Wind Capacity Factor Research Articles

Evaluation of levelized cost of hydrogen produced by wind electrolysis: Argentine and Italian production scenarios

Green hydrogen will play a key role in the future energy transition to achieve the main decarbonization goals. However, many large economies have limited renewable energy generation capacity, which leads to the identification of strategic sites for the global export of hydrogen. The aim of this work is to investigate the techno-economic feasibility of green hydrogen production in Argentine Patagonia with subsequent ship transport to Italy. The study includes the entire supply chain, from the production of hydrogen with renewable energy sources to its final use as a fuel for mobility in Italy. Different scenarios have been identified and compared, considering that the hydrogen production can take place directly in Italy or in Patagonia, followed by a transport to Italy. It was assumed that hydrogen is produced through PEM electrolyzers powered by wind energy. Various hydrogen transport and storage methods were analyzed to assess which is the most cost-effective solution. In addition, the influence of wind capacity factor and electricity cost on the levelized cost of hydrogen (LCOH) was analyzed to provide an in-depth comparative assessment of the two production scenarios. Even considering the additional transport costs from Argentina to Italy, the scenario with hydrogen production in Patagonia was found to be more convenient than the production in Italy. This is due to the high wind potential in Patagonia, where the average wind capacity factor is about 50% compared to 25% in Italy. The minimum LCOH was obtained considering the storage and transport of hydrogen by means of liquid organic hydrogen carriers, with a final cost of 8.60 €/kgH2 and 11.17 €/kgH2 for the Argentine and Italian production scenarios, respectively. The results of the complete supply chains were analyzed step by step, and the reasons behind the most competitive option elucidated in detail.

Examining the Effects of Installed Capacity Mix and Capacity Factor on Aggregate Carbon Intensity for Electricity Generation in China.

Promoting technological advancements and energy transitions in electricity generation are crucial for achieving carbon reduction goals. Some studies have examined the effectiveness of these measures by analysing the driving forces of “aggregate carbon intensity” (ACI) change. However, only a few studies have considered the effect of the installed capacity mix and capacity factor. Moreover, such analysis has never been applied at China’s provincial level after 2015. To alleviate this gap, our study applied a temporal and multi-regional spatial IDA-LMDI model to analyse the driving factors of ACI changes and disparities among the provinces of China from 2005 to 2019. The model notably includes the effects of the installed capacity mix, thermal capacity factor, and overall capacity factor. The analysis revealed that the decline in China’s ACI was diminished after 2015, while an ACI rebound was identified in five provinces. The changes in the ACI from 2015 to 2019 were mainly driven by the effect of the installed capacity mix rather than by the thermal efficiency and thermal capacity factor. The overall capacity factor was the only factor with a negative impact on the ACI change. We also found that its combined effect with the thermal capacity factor on increasing ACI can offset the effect of the installed capacity mix by reducing the ACI in provinces with significant additions of renewable energy installed capacity. The analysis of the influencing factors on the provincial ACI differences revealed that the share of hydropower installed capacity was significant. Moreover, the thermal efficiency and thermal capacity factor both played key roles in the ACI disparities in northeast, northwest, and central China. Overall, this study paves the way for data-driven measures of China’s carbon peak and carbon neutrality goals by improving the capacity factor of wind and solar power, leveraging the critical impact of hydropower, and narrowing the differences in the thermal power sector among provinces.

Trend decomposition aids short-term countrywide wind capacity factor forecasting with machine and deep learning methods

A straightforward decomposition technique applied to hourly wind capacity factor time-series data compiled at the country level can enhance short-term forecasting over the coming twelve hours. The method overcomes the limitations of relying upon limited exogeneous input variables and incorporates near-term impacts of system curtailments and market irregularities, potentially aiding system operators and regulators in optimizing ongoing supply–demand balances. The method establishes multiple attributes from the previous twelve hours of wind capacity data. It then uses those attributes as input variables for forecasting with multiple machine- and deep-learning methods. The method is evaluated using 43,824 hourly data records for combined onshore and offshore wind capacity for Britain from 2015 to 2019. Fourteen machine- and deep-learning models are trained and validated with 2015–2018 data and the trained models are applied to forecast the 8760 hourly records for 2019. 10-fold cross validation is used to verify the robustness of the validation procedure. Forecasting performance is benchmarked against autoregressive integrated moving average and multi-linear regression models. The best-performing models (extreme learning machine, support vector regression and extreme gradient boosting) outperform both autoregressive integrated moving average and multi-linear regression methods in forecasting t0 to t + 12 h ahead. Machine learning models substantially outperform the five deep learning models evaluated in forecasting accuracy and execution time. The transparency of the multi-linear regression model provides useful insight to the relative contributions of each input variable to the forecasts, highlighting that the trend attribute impacts vary significantly across the t0 to t + 12 time periods forecast.

