Capacity Factor Research Articles

High-resolution gridded dataset of China’s offshore wind potential and costs under technical change

Assessing the dynamics of offshore wind potential and costs is essential for low-carbon energy policy decision-making and energy modeling, but no open-source, spatial explicit and technologically detailed dataset is available. This study addresses this gap by employing a consistent assessment framework that integrates GIS analysis, a wind reanalysis model, a component-based cost model and scenario analysis. It identifies suitable space for offshore wind deployment considering 12 technical and policy constraints, estimates hourly output curves, capacity factors, and technology cost dynamics by components across 5058 grid points with a 10 km resolution from 2020 to 2035 under three technical change scenarios. The dataset has been validated through comparisons with existing offshore wind projects and datasets, and is stored in two formats (GeoTIFF and NetCDF4). This dataset offers extensive potential for use as an input in climate policy and energy system research.

Energy-Economy-Environment Nexus Toward Sustainable Development: The Function of Globalization and Technological Innovation on Load Capacity Factor in Thailand

Energy-Economy-Environment Nexus Toward Sustainable Development: The Function of Globalization and Technological Innovation on Load Capacity Factor in Thailand

After the Johannesburg II Declaration, What Next for Ecological Sustainability in BRICS? Perspectives on Green Innovations and Green Transitions

ABSTRACTAdvancing ecological sustainability, a major focus of the SDGs, necessitates evaluating the influence of green energy, sustainable technology, and resource utilization on ecological load capacity factors—an area that has remained underexplored in existing research. Also, the LCF method, which elucidates the unique features of the supply and demand dynamics in the natural environment, is still not used by BRICS environmental modelers. This study examined panel data of pertinent LCF predictors spanning from 1980 to 2021. The analysis utilized several advanced methodologies, and a new viewpoint was presented through the application of the panel‐frequency domain Granger causality test. The results demonstrate that (i) green transitions notably enhance LCF across various quantile distributions, and (ii) green technologies exert a moderately positive influence on LCF. The analysis suggests that ongoing shifts towards clean energy and the encouragement of eco‐friendly technological innovations necessitate well‐planned policies to enhance LCF within the BRICS nations.

Hybrid hydrogen-electricity production using Spherical Tokamaks: a cost-driver sensitivity study and techno-economic analysis

Abstract Hybrid fusion power plants able to produce hydrogen and electricity are proposed here as a way of decarbonising the fossil-fuel-dominated primary energy market and to improve the plant economics. The main cost drivers of a fusion power plant based on a spherical tokamak have been identified using statistical analysis (Morris and Sobol methods) from a wide range of cases obtained with the systems code PROCESS. Showing the relevance of parameters such as 
β, A, BT max.

Three scenarios of advancing technophysical assumptions (conservative, moderate and optimistic) have been chosen to study the integration of the fusion reactor with the power block (Rankine, He-Brayton or super-critical-CO2-Feher) and with the PEM electrolyser. The super-critical-CO2 cycle returns the best results for the studied temperature range (500ºC-800ºC), with an efficiency between 40%-56%. The modelled PEM is in line with current commercial models with a consumption of 51.97 kWh/kg H2.

The economic feasibility of these scenarios has been explored for a set of learning factors that consider the cheapening of the capital costs tied to experience. The LCOE of these scenarios have been compared against current price ranges of solar, wind and fission power and the LCOH against PEM prices, showing that the moderate and optimistic scenarios could be competitive for learning factors lower than 0.5 and capacity factors larger than 0.7. A showcase case using the optimistic scenario shows that the hybrid fusion power plant in the French and German market can improve the plant profits by 15% and 66% respectively.

Simultaneous Profiling of Multiple Phosphorylated Metabolites in Typical Biological Matrices via Ion-Pair Reversed-Phase Ultrahigh-Performance Liquid Chromatography and Mass Spectrometry.

Simultaneous analysis of multiple phosphorylated metabolites (phosphorylated metabolome) in biological samples is vital to reveal their physiological and pathophysiological functions, which is extremely challenging due to their low abundance in some biological matrices, high hydrophilicity, and poor chromatographic behavior. Here, we developed a new method with ion-pair reversed-phase ultrahigh-performance liquid chromatography and mass spectrometry using BEH C18 columns modified with hybrid surface technology. This method demonstrated good performances for various phosphorylated metabolites, including phosphorylated sugars and amino acids, nucleotides, NAD-based cofactors, and acyl-CoAs in a single run using standard LC systems. Specifically, the method showed good retention (capacity factor > 2) and reproducibility (ΔtR < 0.09 min, n = 6), peak symmetry (tailing factor < 2), and sensitivity (limit-of-detection < 238 fmol-on-column with QTOFMS) for all tested analytes especially for the medium- and/or long-chain acyl-CoAs. The method demonstrated reproducible applicability across numerous biological matrices, including tissue (liver), human biofluids (urine, plasma), cells, and feces, and revealed significant molecular phenotypic differences in phosphorylated metabolite composition.

