Abstract
Photovoltaic (PV) systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing. Because of the uncertain nature of the solar energy resource, PV power forecasting models are crucial in any energy management system for smart distribution networks. Although point forecasts can suit many scopes, probabilistic forecasts add further flexibility to an energy management system and are recommended to enable a wider range of decision making and optimization strategies. This paper proposes methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model, in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model. A novel procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of the related coefficients, raising the predictive ability of the final forecasts. Numerical experiments based on actual data quantify an enhancement of the performance of up to 2.2% when compared to relevant benchmarks.
Highlights
The recent growth in distributed Photovoltaic (PV) power generation systems fosters the exploitation of renewable energy and adds further flexibility to electrical distribution systems, which experience a significant amount of generation in close proximity to load, with obvious advantages in terms of reduced line congestion and losses
This paper provides a contribution to probabilistic ensemble PV power forecasting within the bagging framework, based on the interaction between a Quantile Regression (QR) model and a Bayesian bootstrap
Dataset #2 consists of zone-1 data published in the framework of the Global Energy Forecasting Competition 2014 [8] at an unspecified location in Australia, and data span April 1, 2012 to June 30, 2014
Summary
The recent growth in distributed Photovoltaic (PV) power generation systems fosters the exploitation of renewable energy and adds further flexibility to electrical distribution systems, which experience a significant amount of generation in close proximity to load, with obvious advantages in terms of reduced line congestion and losses. PV power is nondeterministic, as it strictly depends on weather and environmental conditions [1] This brings significant challenges for transmission and distribution system operators and for market operators. They continually have to deal with local production that may not respond coherently to day-ahead prior generation programs [2]. Large margins of energy reserve are necessary to be able to deal with the deviations from prior
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
