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A novel approach to the construction of absolutely continuous distributions over the unit interval is proposed. Considering two absolutely continuous random variables with positive support, this method conditions on their convolution to generate a new random variable in the unit interval. This approach is demonstrated using some popular choices of positive random variables, such as the exponential, Lindley, and gamma. Some existing distributions, like the uniform, the beta, and the Kummer-beta, are formulated with this method. Several new structures of density functions having potential for future applications in real-life problems are also provided. One of the new distributions, namely the LCG, is considered for detailed study along with a related distribution, namely the GCL. The moments, hazard rate, cumulative distribution function, stress-strength reliability, random sample generation using the quantile function, method of moments along with maximum likelihood estimation, and regression modeling are discussed for both the distributions. Real-life applications of the proposed models and the corresponding regression models show promising results. [ABSTRACT FROM AUTHOR]

By multivariate econometric analysis of UK offshore wind farms, we demonstrate a life cycle effect of the capacity factor of offshore wind. From the bathtub curve findings in reliability engineering, we would expect a long, flat segment of stable operation in the middle of the life cycle production schedule. Our general findings deviate from the standard curve as it does not have a long segment where production is stable. Production starts falling too early. Lack of stable operation period is harmful to wind farm economics. It indicates immature technology and a potential for improvement. We examine the effect life cycle production schedules have on project economics and compare with a common assumption of a flat production schedule. We find that project profitability (net present value) is very sensitive to the life cycle profile of production. [ABSTRACT FROM AUTHOR]

This paper investigates the decarbonization of Tabriz-City focusing on its wind power for electrification. Statistical, quantitative, comparative, and simulation-research-methods used to analyze the existing and future total energy consumption, demand, and cost in the city according to carbon-based and wind-based electricity. The Monte Carlo Simulation Method has been used to estimate the probability of the Levelized Cost of Electricity. Tabriz thermal power plant generates 1kWh electricity that costs 0.15 US$/kW without subsidies and produces 575 g.CO2/kW.h overall while it will plunge to 0.05US$/kWh producing 7 g.CO2/kWh using wind. The NPV and IRR (32%) analysis show that that investment in wind-based electricity is three times cheaper than thermal power electricity in Tabriz. It is shown that the electrification of an oil-based economy with wind-based power plants is an economical investment for the city. Besides the hub-height and rotor sweep area, the capacity factor is the most decisive in the productivity of the alternative turbines. [ABSTRACT FROM AUTHOR]

This document delves into evaluating wind power potential within Morocco's Oriental region, encompassing an extensive study of 23 locations over 43 years. The analysis was conducted using the advanced MERRA2 data reanalysis system coupled with MATLAB software. Our comprehensive study aims to map the wind energy capabilities across these sites. We employed eight distinct algorithms to adapt the Weibull distribution for the wind speed data. Additionally, the research includes an analysis of the wind rose and assesses the Capacity Factor (CF) to determine the most efficient periods for wind energy production. Our findings highlight that sites S4, S7, and S11 create an ideal geographic formation for wind farm placement. Within this formation, site S8, boasting a (CF) of 36.97%, emerges as a critical location, especially when paired with the EWT DW54 500 wind turbine model. This investigation opens new avenues for advancing wind energy in the region. [ABSTRACT FROM AUTHOR]

Nowadays, numerous governments have instituted diverse regulatory frameworks aimed at fostering the assimilation of sustainable energy sources characterized by reduced environmental footprints. Solar, wind, geothermal, and ocean energies were subject to extensive scrutiny, owing to their ecological merits. However, these sources exhibit pronounced temporal fluctuations. Notably, ocean dynamics offer vast energy reservoirs, with oceanic waves containing significant amounts of energy. In the Central American Pacific context, the exploration of wave energy resources is currently underway. Accurate numerical wave models are required for applied studies such as those focused on the estimation of exploitable wave power; and even more so in Central American region of the Pacific Ocean where existing numerical models simulations have so far relied on coarse resolution and limited validation field data. This work presents a high-resolution unstructured wave hindcast over the Central American Pacific region, implemented using the third-generation spectral wave model WAVEWATCH III over the period between 1979 and 2021. The results of the significant wave height have been bias-corrected on the basis of satellite information spanning 2005 to 2015, and further validation was performed using wave buoy and acoustic Doppler current profiler (ADCP) records located in the nearshore region of the Central America Pacific coast. After correction and validation of the wave hindcast, we employed the dataset for the evaluation and assessment of wave energy and its possible exploitation using different wave energy converters (WECs). This evaluation addressed the need to diverse the energy portfolio within the exclusive economic zones of Guatemala, El Salvador, Honduras, Nicaragua, Costa Rica, Panama, Colombia, and Ecuador in a sustainable manner. Moreover, a comprehensive analysis was carried out on the advantages of harnessing wave energy, juxtaposed with the imperative of regulatory frameworks and the current dearth of economic and environmental guidelines requisite for development within the region. [ABSTRACT FROM AUTHOR]

Ghana, being blessed with abundant solar resources, has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority (BPA) exemplifies the nation's dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system's performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one year's worth of monitoring data for the climatic conditions at the facility and AC energy output fed into the grid. These data were used to analyze PV performance on each month's sunniest and cloudiest days. The goal is to aid in predicting the system's output over the next 365 days based on the system design and weather forecast and identify opportunities for system optimization to improve grid dependability. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between 16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analyses showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant's performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant's performance from deteriorating. Optimizing a solar PV system's design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days. The study reveals the necessity of hydropower backup during cloudy days, enabling BPA to calculate the required hydropower for a consistent grid supply. Being able to predict the daily output of the system allows BPA to optimize dispatch strategies and determine the most efficient mix of solar and hydropower. It also assists BPA in identifying areas of the solar facility that require optimization to improve grid reliability. [ABSTRACT FROM AUTHOR]

