Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive cont...Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.展开更多
The transportation sector is characterized by high emissions of greenhouse gases(GHG)into the atmosphere.Consequently,electric vehicles(EVs)have been proposed as a revolutionary solution to mitigate GHG emissions and ...The transportation sector is characterized by high emissions of greenhouse gases(GHG)into the atmosphere.Consequently,electric vehicles(EVs)have been proposed as a revolutionary solution to mitigate GHG emissions and the dependence on petroleum products,which are fast depleting.EVs are proliferating in many countries worldwide and the fast adoption of this technology is significantly dependent on the expansion of charging stations.This study proposes the use of the hybrid genetic algorithm and particle swarm optimization(GA-PSO)for the optimal allocation of plug-in EV charging stations(PEVCS)into the distribution network with distributed generation(DG)in high volumes and at selected buses.Photovoltaic(PV)systems with a power factor of 0.95 are used as DGs.The PVs are penetrated into the distribution network at 60%and six penetration cases are considered for the optimal placement of the PEVCSs.The optimization problem is formulated as a multi-objective problem minimizing the active and reactive power losses as well as the voltage deviation index.The IEEE 33 and 69 bus distribution networks are used as test networks.The simulation was performed using MATLAB and the results obtained validate the effectiveness of the hybrid GA-PSO.For example,the integration of PEVCSs results in the minimum bus voltage still within accepted margins.For the IEEE 69 bus network,the resulting minimum voltage is 0.973 p.u in case 1,0.982 p.u in case 2,0.96 p.u in case 3,0.961 p.u in case 4,0.954 p.u in case 5,and 0.965 p.u in case 6.EVs are a sustainable means of significantly mitigating emissions from the transportation sector and their utilization is essential as the worldwide concern of climate change and a carbon-free society intensifies.展开更多
文摘Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.
文摘The transportation sector is characterized by high emissions of greenhouse gases(GHG)into the atmosphere.Consequently,electric vehicles(EVs)have been proposed as a revolutionary solution to mitigate GHG emissions and the dependence on petroleum products,which are fast depleting.EVs are proliferating in many countries worldwide and the fast adoption of this technology is significantly dependent on the expansion of charging stations.This study proposes the use of the hybrid genetic algorithm and particle swarm optimization(GA-PSO)for the optimal allocation of plug-in EV charging stations(PEVCS)into the distribution network with distributed generation(DG)in high volumes and at selected buses.Photovoltaic(PV)systems with a power factor of 0.95 are used as DGs.The PVs are penetrated into the distribution network at 60%and six penetration cases are considered for the optimal placement of the PEVCSs.The optimization problem is formulated as a multi-objective problem minimizing the active and reactive power losses as well as the voltage deviation index.The IEEE 33 and 69 bus distribution networks are used as test networks.The simulation was performed using MATLAB and the results obtained validate the effectiveness of the hybrid GA-PSO.For example,the integration of PEVCSs results in the minimum bus voltage still within accepted margins.For the IEEE 69 bus network,the resulting minimum voltage is 0.973 p.u in case 1,0.982 p.u in case 2,0.96 p.u in case 3,0.961 p.u in case 4,0.954 p.u in case 5,and 0.965 p.u in case 6.EVs are a sustainable means of significantly mitigating emissions from the transportation sector and their utilization is essential as the worldwide concern of climate change and a carbon-free society intensifies.
