One of the Sustainable Development Goals set in 2015 by the United Nations aims to ensure access to affordable, reliable, sustainable, and modern energy for all, increasing the global share of renewable energy to 32-35% by 2030. Moving towards this goal, the University of Trento funded the interdepartmental strategic project ERiCSol (Energie Rinnovabili e Combustibili Solari), in order to promote the research on renewable energy storage and solar fuels. The research activity presented in this thesis lies in the framework of this project, focusing on the development of new advanced simulation approaches to improve the estimation of the wind resource availability and the related power production for Italian wind farms in complex terrain. The wind farms, operated by the company AGSM S.p.A., are located in two different geographical contexts: Rivoli Veronese and Affi are at the inlet of the Adige Valley, while Casoni di Romagna and Carpinaccio Firenzuola, are on the crest of the Apennines close to the borders between the provinces of Bologna e Firenze. The analysis of data from year-long field measurements highlighted the different peculiarities of these areas. The wind farms at the mouth of the Adige Valley are influenced by a daily periodic thermally-driven circulation, characterised by a nocturnal intense down-valley wind alternating with a diurnal weaker up-valley wind, while the Apennines wind farms are primarily affected by synoptic-scale winds. Simulations, with the mesoscale Weather Research and Forecasting (WRF) model, are performed and compared with field measurements in both cases, to highlight strengths and weaknesses. The results show that the model is able to capture with good accuracy wind speed and direction in the Apennines wind farms, while larger errors arise for Rivoli Veronese and Affi wind farms, where the intensity of the nocturnal down-valley wind is generally underestimated. Considering the former case, modelled and observed yearly wind speed density distributions are compared, in order to evaluate the impact of model errors in the estimation of the wind resource at these sites. Since reliable simulations of the wind resource are also essential to ensure the security in power transmission and to prevent penalties to energy operators, an analysis of the power production is also performed, to evaluate how errors in the estimate of the resource translate into errors in the estimate of the production. Considering the wind farms at the mouth of the Adige Valley, the research work mainly focuses on the evaluation of the impact of data assimilation by means of observational nudging on model results, in order to optimize the setup for operational forecasts. Different configurations are tested and compared, varying the temporal window for the assimilation of local data.

Improving numerical simulation methods for the assessment of wind source availability and related power production for wind farms over complex terrain / Ive, Federica. - (2022 Jul 26).

Improving numerical simulation methods for the assessment of wind source availability and related power production for wind farms over complex terrain

Ive, Federica
2022-07-26

Abstract

One of the Sustainable Development Goals set in 2015 by the United Nations aims to ensure access to affordable, reliable, sustainable, and modern energy for all, increasing the global share of renewable energy to 32-35% by 2030. Moving towards this goal, the University of Trento funded the interdepartmental strategic project ERiCSol (Energie Rinnovabili e Combustibili Solari), in order to promote the research on renewable energy storage and solar fuels. The research activity presented in this thesis lies in the framework of this project, focusing on the development of new advanced simulation approaches to improve the estimation of the wind resource availability and the related power production for Italian wind farms in complex terrain. The wind farms, operated by the company AGSM S.p.A., are located in two different geographical contexts: Rivoli Veronese and Affi are at the inlet of the Adige Valley, while Casoni di Romagna and Carpinaccio Firenzuola, are on the crest of the Apennines close to the borders between the provinces of Bologna e Firenze. The analysis of data from year-long field measurements highlighted the different peculiarities of these areas. The wind farms at the mouth of the Adige Valley are influenced by a daily periodic thermally-driven circulation, characterised by a nocturnal intense down-valley wind alternating with a diurnal weaker up-valley wind, while the Apennines wind farms are primarily affected by synoptic-scale winds. Simulations, with the mesoscale Weather Research and Forecasting (WRF) model, are performed and compared with field measurements in both cases, to highlight strengths and weaknesses. The results show that the model is able to capture with good accuracy wind speed and direction in the Apennines wind farms, while larger errors arise for Rivoli Veronese and Affi wind farms, where the intensity of the nocturnal down-valley wind is generally underestimated. Considering the former case, modelled and observed yearly wind speed density distributions are compared, in order to evaluate the impact of model errors in the estimation of the wind resource at these sites. Since reliable simulations of the wind resource are also essential to ensure the security in power transmission and to prevent penalties to energy operators, an analysis of the power production is also performed, to evaluate how errors in the estimate of the resource translate into errors in the estimate of the production. Considering the wind farms at the mouth of the Adige Valley, the research work mainly focuses on the evaluation of the impact of data assimilation by means of observational nudging on model results, in order to optimize the setup for operational forecasts. Different configurations are tested and compared, varying the temporal window for the assimilation of local data.
XXXIV
Ingegneria civile, ambientale e mecc (29/10/12-)
Economia e management (29/10/12-)
Civil, Environmental and Mechanical Engineering
Zardi, Dino
Giovannini, Lorenzo
Coller, Graziano
no
Inglese
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/350981
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