IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v81y2018ip2p1953-1965.html
   My bibliography  Save this article

Stochastic modelling of renewable energy sources from operators' point-of-view: A survey

Author

Listed:
  • Talari, Saber
  • Shafie-khah, Miadreza
  • Osório, Gerardo J.
  • Aghaei, Jamshid
  • Catalão, João P.S.

Abstract

High penetration of renewable energy sources, especially weather-dependent sources, has increased the power systems uncertainties. For any analysis in power systems such as planning and operation, it is essential to confront the stochastic nature of these sources in order to get much more precise results. Since operators need proper strategies and methods to decline negative effects of the stochastic behaviour of renewable power generators, such as total operation cost growth, this paper provides a review of different state-of-the-art approaches from the operator's viewpoint for handling the stochastic behaviour of renewable sources. Hence, in this paper, three different strategies are categorized for stochastic analysis of these sources. The first strategy is mathematical modelling including stochastic dependency and independency, multi-dimensional dependence, forecast and scenarios. Afterwards, demand side management, which is one of the other approaches for dealing with these uncertainties, is investigated and different demand response programs and some methods to model them are presented. Finally, the effect of different electricity market schemes and relevant optimization methods to mitigate the variations of renewable energy sources are discussed. The study demonstrates that an operator should choose one or a combination of these three approaches based on its requirements.

