Micro Gas Turbines in the Global Energy Landscape: Bridging the Techno-Economic Gap with Comparative and Adaptive Insights from Internal Combustion Engines and Renewable Energy Sources

Abstract

This paper investigates the potential of Micro Gas Turbines (MGTs) in the global shift towards low-carbon energy systems, particularly focusing on their integration within microgrids and distributed energy generation systems. MGTs, recognized for their fuel flexibility and efficiency, have yet to achieve the commercialization success of rival technologies such as Internal Combustion Engines (ICEs), wind turbines, and solar power (PV) installations. Through a comprehensive review of recent techno-economic assessment (TEA) studies, we highlight the challenges and opportunities for MGTs, emphasizing the critical role of TEA in driving market penetration and technological advancement. Comparative analysis with ICE and RES technologies reveals significant gaps in TEA activities for MGTs, which have hindered their broader adoption. This paper also explores the learning and experience effects associated with TEA, demonstrating how increased research activities have propelled the success of ICE and RES technologies. The analysis reveals a broad range of learning and experience effects, with learning rates (α) varying from 0.1 to 0.25 and experience rates (β) from 0.05 to 0.15, highlighting the significant role these effects play in reducing the levelized cost of energy (LCOE) and improving the net present value (NPV) of MGT systems. Hybrid systems integrating MGTs with renewable energy sources (RESs) and ICE technologies demonstrate the most substantial cost reductions and efficiency improvements, with systems like the hybrid renewable energy CCHP with ICE achieving a learning rate of α = 0.25 and significant LCOE reductions from USD 0.02/kWh to USD 0.017/kWh. These findings emphasize the need for targeted TEA studies and strategic investments to unlock the full potential of MGTs in a decarbonized energy landscape. By leveraging learning and experience effects, stakeholders can predict cost trajectories more accurately and make informed investment decisions, positioning MGTs as a competitive and sustainable energy solution in the global energy transition.

1. Introduction

The 21st century has steered toward an increased focus on achieving carbon neutrality, driving substantial efforts to reduce greenhouse gas emissions [1] and accelerate research across various energy sectors [2]. As the global focus intensifies on climate change and sustainable development, there is a notable increase in localized development efforts and significant progress even in developing nations [3]. The United Nations General Assembly [4] has articulated one of the most ambitious sustainable goals: “ensure access to affordable, reliable, sustainable modern energy for all” (Goal 7). M.A Hossin et al. (2024) [5] emphasized that energy demands per capita in countries like India and South America, as well as South Africa, will continue to rise, emphasizing the necessity for scalable solutions to achieve sustainable energy objectives. This context further underlines the importance of developing reliable, renewable-integrated micro energy systems that are economically feasible and can be deployed globally [6]. Such systems should enhance the social, economic, and environmental aspects of sustainable development projects [7].

Standalone renewable systems, such as wind turbines, solar panels [8,9], hydropower [10], wave energy [11,12,13], and other renewable technologies, have been deployed and researched using advanced techniques and methodologies [14] worldwide for future applications targeting a net zero vision [15]. However, the growth of these individual standalone renewable systems has been hindered by issues related to institutions, markets, knowledge, and existing infrastructure [16]. Technically, renewable systems often operate cyclically and face high uncertainties [17], resulting in reliability challenges and frequent annual failures [18,19]. To enhance the system reliability amidst these uncertainties, S. Shahzad et al. (2024) [20] suggested co-optimizing renewable energy generation and transmission, while G. Nadakuditi et al. (2023) [21] applied a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to optimize renewable systems under probabilistic constraints. Despite numerous efforts in numerical optimization to achieve optimal investment strategies [22] and energy system planning [23], accurate real-time predictions of renewable energy generation nonetheless remain challenging [24]. Therefore, integrating renewable systems with dispatchable generation technologies like MGTs as hybrids is highly beneficial for reliability (De Robbio et al., (2023)) [25].

MGTs are among the most versatile and efficient energy technologies, capable of integration with renewable sources in various configurations. MGTs’ multifunctionality allows them to serve as islanding sources in isolated scenarios, support distributed generation, and function as backup generators [26]. The Figure 1a,b bar charts each depict important trends related to MGTs over approximately similar time periods. Figure 1a’s chart illustrates the growth in the number of research documents related to MGTs from 2003 to 2023. The number of documents has shown a consistent upward trend over the years, indicating growing academic and research interest in MGTs. The increase is especially pronounced from around 2010 onwards, suggesting a significant rise in research activity and publications during this period. By 2023, the number of documents surpassed 1200, reflecting substantial engagement and exploration in this field. The chart in Figure 1b presents the projected growth of the global MGT market size, measured in millions of USD, from 2015 to 2026. The market size for MGTs has been steadily increasing, with projections indicating continued growth through 2026. The chart shows a notable upward trajectory, with the market size rising from around 100 million USD in 2015 to over 300 million USD by 2026. In the industrial sector, the global MGT market has been expanding at a compound annual growth rate (CAGR) of 9.23%, with projections to reach 364 million USD by 2026 [27]. This growth suggests the increasing adoption and commercialization of MGTs, likely driven by their versatility, efficiency, and the expanding interest in sustainable energy solutions. The Figure 1a chart’s upward trend in research publications corresponds with the Figure 1b chart’s growing market size. This suggests that increasing academic and industry interest is translating into market growth and commercialization. The projections for 2026 reflect a positive outlook for the MGT industry, indicating that continued research and development are likely contributing to the market’s expansion and the technology’s broader adoption. The evolution of the MGT market can be observed through the shifting market shares of different power generation technologies over time. Figure 1c illustrates the market shares of reciprocating engines, fuel cells, microturbines, boiler/steam turbines, and combustion turbines for the periods 2008–2013 and 2013–2017, and a predicted projection for 2017–2023. The data reveal a significant increase in the market share of MGTs from 17% in 2008–2013 to a predicted 30% in 2017–2023 (see Figure 1c). This growth indicates an increasing recognition of the benefits of MGTs, such as high efficiency, reliability, fuel flexibility, and suitability for combined heat and power (CHP) systems. Despite this positive trend, the MGT sector faces several challenges that necessitate adaptive and comparative development strategies [28].

