Automatic generation control optimization for power system resilience under real world load variations using genetic algorithm
Abstract Modern power systems must be resilient to sudden load variations in order to keep the system stable. For Automatic Generation Control (AGC), single load change is impractical and need further analysis. This study comprehensively explore the performance of AGC in a two-area interconnected po...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-03608-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849769323427528704 |
|---|---|
| author | Muhammad Ayaz Dur-e-Zehra Baig Syed Muhammad Hur Rizvi Salah S. Alharbi Sheeraz Iqbal Md. Shafiullah |
| author_facet | Muhammad Ayaz Dur-e-Zehra Baig Syed Muhammad Hur Rizvi Salah S. Alharbi Sheeraz Iqbal Md. Shafiullah |
| author_sort | Muhammad Ayaz |
| collection | DOAJ |
| description | Abstract Modern power systems must be resilient to sudden load variations in order to keep the system stable. For Automatic Generation Control (AGC), single load change is impractical and need further analysis. This study comprehensively explore the performance of AGC in a two-area interconnected power system, focusing on a wide range load variations that can exists in realistic power systems consisting from 100 to 300 MW in both increments and decrements. The performance of three control strategies-Conventional AGC (CAGC), Tie-Line Bias (TLB) Control, and Genetic Algorithm-Optimized PID (GA-PID)-is assessed across 12 distinct cases, each tested under these three scenarios. A total of 360 tests are conducted, with performance measured by key metrics, including overshoot, undershoot, settling time, and steady-state accuracy for both areas. The results demonstrate that GA-PID consistently outperforms CAGC and TLB in minimizing transient deviations, ensuring faster stabilization, and maintaining steady-state accuracy. For load increases, GA-PID reduces overshoot by up to 90% and eliminates undershoot in several cases. In comparison, CAGC and TLB show notable weaknesses when dealing with larger disturbances, such as extended oscillations and bigger deviations. The results highlight how effective GA-PID is as a strong and flexible control method, which is crucial for today’s power systems that need to manage unpredictable changes in load. |
| format | Article |
| id | doaj-art-7876ee3fafdd4f71987161387fa89063 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7876ee3fafdd4f71987161387fa890632025-08-20T03:03:27ZengNature PortfolioScientific Reports2045-23222025-07-0115112910.1038/s41598-025-03608-1Automatic generation control optimization for power system resilience under real world load variations using genetic algorithmMuhammad Ayaz0Dur-e-Zehra Baig1Syed Muhammad Hur Rizvi2Salah S. Alharbi3Sheeraz Iqbal4Md. Shafiullah5Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyGhulam Ishaq Khan Institute of Engineering Sciences and TechnologyDhanani School of Science and Engineering, Habib UniversityDepartment of Electrical Engineering, Faculty of Engineering, Al-Baha UniversityInterdisciplinary Research Center for Sustainable Energy Systems (IRC-SES), Research and Innovation, King Fahd University of Petroleum & MineralsInterdisciplinary Research Center for Sustainable Energy Systems (IRC-SES), Research and Innovation, King Fahd University of Petroleum & MineralsAbstract Modern power systems must be resilient to sudden load variations in order to keep the system stable. For Automatic Generation Control (AGC), single load change is impractical and need further analysis. This study comprehensively explore the performance of AGC in a two-area interconnected power system, focusing on a wide range load variations that can exists in realistic power systems consisting from 100 to 300 MW in both increments and decrements. The performance of three control strategies-Conventional AGC (CAGC), Tie-Line Bias (TLB) Control, and Genetic Algorithm-Optimized PID (GA-PID)-is assessed across 12 distinct cases, each tested under these three scenarios. A total of 360 tests are conducted, with performance measured by key metrics, including overshoot, undershoot, settling time, and steady-state accuracy for both areas. The results demonstrate that GA-PID consistently outperforms CAGC and TLB in minimizing transient deviations, ensuring faster stabilization, and maintaining steady-state accuracy. For load increases, GA-PID reduces overshoot by up to 90% and eliminates undershoot in several cases. In comparison, CAGC and TLB show notable weaknesses when dealing with larger disturbances, such as extended oscillations and bigger deviations. The results highlight how effective GA-PID is as a strong and flexible control method, which is crucial for today’s power systems that need to manage unpredictable changes in load.https://doi.org/10.1038/s41598-025-03608-1 |
| spellingShingle | Muhammad Ayaz Dur-e-Zehra Baig Syed Muhammad Hur Rizvi Salah S. Alharbi Sheeraz Iqbal Md. Shafiullah Automatic generation control optimization for power system resilience under real world load variations using genetic algorithm Scientific Reports |
| title | Automatic generation control optimization for power system resilience under real world load variations using genetic algorithm |
| title_full | Automatic generation control optimization for power system resilience under real world load variations using genetic algorithm |
| title_fullStr | Automatic generation control optimization for power system resilience under real world load variations using genetic algorithm |
| title_full_unstemmed | Automatic generation control optimization for power system resilience under real world load variations using genetic algorithm |
| title_short | Automatic generation control optimization for power system resilience under real world load variations using genetic algorithm |
| title_sort | automatic generation control optimization for power system resilience under real world load variations using genetic algorithm |
| url | https://doi.org/10.1038/s41598-025-03608-1 |
| work_keys_str_mv | AT muhammadayaz automaticgenerationcontroloptimizationforpowersystemresilienceunderrealworldloadvariationsusinggeneticalgorithm AT durezehrabaig automaticgenerationcontroloptimizationforpowersystemresilienceunderrealworldloadvariationsusinggeneticalgorithm AT syedmuhammadhurrizvi automaticgenerationcontroloptimizationforpowersystemresilienceunderrealworldloadvariationsusinggeneticalgorithm AT salahsalharbi automaticgenerationcontroloptimizationforpowersystemresilienceunderrealworldloadvariationsusinggeneticalgorithm AT sheeraziqbal automaticgenerationcontroloptimizationforpowersystemresilienceunderrealworldloadvariationsusinggeneticalgorithm AT mdshafiullah automaticgenerationcontroloptimizationforpowersystemresilienceunderrealworldloadvariationsusinggeneticalgorithm |