Deep learning technique for forecasting solar radiation and wind speed for dynamic microgrid analysis

• Autorzy: Islam Md Mainul , Shareef Hussain , Al Hassan Eslam Salah Fayez

Identyfikatory

DOI

10.15199/48.2023.04.27

Warianty tytułu

PL

Technika głębokiego uczenia się do prognozowania promieniowania słonecznego i prędkości wiatru na potrzeby dynamicznej analizy mikrosieci

• Języki publikacji: EN

Abstrakty

EN

The key variables in the development and operation of wind and solar power systems are wind speed and solar radiation. The prediction of solar and wind energy parameters is important to alleviate the effects of power generation fluctuations. Consequently, it is essential to predict renewable energy sources like solar radiation and wind speed precisely. An artificial intelligence-based random forest method is recommended in this paper to estimate wind speed and solar radiation. The number of decision trees in the random forest model is suggested to be optimised using a novel coot algorithm (CA), and the effectiveness of the CA is evaluated to that of the currently used particle swarm optimisation (PSO) method. The best forecasting data are used in this work to develop a dynamic Microgrid (MG) in MATLAB/SIMULINK. A novel binary CA is proposed to control the MG to minimize the cost. The effect of the energy storage system is also investigated during the simulation of the MG.

PL

Kluczowymi zmiennymi w rozwoju i działaniu systemów energii wiatrowej i słonecznej są prędkość wiatru i promieniowanie słoneczne. Prognozowanie parametrów energii słonecznej i wiatrowej jest ważne dla złagodzenia skutków wahań produkcji energii. W związku z tym niezbędne jest precyzyjne przewidywanie źródeł energii odnawialnej, takich jak promieniowanie słoneczne i prędkość wiatru. W tym artykule zaleca się metodę lasów losowych opartą na sztucznej inteligencji w celu oszacowania prędkości wiatru i promieniowania słonecznego. Sugeruje się optymalizację liczby drzew decyzyjnych w modelu losowego lasu przy użyciu nowego algorytmu łyski (CA), a skuteczność CA jest oceniana na podstawie obecnie stosowanej metody optymalizacji roju cząstek (PSO). W tej pracy wykorzystano najlepsze dane prognostyczne do opracowania dynamicznej mikrosieci (MG) w MATLAB/SIMULINK. Proponuje się nowy binarny CA do sterowania MG w celu zminimalizowania kosztów. Wpływ systemu magazynowania energii jest również badany podczas symulacji MG.

Słowa kluczowe

EN

solar power; wind power; random forest method; coot algorithm; microgrid; forecasting

PL

energia słoneczna; energia wiatrowa; prognozowanie; głębokie uczenie

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2023
• Tom: R. 99, nr 4
• Strony: 162--170
• Opis fizyczny: Bibliogr. 40 poz., rys., tab.

Twórcy

autor

Islam Md Mainul

• School of Engineering, Design and Built environment, Western Sydney University, Locked Bag 1797, NSW, 2751, Australia

• https://orcid.org/0000-0002-8079-2065

autor

Shareef Hussain

• Department of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, 15551, Al Ain, UAE

• https://orcid.org/0000-0001-7708-6904

autor

Al Hassan Eslam Salah Fayez

• Department of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, 15551, Al Ain, UAE

