IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v82y2015icp949-959.html
   My bibliography  Save this article

A probabilistic approach to solve the economic dispatch problem with intermittent renewable energy sources

Author

Listed:
  • Osório, G.J.
  • Lujano-Rojas, J.M.
  • Matias, J.C.O.
  • Catalão, J.P.S.

Abstract

In this paper, a methodology for solving the ED (economic dispatch) problem considering the uncertainty of wind power generation and generators reliability is presented. The corresponding PDF (probability distribution function) of available wind power generation is discretized and introduced in the optimization problem in order to probabilistically describe the power generation of each thermal unit, wind power curtailment, ENS (energy not supplied), excess of power generation, and total generation cost. The reliability of each unit is incorporated by estimating the joint PDF of power generation and failure events, while the PDF of ENS is incorporated by convoluting the PDF of ENS due to the forecasting error and any failure event. The performance of the proposed approach is analyzed by studying two power systems of 5 and 10 units. The proposed method is compared to MCS (Monte Carlo Simulation) approach, being able to reproduce the PDF in a reasonable manner, specifically when system reliability is not taken into account.

Suggested Citation

  • Osório, G.J. & Lujano-Rojas, J.M. & Matias, J.C.O. & Catalão, J.P.S., 2015. "A probabilistic approach to solve the economic dispatch problem with intermittent renewable energy sources," Energy, Elsevier, vol. 82(C), pages 949-959.
    • Handle: RePEc:eee:energy:v:82:y:2015:i:c:p:949-959
    DOI: 10.1016/j.energy.2015.01.104
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544215001280
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2015.01.104?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Timilsina, Govinda R. & Cornelis van Kooten, G. & Narbel, Patrick A., 2013. "Global wind power development: Economics and policies," Energy Policy, Elsevier, vol. 61(C), pages 642-652.
    2. Ravindra, Kumudhini & Iyer, Parameshwar P., 2014. "Decentralized demand–supply matching using community microgrids and consumer demand response: A scenario analysis," Energy, Elsevier, vol. 76(C), pages 32-41.
    3. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)," Energy, Elsevier, vol. 55(C), pages 1044-1054.
    4. Pye, Steve & Usher, Will & Strachan, Neil, 2014. "The uncertain but critical role of demand reduction in meeting long-term energy decarbonisation targets," Energy Policy, Elsevier, vol. 73(C), pages 575-586.
    5. Tuohy, Aidan & Meibom, Peter & Denny, Eleanor & O'Malley, Mark, 2009. "Unit commitment for systems with significant wind penetration," MPRA Paper 34849, University Library of Munich, Germany.
    6. Antonio Punzo & Alessandro Zini, 2012. "Discrete approximations of continuous and mixed measures on a compact interval," Statistical Papers, Springer, vol. 53(3), pages 563-575, August.
    7. Grünewald, Philipp & Cockerill, Tim & Contestabile, Marcello & Pearson, Peter, 2011. "The role of large scale storage in a GB low carbon energy future: Issues and policy challenges," Energy Policy, Elsevier, vol. 39(9), pages 4807-4815, September.
    8. Bahmani-Firouzi, Bahman & Farjah, Ebrahim & Azizipanah-Abarghooee, Rasoul, 2013. "An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties," Energy, Elsevier, vol. 50(C), pages 232-244.
    9. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Error analysis of short term wind power prediction models," Applied Energy, Elsevier, vol. 88(4), pages 1298-1311, April.
    10. Santos-Alamillos, F.J. & Pozo-Vázquez, D. & Ruiz-Arias, J.A. & Lara-Fanego, V. & Tovar-Pescador, J., 2014. "A methodology for evaluating the spatial variability of wind energy resources: Application to assess the potential contribution of wind energy to baseload power," Renewable Energy, Elsevier, vol. 69(C), pages 147-156.
    Full references (including those not matched with items on IDEAS)

    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. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    2. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2017. "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 341-363.
    3. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    4. Jan Abrell & Friedrich Kunz, 2015. "Integrating Intermittent Renewable Wind Generation - A Stochastic Multi-Market Electricity Model for the European Electricity Market," Networks and Spatial Economics, Springer, vol. 15(1), pages 117-147, March.
    5. Barbaro, Marco & Castro, Rui, 2020. "Design optimisation for a hybrid renewable microgrid: Application to the case of Faial island, Azores archipelago," Renewable Energy, Elsevier, vol. 151(C), pages 434-445.
    6. Jordehi, A. Rezaee, 2019. "Optimisation of demand response in electric power systems, a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 308-319.
    7. Després, Jacques & Hadjsaid, Nouredine & Criqui, Patrick & Noirot, Isabelle, 2015. "Modelling the impacts of variable renewable sources on the power sector: Reconsidering the typology of energy modelling tools," Energy, Elsevier, vol. 80(C), pages 486-495.
    8. Hao Chen & Qiulan Wan & Yurong Wang, 2014. "Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models," Energies, MDPI, vol. 7(7), pages 1-14, July.
    9. Ettore Bompard & Daniele Grosso & Tao Huang & Francesco Profumo & Xianzhang Lei & Duo Li, 2018. "World Decarbonization through Global Electricity Interconnections," Energies, MDPI, vol. 11(7), pages 1-29, July.
    10. Vithayasrichareon, Peerapat & MacGill, Iain F., 2013. "Assessing the value of wind generation in future carbon constrained electricity industries," Energy Policy, Elsevier, vol. 53(C), pages 400-412.
    11. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. Pye, Steve & Sabio, Nagore & Strachan, Neil, 2015. "An integrated systematic analysis of uncertainties in UK energy transition pathways," Energy Policy, Elsevier, vol. 87(C), pages 673-684.
    13. Bingke Yan & Bo Wang & Lin Zhu & Hesen Liu & Yilu Liu & Xingpei Ji & Dichen Liu, 2015. "A Novel, Stable, and Economic Power Sharing Scheme for an Autonomous Microgrid in the Energy Internet," Energies, MDPI, vol. 8(11), pages 1-24, November.
    14. Romero-Quete, David & Garcia, Javier Rosero, 2019. "An affine arithmetic-model predictive control approach for optimal economic dispatch of combined heat and power microgrids," Applied Energy, Elsevier, vol. 242(C), pages 1436-1447.
    15. Tan, Xiujie & Sun, Qian & Wang, Meiji & Se Cheong, Tsun & Yan Shum, Wai & Huang, Jinpeng, 2022. "Assessing the effects of emissions trading systems on energy consumption and energy mix," Applied Energy, Elsevier, vol. 310(C).
    16. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    17. Katrin Trepper & Michael Bucksteeg & Christoph Weber, 2013. "An integrated approach to model redispatch and to assess potential benefits from market splitting in Germany," EWL Working Papers 1319, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Apr 2014.
    18. Gokturk Poyrazoglu & HyungSeon Oh, 2019. "Co-optimization of Transmission Maintenance Scheduling and Production Cost Minimization," Energies, MDPI, vol. 12(15), pages 1-18, July.
    19. Li, Xiao Hui & Hong, Seung Ho, 2014. "User-expected price-based demand response algorithm for a home-to-grid system," Energy, Elsevier, vol. 64(C), pages 437-449.
    20. Nyamdash, Batsaikhan & Denny, Eleanor, 2013. "The impact of electricity storage on wholesale electricity prices," Energy Policy, Elsevier, vol. 58(C), pages 6-16.

    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:eee:energy:v:82:y:2015:i:c:p:949-959. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.