Optimal Engagement of Residential Battery Storage to Alleviate Grid Upgrades Caused by EVs and Solar Systems
Volume 9, Issue 2, Page No 01-08, 2024
Author’s Name: Rafi Zahedi1,a), Amirhossein Ahmadian1, Chen Zhang1, Shashank Narayana Gowda1, Kourosh SedghiSigarchi2, Rajit Gadh1
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1 Smart Grid Energy Research Center, University of California, Los Angeles, Los Angeles, 90095, USA
2 ECE Department, California State University, Northridge, Northridge, 91330, USA
a)whom correspondence should be addressed. E-mail: rafi73@g.ucla.edu
Adv. Sci. Technol. Eng. Syst. J. 9(2), 1-8 (2024); 
DOI: 10.25046/aj090201
Keywords: Electric Vehicle, Solar Energy, Distributed Energy Resources, Optimization
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The integration of distributed energy resources has ushered in a host of complex challenges, significantly impacting power quality in distribution networks. This work studies these challenges, exploring issues such as voltage fluctuations and escalating power losses caused by the integration of solar systems and electric vehicle (EV) chargers. We present a robust methodology focused on mitigating voltage deviations and power losses, emphasizing the allocation of a Permitted Percentage (PP) of battery-based solar systems within residential areas endowed with storage capabilities.
A key facet of this research lies in its adaptability to the changing landscape of electric transportation. With the rapid increase of electric trucks on the horizon, our proposed model gains relevance. By tactically deploying PP to oversee the charging and discharging of batteries within residential solar systems, utilities are poised not only to assist with grid resilience but also to cater to the upcoming demands spurred by the advent of new EVs, notably trucks.
To validate the efficacy of our proposed model, rigorous simulations were conducted using the IEEE 33-bus distribution network as a designed testbed. Leveraging advanced Particle Swarm Optimization techniques, we have deciphered the optimal charging and discharging commands issued by utilities to energy storage systems. The outcomes of these simulations help us understand the transformative potential of various PP allocations, shedding light on the balance between non-battery-based and battery-based solar residences. яндекс This research underscores the need for carefully crafted approaches in navigating the complexities of modern grid dynamics amid the anticipated increase in electric vehicles.
Received: 16 October 2023, Revised: 12 January 2024, Accepted: 13 January 2024, Published Online: 22 March 2024
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