One of the directions of development of the electric power industry is decentralization, aimed at improving the reliability of energy supply, reducing losses during transmission of electric energy and ensuring energy independence of consumers. It is possible to simulate decentralized power systems, including distributed generation facilities, by implementation of multi-agent systems that allow solving design and control problems taking into account the needs of each participant in the process of production, transmission, distribution and consumption of electricity. The development of distributed generation using a multi-agent approach requires the creation of models for assessing the technical and economic efficiency of decisions made by each agent, both at the strategic and tactical levels. The strategic decisions of agents related to distributed generation include, among other things, the creation of power facilities and power plants based on renewable energy sources. An important factor for making such decisions is the estimation of the capacity factor. However, currently there are no models for its estimation with high reliability. The present paper proposes a new algorithm for estimating the capacity factor for the entire territory of a certain administrative unit and a model for its forecasting based on climatic and geographical parameters. The study was conducted on a data sample of 221 generation facilities (solar and wind power plants) in four oblasts (regions) of the Russian Federation. It has been determined that the capacity factor can be forecasted with a mean error within 4 % for photovoltaic power plants and 9 % for wind power plants. Therefore, it is possible to use the developed algorithm and model both in decision support systems when choosing the location of this types of power plants, and in systems that model the development of power systems using a multi-agent approach.

Ghana, being blessed with abundant solar resources, has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority (BPA) exemplifies the nation’s dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system’s performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one year’s worth of monitoring data for the climatic conditions at the facility and AC energy output fed into the grid. These data were used to analyze PV performance on each month’s sunniest and cloudiest days. The goal is to aid in predicting the system’s output over the next 365 days based on the system design and weather forecast and identify opportunities for system optimization to improve grid dependability. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between 16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analyses showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant’s performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant’s performance from deteriorating. Optimizing a solar PV system’s design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days. The study reveals the necessity of hydropower backup during cloudy days, enabling BPA to calculate the required hydropower for a consistent grid supply. Being able to predict the daily output of the system allows BPA to optimize dispatch strategies and determine the most efficient mix of solar and hydropower. It also assists BPA in identifying areas of the solar facility that require optimization to improve grid reliability.

To reduce greenhouse gas emissions and the environmental impact of fossil fuels, Morocco has decided to increase the use of renewable energy resources. The intermittent nature of renewable energy resources causes instability in the power grid. Energy storage is the appropriate solution to this problem. Compressed air energy storage is a technology that stores energy in the form of high-pressure compressed air in above ground tanks or underground caverns. Large-scale storage of compressed air energy requires the storage of large volumes in salt caverns or aquifers. The aim of this paper is to find out the benefits of integrating underground compressed air energy storage technology. A case study in Morocco is used to estimate the levelized cost of energy plus storage (LCOES). The annual capacity factor for solar and wind power plants and the potential of underground caverns in Morocco were analyzed. The results illustrate that for a system with 100 MW capacity installed in the Casablanca region, the combination of an adiabatic compressed air energy storage system (ACAES) with a wind turbine installation offers the lowest electricity price per kWh, with average LCOES of 0.04 $/kWh. [ABSTRACT FROM AUTHOR]

Renewable energy stands as a cornerstone for a sustainable future, yet it grapples with a significant obstacle: curtailment—deliberate energy generation reduction to avert grid overload. Curtailment not only squanders clean energy but also poses economic and environmental challenges. This study seeks to enhance the value of renewable energy by proposing a comprehensive methodology to evaluate and mitigate curtailment, with a particular focus on repurposing curtailed energy for ammonia production in Jordan, a pioneering nation in renewable energy adoption. The study unfolds several pivotal contributions to the field: (1) Incorporating curtailment in the capacity factor equation for the first time. (2) Exploring green ammonia potential in Jordan for the first time. (3) Investigating, for the first time, the effects of by-products (specifically oxygen and rare gases) on the investment in green hydrogen and ammonia, utilizing system dynamics (4) Outlining hydrogen applications and highlighting the breakthrough hydrogen energy release optimizer for combustion-free hydrogen extraction. In a scenario where curtailment is restrained (514.1 GWh/year), our findings unveil promising prospects: hydrogen and ammonia can be produced at levelized costs of 2.4 USD/kg and 600 USD/ton, respectively. More significantly, the integration of free energy (curtailed energy) and the synergistic utilization of oxygen and rare gases, notably argon, precipitate a remarkable cost reduction to 1.5 USD/kg and 401 USD/ton for hydrogen and ammonia production, respectively. This remarkable cost reduction underscores a substantial improvement, with the cost of ammonia being USD 219 lower than the average market price (USD 620). We advocate for the adoption of the hydrogen energy release optimizer and strategic utilization of by-products as pivotal strategies to significantly mitigate the costs associated with hydrogen and ammonia production, thereby amplifying the value and viability of renewable energy initiatives. • Mitigation methodology for renewable energy curtailment in green ammonia production. • Integration of curtailment into the capacity factor equation for accurate assessment. • Pioneering exploration of green ammonia potential in Jordan's renewable energy landscape. • System dynamics analysis of by-products (oxygen, rare gases) impact on investment. • Utilizing by-products and deploying HERO for cost-effective hydrogen and ammonia production. [ABSTRACT FROM AUTHOR]