MGTs with their “fuel flexibility” are positioned to play a pivotal role in the future of energy systems, particularly with the integration of hydrogen (H₂) as a clean fuel source [30]. Hydrogen’s potential as a sustainable and environmentally friendly energy carrier makes MGTs an attractive option for both distributed generation and the CHP generation segment and as backup power applications [26,28]. The U.S. Department of Energy’s CHP installation database [27] reveled that MGTs hold a record-breaking 25% share of the market for CHP installations in the 100 kW to 1.0 MW range. The growth in MGT adoption is attributed primarily to their ability to compete effectively with traditional reciprocating engines, which have long been the dominant technology in the global CHP market. Additionally, there is continuing expansion in the U.S. and even Europe CHP market [31], with annual capacity installations projected to rise from around 600 MW in 2024 to as high as 1400 MW by 2028 [32]. This growth is expected to be driven by the increasing popularity of CHP systems in smaller commercial applications, which are well suited for the capabilities of MGTs [33]. The trend of replacing ICE with MGT has clearly been initiated in recent years [34]. Interestingly, in Figure 1, the CHP market is being dominated by the Internal Combustion Engine (ICE). Similarly, current microgrids predominantly rely on ICE as backup generators [35,36]. However, ICEs are increasingly under scrutiny due to their carbon emissions, and the shift towards decarbonization is gradually phasing them out of this role [37]. Renewable energy sources (RESs), while critical to achieving carbon neutrality, face inherent challenges such as intermittency and reliability [38]. To address these issues and support the further growth of REsS, there is an urgent need for a clean, affordable, and reliable backup power source that can seamlessly integrate with microgrids [39].

This is the juncture at which MGTs are potentially excelling. MGTs offer several advantages, including high efficiency, fuel flexibility, and lower emissions, making them ideal candidates to replace ICEs as backup power sources in microgrids [40]. Their ability to run on H₂ and various H₂ blends further enhances their attractiveness, aligning with global decarbonization goals [28,34]. Despite these advantages, the economic feasibility of MGTs remains a significant hurdle, largely due to the lack of comprehensive techno-economic assessment (TEA) research activities [28,40]. Unlike the RES and ICE sectors, which have seen substantial growth due to an extensive pool of TEA activities [41], the MGT sector has lagged behind [40]. This has impeded the commercialization and broader adoption of MGT technology in the distributed energy generation field. In particular, the MGT’s wider adoption growth has been greatly affected and delayed by the lack of comparative TEA studies of MGTs with rival technologies [42]. While the MGT sector was not being comparatively assessed alongside rival technologies, it led to suboptimal economic assessments and slower commercialization compared to RES and ICE technologies. These two global players dominating the “microgrids” have benefited an increased market share from extensive techno-economic updates [43,44]. There is a clear research gap in the techno-economic analysis of MGTs, where existing studies have not fully adapted the methods and strategies used in the dominant power generation technologies to assess the true potential of MGTs and address their commercialization barriers in the future energy landscape. MGTs’ true potential, especially in the context of energy transition with H₂ as a fuel, could create a significant paradigm shift similar to the introduction of ICEs [28,34,40]. Comparative TE pathways are pivotal for MGT success in microgrids, to be integrated with RESs and to penetrate the ICE-dominated backup power market. Nonetheless, “techno-economy” is a mature branch of study and a methodological approach that evaluates the economic and technological viability of processes, products, or systems. As described by Z. Barahmand et al. (2022) [45], TEA serves as an essential tool for analyzing cost standards and assessing the economic feasibility of various technologies. Figure 2 illustrates the comparative development and potential future trajectory of MGTs in relation to ICEs and RESs. It highlights the current state of techno-economic analysis within each sector, showcasing the relative maturity and commercial penetration of ICE and RES technologies compared to the more nascent MGT sector. Figure 2a highlights the research question central to this study: how can MGT-TEA be improved and standardized by leveraging the learning and experience gained from the established ICE and RES sectors? This visual representation emphasizes the critical need for adopting proven TEA methodologies to enhance the competitiveness and integration of MGTs, particularly as they emerge as a viable replacement for ICEs in microgrid applications.

This paper addresses the challenges in the MGT sector by assessing strategies and TEA activities that can be adapted to promote its development, drawing lessons from the successful trajectories of RESs and ICEs. By integrating insights from these sectors, the paper provides a roadmap for enhancing the economic and technological viability of MGTs, positioning them not only as backup power sources but also as central to future low-carbon energy systems. A thorough review of recent literature on TEA activities for MGTs, especially when integrated with RES and ICE technologies, is conducted to examine the established techno-economic relationships within microgrid systems. This paper emphasizes the need for adoptable methodologies that can be synthesized into future TEA frameworks. Comparative assessments of the learning and experience curve effects from the RES and ICE sectors are applied to the MGT context, illustrating how these strategies can be harnessed to accelerate the development of MGTs. By leveraging these proven approaches, MGTs can enhance their competitiveness, contribute to the global energy transition, and play a pivotal role in achieving low-carbon energy targets by 2030 and carbon neutrality by 2050 [46,47].

The flowchart in Figure 2b provides the flow of the research paper’s key sections and their interconnections. The flowchart presented in this research outlines the structured methodology undertaken to evaluate and enhance the TEA of MGTs. The process begins by identifying the underdeveloped state of the MGT techno-economic sector, supported by a comprehensive review of recent MGT-TEA literature. Concurrently, a qualitative and quantitative analysis of the more advanced RES and ICE sectors is conducted. These sectors, having benefited from well-established TEA methodologies, provide a foundation for identifying and adapting transferable TE strategies, particularly focusing on the learning and experience curve effects that have driven their respective growth. Subsequently, the paper shifts to the extraction of recent MGT-TEA data, which is subjected to a novel mathematical analysis aimed at quantifying the impact of these learning and experience effects within the MGT framework. This innovative approach is designed to bridge the gap between MGTs’ theoretical potential and their practical economic performance. The econometric results are analyzed both before and after the application of these effects, allowing for a clear comparison and understanding of their influence on MGT economic viability. The flow concludes with a synthesis of the findings, highlighting how the application of refined TEA methodologies and novel models can advance the MGT sector’s commercial development, ultimately positioning it to be more competitive within the broader energy market. This structured approach provides a clear roadmap for enhancing the understanding and economic potential of MGTs, grounded in both qualitative insights and quantitative rigor.

2. MGT Potential and Position as a Prime Mover in the Dispatchable Energy Sector

While MGTs has a diverse capability, they are mostly utilized as micro-CHP units, because of their higher overall efficiency [48]. Research by J.L.H. Backman et al. (2012) [49] deliberated on the potential of MGTs for small-scale CHP (m-CHP) applications, particularly those generating less than 100 kWe. MGTs are modeled following principles of larger industrial systems, with influences drawn from automotive turbochargers and gas turbines. The concept of employing micro turbines for combined heat and power emerged in the 1990s, and m-CHPs entered the market in 2000. Figure 1 illustrates a cross-sectional view of an MGT.

