case, ants walk randomly around the search space, while antlions constrain the movement of ants. The best antlions are selected to be elite solutions to direct their search. The ALO algorithm strikes an effective balance between exploration and exploitation, while achieving near-optimal solutions with high accuracy. The primary disadvantage of ALO is that it incurs a higher computational cost compared to large-scale algorithms.

Reference [154], designed an optimization framework for planning a completely renewable islanded hybrid power system supplying electricity and thermal energy simultaneously, as well as integrating EV parking lots with intelligent charging. The main innovation is in four aspects: (i) joint modeling of heat and electricity demand simultaneously using the same model, (ii) inclusion of a bio-waste power generation system with combined heat and power capability, (iii) utilization of CAES rather than batteries or hydrogen storage, and (iv) inclusion of EV smart charging in the planning model. In order to manage uncertainty in bio-waste gas generation, wind velocity, load, and EV parameters, the point estimate method has been used. The ALO has been used for solving the nonlinear optimization issue. A case study was performed in Espoo, Finland, taking into consideration realistic electrical/thermal load, renewable production, and EV behavior. Five scenarios were simulated, ranging from solely electrical supply with no EVs to full integration of electricity, heat, and EV smart charging. The outcomes showed that:

Utilization of CAES reduced planning expenditure by 7.7% compared to batteries and by 12.9% compared to hydrogen storage, thereby determining its economic advantage [154].

Parallel supply of electricity and heat increased planning cost by about 57%, but offered a more sustainable and efficient system.

Overall, this study demonstrated that CAES has the potential to propel economic design in renewable-based off-grid systems through providing secure large-scale storage at more affordable costs, especially in combination with bio-waste CHP units and EV smart charging technologies.

4.6. Honey Bee Mating Optimization (HBMO)

HBMO Honey Bee Mating Optimization is based on the natural mating of a queen bee with drones [155]. The queen bee represents the best solution, and the drones represent other potential solutions. The process generates offspring solutions through a crossover mechanism, which helps to increase population diversity and reduce the chances of the algorithm getting trapped in a local optimal solution. Although HBMO is computationally more complex than other simple swarm-based algorithms, it is capable of generating better quality solutions.

Work [156], demonstrated the optimal planning of a renewable off-grid system with a WT and biowaste systems for heat and electricity, combined with stationary (batteries) and mobile EV storage. The novelty of the study is two-fold: (i) the plan considered the degradation cost of both stationary and mobile storage in a unified approach; and (ii) made use of an innovative hybrid heuristic algorithm (HBMO + ABC) that allows faster convergence time and has been shown to lead to more reliable solutions than independent meta-heuristics. The study includes a case study for Espoo, Finland, using three planning scenarios: (i) without EVs; (ii) EVs with conventional charging; and (iii) EVs with smart charging. Smart charging of EVs was shown to provide a 7.6% reduction in planning costs, while conventional charging of EVs resulted in a nearly 39.7% increase in costs [156]. The hybrid HBMO + ABC algorithm required fewer than half the iterations and had a very low solution variance (0.94%), and found a more reliable solution than other methods, including krill herd optimization, sine cosine algorithm, and teaching learning based optimization. This work illustrates how solar generation, combined with stationary and mobile storage and support through smart charging, can provide a reliable and cost-effective strategy for off-grid energy systems. The HBMO + ABC algorithm demonstrates efficiency and robustness as an optimization method for minimizing costs.

4.7. Harris Hawks Algorithm (HHA)

The HHA is an evolutionary metaheuristic inspired by the cooperative hunting strategy of Harris’ hawks, developed to solve complex optimization problems. The HHA mimics hunting variations such as soft and hard besiege, sudden dives, and unexpected dives, and therefore modifies the process of balance between exploring the search space vs. exploiting promising regions. The main mechanism is connected to prey energy, which behaves adaptively in time and eventually determines the change from global search to local search. Due to its adaptive nature, the algorithm also avoids local optima. It achieves rapid convergence and efficient solutions in a wide range of contexts, including energy systems, feature selection in machine learning, industrial design, and multi-objective optimization problems.