Nowadays, numerous governments have instituted diverse regulatory frameworks aimed at fostering the assimilation of sustainable energy sources characterized by reduced environmental footprints. Solar, wind, geothermal, and ocean energies were subject to extensive scrutiny, owing to their ecological merits. However, these sources exhibit pronounced temporal fluctuations. Notably, ocean dynamics offer vast energy reservoirs, with oceanic waves containing significant amounts of energy. In the Central American Pacific context, the exploration of wave energy resources is currently underway. Accurate numerical wave models are required for applied studies such as those focused on the estimation of exploitable wave power; and even more so in Central American region of the Pacific Ocean where existing numerical models simulations have so far relied on coarse resolution and limited validation field data. This work presents a high-resolution unstructured wave hindcast over the Central American Pacific region, implemented using the third-generation spectral wave model WAVEWATCH III over the period between 1979 and 2021. The results of the significant wave height have been bias-corrected on the basis of satellite information spanning 2005 to 2015, and further validation was performed using wave buoy and acoustic Doppler current profiler (ADCP) records located in the nearshore region of the Central America Pacific coast. After correction and validation of the wave hindcast, we employed the dataset for the evaluation and assessment of wave energy and its possible exploitation using different wave energy converters (WECs). This evaluation addressed the need to diverse the energy portfolio within the exclusive economic zones of Guatemala, El Salvador, Honduras, Nicaragua, Costa Rica, Panama, Colombia, and Ecuador in a sustainable manner. Moreover, a comprehensive analysis was carried out on the advantages of harnessing wave energy, juxtaposed with the imperative of regulatory frameworks and the current dearth of economic and environmental guidelines requisite for development within the region.

Abstract Only 32 countries in the world have geothermal power plants in operation, with a combined capacity of 16,318 MW installed in 198 geothermal fields with 673 individual power units. Almost 37% of those units are of flash type with a combined capacity of 8598 MW (52.7% of total), followed by binary ORC type units with 25.1% of the installed capacity. The select list of geothermal power countries continues to be headed by the US, followed by Indonesia, the Philippines and Türkiye, and generated 96,552 GWh of electricity, at an average annual capacity factor of 67.5%, which represented 0.34% of the worldwide electric generation. Electricity from geothermal origin represented more than 10% of the total generated in at least seven countries, headed by Kenya, Iceland, and El Salvador. Practically, all geothermal fields in operation are harnessing resources from hydrothermal, conventional reservoirs, through an estimate of 3700 production wells at an annual average production of almost 3 MWh per well. Things could be similar in the next few years if the current trend continues, but all can change due to the world urgency to maintain global warming below the 1.5 °C threshold in the following years.

It is shown here that if the PDF (Probability Distribution Function) of wind speed is a Rayleigh distribution with parameter [Formula: see text], then the PDF of wind power is a Weibull distribution with parameters [Formula: see text], where [Formula: see text] is the air density; if the wind speed PDF is a Weibull distribution with parameters [Formula: see text], then the wind power PDF is also a Weibull distribution with parameters [Formula: see text]; if the PDF of wind speed is a log-normal distribution with parameters [Formula: see text], then the PDF of wind power is also a log-normal distribution with parameters [Formula: see text]. The derived relationships allow us to quickly estimate parameters that indicate the wind power potential of the considered site, and the amount of wind energy generated by the turbine.

Amidst the current global pursuit for access to energy by all as well as energy transition, the actual performance of existing power plants, particularly in developing countries, has often attracted little interest in scientific research. This study evaluated the performance of selected power plants in Uganda using the techno-economic performance assessment model based on actual operational data of hydropower, solar photovoltaic, cogeneration, and thermal power plants for the period 2010–2021. The analysis revealed that the average combined capacity factors are 19.8%, 22.9%, 18.4% and 58.6%, respectively, for thermal power plants, co-generation power plants, solar power plants and hydropower power plants. Furthermore, the levelised cost of electricity for the selected power plants’ financial performance is found to be lower than the retail price for a unit of electricity in Uganda. Overall, the study revealed that the real-life techno-economic performance of the selected power plants in Uganda agrees with the globally expected performance ranges.

Only 32 countries in the world have geothermal power plants in operation, with a combined capacity of 16,318 MW installed in 198 geothermal fields with 673 individual power units. Almost 37% of those units are of flash type with a combined capacity of 8598 MW (52.7% of total), followed by binary ORC type units with 25.1% of the installed capacity. The select list of geothermal power countries continues to be headed by the US, followed by Indonesia, the Philippines and Türkiye, and generated 96,552 GWh of electricity, at an average annual capacity factor of 67.5%, which represented 0.34% of the worldwide electric generation. Electricity from geothermal origin represented more than 10% of the total generated in at least seven countries, headed by Kenya, Iceland, and El Salvador. Practically, all geothermal fields in operation are harnessing resources from hydrothermal, conventional reservoirs, through an estimate of 3700 production wells at an annual average production of almost 3 MWh per well. Things could be similar in the next few years if the current trend continues, but all can change due to the world urgency to maintain global warming below the 1.5 °C threshold in the following years. [ABSTRACT FROM AUTHOR]