Wind Energy Data Analysis and Resource Mapping of Dangla, Gojjam, Ethiopia

Energy is one of the most important factors in socio-economic development. The rapid increase in energy demand and air pollution has increased the number of ways to generate energy in the power sector. Currently, wind energy capacity in Ethiopia is estimated at 10,000 MW. Of these, however, only eight percent of its capacity has been used in recent years. One of the reasons for the low use of wind energy is the lack of accurate wind atlases in the country. Therefore, the purpose of this study is to develop an accurate wind atlas and review the wind resources using Wind Atlas Analysis and Application Program in Dangla so that the best sites used for the installation of wind farms can be easily identified. Wind climatology data, vector maps, and wind turbine data were used to model the Wind Atlas Analysis and Application Program. The wind data collected from National Meteorology Survey Agency over three years were used for the analysis. Besides, vector data for the survey area was prepared using Global Mapper V20.1. In this study, the Wind Atlas Analysis and Application Program tool was used to estimate wind resources. The wind resource map was developed at an altitude of 80 m above ground level, and a horizontal axis type wind generator called (i.e., GridStreamer Vestas V100 1.8 MW) was selected from the Wind Atlas Analysis and Application Program catalog to mimic the wind farm. The results obtained from the analysis of actual wind data showed that the minimum wind speed was 0.12083 m/s while the maximum wind speed was 9.96389 m/s. At a height of 10 m a.g.l., the mean wind speed and wind power density were 1.8 m/s and 9 w/m2, respectively. The most common wind direction was also discovered to be 210 degrees. The maximum wind speed and capacity factor were available around hills, according to the wind resource map, and the net annual energy output of the Dangla wind farm at a turbine height of 80 m a.g.l. was found to be 282.726 GWh (or, 32.27 MW). The farm’s capacity factor was also discovered to be 9.54 percent, indicating that the site is rated as low potential.

High-frequency estimation of the Lévy-driven Graph Ornstein-Uhlenbeck process

We consider the Graph Ornstein-Uhlenbeck (GrOU) process observed on a non-uniform discrete timegrid and introduce discretised maximum likelihood estimators with parameters specific to the whole graph or specific to each component of the graph. Under a high-frequency sampling scheme, we study the asymptotic behaviour of those estimators as the mesh size of the observation grid goes to zero. We prove two stable central limit theorems to the same distribution as in the continuously-observed case under both finite and infinite jump activity for the Lévy driving noise. In addition to providing the consistency of the estimators, the stable convergence allows us to consider probabilistic sparse inference procedures on the edges themselves when a graph structure is not explicitly available. It also preserves its asymptotic properties. In particular, we also show the asymptotic normality and consistency of an Adaptive Lasso scheme. We apply the new estimators to wind capacity factor measurements, i.e. the ratio between the wind power produced locally compared to its rated peak power, across fifty locations in Northern Spain and Portugal. We compare those estimators to the standard least squares estimator through a simulation study extending known univariate results across graph configurations, noise types and amplitudes.

Multiple criteria application in determining wind power potential: A case study of Adama Zuria woreda, Ethiopia

The total predicted wind energy in Ethiopia is around 10 GW. However, location specific assessment is required to utilize the resource. In this work, a ten-year wind data (2008–2017) at 10 m was analyzed. The objectives of the study were to characterize the wind power potential via the wind rose representation, Weibull distribution functions and geospatial analysis at various elevations (10, 50 and 100 m). The performance assessment was also judged based on the degree of the fitness by evaluating shape factor (k) and scale factor (c) from an empirical, graphical and moment methods. The three wind parameters such as wind speeds, power densities and capacity factors were geospatially determined by GIS modeling. Annual electricity output and turbine's capacity factors for various models were also discussed. The results show Weibull way proved that 4 m/s and 6 m/s are the frequent wind speeds in the distribution. In the assessment, the k's and c's in winter and summer were more likely closer to 3 and 6 m/s proving regular-steady flows and a high class of wind energy in the site. For the remaining season, the k's and c's were found to be very close to 2 and 4 m/s indicating moderate flow and medium energy class in the area. On the other hand, the degree of the fitness using Root Mean Square (RMSE) lied between 0.025 and 0.176 proving lower errors in statistical modeling whereas the R2 was ranged between 0.740 and 1 indicating stronger explanatory powers. The study highlights the importance of multiple tool assessment to determine the proper location of windy sites for wind energy development. The capacity factor (CF) and present value cost (PVC) analysis are also essential tools for installing proper turbine model at the site suitable for energy developers.

Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers

The reliability of ERA5 reanalyses for directly predicting wind resources and energy production has been assessed against observations from six tall towers installed over very heterogeneous sites around the world. Scores were acceptable at the FINO3 (Germany) offshore platform for both wind speed (bias within 1%, r = 0.95−0.96) and capacity factor (CF, at worst biased by 6.70%) and at the flat and sea-level site of Cabauw (Netherlands) for both wind speed (bias within 7%, r = 0.93−0.94) and CF (bias within 6.82%). Conversely, due to the ERA5 limited resolution (~31 km), large under-predictions were found at the Boulder (US) and Ghoroghchi (Iran) mountain sites, and large over-predictions were found at the Wallaby Creek (Australia) forested site. Therefore, using ERA5 in place of higher-resolution regional reanalysis products or numerical weather prediction models should be avoided when addressing sites with high variation of topography and, in particular, land use. ERA5 scores at the Humansdorp (South Africa) coastal location were generally acceptable, at least for wind speed (bias of 14%, r = 0.84) if not for CF (biased by 20.84%). However, due to the inherent sea–land discontinuity resulting in large differences in both surface roughness and solar irradiation (and thus stability conditions), a particular caution should be paid when applying ERA5 over coastal locations.

The effect of increased coupling strength between electricity and heating systems in different climate scenarios for Europe

Over the last two centuries, the growth of fossil fuel combustion is the dominant cause of the increasing concentration of CO2 in the atmosphere. Timely mitigation strategies are therefore important and can be achieved through energy system transformation, but time is limited. We investigate into sector coupling as a first step towards a carbon neutral energy economy by imposing additional synergy between the European electricity and heating systems. Because future climate change is unpredictable, we follow three reconstructed concentration pathways (RCP2.6, RCP4.5 and RCP8.5) from the IPCC AR5 covering the years 2000–2100. Three-hourly time series of the national wind and solar capacity factors, electricity and space heat consumption, and coefficient of performance of heat pumps were generated from climate change affected weather data from an ensemble of nine climate models. By using a greenfield cost-optimisation approach, we find that net zero-emission scenarios as, e.g., by the end-century of RCP26 result in extremely electrified energy systems, which almost doubles the system costs compared to historical levels. Carbon intensive pathways as, e.g., the end-century of RCP8.5 result in fossil-fuel dominated energy systems with significant cost reductions. Independent of the climate outcome, the cost of a strongly coupled system is comparable to that of a weakly coupled system. This result states the importance of transforming energy systems towards higher electrification even if strong coupling technologies such as heat pumps and combined heat and power plants are not implemented at large scale.

The strong effect of network resolution on electricity system models with high shares of wind and solar

Energy system modellers typically choose a low spatial resolution for their models based on administrative boundaries such as countries, which eases data collection and reduces computation times. However, a low spatial resolution can lead to sub-optimal investment decisions for wind and solar generation. Ignoring power grid bottlenecks within regions tends to underestimate system costs, while combining locations with different wind and solar capacity factors in the same resource class tends to overestimate costs. We investigate these two competing effects in a capacity expansion model for Europe’s power system with a high share of renewables, taking advantage of newly-available high-resolution datasets as well as computational advances. We vary the number of nodes, interpolating between a 37-node model based on country and synchronous zone boundaries, and a 1024-node model based on the location of electricity substations. If we focus on the effect of renewable resource resolution and ignore network restrictions, we find that a higher resolution allows the optimal solution to concentrate wind and solar capacity at sites with better capacity factors and thus reduces system costs by up to 10% compared to a low resolution model. This results in a big swing from offshore to onshore wind investment. However, if we introduce grid bottlenecks by raising the network resolution, costs increase by up to 23% as generation has to be sourced more locally at sites with worse capacity factors. These effects are most pronounced in scenarios where grid expansion is limited, for example, by low local acceptance. We show that allowing grid expansion mitigates some of the effects of the low grid resolution, and lowers overall costs by around 16%.

