Mohamed A.M. Shaheen , Hany M. Hasanien , Ibrahim Alsaleh , Abdullah Alassaf , Miao Zhang , Ayoob Alateeq
{"title":"地热发电厂等可再生能源不确定电力系统的最优潮流","authors":"Mohamed A.M. Shaheen , Hany M. Hasanien , Ibrahim Alsaleh , Abdullah Alassaf , Miao Zhang , Ayoob Alateeq","doi":"10.1016/j.asej.2025.103784","DOIUrl":null,"url":null,"abstract":"<div><div>This article proposes an innovative application of the Catch Fish Optimization method (CFOA) to effectively handle the complex Probabilistic Optimal Power Flow (POPF) optimization problem in modern power grids. The growing penetration of stochastic renewable energy sources, photovoltaic (PV) and wind energy, plus the presence of geothermal generation, causes uncertainties. The classical Optimal Power Flow (OPF) can’t address such uncertainties. This paper introduces the capabilities of the CFOA in addressing such uncertainties. The target is to determine optimal design variables considering the probabilistic models of generation. The introduced algorithm has been investigated on IEEE 30- and 118-bus networks. Moreover, these systems are modified to include PV, wind, and geothermal units. Both fixed and dynamic load profiles are included in the study. The simulation results for the 30-bus system show a reduction in total daily fuel costs of approximately 9.64% when compared with the no-renewables baseline. For the larger 118-bus system, the daily fuel cost reduction was even more significant, at approximately 15.91%. The results obtained using the CFOA are compared with those from other well-established algorithms. The comparative analysis confirms the greater CFOA performance in terms of the convergence speed besides the robustness. This analysis affirms the effectiveness of the introduced optimization techniques in tackling the POPF problem. The current research paves the way for further investigation into the application and enhancement of the CFOA for various power system optimization problems.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 12","pages":"Article 103784"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimal power flow in power systems with renewable energy resources uncertainty including geothermal power plants\",\"authors\":\"Mohamed A.M. Shaheen , Hany M. Hasanien , Ibrahim Alsaleh , Abdullah Alassaf , Miao Zhang , Ayoob Alateeq\",\"doi\":\"10.1016/j.asej.2025.103784\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This article proposes an innovative application of the Catch Fish Optimization method (CFOA) to effectively handle the complex Probabilistic Optimal Power Flow (POPF) optimization problem in modern power grids. The growing penetration of stochastic renewable energy sources, photovoltaic (PV) and wind energy, plus the presence of geothermal generation, causes uncertainties. The classical Optimal Power Flow (OPF) can’t address such uncertainties. This paper introduces the capabilities of the CFOA in addressing such uncertainties. The target is to determine optimal design variables considering the probabilistic models of generation. The introduced algorithm has been investigated on IEEE 30- and 118-bus networks. Moreover, these systems are modified to include PV, wind, and geothermal units. Both fixed and dynamic load profiles are included in the study. The simulation results for the 30-bus system show a reduction in total daily fuel costs of approximately 9.64% when compared with the no-renewables baseline. For the larger 118-bus system, the daily fuel cost reduction was even more significant, at approximately 15.91%. The results obtained using the CFOA are compared with those from other well-established algorithms. The comparative analysis confirms the greater CFOA performance in terms of the convergence speed besides the robustness. This analysis affirms the effectiveness of the introduced optimization techniques in tackling the POPF problem. The current research paves the way for further investigation into the application and enhancement of the CFOA for various power system optimization problems.</div></div>\",\"PeriodicalId\":48648,\"journal\":{\"name\":\"Ain Shams Engineering Journal\",\"volume\":\"16 12\",\"pages\":\"Article 103784\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ain Shams Engineering Journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2090447925005258\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ain Shams Engineering Journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2090447925005258","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Optimal power flow in power systems with renewable energy resources uncertainty including geothermal power plants
This article proposes an innovative application of the Catch Fish Optimization method (CFOA) to effectively handle the complex Probabilistic Optimal Power Flow (POPF) optimization problem in modern power grids. The growing penetration of stochastic renewable energy sources, photovoltaic (PV) and wind energy, plus the presence of geothermal generation, causes uncertainties. The classical Optimal Power Flow (OPF) can’t address such uncertainties. This paper introduces the capabilities of the CFOA in addressing such uncertainties. The target is to determine optimal design variables considering the probabilistic models of generation. The introduced algorithm has been investigated on IEEE 30- and 118-bus networks. Moreover, these systems are modified to include PV, wind, and geothermal units. Both fixed and dynamic load profiles are included in the study. The simulation results for the 30-bus system show a reduction in total daily fuel costs of approximately 9.64% when compared with the no-renewables baseline. For the larger 118-bus system, the daily fuel cost reduction was even more significant, at approximately 15.91%. The results obtained using the CFOA are compared with those from other well-established algorithms. The comparative analysis confirms the greater CFOA performance in terms of the convergence speed besides the robustness. This analysis affirms the effectiveness of the introduced optimization techniques in tackling the POPF problem. The current research paves the way for further investigation into the application and enhancement of the CFOA for various power system optimization problems.
期刊介绍:
in Shams Engineering Journal is an international journal devoted to publication of peer reviewed original high-quality research papers and review papers in both traditional topics and those of emerging science and technology. Areas of both theoretical and fundamental interest as well as those concerning industrial applications, emerging instrumental techniques and those which have some practical application to an aspect of human endeavor, such as the preservation of the environment, health, waste disposal are welcome. The overall focus is on original and rigorous scientific research results which have generic significance.
Ain Shams Engineering Journal focuses upon aspects of mechanical engineering, electrical engineering, civil engineering, chemical engineering, petroleum engineering, environmental engineering, architectural and urban planning engineering. Papers in which knowledge from other disciplines is integrated with engineering are especially welcome like nanotechnology, material sciences, and computational methods as well as applied basic sciences: engineering mathematics, physics and chemistry.