Previous studies have shown that the average losses of wind power production due to the wind turbine wake effect within operating wind farms is between 10% to 20% of the overall power output. Among other factors, it is reviled that, the wind farm array layout can contribute significantly to both wake effect and power loss at the wind farm site. This study employs mesoscale modelling techniques to assess the effect of geometric layout on the onshore wind farms performance. Geometric layout can be defined by the spacing and alignment (e.g. staggered or aligned) of the wind turbines with respect to the prevailing wind direction. The Weather Research and Forecasting (WRF) model in this study utilised Fitch's wind farm parameterization to simulate the interaction between wind turbine blades rotation and the atmosphere. To examine a wide range of operating conditions observed within the real-world operating wind farms, two idealised numerical simulations are carried out for each designed wind farm geometric layout, one with the convective condition and another with stable condition. Among the four different designed wind farm geometric layouts, the triangular wind farm layout which offered staggering after every next row was noted to be the easiest method for improving the wind farm performance by increasing the capacity factor from 0.55 to 0.71 and decreasing array losses from 9.15% to 4.63%. Comparison between stable and convective regime indicates that the highest capacity factor was obtained during the stable case with the highest power loss owing to increased wake impacts downstream. The lowest value of the capacity factor was obtained during the convective case with the lowest power loss for both four designed wind farms. [ABSTRACT FROM AUTHOR]

Coal-Fired Steam Power Plant (PLTU) Rembang is an important power plant in the Central Java electricity system. Like other coal-fired steam power plants, fuel cost is the most significant expense when operating the PLTU Rembang. During the 2019-2021 period, the average fuel cost was 73.88% of total costs. One of the ways to reduce fuel costs is by improving the accuracy of fuel demand planning. Fuel procurement planning is very dependent on the projected amount of electricity sales from power plant, which is largely determined by the power plant's Capacity Factor (CF). However, PLTU Rembang does not have any CF prediction modeling. This research developed and compared four prediction models: random forest regression, support vector regression, multiple polynomial regression, and multiple linear regression. Based on the comparison of validation from the four prediction model with MAPE and R-squared parameters, the multiple linear regression models is the best model, with the lowest MAPE of 7.83% and the highest R-squared of 0.8814. This multiple linear regression model can be used to predict the CF of PLTU Rembang in the future so that fuel demand planning is more accurate. [ABSTRACT FROM AUTHOR]

The power generation sector accounts for a significant portion of GHG emissions, and many countries strive for the large-scale adoption of renewable generation. Although the intermittent nature of renewables brings about complications in energy system planning, the share of renewable generations is increasing to the greatest extent. The wind generation has drawn increasing attention to expanding the use of renewable energy to reduce carbon emissions from the power generation sector, and the estimation of capacity factor is crucial in energy system modeling. This study develops a mathematical model for estimating the capacity factor of a wind farm with the consideration of outage probability of individual turbines. In addition, the power curves and wind speed distribution of the wind farm need to be estimated, which is demonstrated with a wind farm in Korea. It is asserted that the proposed method may render the wind farm capacity factor effectively. Thus, the results from this study can be useful for energy system modeling involving wind generations.

The past decade of wind power growth was supported by capacity factor improvements and associated cost reductions. But are higher capacity factors a technology success story or, as suggested by recent research, has the influence of technology been overstated by ignoring positive surface wind speed trends? The answer could influence estimates of wind energy's cost and even future deployment rates. We find that US surface wind speed observations imply a 2.6% improvement in capacity factors from 2010 to 2019. Yet newer vintages of wind plants have recorded capacity factors that are ~25% larger than plants built close to 2010. It follows that technological factors and improved site quality, not higher wind speeds, drove most of the improvement in capacity factors. Additionally, we match hundreds of meteorological stations to nearby (

This research discusses the hydropower potential of hydraulic structures that were originally built for other uses, including hydrological and energy data. The historically known average specific water discharge of rivers in Romania is used as input data in hydropower potential determination. Recent flow measurements confirm that the available hydrological data are consistent and can be used in the analysis. The work defines a connection between the capacity factor, as an energy parameter of a location on a river, and the values of the standardized daily flow duration curve. Therefore, only one value of the daily flows' duration curve is needed in order to determine the energy potential of a location by using the capacity factor. Thus, in the future, if the hydropower potential of a certain site located on a river needs to be determined, it will only be necessary for the standardized flow duration curve to be outlined, without having to calculate the hydropower parameters (installed power and amount of energy obtained). From the graph, the probability value exceeding the 0.5 ratio (flow/multiannual average flow) should be extracted. Using this value, the capacity factor (with the correlation found in this study) can be calculated and the energy attractiveness of the site can be estimated. As a result, a rather accurate estimation of the potential installed power and of the annual energy output can be obtained. Such an approach was not found in the specialized literature. [ABSTRACT FROM AUTHOR]

The useful wind energy potential in the Sahelian part of Chad is estimated by considering two representative sites namely, Mongo and Abeche from a long series of in situ wind speed measurements over a period of thirty years at 10 m height from the ground. The Weibull distribution statistic was used and its parameters were used to show the influence of turbulence sources on the wind speed at 10 m. Vertical extrapolation of the Weibull parameters and wind speed at different heights from 10 m to 100 m provided more usable wind speeds. The results show that on average the wind is more intense and stable in Mongo than in Abeche and that the wind speed values increase with height with the influence of turbulence sources quantified at about 22.15% in Abeche and 19.93% in Mongo. The comparative study of the five turbines used shows that the De Wind D7 turbines with $0.057/kWh at 70 m in Abeche and Bonus 1MW/54 turbines with $0.067/kWh at 50 m in Mongo are better suited to produce energy according to their production capacity. The minimum and maximum electricity costs per kilowatt hour obtained using these two best turbines are very competitive compared to the cost of electricity in Chad (about $0.16/kWh) and that the installation of a wind farm in these cities could significantly improve the socio-economic situation of households. [ABSTRACT FROM AUTHOR]

