Hybrid model for stochastic clearing of joint energy and reserves market
Day-ahead market clearing is usually executed by independent system operator to determine the accepted energy and reserve bids and their payments. In this study, an effective multi-objective stochastic framework is presented for joint energy and reserves market clearing problem considering the power...
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| Vydané v: | IET generation, transmission & distribution Ročník 11; číslo 10; s. 2608 - 2617 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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The Institution of Engineering and Technology
13.07.2017
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| ISSN: | 1751-8687, 1751-8695 |
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| Abstract | Day-ahead market clearing is usually executed by independent system operator to determine the accepted energy and reserve bids and their payments. In this study, an effective multi-objective stochastic framework is presented for joint energy and reserves market clearing problem considering the power system security. To this end, the 24 h scenarios are generated using the Monte Carlo simulation (MCS). Besides, a scenario reduction technique is also presented to reduce the computational burden of the proposed stochastic market clearing procedure. A new validation method based on MCS which is called MCVM method is proposed to verify the proposed strategy against the deterministic model. Direction scalarisation method (DSM) is proposed to solve the proposed multi-objective problem. A new method as the combination of gravitational search algorithm and primal-dual interior point method is employed to solve the proposed single-objective optimisation problem obtained by the DSM. IEEE 24-bus test system is used to address the effectiveness of the proposed framework. |
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| AbstractList | Day‐ahead market clearing is usually executed by independent system operator to determine the accepted energy and reserve bids and their payments. In this study, an effective multi‐objective stochastic framework is presented for joint energy and reserves market clearing problem considering the power system security. To this end, the 24 h scenarios are generated using the Monte Carlo simulation (MCS). Besides, a scenario reduction technique is also presented to reduce the computational burden of the proposed stochastic market clearing procedure. A new validation method based on MCS which is called MCVM method is proposed to verify the proposed strategy against the deterministic model. Direction scalarisation method (DSM) is proposed to solve the proposed multi‐objective problem. A new method as the combination of gravitational search algorithm and primal‐dual interior point method is employed to solve the proposed single‐objective optimisation problem obtained by the DSM. IEEE 24‐bus test system is used to address the effectiveness of the proposed framework. |
| Author | Goroohi Sardou, Iman Goroohi, Ali |
| Author_xml | – sequence: 1 givenname: Iman orcidid: 0000-0002-7195-7665 surname: Goroohi Sardou fullname: Goroohi Sardou, Iman email: imangoroohi@ujiroft.ac.ir organization: Electrical Engineering Department, University of Jiroft, Jiroft, Iran – sequence: 2 givenname: Ali surname: Goroohi fullname: Goroohi, Ali organization: Electrical Engineering Department, University of Jiroft, Jiroft, Iran |
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| Cites_doi | 10.1016/j.ijepes.2015.03.002 10.1016/j.epsr.2013.02.008 10.1002/etep.2039 10.1016/j.apenergy.2009.01.021 10.1016/j.ijepes.2013.07.014 10.1007/s00500-014-1580-6 10.1002/9780470608555 10.1109/TSG.2015.2406879 10.1016/j.apenergy.2011.01.034 10.1016/j.asoc.2011.02.022 10.1109/TSG.2015.2408114 10.1007/978-3-540-79159-1 10.1109/59.780914 10.1016/j.epsr.2007.04.007 10.1016/S0377-0427(00)00433-7 10.1016/j.energy.2009.06.048 10.1016/j.ijepes.2013.03.032 10.1109/TPWRD.2014.2307279 10.1109/SIS.2013.6615191 10.1049/iet-gtd.2011.0842 10.1007/s11708-016-0431-9 10.1007/BF00934564 10.1002/047122412X 10.1016/j.ijepes.2012.06.067 10.1016/j.ijepes.2013.05.050 10.1109/JSYST.2013.2265664 10.1016/j.ins.2009.03.004 10.1109/TPWRS.2011.2163949 10.1016/j.enconman.2008.12.027 10.1016/j.ijepes.2011.10.004 10.1002/9780470225868 10.1109/TSG.2012.2213348 10.1109/TPWRS.2009.2030364 |
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| Copyright | The Institution of Engineering and Technology 2017 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology |
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| Keywords | deterministic model stochastic programming stochastic market clearing procedure reserves market Monte Carlo methods MCVM method direction scalarisation method independent system operator power markets search problems DSM IEEE 24-bus test system day-ahead market clearing single-objective optimisation problem power system security hybrid model joint energy market energy bids primal-dual interior point method gravitational search algorithm reserve bids effective multiobjective stochastic framework validation method MCS scenario reduction technique Monte Carlo simulation |
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| StartPage | 2608 |
| SubjectTerms | day‐ahead market clearing deterministic model direction scalarisation method DSM effective multiobjective stochastic framework energy bids gravitational search algorithm hybrid model IEEE 24‐bus test system independent system operator joint energy market MCS MCVM method Monte Carlo methods Monte Carlo simulation power markets power system security primal‐dual interior point method Research Article reserve bids reserves market scenario reduction technique search problems single‐objective optimisation problem stochastic market clearing procedure stochastic programming validation method |
| Title | Hybrid model for stochastic clearing of joint energy and reserves market |
| URI | http://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2016.1757 https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fiet-gtd.2016.1757 |
| Volume | 11 |
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