• https://orcid.org/0000-0002-4909-5106

Bibliografia

• [1] B. Kumar Sahu, "A study on global solar PV energy developments and policies with special focus on the top ten solar PV power producing countries," Renewable and Sustainable Energy Reviews, vol. 43, pp. 621-634, 2015/03/01/ 2015,doi: https://doi.org/10.1016/j.rser.2014.11.058.
• [2] M. M. Islam, M. Nagrial, J. Rizk, and A. Hellany, "General Aspects, Islanding Detection, and Energy Management in Microgrids: A Review," Sustainability, vol. 13, no. 16, 2021, doi: 10.3390/su13169301.
• [3] W. Zhao et al., "A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants," Energy, vol. 223, p. 120026, 2021/05/15/ 2021, doi: https://doi.org/10.1016/j.energy.2021.120026.
• [4] M. W. Zafar, M. Shahbaz, A. Sinha, T. Sengupta, and Q. Qin, "How renewable energy consumption contribute to environmental quality? The role of education in OECD countries," Journal of Cleaner Production, vol. 268, p. 122149, 2020/09/20/ 2020,doi: https://doi.org/10.1016/j.jclepro.2020.122149.
• [5] D. Zhu, S. M. Mortazavi, A. Maleki, A. Aslani, and H. Yousefi, "Analysis of the robustness of energy supply in Japan: Role of renewable energy," Energy Reports, vol. 6, pp. 378-391, 2020/11/01/ 2020, doi: https://doi.org/10.1016/j.egyr.2020.01.011.
• [6] V. Gaigalis and V. Katinas, "Analysis of the renewable energy implementation and prediction prospects in compliance with the EU policy: A case of Lithuania," Renewable Energy, vol. 151, pp. 1016-1027, 2020/05/01/ 2020, doi: https://doi.org/10.1016/j.renene.2019.11.091.
• [7] T. Ahmad, D. Zhang, and C. Huang, "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, vol. 231, p. 120911, 2021/09/15/ 2021, doi: https://doi.org/10.1016/j.energy.2021.120911.
• [8] S. Goodarzi, H. N. Perera, and D. Bunn, "The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices," Energy Policy, vol. 134, p. 110827, 2019/11/01/ 2019, doi: https://doi.org/10.1016/j.enpol.2019.06.035.
• [9] D. P. Larson, L. Nonnenmacher, and C. F. M. Coimbra, "Dayahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, vol. 91, pp. 11-20, 2016/06/01/ 2016, doi: https://doi.org/10.1016/j.renene.2016.01.039.
• [10] A. Mellit and A. M. Pavan, "A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy," Solar Energy, vol. 84, no. 5, pp. 807-821, 2010/05/01/ 2010, doi: https://doi.org/10.1016/j.solener.2010.02.006.
• [11] Y. Jiang, "Computation of monthly mean daily global solar radiation in China using artificial neural networks and comparison with other empirical models," Energy, vol. 34, no. 9, pp. 1276-1283, 2009/09/01/ 2009, doi: https://doi.org/10.1016/j.energy.2009.05.009.
• [12] H. Khorasanizadeh and K. Mohammadi, "Introducing the best model for predicting the monthly mean global solar radiation over six major cities of Iran," Energy, vol. 51, pp. 257-266, 2013/03/01/ 2013, doi: https://doi.org/10.1016/j.energy.2012.11.007.
• [13] K. Nam, S. Hwangbo, and C. Yoo, "A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea," Renewable and Sustainable Energy Reviews, vol. 122, p. 109725, 2020/04/01/ 2020, doi: https://doi.org/10.1016/j.rser.2020.109725.
• [14] D. Yang, Z. Ye, L. H. I. Lim, and Z. Dong, "Very short term irradiance forecasting using the lasso," Solar Energy, vol. 114, pp. 314-326, 2015/04/01/ 2015, doi: https://doi.org/10.1016/j.solener.2015.01.016.
• [15] X. Huang et al., "Hybrid deep neural model for hourly solar irradiance forecasting," Renewable Energy, vol. 171, pp. 1041-1060, 2021/06/01/ 2021, doi: https://doi.org/10.1016/j.renene.2021.02.161.
• [16] J. Wang and Z. Yang, "Ultra-short-term wind speed forecasting using an optimized artificial intelligence algorithm," Renewable Energy, vol. 171, pp. 1418-1435, 2021/06/01/ 2021, doi: https://doi.org/10.1016/j.renene.2021.03.020.
• [17] Y. Nie, N. Liang, and J. Wang, "Ultra-short-term wind-speed biforecasting system via artificial intelligence and a double-forecasting scheme," Applied Energy, vol. 301, p. 117452, 2021/11/01/ 2021, doi: https://doi.org/10.1016/j.apenergy.2021.117452.
• [18] Z. Liu, R. Hara, and H. Kita, "Hybrid forecasting system based on data area division and deep learning neural network for short-term wind speed forecasting," Energy Conversion and Management, vol. 238, p. 114136, 2021/06/15/ 2021, doi: https://doi.org/10.1016/j.enconman.2021.114136.
• [19] M. Yang, S. Zhu, M. Liu, and W. Lee, "One Parametric Approach for Short-Term JPDF Forecast of Wind Generation," IEEE Transactions on Industry Applications, vol. 50, no. 4, pp. 2837-2843, 2014, doi: 10.1109/TIA.2014.2300188.
• [20] K. Mason, J. Duggan, and E. Howley, "Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks," Energy, vol. 155, pp. 705-720, 2018/07/15/ 2018, doi: https://doi.org/10.1016/j.energy.2018.04.192.
• [21] S. A. Kalogirou, "Artificial Neural Networks and Genetic Algorithms for the Modeling, Simulation, and Performance Prediction of Solar Energy Systems," in Assessment and Simulation Tools for Sustainable Energy Systems: Theory and Applications, F. Cavallaro Ed. London: Springer London, 2013, pp. 225-245.
