Web tools concerning performance analysis and planning support for solar energy plants starting from remotely sensed optical images
- Department of Physics, University of Milano, Via Celoria 16, 20133 Milano (Italy)
- Flyby S.r.l., Via Puini 97, 57128 Livorno (Italy)
- i-EM S.r.l., Via Lampredi 45, 57121 Livorno (Italy)
We present innovative web tools, developed also in the frame of the FP7 ENDORSE (ENergy DOwnstReam SErvices) project, for the performance analysis and the support in planning of solar energy plants (PV, CSP, CPV). These services are based on the combination between the detailed physical model of each part of the plants and the near real-time satellite remote sensing of incident solar irradiance. Starting from the solar Global Horizontal Irradiance (GHI) data provided by the Monitoring Atmospheric Composition and Climate (GMES-MACC) Core Service and based on the elaboration of Meteosat Second Generation (MSG) satellite optical imagery, the Global Tilted Irradiance (GTI) or the Beam Normal Irradiance (BNI) incident on plant's solar PV panels (or solar receivers for CSP or CPV) is calculated. Combining these parameters with the model of the solar power plant, using also air temperature values, we can assess in near-real-time the daily evolution of the alternate current (AC) power produced by the plant. We are therefore able to compare this satellite-based AC power yield with the actually measured one and, consequently, to readily detect any possible malfunctions and to evaluate the performances of the plant (so-called “Controller” service). Besides, the same method can be applied to satellite-based averaged environmental data (solar irradiance and air temperature) in order to provide a Return on Investment analysis in support to the planning of new solar energy plants (so-called “Planner” service). This method has been successfully applied to three test solar plants (in North, Centre and South Italy respectively) and it has been validated by comparing satellite-based and in-situ measured hourly AC power data for several months in 2013 and 2014. The results show a good accuracy: the overall Normalized Bias (NB) is − 0.41%, the overall Normalized Mean Absolute Error (NMAE) is 4.90%, the Normalized Root Mean Square Error (NRMSE) is 7.66% and the overall Correlation Coefficient (CC) is 0.9538. The maximum value of the Normalized Absolute Error (NAE) is about 30% and occurs for time periods with highly variable meteorological conditions. - Highlights: • We developed an online service (Controller) dedicated to solar energy plants real-time monitoring • We developed an online service (Planner) that supports the planning of new solar energy plants • The services are based on the elaboration of satellite optical imagery in near real-time • The validation with respect to in-situ measured hourly AC power data for three test solar plants shows good accuracy • The maximum value of the Normalized Absolute Error is about 30% and occurs for highly variable meteorological conditions.
- OSTI ID:
- 22479732
- Journal Information:
- Environmental Impact Assessment Review, Vol. 52; Other Information: Copyright (c) 2014 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); ISSN 0195-9255
- Country of Publication:
- United States
- Language:
- English
Similar Records
A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting
Utility-Scale Solar 2014. An Empirical Analysis of Project Cost, Performance, and Pricing Trends in the United States