Short-Term Forecasts of DNI from an Integrated Forecasting System (ECMWF) for Optimized Operational Strategies of a Central Receiver System

Lopes, Francis M.; Conceição, Ricardo; Silva, Hugo; Fasquelle, Thomas; Salgado, Rui; Canhoto, Paulo; Collares-Pereira, Manuel

doi:10.3390/en12071368

Cited by 23 publications

(12 citation statements)

References 30 publications

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“…The difference in the optimal value of the offset is mainly due to the different meteorological product used for the cloud cover estimation. The error and skill score values are in line with those found both in the works already mentioned, and, in the most recent studies on the subject, see, for example, [11,18] with variations that are related to the meteorological characteristics of the geographical areas considered.…”

Section: Forecast Resultssupporting

confidence: 91%

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A Forecasting-Based Control Algorithm for Improving Energy Managment in High Concentrator Photovoltaic Power Plant Integrated with Energy Storage Systems

et al. 2020

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The High Concentrator Photovoltaic (HCPV) technology, due to its high efficiency, is considered one of the most promising solutions for the exploitation of sun-irradiation-based Renewable Energy Sources (RES). Nevertheless, the HCPV production is strictly connected to the Direct Normal Irradiation (DNI) making this photovoltaic technology more sensible to cloudiness than traditional ones. In order to mitigate the power intermittence and improve production programmability, the integration between Energy Storage Systems (ESSs) and HCPV, resorting to forecasting algorithms, has been investigated. Specifically, a local weather forecasting algorithm has been used for estimating the daily time evolution of DNI, air Temperature (T), Wind Speed (WS), and Air Mass (AM). These data are subsequently processed by means of an accurate HCPV model for the estimation of one day-ahead daily power production profile. The processing of HCPV forecasted generation by means of a properly tuned filter-based algorithm allows one day-ahead the definition of power profiles of ESS and power plant respectively, considering also the ESS constraints and the characteristic of the implemented real-time control algorithm. The effectiveness of the proposed forecasting model and control algorithm is verified through a simulation study referring to the solar power plant constituted by HCPV and ESS installed in Ottana, Italy. The results highlight that the application of the proposed approach lessens the power fluctuation effect caused by HCPV generation preserving the batteries at the same time. The feasibility and advantages of the proposed approach are finally presented.

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Section: Forecast Resultssupporting

confidence: 91%

“…1 T t t−T P l (t)dt (18) In Figure 6, the block scheme of this algorithm has been shown. The feedback action allows for considering the E bat values at the beginning and at the end of the day equal to each other because it compensates losses and the mean value intrinsically satisfies this requirement.…”

Section: Moving Average Algorithmmentioning

confidence: 99%

A Forecasting-Based Control Algorithm for Improving Energy Managment in High Concentrator Photovoltaic Power Plant Integrated with Energy Storage Systems

et al. 2020

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“…In the specific section Concentrating Solar Power Validation [30], NREL reports about one parabolic trough and one solar tower studies performed with SAM 2013.1.15. Additionally, the only other study mentioned in the validation section of SAM is the recently published paper [31] on a solar tower study with SAM 2017.9.5. Also, this model is available for download in [32].…”

