Characterisation of Large Changes in Wind Power for the Day-Ahead Market Using a Fuzzy Logic Approach

Abstract

Wind power has become one of the renewable resources with a major growth in the electricity market. However, due to its inherent variability, forecasting techniques are necessary for the optimum scheduling of the electric grid, specially during ramp events. These large changes in wind power may not be captured by wind power point forecasts even with very high resolution Numerical Weather Prediction (NWP) models. In this paper, a fuzzy approach for wind power ramp characterisation is presented. The main benefit of this technique is that it avoids the binary definition of ramp event, allowing to identify changes in power output that can potentially turn into ramp events when the total percentage of change to be considered a ramp event is not met. To study the application of this technique, wind power forecasts were obtained and their corresponding error estimated using Genetic Programming (GP) and Quantile Regression Forests. The error distributions were incorporated into the characterisation process, which according to the results, improve significantly the ramp capture. Results are presented

A review on the recent history of wind power ramp forecasting

“…Some applications of the ramp function were described in [50] and [53]. In this line, a fuzzy approach for wind power ramp characterisation was presented in MartnezArellano et al [62]. Bossavy et al [63] proposed a comprehensive framework for evaluating and comparing different continuous-valued approaches for wind power ramp characterisation.…”

“…Table 1 References of the works considered in Fig. 1 [57][58][59][60] - [61,62] [ [63][64][65][66] *Denotes data up to August 2014.…”

Characterisation of intra-hourly wind power ramps at the wind farm scale and associated processes

“…This is partly due to the wide variety of timescales over which they occur, ranging from a few minutes up to several hours (Worsnop et al, 2018). At the wind farm scale, numerical weather prediction models struggle with forecasting wind power fluctuations occurring within an hour and often fail to predict accurately the timing and the amplitude of the ramps (Zack et al, 2011;Magerman, 2014). In practice, the vast majority of operational short-term wind forecasts rely primarily on variations of the persistence method (or "naive predictor") (Wurth et al, 2018), which assumes that there will be no variation between the current conditions and the conditions at the time of the forecast.…”

“…To alleviate these shortcomings, the so-called "ramp functions" have been introduced, which provide an estimation of the ramp intensity at each time step. Gallego et al (2013Gallego et al ( , 2014 first introduced a ramp function based on a continuous wavelet transform (the "Haar" wavelet) of a wind power time series, and Martínez-Arellano et al (2014) proposed a ramp function based on a fuzzy-logic approach to characterise ramps for the dayahead market. More recently, a continuous wavelet transform (CWT) based on a Gaussian wavelet was used by Hannesdóttir and Kelly (2019).…”

Comparative Analysis of Lstm, Rf and SVM Architectures for Predicting Wind Nature for Smart City Planning

“…In ML various concepts can be used such as fuzzy logic (Monfared et al, 2009), neural networks (El-Fouly and El-Saadany, 2008, Daraeepour and Echeverri, 2014, Yesilbodak et al, 2017 and statistical models (Miranda and Dunn, 2006, Jursa and Rohrig, 2008, Zhou et al, 2011. Regression models using neural networks along with techniques like particle swarm optimization, wavelet transform (Martnez-Arellano et al, 2014), REP tree, M5P tree, bagging tree (Kusiak et al, 2009b, Kusiak andZhang, 2010), K-nearest neighbor algorithm (Jursa andRohrig, 2008, Treiber et al, 2016), principal component analysis, moving average models (De Giorgi et al, 2009, Vargas et al, 2010, Markov chain (Kusiak et al, 2009a, Treiber et al, 2016 have been used for wind analysis. Support Vector Machines (SVM) and its variation, Least Square Support Vector Machines (LSSVM) have also been used for forecasting wind speed (De Giorgi et al, 2009, De Giorgi et al, 2014.…”