A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem
The uncertainty related to the massive integration of intermittent energy sources (e.g., wind and solar generation) is one of the biggest challenges for the economic, safe and reliable operation of current power systems. One way to tackle this challenge is through a stochastic security constraint un...
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| Veröffentlicht in: | Energies (Basel) Jg. 13; H. 15; S. 3777 |
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| Abstract | The uncertainty related to the massive integration of intermittent energy sources (e.g., wind and solar generation) is one of the biggest challenges for the economic, safe and reliable operation of current power systems. One way to tackle this challenge is through a stochastic security constraint unit commitment (SSCUC) model. However, the SSCUC is a mixed-integer linear programming problem with high computational and dimensional complexity in large-scale power systems. This feature hinders the reaction times required for decision making to ensure a proper operation of the system. As an alternative, this paper presents a joint strategy to efficiently solve a SSCUC model. The solution strategy combines the use of linear sensitivity factors (LSF) to compute power flows in a quick and reliable way and a method, which dynamically identifies and adds as user cuts those active security constraints N − 1 that establish the feasible region of the model. These two components are embedded within a progressive hedging algorithm (PHA), which breaks down the SSCUC problem into computationally more tractable subproblems by relaxing the coupling constraints between scenarios. The numerical results on the IEEE RTS-96 system show that the proposed strategy provides high quality solutions, up to 50 times faster compared to the extensive formulation (EF) of the SSCUC. Additionally, the solution strategy identifies the most affected (overloaded) lines before contingencies, as well as the most critical contingencies in the system. Two metrics that provide valuable information for decision making during transmission system expansion are studied. |
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| AbstractList | The uncertainty related to the massive integration of intermittent energy sources (e.g., wind and solar generation) is one of the biggest challenges for the economic, safe and reliable operation of current power systems. One way to tackle this challenge is through a stochastic security constraint unit commitment (SSCUC) model. However, the SSCUC is a mixed-integer linear programming problem with high computational and dimensional complexity in large-scale power systems. This feature hinders the reaction times required for decision making to ensure a proper operation of the system. As an alternative, this paper presents a joint strategy to efficiently solve a SSCUC model. The solution strategy combines the use of linear sensitivity factors (LSF) to compute power flows in a quick and reliable way and a method, which dynamically identifies and adds as user cuts those active security constraints N − 1 that establish the feasible region of the model. These two components are embedded within a progressive hedging algorithm (PHA), which breaks down the SSCUC problem into computationally more tractable subproblems by relaxing the coupling constraints between scenarios. The numerical results on the IEEE RTS-96 system show that the proposed strategy provides high quality solutions, up to 50 times faster compared to the extensive formulation (EF) of the SSCUC. Additionally, the solution strategy identifies the most affected (overloaded) lines before contingencies, as well as the most critical contingencies in the system. Two metrics that provide valuable information for decision making during transmission system expansion are studied. The uncertainty related to the massive integration of intermittent energy sources (e.g., wind and solar generation) is one of the biggest challenges for the economic, safe and reliable operation of current power systems. One way to tackle this challenge is through a stochastic security constraint unit commitment (SSCUC) model. However, the SSCUC is a mixed-integer linear programming problem with high computational and dimensional complexity in large-scale power systems. This feature hinders the reaction times required for decision making to ensure a proper operation of the system. As an alternative, this paper presents a joint strategy to efficiently solve a SSCUC model. The solution strategy combines the use of linear sensitivity factors (LSF) to compute power flows in a quick and reliable way and a method, which dynamically identifies and adds as user cuts those active security constraintsN−1that establish the feasible region of the model. These two components are embedded within a progressive hedging algorithm (PHA), which breaks down the SSCUC problem into computationally more tractable subproblems by relaxing the coupling constraints between scenarios. The numerical results on the IEEE RTS-96 system show that the proposed strategy provides high quality solutions, up to 50 times faster compared to the extensive formulation (EF) of the SSCUC. Additionally, the solution strategy identifies the most affected (overloaded) lines before contingencies, as well as the most critical contingencies in the system. Two metrics that provide valuable information for decision making during transmission system expansion are studied. |
| Author | Marín-Cano, Cristian Camilo Sierra-Aguilar, Juan Esteban Villegas, Juan G. López-Lezama, Jesús M. Jaramillo-Duque, Álvaro |
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| Cites_doi | 10.1109/TPWRS.2010.2045161 10.1109/TPWRS.2019.2892620 10.1016/j.orl.2015.03.008 10.1109/TPWRS.2007.907528 10.1109/TPWRS.2008.926719 10.1109/PSCC.2016.7540845 10.1007/s10107-016-1000-z 10.1007/s40010-017-0475-1 10.1007/BF02204860 10.1109/PESGM.2014.6938802 10.1109/TPWRS.2011.2164947 10.1016/j.egypro.2015.12.360 10.1109/TPWRS.2014.2355204 10.1109/TPWRS.2017.2686701 10.1109/PESMG.2013.6673013 10.1109/PSCC.2016.7540910 10.1109/TSTE.2013.2289853 10.1109/JPROC.2005.857490 10.1287/moor.16.1.119 10.3390/en12071399 10.1109/TPWRS.2011.2165087 10.1109/TPWRS.2015.2494590 10.1007/s10479-018-3003-z 10.1016/j.ijepes.2018.04.026 10.1109/PESMG.2013.6672719 10.1109/TPWRS.2006.873407 10.1109/IGBSG.2018.8393518 10.1016/j.epsr.2011.09.006 10.1016/j.ijepes.2009.03.032 10.1109/TLA.2013.6502840 10.1109/PTC.2015.7232629 10.1007/s10287-010-0125-4 10.1016/j.ijepes.2015.02.017 10.1016/S0142-0615(97)00058-6 10.1109/TPWRS.2007.894843 10.1061/(ASCE)EY.1943-7897.0000187 |
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| SubjectTerms | Decision making Decomposition Integer programming Optimization power system optimization progressive hedging algorithm Security-Constraint Unit Commitment Variables |
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| Title | A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem |
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