Mean Capacity Factor Research Articles

Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas

There is an urgent need to develop accurate predictions of power production, wake losses and array–array interactions from multi-GW offshore wind farms in order to enable developments that maximize power benefits, minimize levelized cost of energy and reduce investment uncertainty. New, climatologically representative simulations with the Weather Research and Forecasting (WRF) model are presented and analyzed to address these research needs with a specific focus on offshore wind energy lease areas along the U.S. east coast. These, uniquely detailed, simulations are designed to quantify important sources of wake-loss projection uncertainty. They sample across different wind turbine deployment scenarios and thus span the range of plausible installed capacity densities (ICDs) and also include two wind farm parameterizations (WFPs; Fitch and explicit wake parameterization (EWP)) and consider the precise WRF model release used. System-wide mean capacity factors for ICDs of 3.5 to 6.0 MWkm−2 range from 39 to 45% based on output from Fitch and 50 to 55% from EWP. Wake losses are 27–37% (Fitch) and 11–19% (EWP). The discrepancy in CF and wake losses from the two WFPs derives from two linked effects. First, EWP generates a weaker ‘deep array effect’ within the largest wind farm cluster (area of 3675 km2), though both parameterizations indicate substantial within-array wake losses. If 15 MW wind turbines are deployed at an ICD of 6 MWkm−2 the most heavily waked wind turbines generate an average of only 32–35% of the power of those that experience the freestream (undisturbed) flow. Nevertheless, there is no evidence for saturation of the resource. The wind power density (electrical power generation per unit of surface area) increases with ICD and lies between 2 and 3 Wm−2. Second, EWP also systematically generates smaller whole wind farm wakes. Sampling across all offshore wind energy lease areas and the range of ICD considered, the whole wind farm wake extent for a velocity deficit of 5% is 1.18 to 1.38 times larger in simulations with Fitch. Over three-quarters of the variability in normalized wake extents is attributable to variations in freestream wind speeds, turbulent kinetic energy and boundary layer depth. These dependencies on meteorological parameters allow for the development of computationally efficient emulators of wake extents from Fitch and EWP.

Theoretic and experimental performance of a grid-connected photovoltaic system: Multiple prediction model of efficiency and annual energy generation

In this article, the performance of a 3.36 kWp grid-connected photovoltaic system (GCPVS) under warm and subhumid weather conditions and the development of a predictive mathematical model is presented. Climate data of the 2021 year were used to evaluate energy generation, different types of performance, and efficiency. The average annual yield, corrected yield, array, and final yields were 6.45 h/day, 6.18 h/day, 5.16 h/day, and 4.97 h/day, respectively. The overall annual mean capacity factor and efficiency ratios were 20.73% and 77.22%, correspondingly. Experimental data were analyzed and correlated by multivariate linear regression (MLR) prediction and simulation to validate models. The MLR analysis showed that the efficiency is highly dependent on the temperature of the PV modules and that climatic parameters significantly affect the efficiency and output electric power. The prediction models for PV module efficiency, system efficiency, and direct current energy exhibit an uncertainty of ±1.04%, ±0.57%, and ±35.38 kWh, one-to-one. The monthly generation was compared with results obtained by Energy3D simulation-free software, showing an absolute error of ±2.33 kWh. This information can be used as a methodological tool for predicting efficiency and power generation in direct current.

Reasons for the Recent Onshore Wind Capacity Factor Increase

Increasing wind capacity and capacity factors (CF) are essential for achieving the goals set by the Paris Climate Agreement. From 2010–2012 to 2018–2020, the 3-year mean CF of the global onshore wind turbine fleet rose from 0.22 to 0.25. Wind turbine siting, wind turbine technology, hub height, and curtailed wind energy are well-known CF drivers. However, the extent of these drivers for CF is unknown. Thus, the goal is to quantify the shares of the four drivers in CF development in Germany as a case. Newly developed national power curves from high-resolution wind speed models and hourly energy market data are the basis for the study. We created four scenarios, each with one driver kept constant at the 2010–2012 level, in order to quantify the share of a driver for CF change between 2010–2012 and 2019–2021. The results indicated that rising hub heights increased CF by 10.4%. Improved wind turbine technology caused 7.3% higher CF. However, the absolute CF increase amounted to only 11.9%. It is because less favorable wind turbine sites and curtailment in the later period moderated the CF increase by 2.1% and 3.6%, respectively. The drivers are mainly responsible for perennial CF development. In contrast, variations in wind resource availability drive the enormous CF inter-annual variability. No multi-year wind resource change was detected.