Evaluating the effectiveness of cost-minimal planning of decarbonized electricity systems in reducing life cycle greenhouse gas emissions

Abstract The decarbonization of regional electricity systems is a critical enabler of broader economy-wide decarbonization strategies and has motivated robust research on how to decarbonize electricity systems at minimum monetary cost. Since these efforts often focus on the operation of electricity systems, however, they do not account for emissions of the full life cycle associated with different resources. Different resource mixes can achieve a similar level of decarbonization apparent decarbonization through operational emissions, while achieving very different levels of life cycle greenhouse gas (GHG) emissions reductions. To explore these differences, we model the expansion of the California electricity system from 2030 to 2045 using a simplified electricity dispatch model to investigate how the planning of future electricity systems may differ between prioritizing minimum cost versus minimum life cycle GHG emissions under a common target for operational GHG reductions. We find that explicitly planning for minimum life cycle GHG emissions yields an additional 1.6-2.0% reduction in annual life cycle GHG emissions at a cost penalty of 3.2–9.6% and that electricity resource mixes that minimize life cycle GHG emissions tend to favor high capacity factor zero-carbon resources and long-duration storage compared to a minimum cost approach. Comparatively, we find that aggressive supply chain decarbonization of generation and storage technologies reduces life cycle GHG emissions of electricity supplies by 3.0% to 14% and combining both increases these reductions to 5.0% to 16%. Further, we find that applying a carbon tax to minimum cost-based capacity expansion can incentivize planning to account for life cycle GHG emissions. Our results indicate that a planning approach focused on minimizing life cycle GHG emissions may not be palatable, but significant reductions in life cycle emissions can be realized through conventional mechanisms like carbon taxes, import standards, and targeted supply chain decarbonization.

Linking Resource Richness, Digital Economy, and Clean Energy to Ecological Footprint and Load Capacity Factor in Emerging Markets

Linking Resource Richness, Digital Economy, and Clean Energy to Ecological Footprint and Load Capacity Factor in Emerging Markets

The Impact of Uncertainty in Economic Policy on the Load Capacity Factors in China and the United States (US): New Evidence from Novel Fourier Bootstrap ARDL Approach

The Impact of Uncertainty in Economic Policy on the Load Capacity Factors in China and the United States (US): New Evidence from Novel Fourier Bootstrap ARDL Approach

Green hydrogen production from offshore wind: A techno-economic analysis for Türkiye

This study investigates the economic viability of green hydrogen production using offshore wind energy in Türkiye, focusing on four regions: Ayvacık, Bozcaada, Edremit, and Bandırma. A techno-economic model was developed to estimate the levelized cost of hydrogen (LCOH) using 50 MW alkaline and polymer electrolyte membrane (PEM) electrolyzers. Results indicate that Ayvacık, with an LCOH of $4.33/kg H₂ for alkaline electrolysis, is the most cost-effective location, while Bandırma, with the highest production costs, reaches an LCOH of $5.85/kg H₂. Key cost drivers include capacity factor, capital expenditures (CAPEX), and government incentives such as local content support, which lowers Ayvacık’s LCOH to $4.13/kg H₂. These findings underscore offshore wind’s potential to drive green hydrogen production in Türkiye, although regional factors significantly impact economic feasibility. This analysis highlights the strategic importance of optimal site selection, supportive policies, and technology advancements for expanding Türkiye’s green hydrogen industry.

Achieving environmental quality through stringent environmental policies: Comparative evidence from G7 countries by multiple environmental indicators

Compatible with the increasing public interest on climate change, countries have taken measures to combat climate change and support environmental sustainability. Considering this fact, this study investigates whether environmental measures, proxied by the environmental policy stringency (EPS) index, are efficient in achieving sustainability of environment in G7 countries as the leading economies; uses multiple environmental sustainability indicators, and applies quantile methods from 1991/Q1 to through 2020/Q4. The results show that (i) EPS curbs carbon dioxide emissions in France and the United States across all quantiles. Also, it has a declining effect in Germany and Italy at lower quantiles and in Canada at lower and higher quantiles; (ii) EPS declines ecological footprint in United States across all quantiles, while it curbs in Canada and Germany at lower quantiles as well as in Italy and United Kingdom at higher quantiles; (iii) EPS stimulates load capacity factor in France, United Kingdom, and United States across all quantiles and in Canada at higher quantiles; (iv) causal effect of EPS on the environment varies throughout quantiles; (v) the robustness of the results by quantile regression method is verified. Overall, the results reveal that the effect of EPS on environmental sustainability differentiates across environmental indicators, countries, and quantiles. In ensuring environmental quality, EPS is completely helpful in the United States, fully inefficient in Japan, and has a mixed effect in remaining G7 countries.

