IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v28y2013icp726-737.html
   My bibliography  Save this article

Scenario-based security-constrained hydrothermal coordination with volatile wind power generation

Author

Listed:
  • Karami, M.
  • Shayanfar, H.A.
  • Aghaei, J.
  • Ahmadi, A.

Abstract

This paper presents the application of Mixed-Integer Programming (MIP) approach for solving the security-constrained daily hydrothermal generation Scheduling which takes into account the intermittency and volatility of wind power generation, which is called Security-Constrained Wind Hydrothermal Coordination (WHTC). In restructured power systems, Independent System Operators (ISOs) execute the Security-Constrained Unit Commitment (SCUC) program to plan a secure and economical hourly generation schedule for the daily/weekly-ahead market. The objective of security-constrained daily hydrothermal generation scheduling is to determine an optimum schedule of generating units for minimizing the cost of supplying energy and ancillary services with considering network security constraints. The problem formulation includes dynamic ramp-rate constraints for generation schedules and reserve activation, and minimum up-time and down-time of conventional units. Of particular interest in this study are considering more practical constraints and rigorous modeling of thermal and hydro units such as prohibited operating zones and valve loading effects. Furthermore, for the hydro plants, multi performance curve with spillage and time delay between reservoirs are considered. To assess the efficiency and powerful performance of mentioned method, a typical case study based on modified IEEE-118 bus system is investigated and the results are compared to each other in different test system.

Suggested Citation

  • Karami, M. & Shayanfar, H.A. & Aghaei, J. & Ahmadi, A., 2013. "Scenario-based security-constrained hydrothermal coordination with volatile wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 726-737.
    • Handle: RePEc:eee:rensus:v:28:y:2013:i:c:p:726-737
    DOI: 10.1016/j.rser.2013.07.052
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032113004978
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2013.07.052?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. Moghimi Ghadikolaei, Hadi & Ahmadi, Abdollah & Aghaei, Jamshid & Najafi, Meysam, 2012. "Risk constrained self-scheduling of hydro/wind units for short term electricity markets considering intermittency and uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4734-4743.
    2. Shamshad, A. & Bawadi, M.A. & Wan Hussin, W.M.A. & Majid, T.A. & Sanusi, S.A.M., 2005. "First and second order Markov chain models for synthetic generation of wind speed time series," Energy, Elsevier, vol. 30(5), pages 693-708.
    3. F.-Javier Heredia & Marcos Rider & Cristina Corchero, 2012. "A stochastic programming model for the optimal electricity market bid problem with bilateral contracts for thermal and combined cycle units," Annals of Operations Research, Springer, vol. 193(1), pages 107-127, 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. Lu, Xin & Qiu, Jing & Lei, Gang & Zhu, Jianguo, 2022. "Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia," Applied Energy, Elsevier, vol. 308(C).
    2. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
    3. Esmaeily, Ali & Ahmadi, Abdollah & Raeisi, Fatima & Ahmadi, Mohammad Reza & Esmaeel Nezhad, Ali & Janghorbani, Mohammadreza, 2017. "Evaluating the effectiveness of mixed-integer linear programming for day-ahead hydro-thermal self-scheduling considering price uncertainty and forced outage rate," Energy, Elsevier, vol. 122(C), pages 182-193.
    4. 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.
    5. Ahmadi, Abdollah & Charwand, Mansour & Siano, Pierluigi & Nezhad, Ali Esmaeel & Sarno, Debora & Gitizadeh, Mohsen & Raeisi, Fatima, 2016. "A novel two-stage stochastic programming model for uncertainty characterization in short-term optimal strategy for a distribution company," Energy, Elsevier, vol. 117(P1), pages 1-9.
    6. Khan, Mohammad Junaid & Yadav, Amit Kumar & Mathew, Lini, 2017. "Techno economic feasibility analysis of different combinations of PV-Wind-Diesel-Battery hybrid system for telecommunication applications in different cities of Punjab, India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 577-607.
    7. Suresh K. Damodaran & T. K. Sunil Kumar, 2018. "Hydro-Thermal-Wind Generation Scheduling Considering Economic and Environmental Factors Using Heuristic Algorithms," Energies, MDPI, vol. 11(2), pages 1-19, February.
    8. Srikanth Reddy, K. & Panwar, Lokesh & Panigrahi, B.K. & Kumar, Rajesh, 2018. "Modeling and analysis of profit based self scheduling of GENCO in electricity markets with renewable energy penetration and emission constraints," Renewable Energy, Elsevier, vol. 116(PA), pages 48-63.
    9. Hossain, Md Maruf & Ali, Mohd. Hasan, 2015. "Future research directions for the wind turbine generator system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 481-489.
    10. Khan, Komal S. & Tariq, Muhammad, 2018. "Wind resource assessment using SODAR and meteorological mast – A case study of Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2443-2449.
    11. 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.
    12. Zeng, Ming & Yang, Yongqi & Fan, Qiannan & Liu, Yingxin & Zou, Zhuojun, 2015. "Coordination between clean energy generation and thermal power generation under the policy of “direct power-purchase for large users” in China," Utilities Policy, Elsevier, vol. 33(C), pages 10-22.
    13. Surroop, Dinesh & Raghoo, Pravesh, 2017. "Energy landscape in Mauritius," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 688-694.
    14. Margaret Amutha, W. & Rajini, V., 2015. "Techno-economic evaluation of various hybrid power systems for rural telecom," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 553-561.
    15. Sarimuthu, Charles R. & Ramachandaramurthy, Vigna K. & Agileswari, K.R. & Mokhlis, Hazlie, 2016. "A review on voltage control methods using on-load tap changer transformers for networks with renewable energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1154-1161.
    16. Shen, Jianjian & Cheng, Chuntian & Wang, Sen & Yuan, Xiaoye & Sun, Lifei & Zhang, Jun, 2020. "Multiobjective optimal operations for an interprovincial hydropower system considering peak-shaving demands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).