Reference [157], has provided a valuable study on the application of this algorithm. This research performed techno-economic and environmental optimization of hydrogen-based hybrid energy systems for remote off-grid communities in Broken Hill, New South Wales, Australia. The innovations are four in type: (i) comparing two configurations a PV, BESS, fuel cell, electrolyzer, and hydrogen storage included one, and a BESS -excluded one, (ii) applying state-of-the-art metaheuristic algorithms such as the Harris Hawks Algorithm (HHA), red-tailed hawk algorithm, and NSGA-II to determine the optimal component sizes, (iii) designing a rule-based energy management framework for real-time supply-demand balance and stability of the system, and (iv) including sensitivity, environmental, and socio-economic analyses to provide an integrated sustainability appraisal. The case study results showed that HHA–Configuration 1 (PV + BESS + fuel cell + electrolyzer + hydrogen storage) had the best performance with a net present cost of $338,111, a levelized cost of electricity of $0.185/kWh, and a levelized cost of hydrogen of $4.60/kg [157]. Sensitivity analysis showed the cost of PV module and hydrogen storage as the most significant parameters, and lowering the efficiency of fuel cell from 40% to 60% would decrease the costs by up to 40% [157]. From an environmental perspective, the estimated HES prevented over 500,000 kg of CO₂ annually from being released compared to a comparable diesel system. Socio-economic assessment also showed that the system had the potential to raise the human development index by providing access to healthcare, education, and economic opportunities, as well as providing employment opportunities in PV installation, BESS servicing, and hydrogen infrastructure. Overall, the studies demonstrated how PV-based hydrogen hybrid systems represent a cost-effective, sustainable, and scalable solution for energy access in remote regions with the HHA emerging as a useful tool for the determination of optimal system designs.

4.8. Artificial Bee Colony (ABC)

The ABC algorithm simulates the foraging behavior of honey bees [158]. It divides the population into employed bees, onlooker bees, and scout bees, which cooperate to exploit previously known food sources and search for new ones. ABC is simple, flexible, and efficient in nonlinear problem spaces with an outstanding balance between global exploration and local exploitation. However, it may be hindered by slow convergence in very complex or large-scale problems.

Study [159], aimed to address the capacity and knowledge gaps of local government units in formulating successful energy transition plans through the techno-economic optimization of an HRES for Tagoloan, Misamis Oriental, Philippines. This study approach brought together renewable resource assessment, mathematical and economic modeling, and the use of the ABC algorithm for optimization of the system. The optimization analysis using the ABC-based algorithm, which identified the least-cost scenario, revealed a 65 kW PV system with 250 BESS units, excluding WT and biomass gasifiers. This configuration achieved a net present cost (NPC) of ₱20.68 million, a LCOE of ₱16.95/kWh, and annual operating costs of ₱930,201.61, each lower than values obtained from using HOMER software [159]. The LCOE is still higher than grid electricity tariffs; however, the optimized HRES is a significant move closer to sustainable and potentially competitive local energy solutions. The study provides data-driven insights, which give the local government units a solid foundation for renewable energy policy and contribute to accelerating the local energy transition.

4.9. Non-Dominated Sorting Genetic Algorithm III (NSGA-III)

NSGA-III is a method of evolutionary algorithms to tackle multi-objective optimization problems, particularly for problems that have more than three objectives (i.e., many-objective optimization). NSGA-III is an extension of NSGA-II, which performs very well when a problem has two or three objectives but becomes unmanageable as the number of objectives becomes large [160].

In work [161], aimed to optimize the configuration of off-grid residential HRES using an improved NSGA-III integrated with a Hypervolume indicator. The primary objectives were to minimize total annual cost, enhance system reliability by reducing the LPSP, and minimize renewable energy loss. This study proposed a comprehensive approach for optimizing HRES, including PV, WT, BESS, hydrogen storage, and proton exchange membrane fuel cells, for residential applications independent of the grid. The study utilized the NSGA-III algorithm, a popular multi-objective optimization tool, improved with the Hypervolume indicator to enhance convergence speed and maintain diversity within the Pareto front. The system under study involved several renewable energy sources, such as PV and wind energy, alongside hydrogen storage and BESS for energy management. The improved NSGA-III with the Hypervolume indicator showed excellent performance in solving complex multi-objective optimization problems. The Hypervolume improvement ensured better convergence and diversity in the Pareto front. The optimization led to a Pareto Front Superiority Index (SI) of 82.19%, indicating the effectiveness of the algorithm in achieving high-quality solutions for the multi-dimensional problem [161].

To enhance the real-world applicability of optimization algorithms in designing and planning renewable energy systems, technical optimization is insufficient; their economic and social aspects also need consideration. Algorithms such as HHA, NSGA-III, PSO, and RPO can reduce system cost by minimizing the system composition, size, and production and storage scheduling. These cost savings can render renewable energy options more financially viable for off-grid communities and, over the long term, help support the development of sustainable energy infrastructure in remote areas. Apart from the direct economic effects, the deployment of renewable energy systems creates new employment opportunities in installation, operation, maintenance, equipment manufacturing, and intelligent energy management. If properly supported by policies, the new employment opportunities can lead to improvements in the local economy and social conditions.