Enhanced extreme learning machine via competitive learning SSA (CL-SSA) for load capacity factor prediction

Enhanced extreme learning machine via competitive learning SSA (CL-SSA) for load capacity factor prediction

Techno-economic analysis of floating solar photovoltaic plant in reservoir-based hydropower station in India: a case study

ABSTRACT India's grid stability and sustainable energy transition encounter challenges posed by the unpredictable nature of solar power and the low plant load factor (PLF) of hydroelectric stations caused by water scarcity. It is worth noting that traditional energy sources still account for 78% of the country's energy demand. This study aims to present a solution to these problems by analysing the feasibility of a floating solar photovoltaic (FSPV) plant on the reservoir of Tehri hydro plant, Uttarakhand, India. The study determined that a 175 MW FSPV system has the potential to generate 298 million units (MU) of energy and reduce CO2 emissions by 265 million kg. The estimated increase in hydropower production by preventing water loss through evaporation amounts to 7.05 million units per year. The estimated tariff for the proposed 175 MW FSPV is 5.25 US Cents/Unit, in accordance with current regulations. In addition, the System Advisor Model (SAM) software calculates the net present value and payback duration of capital invested to be 18.05 million US$ and 8.8 years, respectively. The analysis also determined that there is no need for additional transmission infrastructure to evacuate FSPV power from the existing Tehri hydro plant. The proposed model of FSPV with hydropower stations enables the reservoirs to function as virtual batteries, hence optimising power generation. Furthermore, the viability of FSPV installations have been assessed at sixteen reservoir-based hydropower stations with a capacity factor less than 30%. Annually, installing FSPV systems at these locations may yield total power output of 11446 MW and generate 17310 MU of electricity. The integration of these sites would lead to substantial reductions in CO2 emissions, amounting to 15406 million kg, as well as an increase in hydropower generation by 341.30 million units per year.

Implications of Large-Scale PV Integration on Grid Operation, Costs, and Emissions: Challenges and Proposed Solutions

This study examines integrating large-scale photovoltaic (PV) systems into the power grid to achieve a 30% PV share, addressing operational and economic challenges such as backup generation, storage, and grid stability. Applying an electricity dispatch model to the test case of Israel, it highlights significant impacts on fuel consumption, cost, and carbon emissions. Key findings include an 8% drop in the capacity factor of natural gas combined cycle (NGCC) plants, leading to increased starts, stops, and higher fuel consumption. Annual power generation will grow from 81 to 104 TWh, with PV generation increasing from 8.1 to 31.1 TWh. Open cycle gas turbine (OCGT) output will grow from 2.4 to 10.2 TWh, increasing OCGT’s market share from 3% to 10%. NGCC operations’ intermittency will double annual starts from 3721 to 7793, causing a 1.1% efficiency drop and a 2% rise in natural gas consumption. 3.45 GWh of Li-ion battery capacity will be needed. The LCoE is expected to increase from 6.6 to 7.0 c$/kWh without a carbon tax and from 8.7 to 8.8 c$/kWh with a $40/t carbon tax. Annual emissions will rise from 41.8 to 46.5 Mt. This study provides insights for sunny Mediterranean countries with similar renewable energy goals.

Assessing the Efficiency of the Zagtouli Solar Plant: A Large-Scale Grid-Connected PV System in Burkina Faso

