Optimal Engagement of Residential Battery Storage to Alleviate Grid Upgrades Caused by EVs and Solar Systems

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

View Affiliations

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); a  DOI: 10.25046/aj090201

Keywords: Electric Vehicle, Solar Energy, Distributed Energy Resources, Optimization

Share

31 Downloads

Export Citations

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

  1. R. Seguin, J. Woyak, D. Costyk, J. Hambrick, B. Mather, “High-Penetration PV Integration Handbook for Distribution Engineers,” NREL – National Renewable Energy Laboratory, 1–109, 2016.
  2. B.A. Mather, B.L. Norris, J.H. Dise, L. Yu, D. Paradis, F. Katiraei, R. Seguin, D. Costyk, J. Woyak, J. Jung, K. Russell, R. Broadwater, B.A. Mather, B.L. Norris, J.H. Dise, L. Yu, D. Paradis, F. Katiraei, R. Seguin, D. Costyk, J. Jung, K. Russell, “NREL / SCE High Penetration PV Integration Project : FY13 Annual Report,” (June 2014), 2014.
  3. A. Ahmadian, V. Ghodrati, R. Gadh, “Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework,” Applied Energy, 352, 121884, 2023.
  4. B. Borlaug, M. Muratori, M. Gilleran, D. Woody, W. Muston, T. Canada, A. Ingram, H. Gresham, C. McQueen, “Heavy-duty truck electrification and the impacts of depot charging on electricity distribution systems,” Nature Energy, 6(6), 673–682, 2021.
  5. M. Muratori, B. Borlaug, Perspectives on Charging Medium-and Heavy-Duty Electric Vehicles, National Renewable Energy Lab.(NREL), Golden, CO (United States), 2022.
  6. F.E.R. Commission, FERC Order No. 2222: Fact Sheet | Federal Energy Regulatory Commission, 2022.
  7. T. Bowen, C. Gokhale-Welch, “Behind-The-Meter Battery Energy Storage: Frequently Asked Questions What Is Behind-The-Meter Battery Energy Storage?,” (2018), 1–10, 2021.
  8. O. Zinaman, T. Bowen, A. Aznar, “An overview of behind-the-meter solar-plus-storage regulatory design: Approaches and case studies to inform international applications (Report no. NREL/TP-7A40-75283),” NREL Report, (March), 1–76, 2020.
  9. U.S.D. of Energy, Department of Energy National Laboratories and Plants: Mobility Across the Complex, 2013.
  10. T.B. and C. Gokhale-Welch, Behind-The-Meter Battery Energy Storage: Frequently Asked Questions What Is Behind-The-Meter Battery Energy Storage?, 2021.
  11. M. Lavillotti, DER Energy Market Design: Dual Participation, New York Independent System Operator,.
  12. L. Bird, F. Flores, C. Volpi, K. Ardani, D. Manning, R. McAllister, “Review of Interconnection Practices and Costs in the Western States,” National Renewable Energy Laboratory, (April), 2018.
  13. Z. Peterson, M. Coddington, F. Ding, B. Sigrin, D. Saleem, K. Horowitz, S.E. Baldwin, B. Lydic, “An overview of distributed energy resource (DER) interconnection: Current practices and emerging solutions,” NREL Technical Report, (April 2019), 2019.
  14. R. Zahedi, A. Ahmadian, K. SedghiSigarchi, R. Gadh, “An Optimal Methodology for Mitigating the Impacts of EVs and Solar Systems on the Grid by Utilizing Existing Residential Battery Storage Capacity With No Further Grid Upgrades,” in 2023 IEEE Transportation Electrification Conference & Expo (ITEC), IEEE: 1–5, 2023.
  15. R. Zahedi, G.B. Gharehpetian, H. Rastegar, “Environment Compatible Management Strategy of Distributed Generation Based on Neural Network with a Power Capacity Index,” in Electrical Engineering (ICEE), Iranian Conference on, 1022–1026, 2018, doi:10.1109/ICEE.2018.8472698.
  16. S. Uimonen, M. Lehtonen, “Simulation of electric vehicle charging stations load profiles in office buildings based on occupancy data,” Energies, 13(21), 2020, doi:10.3390/en13215700.
  17. R. Zahedi, M. Moeini-Aghtaie, “Operational strategy optimization of a hybrid green power system based on fuzzy logic controller with considering for optimal sizing and analysis of different priorities for energy storage,” Sustainable Energy, Grids and Networks, 32, 100809, 2022, doi:10.1016/j.segan.2022.100809.
  18. J.A.M. Rupa, S. Ganesh, “Power flow analysis for radial distribution system using backward/forward sweep method,” International Journal of Electrical, Computer, Electronics and Communication Engineering, 8(10), 1540–1544, 2014.
  19. A. Savić, Ž. Durišić, “Optimal sizing and location of SVC devices for improvement of voltage profile in distribution network with dispersed photovoltaic and wind power plants,” Applied Energy, 134, 114–124, 2014, doi:10.1016/j.apenergy.2014.08.014.

Citations by Dimensions

Citations by PlumX

No. of Downloads Per Month

No. of Downloads Per Country