An Action‐Oriented Approach to Make the Most of the Wind and Solar Power Complementarity

AbstractSolar and wind power are called to play a main role in the transition toward decarbonized electricity systems. However, their integration in the energy mix is highly compromised due to the intermittency of their production caused by weather and climate variability. To face the challenge, here we present research about actionable strategies for wind and solar photovoltaic facilities deployment that exploit their complementarity in order to minimize the volatility of their combined production while guaranteeing a certain supply. The developed methodology has been implemented in an open‐access step‐wise model called CLIMAX. It first identifies regions with homogeneous temporal variability of the resources, and then determines the optimal shares of each technology over such regions. In the simplistic application performed here, we customize the model to narrow the monthly deviations of the total wind‐plus‐solar electricity production from a given curve (here, the mean annual cycle of the total production) across five European domains. For the current shares of both technologies, the results show that an optimal siting of the power units would reduce the standard deviation of the monthly anomalies of the total wind‐plus‐solar power generation by up to 20% without loss in the mean capacity factor as compared to a baseline scenario with an evenly spatial distribution of the installations. This result further improves (up to 60% in specific regions) if the total shares of each technology are also optimized, thus encouraging the use of CLIMAX for practical guidance of next‐generation renewable energy scenarios.

Potential impact of global stilling on wind energy production in China

The rapid increase in world's installed wind energy capacity may have masked the power loss caused by declining global surface wind speed (termed ‘global stilling’), particularly for China with huge wind energy investments. Here, we estimated the potential impact of global stilling on wind energy production in China, using data from 1226 meteorological stations between 1971 and 2015. We show that surface wind speeds have on average declined at a rate of 5.5% decade−1, while the corresponding wind power density dropped by 24.5% decade−1. Although the decline in wind speeds has slowed after 1991, the decline in wind power density did not show this slowdown. This was attributed to the absence of slowdown in the declining trend of strong winds. Compared with the wind speed levels in the 2000s, the mean capacity factor would have dropped from 20.7% in 2001 to 14.9% in 2015, with the largest absolute change in the “Three North” regions. Furthermore, wind electricity generation would have declined by 40 TWh/a using the installed capacity of 2015, representing a decrease of 16%. The correlation analysis indicates that the decreased wind speed is most likely caused by changes in the large-scale atmospheric circulation rather than increased surface roughness.

Potential zones for offshore wind power development in the Gulf of Mexico using reanalyses data and capacity factor seasonal analysis

Mexico is an attractive candidate for offshore wind energy development due to its geographical location with extensive coasts in the Pacific Ocean and Mexico's Gulf. Although potential offshore wind areas have been geographically assessed, an evaluation of the seasonal variations of the capacity factors has not been considered for the feasibility of the locations. This research identifies potential zones for offshore wind development in the Gulf of Mexico, implementing geographical restrictions such as the Economic Exclusive Zone, distance from the coast, protected areas, bathymetry, and capacity factor seasonality. Wind speeds were obtained from 39 years of reanalyses historical data and two reference wind turbines of 5 and 10 MW were included in the analysis. Three potential areas were identified from the results: the northeast Tamaulipas, the western Campeche, and the northern Yucatan. Monthly mean capacity factors above 45% were estimated from October to June, with the maximum values near 60% between March and April. Conversely, minimum values were observed from July to September but consistently higher than 30%. The analyzed zones show suitable technical conditions for offshore wind development. Further analysis is needed to validate the wind speed conditions, in addition to the evaluation of economic factors, the study of extreme weather conditions like tropical cyclones as well as characteristics in the intertropical region.

