Economic merits of a state-of-the-art concentrating solar power forecasting system for participation in the Spanish electricity market

Kraas, Birk; Schroedter-Homscheidt, Marion; Madlener, Reinhard

doi:10.1016/j.solener.2013.04.012

Cited by 100 publications

(49 citation statements)

References 11 publications

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“…As stated before, the available DNI at a CSP plant's location has a significant impact on both the LCOE and the LPOE of the plant [31], [37], [44], [45]. In its report, The International Energy Agency (IEA) [15] showed that when a baseline of 2100 kWh/m 2 /year (Spain's average DNI) is assumed, the calculated LCOE of a CSP plant declines by 4.5 per cent for every 100 kWh/m 2 /year that the DNI exceeds 2100.…”

Section: Dni Lcoe and Lpoementioning

confidence: 88%

“…The cost has fallen globally over the years from 80 USD cents/kWh in the 1980s to around 20 to 30 USD cents/kWh in 2010, 17 to 25 USD cents/kWh in 2013, and an expected six USD cents//kWh in 2050 [15], [35], [58]. However, this cost of electricity from CSP systems varies globally, depending on the DNI resources of the location [2], [45], [59], [60].…”

Section: Experience Curve and Cumulative Installed Capacitymentioning

confidence: 99%

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The Current and Future Energy Economics of Concentrating Solar Power (Csp) in South Africa

Craig

Brent

Dinter

2017

SAJIE

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For South Africa, an increase in energy demand is associated with higher CO2 emissions. In order to overcome this challenge, the government started the Renewable Energy Independent Power Producer Procurement Programme (REI4P) to allow easy integration of renewable energy technologies into the existing energy mix. The country has an abundant solar resource, and the potential to harvest this resource through concentrating solar power (CSP) has been proven. 600 MW of CSP have been bought in the REI4P, but the future of CSP in SA looks bleak, as the government's recent Integrated Resource Plan (IRP) updates gave no allocation to new CSP plants beyond 2030. Very few CSP plants are connected to the grid, and there is limited research and literature on its learning effect and economics of scale. This study analyses the state of CSP and uses a mathematical relationship to determine the progress ratio, the learning effect, and the likely future of CSP in South Africa. The results address the difficulties involved in determining the learning effect of CSP. OPSOMMINGVir Suid-Afrika word 'n toename in die vraag na energie geassosieer met hoër CO2-uitlate. Om hierdie uitdaging te oorkom, het die regering die Renewable Energy Independent Power Produsent Procurement Program (REI4P) begin om maklike integrasie van hernubare energie tegnologieë in die bestaande energiemengsel moontlik te maak. Die land het 'n oorvloed in sonkrag, en die potensiaal om hierdie hulpbron te benut deur konsentrasie van sonkrag (CSP) is alreeds bewys. 600 MW CSP is in met die REI4P gekoop, maar die toekoms van CSP in SA lyk bleek, aangesien die regering se onlangse opdaterings van die Geïntegreerde Hulpbronplan (IRP) geen toewysing na 2030 aan nuwe CSP-aanlegte gedoen het nie. Baie min CSP-aanlegte is verbind met die netwerk, en daar is beperkte navorsing en literatuur oor sy leer-effek en skaalbesparing. Hierdie studie analiseer die stand van CSP en gebruik 'n wiskundige verhouding om die vorderingsverhouding, die leer effek, en die waarskynlike toekoms van CSP in Suid-Afrika te bepaal. Die resultate spreek die probleme aan in de bepaling van die leer-effek van CSP.

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Section: Dni Lcoe and Lpoementioning

confidence: 88%

Section: Experience Curve and Cumulative Installed Capacitymentioning

confidence: 99%

The Current and Future Energy Economics of Concentrating Solar Power (Csp) in South Africa

Craig

Brent

Dinter

2017

SAJIE

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“…Altogether they are called the 'Group of low indices' (yellow). Direction changes after [4], and ΔDNI_mean, ΔDNI_σ and ΔDNI_max after [1] together with the variability index VI after [3] are the final group of indices called 'Medium sized indices' and given in green. The index values are far from being constant for each individual case, but compared to the other variability classes, the distributions of values occurring in each variability class differ systematically.…”

Section: Automatic Detection Of Variability Classesmentioning

confidence: 99%

Classifying 1 minute temporal variability in global and direct normal irradiances within each hour from ground-based measurements

Schroedter-Homscheidt¹,

Jung²,

Kosmale³

2017

AIP Conference Proceedings

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Abstract. This study aims at classifying the 1 min resolved DNI and GHI variability in an hour in generic variability classes. It couples existing variability indices and adds new indicators to quantify aspects of the time series not being quantified so far. Additionally, the focus is laid on an automatic detection scheme. Eight classes are differentiated and described with respect to several aspects of variability.

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“…Thus, the knowledge and the prediction of cloud properties for the derivation of DNI is essential for the optimization of the CSP operation strategy because for day-ahead and intra-day electricity markets the electricity production must be announced to the market operator and deviations from the production schedule may lead to deviation penalties. Kraas et al (2013) show the economic merits of a forecasting system for day-ahead forecasts for CSP, which reduces penalties by 47.6 % compared to a persistence model. The persistence model (the cloud distribution observed at the forecast starting point is assumed to stay unchanged during the entire forecast time) is the simplest forecast model and works well for periods of low cloud variability and obviously for clear-sky cases.…”

Section: Introductionmentioning

confidence: 98%

Cloud and DNI nowcasting with MSG/SEVIRI for the optimized operation of concentrating solar power plants

Sirch

Bugliaro

Zinner³

et al. 2017

Atmos. Meas. Tech.

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Abstract. A novel approach for the nowcasting of clouds and direct normal irradiance (DNI) based on the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG) satellite is presented for a forecast horizon up to 120 min. The basis of the algorithm is an optical flow method to derive cloud motion vectors for all cloudy pixels. To facilitate forecasts over a relevant time period, a classification of clouds into objects and a weighted triangular interpolation of clear-sky regions are used. Low and high level clouds are forecasted separately because they show different velocities and motion directions. Additionally a distinction in advective and convective clouds together with an intensity correction for quickly thinning convective clouds is integrated. The DNI is calculated from the forecasted optical thickness of the low and high level clouds. In order to quantitatively assess the performance of the algorithm, a forecast validation against MSG/SEVIRI observations is performed for a period of 2 months. Error rates and Hanssen-Kuiper skill scores are derived for forecasted cloud masks. For a forecast of 5 min for most cloud situations more than 95 % of all pixels are predicted correctly cloudy or clear. This number decreases to 80-95 % for a forecast of 2 h depending on cloud type and vertical cloud level. Hanssen-Kuiper skill scores for cloud mask go down to 0.6-0.7 for a 2 h forecast. Compared to persistence an improvement of forecast horizon by a factor of 2 is reached for all forecasts up to 2 h. A comparison of forecasted optical thickness distributions and DNI against observations yields correlation coefficients larger than 0.9 for 15 min forecasts and around 0.65 for 2 h forecasts.

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Economic merits of a state-of-the-art concentrating solar power forecasting system for participation in the Spanish electricity market

Cited by 100 publications

References 11 publications

The Current and Future Energy Economics of Concentrating Solar Power (Csp) in South Africa

The Current and Future Energy Economics of Concentrating Solar Power (Csp) in South Africa

Classifying 1 minute temporal variability in global and direct normal irradiances within each hour from ground-based measurements

Cloud and DNI nowcasting with MSG/SEVIRI for the optimized operation of concentrating solar power plants

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