Stochastic Resource Allocation for Electricity Distribution Network Resilience
In recent years, it has become crucial to improve the resilience of electricity distribution networks (DNs) against storm-induced failures. Microgrids enabled by Distributed Energy Resources (DERs) can significantly help speed up re-energization of loads, particularly in the complete absence of bulk...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/132682 |
| _version_ | 1826200454400311296 |
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| author | Chang, Derek Shelar, Devendra Amin, Saurabh |
| author2 | Massachusetts Institute of Technology. Center for Computational Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Center for Computational Science and Engineering Chang, Derek Shelar, Devendra Amin, Saurabh |
| author_sort | Chang, Derek |
| collection | MIT |
| description | In recent years, it has become crucial to improve the resilience of electricity distribution networks (DNs) against storm-induced failures. Microgrids enabled by Distributed Energy Resources (DERs) can significantly help speed up re-energization of loads, particularly in the complete absence of bulk power supply. We describe an integrated approach which considers a pre-storm DER allocation problem under the uncertainty of failure scenarios as well as a post-storm dispatch problem in microgrids during the multi-period repair of the failed components. This problem is computationally challenging because the number of scenarios (resp. binary variables) increases exponentially (resp. quadratically) in the network size. Our overall solution approach for solving the resulting two-stage mixed-integer linear program (MILP) involves implementing the sample average approximation (SAA) method and Benders Decomposition. Additionally, we implement a greedy approach to reduce the computational time requirements of the post-storm repair scheduling and dispatch problem. The optimality of the resulting solution is evaluated on a modified IEEE 36-node network. |
| first_indexed | 2024-09-23T11:36:43Z |
| format | Article |
| id | mit-1721.1/132682 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:36:43Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1326822024-05-21T18:44:23Z Stochastic Resource Allocation for Electricity Distribution Network Resilience Chang, Derek Shelar, Devendra Amin, Saurabh Massachusetts Institute of Technology. Center for Computational Science and Engineering Massachusetts Institute of Technology. Department of Civil and Environmental Engineering In recent years, it has become crucial to improve the resilience of electricity distribution networks (DNs) against storm-induced failures. Microgrids enabled by Distributed Energy Resources (DERs) can significantly help speed up re-energization of loads, particularly in the complete absence of bulk power supply. We describe an integrated approach which considers a pre-storm DER allocation problem under the uncertainty of failure scenarios as well as a post-storm dispatch problem in microgrids during the multi-period repair of the failed components. This problem is computationally challenging because the number of scenarios (resp. binary variables) increases exponentially (resp. quadratically) in the network size. Our overall solution approach for solving the resulting two-stage mixed-integer linear program (MILP) involves implementing the sample average approximation (SAA) method and Benders Decomposition. Additionally, we implement a greedy approach to reduce the computational time requirements of the post-storm repair scheduling and dispatch problem. The optimality of the resulting solution is evaluated on a modified IEEE 36-node network. 2021-10-01T15:40:47Z 2021-10-01T15:40:47Z 2020-07 2021-10-01T14:18:43Z Article http://purl.org/eprint/type/ConferencePaper 2378-5861 https://hdl.handle.net/1721.1/132682 D. Chang, D. Shelar and S. Amin, "Stochastic Resource Allocation for Electricity Distribution Network Resilience," 2020 American Control Conference (ACC), 2020, pp. 198-203 © 2020 AACC. en 10.23919/ACC45564.2020.9147879 Proceedings of the American Control Conference Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Chang, Derek Shelar, Devendra Amin, Saurabh Stochastic Resource Allocation for Electricity Distribution Network Resilience |
| title | Stochastic Resource Allocation for Electricity Distribution Network Resilience |
| title_full | Stochastic Resource Allocation for Electricity Distribution Network Resilience |
| title_fullStr | Stochastic Resource Allocation for Electricity Distribution Network Resilience |
| title_full_unstemmed | Stochastic Resource Allocation for Electricity Distribution Network Resilience |
| title_short | Stochastic Resource Allocation for Electricity Distribution Network Resilience |
| title_sort | stochastic resource allocation for electricity distribution network resilience |
| url | https://hdl.handle.net/1721.1/132682 |
| work_keys_str_mv | AT changderek stochasticresourceallocationforelectricitydistributionnetworkresilience AT shelardevendra stochasticresourceallocationforelectricitydistributionnetworkresilience AT aminsaurabh stochasticresourceallocationforelectricitydistributionnetworkresilience |