&lt;p&gt;This paper presents an evaluation and analysis of the energy performance of a 33.7 MWp solar photovoltaic plant. Monitoring data for 36 months (January 2019-December 2021) have been used to evaluate the performance of the power plant according to the IEC 61724 standard. Normalized parameters that are (i) performance ratio, (ii) reference yield, (iii) array yield, (iv) final yield, (v) array capture losses, (vi) system losses, (vii) system efficiency, and (viii) capacity factor were quantified. During the study period, monthly averages of the normalized yields Y&lt;sub&gt;r&lt;/sub&gt;, Y&lt;sub&gt;a,&lt;/sub&gt; and Y&lt;sub&gt;f&lt;/sub&gt; obtained are respectively 5.81 h/d, 4.59 h/d, and 4.52 h/d with average total losses (T&lt;sub&gt;L&lt;/sub&gt;) estimated at 1.29 h/d and an average performance ratio (PR) of 78%. In light of this study, more than 80% of array capture losses are related to miscellaneous capture losses. Thus, periods of low production due to a drop in performance are compounded by other external climatic factors. The performance ratio becomes increasingly sensitive to the effect of temperature over time. This study could constitute a decision support tool for solar power plant projects underway at the national level and in the Sudan-Sahelian zone.&lt;/p&gt;

The impact of human capital on the load capacity factor in the middle east and north Africa

Although there has been a substantial body of research analysing the factors influencing environmental sustainability, the role of human capital has been relatively understudied. The objective of this research is to fill this gap by examining the impact of human capital on load capacity factor (LCF) across 14 MENA countries from 1990 to 2019. The empirical investigation employs the Method of Moments Quantile Regression (MMQR) alongside a variety of second-generation panel data techniques. The empirical analysis confirms the presence of a long-term linkage between human capital and environmental sustainability. Furthermore, the MMQR suggests a negative impact of human capital on LCF across all quantile orders, indicating that enhanced human capital reduces LCF and deteriorates environmental quality. Although the detrimental effects of human capital on the environment is observed in all countries, they are more pronounced in countries with good environmental performance. Additionally, the MMQR indicates the validity of the Load Capacity Curve hypothesis within MENA countries. Finally, economic globalisation and population have detrimental impacts on the environment, whereas clean energy consumption has a positive effect. This study emphasises the necessity of increasing public awareness of environmental challenges, as well as the implementation of strategies for mitigating climate change in the MENA region.

Study on the Influencing Factors of Financing Ability of Real Estate Listed Companies

In recent years, due to the rich profits in the real estate industry, more and more companies join the ranks to share a share of the pie, which leads to the uneven quality of the real estate industry, and the continuous emergence of bad market behaviors such as real estate speculation and bubble real estate, which has brought a negative impact on the real estate industry. In order to reverse this unfavorable situation, the state has also introduced some policies to regulate the real estate industry. With the introduction of some national tax policies and financial policies, the real estate enterprises have some capital difficulties, which will eventually hinder the further development of the real estate industry. Therefore, the real estate industry should develop vigorously, especially the development of listed real estate companies are inseparable from stable financing. Therefore, it is of great theoretical and practical significance to refine the influencing factors of the financing ability of real estate listed companies and determine the influence intensity and direction, which will enrich the theory of enterprise financing and optimize the financing structure of real estate listed companies. This article first related affecting factors of real estate financing ability of listed companies at home and abroad literature, using the factor analysis method to the real estate listed company financing ability influence factors of multiple indexes for sorting, construct the real estate financing ability evaluation index system of influencing factors of listed companies, on the basis of this place, design with the use of multiple regression analysis the real estate listed companies financing ability and the factors influencing the regression model. Excel, SPSS and other software were used to conduct data operation and processing on the model, and the output results of operation were summarized and the following research conclusions were drawn. Firstly, using the factor analysis method to the real estate listed company financing ability will influence factors of multiple indices for sorting, finally get the real estate listed company financing ability of the five key factors of affecting factors namely profitability factor (F1), short-term debt paying ability factor (F2), capacity factor (F3), development capacity factor (F4), the company scale factor (F5). Secondly, the short-term solvency factor has a significant negative effect on the financing ability (asset-liability ratio) of real estate listed companies. Thirdly, the development ability factor has a significant positive effect on the financing ability (asset-liability ratio) of real estate listed companies. Fourthly, the company size factor has a significant positive effect on the financing ability (asset-liability ratio) of real estate listed companies. Fifthly, there is no significant causal relationship between profitability factor and operating ability factor and financing ability (asset-liability ratio) of real estate listed companies.

Evaluating the Performance of Parabolic Trough Solar Power Plants in Algerian Deserts: A Case Study of Andasol-1

Electricity generation through renewable energy sources is essential for addressing environmental and economic challenges caused by reliance on fossil fuels. The global energy sector is rapidly transitioning towards sustainable and renewable energy sources, with concentrated solar power (CSP) emerging as a promising technology, particularly parabolic trough solar power plants. This study examines the performance of the Andasol-1 Parabolic Trough Solar Power Plant, with a 50 MWe power output, in various locations of the Algerian desert, including Bechar, Djanet, and Tamanrasset regions. A detailed overview of the technical specifications of the Andasol-1 facility is presented, and a comprehensive economic and energy analysis is carried out using the System Advisor Model (SAM) software. Our findings indicate that the Djanet region emerged as the most favorable site for CSP deployment, with a capacity factor of 53.7% and a Levelized Cost of Electricity (LCOE) of 16.84 ¢/kWh, offering the best balance of energy yield and cost efficiency. These results contribute to the global transition to clean and economically advantageous energy sources and provide valuable insights into the viability and efficiency of CSP technologies in dry climates.

