Hybrid Forecasting Model for Short-Term Electricity Market Prices with Renewable Integration

In recent years, there have been notable commitments and obligations by the electricity sector for more sustainable generation and delivery processes to reduce the environmental footprint. However, there is still a long way to go to achieve necessary sustainability goals while ensuring standards of robustness and the quality of power grids. One of the main challenges hindering this progress are uncertainties and stochasticity associated with the electricity sector and especially renewable generation. In this paradigm shift, forecasting tools are indispensable, and their utilization can significantly improve system operation and minimize costs associated with all related activities. Thus, forecasting tools have an essential key role in all decision-making stages. In this work, a hybrid probabilistic forecasting model (HPFM) was developed for short-term electricity market prices (EMP) combining wavelet transforms (WT), hybrid particle swarm optimization (DEEPSO), adaptive neuro-fuzzy inference system (ANFIS), and Monte Carlo simulation (MCS). The proposed hybrid probabilistic forecasting model (HPFM) was tested and validated with real data from the Spanish and Pennsylvania-New Jersey-Maryland (PJM) markets. The proposed model exhibited favorable results and performance in comparison with previously published work considering electricity market prices (EMP) data, which is notable.

[1]  Joao P. S. Catalao,et al.  Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach , 2011 .

[2]  Mohammad-Hassan Khooban,et al.  Secondary Load Frequency Control of Time-Delay Stand-Alone Microgrids With Electric Vehicles , 2018, IEEE Transactions on Industrial Electronics.

[3]  Joao P. S. Catalao,et al.  Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term , 2016 .

[4]  Vladimiro Miranda,et al.  Training a FIS with EPSO under an Entropy Criterion for Wind Power prediction , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.

[5]  Nima Amjady,et al.  Day‐ahead price forecasting of electricity markets by a hybrid intelligent system , 2009 .

[6]  Nikolaos G. Bourbakis,et al.  Genetic Optimal Regression of Relevance Vector Machines for Electricity Pricing Signal Forecasting in Smart Grids , 2015, IEEE Transactions on Smart Grid.

[7]  Mohammad Hassan Khooban,et al.  A Novel Predictive Fuzzy Logic-Based Energy Management System for Grid-Connected and Off-Grid Operation of Residential Smart Microgrids , 2020, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[8]  Ponnuthurai Nagaratnam Suganthan,et al.  Ensemble methods for wind and solar power forecasting—A state-of-the-art review , 2015 .

[9]  Reza Kheirollahi,et al.  Point and interval forecasting of real-time and day-ahead electricity prices by a novel hybrid approach , 2017 .

[10]  Ricardo Bessa,et al.  Probabilistic forecasting of day-ahead electricity prices for the Iberian electricity market , 2016, 2016 13th International Conference on the European Energy Market (EEM).

[11]  . Anamika,et al.  Electricity Price Forecasting and Classification Through Wavelet–Dynamic Weighted PSO–FFNN Approach , 2018, IEEE Systems Journal.

[12]  R. Weron,et al.  Recent advances in electricity price forecasting: A review of probabilistic forecasting , 2016 .

[13]  J. Contreras,et al.  ARIMA models to predict next-day electricity prices , 2002 .

[14]  Mun-Kyeom Kim,et al.  Short-term price forecasting of Nordic power market by combination Levenberg–Marquardt and Cuckoo search algorithms , 2015 .

[15]  Taher Niknam,et al.  Probabilistic Forecasting of Hourly Electricity Price by Generalization of ELM for Usage in Improved Wavelet Neural Network , 2017, IEEE Transactions on Industrial Informatics.

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

[17]  O. Weck,et al.  A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM , 2005 .

[18]  Arash Miranian,et al.  Day-ahead electricity price analysis and forecasting by singular spectrum analysis , 2013 .

[19]  Joao P. S. Catalao,et al.  Electricity prices forecasting by a hybrid evolutionary-adaptive methodology , 2014 .

[20]  Duehee Lee,et al.  Bivariate Probabilistic Wind Power and Real-Time Price Forecasting and Their Applications to Wind Power Bidding Strategy Development , 2018, IEEE Transactions on Power Systems.

[21]  Vladimiro Miranda,et al.  Differential Evolutionary Particle Swarm Optimization (DEEPSO): A Successful Hybrid , 2013, 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence.

[22]  Amir F. Atiya,et al.  A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[23]  José L. Bernal-Agustín,et al.  New Methodology for the Optimization of the Management of Wind Farms, Including Energy Storage , 2013 .

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

[25]  Tomonobu Senjyu,et al.  A Novel Hybrid Approach Based on Wavelet Transform and Fuzzy ARTMAP Networks for Predicting Wind Farm Power Production , 2013 .

[26]  Thomas Lidebrandt,et al.  Variance Reduction Three Approaches to Control Variates , 2007 .

[27]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[28]  Taher Niknam,et al.  A novel intelligent strategy for probabilistic electricity price forecasting: Wavelet neural network based modified dolphin optimization algorithm , 2016, J. Intell. Fuzzy Syst..

[29]  Ke Meng,et al.  Day-ahead electricity price forecasting based on panel cointegration and particle filter , 2013 .

[30]  İnci Okumuş,et al.  Current status of wind energy forecasting and a hybrid method for hourly predictions , 2016 .

[31]  Taher Niknam,et al.  Probabilistic electricity price forecasting by improved clonal selection algorithm and wavelet preprocessing , 2016, Neural Computing and Applications.

[32]  Dilan Jayaweera,et al.  Modelling and Simulation of Power Systems , 2016 .

[33]  V M F Mendes,et al.  Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting , 2011, IEEE Transactions on Power Systems.

[34]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[35]  Fevrier Valdez,et al.  Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot , 2012, Inf. Sci..

[36]  Omar S. Soliman,et al.  A Bio-Inspired Memetic Particle Swarm Optimization Algorithm for  Multi-objective Optimization Problems , 2012, 2012 Third International Conference on Innovations in Bio-Inspired Computing and Applications.