The widespread use of green energy sources creates a significant demand for energy storage. Hybrid floating photovoltaic (FPV) and pumped hydro storage (PHS) represent one of the most dependable and cost-effective solutions, which uses the PV system on the water body combined with a pair of lakes with different heights. This study focuses on the load side as well as the PHS capacity factor and aims to lower the cost of energy (COE) by raising the PHS capacity factor. Since building a PHS requires a significant upfront investment, doing so will also lower the COE and enable the acquisition of PHS for large-scale power production, which will guarantee power access in large cities. To simulate the FPV-PHS system, the multi-objective genetic algorithm (MOGA) is employed. Sufficient power management is a prerequisite for achieving system reliability in the best possible hybrid energy system design and implementation. Considering the 60-year system lifespan, the net present cost (NPC) analysis shows that, out of all the communities evaluated, the FPV-PHS system has the lowest NPC and COE. For the optimal configuration (FPV (Block A) 105 MW, PHS 80 MW, FPV (Block B) 357 MW, and the current hydropower plant), the expected NPC and energy costs for implementing the hybrid energy system (HRS) at the chosen location are $44,737,613 and $40/MWh, respectively. The existing FPV system spans 4.65 km2 and lowers the evaporation fraction by 17,279,400 m3. The hybrid FPV-PHS system reduces annual CO2 emissions by 581,830 tons. Our research reveals that a hybrid floating PV and pump storage hydropower system offers more steady clean electricity, implying a great significance for power grid infrastructure.

The sole reliance on conventional energy sources proves inadequate to meet the surging energy demand. To address this challenge, a self-sustainable hybrid system based on solar PV emerges as a promising solution. In light of this issue, it becomes important to analyze the performance of a solar PV array. The study aims to assess the performance and degradation of two distinct solar PV technologies, polycrystalline silicon PV (Poly-Si PV) and monocrystalline silicon PV (Mono-Si PV), under the tropical climatic conditions of eastern India. Various performance indices of solar PV arrays, such as reference yield, array yield, final yield, array capture loss, capacity factor (CF) and performance ratio (PR) for the two PV technologies, have been meticulously calculated to analyze the performance of solar PV arrays. The Linear regression (LR) method is employed to assess the degradation rate of both technologies. The results show better performance for Mono-Si PV in terms of yield outcomes and losses compared to its Poly-Si PV counterpart. The yield outcome of Mono-Si PV is approximately 12 % higher, whereas losses are around 32 % lower than Poly-Si PV. The annual average PR for Mono-Si PV and Poly-Si PV is obtained as 77.5 % and 72.42 %, respectively. Correspondingly, the average yearly CF is 16.78 % for Mono-Si PV and 15.44 % for Poly-Si PV. Results also show that Poly-Si PV degrades faster as compared to Mono-Si PV, with an annual average degradation rate of 0.67 % for Mono-Si PV and 0.73 % for Poly-Si PV. Finally, economic analysis of the two systems reveals that Mono-Si PV provides energy at a 9 % lower cost than Poly-Si PV, with a levelized cost of energy (LCOE) of 0.248 $/kWh for Mono-Si PV and 0.273 $/kWh for Poly-Si PV.

Increasing use of renewable energy requires sufficient grid flexibility to address uncertainty and variability in electricity generation. Previous studies suggest that combined heat and power (CHP) systems may support grid flexibility but they do not consider operating hours. In this paper, we used CHP operating data and determined annual and monthly availability of the installed CHP capacity from various sectors (e.g., utility, independent power producer, commercial, and industrial) in all seven U.S. independent system operators (ISOs) and regional transmission organizations (RTOs). Also, we estimated hourly CHP availability installed in five facility types (i.e., hospitals, universities, hotels, offices, and manufacturing) in the state of New York. The results show that regardless of ISO/RTO, sector, or season, more than 40% of the installed CHP capacity (0.7–8.7 GW) was not fully utilized in 2019; the results are similar for 2018. This available CHP capacity accounted for up to 9% of the ISO/RTO's peak electric demand, which may yield cost savings up to $16 billion by avoiding installation costs of new natural gas combustion or combined-cycle turbines. To exploit the available CHP capacity to enhance grid flexibility, we recommend different policy implications including flexible contract lengths between CHP owners and grid operators, improved market designs, and simplified interconnection standards.

Wind energy has gradually become one of the most efficient and reliable forms of sustainable energy, and it is being globally utilized to accelerate the expansion of green power production. However, recent projections of onshore & offshore wind energy systems indicate that further improvements are continuously required in terms of the deployments, capacity factors, costs, etc. to achieve an optimal wind power generation. According to these projections, the most critical current issue is the offshore systems' high levelized cost of energy, which demands the enhancements of the technologies to ensure the cost reduction in the future. Moreover, a number of reports are underscoring that offshore power systems can also leave a larger carbon footprints through their life-cycles compared to the onshore counterpart. On the other hand, the modelling methods of the power plants' life-cycle impact assessment (LCIA) have nowadays become among the most compelling research problems in the field of wind power systems engineering. In addition to comparatively assessing the characteristics of onshore & offshore energy systems based on some reports & studies, this work is therefore aimed at rigorously exploring the existing study practices in modelling the wind power plants' LCIA. A number of these practices were based on the implementation of various conventional modelling methods that may not help to make accurate assessments of the wind power plants’ life-cycle impacts. Hence, one of the main contributions of this study is to summarize a novel LCIA modelling approach based on Internet of Things (IoT) and predictive digital twin technologies. Unlike the conventional methods, real-time data extraction from the power plants makes these technologies possible to conduct the most accurate assessments of the life-cycle impacts.

This research aims to compare the energy output potential of land-based and floating bifacial photovoltaic (PV) systems of 50 MW and 400 kW with an existing land-based and floating monofacial PV system of the same dimensions and design in Bui, Ghana. The study uses ground weather files and PV system configurations to compare the energy yield from simulations of the different PV systems based on capacity factor, performance ratio, and monthly, final/annual energy yield. The results show that the bifacial gain of land-based and floating bifacial PV systems is 2.51% and 4.57%, respectively, and the capacity factor and performance ratio of the bifacial PV system are higher than the monofacial PV system. However, the additional energy generated by the bifacial PV system is not significant enough to justify the installation of a new system. Therefore, the study recommends optimizing system parameters such as albedo, tilt angle, and ground cover ratio to 0.5, 15°, and 0.3 respectively to enhance the performance and increase the bifacial gain of the PV system. Overall, this study provides valuable insights into the potential of bifacial PV systems in Bui, Ghana, and highlights the importance of optimizing system parameters for maximum energy output.

Distributed generation (DG) has been increasingly integrated into distribution systems in the last decade. The selection of the optimal location and capacity of these DGs is an important issue. This selection is based on minimizing losses, reducing emissions, reducing voltage deviation, and much more. This paper proposes a new optimization approach with a multi-objective function for maximizing DG capacities and minimizing both system losses and voltage deviation, which are subject to system constraints. The weight of each attribute of the multi-objective function is estimated using the analytical hierarchical process (AHP). The optimal DG size is calculated for all possible buses, and hence, the optimal bus can be located. Moreover, an algorithm is introduced to decrease the number of iterations when integrating more than one DG. The proposed method is an iterative linear programming method based on the familiar Newton-Raphson sensitivity matrix. The proposed approach has been tested on 69 and IEEE 33 bus systems. The simulation results are compared with previous literature results. [ABSTRACT FROM AUTHOR]

A wind turbine’s “specific power” rating relates its capacity to the swept area of its rotor in terms of Watt per square meter. For a given generator capacity, specific power declines as rotor size increases. In land-rich but capacity-constrained wind power markets, such as the United States, developers have an economic incentive to maximize megawatt-hours per constrained megawatt, and so have favored turbines with ever-lower specific power. To date, this trend toward lower specific power has pushed capacity factors higher while reducing the levelized cost of energy. We employ geospatial levelized cost of energy analysis across the United States to explore whether this trend is likely to continue. We find that under reasonable cost scenarios (i.e. presuming that logistical challenges from very large blades are surmountable), low-specific-power turbines could continue to be in demand going forward. Beyond levelized cost of energy, the boost in market value that low-specific-power turbines provide could become increasingly important as wind penetration grows.

The flexible DC interconnection system, which cascades and unitedly controls several converter stations, can effectively improve the new energy consumption and significantly increase the power quality, thus has been widely used in the power grid. This interconnection system generally uses master–slave control strategy, where chooses one station as the master station to ensure the direct-side voltage’s stability and maintain the input and output power balance. However, in some emergency cases, the input or output power can increase to a huge value that even exceeds the capacity of the master station, which not only threatens the safety of the master station but also leads to power imbalance and abnormal fluctuation of the direct-side voltage. Therefore, to avoid the capacity exceedance of the master station, a multi-architecture power distribution strategy is proposed in this study, which sufficiently considers the capacity limitation in the system architecture and innovatively prioritizes the capacity factor in the control strategy of the master station. Furthermore, the proposed strategy also fully considers cases where the system contains different types of slave stations, such as the station with redundant power and the station that can support bidirectional power flow. The strategy then optimizes the power distribution and adjusts the power flow based on the system’s different architectures. The reliability and effectiveness of the proposed strategy are finally verified by simulation.

This paper presents the assessment of solar resources, investigation of performance, and estimation of normalized performance indices of a 1 kWp Solar PV system in India's climatology of an isolated hilly area. The monthly and yearly mean normalized performance indices are estimated. The annual mean values of the daylight hours, cell temperature, and ambient temperature are 12.7 h/d, 10.89°C, and 1.25°C, respectively. The yearly mean values and corresponding ranges of monthly mean values of capacity factor, performance ratio, array yield, reference yield, final yield, array capture loss and system loss are obtained as 23.60%, 59.82%, 2.83 h/d, 4.63 h/d, 2.69 h/d, 1.8 h/d, 0.14 h/d and (17.56–29.07)%, (47.93–75.55)%, (2.24–3.42) h/d, (2.85–6.1) h/d, (2.13–3.25) h/d, (0.60–2.99) h/d and (0.11–0.17) h/d, respectively. The Levelized Cost of energy is reduced to 85% and 68.22% by using the proposed solar PV/ battery system compared to DG and DG/Battery systems. A saving of 14.8 kl of diesel and the corresponding reduction of emissions of carbon monoxide (244.22 kilograms), unburned hydrocarbons (10.66 kg), particulates (1.48 kg), and nitrogen Oxides (229.42 kg) have also been achieved per year. These findings effectively provide optimal power solution strategies for policymakers in the concerned area. [ABSTRACT FROM AUTHOR]