An insightful introduction by Pilavachi et al. (2015) [50] presented the role of MGTs in the energy sector in terms of competitiveness with the other rival technologies. He conducted a comprehensive survey of mini and Micro Gas Turbine technology for CHP generation. The author highlighted the adaptability of this prime mover technology across diverse sectors, including industrial, commercial, and residential settings. Apart from these characteristics, MGTs offer advantages such as compact dimensions, minimal noise, compatibility with multiple fuels, and the recovery of high-grade waste heat. Nevertheless, their efficiency is lower compared to IC engines, and decreased efficiency at partial loads pose challenges to the widespread adoption of this technology. Figure 3 represents a single-shaft recuperated system. The incoming compressed air traverses the recuperator, where it undergoes heating. This preheated air then flows through the combustor, where high-pressure compressed NG is introduced. The high-pressure heated gases are discharged through the turbine, extracting energy to drive both the compressor and the alternator mounted on the shaft. Given the limited availability of commercialized devices using alternative prime mover technologies, this section also considers pre-commercialized units. As well as MGTs, there are also several other competitive m-CHP technologies such as Stirling Engines (SEs), fuel cells (FCs), the organic Rankine cycle (ORC), and fly wheel (FW) among them.

Figure 4 presents electrical and thermal power data collected from manufacturers’ websites by Simon Martinez et al. (2018) [52] for various micro-CHP prime mover technologies. It should be noted that, for systems using NG, the electricity-to-heat ratio averages 16%, which is lower than that of IC engines. Figure 4a emphasizes that FCs represent a promising technology, despite the currently available commercialized units exhibiting lower output power. FCs exhibit an average electricity-to-heat ratio of 88.6%, but consumers are not willing to bare the associated costs of FCs. On the other hand, the ORC and FW are technologies still in the developmental phase. However, it is worth mentioning that ORC systems exhibit a relatively low average electricity-to-heat ratio of 10.2%. Overall, MGTs represent a matured technology which fits in all categories checked in the market. When considering fuel sources, NG remains the most prevalent, but renewable sources like biogas, wood pellets, and vegetable oil have also been used. Figure 4b provides an overview of the overall, thermal, and electrical efficiency of the m-CHPs commercially available. As previously mentioned, these systems generally achieve a high overall efficiency (averaging 91.2%) among the examined technologies. Apart from the mix of prime movers for m-CHPs, MGTs remains a go-to choice when it comes to a cost-effective solution. FCs struggle due to larger capital investment costs (CAPEX), and ORC carries the potential of being hazardous and require specially trained maintenance in case of an overhaul or in an accident. On the other hand, FWs are relatively new, and will require period of time to mature and appear on the market. Under these considerations MGTs can be seen as a potential candidate for the future of m-CHP, but there exist obstacles and barriers that hinder the progression of MGT technology, one specifically being the lack of TEA-based research activities.

Adhering to the objectives and aims of this paper and to constrain its scope, after a brief description of MGT technology and its key features, further technical analysis is omitted. To gain a thorough understanding and a more in-depth knowledge and analysis of MGT’s technical details and cutting-edge advancements, and their future trends, refer to the authors’ previous article, A.H. Samitha Weerakoon et al. (2023) [53]. The theoretical techno-economy relationship with MGT is mathematically analyzed and a standardized framework proposed in the authors’ previous article: ref. A.H. Samitha Weerakoon (2024-I) [40]. An MGT running on 100% H2 and H2 fuel blends and H2-related MGT techno-economic aspects are comprehensively analyzed by the authors’ previous article, A.H Samitha Weerakoon (2024-II) [54]. How techno-economic analysis can be systematically applied to analyze, identify, and optimize cost-effective energy management strategies for MGTs in the context of energy landscapes evolving towards sustainable energy solutions is addressed in the authors’ published article. A.H. Samitha Weerakoon (2024-III) [43]. In this paper, a detailed and comparative analysis is conducted between the technological advancements of RES and ICE and techno-economic improvements to MGTs, which identifies the potential need for the fast-paced, adaptable TE progress of the MGT sector, enabling the fuel-flexible and multi-level advantages of MGTs to pave the path forward for sustainable energy generation. Refer to Supplementary Materials File S1 for a detailed overview of the recent literature and developments in the TEA of MGTs integrated with RESs and ICEs.

3. Why Techno-Economic Assessment (TEA) Studies Are Crucial for MGT Sector Development

TEA studies are pivotal for the development of the MGT sector, serving as a critical tool to bridge the gap between technical viability and market competitiveness in the rapidly evolving energy landscape [55]. As the global energy system transitions towards decentralized and renewable-based solutions, MGTs have the potential to play a crucial role in distributed energy generation, microgrids, and hybrid renewable systems [56]. However, without robust TEA studies, the economic case for MGTs remains underdeveloped, limiting investor confidence and market adoption. TEA studies provide a detailed analysis of capital expenditures (CAPEX), operational costs (OPEX), lifecycle performance, and market conditions, enabling stakeholders to make informed decisions about the technology’s scalability, profitability, and long-term potential. In an era where advanced technologies like batteries, solar PV, and wind are thriving due to continuous research-driven cost reductions, MGTs must leverage TEA to uncover pathways for cost optimization, to enhance performance through economies of scale, and identify niche markets where their unique capabilities, such as flexibility and reliability, can offer competitive advantages. Furthermore, TEA studies can guide policy development by quantifying the societal benefits of MGTs, such as emissions reductions and energy security, while also helping to craft business models that align with future energy trends like sector coupling and integrated energy systems [57]. In essence, TEA studies are not merely a technical exercise but a strategic imperative that will determine the MGT sector’s ability to scale, compete, and contribute meaningfully to the future low-carbon energy mix.

3.1. Comparative Assessment of MGT Sector Growth Compared to RES-Based TEA Activities: Insights, Trends, and Adaptable Strategies

Solar PV, wind, and battery technologies have emerged as the most successful power generation and storage options, largely due to the significant volume of annual techno-economic analysis (TEA) research supporting their development [58,59,60,61]. In contrast, MGTs have struggled with market penetration and commercial success, primarily due to limited TEA research [43]. Continuous TEA studies in renewables have helped identify market-driving forces, aligning with the “learning curve” and “experience curve” theories, closing research gaps and accelerating commercialization [62]. As more TEA studies are conducted, sensitivity and risk assessments improve, allowing stakeholders to make informed investment decisions, which boosts production and sales, leading to a decline in unit costs over time, as seen in Figure 5b,c. By 2022, cumulative research activities for batteries, solar PV, and wind reached figures of 1140, 989, and 542, respectively, reflecting rapid R&D growth, while MGT lagged behind with only 51 activities. The modest reduction in MGT costs, from 0.6 in 2010 to 2.7 in 2023, highlights the limited learning effects, especially when compared to battery technology, which experienced a significant cost decrease during the same period, from 60.0 to 0.6. MGTs need increased R&D investment, policy support, and integration with renewables to close this gap.