    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. Jin, Xin & Zhang, Zhaolong & Shi, Xiaoqiang & Ju, Wenbin, 2014. "A review on wind power industry and corresponding insurance market in China: Current status and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 1069-1082.
    2. Tang, Jie & Brouste, Alexandre & Tsui, Kwok Leung, 2015. "Some improvements of wind speed Markov chain modeling," Renewable Energy, Elsevier, vol. 81(C), pages 52-56.
    3. Esmaeili Aliabadi, Danial & Kaya, Murat & Sahin, Guvenc, 2017. "Competition, risk and learning in electricity markets: An agent-based simulation study," Applied Energy, Elsevier, vol. 195(C), pages 1000-1011.
    4. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    5. Chiacchio, Ferdinando & D’Urso, Diego & Famoso, Fabio & Brusca, Sebastian & Aizpurua, Jose Ignacio & Catterson, Victoria M., 2018. "On the use of dynamic reliability for an accurate modelling of renewable power plants," Energy, Elsevier, vol. 151(C), pages 605-621.
    6. Wenming Xie & Yingxue Zhao & Zhibin Jiang & Pui-Sze Chow, 2016. "Optimizing product service system by franchise fee contracts under information asymmetry," Annals of Operations Research, Springer, vol. 240(2), pages 709-729, May.
    7. Hong, Ying-Yi & Chang, Huei-Lin & Chiu, Ching-Sheng, 2010. "Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs," Energy, Elsevier, vol. 35(9), pages 3870-3876.
    8. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    9. Amanda S. Hering & Karen Kazor & William Kleiber, 2015. "A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales," Resources, MDPI, vol. 4(1), pages 1-23, February.
    10. Meschede, Henning & Dunkelberg, Heiko & Stöhr, Fabian & Peesel, Ron-Hendrik & Hesselbach, Jens, 2017. "Assessment of probabilistic distributed factors influencing renewable energy supply for hotels using Monte-Carlo methods," Energy, Elsevier, vol. 128(C), pages 86-100.
    11. Zhang, Jin-hua & Liu, Yong-qian & Tian, De & Yan, Jie, 2015. "Optimal power dispatch in wind farm based on reduced blade damage and generator losses," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 64-77.
    12. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
    13. Ming, Bo & Liu, Pan & Guo, Shenglian & Cheng, Lei & Zhou, Yanlai & Gao, Shida & Li, He, 2018. "Robust hydroelectric unit commitment considering integration of large-scale photovoltaic power: A case study in China," Applied Energy, Elsevier, vol. 228(C), pages 1341-1352.
    14. D׳Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2015. "Reliability measures for indexed semi-Markov chains applied to wind energy production," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 170-177.
    15. 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.
    16. Goić, R. & Krstulović, J. & Jakus, D., 2010. "Simulation of aggregate wind farm short-term production variations," Renewable Energy, Elsevier, vol. 35(11), pages 2602-2609.
    17. Yirgalem Chebud & Assefa Melesse, 2011. "Operational Prediction of Groundwater Fluctuation in South Florida using Sequence Based Markovian Stochastic Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2279-2294, July.
    18. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2013. "First and second order semi-Markov chains for wind speed modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1194-1201.
    19. Khasanzoda, Nasrullo & Zicmane, Inga & Beryozkina, Svetlana & Safaraliev, Murodbek & Sultonov, Sherkhon & Kirgizov, Alifbek, 2022. "Regression model for predicting the speed of wind flows for energy needs based on fuzzy logic," Renewable Energy, Elsevier, vol. 191(C), pages 723-731.
    20. Lei, Yang & Wang, Dan & Jia, Hongjie & Li, Jiaxi & Chen, Jingcheng & Li, Jingru & Yang, Zhihong, 2021. "Multi-stage stochastic planning of regional integrated energy system based on scenario tree path optimization under long-term multiple uncertainties," Applied Energy, Elsevier, vol. 300(C).

    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:28:y:2013:i:c:p:726-737. 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.