Long-term changes in offshore wind power density and wind turbine capacity factor in the Iberian Peninsula (1900–2010)

This study analysed temporal and spatial changes in offshore wind power density (WPD) and capacity factor (CF) around the Iberian Peninsula during the 20th century by analysing data from ERA20 and ERA5. Both WPD and CF were calculated using reanalysis data considering a wind turbine with a hub height of 90 m and incorporating the effect of air density changes. Since ERA5 assimilates more observations, the data from ERA20 was bias-corrected using quantile matching, with ERA5 reanalysis data as the reference. As both variables are based on wind speed (WS), this variable was also corrected and analysed. The results show that the mean values for WPD, CF, and WS during the 20th century were highest in the Atlantic zone and the Gulf of Lyon and lowest around the Balearic Islands. The results of the assessment of decadal trends using the Theil–Sen estimator show that all indicators increased significantly in the waters of the Iberian Peninsula during the study period (1900–2010). Considering the mean slope over this period, the change over the entire period could amount to 174 Wm-2 for WPD, 8.8% for CF, and 1.1 ms−1 for WS. Based on these changes, offshore wind turbines would have increased their returns by approximately 20% over the 11 decades.

Designing Optimal Integrated Electricity Supply Configurations for Renewable Hydrogen Generation in Australia

Renewable derived hydrogen is increasingly seen as key to energy sector and wider decarbonization over coming decades. However, costs are still high by comparison with fossil fuel alternatives. Fortunately, falling costs with growing deployment of wind and solar renewables, and technology progress with electrolyzers promise improving economics for green hydrogen to 2030 and beyond. Wind and solar are now the cheapest electricity supply option in many jurisdictions including Australia. However, their high variable and somewhat unpredictable output poses challenges for electrolyzer operation and economics. In particular, there is a potential tradeoff between achieved electrolyzer capacity factor and renewable electricity capacity factors. We develop a comprehensive and integrated cost framework for design of renewable electricity supply configurations to minimize levelized hydrogen production costs. The electrolyzer model incorporates important and often neglected factors including performance degradation over time, financing rates, and indirect costs to assess the economics of 10 MW scale Alkaline and PEM electrolyzers. Our scenario analysis explores three possible configurations of electricity provision: (i) grid supply using both average and time varying whole saleprices, using current and projected wholesale electricity market data from the Australian National Electricity Market (NEM), (ii) the application of renewable power purchase agreements within electricity markets matched to flexible electrolyzer operation, using existing NEM solar/wind farm generation curves and (iii) off-grid electrolyzers matched to co-located wind and solar projects, including consideration of potential oversizing of the renewables to achieve higher electrolyzer capacity factors. We assess possible levelized H2 production costs of these different configurations in the near term (2020 costs) as well as for possible 2030 electrolyzer and renewables costs. Our findings highlight that the higher capacity factor of wind is a significant advantage for electrolyzer operation despite higher wind than PV project costs. It also highlights the significance of degradation rates and major refurbishments, as well as financing costs on total production costs, impacting LCH2 by around 30%. Overall, a wind-powered electrolyzer configuration currently likely offers the most economical generation pathway (A$4 – 9 kg-1) using both PPAs and off-grid supply. However, PV costs are projected to fall more than wind costs over the coming decade and beyond. Oversizing off-grid renewables up to 50% can improve overall economics by up to 10%. Our modelling suggests that future electrolyzer costs reduction (<A$700 kW-1, favorable financing schemes (weighted average cost of capital < 4%) and readily available low-cost renewable electricity (<30 MWh), can deliver hydrogen at costs of around A$2 - 3 kg-1 in Australia.

The Value of Inter-Regional Coordination and Transmission in Decarbonizing the US Electricity System

Summary Preventing global warming in excess of 1.5°C–2°C requires a transition to zero-carbon electricity systems by midcentury along with the widespread electrification of other sectors. Current state-level renewable portfolio standards and regional transmission arrangements do not capture the benefits of inter-regional transmission or coordination of planning and dispatch for renewable-energy integration. Here, using a co-optimized capacity-planning and dispatch model over 7 years of hourly operation, we show that inter-state coordination and transmission expansion reduce the system cost of electricity in a 100%-renewable US power system by 46% compared with a state-by-state approach, from 135 $/MWh to 73 $/MWh. Sensitivity analyses show that reductions in the cost of photovoltaics, wind, and lithium-ion batteries lead to the lowest electricity costs for systems in which transmission expansion is allowed, while cost reductions for nuclear power or long-duration energy storage lead to greater electricity cost reductions for isolated systems.