At the current stage of the integration of renewable energy sources into the power systems of many countries, requirements for compliance with established technical characteristics are being applied to power generation. One such requirement is the installed capacity utilization factor, which is extremely important for optimally placing power facilities based on renewable energy sources and for the successful development of renewable energy. Efficient placement maximizes the installed capacity utilization factor of a power facility, increasing energy efficiency and the payback period. The installed capacity utilization factor depends on the assumed meteorological factors relating to geographical location and the technical characteristics of power generation. However, the installed capacity utilization factor cannot be accurately predicted, since it is necessary to know the volume of electricity produced by the power facility. A novel approach to the optimization of placement of renewable energy source power plants and their capacity factor forecasting was proposed in this article. This approach combines a machine learning forecasting algorithm (random forest regressor) with a metaheuristic optimization algorithm (grey wolf optimizer). Although the proposed approach assumes the use of only open-source data, the simulations show better results than commonly used algorithms, such as random search, particle swarm optimizer, and firefly algorithm. [ABSTRACT FROM AUTHOR]

It is well known that the wind regime around the globe is different depending on the geographical position, season, and dynamics of climate changes. For this reason, studying the evolution of the wind is crucial both for the activity of human communities and especially for the exploitation of its energy. The current development of offshore wind farms is possible by knowing the wind regime in points of interest around the globe. The present study is a brief analysis of wind speed in twelve geographical locations on the world’s seas and oceans, based on data provided by ERA 5. The criteria for the comparative analysis considered in the study are Annual Electricity Production (AEP) and capacity factor (Cf).

Over the last few decades, and more prominently currently, many countries have launched and reinforced campaigns to reduce CO2 emissions from all human activities and, in the area of energy, promote energy generating technologies from low carbon, renewable sources, especially wind and solar. In recent years, this promotion of renewables can be seen in statistics as well as an extraordinary increase in plants using renewable sources. There is more activity surrounding the use of small devices installed close to consumers, such as small wind turbines (SWT). In cities, the best places to install SWT are tall buildings. The Institute of Energy and Environment (IEE-USP) has installed a 1.8 kW SWT on the University of São Paulo campus in São Paulo, Brazil. Even with low-magnitude winds at the site, the SWT installation was carried out to serve as a didactic apparatus and demonstration initiative of wind energy generation connected directly to the University’s electric grid, which already has other embedded renewable sources installed, namely photovoltaic and biogas plants. The turbine was placed on the roof of the existing High Voltage Laboratory building, leading to an operating height of 35 m. This paper presents previous local wind data measurements using a Lidar system, annual energy yield estimation calculations, and measurements, also bringing all implementation details. It reports and analyzes the operation and energy production data from three full operational years, from 2018 to 2020, discussing and concluding with further improvements of SWT from technical and economic aspects.

The investment in renewable energies like solar power became a priority in order to protect the world from an environmental crisis caused by greenhouse gas emissions. Over the past few decades, human activities like burning fossil fuels for energy have increased greenhouse emissions to an unprecedented level which warn to increase the global average temperature up to 1.5 °C above pre-industrial levels. Such increase in global temperature would cause massive environmental damage and affect people’s lives. Currently, around 65% of the total CO2 emissions are related to fossil fuel burning activities, thus it is necessary to find alternative sources of energy that are environmentally friendly. Renewable energy like solar power is considered one of the most reliable sources of energy as it covers a wide range of the earth’s surface and is constantly available. Several research studies focused on concentrated solar power (CSP) which revealed its high performance in producing electric power. Thus, this paper aims to study the feasibility of constructing a100 MW CSP Linear Fresnel solar power in Riyadh city to support meeting the energy demand in Saudi Arabia and reduce the dependence on fossil fuels. In this study, the National Renewable Energy Laboratory (NREL) System Advisor Model (SAM) was used to design, simulate, and analyze the system. The system was designed based on CSP LF technology, and it was tested with two types of thermal fluid namely molten salt and liquid sodium to examine the potential of each fluid to generate competitive energy at a lower cost. The thermal fluids serve as heat transfer fluids (HTF) and thermal energy storage (TES), where TES supplies the power cycle with needed energy during sunset time. The system simulation shows the capability to produce enough solar power throughout the year. The model achieved a high level of energy production with a capacity factor exceeding 90% during summer for both operating fluids. It was also found that liquid sodium yields higher energy as compared to molten salt with a capacity factor exceeding 0.5–1.2%. During the summertime, it was shown that Saudi Arabia has a high potential to adopt the CSP LF technology to produce sustainable solar power.

This study evaluates two grid-connected solar photovoltaic (PV) systems using five criteria: final energy output, system yield, performance ratio, capacity factor, and system efficiency. The PV systems were installed on the rooftop of the Engineering building, Kyungpook National University, the Republic of Korea. The system was monitored, and necessary environmental data was recorded from January 2021 to December 2021. From the result, the annual electricity exported to the grid by the m-si solar PV was 9521 kWh, and that of the p-si was 8263 kWh. The m-si has a better annual average daily final and array yield of 5.23 h/day and 5.45 h/day than the annual final and array yield of p-si with 4.93 h/day and 5.12 h/day, respectively The composite solar PV saved the environment from 9.7 tons of CO2per year, equivalent to 9.7% of people reducing energy use by 20%. Based on the analysis for this location, the m-si solar cell technology showed a better annual performance than the p-si solar cell when considering the five evaluations, except for the performance ratio where the p-si solar cell outperformed the m-si solar technology. Finally, this study provides insightful information to researchers, PV installers, and PV manufacturing companies on the actual performance of grid-connected solar PV in Korea.

