Heterogeneous Unit Clustering for Efficient Operational Flexibility Modeling for Strategic Models
The increasing penetration of wind generation has led to significant improvements in unit commitment models. However, long-term capacity planning methods have not been similarly modified to address the challenges of a system with a large fraction of generation from variable sources. Designing future...
| Main Authors: | , |
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| Format: | Working Paper |
| Language: | en_US |
| Published: |
Massachusetts Institute of Technology. Engineering Systems Division
2016
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| Online Access: | http://hdl.handle.net/1721.1/102960 |
| _version_ | 1811090080248365056 |
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| author | Palmintier, Bryan S. Webster, Mort D. |
| author_facet | Palmintier, Bryan S. Webster, Mort D. |
| author_sort | Palmintier, Bryan S. |
| collection | MIT |
| description | The increasing penetration of wind generation has led to significant improvements in unit commitment models. However, long-term capacity planning methods have not been similarly modified to address the challenges of a system with a large fraction of generation from variable sources. Designing future capacity mixes with adequate flexibility requires an embedded approximation of the unit commitment problem to capture operating constraints. Here we propose a method, based on clustering units, for a simplified unit commitment model with dramatic improvements in solution time that enable its use as a submodel within a capacity expansion framework. Heterogeneous clustering speeds computation by aggregating similar but non-identical units thereby replacing large numbers of binary commitment variables with fewer integers that still capture individual unit decisions and constraints. We demonstrate the trade-off between accuracy and run-time for different levels of aggregation. A numeric example using an ERCOT-based 205-unit system illustrates that careful aggregation introduces errors of 0.05-0.9% across several metrics while providing several orders of magnitude faster solution times (400x) compared to traditional binary formulations and further aggregation increases errors slightly (~2x) with further speedup (2000x). We also compare other simplifications that can provide an additional order of magnitude speed-up for some problems. |
| first_indexed | 2024-09-23T14:32:43Z |
| format | Working Paper |
| id | mit-1721.1/102960 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:32:43Z |
| publishDate | 2016 |
| publisher | Massachusetts Institute of Technology. Engineering Systems Division |
| record_format | dspace |
| spelling | mit-1721.1/1029602019-04-12T16:25:46Z Heterogeneous Unit Clustering for Efficient Operational Flexibility Modeling for Strategic Models Palmintier, Bryan S. Webster, Mort D. The increasing penetration of wind generation has led to significant improvements in unit commitment models. However, long-term capacity planning methods have not been similarly modified to address the challenges of a system with a large fraction of generation from variable sources. Designing future capacity mixes with adequate flexibility requires an embedded approximation of the unit commitment problem to capture operating constraints. Here we propose a method, based on clustering units, for a simplified unit commitment model with dramatic improvements in solution time that enable its use as a submodel within a capacity expansion framework. Heterogeneous clustering speeds computation by aggregating similar but non-identical units thereby replacing large numbers of binary commitment variables with fewer integers that still capture individual unit decisions and constraints. We demonstrate the trade-off between accuracy and run-time for different levels of aggregation. A numeric example using an ERCOT-based 205-unit system illustrates that careful aggregation introduces errors of 0.05-0.9% across several metrics while providing several orders of magnitude faster solution times (400x) compared to traditional binary formulations and further aggregation increases errors slightly (~2x) with further speedup (2000x). We also compare other simplifications that can provide an additional order of magnitude speed-up for some problems. 2016-06-06T13:39:27Z 2016-06-06T13:39:27Z 2013-01 Working Paper http://hdl.handle.net/1721.1/102960 en_US ESD Working Papers;ESD-WP-2013-04 application/pdf Massachusetts Institute of Technology. Engineering Systems Division |
| spellingShingle | Palmintier, Bryan S. Webster, Mort D. Heterogeneous Unit Clustering for Efficient Operational Flexibility Modeling for Strategic Models |
| title | Heterogeneous Unit Clustering for Efficient Operational Flexibility Modeling for Strategic Models |
| title_full | Heterogeneous Unit Clustering for Efficient Operational Flexibility Modeling for Strategic Models |
| title_fullStr | Heterogeneous Unit Clustering for Efficient Operational Flexibility Modeling for Strategic Models |
| title_full_unstemmed | Heterogeneous Unit Clustering for Efficient Operational Flexibility Modeling for Strategic Models |
| title_short | Heterogeneous Unit Clustering for Efficient Operational Flexibility Modeling for Strategic Models |
| title_sort | heterogeneous unit clustering for efficient operational flexibility modeling for strategic models |
| url | http://hdl.handle.net/1721.1/102960 |
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