Potential impacts of climate change on wind and solar electricity generation in Texas

Wind and solar energy sources are climate and weather dependent, therefore susceptible to a changing climate. We quantify the impacts of climate change on wind and solar electricity generation under high concentrations of greenhouse gases in Texas. We employ mid-twenty-first century climate projections and a high-resolution numerical weather prediction model to generate weather variables in the future and produce wind and solar generation time series. We find that mid-twenty-first century projections based on five global climate models agree on the multiyear average increases across Texas in direct normal irradiance, global horizontal irradiance, surface air temperature, and 100-m wind speed of up to 5%, 4%, 10%, and 1%, respectively. These changes lead to multiyear average relative changes across Texas of − 0.6 to + 2.5% and of + 1.3 to + 3.5% in solar and wind capacity factors, respectively, with significant regional, seasonal, and diurnal differences. Areas with low solar resource show an increase in solar capacity factors but reductions in wind capacity factors. Areas with high solar resource show reductions in solar capacity factors. The spatial and temporal differences in our results highlight the importance of using high-resolution data sets to study the potential impacts of climate change on wind and solar power.

Estimating wind speed and capacity factors in Mexico using reanalysis data

In the fundamental stage of resource assessment, high-quality wind speed measurements are required to estimate power production. However, this high-quality data is not always available, and therefore the analysis of alternative sources becomes essential. In this work, we analyze the ability of MERRA-2 to represent wind speed characteristics at 24 anemometric stations in Mexico. The assessment was carried out using the Pearson correlation coefficient between the observed time series, and the obtained by interpolating bias-corrected reanalysis-estimated wind speed to all locations for different time-averaging periods. Results showed that the reanalysis' performance is not uniform throughout the country; it depends on the time resolution, local orographic conditions, and the relationship between the local flow and the large-scale circulation. Based on these results, the country was subdivided into eight regions. The best-represented region was the Chivela Pass, where the winds are tightly linked to the interaction between the large-scale circulation and the local orography. The worst performing regions were located where the land sea-mask and orography at the reanalysis' resolution may not be accurate enough to reproduce the station's wind speeds. Reanalysis-estimated capacity factors exhibit large interannual variability in some stations, which can have significant consequences for the operation of individual wind farms and the power grid. The results show that, while caution should be exercised when applying reanalyses to wind resource assessment in Mexico, reanalysis wind power estimates can be a valuable tool to investigate the feasibility and installed capacity requirements for Mexico to meet its renewable energy targets.

Two-stage robust generation expansion planning considering long- and short-term uncertainties of high share wind energy

This paper introduces a two-stage robust generation expansion planning (GEP) methodology under the presence of wind energy uncertainty. Both long- and short-term uncertainties are incorporated into the proposed GEP model. The first stage concerns the impact of long-term wind uncertainty through the annual variation of wind capacity factor. The second stage deals with the problem of short-term uncertainty using the autoregressive moving average (ARMA) model. A polyhedral uncertainty set is introduced through its variance-covariance matrix considering the correlation between the uncertain coefficients of different wind sites. Various GEP results are presented for different wind uncertainty scenarios. The results have demonstrated that the correlation among the uncertain coefficients yields a less conservatism robust solution. Various solutions to cope with the short-term wind uncertainty impact are proposed. The most economical method among the proposed solutions involves developing a tailored objective function to achieve economic optimality.

Frequency and duration of low-wind-power events in Germany

In the transition to a renewable energy system, the occurrence of low-wind-power events receives increasing attention. We analyze the frequency and duration of such events for onshore wind power in Germany, based on 40 years of reanalysis data and open software. We find that low-wind-power events are less frequent in winter than in summer, but the maximum duration is distributed more evenly between months. While short events are frequent, very long events are much rarer. Every year, a period of around five consecutive days with an average wind capacity factor below 10% occurs, and every ten years a respective period of nearly eight days. These durations decrease if only winter months are considered. The longest event in the data lasts nearly ten days. We conclude that public concerns about low-wind-power events in winter may be overrated, but recommend that modeling studies consider multiple weather years to properly account for such events.