Wave Energy Resource Harnessing Assessment in a Subtropical Coastal Region of the Pacific

Most wave energy converters (WECs) are designed to operate in high-latitude energetic seas, limiting their performance in regions usually dominated by milder conditions. The present study assesses the performance of complete test-stage WECs in farms that satisfy a decentralized energy scheme (DES) on the coast of Baja California, which is considered one of the most energetic regions along the Mexican Pacific. A high-resolution 11-year nearshore wave hindcast was performed and validated with Acoustic Doppler Current Profilers (ADCPs) data to characterize the wave energy resource in the study area. Two hotspots were identified from the wave power climatology. In these sites, the extractive capacities of seven well-known WEC technologies were determined based on their power matrices. Finally, the power extracted by small WEC farms, with the minimum number of devices required to satisfy a DES, was estimated. The studied region has moderate wave power availability with marked seasonality and low inter-annual variability. Out of all the evaluated devices, WaveDragon extracts the highest wave power; however, Pelamis has the best performance, with maximum monthly mean capacity factors up to 40%. Coupling WEC farms with storage modules or hybrid renewable systems are recommended to satisfy a continuous DES during the less energetic summer months.

Statistical analysis of wind resource and energy potential assessment of Sanghar site, Sindh Pakistan

PurposeThe purpose of this paper is to assess the wind resource and energy potential of the Sanghar site for minimizing the dependence on fossil fuels and improving the environment.Design/methodology/approachThe Sanghar site wind shear coefficient and turbulence intensity factor are investigated for a period of a year. The two-parameter k and c Weibull distribution function is used to analyze the wind speed of the Sanghar site. The standard deviation, coefficient of variation, wind power density and energy density; and capacity factor was assessed for a period of a year. The economic assessment of energy/kWh is investigated for the selection of appropriate wind turbines.FindingsThe mean wind shear of the Sanghar site was found to be 0.2509. The mean wind speed was found to be 4.766, 5.534 and 6.121 at 20, 40 and 60 m above the ground level. The mean value of the k parameter was observed to be 2.433, 2.777 and 2.862 at 20, 40 and 60 m for a period of a year. The Weibull c m/s parameter was found to be 5.377, 6.245 and 6.906 m/s at 20, 40 and 60 m. The major portion of values of standard deviation was found to be in between 0.1 to 2.00 at 20, 40 and 60 m. The mean wind power density values were observed to be 88.33, 93.5 and 110.16 W/m2 at 20, 40 and 60 m; respectively, for a period of a year. The mean coefficient of variation was found to be 0.1478, 0.1205 and 0.1033 at 20, 40 and 60 m; respectively. The mean energy density was found to be 476.75, 683.08 and 866.33 kWh/m2 at 20, 40 and 60 m; respectively. The mean capacity factor for different wind turbines was ranged between 18 to 24.83 for a period of a year. The economic assessment showed that wind turbine B has the minimum cost (US$) 0.0484/kWh.Practical implicationsThe assessment provides the solution to sustainable energy generation which reduces the consumption of fuel and the effect of fluctuating price of fuel in the world market on local consumers.Social implicationsWind energy may have social implications including environmentally friendly, consistent supply of energy during the peak summer season, less unit per cost, etc.Originality/valueThe Sanghar site is new and assessed for the first time in this research work. The Sanghar site is suitable for installing utility wind turbines for energy generation at the lowest cost.