Suggested Citation

  • Talari, Saber & Shafie-khah, Miadreza & Osório, Gerardo J. & Aghaei, Jamshid & Catalão, João P.S., 2018. "Stochastic modelling of renewable energy sources from operators' point-of-view: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1953-1965.
    • Handle: RePEc:eee:rensus:v:81:y:2018:i:p2:p:1953-1965
    DOI: 10.1016/j.rser.2017.06.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032117309437
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2017.06.006?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. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    2. Pandey, Shashi Kant & Mohanty, Soumya R. & Kishor, Nand, 2013. "A literature survey on load–frequency control for conventional and distribution generation power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 318-334.
    3. Janssen, Hans, 2013. "Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 123-132.
    4. Aghaei, Jamshid & Alizadeh, Mohammad-Iman & Siano, Pierluigi & Heidari, Alireza, 2016. "Contribution of emergency demand response programs in power system reliability," Energy, Elsevier, vol. 103(C), pages 688-696.
    5. Niknam, Taher & Golestaneh, Faranak & Malekpour, Ahmadreza, 2012. "Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational," Energy, Elsevier, vol. 43(1), pages 427-437.
    6. Li, Yanfu & Zio, Enrico, 2012. "Uncertainty analysis of the adequacy assessment model of a distributed generation system," Renewable Energy, Elsevier, vol. 41(C), pages 235-244.
    7. Heydarian-Forushani, E. & Golshan, M.E.H. & Shafie-khah, M., 2015. "Flexible security-constrained scheduling of wind power enabling time of use pricing scheme," Energy, Elsevier, vol. 90(P2), pages 1887-1900.
    8. Shariatzadeh, Farshid & Mandal, Paras & Srivastava, Anurag K., 2015. "Demand response for sustainable energy systems: A review, application and implementation strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 343-350.
    9. Gerd Infanger (ed.), 2011. "Stochastic Programming," International Series in Operations Research and Management Science, Springer, number 978-1-4419-1642-6, December.
    10. Frieder Borggrefe & Karsten Neuhoff, 2011. "Balancing and Intraday Market Design: Options for Wind Integration," Discussion Papers of DIW Berlin 1162, DIW Berlin, German Institute for Economic Research.
    11. O׳Connell, Niamh & Pinson, Pierre & Madsen, Henrik & O׳Malley, Mark, 2014. "Benefits and challenges of electrical demand response: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 686-699.
    12. Georgilakis, Pavlos S., 2008. "Technical challenges associated with the integration of wind power into power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(3), pages 852-863, April.
    13. Mohammed, Y.S. & Mustafa, M.W. & Bashir, N., 2014. "Hybrid renewable energy systems for off-grid electric power: Review of substantial issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 527-539.
    14. Jooyoung Jeon & James W. Taylor, 2012. "Using Conditional Kernel Density Estimation for Wind Power Density Forecasting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 66-79, March.
    15. Costa, Alexandre & Crespo, Antonio & Navarro, Jorge & Lizcano, Gil & Madsen, Henrik & Feitosa, Everaldo, 2008. "A review on the young history of the wind power short-term prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(6), pages 1725-1744, August.
    16. Carpinelli, Guido & Caramia, Pierluigi & Varilone, Pietro, 2015. "Multi-linear Monte Carlo simulation method for probabilistic load flow of distribution systems with wind and photovoltaic generation systems," Renewable Energy, Elsevier, vol. 76(C), pages 283-295.
    17. Hagspiel, Simeon & Papaemannouil, Antonis & Schmid, Matthias & Andersson, Göran, 2012. "Copula-based modeling of stochastic wind power in Europe and implications for the Swiss power grid," Applied Energy, Elsevier, vol. 96(C), pages 33-44.
    18. Li, Y.F. & Huang, G.H. & Li, Y.P. & Xu, Y. & Chen, W.T., 2010. "Regional-scale electric power system planning under uncertainty--A multistage interval-stochastic integer linear programming approach," Energy Policy, Elsevier, vol. 38(1), pages 475-490, January.
    19. Finn, Paddy & Fitzpatrick, Colin, 2014. "Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing," Applied Energy, Elsevier, vol. 113(C), pages 11-21.
    20. Quan, Hao & Srinivasan, Dipti & Khambadkone, Ashwin M. & Khosravi, Abbas, 2015. "A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources," Applied Energy, Elsevier, vol. 152(C), pages 71-82.
    21. Bruno, Sergio & Ahmed, Shabbir & Shapiro, Alexander & Street, Alexandre, 2016. "Risk neutral and risk averse approaches to multistage renewable investment planning under uncertainty," European Journal of Operational Research, Elsevier, vol. 250(3), pages 979-989.
    22. Chen, Z. & Blaabjerg, F., 2009. "Wind farm--A power source in future power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1288-1300, August.
    23. Rahimiyan, Morteza, 2014. "A statistical cognitive model to assess impact of spatially correlated wind production on market behaviors," Applied Energy, Elsevier, vol. 122(C), pages 62-72.
    24. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
    25. 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.
    26. Moghaddam, M. Parsa & Abdollahi, A. & Rashidinejad, M., 2011. "Flexible demand response programs modeling in competitive electricity markets," Applied Energy, Elsevier, vol. 88(9), pages 3257-3269.
    27. Soroudi, Alireza & Ehsan, Mehdi, 2011. "A possibilistic-probabilistic tool for evaluating the impact of stochastic renewable and controllable power generation on energy losses in distribution networks--A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 794-800, January.
    28. Valizadeh Haghi, H. & Tavakoli Bina, M. & Golkar, M.A. & Moghaddas-Tafreshi, S.M., 2010. "Using Copulas for analysis of large datasets in renewable distributed generation: PV and wind power integration in Iran," Renewable Energy, Elsevier, vol. 35(9), pages 1991-2000.
    29. Pousinho, H.M.I. & Mendes, V.M.F. & Catalão, J.P.S., 2011. "A risk-averse optimization model for trading wind energy in a market environment under uncertainty," Energy, Elsevier, vol. 36(8), pages 4935-4942.
    30. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Demand response in smart electricity grids equipped with renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 64-72.
    31. Weber, Christoph, 2010. "Adequate intraday market design to enable the integration of wind energy into the European power systems," Energy Policy, Elsevier, vol. 38(7), pages 3155-3163, July.
    32. Aien, Morteza & Hajebrahimi, Ali & Fotuhi-Firuzabad, Mahmud, 2016. "A comprehensive review on uncertainty modeling techniques in power system studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1077-1089.
    33. Erdinc, O. & Uzunoglu, M., 2012. "Optimum design of hybrid renewable energy systems: Overview of different approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1412-1425.
    34. Torriti, Jacopo & Hassan, Mohamed G. & Leach, Matthew, 2010. "Demand response experience in Europe: Policies, programmes and implementation," Energy, Elsevier, vol. 35(4), pages 1575-1583.
    35. Wang, Yong & Li, Lin, 2013. "Time-of-use based electricity demand response for sustainable manufacturing systems," Energy, Elsevier, vol. 63(C), pages 233-244.
    36. Lujano-Rojas, Juan M. & Monteiro, Cláudio & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2012. "Optimum residential load management strategy for real time pricing (RTP) demand response programs," Energy Policy, Elsevier, vol. 45(C), pages 671-679.
    37. Dupont, B. & De Jonghe, C. & Olmos, L. & Belmans, R., 2014. "Demand response with locational dynamic pricing to support the integration of renewables," Energy Policy, Elsevier, vol. 67(C), pages 344-354.
    38. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    39. Chauhan, Anurag & Saini, R.P., 2014. "A review on Integrated Renewable Energy System based power generation for stand-alone applications: Configurations, storage options, sizing methodologies and control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 99-120.
    40. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
    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. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    2. Tiago P. Abud & Andre A. Augusto & Marcio Z. Fortes & Renan S. Maciel & Bruno S. M. C. Borba, 2022. "State of the Art Monte Carlo Method Applied to Power System Analysis with Distributed Generation," Energies, MDPI, vol. 16(1), pages 1-24, December.
    3. Naser Hossein Motlagh & Ali Khatibi & Alireza Aslani, 2020. "Toward Sustainable Energy-Independent Buildings Using Internet of Things," Energies, MDPI, vol. 13(22), pages 1-17, November.
    4. Ray, Manojit & Chakraborty, Basab, 2019. "Impact of evolving technology on collaborative energy access scaling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 13-27.
    5. Patel, Ismail & Shah, Adil & Shen, Boyang & Wei, Haigening & Hao, Luning & Hu, Jintao & Wang, Qi & Coombs, Tim, 2023. "Stochastic optimisation and economic analysis of combined high temperature superconducting magnet and hydrogen energy storage system for smart grid applications," Applied Energy, Elsevier, vol. 341(C).
    6. Yang, Dazhi & Yagli, Gokhan Mert & Srinivasan, Dipti, 2022. "Sub-minute probabilistic solar forecasting for real-time stochastic simulations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    7. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    8. Guk-Hyun Moon & Rakkyung Ko & Sung-Kwan Joo, 2020. "Integration of Smart Grid Resources into Generation and Transmission Planning Using an Interval-Stochastic Model," Energies, MDPI, vol. 13(7), pages 1-12, April.
    9. Hlalele, Thabo G. & Naidoo, Raj M. & Bansal, Ramesh C. & Zhang, Jiangfeng, 2020. "Multi-objective stochastic economic dispatch with maximal renewable penetration under renewable obligation," Applied Energy, Elsevier, vol. 270(C).
    10. Alshawaf, Mohammad & Poudineh, Rahmatallah & Alhajeri, Nawaf S., 2020. "Solar PV in Kuwait: The effect of ambient temperature and sandstorms on output variability and uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    11. Siripha Junlakarn & Radhanon Diewvilai & Kulyos Audomvongseree, 2022. "Stochastic Modeling of Renewable Energy Sources for Capacity Credit Evaluation," Energies, MDPI, vol. 15(14), pages 1-27, July.