Impact of weather regimes on wind power variability in western Europe

We have assessed the dependency of wind power resources in Western Europe on the atmospheric circulation as represented by a new set of 8 tailored weather regimes (WRs). For this purpose, we have derived wind capacity factors (CFs) from a meteorological reanalysis dataset and from high-resolution data simulated by the Weather Research and Forecasting (WRF) model. We first show that WRs capture effectively year-round onshore wind power production variability across Europe, especially over northwestern/central Europe and Iberia. Since the influence of the large-scale circulation on wind energy production is regionally dependent, we have then examined the high-resolution CF data interpolated to the location of more than 100 wind farms in two regions with different orography and climatological features, the United Kingdom and the Iberian Peninsula. The use of the monthly frequencies of occurrence of WRs as predictors in a multi-linear regression model allows explaining up to two thirds of the month-to-month CF variability for most seasons and sub-regions. These results outperform those previously reported based on Euro-Atlantic modes of atmospheric circulation, indicating that the use of WRs customized to the region of study is preferred to reproduce the evolution of wind energy resources. Finally, we have applied these WRs to understand the day-to-day evolution of specific episodes with anomalous regional wind power production. In particular, the wind energy deficit of summer 2018 in the United Kingdom and the surplus of March 2018 in Iberia stemmed from the combination of WRs associated with low and high CFs, respectively. These findings are relevant for the forecast of wind energy resources as the large-scale features of the atmospheric circulation captured by WRs can be modelled with considerably less uncertainty than wind speeds at wind farm sites.

Experimental validation of wind energy estimation

Wind power assessment in complex terrain is a very demanding task. Modeling wind conditions with standard linear models does not sufficiently reproduce wind conditions in complex terrains, especially on leeward sides of terrain slopes, primarily due to the vorticity. A more complex non-linear model, based on Reynolds averaged Navier-Stokes equations has been used. Turbulence was modeled by modified two-equations k-? model for neutral atmospheric boundary-layer conditions, written in general curvelinear non-orthogonal co-ordinate system. The full set of mass and momentum conservation equations as well as turbulence model equations are numerically solved, using the as CFD technique. A comparison of the application of linear model and non-linear model is presented. Considerable discrepancies of estimated wind speed have been obtained using linear and non-linear models. Statistics of annual electricity production vary up to 30% of the model site. Even anemometer measurements directly at a wind turbine?s site do not necessarily deliver the results needed for prediction calculations, as extrapolations of wind speed to hub height is tricky. The results of the simulation are compared by means of the turbine type, quality and quantity of the wind data and capacity factor. Finally, the comparison of the estimated results with the measured data at 10, 30, and 50 m is shown.

Sizing a renewable microgrid for flow shop manufacturing using climate analytics

Abstract A variety of methods have been proposed to assist the integration of microgrid in flow shop systems with the goal of attaining eco-friendly operations. There is still a lack of integrated planning models in which renewable portfolio, microgrid capacity and production plan are jointly optimized under power demand and generation uncertainty. This paper aims to develop a two-stage, mixed-integer programming model to minimize the levelized cost of energy of a flow shop powered by onsite renewables. The first stage minimizes the annual energy use subject to a job throughput requirement. The second stage aims at sizing wind turbine, solar panels and battery units to meet the hourly electricity needs during a year. Climate analytics are employed to characterize the stochastic wind and solar capacity factor on an hourly basis. The model is tested in four locations with a wide range of climate conditions. Three managerial insights are derived from the numerical experiments. First, time-of-use tariff significantly stimulates the wind penetration in locations with medium or low wind speed. Second, regardless of the climate conditions, large-scale battery storage units are preferred under time-of-use rate but it is not the case under a net metering policy. Third, wind- and solar-based microgrid is scalable and capable of meeting short-term demand variation and long-term load growth with a stable energy cost rate.

Atypical variability in TMY-based power systems

This paper presents the results of an analysis that explores how Typical Meteorological Year (TMY) informed power system models perform when exposed to atypical variability. A simplified power system planning model is tested for two case studies in Guinea-Bissau and Turkey. A TMY proxy is compared against 24-year timeseries datasets containing hourly resolution solar PV and wind capacity factor data. Results show that unforeseen shortages in VRE generation and additional expenses are approximately 36 GWh and 10 million USD for Guinea-Bissau, and 92 GWh and 232,000 million TL for Turkey. Moreover, Turkey faces unmet demand of up to 50 TWh. For Turkey, unmet demand is penalized at 5000 TL/MWh, explaining the high cost increase. The underperformance of the TMY-based simulated system also is responsible for 45.77 million kg of unforeseen CO2 emissions in the case of Guinea-Bissau.