Optimal power flow analysis considering renewable energy resources uncertainty based on an improved wild horse optimizer
In recent years, electricity networks across the globe have undergone rapid development, especially with the incorporation of various renewable energy sources (RES). The goal is to increase the penetration level of RES in the power grid to maximize energy efficiency. However, the optimal power flow...
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| Vydáno v: | IET generation, transmission & distribution Ročník 17; číslo 16; s. 3582 - 3606 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Wiley
01.08.2023
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| ISSN: | 1751-8687, 1751-8695, 1751-8695 |
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| Abstract | In recent years, electricity networks across the globe have undergone rapid development, especially with the incorporation of various renewable energy sources (RES). The goal is to increase the penetration level of RES in the power grid to maximize energy efficiency. However, the optimal power flow (OPF) problem for conventional power generation with RES integration is highly complex, non‐linear, and non‐convex, and this complexity is further compounded when stochastic RES is integrated into the network. To address this problem, this article proposes an elite evolutionary strategy (EES) based on evolutionary approaches to improve the Wild Horse Optimizer (WHO), creating an enhanced hybrid technique called EESWHO. The proposed technique's effectiveness and robustness were tested on 23 numerical optimization test functions, including seven unimodal, six multimodal, and ten composite test functions. Furthermore, the EESWHO was applied to the modified IEEE‐30 bus test system to demonstrate its supremacy and efficacy in achieving the optimal solution. The simulation results show that the proposed EESWHO algorithm is highly effective and robust in achieving the optimal solution to the OPF problem with stochastic RES. This approach provides a practical solution to the challenges posed by the integration of RES into power networks, allowing for maximum energy efficiency while minimizing system complexity.
An elite evolutionary strategy (EES) based on evolutionary approaches to improve the Wild Horse Optimizer (WHO), creating an enhanced hybrid technique called EESWHO. The proposed technique's effectiveness and robustness were tested on 23 numerical optimization test functions, including seven unimodal, six multimodal, and ten composite test functions. Furthermore, the EESWHO was applied to the modified IEEE‐30 bus test system to demonstrate its supremacy and efficacy in achieving the optimal solution. The simulation results show that the proposed EESWHO algorithm is highly effective and robust in achieving the optimal solution to the OPF problem with stochastic RES. |
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| AbstractList | In recent years, electricity networks across the globe have undergone rapid development, especially with the incorporation of various renewable energy sources (RES). The goal is to increase the penetration level of RES in the power grid to maximize energy efficiency. However, the optimal power flow (OPF) problem for conventional power generation with RES integration is highly complex, non‐linear, and non‐convex, and this complexity is further compounded when stochastic RES is integrated into the network. To address this problem, this article proposes an elite evolutionary strategy (EES) based on evolutionary approaches to improve the Wild Horse Optimizer (WHO), creating an enhanced hybrid technique called EESWHO. The proposed technique's effectiveness and robustness were tested on 23 numerical optimization test functions, including seven unimodal, six multimodal, and ten composite test functions. Furthermore, the EESWHO was applied to the modified IEEE‐30 bus test system to demonstrate its supremacy and efficacy in achieving the optimal solution. The simulation results show that the proposed EESWHO algorithm is highly effective and robust in achieving the optimal solution to the OPF problem with stochastic RES. This approach provides a practical solution to the challenges posed by the integration of RES into power networks, allowing for maximum energy efficiency while minimizing system complexity.