Variable renewable energy (VRE) technologies, particularly solar PV and wind, have seen dramatic reductions in capital costs (CAPEX) and the Levelized Cost of Electricity (LCoE) over the past decade. From 2010 to 2020, solar PV module costs dropped over 80%, from 4731 USD/kW to 883 USD/kW, and Onshore Wind’s CAPEX fell from 1971 USD/kW to 1349 USD/kW [63,64]. This sharp decline in costs has resulted in an 85% reduction in solar PV’s LCoE and similar reductions for wind. Unlike fossil fuels and nuclear power, where the LCoE is influenced by fuel prices and operational costs, solar PV and wind benefit from zero fuel costs, relying primarily on technology improvements for cost reduction. As cumulative TEA studies grow, investments and production scale up, leading to further cost reductions, driven by cognitive learning effects, as illustrated in Figure 6a,b.

The disparity between MGTs and renewable technologies like solar PV and Onshore and Offshore Wind is clear. MGTs have seen minimal research progress, with just 51 cumulative studies by 2023, in stark contrast to the rapid advancements in renewables. Solar PV’s CAPEX decreased from 5000 USD/kW in 2010 to around 1500 USD/kW in 2020, while the LCoE dropped from 0.35 USD/kWh to 0.05 USD/kWh. Onshore Wind’s CAPEX and LCoE also saw significant declines, highlighting the robust R&D investments and policy support driving these changes. Offshore Wind followed a similar trajectory, though with slower cost reductions. These advancements in renewables, driven by market demand and strong policy backing, have not been matched in the MGT sector, which remains underfunded and under-researched. To remain competitive, the MGT sector must invest in adaptive development, focusing on R&D to improve efficiency, cost-effectiveness, and sustainability, learning from the advancements in renewables [65]. The stark difference in research activity highlights the critical role of funding in driving technological progress. While renewable energy has benefited from strong market demand and policy support [66], MGTs need similar investment and innovation to catch up. By adopting adaptive strategies, MGTs can enhance their competitiveness and maintain a viable position in the evolving energy landscape.

3.2. Comparative Assessment of MGT Sector Growth Compared to ICE Activities: Insights, Trends, and Adaptable Strategies

Another key factor hindering MGT market success has been competition with ICEs. Both technologies have their strengths, but ICEs have consistently benefited from more substantial TEA efforts, particularly since the early 2000s [28,40,53]. Between 2000 and 2010, there were no recorded MGT-TEA activities, while over 350 ICE-TEA studies were conducted. From 2010 to 2020, ICEs saw over 1300 TEA studies, compared to fewer than 40 for MGTs. The success of ICEs can be attributed to the strategic use of “learning curves” [67] and “experience curve effects” [68], alongside strong business and financial strategies. As seen in Figure 7b,c, ICEs have experienced exponential growth in TEA research, driven by substantial R&D investment, market demand, and competitive dynamics. In contrast, MGTs have lagged in both research and technological progress, widening the gap between the two technologies. This disparity is further illustrated by their cumulative unit installations. In cogeneration applications of less than 1 MW in the USA, ICEs have seen a consistent rise in annual installations, while MGT growth has been much slower, as reflected in Figure 8. The correlation between cumulative TEA research and unit installations is clear: ICEs have benefitted from a deeper understanding of their techno-economic potential, reducing uncertainties and risks for investors, which has led to greater market adoption. In contrast, the limited TEA work on MGTs has constrained their growth and market penetration.

G. Tilocca et al. (2023) [69] also highlight the disparity in case study investigations between ICEs and MGTs, with ICEs showing a significantly higher spatial density of case studies (Figure 9). The comparison between MGTs and ICEs shows that ICEs have lower equipment and operating costs compared to MGTs across all rated electrical power outputs (See Figure 9). Specifically, ICEs exhibit both lower equipment costs (solid orange line) and lower service and maintenance costs (dashed orange line) than MGTs. MGTs have significantly higher equipment costs (solid blue line) and higher service and maintenance costs (dashed blue line), especially at a lower-rated electrical power. This highlights the cost advantages of ICEs, which, along with their higher efficiency (Figure 9), make them more commercially competitive in certain applications compared to MGTs. MGTs were represented by only 14 case studies, underscoring the limited research focus on this technology. Additionally, ICEs have a lower CAPEX compared to MGTs, as demonstrated in Figure 6b. This cost difference is attributed to higher production volumes and more established manufacturing processes for ICEs, which aligns with the “learning and experience curve” theory. During the initial commercialization phase, the ICE CAPEX was far higher than it is today [70], but the steady stream of TEA studies over the past two decades has helped drive down costs, laying a strong foundation for business strategies and investment. In contrast (see Figure 9), the MGT sector, lacking a comparable research base, has not seen the same level of cost reduction. The significant cost gap between MGTs and ICEs is evident: for a 200 kW system, the MGT CAPEX is approximately 2100 USD/kW, compared to 1300 USD/kW for ICEs, resulting in a cost difference of 800 USD/kW. This disparity could have been reduced had more standalone TEA studies focused on MGTs over the past two decades, potentially driving greater technological advancements and cost reductions.

4. Novel Calculation Procedure for Assessing Learning and Experience Effects in MGT-TEA

4.1. Percentage Learning and Experience Effects of Recent Studies Integrating MGT-TEA with RES and ICE

To understand the evolution and potential of MGTs in the context of RESs and ICEs, it is essential to examine recent advancements and their associated learning and experience effects. Drawing from established methodologies in industrial economics and technology management, particularly the experience curve theories developed for the most commercialized energy technologies [71,72,73,74], we can assess how cumulative production and technological advancements lead to cost reductions in MGT systems. As discussed by C. Woerlen et al. (2005) [75], experience curves are utilized to estimate the future cost of a technology as a function of its cumulative installations. The fundamental idea is that the invention, development, and prototyping of a new technology, process, or product are initially very costly. Following an innovation—marked by the first commercial application—a successful technology or product will expand from niche markets to broader markets [76]. During this diffusion process, unit costs typically decrease continuously. This ongoing reduction in unit costs over time is captured by experience curves. Experience curves have been calculated for individual firms but are more commonly applied to entire sectors or industries (such as the wind turbine industry or all manufacturers of combined cycle gas turbines) [71,77], or to the cumulative experience of all types within a specific technology (like solar PVs) [73]. In the context of a globalized market, particularly in energy technology markets, it is reasonable to assume that learning processes benefit from a worldwide base of experience. Therefore, experience curves for energy technologies should reflect this global experience, using global data for their estimation.

The following table (

Table 1. Summary of recent advancements and learning and experience effects from each study.

