Cleaner Energy Systems最新文献

{"title":"Renewable energy adoption in european small and medium-sized enterprises: A comparative sectoral analysis","authors":"Mercedes Rodríguez,&nbsp;José A. Camacho","doi":"10.1016/j.cles.2025.100227","DOIUrl":"10.1016/j.cles.2025.100227","url":null,"abstract":"<div><div>This study explores sectoral differences in renewable energy adoption among Small and Medium-Sized Enterprises (SMEs) in the European Union, with a particular focus on the understudied service sector, which has received considerably less attention than industrial and manufacturing activities. Drawing on data from the Flash Eurobarometer 498 on SMEs, Resource Efficiency, and Green Markets, the analysis examines how internal resources, especially financial capacity, and external support—such as public funding and advisory services—shapes renewable energy decisions. The findings reveal that industrial (48.7 %) and manufacturing (48.1 %) SMEs are significantly more likely to plan renewable energy adoption in the near future compared to service-sector SMEs (41.1 %). Internal financial resources emerge as a key driver across all sectors, while the effectiveness of external support varies. In particular, public funding is notably underutilized in the service sector, despite its strategic potential. These sectoral disparities reflect deeper differences in energy consumption patterns, investment priorities, and the perceived returns from renewable energy. The study highlights the need for tailored, sector-specific policy interventions to remove adoption barriers in the service sector and improve the alignment of financial and advisory mechanisms. By identifying the distinct challenges and enablers across sectors, this research contributes to the design of more effective renewable energy policies for SMEs, supporting a more inclusive and sustainable energy transition in line with broader Sustainable Development Goals.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"13 ","pages":"Article 100227"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Critical success factors for large-scale solar projects: Insights for Saudi Arabia’s energy transition","authors":"Sikandar Abdul Qadir ,&nbsp;Amjad Ali ,&nbsp;Md Tasbirul Islam ,&nbsp;Muhammad Shahid ,&nbsp;Shoaib Ahmed","doi":"10.1016/j.cles.2026.100234","DOIUrl":"10.1016/j.cles.2026.100234","url":null,"abstract":"<div><div>Energy policies formulated after the Paris agreement place decarbonisation at the core of the energy system. The use of renewable energy resources has become imperative to combat the climate change. The global share of renewable electric capacity grew by 22% in 2024, dominated by solar capacity additions. Solar energy technologies are classified on various factors such as scale, application, technology, and installation. The most economical option is large-scale solar PV (LSPV). This work investigates the role of policies in ensuring LSPV projects’ success. A structured methodology is applied to determine the critical success factors for LSPV projects. Based on the empirical evidence and case studies across selected regions, the paper integrates policies supporting the commercial viability of these projects. It also discusses challenges, opportunities, and proposes recommendations for policymakers, investors, and stakeholders to maximise the potential of LSPV projects. Additionally, the paper presents the case of the Kingdom of Saudi Arabia, highlighting its LSPV potential and key insights from the analysis. A framework is proposed, incorporating successful design considerations from analysed cases to facilitate investment and improve project outcomes of LSPV projects.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"13 ","pages":"Article 100234"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sizing renewable energy microgrids for supercomputing centers using particle swarm optimization","authors":"Lester Acevedo-Montoya ,&nbsp;Carlos J. Vega-Gómez ,&nbsp;Alberto Coronado-Mendoza ,&nbsp;César A. García-García","doi":"10.1016/j.cles.2026.100236","DOIUrl":"10.1016/j.cles.2026.100236","url":null,"abstract":"<div><div>High-performance computing (HPC) centers face growing challenges in managing energy consumption, operational costs, and environmental impact due to their continuous, high-density loads. This study presents a data-driven optimization framework for sizing hybrid renewable energy microgrids tailored to the specific demands of HPC facilities. Using one-minute resolution data collected over a full year from the Data Analysis and Supercomputing Center (CADS) at the Universidad de Guadalajara, we applied Particle Swarm Optimization (PSO) to determine the optimal number of photovoltaic (PV) panels and wind turbines.</div><div>The optimization simultaneously minimizes total system cost, energy deficit, and operational penalties under strict reliability constraints. Results show that a PV-only system comprising 960 panels (no wind turbines) yields the optimal configuration, covering 75% of the annual energy demand. The solution achieves a Levelized Cost of Energy (LCOE) of 1.42 MXN/kWh and a payback period of 7.8 years. Due to low average wind speeds (2.1 m/s), wind turbines were excluded. A comparative validation using a Genetic Algorithm (GA) confirmed the robustness of the PSO solution, with a cost deviation below 1.3%.