    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. Alasseri, Rajeev & Tripathi, Ashish & Joji Rao, T. & Sreekanth, K.J., 2017. "A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 617-635.
    2. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    3. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    4. Zhou, Kaile & Yang, Shanlin, 2015. "Demand side management in China: The context of China’s power industry reform," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 954-965.
    5. Xu, Bing & Nayak, Amar & Gray, David & Ouenniche, Jamal, 2016. "Assessing energy business cases implemented in the North Sea Region and strategy recommendations," Applied Energy, Elsevier, vol. 172(C), pages 360-371.
    6. Leinauer, Christina & Schott, Paul & Fridgen, Gilbert & Keller, Robert & Ollig, Philipp & Weibelzahl, Martin, 2022. "Obstacles to demand response: Why industrial companies do not adapt their power consumption to volatile power generation," Energy Policy, Elsevier, vol. 165(C).
    7. Ampimah, Benjamin Chris & Sun, Mei & Han, Dun & Wang, Xueyin, 2018. "Optimizing sheddable and shiftable residential electricity consumption by incentivized peak and off-peak credit function approach," Applied Energy, Elsevier, vol. 210(C), pages 1299-1309.
    8. Alcázar-Ortega, Manuel & Calpe, Carmen & Theisen, Thomas & Carbonell-Carretero, José Francisco, 2015. "Methodology for the identification, evaluation and prioritization of market handicaps which prevent the implementation of Demand Response: Application to European electricity markets," Energy Policy, Elsevier, vol. 86(C), pages 529-543.
    9. Feuerriegel, Stefan & Neumann, Dirk, 2016. "Integration scenarios of Demand Response into electricity markets: Load shifting, financial savings and policy implications," Energy Policy, Elsevier, vol. 96(C), pages 231-240.
    10. 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.
    11. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2014. "Agent-based model for electricity consumption and storage to evaluate economic viability of tariff arbitrage for residential sector demand response," Applied Energy, Elsevier, vol. 126(C), pages 297-306.
    12. Dehnavi, Ehsan & Abdi, Hamdi, 2016. "Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem," Energy, Elsevier, vol. 109(C), pages 1086-1094.
    13. Tahir, Muhammad Faizan & Chen, Haoyong & Khan, Asad & Javed, Muhammad Sufyan & Cheema, Khalid Mehmood & Laraik, Noman Ali, 2020. "Significance of demand response in light of current pilot projects in China and devising a problem solution for future advancements," Technology in Society, Elsevier, vol. 63(C).
    14. Wang, Yong & Li, Lin, 2015. "Time-of-use electricity pricing for industrial customers: A survey of U.S. utilities," Applied Energy, Elsevier, vol. 149(C), pages 89-103.
    15. Paterakis, Nikolaos G. & Erdinç, Ozan & Catalão, João P.S., 2017. "An overview of Demand Response: Key-elements and international experience," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 871-891.
    16. Schreiber, Michael & Wainstein, Martin E. & Hochloff, Patrick & Dargaville, Roger, 2015. "Flexible electricity tariffs: Power and energy price signals designed for a smarter grid," Energy, Elsevier, vol. 93(P2), pages 2568-2581.
    17. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "Residential demand response scheme based on adaptive consumption level pricing," Energy, Elsevier, vol. 113(C), pages 301-308.
    18. González-Aparicio, I. & Zucker, A., 2015. "Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain," Applied Energy, Elsevier, vol. 159(C), pages 334-349.
    19. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    20. Zehir, Mustafa Alparslan & Batman, Alp & Bagriyanik, Mustafa, 2016. "Review and comparison of demand response options for more effective use of renewable energy at consumer level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 631-642.

    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:rensus:v:81:y:2018:i:p2:p:1953-1965. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.