• [22] I. Song, W. Jung, J. Kim, S. Yun, J. Choi, and S. Ahn, "Operation Schemes of Smart Distribution Networks With Distributed Energy Resources for Loss Reduction and Service Restoration," IEEE Transactions on Smart Grid, vol. 4, no. 1, pp. 367-374, 2013, doi: 10.1109/TSG.2012.2233770.
• [23] M. Falahi, S. Lotfifard, M. Ehsani, and K. Butler-Purry, "Dynamic Model Predictive-Based Energy Management of DG Integrated Distribution Systems," IEEE Transactions on Power Delivery, vol. 28, no. 4, pp. 2217-2227, 2013, doi: 10.1109/TPWRD.2013.2274664.
• [24] A. Ignat, E. Szilagyi, and D. Petreuş, "Renewable Energy Microgrid Model using MATLAB — Simulink," in 2020 43rd International Spring Seminar on Electronics Technology (ISSE), 14-15 May 2020 2020, pp. 1-6, doi: 10.1109/ISSE49702.2020.9120923.
• [25] M. G. M. Abdolrasol, M. A. Hannan, A. Mohamed, U. A. U. Amiruldin, I. B. Z. Abidin, and M. N. Uddin, "An Optimal Scheduling Controller for Virtual Power Plant and Microgrid Integration Using the Binary Backtracking Search Algorithm," IEEE Transactions on Industry Applications, vol. 54, no. 3, pp. 2834-2844, 2018, doi: 10.1109/TIA.2018.2797121.
• [26] R. Nazir, H. D. Laksono, E. P. Waldi, E. Ekaputra, and P. Coveria, "Renewable Energy Sources Optimization: A Micro Grid Model Design," Energy Procedia, vol. 52, pp. 316-327, 2014/01/01/ 2014, doi: https://doi.org/10.1016/j.egypro.2014.07.083.
• [27] S. Chalise, J. Sternhagen, T. M. Hansen, and R. Tonkoski, "Energy management of remote microgrids considering battery lifetime," The Electricity Journal, vol. 29, no. 6, pp. 1-10, 2016/07/01/ 2016, doi: https://doi.org/10.1016/j.tej.2016.07.003.
• [28] M. Marzband, E. Yousefnejad, A. Sumper, and J. L. Domínguez-García, "Real time experimental implementation of optimum energy management system in standalone Microgrid by using multi-layer ant colony optimization," International Journal of Electrical Power & Energy Systems, vol. 75, pp. 265-274, 2016/02/01/ 2016, doi: https://doi.org/10.1016/j.ijepes.2015.09.010.
• [29] J. Radosavljević, M. Jevtić, and D. Klimenta, "Energy and operation management of a microgrid using particle swarm optimization," Engineering Optimization, vol. 48, no. 5, pp. 811-830, 2016/05/03 2016, doi: 10.1080/0305215X.2015.1057135.
• [30] M. M. Islam, M. Nagrial, J. Rizk, and A. Hellany, "Dual stage microgrid energy resource optimization strategy considering renewable and battery storage systems," International Journal of Energy Research, https://doi.org/10.1002/er.7185 vol. n/a, no. n/a, 2021/08/22 2021, doi: https://doi.org/10.1002/er.7185.
• [31] H. Shareef, A. H. Mutlag, and A. Mohamed, "Random Forest-Based Approach for Maximum Power Point Tracking of Photovoltaic Systems Operating under Actual Environmental Conditions," Computational Intelligence and Neuroscience, vol. 2017, p. 1673864, 2017/06/15 2017, doi: 10.1155/2017/1673864.
• [32] I. Naruei and F. Keynia, "A new optimization method based on COOT bird natural life model," Expert Systems with Applications, vol. 183, p. 115352, 2021/11/30/ 2021, doi: https://doi.org/10.1016/j.eswa.2021.115352.
• [33] J. Antonanzas, N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de-Pison, and F. Antonanzas-Torres, "Review of photovoltaic power forecasting," Solar Energy, vol. 136, pp. 78-111, 2016/10/15/ 2016, doi: https://doi.org/10.1016/j.solener.2016.06.069.
• [34] "Australian Bureau of Meteorology.." [Online]. Available: http://www.bom.gov.au/jsp/awap/solar/index.jsp?colour=colour&time=latest&step=0&map=solarave&period=month&area=nat.
• [35] X. Chen, Z. Y. Dong, K. Meng, Y. Xu, K. P. Wong, and H. W.Ngan, "Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping," IEEE Transactions on Power Systems, vol. 27, no. 4, pp. 2055-2062, 2012, doi: 10.1109/TPWRS.2012.2190627.
• [36] M. Q. Raza, M. Nadarajah, and C. Ekanayake, "On recent advances in PV output power forecast," Solar Energy, vol. 136, pp. 125-144, 2016/10/15/ 2016, doi: https://doi.org/10.1016/j.solener.2016.06.073.
• [37] L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5-32, 2001/10/01 2001, doi: 10.1023/A:1010933404324.
• [38] M. M. Islam, Performance comparison of various probability gate assisted binary lightning search algorithm. Institute of Advanced Engineering and Science (in English), 2019.
• [39] Y. Jeong, J. Park, S. Jang, and K. Y. Lee, "A New Quantum Inspired Binary PSO: Application to Unit Commitment Problems for Power Systems," IEEE Transactions on Power Systems, vol. 25, no. 3, pp. 1486-1495, 2010, doi: 10.1109/TPWRS.2010.2042472.
• [40] "Renewable Energy Investment in Australia." [Online]. Available: https://www.rba.gov.au/publications/bulletin/2020/mar/pdf/renewable-energy-investment-in-australia.pdf.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).