The Optimized Charge-Discharge Strategy of EVs Considering the Fluctuation of Wind Power

2014 ◽  
Vol 521 ◽  
pp. 151-156
Author(s):  
Sheng Wei Tang ◽  
Yi Tan ◽  
Juan Liu ◽  
Jian Wei Sun
Keyword(s):  
Genetic Algorithm ◽  
Wind Power ◽  
Large Scale ◽  
Control Model ◽  
Power Fluctuation ◽  
Discharge Characteristics ◽  
The Monte Carlo Method ◽  
Large Scale Integration ◽  
Scale Integration ◽  
Charge Discharge

The fluctuation is an important factor that limits large-scale integration of wind power into power grid. In order to improve penetration level of wind power, the EVs based on V2G are considered to participate in regulating wind power while considering charge-discharge characteristics of EV battery. Thus, in this paper, an optimized EV charge-discharge control model is proposed to reduce output fluctuation of wind power. The Monte-Carlo method is used to simulate the stochastic wind speed based on Weibull probability density function. Finally, Genetic Algorithm (GA) is adopted to solve the problem. Results indicate that the EVs based on V2G can reduce the wind power fluctuation level to some extent, absorbing the wind power surplus and compensating the of wind power shortage.

scholarly journals Genetic Algorithm-based thermal uniformity–aware X-filling to reduce peak temperature during testing

2018 ◽  
Vol 51 (7-8) ◽  
pp. 235-242 ◽  
Author(s):  
Arulmurugan Azhaganantham ◽  
Murugesan Govindasamy
Keyword(s):  
Genetic Algorithm ◽  
Power Consumption ◽  
Power Distribution ◽  
Large Scale ◽  
Heat Dissipation ◽  
Peak Temperature ◽  
Heating Event ◽  
Large Scale Integration ◽  
On Chip ◽  
Scale Integration

High temperature occurs in testing of complex System-on-Chip designs and it may become a critical concern to be carefully taken into account with continual development in Very Large Scale Integration technology. Peak temperature significantly affects the reliability and the performance of the chip. So it is essential to minimize the peak temperature of the chip. Heat generation by power consumption and heat dissipation to the surrounding blocks are the two prominent factors for the peak temperature. Power consumption can be minimized by a careful mapping of don’t cares in precomputed test set. However, it does not provide the solution to peak temperature minimization because the non-uniformity in spatial power distribution may create localized heating event called “hotspot.” The peak temperature on the hotspot is minimized by Genetic Algorithm–based don’t care filling technique that reduces the non-uniformity in spatial power distribution within the circuit under test while maintaining the overall power consumption at a lower level. Experimental results on ISCAS89 benchmark circuits demonstrate that 6%–28% peak temperature reduction can be achieved.

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