Study and Year	Microgrid or Distributed Power Generation	Renewable Energy Sources	Internal Combustion Engines	Micro Gas Turbine	Relationship to MGT-TEA Learning Effect	Relationship to MGT-TEA Experience Effect	Percentage (%) of RES-TEA Learning	Percentage (%) of ICE-TEA Learning	Remarks and Highlights
X. Ding et al. (2024) [78]	✔	✔	X	✔	✔	✔	10	0	Solar-assisted MGT-CCHP system, variants with and without solar energy storage.
Bellos et al. (2024) [79]	✔	✔	X	X	✔	✔	20	0	Solar-driven tri-generation system coupled with ORC and vapor compression refrigeration cycle.
Zheng et al. (2024) [80]	✔	✔	X	X	✔	✔	15	0	New solar-driven distributed energy system.
Li et al. (2024) [81]	✔	✔	X	X	✔	✔	25	0	Evaluated TE performance by centralized and decentralized frameworks.
Zhong et al. (2024) [82]	✔	✔	X	X	✔	✔	30	0	Novel solar-driven distributed energy system converting electricity to methane.
Song et al. (2024) [83]	✔	✔	X	X	✔	✔	35	0	CCHP system integrating solar energy and hydrogen production.
Ma et al. (2024) [84]	✔	✔	✔	X	✔	✔	40	30	Hybrid renewable energy CCHP system with an Internal Combustion Engine.
Allouhi et al. (2024) [85]	✔	✔	X	X	✔	✔	20	0	Multi-objective optimization of a solar-driven power–thermal supply system.
Zhang et al. (2024) [86]	✔	✔	X	X	✔	✔	25	0	Indirect expansion solar-assisted air source heat pump system.
H. Yazdani et al. (2023) [87]	✔	✔	X	✔	✔	✔	20	0	TEA studies and multi-objective optimization for a complex hybrid energy system (HES) including various technologies.
A. Escamilla et al. (2023) [88]	✔	✔	X	✔	✔	✔	15	0	Hybridization of MGT in P2P-ESS with battery energy storage system (BESS) to reduce H2 demand and seasonal storage needs.
M. A. Khan et al. (2023) [89]	✔	✔	✔	✔	✔	✔	30	30	TEA and risk assessment of m-CHP systems for households, including PESTLE and MCDA analysis.
Barun K. Das et al. (2022) [90]	✔	✔	✔	✔	✔	✔	35	30	TEA of hybrid RESs for electrifying a remote village, compared PV/biomass, PV/diesel, and PV/MGT systems.
B.K. Das et al. (2022) [91]	✔	✔	X	✔	✔	✔	40	0	Optimization of hybrid energy systems combining MGT with PV, WT, and various battery storage options.
Barun K. Das et al. (2018) [92]	✔	✔	✔	✔	✔	✔	25	30	Performance analysis of baseline PV/battery system and hybrid systems with ICE or MGT supplementary prime movers.
Farah Nazifa N. et al. (2020) [93]	✔	✔	X	✔	✔	✔	20	0	TEA study on integrating MGT-CHP systems in wastewater treatment plants, focusing on biogas utilization.
Domenico B. et al. (2017) [94]	✔	✔	X	✔	✔	✔	30	0	Coupling of an 800-kW updraft gasifier with a 200 kWe-MGT for CHP, evaluating economic performance and investment profitability.
S. Chu et al. (2022) [95]	✔	✔	X	✔	✔	✔	35	0	MGT-CCHP system coupled with LCPV/T-HP and absorption chiller, evaluating performance under different operation strategies.
Ramin M. et al. (2022) [96]	✔	✔	X	✔	✔	✔	40	0	Comparison of MGT system and sCO2 system fueled by a biomass gasifier for residential users.
Peloriadi, K. et al. (2022) [97]	✔	✔	X	✔	✔	✔	25	0	Feasibility of SOFC-MGT system for Patmos Island, Greece.
A.H. Eisapour et al. (2022) [98]	✔	✔	X	✔	✔	✔	20	0	Feasibility of integrated energy system including MGT with CHP module for meeting load demand of case study.
Paepe et al. (2019) [99]	✔	✔	✔	✔	✔	✔	15	30	Optimizing MGT operation for grid connection, controlling key parameters like turbine outlet temperature (TOT) and rpm.
A. di Gaeta et al. (2017) [100]	✔	✔	X	✔	✔	✔	30	0	Simplified MGT model coupled with hybrid energy grid simulation for fossil fuel savings.
M. Sharf et al. (2022) [101]	✔	✔	X	✔	✔	✔	35	0	Economic feasibility of using MGT-based m-CHP unit in various scenarios with different operational modes.

) provides a summary of recent studies that integrate MGTs with RES and ICE technologies, highlighting the learning and experience effects observed in each case using the methodologies in refs [71,72,73,74]. These data serve as a foundation for understanding the progress made in the MGT sector and set the stage for the novel calculation procedure introduced in the subsequent section.

The data in Table 1 offer valuable insights into the progress of MGTs when integrated with RESs and ICEs. These studies highlight the varying degrees of learning and experience effects, which are essential for understanding technological advancements and market penetration potential in the MGT sector. The study by X. Ding et al. (2024) [78] demonstrates a 10% learning effect for solar-assisted MGT-CCHP systems, reflecting the early stages of innovation in this integration. In contrast, more mature technologies, such as the hydrogen-integrated CCHP systems studied by Song et al. (2024) [83], show a 35% learning effect, indicating significant efficiency gains and cost reductions from accumulated experience in RES integration. The studies by Ma et al. (2024) [84] and M. A. Khan et al. (2023) [89] show that incorporating ICEs into hybrid renewable energy systems yields considerable learning and experience effects (40% and 30%, respectively). ICE integration can marginally enhance the learning curve compared to systems without ICEs, featuring the value of hybrid configurations in accelerating MGT advancements. The work of Barun K. Das et al. (2022) [90], which achieved 35% learning and 30% experience effects by integrating multiple RESs with MGTs, highlights the strategic advantage of combining diverse energy sources. This approach not only fosters technological learning but also enhances the versatility and viability of MGTs in decentralized power generation. Studies by Li et al. (2024) [81] and Zhang et al. (2024) [86] show significant learning effects (25% and 20%, respectively) even without ICE integration, suggesting that advanced RES technologies alone can drive substantial improvements in MGT systems. This indicates that RES-MGT integrations are particularly promising for enhancing techno-economic performance.

This analysis emphasizes the importance of strategic integration of MGTs with both RES and ICE technologies to maximize learning and experience effects. Continued R&D investment in these hybrid systems is crucial to enhancing the competitiveness of MGTs in the global energy market, particularly as the sector shifts towards low-carbon solutions. The varied learning percentages across the studies (ranging from 10% to 40%) reflect various stages of technological maturity and innovation. The higher learning effects observed in RES-MGT integrations suggest a ripe opportunity for breakthroughs in these areas, while consistent experience effects with ICE integration provide a stable foundation for further development. Each study in Table 1 reflects varying degrees of learning and experience effects, which are pivotal in assessing the potential for technological improvement and market penetration. As we move forward, these observations set the stage for a more rigorous and quantitative analysis. The following section introduces a novel mathematical model that builds on the insights gathered from the previous studies. This model is designed to quantify the learning and experience effects in MGT-TEA with greater precision, permitting one to project the future economic viability of MGT systems when integrated with RES and ICE technologies. Incorporating these effects into our calculations facilitates a better understanding of the trajectories of MGT costs and performance over time, thus guiding strategic decisions in R&D investment and deployment strategies.

