IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i22p4392-d288509.html
   My bibliography  Save this article

Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review

Author

Listed:
  • Manzoor Ellahi

    (Electrical Engineering Department, The University of Lahore, Lahore 54000, Pakistan)

  • Ghulam Abbas

    (Electrical Engineering Department, The University of Lahore, Lahore 54000, Pakistan)

  • Irfan Khan

    (Marine Engineering Technology Department in a joint appointment with Electrical and Computer Engineering Department, Texas A&M University, Galveston, TX 77554, USA)

  • Paul Mario Koola

    (Ocean Engineering Department, Texas A&M University, Galveston, TX 77554, USA)

  • Mashood Nasir

    (Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark)

  • Ali Raza

    (Electrical Engineering Department, The University of Lahore, Lahore 54000, Pakistan)

  • Umar Farooq

    (Department of Electrical Engineering, University of the Punjab, Lahore 54590, Pakistan)

Abstract

Renewable energy sources (RESs) are the replacement of fast depleting, environment polluting, costly, and unsustainable fossil fuels. RESs themselves have various issues such as variable supply towards the load during different periods, and mostly they are available at distant locations from load centers. This paper inspects forecasting techniques, employed to predict the RESs availability during different periods and considers the dispatch mechanisms for the supply, extracted from these resources. Firstly, we analyze the application of stochastic distributions especially the Weibull distribution (WD), for forecasting both wind and PV power potential, with and without incorporating neural networks (NN). Secondly, a review of the optimal economic dispatch (OED) of RES using particle swarm optimization (PSO) is presented. The reviewed techniques will be of great significance for system operators that require to gauge and pre-plan flexibility competence for their power systems to ensure practical and economical operation under high penetration of RESs.

