Humanity has used and continues to consume in great proportion nonrenewable energy resources of the planet such as oil, natural gas and coal in order to fulfil its energy needs. It was only during the past two decades that other sources of renewable energy such as solar photovoltaics (PV) and wind energy became somewhat relevant towards electricity generation in the world. PV installations worldwide have reached a compound annual growth rate of ~40% for the last fifteen years. However, the great majority of these systems (over 90% of them) are located where the solar energy resource is not the most abundant – outside of the tropical regions of the planet. While trying to incorporate solar energy PV into electrical power grids, one common question which arises is related to the variable aspect of this form of energy generation – with alternating production during the day due to cloud motion, and total absence during night time. Nonetheless, in some countries, contribution ratios of 5 to 10% of electrical energy from solar PV have been achieved. It becomes then challenging to integrate this source of energy into grids in a professional way, in parallel with existing resources (mostly still fossil-fuel-based). In this thesis, short-term forecasting (for time horizons such as 15-min, 30- min and 1-hour) of the solar resource was investigated in a tropical region of the world – in Singapore, 1° North of the Equator, in Southeast Asia. This thesis focuses on existing methods for irradiance forecasting, but also explores a novel Hybrid proposal, tailored to the tropical environment at hand. Beyond the forecast of the solar energy irradiance ahead of time, PV system simulation and performance assessment were studied and evaluated with the goal of predicting how much electricity is produced in the same time frame given by the solar irradiance forecasting products. The influence of haze was a particular phenomenon, common in today’s Singapore, which affects PV system performance and which was investigated in detail. All work has been validated by a comprehensive network of ground-based meteorological stations, as well as by various PV system monitoring sites throughout Singapore.
André Maia Nobre
Data de defesa: