Assessing wind, solar, and wave energy sources in the southwest of Buenos Aires province (Argentina)

Genchi, Sibila Andrea; Vitale, Alejandro José; Perillo, Gerardo M. E.

doi:10.14350/rig.59657

Cited by 5 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Argentina is a mid‐latitude country located in southern South America. In 2019, the agricultural activity contributed 9.92% to its gross domestic production while investment for the production of wind energy has been promoted since 2015, especially in Patagonia (Barros, 1983; Labraga, 1994; Palese et al ., 2000; Recalde, 2010; Genchi et al ., 2014; 2018). As a result, a decrease in SWS would have a huge impact in its economy.…”

Section: Introductionmentioning

confidence: 99%

Wind trends analysis in southern South America from weather station and reanalysis data

Merino

Gassmann

2021

Intl Journal of Climatology

View full text Add to dashboard Cite

Surface and upper‐level wind speed changes were analysed for several weather stations distributed throughout Argentina from 1990 to 2020. Both annual and seasonal wind speed trends were estimated for individual surface stations and available rawinsonde data. In order to explore the nature of these changes, different percentile time series were evaluated. NCEP/NCAR and ERA5 data were also assessed to verify how these long‐term changes are represented by the different reanalysis time series. A significant decline in measured surface wind speed is observed for most stations, with a reduction up to 13%·decade−1 in some of them. Despite differences on each station's wind speed distributions due to regional characteristics, the estimated trends were mostly negative for the annual 75th and 95th percentile time series. Upper‐level stations registered positive trends especially in the upper troposphere (200 hPa) in northern Argentina, while Patagonia's wind profile showed significant positive wind speed trends at most levels. Reanalysis data have severe limitations to reproduce the observed behaviour in the near‐surface levels, but managed to generate adequate estimates for the medium to high troposphere.

show abstract

Section: Introductionmentioning

confidence: 99%

Wind trends analysis in southern South America from weather station and reanalysis data

Merino

Gassmann

2021

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…Correlation coefficients are by far the most widely used metric for assessing complementarity between energy sources' time series. This approach has been used, for example, in Poland [1], Portugal [2,3], Spain [4], Italy [5,6], USA [7], Germany [8], Australia [9], Brazil [10][11][12][13], China [14][15][16], Sweden [17], Mexico [18], Canada [19], Lesotho [20], Finland [21], Argentina [22], Britain [23], Saudi Arabia [24], and Chile [25]. It has also been used at the international level (e.g., Latin America [26], Iberian Peninsula [27]), and even at the intercontinental level (e.g., Canada vs. Noruega [19] and Europe vs. Africa [28]).…”

Section: Introductionmentioning

confidence: 99%

Total Variation-Based Metrics for Assessing Complementarity in Energy Resources Time Series

2022

View full text Add to dashboard Cite

The growing share of intermittent renewable energy sources raised complementarity to a central concept in the electricity supply industry. The straightforward case of two sources suggests that to guarantee supply, the time series of both sources should be negatively correlated. Extrapolation made Pearson’s correlation coefficient (ρ) the most widely used metric to quantify complementarity. This article shows several theoretical and practical drawbacks of correlation coefficients to measure complementarity. Consequently, it proposes three new alternative metrics robust to those drawbacks based on the natural interpretation of the concept: the Total Variation Complementarity Index (ϕ), the Variance Complementarity Index (ϕ′), and the Standard Deviation Complementarity Index (ϕs). We illustrate the use of the three indices by presenting one theoretical and three real case studies: (a) two first-order autoregressive processes, (b) one wind and one hydropower energy time series in Colombia at the daily time resolution, (c) monthly water inflows to two hydropower reservoirs of Colombia with different hydrologic regimes, and (d) monthly water inflows of the 15 largest hydropower reservoirs in Colombia. The conclusion is that ϕ outperforms the use of ρ to quantify complementarity because (i) ϕ takes into account scale, whereas ρ is insensitive to scale; (ii) ρ does not work for more than two sources; (iii) ρ overestimates complementarity; and (iv) ϕ takes into account other characteristics of the series. ϕ′ corrects the scale insensitivity of ρ. Moreover, it works with more than two sources. However, it corrects neither the overestimation nor the importance of other characteristics. ϕs improves ϕ′ concerning the overestimation, but it lets out other series characteristics. Therefore, we recommend total variation complementarity as an integral way of quantifying complementarity.

show abstract

“…Correlation coefficients are by far the most widely used metric for assessing complementarity between energy sources time series. This approach has been used, for example, in Poland (Jurasz, 2017), Portugal (Moura and de Almeida, 2010;Castro and Crispim, 2018), Spain (Ren et al, 2019), Italy (Monforti et al, 2014;François et al, 2014), USA (Slusarewicz and Cohan, 2018), Colombia 20 (Parra et al, 2020;Peña Gallardo et al, 2020b;Henao et al, 2020;Cantor et al, 2019), Germany (Schindler et al, 2020), Australia (Kay, 2015), Brazil (Silva et al, 2016;de Oliveira Costa Souza Rosa et al, 2017;dos Anjos et al, 2015;Cantão 25 et al, 2017), China (Xu et al, 2017;Li et al, 2019;Cao et al, 2019), Sweden (Widen, 2011), Mexico (Peña Gallardo et al, 2020a), Canada (Denault et al, 2009), Lesotho (D'Isidoro et al, 2020), Finland (Solomon et al, 2020), Argentina 30 (Genchi et al, 2018), Britain (Bett and Thornton, 2016), Saudi Arabia (Sahin, 2000) and Chile (Odeh and Watts, 2019). It has also been used at the international level (e.g.…”

Section: Introductionmentioning

confidence: 99%

Total variation as a metric for complementarity in energy resources time series

Cantor¹,

Ochoa²,

Sánchez³

2021

Preprint

View full text Add to dashboard Cite

Complementarity has become an essential concept in energy supply systems. Although there are some other metrics, most studies use correlation coefficients to quantify complementarity. The standard interpretation is that a high negative correlation indicates a high degree of complementarity. However, we show that the correlation is not an entirely satisfactory measure of complementarity. As an alternative, we propose a new index based on the mathematical concept of the total variation. For two time series, the new index φ is one minus the ratio of the total variation of the sum to the sum of the two series' total variation. We apply the index first to an auto-regressive (AR) process and then to various Colombian electric system series. The AR case clearly illustrates the limitations of the correlation coefficient as a measure of complementarity. We then evaluate complementarity across various space-time scales in the Colombian power sectors, considering hydro and wind projects. The complementarity assessment on a broad temporal and geographical scale helps analyze large power systems with different energy sources. The case study of the Colombian hydropower systems suggests that φ is better than ρ because (i) it considers scale, whereas ρ, being non-dimensional, is insensitive to the scale and even to the physical dimensions of the variables; (ii) one can apply φ to more than two resources; and (iii) ρ tends to overestimate complementarity.

show abstract

Assessing wind, solar, and wave energy sources in the southwest of Buenos Aires province (Argentina)

Cited by 5 publications

References 0 publications

Wind trends analysis in southern South America from weather station and reanalysis data

Wind trends analysis in southern South America from weather station and reanalysis data

Total Variation-Based Metrics for Assessing Complementarity in Energy Resources Time Series

Total variation as a metric for complementarity in energy resources time series

Contact Info

Product

Resources

About