Utility-Scale PV-Battery versus CSP-Thermal Storage in Morocco: Storage and Cost Effect under Penetration Scenarios

In this study, we examine how Battery Storage (BES) and Thermal Storage (TES) combined with solar Photovoltaic (PV) and Concentrated Solar Power (CSP) technologies with an increased storage duration and rental cost together with diversification would influence the Moroccan mix and to what extent the variability (i.e., adequacy risk) can be reduced; this is done using recent (2013) cost data and under various penetration scenarios. To do this, we use MERRA-2 climate reanalysis to simulate hourly demand and capacity factors (CFs) of wind, solar PV and CSP without and with increasing storage capabilities—as defined by the CSP Solar Multiple (SM) and PV Inverter Loading Ratio (ILR). We adjust these time series to observations for the four Moroccan electrical zones over the year 2018. Our objective is to maximize the renewable (RE) penetration and minimize the imbalances between RE production and consumption considering three optimization strategies. We analyze mixes along Pareto fronts using the Mean-Variance Portfolio approach—implemented in the E4CLIM model—in which we add a maximum-cost constraint to take into account the different rental costs of wind, PV and CSP. We propose a method to calculate the rental cost of storage and production technologies taking into account the constraints on storage associated with the increase of SM and ILR in the added PV-BES and CSP-TES modules, keeping the mean solar CFs fixed. We perform some load bands-reduction diagnostics to assess the reliability benefits provided by each RE technology. We find that, at low penetrations, the maximum-cost budget is not reached because a small capacity is needed. The higher the ILR for PV, the larger the share of PV in the mix compared to wind and CSP without storage is removed completely. Between PV-BES and CSP-TES, the latter is preferred as it has larger storage capacity and thus stronger impact in reducing the adequacy risk. As additional BES are installed, more than TES, PV-BES is favored. At high penetrations, optimal mixes are impacted by cost, the more so as CSP (resp., PV) with high SM (resp., ILR) are installed. Wind is preferably installed due to its high mean CF compared to cost, followed by either PV-BES or CSP/CSP-TES. Scenarios without or with medium storage capacity favor CSP/CSP-TES, while high storage duration scenarios are dominated by low-cost PV-BES. However, scenarios ignoring the storage cost and constraints provide more weight to PV-BES whatever the penetration level. We also show that significant reduction of RE variability can only be achieved through geographical diversification. Technological complementarity may only help to reduce the variance when PV and CSP are both installed without or with a small amount of storage. However, the diversification effect is slightly smaller when the SM and ILR are increased and the covariances are reduced as well since mixes become less diversified.

Simulations of energy storage requirements of wind energy facilities

AbstractWind‐energy facilities' key parameters for the attribution of the energy‐storage (ES) cost at the grid level are the nominal capacity of the facility P, the annual mean capacity factorεaand its SD using high‐frequency (HF) samplingσ. Here, we compareεaandσcalculated from measurements and resource‐based simulations. The energy production to the grid is available every 5 minutes for a specific year. Meteorological data are available hourly and not for the specific year, but only for the typical year, about ground level, and from stations located in towns and cities relatively far from the wind‐energy facilities. While the computed and measuredεaare in reasonable agreement, after tuning, the difference between the hourly σ is large. It is concluded that contemporary HF data of energy production and speed of the wind at hub height, plus other meteorological data, in the wind‐energy‐facility, are essential for validating presently inaccurate wind energy simulations.

Techno-economic analysis with energy flow modeling for investigating the investment risks related to consumption changes within a standalone microgrid in Sweden

A techno-economic energy flow model for a standalone microgrid was developed to investigate the investment risks related to consumption changes and compare the results to a conventional grid-connection in Sweden. Two different design strategies for a standalone microgrid was used, one with the objective to minimize the life-cycle cost and the other to provide a lower investment risk. It was shown that the largest investment risk for both design strategies was a potential increase in annual energy consumption within the standalone microgrid. The design strategy with the objective to reduce the investment risk eliminated the influence on the life-cycle cost from an increase in peak consumption and reduced the overall investment risk in comparison to the design strategy with the objective to minimize the life-cycle cost. However, a larger life-cycle cost was the drawback of that design strategy. It was concluded that locations with larger annual mean capacity factors reduced the investment risk for standalone microgrids due to lower diesel fuel dependence. It was also concluded that a conventional grid-connection had a lower investment risk than a standalone microgrid, since adverse changes in consumption always increased the life-cycle cost less for a conventional grid-connection than for a standalone microgrid.

