Wind Farm Cable Connection Layout Optimization Using a Genetic Algorithm and Integer Linear Programming
This work addresses the wind farm (WF) optimization layout considering several substations. It is given a set of wind turbines jointly with a set of substations, and the goal is to obtain the optimal design to minimize the infrastructure cost and the cost of electrical energy losses during the wind...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Computation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-3197/11/12/241 |
| _version_ | 1797381517513588736 |
|---|---|
| author | Eduardo J. Solteiro Pires Adelaide Cerveira José Baptista |
| author_facet | Eduardo J. Solteiro Pires Adelaide Cerveira José Baptista |
| author_sort | Eduardo J. Solteiro Pires |
| collection | DOAJ |
| description | This work addresses the wind farm (WF) optimization layout considering several substations. It is given a set of wind turbines jointly with a set of substations, and the goal is to obtain the optimal design to minimize the infrastructure cost and the cost of electrical energy losses during the wind farm lifetime. The turbine set is partitioned into subsets to assign to each substation. The cable type and the connections to collect wind turbine-produced energy, forwarding to the corresponding substation, are selected in each subset. The technique proposed uses a genetic algorithm (GA) and an integer linear programming (ILP) model simultaneously. The GA creates a partition in the turbine set and assigns each of the obtained subsets to a substation to optimize a fitness function that corresponds to the minimum total cost of the WF layout. The fitness function evaluation requires solving an ILP model for each substation to determine the optimal cable connection layout. This methodology is applied to four onshore WFs. The obtained results show that the solution performance of the proposed approach reaches up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.17</mn></mrow></semantics></math></inline-formula>% of economic savings when compared to the clustering with ILP approach (an exact approach). |
| first_indexed | 2024-03-08T20:52:43Z |
| format | Article |
| id | doaj.art-68ce49c536644058921678fb53ac59fd |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-08T20:52:43Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-68ce49c536644058921678fb53ac59fd2023-12-22T14:01:16ZengMDPI AGComputation2079-31972023-12-01111224110.3390/computation11120241Wind Farm Cable Connection Layout Optimization Using a Genetic Algorithm and Integer Linear ProgrammingEduardo J. Solteiro Pires0Adelaide Cerveira1José Baptista2Department of Engineering, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, PortugalINESC-TEC UTAD’s Pole, Quinta de Prados, 5000-801 Vila Real, PortugalDepartment of Engineering, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, PortugalThis work addresses the wind farm (WF) optimization layout considering several substations. It is given a set of wind turbines jointly with a set of substations, and the goal is to obtain the optimal design to minimize the infrastructure cost and the cost of electrical energy losses during the wind farm lifetime. The turbine set is partitioned into subsets to assign to each substation. The cable type and the connections to collect wind turbine-produced energy, forwarding to the corresponding substation, are selected in each subset. The technique proposed uses a genetic algorithm (GA) and an integer linear programming (ILP) model simultaneously. The GA creates a partition in the turbine set and assigns each of the obtained subsets to a substation to optimize a fitness function that corresponds to the minimum total cost of the WF layout. The fitness function evaluation requires solving an ILP model for each substation to determine the optimal cable connection layout. This methodology is applied to four onshore WFs. The obtained results show that the solution performance of the proposed approach reaches up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.17</mn></mrow></semantics></math></inline-formula>% of economic savings when compared to the clustering with ILP approach (an exact approach).https://www.mdpi.com/2079-3197/11/12/241wind farmcable connection layoutgenetic algorithmsinteger linear programming |
| spellingShingle | Eduardo J. Solteiro Pires Adelaide Cerveira José Baptista Wind Farm Cable Connection Layout Optimization Using a Genetic Algorithm and Integer Linear Programming Computation wind farm cable connection layout genetic algorithms integer linear programming |
| title | Wind Farm Cable Connection Layout Optimization Using a Genetic Algorithm and Integer Linear Programming |
| title_full | Wind Farm Cable Connection Layout Optimization Using a Genetic Algorithm and Integer Linear Programming |
| title_fullStr | Wind Farm Cable Connection Layout Optimization Using a Genetic Algorithm and Integer Linear Programming |
| title_full_unstemmed | Wind Farm Cable Connection Layout Optimization Using a Genetic Algorithm and Integer Linear Programming |
| title_short | Wind Farm Cable Connection Layout Optimization Using a Genetic Algorithm and Integer Linear Programming |
| title_sort | wind farm cable connection layout optimization using a genetic algorithm and integer linear programming |
| topic | wind farm cable connection layout genetic algorithms integer linear programming |
| url | https://www.mdpi.com/2079-3197/11/12/241 |
| work_keys_str_mv | AT eduardojsolteiropires windfarmcableconnectionlayoutoptimizationusingageneticalgorithmandintegerlinearprogramming AT adelaidecerveira windfarmcableconnectionlayoutoptimizationusingageneticalgorithmandintegerlinearprogramming AT josebaptista windfarmcableconnectionlayoutoptimizationusingageneticalgorithmandintegerlinearprogramming |