Feasibility Study and Economic Analysis for Hydroelectric-PV Hybrid System Performed with PVsyst Software

While energy demand is increasing day by day with the developing technology, the decrease in traditional energy sources and the negative effects of carbon emissions on the environment encourage countries to turn to sustainable and environmentally friendly renewable energy sources. Requirements such as the periodic change of renewable energy resources depending on weather conditions and the optimum use of existing resources have made it attractive to use different renewable energy resources together. In this paper, the feasibility study of a hybrid energy system for Akfen Renewable Energy's Gelinkaya Hydroelectric Power Plant (HPP) with an installed capacity of 6,866 MW, located in Aziziye district of Erzurum province. In the study, the economic analysis of the hybrid model resulting from the integration of Gelinkaya HPP with SPP (Solar Power Plant) using PVsyst software is discussed. The acceptance of Gelinkaya HPP by the Ministry of Energy was made on June 14, 2013 and the power plant started electricity generation activities. Since this date, the average annual electricity generation value of the power plant has been 10.062.411 MWh and the capacity factor has been realized as 16.73%. With the analysis of the HPP-SPP hybrid system, it was observed that the power plant was able to generate an average of 12.491 MWh more electricity per year by contributing to electricity generation, especially during irrigation periods, and the capacity factor increased to 37.50%.

Evaluating a Solar–Biogas Hybrid Renewable Power Plant by Heating the Anaerobic Digester Using the Rejected Heat of Rankine Cycle in Idlib, Syria

This research includes modeling and studying the performance improvement of a hybrid renewable energy power plant using the modeling software Greenius in Idlib, Syria. The system consists of solar parabolic trough collectors and an anaerobic digester for generating biogas. This study included a practical experiment for generating biogas using five identical digesters operating at five different temperatures. The raw material was a mixture of 81% food waste and 19% human waste, and average temperatures were as follows: 49.6, 45.9, 43.5, 37.5, and 33.2 °C. Modeling operations were conducted for each case, as well as for the case corresponding to the highest growth rate of methanogenic bacteria theoretically. The modeling processes were conducted at 11 different values for the storage capacity from Full Load Hours (FLHs) 0 to 10 and by varying the solar multiple factor (SM) from 1 to 8. This study showed that when operating as a net solar plant, the lowest value for the cost of produced electricity (LCOE) was 0.1785 EUR/kWh at FLHs = 5 h and SM = 2, while the annual electricity production was 25.21 GWh. The maximum annual electricity production was 48.66 GWh, achieved at FLHs = 10 h, SM = 8, and the LCOE = 0.2896 EUR/kWh. It is possible to obtain annual electrical energy of 39.7 GWh, which was about 82% of the maximum possible annual production, at a cost of LCOE = 0.1864 EUR/kWh, which is less than 5% higher than the lowest possible cost. When operating as a hybrid plant with an annual capacity factor of 1 (full load), it is discovered that the lowest value of energy produced is in the third scenario at tAD = 43.52 °C and tc = 63.5 °C, with FLHs = 0 h and SM = 1, with the LCOE = 0.1283 EUR/kWh.

Jurisdictional Resource Factors Influencing Adoption and Implementation of the Public Safety Assessment During Pretrial

Criminal legal systems are increasingly adopting actuarial pretrial assessments which use statistical formulas to estimate individuals’ probability of adhering to pretrial requirements and guide the conditions of their supervision. A large gap in the study of pretrial assessments is due to inattention to implementation of these assessments in real-world settings. We conducted qualitative research examining personnel and data resource factors that influenced adoption and implementation of one pretrial assessment, the Public Safety Assessment (PSA) in seven counties in the United States. Qualitative interviews with legal and community actors were conducted and supplemented with implementation process data to elucidate personnel and data capacity factors impacting PSA adoption and implementation. Findings suggest that generally, jurisdictions with existing pretrial services programs were more likely to adopt the PSA and to encounter fewer barriers to implementation due to personnel and data infrastructure. Implications for PSA adoption and implementation in future pretrial settings are discussed.