Data investigation of installed and output power densities of onshore and offshore wind turbines worldwide

Differing estimates have emerged about how much land or water area is used by existing wind farms and how much power can be obtained from that area. Whereas, no single unique method exists to define wind farm spacing area, the spacing area (thus installed and output power densities) of a wind farm can be determined in a way to meet specific logical criteria, This study proposes a new, intuitive, data based, automatized method of estimating spacing areas occupied by existing onshore and offshore wind farms worldwide. The method eliminates the erroneous counting of space outside of wind farm boundaries, space between clusters of turbines, and overlapping space that results when assuming a large fixed area around each turbine. At least one of three types of extra space has incorrectly been included in all calculations of wind farm areas to date. Unlike most previous methods, this method also ensures that the addition of a wind turbine to a farm increases the overall required spacing area of the farm. The study then uses data from over 1600 operating wind turbines in 16 onshore and 7 offshore wind farms in 13 countries across 5 continents during the period 2016–2018 to quantify installed and output power densities of these farms. Finally, it compares results with estimates from other studies. Results indicate that the mean (range) installed and output power densities of onshore wind farms in Europe are 19.8 (6.2–46.9) MW/km2 and 6.64 (2.3–8.2) W/m2, respectively; of onshore wind farms outside of Europe are similarly 20.5 (16.5–48) MW/km2 and 6.84 (4.81–11.2) W/m2, respectively; and of offshore wind farms in Europe are 7.2 (3.3–20.2) MW/km2 and 2.94 (1.15–6.32) W/m2, respectively. The mean capacity factors in each case are thus 33.5%, 33.4%, and 40.8%, respectively. These results indicate substantially higher installed and output power densities than previously reported, based simply on different definitions of land area, with no impact on capacity factor. Thus, existing wind turbines may extract more wind power over less land or water than previously thought.

Performance analysis and evaluation of 10 kWp solar photovoltaic array for remote islands of Andaman and Nicobar

The Andaman and Nicobar island, an union territory of India, is a group of islands located in the Indian Ocean where 93.63% of total power is generated by the diesel generators for which solar photovoltaic (PV) generation system is a viable alternative to meet the load demand of the islands. The proposed paper presents the solar PV resource assessment, performance analysis, and evaluation of normalized performance indices obtained from 10 kWp solar PV array considered at the climatic conditions of five islands of Andaman and Nicobar, mainly Smith, Middle Andaman, Baratang, Car Nicobar and Katchal. The yearly mean normalized performance indices like array yield (Yarr), reference yield (Yref), final yield (Yfin), performance ratio (PR), array capture loss (Lcap), system loss (Lsys) and capacity factor (CF) have been evaluated. The variation of ambient temperature (Tamb) and cell temperature (Tcell) have also been presented. The obtained results show yearly mean capacity factor and performance ratio are in the range of (13.73–14.61)% and (64.70–64.93)%, respectively. The yearly mean array yield, reference yield, final yield, array capture loss and system loss are (3.66–3.90) h/d, (5.08–5.43) h/d, (3.30–3.51) h/d, (1.42–1.53) h/d and (0.37–0.39) h/d, respectively.

The annual cycle and intra-annual variability of the global wind power distribution estimated by the system of wind speed distributions

The intermittent nature of wind energy is a major challenge transforming the energy sector from fossil fuels to renewables. Depending on the location, results from previous studies show that the availability of wind energy can strongly vary over a year. However, although global temporal wind speed fluctuations are complex on the monthly and seasonal scales, they have been rarely quantified so far. Thus, the goals of this study were to assess the annual cycle and intra-annual variability of wind power around the world. A comprehensive dataset of more than 7000 globally distributed near-surface wind speed time series was analyzed. After extrapolation to a typical wind turbine hub height of 120 m, the monthly and seasonal mean wind speed, wind power density, and intra-annual variability were calculated. The system of wind speed distributions, which consists of the Burr-Generalized Extreme Value, Kappa, and Wakeby distributions, was fitted to all wind speed time series and used to estimate wind turbine related capacity factors. The greatest global wind resource was found for spring (global mean capacity factor: 0.272). In summer, the global wind resource decreased by 20.7%. The results reveal the greatest intra-annual variability in regions affected by the Indian monsoon circulation.