An elite evolutionary strategy (EES) based on evolutionary approaches to improve the Wild Horse Optimizer (WHO), creating an enhanced hybrid technique called EESWHO. The proposed technique's effectiveness and robustness were tested on 23 numerical optimization test functions, including seven unimodal, six multimodal, and ten composite test functions. Furthermore, the EESWHO was applied to the modified IEEE‐30 bus test system to demonstrate its supremacy and efficacy in achieving the optimal solution. The simulation results show that the proposed EESWHO algorithm is highly effective and robust in achieving the optimal solution to the OPF problem with stochastic RES. In recent years, electricity networks across the globe have undergone rapid development, especially with the incorporation of various renewable energy sources (RES). The goal is to increase the penetration level of RES in the power grid to maximize energy efficiency. However, the optimal power flow (OPF) problem for conventional power generation with RES integration is highly complex, non-linear, and non-convex, and this complexity is further compounded when stochastic RES is integrated into the network. To address this problem, this article proposes an elite evolutionary strategy (EES) based on evolutionary approaches to improve the Wild Horse Optimizer (WHO), creating an enhanced hybrid technique called EESWHO. The proposed techniques effectiveness and robustness were tested on 23 numerical optimization test functions, including seven unimodal, six multimodal, and ten composite test functions. Furthermore, the EESWHO was applied to the modified IEEE-30 bus test system to demonstrate its supremacy and efficacy in achieving the optimal solution. The simulation results show that the proposed EESWHO algorithm is highly effective and robust in achieving the optimal solution to the OPF problem with stochastic RES. This approach provides a practical solution to the challenges posed by the integration of RES into power networks, allowing for maximum energy efficiency while minimizing system complexity. Abstract In recent years, electricity networks across the globe have undergone rapid development, especially with the incorporation of various renewable energy sources (RES). The goal is to increase the penetration level of RES in the power grid to maximize energy efficiency. However, the optimal power flow (OPF) problem for conventional power generation with RES integration is highly complex, non‐linear, and non‐convex, and this complexity is further compounded when stochastic RES is integrated into the network. To address this problem, this article proposes an elite evolutionary strategy (EES) based on evolutionary approaches to improve the Wild Horse Optimizer (WHO), creating an enhanced hybrid technique called EESWHO. The proposed technique's effectiveness and robustness were tested on 23 numerical optimization test functions, including seven unimodal, six multimodal, and ten composite test functions. Furthermore, the EESWHO was applied to the modified IEEE‐30 bus test system to demonstrate its supremacy and efficacy in achieving the optimal solution. The simulation results show that the proposed EESWHO algorithm is highly effective and robust in achieving the optimal solution to the OPF problem with stochastic RES. This approach provides a practical solution to the challenges posed by the integration of RES into power networks, allowing for maximum energy efficiency while minimizing system complexity. In recent years, electricity networks across the globe have undergone rapid development, especially with the incorporation of various renewable energy sources (RES). The goal is to increase the penetration level of RES in the power grid to maximize energy efficiency. However, the optimal power flow (OPF) problem for conventional power generation with RES integration is highly complex, non‐linear, and non‐convex, and this complexity is further compounded when stochastic RES is integrated into the network. To address this problem, this article proposes an elite evolutionary strategy (EES) based on evolutionary approaches to improve the Wild Horse Optimizer (WHO), creating an enhanced hybrid technique called EESWHO. The proposed technique's effectiveness and robustness were tested on 23 numerical optimization test functions, including seven unimodal, six multimodal, and ten composite test functions. Furthermore, the EESWHO was applied to the modified IEEE‐30 bus test system to demonstrate its supremacy and efficacy in achieving the optimal solution. The simulation results show that the proposed EESWHO algorithm is highly effective and robust in achieving the optimal solution to the OPF problem with stochastic RES. This approach provides a practical solution to the challenges posed by the integration of RES into power networks, allowing for maximum energy efficiency while minimizing system complexity. |
| Author | Kamel, Salah Hussien, Abdelazim G. Hassan, Mohamed H. |
| Author_xml | – sequence: 1 givenname: Mohamed H. surname: Hassan fullname: Hassan, Mohamed H. organization: Ministry of Electricity and Renewable Energy – sequence: 2 givenname: Salah orcidid: 0000-0001-9505-5386 surname: Kamel fullname: Kamel, Salah organization: Aswan University – sequence: 3 givenname: Abdelazim G. orcidid: 0000-0001-5394-0678 surname: Hussien fullname: Hussien, Abdelazim G. email: abdelazim.hussien@liu.se organization: Middle East University |
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| Keywords | elite evolutionary strategy optimal power flow wild horse optimizer algorithm stochastic renewable energy sources |
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