Suggested Citation

  • Manzoor Ellahi & Ghulam Abbas & Irfan Khan & Paul Mario Koola & Mashood Nasir & Ali Raza & Umar Farooq, 2019. "Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review," Energies, MDPI, vol. 12(22), pages 1-30, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4392-:d:288509
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/22/4392/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/22/4392/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rodríguez, Fermín & Fleetwood, Alice & Galarza, Ainhoa & Fontán, Luis, 2018. "Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control," Renewable Energy, Elsevier, vol. 126(C), pages 855-864.
    2. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    3. Camilo Carrillo & José Cidrás & Eloy Díaz-Dorado & Andrés Felipe Obando-Montaño, 2014. "An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galicia (Spain)," Energies, MDPI, vol. 7(4), pages 1-25, April.
    4. Celik, Ali Naci, 2004. "A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey," Renewable Energy, Elsevier, vol. 29(4), pages 593-604.
    5. Jordehi, A. Rezaee, 2015. "Particle swarm optimisation (PSO) for allocation of FACTS devices in electric transmission systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1260-1267.
    6. Zhu, Xianwen & Xia, Mingchao & Chiang, Hsiao-Dong, 2018. "Coordinated sectional droop charging control for EV aggregator enhancing frequency stability of microgrid with high penetration of renewable energy sources," Applied Energy, Elsevier, vol. 210(C), pages 936-943.
    7. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    8. Athraa Ali Kadhem & Noor Izzri Abdul Wahab & Ishak Aris & Jasronita Jasni & Ahmed N. Abdalla, 2017. "Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network," Energies, MDPI, vol. 10(11), pages 1-17, October.
    9. Arias-Rosales, Andrés & Mejía-Gutiérrez, Ricardo, 2018. "Optimization of V-Trough photovoltaic concentrators through genetic algorithms with heuristics based on Weibull distributions," Applied Energy, Elsevier, vol. 212(C), pages 122-140.
    10. Hemmati, Reza & Saboori, Hedayat & Jirdehi, Mehdi Ahmadi, 2017. "Stochastic planning and scheduling of energy storage systems for congestion management in electric power systems including renewable energy resources," Energy, Elsevier, vol. 133(C), pages 380-387.
    11. Ghaem Sigarchian, Sara & Paleta, Rita & Malmquist, Anders & Pina, André, 2015. "Feasibility study of using a biogas engine as backup in a decentralized hybrid (PV/wind/battery) power generation system – Case study Kenya," Energy, Elsevier, vol. 90(P2), pages 1830-1841.
    12. Alizadeh, M.I. & Parsa Moghaddam, M. & Amjady, N. & Siano, P. & Sheikh-El-Eslami, M.K., 2016. "Flexibility in future power systems with high renewable penetration: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1186-1193.
    13. Kheshti, Mostafa & Ding, Lei & Ma, Shicong & Zhao, Bing, 2018. "Double weighted particle swarm optimization to non-convex wind penetrated emission/economic dispatch and multiple fuel option systems," Renewable Energy, Elsevier, vol. 125(C), pages 1021-1037.
    14. Jiang, R. & Murthy, D.N.P., 2011. "A study of Weibull shape parameter: Properties and significance," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1619-1626.
    15. Pishgar-Komleh, S.H. & Keyhani, A. & Sefeedpari, P., 2015. "Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 313-322.
    16. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting," Energies, MDPI, vol. 11(1), pages 1-13, January.
    17. Sinha, Sunanda & Chandel, S.S., 2015. "Review of recent trends in optimization techniques for solar photovoltaic–wind based hybrid energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 755-769.
    18. Luis Galván & Juan M. Navarro & Eduardo Galván & Juan M. Carrasco & Andrés Alcántara, 2019. "Optimal Scheduling of Energy Storage Using A New Priority-Based Smart Grid Control Method," Energies, MDPI, vol. 12(4), pages 1-17, February.
    19. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    20. Akbar Maleki & Marc A. Rosen & Fathollah Pourfayaz, 2017. "Optimal Operation of a Grid-Connected Hybrid Renewable Energy System for Residential Applications," Sustainability, MDPI, vol. 9(8), pages 1-20, July.
    21. Tonglin Fu & Chen Wang, 2018. "A Hybrid Wind Speed Forecasting Method and Wind Energy Resource Analysis Based on a Swarm Intelligence Optimization Algorithm and an Artificial Intelligence Model," Sustainability, MDPI, vol. 10(11), pages 1-24, October.