Furthermore, increased access to clean and inexpensive energy can also further boost human development indicators like education, health, and prosperity. As far as such factors are concerned, optimization of renewable energy systems is relevant both technically and economically as well as socially. Such research can provide efficient and practical models that can facilitate sustainable development in plural societies. Nevertheless, consideration should be given to potential constraints, such as initial investment levels, workforce training, and technology deployment in diverse environments.

Figure 8, compares several metaheuristic optimization algorithms based on four key criteria.

For small and isolated systems, quicker convergence and computationally less demanding algorithms like PSO, ABC, and HSA perform better since they are easy and effective. For large and hybrid systems, algorithms like RPO, HHA, and NSGA-III, as they are more powerful in setting up stronger exploration–exploitation balance and are robust, perform better in complex, multi-objective, or dynamic optimization problems.

5. Discussion

The consolidation of recent literature irrevocably demonstrates that the path to cost-efficient and reliable off-grid electricity is through the simultaneous implementation of hybrid storage systems, hybrid AC/DC configurations, and advanced optimization approaches. The storage strategy of the portfolio, combining the quick response of batteries with the long-duration capacity of hydrogen or mechanical storage, becomes essential for addressing the intermittency of solar and wind resources. The hybrid AC/DC topology is also the dominant structure due to its intrinsic flexibility, effectiveness in supporting multiple components, and enhanced resilience. In planning and operation, metaheuristic algorithms, and specifically hybrid models like HBMO + ABC and RPO, have performed superiorly in mapping the intricate, nonlinear, and multi-objective problem space of HRES, leading to significant LCOE reductions and improved reliability measures.

It is a dynamic domain, however, and such a review has limitations inherent in which concurrently emphasize important future research directions:

• Beyond Qualitative Synthesis: As a qualitative synthesis of the subject in its current form as an overall narrative review, the subsequent step in rational order would be a systematic review or meta-analysis quantitatively synthesizing performance data (e.g., LCOE, EENS) from research studies. This would provide statistical inference about the most significant design levers and technologies for given conditions, and provide policymakers and system planners with more generalized and definitive guidance.
• Bridging the Laboratory-to-Field Gap: While simulation case studies and optimization models are everywhere, something is conspicuous by its absence in the form of long-term, real-world operating data on new technologies like green hydrogen storage and flow batteries for off-grid use. Longitudinal pilot deployments following degradation, operating failure, maintenance costs, and real-world efficiency are to be given high priority in future research. This empirical data is key to validating models, minimizing risk to investors, and maximizing lifecycle analysis.
• Future Generation of Intelligent EMS: Optimization’s role is often restricted to the planning stage. The future vision lies in the real-time operation of HRES by AI and Digital Twins. Future research should focus on developing AI-based EMS that utilize predictive analytics for load and generation forecasting, provide predictive maintenance, and autonomously adjust control strategies to maximize system lifetime and profitability, surpassing traditional rule-based methods.
• A Holistic Socio-Techno-Economic Framework: Techno-economic models are typically predominant in the very important human and institutional elements. The sustainability and long-term implementation of HRES in this area is highly dependent on factors such as social acceptance, new business models, and supportive policies. Interdisciplinary research, incorporating technical modeling, socio-behavioral science, political economy analysis, and circular economy frameworks, is needed to develop comprehensive implementation strategies to successfully deliver long-term project outcomes.
• Modularity, Standardization, and Circularity: A lack of standardized system components/elements and control protocols makes it difficult to scale and raises the cost. Researching modular and plug-and-play HRES architectures can help facilitate deployment and maintenance.

At the same time, as the number of batteries facing decommissioning is expected to increase, there is a need to develop sustainable and economically sound circular economy frameworks for reuse, remanufacturing and recycling.

6. Conclusions

This review has presented a critical state-of-the-art of HRES for off-grid electrification under three parts: energy storage technologies, system topologies, and metaheuristic optimization methods. The review reveals that feasible off-grid systems require an integrative approach comprising hybrid storage solutions (e.g., battery-hydrogen or battery-CAES configurations), hybrid AC/DC topologies, and advanced optimization algorithms. Major results indicate that hybrid AC/DC topologies offer the most versatile solution for system stability with the integration of heterogeneous energy sources. Furthermore, hybrid metaheuristic algorithms, namely HBMO + ABC and RPO, offer superior performance in system configuration optimization and cost minimization. Studies have demonstrated 7–8% cost reduction with the integration of smart EV charging and approximately 7.9% improvement in economic performance with the deployment of hydrogen storage. The review also highlights significant issues that warrant further investigation, including the development of standardized protocols, circular economy approaches to component end-of-life management, and the need for AI performance management systems. Future directions should include quantitative meta-analysis of performance parameters, long-term confirmation of new-emerging technologies, using pilot studies, and socio-technical integration within system design.