The competitiveness and cost indicators of power plants operating on non-traditional renewable energy sources (NTRES) are significantly affected by the intensity of power operation. This determined the relevance of this study. The purpose of the study is a comparative analysis of indicators characterizing the using intensity of power plants on NRES in the electric power industry of developed countries (USA, Germany, France and the United Kingdom) and in the Russian Federation. The objectives of the study include the substantiation and evaluation of indicators characterizing the intensity of operation of power plants using renewable energy sources in the Russian Federation and abroad. The study is based on the scientific works of the Russian scientists, materials of foreign reviews and is carried out using the analytical methods and economic analysis. The authors note that abroad these power plants are located in areas with favorable natural and climatic conditions, and then they are distinguished by the highest rates of operation of renewable energy generators (solar power plants in the USA, offshore wind power plants in the United Kingdom). NRES-based power plants in the Russian Federation are characterized by lower rates of capacity exploitation compared to similar power plants in developed countries. One of the reasons for this is the massive localization of renewable energy generation in the United Energy System of the South, accompanied by problems of integrating these energy sources into the energy system. In order to increase the intensity of operation of NRES power plants in the Russian Federation, it is necessary to plan their construction, taking into account the existing scale of NRES power in regional energy systems and the possibility of transmitting the generated electricity to consumers.

Most of the countries in South-East Europe primarily depend on fossil fuels to cover their energy demands. The paper discusses the future perspective on wind energy in the country, where over 90% of energy is generated in coal-fired thermal power plants. Given the energy crisis, that has gripped the world, the possibility of covering the increased energy demand is being studied, especially during the winter. Based on current trends on energy generation, with just symbolic participation of wind, hydro and solar energy, the potential for maximization of the use of wind energy is considered, which means the use of each identified adequate location throughout the country. The main advantage here is that the maximum energy produced by wind is during winter when demand increases. This is important to know that Kosovo faces significant heating problems and its demand is covered with electricity. Analyzes prove that the country has a generous wind capacity, which reduces to a certain extent the need to import and even enables the export of energy under certain conditions. The potential installation capacity in Kosovo is 510.9 MW, of which 32.4 MW is currently in operating conditions. From the analysis made for the current wind farm in operation, the plant capacity factor is 31.8%. The study of the results indicates a direct correlation between the increase in load during the winter season and the electricity production by wind farms, thereby, the energy demand can be sufficiently covered. [ABSTRACT FROM AUTHOR]

Small wind turbines (SWTs) represent an opportunity to promote energy generation technologies from low-carbon renewable sources in cities. Tall buildings are inherently suitable for placing SWTs in urban environments. Thus, the Institute of Energy and Environment of the University of São Paulo (IEE-USP) has installed an SWT in an existing high-height High Voltage Laboratory building on its campus in São Paulo, Brazil. The dataset file contains data regarding the actual electrical and mechanical operational quantities and control parameters obtained and recorded by the internal inverter of a Skystream 3.7 SWT, with 1.8 kW rated power, from 2017 to 2022. The main electrical parameters are the generated energy, voltages, currents, and power frequency in the connection grid point. Rotation, referential wind speed, and temperatures measured in some points at the inverter and in the nacelle are also recorded. Several other parameters concerning the SWT inverter operation, including alarms and status codes, are also presented. This dataset can be helpful for reanalysis, to access information, such as capacity factor, and can also be used as overall input data of actual SWT operation quantities. Dataset: https://doi.org/10.5281/zenodo.7348454. Dataset License: Creative Commons Attribution 4.0 International. [ABSTRACT FROM AUTHOR]

India’s ambitious net-zero climate goals include plans for a four-fold increase in current levels of wind energy generation by 2030. Many existing wind farms in India occupy sites with the best wind resources nationally but use older, smaller turbines that achieve lower capacity factors compared to modern turbine designs. A strategy of replacing existing wind turbines with state-of-the-art models (termed repowering) could boost capacity factors and ensure maximal use of available wind resources. However, a nationwide assessment of the potential wind generation increases resulting from repowering is currently lacking for India. Here, we present the first validated synthetic wind generation dataset for India based on reanalysis data and show that full repowering of the existing fleet of wind turbines could boost capacity factors by 82% nationwide (from 0.19 to 0.35). Our assessment of attainable capacity factors under full repowering exceeds equivalent estimates within the National Electricity Plan of India and national decarbonisation pathways compiled by the Intergovernmental Panel on Climate Change (IPCC), suggesting less total installed capacity is required to achieve specific generation outcomes than previously estimated. Ongoing technological progress, leading to increased turbine dimensions, will drive capacity factors beyond the levels estimated here, which could further add to the generation benefits of repowering. Yet, despite the higher average output from a repowered fleet of wind generators, substantial variability in generation across timescales persists, highlighting the increasing need for power system flexibility within a decarbonised energy system.

Hydroelectric plants broadly sustain Colombian electricity demand. However, wind power emerges to improve the Colombian energy matrix and capacity, satisfying the energy demand. Thus, this study evaluates the technical and economic feasibility of projects based on Small Wind Turbines (SWTs), based on the Colombian wind resource availability. Furthermore, due to the Colombian diversity of energy sources, sensitivity to climate changes and a high percentage of non-interconnected territory, the SWTs excels as an off-grid energy system alternative. For this purpose, the annual energy production and the capacity factor of 24 SWT and the wind resource in Puerto Bolívar, La Guajira, are considered. A techno-economic study that includes cash flow analysis and net present value is used to determine economic viability, considering a financing percentage sensitivity analysis. From the results, it can be concluded that an SWT based project increases its profitability by increasing the IRR value and lowering the financing percentage. Further, the best SWT models can be related to a higher capacity factor value based on the economic phase.