Tools for multi time horizon PV point forecasting and prediction intervals

The massive integration of stochastic renewable generation in modern power systems requires decentralized control schemes that cope with the uncertainties of the distributed energy resources (DERs). Among the DERs, small-scale photovoltaic (PV) systems are expected to represent most of the future power generation capacity. Therefore, solar resource assessment and the associated forecasting at various time scales are of fundamental importance for power systems operators. The activity here summarized focuses on developing forecasting methods based on the integrated use of time series, all-sky camera images and generation models of PV systems, considering short-term temporal horizons (below one hour) and fine spatial resolution (single site installations). We aim to improve the efficiency of forecasting methods developed by the DESL through prediction of clouds movement and associated cover of the sun disk. For this, we developed and validated different machine-learning techniques relying on the integrated use of Global Horizonal Irradiance (GHI) time series, all-sky image processing and cloud motion identification. Furthermore, a methodology to estimate the irradiance from all-sky images is proposed, investigating the possibility of using all-sky cameras as irradiance sensors.

Ultra-short-term prediction intervals of photovoltaic AC active power

Jean-Yves Le Boudec, Mario Paolone, Enrica Scolari, Dimitri Torregrossa

The paper describes a heuristic method for the ultra-short-term computation of prediction intervals (PIs) for photovoltaic (PV) power generation. The method allows for directly forecasting the AC active power output of a PV system by simply extracting information from past time series. Two main approaches are investigated. The former relies on experimentally observed correlations between the time derivative of the PV AC active power output and the errors caused by a generic point forecast technique. The latter approach represents an improvement of the first one, where the mentioned correlations are clustered as a function of the value of the AC active power. The work is framed in the context of microgrids and inertialess power systems control, where accounting for the fastest dynamics of the solar irradiance can become extremely valuable. We validate the proposed model using one month of AC active power measurements and for sub-second time horizons: 100, 250 and 500 ms.

IEEE2016

Cloud Motion Identification Algorithms Based on All-Sky Images to Support Solar Irradiance Forecast

Lydie Catherine Magnone, Mario Paolone, Enrica Scolari, Fabrizio Sossan

Cloud motion is a cause of direct irradiance variations at ground level and determines significant fluctuations of PV generation. In this work, we investigate on how integrating information on clouds motion extracted from all-sky images into a time series-based forecasting tool for global horizontal irradiance (GHI) to enhance its prediction performance. We consider three different cloud motion algorithms: heuristic motion detection (HMD), particle image velocimetry (PIV), and a persistent model. The HMD method is originally proposed in this paper. It consists in choosing the cloud motion vector leading to the best cloud map prediction considering the most recent sky images. Results show that integrating the information of the predicted cloud coverage in the circumsolar area leads to a decrease of the width of the GHI prediction intervals up to 2% for prediction horizons in the range 1 to 10 minutes.

2017

Performance analysis of photovoltaic thermal (Pvt) panels considering thermal parameters

Amarasinghage Tharindu Dasun Perera

Solar PVT panels are getting popular for wider spectrum of applications for concurrent heat and power generation (CHP). These panels can provide the heating demand of buildings while generating electricity which becomes ideal for building applications of urban energy systems. Energy flow analysis of such panels and performance analysis of such systems becomes essential to design PVT systems matching with the operating conditions. A number of studies have used both theoretical and experimental methods to optimize PVT. However, this task is challenging due to interrelation of CHP production based on two different phenomena where classical optimization methods cannot be applied directly. Hence basic performance analysis considering primary design parameters plays a major role. In this study, a computational model is developed to evaluate sensitivity of design, operating and climatic parameters for a hybrid PVT system and to analyze the performances of PVT for five different design configurations. Five main configurations of the PVT system are considered based on the heat transfer fluid and the arrangements of glass and tedlar layers of PVT collector. This study presents comprehensive performance analysis conducted to evaluate the sensitivity of mass flow rate and working fluid temperature for the five different design configurations of PVT panels. Results show that glass-tedlar water collector performs better when compared to other configurations. Subsequently, the sensitivity of wind speed and solar irradiation is evaluated. The behavior of thermal and electrical efficiencies is analyzed at different wind speed and solar irradiation levels for a range of mass flow rates and working fluid temperatures. Important conclusions on the performance of PVT panels are given based on this detailed analysis.

American Society of Mechanical Engineers2016