On the Influence of Renewable Energy Sources in Electricity Price Forecasting in the Iberian Market

The mainstream of EU policies is heading towards the conversion of the nowadays electricity consumer into the future electricity prosumer (producer and consumer) in markets in which the production of electricity will be more local, renewable and economically efficient. One key component of a local short-term and medium-term planning tool to enable actors to efficiently interact in the electric pool markets is the ability to predict and decide on forecast prices. Given the progressively more important role of renewable production in local markets, we analyze the influence of renewable energy production on the electricity price in the Iberian market through historical records. The dependencies discovered in this analysis will serve to identify the forecasts to use as explanatory variables for an electricity price forecasting model based on recurrent neural networks. The results will show the wide impact of using forecasted renewable energy production in the price forecasting.

[1]  Luigi Grossi,et al.  Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources , 2019, Technological Forecasting and Social Change.

[2]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[3]  Ashwani Kumar,et al.  Electricity price forecasting in deregulated markets: A review and evaluation , 2009 .

[4]  Chi-Keung Woo,et al.  The impact of wind generation on the electricity spot-market price level and variance: The Texas experience , 2011 .

[5]  Bert Scholtens,et al.  The impact of renewable energy on electricity prices in the Netherlands , 2013 .

[6]  V. Mendes,et al.  Short-term electricity prices forecasting in a competitive market: A neural network approach , 2007 .

[7]  Martin Wild,et al.  The impact of climate change on photovoltaic power generation in Europe , 2015, Nature Communications.

[8]  I. J. Ramírez-Rosado,et al.  Explanatory information analysis for day-ahead price forecasting in the Iberian electricity market , 2015 .

[9]  Eduardo Caro,et al.  Electricity price forecasting accounting for renewable energies: optimal combined forecasts , 2015, J. Oper. Res. Soc..

[10]  David Wozabal,et al.  The effect of intermittent renewables on the electricity price variance , 2014, OR Spectr..

[11]  Marco van Akkeren,et al.  A GARCH forecasting model to predict day-ahead electricity prices , 2005, IEEE Transactions on Power Systems.

[12]  Ilkay Oksuz,et al.  Electricity Price Forecasting Using Recurrent Neural Networks , 2018 .

[13]  Dipti Srinivasan,et al.  A hybrid intelligent model based on recurrent neural networks and excitable dynamics for price prediction in deregulated electricity market , 2013, Eng. Appl. Artif. Intell..

[14]  S. Borenstein.  The Private and Public Economics of Renewable Electricity Generation , 2011 .

[15]  Hendrik Feldmann,et al.  Future Changes of Wind Speed and Wind Energy Potentials in EURO‐CORDEX Ensemble Simulations , 2018, Journal of Geophysical Research: Atmospheres.

[16]  M. Obersteiner,et al.  Forecasting electricity spot-prices using linear univariate time-series models , 2004 .

[17]  Eva Blasco Hedo.  Real Decreto 900/2015, de 9 de octubre, por el que se regulan las condiciones administrativas, técnicas y económicas de las modalidades de suministro de energía eléctrica con autoconsumo y de producción con autoconsumo , 2015 .

[18]  Asher Tishler,et al.  Can price volatility enhance market power? The case of renewable technologies in competitive electricity markets , 2015 .

[19]  Ping-Huan Kuo,et al.  An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks , 2018 .

[20]  Ingjerd Haddeland,et al.  Climate change: impacts on electricity markets in Western Europe , 2011, Climatic Change.

[21]  Bart De Schutter,et al.  Forecasting spot electricity prices Deep learning approaches and empirical comparison of traditional algorithms , 2018 .

[22]  H. Jacobsen,et al.  Reducing the market impact of large shares of intermittent energy in Denmark , 2010 .

[23]  R. Weron.  Electricity price forecasting: A review of the state-of-the-art with a look into the future , 2014 .

[24]  G. Notton,et al.  Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting , 2018 .

[25]  Narayanan Kumarappan,et al.  Day-Ahead Deregulated Electricity Market Price Forecasting Using Recurrent Neural Network , 2013, IEEE Systems Journal.

[26]  R. Weron,et al.  Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models , 2006 .

[27]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[28]  José Ramón Cancelo,et al.  Forecasting the electricity load from one day to one week ahead for the Spanish system operator , 2008 .

[29]  Monica Giulietti,et al.  Analyzing the Potential Economic Value of Energy Storage , 2018, The Energy Journal.

[30]  Florentina Paraschiv,et al.  The impact of renewable energies on EEX day-ahead electricity prices , 2014 .

[31]  Bri-Mathias Hodge,et al.  The impact of wind power on electricity prices , 2016 .

[32]  J. Contreras,et al.  Forecasting Next-Day Electricity Prices by Time Series Models , 2002, IEEE Power Engineering Review.

[33]  Ashwani Kumar,et al.  Parameter optimisation using genetic algorithm for support vector machine-based price-forecasting model in National electricity market , 2010 .

[34]  R. Green,et al.  Market behaviour with large amounts of intermittent generation , 2010 .

[35]  J. Contreras,et al.  Forecasting electricity prices for a day-ahead pool-based electric energy market , 2005 .

[36]  N. Amjady,et al.  Day-Ahead Price Forecasting of Electricity Markets by Mutual Information Technique and Cascaded Neuro-Evolutionary Algorithm , 2009, IEEE Transactions on Power Systems.

[37]  Ignacio J. Ramirez-Rosado,et al.  Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market , 2016 .

[38]  Javier M. Moguerza,et al.  Electricity Price Forecasting with Dynamic Trees: A Benchmark Against the Random Forest Approach , 2018 .

[39]  Ignacio J. Ramirez-Rosado,et al.  New probabilistic price forecasting models: Application to the Iberian electricity market , 2018, International Journal of Electrical Power & Energy Systems.

[40]  José Mira-McWilliams,et al.  Important variable assessment and electricity price forecasting based on regression tree models: classification and regression trees, Bagging and Random Forests , 2015 .

[41]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[42]  A. Gil,et al.  Forecasting of electricity prices with neural networks , 2006 .

[43]  Torben K. Mideksa,et al.  The impact of climate change on the electricity market: A review , 2010 .

[44]  Jay R. Lund,et al.  Estimated impacts of climate warming on California’s high-elevation hydropower , 2010 .