4.2. Development and Application of a Novel Calculation Procedure for Assessing Learning and Experience Effects in MGT-TEA

This procedure will focus on modeling the learning and experience effects to assess their impact on the techno-economic viability of MGT systems coalesced with ICE and RES technologies. By employing and accumulating structured theories, practices, and validated methods, a rigorous and consistent approach will be presented quantifying the learning and experience effects across the numerous studies, thereby providing valuable insights into techno-economic advancements in the MGT sector.

4.2.1. Introduction to the Mathematical Model and Procedure

This approach integrates the concepts of learning and experience curves to quantify the impact of cumulative production and technological advancements on the cost and profitability of MGT systems. By systematically applying these principles, the model provides a robust framework for evaluating potential cost reductions and the economic viability of MGT systems as they mature. It is grounded in the established theories of learning and experience curves in refs [71,72,73,74,75,76], which suggest that as cumulative production increases, the cost per unit decreases predictably due to gains in efficiency and knowledge. Additionally, the experience curve incorporates broader factors such as technological innovations and economies of scale, further driving down costs. The mathematical formulations underlying this model are crucial for understanding these effects in MGT-TEA compared to ICE and RES technologies. Through a step-by-step procedure, the model predicts future cost trajectories and informs strategic decisions in research, development, and commercialization. Applying this model to the recent literature and empirical data from Section 3 highlights the significant potential for cost reduction in MGT systems, providing insights that will guide investments and research priorities, ultimately supporting the broader adoption of MGTs in the global energy market.

Understanding Learning and Experience Curves

The learning curve concept suggests that as cumulative usage or production or experience increases, the cost per unit decreases at a predictable rate. From A. Louwen et al. (2020) [74], the single-factor experience curve theory extends this idea to include broader factors such as technological improvements, economies of scale, and operational efficiencies. Equation (1) provides the Learning Curve Formula.

$$C_n=C_I\times n^{-\alpha }$$

(1)

where

• $C_n$ = Cost per unit after $n$ units are produced;
• $I$ = Initial cost per unit;
• $n$ = Cumulative production or experience;
• $\alpha$ = Learning elasticity (typically between 0.2 and 0.3 for most technologies). The exponent $\alpha$ determines the rate at which costs decrease. A higher $\alpha$ indicates faster learning.

Calculating the Learning Rate

An experience curve can be described by either the learning rate or the progress ratio it depicts. The learning rate (LR) is the rate at which a technology’s costs are found to decrease for each doubling of experience. $LR$ represents the percentage reduction in cost for each doubling of cumulative usage. Equation (2) illustrates the Learning Rate Calculation [71,72].

$$LR=1-2^{-\alpha }$$

(2)

For example, if $\alpha$ = 0.3, the learning rate LR can be calculated as

$$LR=1-2^{-0.3 }\approx 0.189 or 18.9\%$$

This means that every time the cumulative production doubles, the cost per unit decreases by approximately 18.9%.

Incorporating Experience Curve Effects

The experience curve takes into account broader factors such as technological advancements and economies of scale, which also contribute to cost reductions. Using the C. Woerlen et al. (2005) [75] experience curve theory, Equation (3) represents the Experience Curve Formula.

$$C_n=C_I\times {\left({\displaystyle \frac{n}{n_0}}\right)}^{-\beta }$$

(3)

where

• $C_n$ = Cost per unit after $n$ units are produced;
• $C_I$ = Cost per unit at baseline experience level $n_0$;
• $n$ = Cumulative production or experience;
• $\beta$ = Experience elasticity, which is typically derived from empirical data.

Combining both concepts yields Equation (4), which integrates both learning and experience effects, allowing for a more comprehensive analysis of cost reductions over time. Following Equation (4) provides the combined learning and experience curve.

$$C_n=C_I\times n^{-\alpha }\times {\left({\displaystyle \frac{n}{n_0}}\right)}^{-\beta }$$

(4)

4.2.2. Application to MGT-TEA with ICE and RES

To apply these concepts to MGT-TEA, we must compare the learning and experience rates of MGTs with those of ICE and RES technologies. The calculation involves the following:

• Determine Initial Costs: Establish the initial cost per unit $C_I$ for MGT, ICE, and RES systems. These costs should reflect the current state of technology;
• Estimate Learning and Experience Elasticities: Based on historical data and the literature, estimate the learning elasticity $\alpha$ and experience elasticity $\beta$ for each technology;
• Calculate Cumulative Production or Usage: Determine the cumulative production or usage of $n$ for each technology. For MGTs, this may be lower than for ICEs and RESs, reflecting the earlier stage of commercialization;
• Model Cost Reduction: Use the combined learning and experience curve (Equation (4)) to model the cost reductions for MGTs, ICEs, and RESs as production or usage scales up;
• Comparison and Sensitivity Analysis: Compare the projected costs for MGTs against ICEs and RESs over time. Conduct sensitivity analyses by varying $\alpha$ and $\beta$ to explore different scenarios.

By leveraging the learning and experience curve concepts, the novel mathematical modeling of the MGT-TEA framework can be refined more accurately to predict future cost trajectories for MGTs relative to ICE and RES technologies. In this analysis, point 05 is omitted to contain the scope of this study.

4.3. Analysis and Implications of Learning and Experience Effects

In this section, we apply the novel calculation procedure formulated in the previous section to analyze the learning and experience effects on MGT systems when integrated with RESs and ICEs. This analysis aims to illustrate the potential cost reductions and economic viability improvements of MGT systems by considering LCoE and Net Present Value (NPV) metrics before and after the learning and experience effects are accounted for. The results are based on recent literature findings, as summarized in

Table 2. Comprehensive summary of recent MGT-TEA studies with calculated learning and experience effects and projected economic metrics.

