IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v87y2016ip1p731-743.html
   My bibliography  Save this article

A heuristic methodology to economic dispatch problem incorporating renewable power forecasting error and system reliability

Author

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

Abstract

With the constant increment of wind power generation driven by economic and environmental factors, the optimal utilization of generation resources has become a critical problem discussed by many authors. Within this topic, determination of optimal spinning reserve (SR) requirements is a key and complex issue due to the variable and unpredictable nature of renewable generation besides of generation unit reliability. Cost/benefit relationship has been suggested as a way to determine the optimal amount of power generation to be committed by taking into account renewable power forecasting error and system reliability. In this paper, a technique that combines an analytical convolution process with Monte Carlo Simulation (MCS) approach is proposed to efficiently build cost/benefit relationship. The proposed method uses discrete probability theory and identifies those cases at which convolution analysis can be used by recognizing those situations at which SR does not have any effect; while in the other cases MCS is applied. This approach allows improving significantly the computational efficiency. The proposed technique is illustrated by means of two case studies of 10 and 140 units, demonstrating the capabilities and flexibility of the proposed methodology.

Suggested Citation

  • Lujano-Rojas, J.M. & Osório, G.J. & Matias, J.C.O. & Catalão, J.P.S., 2016. "A heuristic methodology to economic dispatch problem incorporating renewable power forecasting error and system reliability," Renewable Energy, Elsevier, vol. 87(P1), pages 731-743.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:731-743
    DOI: 10.1016/j.renene.2015.11.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148115304316
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2015.11.011?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. 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.
    2. 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.
    3. Fokaides, Paris A. & Kylili, Angeliki, 2014. "Towards grid parity in insular energy systems: The case of photovoltaics (PV) in Cyprus," Energy Policy, Elsevier, vol. 65(C), pages 223-228.
    4. Mc Garrigle, E.V. & Leahy, P.G., 2015. "Quantifying the value of improved wind energy forecasts in a pool-based electricity market," Renewable Energy, Elsevier, vol. 80(C), pages 517-524.
    5. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    6. Barbiero, Alessandro, 2012. "A general discretization procedure for reliability computation in complex stress–strength models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(9), pages 1667-1676.
    7. Papaefthymiou, Stefanos V. & Papathanassiou, Stavros A., 2014. "Optimum sizing of wind-pumped-storage hybrid power stations in island systems," Renewable Energy, Elsevier, vol. 64(C), pages 187-196.
    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. Anfeng Zhu & Qiancheng Zhao & Xian Wang & Ling Zhou, 2022. "Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, Whale Optimisation Algorithm, and Elman Network," Energies, MDPI, vol. 15(9), pages 1-17, April.
    2. Wang, Guang Chao & Ratnam, Elizabeth & Haghi, Hamed Valizadeh & Kleissl, Jan, 2019. "Corrective receding horizon EV charge scheduling using short-term solar forecasting," Renewable Energy, Elsevier, vol. 130(C), pages 1146-1158.
    3. Li Han & Rongchang Zhang & Xuesong Wang & Yu Dong, 2018. "Multi-Time Scale Rolling Economic Dispatch for Wind/Storage Power System Based on Forecast Error Feature Extraction," Energies, MDPI, vol. 11(8), pages 1-27, August.
    4. Pei Zhang & Chunping Li & Chunhua Peng & Jiangang Tian, 2020. "Ultra-Short-Term Prediction of Wind Power Based on Error Following Forget Gate-Based Long Short-Term Memory," Energies, MDPI, vol. 13(20), pages 1-13, October.

    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. Wang, Bo & Wang, Shuming & Zhou, Xianzhong & Watada, Junzo, 2016. "Multi-objective unit commitment with wind penetration and emission concerns under stochastic and fuzzy uncertainties," Energy, Elsevier, vol. 111(C), pages 18-31.
    2. Simoglou, Christos K. & Bakirtzis, Emmanouil A. & Biskas, Pandelis N. & Bakirtzis, Anastasios G., 2016. "Optimal operation of insular electricity grids under high RES penetration," Renewable Energy, Elsevier, vol. 86(C), pages 1308-1316.
    3. Ying-Yi Hong & Gerard Francesco DG. Apolinario, 2021. "Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications," Energies, MDPI, vol. 14(20), pages 1-47, October.
    4. Osório, G.J. & Rodrigues, E.M.G. & Lujano-Rojas, J.M. & Matias, J.C.O. & Catalão, J.P.S., 2015. "New control strategy for the weekly scheduling of insular power systems with a battery energy storage system," Applied Energy, Elsevier, vol. 154(C), pages 459-470.
    5. David Schönheit & Dominik Möst, 2019. "The Effect of Offshore Wind Capacity Expansion on Uncertainties in Germany’s Day-Ahead Wind Energy Forecasts," Energies, MDPI, vol. 12(13), pages 1-23, July.
    6. 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.
    7. Tim Felling & Björn Felten & Paul Osinski & Christoph Weber, 2023. "Assessing Improved Price Zones in Europe: Flow-Based Market Coupling in Central Western Europe in Focus," The Energy Journal, , vol. 44(6), pages 71-112, November.
    8. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    9. Amirinia, Gholamreza & Mafi, Somayeh & Mazaheri, Said, 2017. "Offshore wind resource assessment of Persian Gulf using uncertainty analysis and GIS," Renewable Energy, Elsevier, vol. 113(C), pages 915-929.
    10. 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.
    11. Aruna Kanagaraj & Kumudini Devi Raguru Pandu, 2020. "Investigations of Various Market Models in a Deregulated Power Environment Using ACOPF," Energies, MDPI, vol. 13(9), pages 1-17, May.
    12. Briguglio, Marie & Formosa, Glenn, 2017. "When households go solar: Determinants of uptake of a Photovoltaic Scheme and policy insights," Energy Policy, Elsevier, vol. 108(C), pages 154-162.
    13. Han, Qinkai & Wang, Tianyang & Chu, Fulei, 2022. "Nonparametric copula modeling of wind speed-wind shear for the assessment of height-dependent wind energy in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    14. Rahmani, Shima & Amjady, Nima, 2017. "A new optimal power flow approach for wind energy integrated power systems," Energy, Elsevier, vol. 134(C), pages 349-359.
    15. Rodriguez-Hernandez, O. & Jaramillo, O.A. & Andaverde, J.A. & del Río, J.A., 2013. "Analysis about sampling, uncertainties and selection of a reliable probabilistic model of wind speed data used on resource assessment," Renewable Energy, Elsevier, vol. 50(C), pages 244-252.
    16. Mostafaeipour, Ali, 2010. "Productivity and development issues of global wind turbine industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1048-1058, April.
    17. Pejman Bahramian, 2021. "Integration of wind power into an electricity system using pumped-storage: Economic challenges and stakeholder impacts," Working Paper 1480, Economics Department, Queen's University.
    18. Psarros, Georgios N. & Papathanassiou, Stavros A., 2023. "Generation scheduling in island systems with variable renewable energy sources: A literature review," Renewable Energy, Elsevier, vol. 205(C), pages 1105-1124.
    19. Davis, Dominic & Brear, Michael J., 2024. "Impact of short-term wind forecast accuracy on the performance of decarbonising wholesale electricity markets," Energy Economics, Elsevier, vol. 130(C).
    20. 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.

    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:renene:v:87:y:2016:i:p1:p:731-743. 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/renewable-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.