Section: Methodsmentioning

confidence: 99%

Validation of SAM Modeling of Concentrated Solar Power Plants

2020

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The paper proposes the validation of the latest System Advisor Model (SAM) vs. the experimental data for concentrated solar power energy facilities. Both parabolic trough, and solar tower, are considered, with and without thermal energy storage. The 250 MW parabolic trough facilities of Genesis, Mojave, and Solana, and the 110 MW solar tower facility of Crescent Dunes, all in the United States South-West, are modeled. The computed monthly average capacity factors for the average weather year are compared with the experimental data measured since the start of the operation of the facilities. While much higher sampling frequencies are needed for proper validation, as monthly averaging dramatically filters out differences between experiments and simulations, computational results are relatively close to measured values for the parabolic trough, and very far from for solar tower systems. The thermal energy storage is also introducing additional inaccuracies. It is concluded that the code needs further development, especially for the solar field and receiver of the solar tower modules, and the thermal energy storage. Validation of models and sub-models vs. high-frequency data collected on existing facilities, for both energy production, power plant parameters, and weather conditions, is a necessary step before using the code for designing novel facilities. National Electricity Market data tell us that even installed capacity of 18.1 GW sometimes is not enough to deliver 0.2 GW to the grid in cases, for example, of low wind after sunset), the coupling of concentrating solar power with molten salt thermal energy storage is extremely attractive, as it may produce fully dispatchable energy.While the concentrating solar power solar tower coupled with thermal energy storage has so far performed badly in the real world, the concentrating solar power parabolic trough with thermal energy storage is already delivering good performances, [8][9][10][11][12], even if still less than the predictions. Solar photovoltaic works with annual average ε about 0.27-0.29, but with much larger than the capacity factor high-frequency standard deviations, for coefficients of variability in excess of unity [15]. While in the virtual reality of model computations concentrating solar power with thermal energy storage may achieve much larger and more uniform ε, with average ε well in excess of 0.5, also addressing the issues of lack of production during night times, and drastically reduced production with clouds, with dramatically reduced standard deviations, the best real-world experience has so far delivered an annual average ε of about 0.36 (Solana). By contrast with photovoltaic, concentrating solar power, especially the solar tower, suffers from cloud coverage, and this may considerably affect the accuracy of the models.As concluded in [8][9][10][11][12], the concentrating solar power parabolic trough has the potential, once a satisfactory design will be industrialized, to deliver the same or better than photovoltaic costs of electricity with...

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“…Information concerning the potential that day-ahead forecasts of DNI have in the operation of CSP systems is available in literature, see Ref. [37,44,45], where recommended strategies for typical days (i.e. days with different weather conditions) are proposed.…”

Section: Power Plant Daily Strategiesmentioning

confidence: 99%

Improved ECMWF forecasts of direct normal irradiance: A tool for better operational strategies in concentrating solar power plants

et al. 2021

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To contribute for improved operational strategies of concentrating solar power plants with accurate forecasts of direct normal irradiance, this work describes the use of several post-processing methods on numerical weather prediction. Focus is given to a multivariate regression model that uses measured irradiance values from previous hours to improve next-hour predictions, which can be used to refine daily strategies based on day-ahead predictions. Short-term forecasts provided by the Integrated Forecasting System, the global model from the European Centre for Medium-Range Weather Forecasts (ECMWF), are used together with measurements in southern Portugal. As a nowcasting tool, the proposed regression model significantly improves hourly predictions with a skill score of z0.84 (i.e. an increase of z27.29% towards the original hourly forecasts). Using previous-day measured availability to improve next-day forecasts, the model shows a skill score of z0.78 (i.e. an increase of z6% towards the original forecasts), being further improved if larger sets of data are used. Through a power plant simulator (i.e. the System Advisor Model), a preliminary economic analysis shows that using improved hourly predictions of electrical energy allows to enhance a power plant's profit in z0.44 MV/year, as compared with the original forecasts. Operational strategies are proposed accordingly.

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Short-Term Forecasts of DNI from an Integrated Forecasting System (ECMWF) for Optimized Operational Strategies of a Central Receiver System

Cited by 23 publications

References 30 publications

A Forecasting-Based Control Algorithm for Improving Energy Managment in High Concentrator Photovoltaic Power Plant Integrated with Energy Storage Systems

A Forecasting-Based Control Algorithm for Improving Energy Managment in High Concentrator Photovoltaic Power Plant Integrated with Energy Storage Systems

Validation of SAM Modeling of Concentrated Solar Power Plants

Improved ECMWF forecasts of direct normal irradiance: A tool for better operational strategies in concentrating solar power plants

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