</div><div>This study highlights the importance of high-resolution real-world data and site-specific modeling in optimizing microgrids for mission-critical environments. The proposed framework is generalizable and supports informed decision-making for renewable integration in data-intensive infrastructure.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"13 ","pages":"Article 100236"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonstationary multivariate structural damage risk evaluation method for green energy harvesters","authors":"Oleg Gaidai ,&nbsp;Tao Zhang ,&nbsp;Shicheng He ,&nbsp;Qianlong Ma ,&nbsp;Salwa Noureldine","doi":"10.1016/j.cles.2026.100231","DOIUrl":"10.1016/j.cles.2026.100231","url":null,"abstract":"<div><div>Thorough experimental research is necessary for the development of green Energy Harvesters (EH), in addition to appropriate safety and reliability studies. The dynamic behavior of a particular galloping EH was studied through a number of wind tunnel experiments, carried out at various realistic wind speeds. Utilizing recently developed multivariate statistical approach, an excessive multivariate dynamic response of an experimental galloping EH was investigated. Majority of existing piezoelectric wind EHs, utilize a cantilever beam support structure. The effective mass, damping coefficient, and cantilever beam stiffness were determined by free vibration tests. Stationary aerodynamic loads were generated through wind tunnel experiments. This case study benchmarks recently developed design concept for the structural reliability analysis of multi-dimensional, nonstationary nonlinear dynamic systems.</div><div>Presented case study demonstrates efficiency of combining recently developed multivariate Gaidai reliability method with an accurate deconvolution extrapolation scheme, allowing for accurate estimations of the likelihood of structural damage and dynamic system’s failure. The latter is a distinct practical benefit of the proposed multivariate methodology, since existing structural reliability methodologies often struggle to cope with dynamic system's high dimensionality, along with nonlinear cross-correlations across system parts/dimensions. The aim of this investigation was to establish a baseline for the proposed multivariate structural reliability concept, facilitating the effective retrieval of pertinent statistical information from the underlying measured dataset. System performance or limit state function depends on multiple random variables (e.g., load, resistance, environmental factors).</div><div><em>Engineering relevance</em>: assessing the reliability of high-dimensional structures where failure probabilities <span><math><msub><mi>P</mi><mi>F</mi></msub></math></span> are quite low (e.g., less than <span><math><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>6</mn></mrow></msup></math></span>). Presented study benchmarked novel multivariate structural reliability concept that is more conservative than existing univariate or bivariate reliability schemes. Proposed deconvolution extrapolation scheme enabled assessment of design valued with <span><math><msub><mi>P</mi><mi>F</mi></msub></math></span> four decimal orders of magnitude lower that actually measured data permits, thus enabling device design with over one year service lifetime.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"13 ","pages":"Article 100231"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance analysis of a hybrid power network using a multi-criteria approach (AHP-TOPSIS)","authors":"Aristide Tolok Nelem ,&nbsp;Yannick Antoine Abanda ,&nbsp;Diane Tchuani Tchakonté ,&nbsp;Mimosette Makem ,&nbsp;Mathieu Jean Pierre Pesdjock ,&nbsp;Pierre Ele ,&nbsp;Ndjakomo E. Salomé","doi":"10.1016/j.cles.2025.100224","DOIUrl":"10.1016/j.cles.2025.100224","url":null,"abstract":"<div><div>Cameroon has abundant but underutilized energy resources, with low electricity access and significant urban–rural disparities. Communities increasingly adopt hybrid systems combining conventional and renewable sources, yet intermittency affects load profiles and performance. We propose a LabVIEW-based AHP–TOPSIS framework within a predict–optimize–decide pipeline, where short-term forecasts inform evaluated alternatives for realistic rankings. The criteria—C1 (availability), C2 (demand satisfaction), C3 (CO₂ intensity), and C4 (levelized operating cost)—are weighted via normalized AHP. A local case study (PV, grid, diesel, batteries) with sensitivity analyses (OAT ±5–20 %, Dirichlet sampling) and robustness tests (demand surge, diesel stress, solar variability) shows grid preference under stress, while PV with storage is optimal under favorable irradiance. The lightweight, SCADA-deployable framework enables transparent, indicator-driven switching, enhancing reliability and cost-effectiveness.