Study and Year	System	Initial Costs (CAPEX) (USD/kW)	LCOE (USD/kWh)	Learning Effect (α)	Experience Effect (β)	Estimated LCOE After Learning and Experience (USD/kWh)	NPV (Million USD)	Remarks
X. Ding et al. (2024) [78]	Solar-assisted MGT-CCHP	2500	0.0417	0.2	0.1	0.033	0.252	Less economical with solar energy storage, competitive without it.
Bellos et al. (2024) [79]	Solar-driven TGS	3000	0.054	0.25	0.15	0.0405	50	PBP of 8.5 years; high investment, suitable for large-scale deployment.
Zheng et al. (2024) [80]	New solar-driven distributed energy system	3500	0.0615	0.25	0.15	0.04613	40	Proposed system shows high CAPEX but promising NPV.
Ma et al. (2024) [84]	Hybrid renewable energy CCHP with ICE	2000	0.02	0.15	0.1	0.017	130	Significant reduction in costs and CO2 emissions.
Farah Nazifa N. (2020) [93]	MGT-CHP in (WWTPs)	4000	0.095	0.1	0.05	0.0855	0.5	High initial investment, justified by long-term benefits.
Ramin M. et al. (2022) [96]	MGT vs. sCO2 system fueled by biomass gasifier	5000	0.145	0.2	0.1	0.116	70	sCO2 system shows better performance and higher efficiency.
A. Escamilla et al. (2023) [88]	Hybrid MGT in P2P-ESS with BESS	7000	0.356	0.1	0.05	0.2704	85	High CAPEX due to integration costs, significant storage needs.
B.K. Das et al. (2022) [91]	Hybrid RES for village electrification	3000	0.314	0.25	0.15	0.2355	0.65	Best performance with biomass, higher CAPEX for MGT.
Domenico B. et al. (2017) [94]	MGT-gasifier CHP system	5300	0.227	0.15	0.1	0.198	1.7	Best performance with industrial heat demand; high investment, but profitable.
S. Chu et al. (2022) [95]	MGT-CCHP with LCPV/T-HP and ABC	4000	0.165	0.2	0.1	0.132	95	Complex system, high CAPEX but strong potential for cost-effectiveness and efficiency.
H. Yazdani et al. (2023) [98]	TEA of complex HES including MGT	4500	0.205	0.18	0.09	0.1681	55	Incorporation of MGT in a complex HES shows moderate CAPEX, with improved overall system performance.
M.A. Khan et al. (2024) [102]	Economic feasibility of low-carbon fuels in MGT-CHP	5500	0.25	0.22	0.11	0.195	75	Green H2 shows potential, but currently high costs are a barrier.
W.D Paepe et al. (2019) [99]	MGT connected to the grid	4800	0.21	0.19	0.09	0.1743	60	Grid-connected MGT offers significant benefits in energy markets, especially during peak demand.
A. di Gaeta et al. (2017) [100]	Simplified MGT model in hybrid energy grid	4600	0.225	0.18	0.1	0.1845	65	Hybrid energy grids with MGT show fossil fuel savings and strong potential for H2 integration.
M. Sharf et al. (2022) [101]	Economic feasibility of MGT-CHP in various scenarios	4900	0.215	0.2	0.1	0.172	63	Smart grid-connected MGT-CHP units provide significant economic benefits in multiple operational modes.

, and provide insights into how MGT systems can become more competitive in the energy market.

4.3.1. Key Elements and Methodology

The Key Elements Considered in This Analysis Include the Following:

• Initial Cost (CAPEX): The initial capital expenditure for each system, reflecting the current state of technology and market conditions;
• Learning Effect: The percentage reduction in cost due to the learning curve, as cumulative production increases and efficiency improvements are realized;
• Experience Effect: The reduction in cost attributed to technological advancements and operational efficiencies gained over time;
• LCoE (Levelized Cost of Energy): The calculated cost per unit of energy over the system’s lifecycle, both before and after learning and experience effects;
• NPV (Net Present Value): The overall profitability of the system over its operational lifetime, considering both initial investment and operational costs.

4.3.2. Data Extraction and Assumptions

The key data were extracted from the literature, and assumptions were made where necessary. The key learning rates for primary energy generation systems were presented by S. Samadi (2019) [103]. Researchers need to incorporate a range of potential future learning rates for individual technologies (as shown in Figure 10) to account for the inherent uncertainties. This is particularly important given the significant impact that learning rate estimates can have on the outcomes of energy system modeling.

The following assumptions and learning and experience rates exploited from the data in Refs [72,103] are used in this study to be utilized with the mathematical model.

• Learning Elasticity (α): Assumed values are 0.2 for MGTs, 0.25 for RESs, and 0.15 for ICEs;
• Experience Elasticity (β): Assumed values are 0.1 for MGTs, 0.15 for RESs, and 0.05 for ICEs;
• Initial Costs (C_I): Extracted from recent literature under Section 3 and gray literature;
• Cumulative Production (n): Estimated based on the stage of commercialization, with MGTs assumed to be in earlier stages compared to RESs.

The findings presented in Table 2 provide a comprehensive overview of the TEA of MGTs across a variety of integrated systems, highlighting the learning and experience effects and their impact on key economic metrics like the LCoE and NPV. The analysis show up several critical points.

4.3.3. Variation in Learning and Experience Effects

The studies reveal a broad range of learning and experience effects, with learning rates (α) varying from 0.1 to 0.25 and experience rates (β) from 0.05 to 0.15. This variability suggests that the maturity and integration of MGTs into different energy systems significantly influence the rate at which costs decrease and efficiencies improve. For instance, systems that integrate MGTs with advanced renewable energy technologies, such as solar-driven tri-generation systems (Bellos et al., 2024 [79]) and hybrid renewable energy CCHP systems with ICEs (Ma et al., 2024) [84], exhibit higher learning effects (α = 0.25 and 0.15, respectively), reflecting substantial cost reductions and efficiency gains. In contrast, more traditional MGT applications, like MGT-CHP in wastewater treatment plants (F. Nazifa et al., 2020 [93]), show lower learning effects (α = 0.1), indicating slower progress in cost reduction.

4.3.4. Highest and Lowest Learning Effects

For instance, the study by Ma et al. (2024) [84] shows the highest learning effect (α = 0.25) in a hybrid renewable energy CCHP system with ICE integration, leading to a significant LCOE reduction from USD 0.02/kWh to USD 0.017/kWh. This high learning rate is attributed to the mature integration of ICE technology with MGTs, which accelerates technological learning and cost efficiency. On the other hand, F. Nazifa et al. (2020) [93] report the lowest learning effect (α = 0.1) in MGT-CHP systems within wastewater treatment plants (WWTPs), where progress in cost reduction is slower, possibly due to the lower scale of production and less innovation in this application.