Adequacy of Renewable Energy Mixes with Concentrated Solar Power and Photovoltaic in Morocco: Impact of Thermal Storage and Cost

In this paper, we analyze the sensitivity of the optimal mixes to cost and variability associated with solar technologies and examine the role of Thermal Energy Storage (TES) combined to Concentrated Solar Power (CSP) together with time-space complementarity in reducing the adequacy risk—imposed by variable Renewable Energies (RE)—on the Moroccan electricity system. To do that, we model the optimal recommissioning of RE mixes including Photovoltaic (PV), wind energy and CSP without or with increasing levels of TES. Our objective is to maximize the RE production at a given cost, but also to limit the variance of the RE production stemming from meteorological fluctuations. This mean-variance analysis is a bi-objective optimization problem that is implemented in the E4CLIM modeling platform—which allows us to use climate data to simulate hourly Capacity Factors (CFs) and demand profiles adjusted to observations. We adapt this software to Morocco and its four electrical zones for the year 2018, add new CSP and TES simulation modules, perform some load reduction diagnostics, and account for the different rental costs of the three RE technologies by adding a maximum-cost constraint. We find that the risk decreases with the addition of TES to CSP, the more so as storage is increased keeping the mean capacity factor fixed. On the other hand, due to the higher cost of CSP compared to PV and wind, the maximum-cost constraint prevents the increase of the RE penetration without reducing the share of CSP compared to PV and wind and letting the risk increase in return. Thus, if small level of risk and higher penetrations are targeted, investment must be increased to install more CSP with TES. We also show that regional diversification is key to reduce the risk and that technological diversification is relevant when installing both PV and CSP without storage, but less so as the surplus of energy available for TES is increased and the CSP profiles flatten. Finally, we find that, thanks to TES, CSP is more suited than PV and wind to meet peak loads. This can be measured by the capacity credit, but not by the variance-based risk, suggesting that the latter is only a crude representation of the adequacy risk.

Trends in performance factors of wind energy facilities

This communication discusses the two parameters recently emerged as key performance indicators of wind energy facilities, the mean capacity factor over a year, and the standard deviation of the capacity factor from a high-frequency sampling of 1 min or less (the annual mean does not change if the sampling interval of the statistical population is every month or every minute; the standard deviation does). Both parameters impact the levelised cost of electricity. They permit to quantify the energy production by the specific facility. They also permit to attribute grid energy storage costs to the specific facility. This manuscript shows that the annual mean capacity factors of wind energy facilities are not improving in the U.S. with the year of completion. The average of the annual mean capacity factors of every facility operational in a given year is about 33% now in 2019 as it was in 2012. Additional to the flat trend in the average of the annual mean capacity factor, their variability is large at the individual facility level. With 5 min sampling frequency, the standard deviation of the capacity factor of an individual wind energy facility is about that same magnitude of the mean. This translates into a coefficient of variability—the ratio of the standard deviation to the mean—approaching unity. At the grid level, the variability of all the wind energy supply is still large and necessitates significant energy storage.

Trends in performance factors of large photovoltaic solar plants

Abstract This short communication discusses the two parameters recently emerged as a Key Performance Indicator of solar energy facilities, the mean capacity factor over a year, and the standard deviation of the capacity factor computed with high-frequency sampling. Both parameters impact directly and indirectly on the Levelised Cost Of Electricity (LCOE), permitting to quantify the energy production by the specific facility, and the attribution of the grid energy storage costs to the specific facility. The annual average capacity factors of the latest, largest, 53 photovoltaic (PV) solar energy facilities in the US, vary between 10% and 36%, with a mean value of 27% and a standard deviation of 5%. There are large differences also in between plants located in areas of a similar solar resource. Inference of long term performance degradation or O&M costs is difficult. We know from Australia that solar PV facilities work with high-frequency capacity factors’ standard deviations larger than the mean, for coefficients of variability above unity, and significant variability also at the grid level. This variability necessitates of energy storage. High-frequency data to assess the standard deviation of the individual US facility contribution to the different grids, as well as the energy storage needed for every grid, is unavailable. Construction cost data are less reliable than energy production data and mostly missing. Based on data for 15 plants, completed between 2014 and 2017, the construction cost dramatically varies between different facilities. The specific cost varies between 1,719 [US$/kW] and 7,143 [US$/kW], an average of 3,983 [US$/kW]. Considering the actual generating power vs. the nominal generating power, the specific cost varies between 6,374 and 22,806, an average 14,006 [US$/kW]. Hence, an accurate prediction of the LCOE of large PV facilities in the US is presently difficult.

