Two-Stage Energy Storage Allocation Considering Voltage Management and Loss Reduction Requirements in Unbalanced Distribution Networks DOI Creative Commons
Hu Cao,

Lingling Ma,

Guoying Liu

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(24), P. 6325 - 6325

Published: Dec. 15, 2024

The authors propose a two-stage sequential configuration method for energy storage systems to solve the problems of heavy load, low voltage, and increased network loss caused by large number electric vehicle (EV) charging piles distributed photovoltaic (PV) access in urban, old unbalanced distribution networks. At stage selecting location storage, comprehensive power flow sensitivity variance (CPFSV) is defined determine storage. capacity stage, optimized considering benefits peak shaving valley filling, costs, voltage deviations. Finally, simulations are conducted using modified IEEE-33-node system, results obtained improved beluga whale optimization algorithm show that peak-to-valley difference system after addition decreased 43.7% 51.1% compared original with EV PV resources added, respectively. maximum CPFSV 52% 75.1%, In addition, engineering value this verified through real-machine 199 nodes district Kunming. Therefore, proposed article can provide reference solving outstanding large-scale EVs PVs network.

Language: Английский

Two-Stage Energy Storage Allocation Considering Voltage Management and Loss Reduction Requirements in Unbalanced Distribution Networks DOI Creative Commons
Hu Cao,

Lingling Ma,

Guoying Liu

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(24), P. 6325 - 6325

Published: Dec. 15, 2024

The authors propose a two-stage sequential configuration method for energy storage systems to solve the problems of heavy load, low voltage, and increased network loss caused by large number electric vehicle (EV) charging piles distributed photovoltaic (PV) access in urban, old unbalanced distribution networks. At stage selecting location storage, comprehensive power flow sensitivity variance (CPFSV) is defined determine storage. capacity stage, optimized considering benefits peak shaving valley filling, costs, voltage deviations. Finally, simulations are conducted using modified IEEE-33-node system, results obtained improved beluga whale optimization algorithm show that peak-to-valley difference system after addition decreased 43.7% 51.1% compared original with EV PV resources added, respectively. maximum CPFSV 52% 75.1%, In addition, engineering value this verified through real-machine 199 nodes district Kunming. Therefore, proposed article can provide reference solving outstanding large-scale EVs PVs network.

Language: Английский

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