Stochastic AC optimal power flow: A data-driven approach
There is an emerging need for efficient solutions to stochastic AC Optimal Power Flow (AC-OPF) to ensure optimal and reliable grid operations in the presence of increasing demand and generation uncertainty. This paper presents a highly scalable data-driven algorithm for stochastic AC-OPF that has ex...
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| Published in | Electric power systems research Vol. 189; p. 106567 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
01.12.2020
Elsevier Science Ltd Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0378-7796 1873-2046 1873-2046 |
| DOI | 10.1016/j.epsr.2020.106567 |
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| Abstract | There is an emerging need for efficient solutions to stochastic AC Optimal Power Flow (AC-OPF) to ensure optimal and reliable grid operations in the presence of increasing demand and generation uncertainty. This paper presents a highly scalable data-driven algorithm for stochastic AC-OPF that has extremely low sample requirement. The novelty behind the algorithm’s performance involves an iterative scenario design approach that merges information regarding constraint violations in the system with data-driven sparse regression. Compared to conventional methods with random scenario sampling, our approach is able to provide feasible operating points for realistic systems with much lower sample requirements. Furthermore, multiple sub-tasks in our approach can be easily paralleled and based on historical data to enhance its performance and application. We demonstrate the computational improvements of our approach through simulations on different test cases in the IEEE PES PGLib-OPF benchmark library. |
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| AbstractList | There is an emerging need for efficient solutions to stochastic AC Optimal Power Flow (AC-OPF) to ensure optimal and reliable grid operations in the presence of increasing demand and generation uncertainty. This paper presents a highly scalable data-driven algorithm for stochastic AC-OPF that has extremely low sample requirement. The novelty behind the algorithm's performance involves an iterative scenario design approach that merges information regarding constraint violations in the system with data-driven sparse regression. Compared to conventional methods with random scenario sampling, our approach is able to provide feasible operating points for realistic systems with much lower sample requirements. Furthermore, multiple sub-tasks in our approach can be easily paralleled and based on historical data to enhance its performance and application. We demonstrate the computational improvements of our approach through simulations on different test cases in the IEEE PES PGLib-OPF benchmark library. There is an emerging need for efficient solutions to stochastic AC Optimal Power Flow (AC-OPF) to ensure optimal and reliable grid operations in the presence of increasing demand and generation uncertainty. Herein this paper presents a highly scalable data-driven algorithm for stochastic AC-OPF that has extremely low sample requirement. The novelty behind the algorithm’s performance involves an iterative scenario design approach that merges information regarding constraint violations in the system with data-driven sparse regression. Compared to conventional methods with random scenario sampling, our approach is able to provide feasible operating points for realistic systems with much lower sample requirements. Furthermore, multiple sub-tasks in our approach can be easily paralleled and based on historical data to enhance its performance and application. We demonstrate the computational improvements of our approach through simulations on different test cases in the IEEE PES PGLib-OPF benchmark library. |
| ArticleNumber | 106567 |
| Author | Deka, Deepjyoti Mezghani, Ilyes Misra, Sidhant |
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| Cites_doi | 10.1109/TPWRS.2009.2021235 10.1109/TCNS.2017.2673546 10.1007/s10107-002-0331-0 10.1109/TPWRS.2019.2918363 10.1109/TAC.2015.2494875 10.1137/130910312 10.1109/TPWRS.2017.2743348 10.1109/TPWRS.2017.2745410 10.1109/TAC.2006.875041 10.1109/TPWRS.2017.2748060 10.1109/TPWRS.2017.2656080 10.1109/TAC.2014.2303232 10.1109/TPWRS.2017.2760699 10.1109/TPWRS.2013.2272546 |
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| Keywords | Chance constraints Sparse regression Scenario optimization Monte Carlo Stochastic AC-OPF Data-driven optimization |
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| References | Deka, Misra (bib0021) 2019 Molzahn, Roald (bib0006) 2018 Venzke, Halilbasic, Markovic, Hug, Chatzivasileiadis (bib0007) 2018; 33 Calafiore, Campi (bib0009) 2006; 51 Margellos, Goulart, Lygeros (bib0015) 2014; 59 DallAnese, Baker, Summers (bib0008) 2017; 32 S. Babaeinejadsarookolaee, A. Birchfield, R.D. Christie, C. Coffrin, C. DeMarco, R. Diao, M. Ferris, S. Fliscounakis, S. Greene, R. Huang, et al., The power grid library for benchmarking AC optimal power flow algorithms, arXiv preprint arXiv:1908.02788 (2019). Muhlpfordt, Roald, Hagenmeyer, Faulwasser, Misra (bib0002) 2019; 34 Roald, Misra, Chertkov, Andersson (bib0014) 2015 Chamanbaz, Dabbene, Tempo, Venkataramanan, Wang (bib0016) 2015; 61 Roald, Andersson (bib0001) 2017; 33 Bienstock, Chertkov, Harnett (bib0013) 2014; 56 Ng, Misra, Roald, Backhaus (bib0020) 2018 Dupačová, Gröwe-Kuska, Römisch (bib0018) 2003; 95 Wainwright, Jordan (bib0022) 2008; 1 Mashayekh, Stadler, Cardoso, Heleno, Madathil, Nagarajan, Bent, Mueller-Stoffels, Lu, Wang (bib0011) 2017; 33 Growe-Kuska, Heitsch, Romisch (bib0017) 2003; 3 Stott, Jardim, Alsaç (bib0003) 2009; 24 Lorca, Sun (bib0005) 2018; 33 Deka, Backhaus, Chertkov (bib0004) 2017; 5 Vrakopoulou, Margellos, Lygeros, Andersson (bib0010) 2013; 28 Roald, Oldewurtel, Krause, Andersson (bib0012) 2013 Muhlpfordt (10.1016/j.epsr.2020.106567_bib0002) 2019; 34 Calafiore (10.1016/j.epsr.2020.106567_bib0009) 2006; 51 Molzahn (10.1016/j.epsr.2020.106567_bib0006) 2018 Bienstock (10.1016/j.epsr.2020.106567_bib0013) 2014; 56 Growe-Kuska (10.1016/j.epsr.2020.106567_bib0017) 2003; 3 Dupačová (10.1016/j.epsr.2020.106567_bib0018) 2003; 95 Deka (10.1016/j.epsr.2020.106567_bib0004) 2017; 5 Vrakopoulou (10.1016/j.epsr.2020.106567_bib0010) 2013; 28 Wainwright (10.1016/j.epsr.2020.106567_bib0022) 2008; 1 Roald (10.1016/j.epsr.2020.106567_bib0001) 2017; 33 DallAnese (10.1016/j.epsr.2020.106567_bib0008) 2017; 32 Deka (10.1016/j.epsr.2020.106567_bib0021) 2019 Margellos (10.1016/j.epsr.2020.106567_bib0015) 2014; 59 Ng (10.1016/j.epsr.2020.106567_bib0020) 2018 Roald (10.1016/j.epsr.2020.106567_bib0012) 2013 10.1016/j.epsr.2020.106567_bib0019 Venzke (10.1016/j.epsr.2020.106567_bib0007) 2018; 33 Stott (10.1016/j.epsr.2020.106567_bib0003) 2009; 24 Chamanbaz (10.1016/j.epsr.2020.106567_bib0016) 2015; 61 Mashayekh (10.1016/j.epsr.2020.106567_bib0011) 2017; 33 Lorca (10.1016/j.epsr.2020.106567_bib0005) 2018; 33 Roald (10.1016/j.epsr.2020.106567_bib0014) 2015 |
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| Title | Stochastic AC optimal power flow: A data-driven approach |
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