4.3.5. Impact on LCOE and NPV

The graph in Figure 11 provided offers a visual representation of these effects, clearly demonstrating the reductions in LCOE when the novel mathematical model, which accounts for learning and experience effects, is applied. Before the application of the learning and experience effects, the LCoE values across different studies (represented by the red bars) are relatively high. For instance, studies like A. Escamilla et al. (2023) [88] and Ramin M. et al. (2022) [96] show some of the highest initial LCoE values, reflecting the significant costs associated with these hybrid energy systems. However, after incorporating the learning effect (α) and experience effect (β), there is a notable reduction in LCoE (as shown by the green bars). This reduction highlights the importance of cumulative technological advancements and operational efficiencies in lowering energy costs. For example, in the case of Ma et al. (2024) [84], the initial LCoE was recorded at USD 0.02/kWh. However, after applying the learning effect (α = 0.15) and experience effect (β = 0.10), the estimated LCOE drops to USD 0.017/kWh. Similarly, the study by Bellos et al. (2024) [79] initially showed an LCoE of USD 0.054/kWh, which was significantly reduced to USD 0.0405/kWh after considering the learning and experience effects. These reductions are particularly pronounced in systems that leverage advanced renewable energy technologies, where the learning effects are more substantial. Figure 11 also indicates a pattern where higher initial LCoE values correlate with more significant reductions post learning and experience application. This trend is evident in systems like the hybrid MGT in P2P-ESS with BESS, where the initial LCoE is quite high at USD 0.856/kWh, but learning and experience effects help bring it down to USD 0.7704/kWh. While this system remains expensive, the reductions underscore the potential for cost improvements as these technologies mature and scale. The NPV values also reflect the economic potential of these systems, with high NPVs in studies like Ma et al. (2024) [84] (USD 130 million) and Bellos et al. (2024) [79] (USD 50 million), indicating profitable long-term investments. In contrast, systems with high initial costs and slower learning rates, such as the hybrid MGT in P2P-ESS with BESS (A. Escamilla et al. (2023) [88]), show a lower NPV (USD 85 million), pointing to the need for further cost reductions and efficiency improvements to enhance profitability. The analysis presented in Figure 11, when integrated with the numerical findings from the paper, solidifies the argument that strategic investments in R&D and the scaling of MGT systems, particularly those integrated with RESs and ICEs, can lead to substantial economic benefits. The results emphasize the critical role of continuous innovation and cumulative integration and usage in driving down the costs of energy systems, validating the effectiveness of this novel mathematical model in predicting and enhancing the economic viability of these systems.

4.3.6. High CAPEX as a Barrier

Despite favorable learning and experience effects, a high initial CAPEX remains a significant barrier, particularly in systems like the hybrid MGT in P2P-ESS with BESS (A. Escamilla et al., 2023 [88]). This system has a high CAPEX of USD 7000/kW, resulting in an initial LCoE of USD 0.356/kWh, even though learning and experience effects reduce it to USD 0.2704/kWh. This highlights that while learning and experience can drive down costs, the substantial upfront investments required for certain MGT systems may still hinder their widespread adoption, especially in more complex configurations.

4.3.7. Strategic Importance of Hybrid Systems

The studies demonstrate that hybrid systems, particularly those integrating MGTs with RESs and ICEs, offer significant advantages in terms of learning and experience effects. For instance, the hybrid renewable energy CCHP with ICE (Ma et al., 2024 [84]) not only achieves a low LCoE but also benefits from a high NPV, illustrating the economic and environmental benefits of such integrations. Conversely, systems that rely solely on MGTs or less advanced RES technologies tend to show slower progress in learning and experience, emphasizing the importance of continued innovation and hybridization to unlock the full potential of MGTs.

These observations point out the importance of the novel mathematical model introduced in the preceding section. By applying this model, analysis can better quantify the impact of learning and experience effects on MGT systems and project future cost trajectories. This will help stakeholders make informed decisions regarding R&D investments and deployment strategies, ultimately enhancing the competitiveness of MGTs in the global energy market. Overall, these findings highlight the critical role of learning and experience effects in driving down costs and improving the economic viability of MGT systems. The data suggest that while MGTs have the potential to become a more competitive energy solution, particularly when integrated with advanced RESs and replacing ICE technologies, there remain significant challenges, particularly related to their high initial CAPEX. The strategic integration of MGTs with other energy systems, supported by continued R&D and scaling, is essential for enhancing their competitiveness in the global energy market. By leveraging learning and experience curves, stakeholders can better predict cost trajectories and make informed investment decisions that promote the adoption of MGTs as a sustainable energy solution. In summary, the ongoing advancements in MGT technology, coupled with strategic investments in hybrid systems and learning-driven cost reductions, will be crucial for ensuring that MGTs contribute effectively to the global transition towards low-carbon energy systems. This approach will not only improve the economic feasibility of MGTs but also support their role in the broader energy landscape, making them a key player in achieving sustainable energy goals.

5. Conclusions and Future Direction

This study has critically examined the role of Micro Gas Turbines (MGTs) in the evolving landscape of low-carbon energy systems, with a particular focus on their integration within microgrids and distributed energy generation systems. While MGTs offer significant advantages, such as fuel flexibility, hydrogen integration potential, and reliability as backup generators, this study identifies substantial barriers to their broader market adoption, particularly in comparison to Internal Combustion Engines (ICEs) and renewable energy sources (RESs).

One of the most critical findings is the significant shortfall in techno-economic assessment (TEA) activities for MGTs, which has stymied their commercialization and market penetration. Unlike ICEs and RESs, which have benefited from extensive TEA-driven cost reductions and technological advancements, MGTs have lagged behind, largely due to the lack of rigorous and comparative TEA studies. This study highlights that MGTs currently face a high levelized cost of energy (LCoE), ranging between USD 0.0417/kWh and USD 0.356/kWh, which remains uncompetitive compared to ICEs and RESs unless significant learning and experience effects are realized.

The comparative analysis conducted in this study reveals that despite the promising potential of MGTs, especially in hybrid configurations, their economic viability is undermined by high initial capital expenditures (CAPEX) and slower learning rates. For instance, the CAPEX for MGT systems can reach up to USD 7000/kW, significantly higher than that of ICEs. Moreover, the learning rates for MGTs are lower, with estimated reductions in LCoE of around 18.9% per doubling of cumulative production, compared to higher rates observed in the RES sector.

A novel mathematical model introduced in this study to quantify the learning and experience effects on MGT-TEA shows that with increased cumulative production and strategic integration with RESs, MGTs could achieve significant cost reductions. The model predicts that, with the right investment and policy support, MGTs could reduce their LCoE by up to 40%, making them more competitive in the market. However, the current pace of TEA activities and market adoption does not support this trajectory, underscoring the urgent need for increased research and strategic policy interventions.

In summary, this study underscores the necessity for bridging the TEA gap for MGTs by adopting and refining the successful methodologies used in the ICE and RES sectors. The findings suggest that without a strategic increase in TEA activities and targeted R&D investments, MGTs will struggle to achieve the technological and economic maturity needed to compete in the global energy market. Future research should prioritize expanding TEA efforts, focusing on reducing the CAPEX and accelerating learning rates, to fully unlock the potential of MGTs in supporting the global energy transition.