scholarly journals Flexibilizing Distribution Network Systems via Dynamic Reconfiguration to Support Large-Scale Integration of Variable Energy Sources Using a Genetic Algorithm

2017 ◽  
pp. 72-80
Author(s):  
Marco R. M. Cruz ◽  
Desta Z. Fitiwi ◽  
Sérgio F. Santos ◽  
João P. S. Catalão
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Distribution Network ◽  
Dynamic Reconfiguration ◽  
Energy Sources ◽  
Network Systems ◽  
Large Scale Integration ◽  
Scale Integration ◽  
Variable Energy

scholarly journals Investigation of the Impact of Large-Scale Integration of Electric Vehicles for a Swedish Distribution Network

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4717 ◽  
Author(s):  
Sylvester Johansson ◽  
Jonas Persson ◽  
Stavros Lazarou ◽  
Andreas Theocharis
Keyword(s):  
Electric Vehicles ◽  
Power Distribution ◽  
Large Scale ◽  
Electrical Power ◽  
Distribution Network ◽  
Transport Sector ◽  
Smart Charging ◽  
Large Scale Integration ◽  
Scale Integration ◽  
The Impact

Social considerations for a sustainable future lead to market demands for electromobility. Hence, electrical power distribution operators are concerned about the real ongoing problem of the electrification of the transport sector. In this regard, the paper aims to investigate the large-scale integration of electric vehicles in a Swedish distribution network. To this end, the integration pattern is taken into consideration as appears in the literature for other countries and applies to the Swedish culture. Moreover, different charging power levels including smart charging techniques are examined for several percentages of electric vehicles penetration. Industrial simulation tools proven for their accuracy are used for the study. The results indicate that the grid can manage about 50% electric vehicles penetration at its current capacity. This percentage decreases when higher charging power levels apply, while the transformers appear overloaded in many cases. The investigation of alternatives to increase the grid’s capabilities reveal that smart techniques are comparable to the conventional re-dimension of the grid. At present, the increased integration of electric vehicles is manageable by implementing a combination of smart gird and upgrade investments in comparison to technically expensive alternatives based on grid digitalization and algorithms that need to be further confirmed for their reliability for power sharing and energy management.

scholarly journals Large scale integration of renewable energy sources (RES) in the future Colombian energy system

Energy ◽  
2019 ◽  
Vol 186 ◽  
pp. 115805 ◽  
Author(s):  
Oscar Pupo-Roncallo ◽  
Javier Campillo ◽  
Derek Ingham ◽  
Kevin Hughes ◽  
Mohammed Pourkashanian
Keyword(s):  
Renewable Energy ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Energy System ◽  
Energy Sources ◽  
The Future ◽  
Large Scale Integration ◽  
Scale Integration

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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