Risk-based dynamic generation and transmission expansion planning with propagating effects of contingencies
•A risk-based dynamic generation and transmission expansion planning model is proposed.•The propagating effect of each contingency on the power system is modeled with risk indices.•Post-contingency load-shedding costs are used to penalize high-risk contingencies more dominantly.•The McCormick relaxa...
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| Published in: | International journal of electrical power & energy systems Vol. 118; p. 105762 |
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| Language: | English |
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01.06.2020
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| Abstract | •A risk-based dynamic generation and transmission expansion planning model is proposed.•The propagating effect of each contingency on the power system is modeled with risk indices.•Post-contingency load-shedding costs are used to penalize high-risk contingencies more dominantly.•The McCormick relaxation is tailored to alter the objective function into a linear format.•A second-order cone programming model is applied for power flow representation.
Transmission networks and generating units must be reinforced to satisfy the ever-increasing demand for electricity and to keep power system reliability within an acceptable level. According to the standards, the planned power system must be able to supply demand in the case of outage of a single element (N-1 security criteria), and the possibility of cascading failures must be minimized. In this paper, we propose a risk-based dynamic generation and transmission expansion planning model with respect to the propagating effect of each contingency on the power system. Using the concept of risk, post-contingency load-shedding penalty costs are obtained and added in the objective function to penalize high-risk contingencies more dominantly. The McCormick relaxation is tailored to alter the objective function into a linear format. To keep the practicality of the proposed model, a second-order cone programming model is applied for power flow representation, and the problem is modeled in a dynamic time frame. The proposed model is formulated as a mixed-integer second-order cone programming problem. The numerical studies on the RTS 24-bus test system illustrate the efficacy of the proposed model. |
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| AbstractList | •A risk-based dynamic generation and transmission expansion planning model is proposed.•The propagating effect of each contingency on the power system is modeled with risk indices.•Post-contingency load-shedding costs are used to penalize high-risk contingencies more dominantly.•The McCormick relaxation is tailored to alter the objective function into a linear format.•A second-order cone programming model is applied for power flow representation.
Transmission networks and generating units must be reinforced to satisfy the ever-increasing demand for electricity and to keep power system reliability within an acceptable level. According to the standards, the planned power system must be able to supply demand in the case of outage of a single element (N-1 security criteria), and the possibility of cascading failures must be minimized. In this paper, we propose a risk-based dynamic generation and transmission expansion planning model with respect to the propagating effect of each contingency on the power system. Using the concept of risk, post-contingency load-shedding penalty costs are obtained and added in the objective function to penalize high-risk contingencies more dominantly. The McCormick relaxation is tailored to alter the objective function into a linear format. To keep the practicality of the proposed model, a second-order cone programming model is applied for power flow representation, and the problem is modeled in a dynamic time frame. The proposed model is formulated as a mixed-integer second-order cone programming problem. The numerical studies on the RTS 24-bus test system illustrate the efficacy of the proposed model. |
| ArticleNumber | 105762 |
| Author | Kargarian, Amin Mehrtash, Mahdi |
| Author_xml | – sequence: 1 givenname: Mahdi surname: Mehrtash fullname: Mehrtash, Mahdi email: mmehrt3@lsu.edu – sequence: 2 givenname: Amin surname: Kargarian fullname: Kargarian, Amin email: kargarian@lsu.edu |
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| Cites_doi | 10.1007/s10898-014-0176-0 10.1109/TPWRS.2005.852142 10.1109/TPWRS.2018.2850822 10.1049/iet-gtd:20060465 10.1109/TPWRS.2016.2523998 10.1109/TPWRS.2013.2296352 10.1109/TPWRS.2018.2835663 10.3389/fenrg.2015.00055 10.1016/j.ijepes.2019.105415 10.1109/TPWRS.2012.2228507 10.1049/iet-gtd.2018.6555 10.1016/j.ejor.2014.10.030 10.1109/TPWRS.2015.2443101 10.1080/15325008.2011.567216 |
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| Keywords | McCormick relaxation Generation and transmission expansion planning N-1 security criteria Second-order cone programming Risk index |
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| References | Kargarian, Raoofat, Mohammadi (b0085) 2011; 39 Wood, Wollenberg (b0080) 2012 Aghaei, Amjady, Baharvandi, Akbari (b0040) 2014; 29 Choi, Tran, El-Keib, Thomas, Oh, Billinton (b0035) 2005; 20 Xiao, McCalley, Ou, Adams, Myers (b0075) 2006 Majidi-Qadikolai, Baldick (b0030) 2016; 31 Tsoukalas, Mitsos (b0095) 2014; 59 Armaghani, Naghshbandy, Shahrtash (b0045) 2020; 115 . Conejo, Baringo, Kazempour, Siddiqui (b0010) 2016 Rider, Garcia, Romero (b0110) 2007; 1 Haghighat, Zeng (b0060) 2018; 33 Ruiz, Conejo (b0105) 2015; 242 Schröder, Kuckshinrichs (b0090) 2015; 3 NERC. Transmission System Adequacy and Security [Online]. Available Mehrtash, Mohammadi, Barati, Kargarian (b0050) 2018 Drewes, Ulbrich (b0070) 2009 Jabr (b0055) 2013; 28 Mehrtash (b0005) 2019 Mehrtash, Kargarian, Mohammadi (b0065) 2019; 13 Rashidaee, Amraee, Fotuhi-Firuzabad (b0015) 2018 Majidi-Qadikolai, Baldick (b0025) 2016; 31 IBM ILOG CPLEX Optimizer software. Available 10.1016/j.ijepes.2019.105762_b0020 Wood (10.1016/j.ijepes.2019.105762_b0080) 2012 Mehrtash (10.1016/j.ijepes.2019.105762_b0050) 2018 Choi (10.1016/j.ijepes.2019.105762_b0035) 2005; 20 Mehrtash (10.1016/j.ijepes.2019.105762_b0065) 2019; 13 10.1016/j.ijepes.2019.105762_b0100 Mehrtash (10.1016/j.ijepes.2019.105762_b0005) 2019 Majidi-Qadikolai (10.1016/j.ijepes.2019.105762_b0025) 2016; 31 Conejo (10.1016/j.ijepes.2019.105762_b0010) 2016 Rashidaee (10.1016/j.ijepes.2019.105762_b0015) 2018 Aghaei (10.1016/j.ijepes.2019.105762_b0040) 2014; 29 Haghighat (10.1016/j.ijepes.2019.105762_b0060) 2018; 33 Drewes (10.1016/j.ijepes.2019.105762_b0070) 2009 Schröder (10.1016/j.ijepes.2019.105762_b0090) 2015; 3 Rider (10.1016/j.ijepes.2019.105762_b0110) 2007; 1 Ruiz (10.1016/j.ijepes.2019.105762_b0105) 2015; 242 Majidi-Qadikolai (10.1016/j.ijepes.2019.105762_b0030) 2016; 31 Xiao (10.1016/j.ijepes.2019.105762_b0075) 2006 Armaghani (10.1016/j.ijepes.2019.105762_b0045) 2020; 115 Kargarian (10.1016/j.ijepes.2019.105762_b0085) 2011; 39 Jabr (10.1016/j.ijepes.2019.105762_b0055) 2013; 28 Tsoukalas (10.1016/j.ijepes.2019.105762_b0095) 2014; 59 |
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