This paper examines the intricate issue of Optimal Power Flow(OPF)optimization concerning the incorporation of renewable energy sources(RESs)into power networks.We present the Boosting Circulatory System Based Optimiz...This paper examines the intricate issue of Optimal Power Flow(OPF)optimization concerning the incorporation of renewable energy sources(RESs)into power networks.We present the Boosting Circulatory System Based Optimization(BCSBO)method,a novel modification of the original Circulatory System Based Optimization(CSBO)algorithm.The BCSBO algorithm has innovative movement techniques that markedly improve its exploration and exploitation skills,making it an effective instrument for addressing intricate optimization challenges.The suggested technique is thoroughly assessed utilizing five different objective functions alongside the IEEE 30-bus and IEEE 118-bus systems as test examples.The performance of the BCSBO algorithm is evaluated against many recognized optimization approaches,including CSBO,Moth-Flame Optimization(MFO),Particle Swarm Optimization(PSO),Thermal Exchange Optimization(TEO),and Elephant Herding Optimization(EHO).For the first case with minimizing the fuel cost associated with the thermal power generators,the total cost reported by the BCBSO is obtained as$781.8610,which is lower than other algorithms.For the second case,aimed at minimizing the total generating cost while also imposing a fixed carbon tax for thermal units,the derived total cost by the BCBSO is$810.7654.For the third case,aimed at minimizing the total cost considering prohibited operating zones of thermal units with RESs,the obtained total cost using the BCBSO is$781.9315.For case 4,with network losses included,the value of total costs obtained using the BCBSO is$880.4864.The value of total costs considering voltage deviation in case 5 is also obtained as$961.4354.For the IEEE 118-bus test system,the total cost is obtained$103,415.9315 using the BCBSO.These values reported by the BCBSO are all lower than those obtained by other methods addressed in this paper.The findings highlight the BCSBO algorithm’s potential as a crucial tool for enhancing power systems with renewable energies.展开更多
文摘This paper examines the intricate issue of Optimal Power Flow(OPF)optimization concerning the incorporation of renewable energy sources(RESs)into power networks.We present the Boosting Circulatory System Based Optimization(BCSBO)method,a novel modification of the original Circulatory System Based Optimization(CSBO)algorithm.The BCSBO algorithm has innovative movement techniques that markedly improve its exploration and exploitation skills,making it an effective instrument for addressing intricate optimization challenges.The suggested technique is thoroughly assessed utilizing five different objective functions alongside the IEEE 30-bus and IEEE 118-bus systems as test examples.The performance of the BCSBO algorithm is evaluated against many recognized optimization approaches,including CSBO,Moth-Flame Optimization(MFO),Particle Swarm Optimization(PSO),Thermal Exchange Optimization(TEO),and Elephant Herding Optimization(EHO).For the first case with minimizing the fuel cost associated with the thermal power generators,the total cost reported by the BCBSO is obtained as$781.8610,which is lower than other algorithms.For the second case,aimed at minimizing the total generating cost while also imposing a fixed carbon tax for thermal units,the derived total cost by the BCBSO is$810.7654.For the third case,aimed at minimizing the total cost considering prohibited operating zones of thermal units with RESs,the obtained total cost using the BCBSO is$781.9315.For case 4,with network losses included,the value of total costs obtained using the BCBSO is$880.4864.The value of total costs considering voltage deviation in case 5 is also obtained as$961.4354.For the IEEE 118-bus test system,the total cost is obtained$103,415.9315 using the BCBSO.These values reported by the BCBSO are all lower than those obtained by other methods addressed in this paper.The findings highlight the BCSBO algorithm’s potential as a crucial tool for enhancing power systems with renewable energies.