    22. Mohammadi, Kasra & Shamshirband, Shahaboddin & Yee, Por Lip & Petković, Dalibor & Zamani, Mazdak & Ch, Sudheer, 2015. "Predicting the wind power density based upon extreme learning machine," Energy, Elsevier, vol. 86(C), pages 232-239.
    23. Chiou-Jye Huang & Ping-Huan Kuo, 2018. "A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems," Energies, MDPI, vol. 11(10), pages 1-20, October.
    24. Widén, Joakim & Carpman, Nicole & Castellucci, Valeria & Lingfors, David & Olauson, Jon & Remouit, Flore & Bergkvist, Mikael & Grabbe, Mårten & Waters, Rafael, 2015. "Variability assessment and forecasting of renewables: A review for solar, wind, wave and tidal resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 356-375.
    25. Jordehi, A. Rezaee, 2018. "How to deal with uncertainties in electric power systems? A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 145-155.
    26. Roberto Langella & Daniela Proto & Alfredo Testa, 2016. "Solar Radiation Forecasting, Accounting for Daily Variability," Energies, MDPI, vol. 9(3), pages 1-17, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luana Pontes & Tatiane Costa & Amanda Souza & Nicolau Dantas & Andrea Vasconcelos & Guilherme Rissi & Roberto Dias & Mohamed A. Mohamed & Pierluigi Siano & Manoel Marinho, 2023. "Operational Data Analysis of a Battery Energy Storage System to Support Wind Energy Generation," Energies, MDPI, vol. 16(3), pages 1-20, February.
    2. Ghasemi-Marzbali, Ali & Shafiei, Mohammad & Ahmadiahangar, Roya, 2023. "Day-ahead economical planning of multi-vector energy district considering demand response program," Applied Energy, Elsevier, vol. 332(C).
    3. Muhammad Zeeshan Malik & Haoyong Chen & Muhammad Shahzad Nazir & Irfan Ahmad Khan & Ahmed N. Abdalla & Amjad Ali & Wan Chen, 2020. "A New Efficient Step-Up Boost Converter with CLD Cell for Electric Vehicle and New Energy Systems," Energies, MDPI, vol. 13(7), pages 1-14, April.
    4. Raluca-Andreea Felseghi & Ioan Așchilean & Nicoleta Cobîrzan & Andrei Mircea Bolboacă & Maria Simona Raboaca, 2021. "Optimal Synergy between Photovoltaic Panels and Hydrogen Fuel Cells for Green Power Supply of a Green Building—A Case Study," Sustainability, MDPI, vol. 13(11), pages 1-20, June.
    5. João Fausto L. de Oliveira & Paulo S. G. de Mattos Neto & Hugo Valadares Siqueira & Domingos S. de O. Santos & Aranildo R. Lima & Francisco Madeiro & Douglas A. P. Dantas & Mariana de Morais Cavalcant, 2023. "Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review," Energies, MDPI, vol. 16(18), pages 1-20, September.
    6. Mengqi Qing & Fei Tang & Fusuo Liu & Dichen Liu & Nianchun Du & Benxi Hu, 2020. "An Analytical Method for Estimating the Maximum Penetration of DFIG Considering Frequency Stability," Sustainability, MDPI, vol. 12(23), pages 1-21, November.
    7. Kristia Kristia & Mohammad Fazle Rabbi, 2023. "Exploring the Synergy of Renewable Energy in the Circular Economy Framework: A Bibliometric Study," Sustainability, MDPI, vol. 15(17), pages 1-27, September.
    8. Hasan Erteza Gelani & Faizan Dastgeer & Mashood Nasir & Sidra Khan & Josep M. Guerrero, 2021. "AC vs. DC Distribution Efficiency: Are We on the Right Path?," Energies, MDPI, vol. 14(13), pages 1-26, July.
    9. Rajvikram Madurai Elavarasan & Leoponraj Selvamanohar & Kannadasan Raju & Raghavendra Rajan Vijayaraghavan & Ramkumar Subburaj & Mohammad Nurunnabi & Irfan Ahmad Khan & Syed Afridhis & Akshaya Harihar, 2020. "A Holistic Review of the Present and Future Drivers of the Renewable Energy Mix in Maharashtra, State of India," Sustainability, MDPI, vol. 12(16), pages 1-33, August.
    10. Guan, Hongyu & Yin, Xiuxing & Jiang, Wei, 2024. "Towards the integration of distributed renewables: Operation analysis of pumped storage system under off-design condition based on CFD," Applied Energy, Elsevier, vol. 355(C).
    11. Marta Poncela-Blanco & Pilar Poncela, 2021. "Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques," Energies, MDPI, vol. 14(5), pages 1-16, March.
    12. Onu, Uchenna Godswill & Silva, Giuseppe Scabello & Zambroni de Souza, Antonio Carlos & Bonatto, Benedito Donizeti & Ferreira da Costa, Vinicius Braga, 2022. "Integrated design of photovoltaic power generation plant with pumped hydro storage system and irrigation facility at the Uhuelem-Amoncha African community," Renewable Energy, Elsevier, vol. 198(C), pages 1021-1031.
    13. Yuan-Kang Wu & Cheng-Liang Huang & Quoc-Thang Phan & Yuan-Yao Li, 2022. "Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints," Energies, MDPI, vol. 15(9), pages 1-22, May.