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"13 ","pages":"Article 100224"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards energy requirement reduction for high-strength concrete using brick powder and tyre rubber: A pragmatic runway of sustainability","authors":"David Sinkhonde,&nbsp;Tajebe Bezabih,&nbsp;Derrick Mirindi,&nbsp;Ismael Dabakuyo,&nbsp;Oluwapelumi Abiodun","doi":"10.1016/j.cles.2026.100235","DOIUrl":"10.1016/j.cles.2026.100235","url":null,"abstract":"<div><div>The energy requirement reduction in concrete using waste materials, viewed as a strategy for sustainable construction, requires evaluation among many concrete grades. Waste materials such as waste brick powder (WBP) and waste tyre rubber (WTR) have been receiving an increasing interest due to their abilities to satisfy both sustainability and performance requirements simultaneously. However, the specific impact of these materials on energy demand across different concrete strength classes remains underexplored. This study evaluates the energy requirement reductions of classes 50, 55 and 60 concrete mixes containing WBP and WTR. Response surface methodology was also employed to understand the relationships between input variables (WBP and WTR) and desired outcome (energy requirement). The results highlight that using WTR and WBP in concrete significantly reduces the energy requirements of high-strength concrete mixes. Remarkably, the inclusion of 5% WBP causes higher energy requirement reductions (&gt;4.5% reductions) in contrast with 20% WTR (&lt;0.82% reductions). The response surface methodology results show that WBP inclusion in concrete is more sensitive to energy requirement compared with WTR. However, their combined influence on energy requirements depends on their contents and their basic unit values among the various concrete classes. Moreover, the current results strongly suggest that smaller replacement levels of WTR and WBP are key factors towards sustainable construction and considerable concrete performance even though the research does not include detailed experimental works on concrete properties. This research provides new insights into developing non-conventional concrete mixes using WTR and WBP for sustainable construction.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"13 ","pages":"Article 100235"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A conceptual framework for sustainable energy management modelling in hubs for circularity","authors":"Tobias Løvebakke Nielsen,&nbsp;Daniela Guericke,&nbsp;Alessio Trivella,&nbsp;Devrim Murat Yazan","doi":"10.1016/j.cles.2026.100237","DOIUrl":"10.1016/j.cles.2026.100237","url":null,"abstract":"<div><div>The concept Hubs for Circularity (H4C) represents integrated systems that combine efficient use of clean energy and circular economy to enhance resource efficiency within a region. H4C benefit from the geographical proximity of different industries within industrial zones and the surrounding urban and rural areas, allowing them to share resources, technology, and infrastructure. They reduce the use of virgin resources through Industrial Symbiosis (IS), where one company uses waste of another company as resource. Energy management is crucial in H4C, as these systems often integrate renewable energy sources, involve energy-intensive industries, include numerous energy consumers, and may rely on energy-based industrial symbiosis exchanges.</div><div>This study presents a conceptual modelling framework for energy management in H4C, developed through a systematic literature review of related systems including energy-based IS. The framework provides a guideline for researchers and practitioners on which modelling aspects to consider when optimising the energy flows within a hub. We argue that effective energy management in H4C requires combining conventional modelling aspects, such as objective functions, uncertainty, operational flexibility, and market participation, with IS-specific factors like the type of symbiosis, the degree of information sharing, and collaboration structures. Lastly, temporal and spatial granularity should also be considered. In H4C with extensive IS, decentralised resource and energy exchanges often lead to similarly decentralised information flows and decision-making. Yet, our review shows a persistent reliance on centralised model structures, suggesting a path dependency rooted in traditional energy optimisation approaches. This highlights the need for models that better align with the distributed and collaborative nature of H4C systems.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"13 ","pages":"Article 100237"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk, flexibility, and investment in Fischer–Tropsch fuels: Insights from real options analysis","authors":"Mahsa Mehrara ,&nbsp;Jonas Zetterholm ,&nbsp;Andrea Toffolo ,&nbsp;Elisabeth Wetterlund","doi":"10.1016/j.cles.2026.100232","DOIUrl":"10.1016/j.cles.2026.100232","url":null,"abstract":"<div><div>The transition to sustainable transportation fuels requires investment in emerging biomass-to-liquid production pathways under uncertain market and policy conditions. This study applies a real options analysis framework to evaluate the economic viability and timing of investments in biomass- and power-to-liquid pathways by identifying conditions where an investor should invest, defer, or abandon investments. The analysis is conducted for Sweden, reflected by its large biomass base and well-developed forest industry and ambitious defossilization policies. Results