A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms
The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/2/511 |
| _version_ | 1827372328460222464 |
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| author | Wenlei Bai Wen Zhang Richard Allmendinger Innocent Enyekwe Kwang Y. Lee |
| author_facet | Wenlei Bai Wen Zhang Richard Allmendinger Innocent Enyekwe Kwang Y. Lee |
| author_sort | Wenlei Bai |
| collection | DOAJ |
| description | The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV allocation (sizing and location) is challenging because it involves mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. Meta-heuristic algorithms have proven their effectiveness in many complex engineering problems. Thus, in this study, we propose to achieve optimal PV allocation by using several basic evolutionary algorithms (EAs), particle swarm optimization (PSO), artificial bee colony (ABC), differential evolution (DE), and their variants, all of which are applied for a study of their performance levels. Two modified unbalanced IEEE test feeders (13 and 37 bus) are developed to evaluate these performance levels, with two objectives: one is to maximize PV penetration, and the other is to minimize the voltage deviation from 1.0 p.u. To handle the computational burden of the sequential power flow and unbalanced network, we adopt an efficient iterative load flow algorithm instead of the commonly used and yet highly simplified forward–backward sweep method. A comparative study of these basic EAs shows their general success in finding a near-optimal solution, except in the case of the DE, which is known for solving continuous optimization problems efficiently. From experiments run 30 times, it is observed that PSO-related algorithms are more efficient and robust in the maximum PV penetration case, while ABC-related algorithms are more efficient and robust in the minimum voltage deviation case. |
| first_indexed | 2024-03-08T10:57:57Z |
| format | Article |
| id | doaj.art-31a971cf4bb54443b8f1a8bff114b550 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T10:57:57Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-31a971cf4bb54443b8f1a8bff114b5502024-01-26T16:22:06ZengMDPI AGEnergies1996-10732024-01-0117251110.3390/en17020511A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary AlgorithmsWenlei Bai0Wen Zhang1Richard Allmendinger2Innocent Enyekwe3Kwang Y. Lee4School of Engineering and Computer Science, Baylor University, Waco, TX 76706, USAHankamer School of Business, Baylor University, Waco, TX 76706, USAAlliance Manchester Business School, University of Manchester, Manchester M15 6PB, UKSchool of Engineering and Computer Science, Baylor University, Waco, TX 76706, USASchool of Engineering and Computer Science, Baylor University, Waco, TX 76706, USAThe growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV allocation (sizing and location) is challenging because it involves mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. Meta-heuristic algorithms have proven their effectiveness in many complex engineering problems. Thus, in this study, we propose to achieve optimal PV allocation by using several basic evolutionary algorithms (EAs), particle swarm optimization (PSO), artificial bee colony (ABC), differential evolution (DE), and their variants, all of which are applied for a study of their performance levels. Two modified unbalanced IEEE test feeders (13 and 37 bus) are developed to evaluate these performance levels, with two objectives: one is to maximize PV penetration, and the other is to minimize the voltage deviation from 1.0 p.u. To handle the computational burden of the sequential power flow and unbalanced network, we adopt an efficient iterative load flow algorithm instead of the commonly used and yet highly simplified forward–backward sweep method. A comparative study of these basic EAs shows their general success in finding a near-optimal solution, except in the case of the DE, which is known for solving continuous optimization problems efficiently. From experiments run 30 times, it is observed that PSO-related algorithms are more efficient and robust in the maximum PV penetration case, while ABC-related algorithms are more efficient and robust in the minimum voltage deviation case.https://www.mdpi.com/1996-1073/17/2/511distributed energy resourcesevolutionary algorithmsoptimal PV allocationPV penetration |
| spellingShingle | Wenlei Bai Wen Zhang Richard Allmendinger Innocent Enyekwe Kwang Y. Lee A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms Energies distributed energy resources evolutionary algorithms optimal PV allocation PV penetration |
| title | A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms |
| title_full | A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms |
| title_fullStr | A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms |
| title_full_unstemmed | A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms |
| title_short | A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms |
| title_sort | comparative study of optimal pv allocation in a distribution network using evolutionary algorithms |
| topic | distributed energy resources evolutionary algorithms optimal PV allocation PV penetration |
| url | https://www.mdpi.com/1996-1073/17/2/511 |
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