    14. Juan Moreno-Castro & Victor Samuel Ocaña Guevara & Lesyani Teresa León Viltre & Yandi Gallego Landera & Oscar Cuaresma Zevallos & Miguel Aybar-Mejía, 2023. "Microgrid Management Strategies for Economic Dispatch of Electricity Using Model Predictive Control Techniques: A Review," Energies, MDPI, vol. 16(16), pages 1-24, August.
    15. Faisal Tariq & Salem Alelyani & Ghulam Abbas & Ayman Qahmash & Mohammad Rashid Hussain, 2020. "Solving Renewables-Integrated Economic Load Dispatch Problem by Variant of Metaheuristic Bat-Inspired Algorithm," Energies, MDPI, vol. 13(23), pages 1-36, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhao, Ning & You, Fengqi, 2022. "Sustainable power systems operations under renewable energy induced disjunctive uncertainties via machine learning-based robust optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    2. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    3. Kwami Senam A. Sedzro & Adekunlé Akim Salami & Pierre Akuété Agbessi & Mawugno Koffi Kodjo, 2022. "Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)," Energies, MDPI, vol. 15(22), pages 1-28, November.
    4. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    5. Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Mohammed Elsayed Lotfy & Theophilus Amara & Keifa Vamba Konneh & Tomonobu Senjyu, 2019. "Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning," Future Internet, MDPI, vol. 11(10), pages 1-16, October.
    6. Ren, Simiao & Hu, Wayne & Bradbury, Kyle & Harrison-Atlas, Dylan & Malaguzzi Valeri, Laura & Murray, Brian & Malof, Jordan M., 2022. "Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis," Applied Energy, Elsevier, vol. 326(C).
    7. Laura Canale & Anna Rita Di Fazio & Mario Russo & Andrea Frattolillo & Marco Dell’Isola, 2021. "An Overview on Functional Integration of Hybrid Renewable Energy Systems in Multi-Energy Buildings," Energies, MDPI, vol. 14(4), pages 1-33, February.
    8. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    9. Wang, Jianzhou & Huang, Xiaojia & Li, Qiwei & Ma, Xuejiao, 2018. "Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of China," Energy, Elsevier, vol. 164(C), pages 432-448.
    10. Li, Ke & Shen, Ruifang & Wang, Zhenguo & Yan, Bowen & Yang, Qingshan & Zhou, Xuhong, 2023. "An efficient wind speed prediction method based on a deep neural network without future information leakage," Energy, Elsevier, vol. 267(C).
    11. Musaed Alhussein & Syed Irtaza Haider & Khursheed Aurangzeb, 2019. "Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance," Energies, MDPI, vol. 12(8), pages 1-27, April.
    12. Ninoslav Holjevac & Tomislav Baškarad & Josip Đaković & Matej Krpan & Matija Zidar & Igor Kuzle, 2021. "Challenges of High Renewable Energy Sources Integration in Power Systems—The Case of Croatia," Energies, MDPI, vol. 14(4), pages 1-20, February.
    13. Dagoumas, Athanasios S. & Koltsaklis, Nikolaos E., 2019. "Review of models for integrating renewable energy in the generation expansion planning," Applied Energy, Elsevier, vol. 242(C), pages 1573-1587.
    14. Dongbum Kang & Kyungnam Ko & Jongchul Huh, 2018. "Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea," Energies, MDPI, vol. 11(2), pages 1-19, February.
    15. Mekalathur B Hemanth Kumar & Saravanan Balasubramaniyan & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen, 2019. "Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India," Energies, MDPI, vol. 12(11), pages 1-21, June.
    16. Oh, Eunsung & Son, Sung-Yong, 2018. "Energy-storage system sizing and operation strategies based on discrete Fourier transform for reliable wind-power generation," Renewable Energy, Elsevier, vol. 116(PA), pages 786-794.
    17. Luis Lopez & Ingrid Oliveros & Luis Torres & Lacides Ripoll & Jose Soto & Giovanny Salazar & Santiago Cantillo, 2020. "Prediction of Wind Speed Using Hybrid Techniques," Energies, MDPI, vol. 13(23), pages 1-13, November.
    18. Bouendeu, Jean Jacques & Talla Konchou, Franck Armel & Astrid, Medjo Nouadje Brigitte & Elmorshedy, Mahmoud F. & René, Tchinda, 2023. "A systematic techno-enviro-socio-economic design optimization and power quality of hybrid renewable microgrids," Renewable Energy, Elsevier, vol. 218(C).
    19. Mehr Gul & Nengling Tai & Wentao Huang & Muhammad Haroon Nadeem & Moduo Yu, 2019. "Assessment of Wind Power Potential and Economic Analysis at Hyderabad in Pakistan: Powering to Local Communities Using Wind Power," Sustainability, MDPI, vol. 11(5), pages 1-23, March.
    20. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4392-:d:288509. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.