indicate that large price gaps between feedstock and produced fuels are not by themselves sufficient to trigger investment; in volatile markets, investors may still defer because the option to wait has economic value. Thus, even at identical price levels across scenarios, outcomes range from commitment to inaction depending on volatility. Moreover, when investments do occur, they are consistently deferred until the final year of the investment window. While modest subsidies may suffice under stable price conditions, volatile markets with high drifts require significantly greater support to counteract the incentive to defer investments. Electricity cost structures and carbon pricing must be targeted to support the transition toward electrified fuel production pathways. The insights from this study can inform the design of policy instruments that align investor incentives with global transition goals.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"13 ","pages":"Article 100232"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Techno-economic analysis of large-scale hydrogen storage in salt caverns for renewable energy integration and energy market optimisation","authors":"Jawaria Bint Faheem,&nbsp;Khaled Benyounis,&nbsp;James G Carton","doi":"10.1016/j.cles.2026.100233","DOIUrl":"10.1016/j.cles.2026.100233","url":null,"abstract":"<div><div>This study presents a techno-economic analysis of large-scale hydrogen storage, using salt caverns in Ireland, to mitigate wind energy curtailment and optimise energy market integration. The research explores the feasibility of converting surplus wind energy into hydrogen during off-peak periods and utilising it— in the electricity day-ahead market (DAM) and other sectors such as transportation and heating — when demanded. A modelling framework incorporating geological, operational, and economic constraints was developed to assess hydrogen storage costs and overall system’s profitability for the presented case. The findings indicate that while seasonal hydrogen storage for the supply in electricity market alone is economically challenging (37.6€/kg hydrogen), integrating hydrogen into multiple energy sectors significantly enhances viability (2-3€/kg hydrogen). Sensitivity analysis highlights the critical impact of electrolyser capital costs and operational expenses on economic feasibility. The study concludes that a multi-sectoral hydrogen strategy can improve renewable energy utilization, support grid stability, and advance decarbonization objectives.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"13 ","pages":"Article 100233"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geographic information system–based multi-criteria decision framework for siting photovoltaic-powered electric vehicle charging stations","authors":"Ade Gafar Abdullah ,&nbsp;Samsu Arif ,&nbsp;Muhammad Kunta Biddinika ,&nbsp;Farsha Sabilla Rahman ,&nbsp;Mita Maylani ,&nbsp;Dadang Mohamad","doi":"10.1016/j.cles.2025.100230","DOIUrl":"10.1016/j.cles.2025.100230","url":null,"abstract":"<div><div>The accelerating adoption of electric vehicles (EVs) has created an urgent demand for efficient and sustainable charging infrastructure. Among the viable alternatives, photovoltaic (PV)-powered electric vehicle charging stations (EVCS) stand out for their alignment with global decarbonization and renewable energy targets. However, determining optimal EVCS locations requires integrating diverse criteria, including spatial suitability, technical feasibility, environmental impact, socio-economic considerations, and grid connectivity. This study presents a systematic literature review (SLR) of 43 peer-reviewed articles published between 2010 and 2024, applying the PRISMA methodology to examine the use of geographic information systems (GIS) and multi-criteria decision-making (MCDM) approaches in PV-based EVCS site selection. The analysis is structured around five key themes: analytical frameworks, dominant MCDM methods, artificial intelligence (AI) integration, spatial data utilization, and methodological challenges. Results indicate that AHP and TOPSIS are the most widely applied MCDM techniques, enabling structured evaluation of multiple criteria, while GIS is extensively used for spatial visualization, overlay analysis, and suitability mapping. Emerging studies demonstrate the incorporation of AI and machine learning (ML) methods such as Improved whale optimization algorithm (IWOA), particle swarm optimization (PSO), and random forest to enhance site prediction accuracy, adaptability, and planning under uncertainty. Despite methodological progress, gaps remain in the consistent integration of social sustainability dimensions, dynamic real-time data, and empirical model validation. This review contributes by synthesizing current trends, identifying research limitations, and proposing a hybrid GIS–MCDM–AI framework to support future decision-making processes. The insights generated offer practical implications for infrastructure planners and policymakers aiming to scale up context-sensitive, intelligent, and sustainable EVCS deployment worldwide.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"13 ","pages":"Article 100230"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}