Performance analysis of a grid-connected photovoltaic plant in eastern Turkey

Photovoltaic market is a globally growing market, and this also reflects the current trend in Turkey. In this study, a grid-connected photovoltaic (PV) power plant of 2130.7 kWp rated power located in the eastern part of Turkey was analysed. The photovoltaic plant was assessed in terms of performance parameters such as reference yield, array yield, final yield, inverter efficiency, capture loss, system loss, system efficiency, capacity factor, performance ratio and annual final yield. In 2017, 3519.98 MWh of energy was generated by this PV plant. Mean final yield, mean performance ratio, system efficiency and capacity factor were found as 4.53 h/d, 81.15%, 13.18% and 18.86%, respectively. Besides the real-time analysis, a simulation of energy prediction and performance analysis were also done. A comparison with other PV plants located in different parts of the world lead to the conclusion that insolation and environmental conditions are the primary factors that affect PV plant performance.

Cost of wind energy generation should include energy storage allowance

The statistic of wind energy in the US is presently based on annual average capacity factors, and construction cost (CAPEX). This approach suffers from one major downfall, as it does not include any parameter describing the variability of the wind energy generation. As a grid wind and solar only requires significant storage in terms of both power and energy to compensate for the variability of the resource, there is a need to account also for a parameter describing the variability of the power generation. While higher frequency data every minute or less is needed to design the storage, low-frequency monthly values are considered for different wind energy facilities. The annual capacity factors have an average of 0.34. They vary significantly from facility to facility, from a minimum of 0.15 to a maximum of 0.5. They also change year-by-year and are subjected to large month-by-month variability. It is concluded that a better estimation of performance and cost of wind energy facilities should include a parameter describing the variability, and an allowance for storage should be added to the cost. When high-frequency data will be eventually made available over a full year for all the wind and solar facilities connected to the same grid of given demand, then it will be possible to compute growth factors for wind and solar capacity, total power and energy of the storage, cost of the storage, and finally, attribute this cost to every facility inversely proportional to the annual mean capacity factor and directly proportional to the standard deviation about this value. The novelty of the present work is the recognition of the variability of wind power generation as a performance and cost parameter, and the proposal of a practical way to progress the design of the storage and its cost attribution to the generating facilities.

High‐frequency standard deviation of the capacity factor of renewable energy facilities—Part 2: Wind

AbstractIn terms of performances of wind energy installations, it is important to secure for every installation the highest possible average (mean) capacity factor, as well as the lowest possible standard deviation (SD) computed with high frequency. High‐frequency data of wind energy facilities are, however, very hard to be found and there are no estimations in the literature of the SD of the capacity factor of wind energy installations sampled with high frequency. Here, we show the data sampled every 5 minutes for the wind energy facilities connected to the Australian National Electricity Market (NEM) grid during the year 2018. While the average capacity factors are about 33% to 35%, the SDs have only marginally smaller values at about 30%, for coefficients of variation of about 90%. The analysis highlights the extreme variability of wind energy that has been dramatically downplayed so far, stressing the need for energy storage, that coupled to a more sophisticated control of production, storage, and consumption, is finally emerging as the key factor for making possible an efficient, resilient, renewable energy‐only grid. The collected and interpreted information and the provided analysis are filing a gap in the area of renewable energy and energy harvesting technology based on wind and solar photovoltaics where variability has been so far underevaluated.