Optimal Power Flow Solution of Wind-Integrated Power System Using Novel Metaheuristic Method
Wind energy is particularly significant in the power system today since it is a cheap and clean power source. The unpredictability of wind speed leads to uncertainty in devolved power which increases the difficulty in wind energy system operation. This paper presents a stochastic optimal power flow...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/19/6117 |
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| author | Amr Khaled Khamees Almoataz Y. Abdelaziz Makram R. Eskaros Adel El-Shahat Mahmoud A. Attia |
| author_facet | Amr Khaled Khamees Almoataz Y. Abdelaziz Makram R. Eskaros Adel El-Shahat Mahmoud A. Attia |
| author_sort | Amr Khaled Khamees |
| collection | DOAJ |
| description | Wind energy is particularly significant in the power system today since it is a cheap and clean power source. The unpredictability of wind speed leads to uncertainty in devolved power which increases the difficulty in wind energy system operation. This paper presents a stochastic optimal power flow (SCOPF) for obtaining the best scheduled power from wind farms while lowering total operational costs. A novel metaheuristics method called Aquila Optimizer (AO) is used to address the SCOPF problem due to its highly nonconvex and nonlinear nature. Wind speed is represented by the Weibull probability distribution function (PDF), which is used to anticipate the cost of wind-generated power from a wind farm based on scheduled power. Weibull parameters that provide the best match to wind data are estimated using the AO approach. The suggested wind generation cost model includes the opportunity costs of wind power underestimation and overestimation. Three IEEE systems (30, 57, and 118) are utilized to solve optimal power flow (OPF) using the AO method to prove the accuracy of this method, and results are compared with other metaheuristic methods. With six scenarios for the penalty and reverse cost coefficients, SCOPF is applied to a modified IEEE-30 bus system with two wind farms to obtain the optimal scheduled power from the two wind farms which reduces total operation cost. |
| first_indexed | 2024-03-10T07:03:27Z |
| format | Article |
| id | doaj.art-0feede77e3b04efab6cc551b3ac023b9 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T07:03:27Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0feede77e3b04efab6cc551b3ac023b92023-11-22T15:59:31ZengMDPI AGEnergies1996-10732021-09-011419611710.3390/en14196117Optimal Power Flow Solution of Wind-Integrated Power System Using Novel Metaheuristic MethodAmr Khaled Khamees0Almoataz Y. Abdelaziz1Makram R. Eskaros2Adel El-Shahat3Mahmoud A. Attia4Engineering Physics and Mathematics Department, Ain Shams University, Cairo 11517, EgyptFaculty of Engineering and Technology, Future University in Egypt, Cairo 11835, EgyptEngineering Physics and Mathematics Department, Ain Shams University, Cairo 11517, EgyptEnergy Technology Program, School of Engineering Technology, Purdue University, West Lafayette, IN 46202, USAElectrical Power & Machines Department, Ain Shams University, Cairo 11517, EgyptWind energy is particularly significant in the power system today since it is a cheap and clean power source. The unpredictability of wind speed leads to uncertainty in devolved power which increases the difficulty in wind energy system operation. This paper presents a stochastic optimal power flow (SCOPF) for obtaining the best scheduled power from wind farms while lowering total operational costs. A novel metaheuristics method called Aquila Optimizer (AO) is used to address the SCOPF problem due to its highly nonconvex and nonlinear nature. Wind speed is represented by the Weibull probability distribution function (PDF), which is used to anticipate the cost of wind-generated power from a wind farm based on scheduled power. Weibull parameters that provide the best match to wind data are estimated using the AO approach. The suggested wind generation cost model includes the opportunity costs of wind power underestimation and overestimation. Three IEEE systems (30, 57, and 118) are utilized to solve optimal power flow (OPF) using the AO method to prove the accuracy of this method, and results are compared with other metaheuristic methods. With six scenarios for the penalty and reverse cost coefficients, SCOPF is applied to a modified IEEE-30 bus system with two wind farms to obtain the optimal scheduled power from the two wind farms which reduces total operation cost.https://www.mdpi.com/1996-1073/14/19/6117wind energystochastic optimal power flowWeibull probability distributionAquila Optimizer |
| spellingShingle | Amr Khaled Khamees Almoataz Y. Abdelaziz Makram R. Eskaros Adel El-Shahat Mahmoud A. Attia Optimal Power Flow Solution of Wind-Integrated Power System Using Novel Metaheuristic Method Energies wind energy stochastic optimal power flow Weibull probability distribution Aquila Optimizer |
| title | Optimal Power Flow Solution of Wind-Integrated Power System Using Novel Metaheuristic Method |
| title_full | Optimal Power Flow Solution of Wind-Integrated Power System Using Novel Metaheuristic Method |
| title_fullStr | Optimal Power Flow Solution of Wind-Integrated Power System Using Novel Metaheuristic Method |
| title_full_unstemmed | Optimal Power Flow Solution of Wind-Integrated Power System Using Novel Metaheuristic Method |
| title_short | Optimal Power Flow Solution of Wind-Integrated Power System Using Novel Metaheuristic Method |
| title_sort | optimal power flow solution of wind integrated power system using novel metaheuristic method |
| topic | wind energy stochastic optimal power flow Weibull probability distribution Aquila Optimizer |
| url | https://www.mdpi.com/1996-1073/14/19/6117 |
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