Web tools concerning performance analysis and planning support for solar energy plants starting from remotely sensed optical images

Feb 14, 2022

AUTHORS
Marco Morelli, Andrea Masini, Fabrizio Ruffini, Marco Alberto Carlo Potenza

ABSTRACT
We present innovative web tools, developed also in the frame of the FP7 ENDORSE (ENergy DOwnstReamSErvices) 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 plantsand 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 Compo-sition 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) incidenton plant’s solar PV panels (or solar receivers for CSP or CPV) is calculated. Combining these parameters with themodel of the solar power plant, using also air temperature values, we can assess in near-real-time the daily evo-lution 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 possiblemalfunctions and to evaluate the performances of the plant (so-called“Controller”service). Besides, the samemethod 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 respective-ly) and it has been validated by comparing satellite-based and in-situ measured hourly AC power data for severalmonths in 2013 and 2014. The results show a good accuracy: the overall Normalized Bias (NB) is−0.41%, theoverall Normalized Mean Absolute Error (NMAE) is 4.90%, the Normalized Root Mean Square Error (NRMSE) is7.66% and the overall Correlation Coefficient (CC) is 0.9538. The maximum value of the Normalized AbsoluteError (NAE) is about 30% and occurs for time periods with highly variable meteorological conditions.

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One year Operation of an Innovative Condition Monitoring Technique in Four Hydropower Plants

One year Operation of an Innovative Condition Monitoring Technique in Four Hydropower Plants

Feb 14, 2022

AUTHORS:
Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri and Debora Mucci.
ABSTRACT:
The ability to accurately forecast power generation from renewable sources is nowadays recognized as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper, we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic (PV) plants of different sizes and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation.

NREL Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

NREL Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Apr 27, 2021

AUTHORS:
Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri and Debora Mucci.
ABSTRACT:
The ability to accurately forecast power generation from renewable sources is nowadays recognized as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper, we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic (PV) plants of different sizes and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation.

Condition monitoring and early diagnostics methodologies for hydropower plants

Condition monitoring and early diagnostics methodologies for hydropower plants

Nov 15, 2019

AUTHORS:
Alessandro Betti, Emanuele Crisostomi, Gianluca Paolinelli, Antonio Piazzi, Fabrizio Ruffini and Mauro Tucci.
ABSTRACT:
Hydropower plants are one of the most convenient option for power generation, as they generate energy exploiting a renewable source, they have relatively low operating and maintenance costs, and they may be used to provide ancillary services, exploiting the large reservoirs of available water. The recent advances in Information and Communication Technologies (ICT) and in machine learning methodologies are seen as fundamental enablers to upgrade and modernize the current operation of most hydropower plants, in terms of condition monitoring, early diagnostics and eventually predictive maintenance. While very few works, or running technologies, have been documented so far for the hydro case, in this paper we propose a novel Key Performance Indicator (KPI) that we have recently developed and tested on operating hydropower plants. In particular, we show that after more than one year of operation it has been able to identify several faults, and to support the operation and maintenance tasks of plant operators. Also we show that the proposed KPI outperforms conventional multivariable process control charts, like the Hotelling t2 index.

MOWGLI (MicrO reneWable Grid for ruraL Indian areas) project on ETN magazine

MOWGLI (MicrO reneWable Grid for ruraL Indian areas) project on ETN magazine

Oct 15, 2019

AUTHORS:
Ciro Lanzetta and Fabrizio Ruffini.
ABSTRACT:
The Mowgli feasibility study started in 2018 funded by the European Space Agency (ESA) with the involvement of the India Energy Storage Alliance (IESA) as stakeholder. The aim is to evaluate the technical and economic feasibility of satellite-based services for microgrids. Designed by i-EM, Mowgli is a solution that provides a set of services for optimal microgrid planning, designing and operational and maintenance (O&M) applications in urban and rural areas developing countries, with focus on India as a user case.

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