A Hybrid Optimization Method Combining Network Expansion Planning and Switching State Optimization
| dc.date.accessioned | 2020-09-09T12:05:29Z | |
| dc.date.available | 2020-09-09T12:05:29Z | |
| dc.date.issued | 2020-07-01 | |
| dc.identifier | doi:10.17170/kobra-202009091753 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/11792 | |
| dc.description.sponsorship | Gefördert durch den Publikationsfonds der Universität Kassel | ger |
| dc.language.iso | eng | eng |
| dc.rights | Namensnennung 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | greedy algorithms | eng |
| dc.subject | heuristic algorithms | eng |
| dc.subject | mathematical programming | eng |
| dc.subject | optimization methods | eng |
| dc.subject | power system planning | eng |
| dc.subject.ddc | 004 | |
| dc.subject.ddc | 510 | |
| dc.title | A Hybrid Optimization Method Combining Network Expansion Planning and Switching State Optimization | eng |
| dc.type | Aufsatz | |
| dcterms.abstract | Combining switching state optimization (SSO) and network expansion planning (NEP) in AC systems results in a mixed-integer non-linear optimization problem. Two methodically different solution approaches are mathematical programming and heuristic methods. In this paper, we develop a hybrid optimization method combining both methods to solve the combined optimization of SSO and NEP. The presented hybrid method applies a DC programming model as an initialization strategy to reduce the search space of the heuristic. A greedy heuristic ensures that the obtained solutions are AC feasible. We compare the hybrid method with other heuristic methods and three mathematical programming models on the same set of planning problems. We show optimization results for four realistic sized power system study cases. Evaluation criteria are convergence, solution cost, and run time. Results show that the hybrid method is able to find a higher number of valid AC-solutions in comparisons to the mathematical programming methods. Furthermore, the obtained solutions have lower expansions costs and are obtained in a shorter run-time compared to the remaining methods for the analyzed study cases. As an addition to this paper, the hybrid implementation and the defined benchmark cases are available as open-source software. | eng |
| dcterms.accessRights | open access | |
| dcterms.creator | Schäfer, Florian | |
| dcterms.creator | Scheidler, Alexander | |
| dcterms.creator | Braun, Martin | |
| dc.relation.doi | doi:10.1109/OAJPE.2020.3006344 | |
| dc.subject.swd | Greedy-Algorithmus | ger |
| dc.subject.swd | Heuristik | ger |
| dc.subject.swd | Programmierung | ger |
| dc.subject.swd | Optimierung | ger |
| dc.type.version | publishedVersion | |
| dcterms.source.identifier | EISSN: 2687-7910 | |
| dcterms.source.journal | IEEE Open Access Journal of Power and Energy | eng |
| dcterms.source.pageinfo | 234-242 | |
| dcterms.source.volume | Volume 7 | |
| kup.iskup | false |
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