TY - GEN
T1 - Optimizing Energy Management in a Renewable Energy-based Microgrid With Built-in Frequency Support Features
AU - Gbadega, Peter Anuoluwapo
AU - Abolaji Balogun, Olufunke
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The growing adoption of Plug-in Electric Vehicles (PEVs) is expected to challenge the stability of electricity networks due to increased demand. However, Vehicle-to-Grid (V2G) technology offers a solution by enabling PEVs to provide ancillary services that enhance grid stability and reliability. This paper introduces an optimal power management system with built-in frequency support capabilities, leveraging the Squid Game Optimizer (SGO) algorithm to determine the optimal power exchange between PEV batteries and the grid throughout the day. The study further explores the potential of PEVs to provide frequency support through a frequency-PEV load droop control approach, which dynamically adjusts the PEV's power output in response to frequency deviations. After the need for frequency support is met, the SGO algorithm reschedules the PEV's operations, ensuring that all pre-set objectives and constraints are satisfied. This dual-purpose approach optimizes energy management within a renewable energy-based microgrid and contributes to maintaining grid frequency stability, thereby enhancing the resilience and efficiency of future power systems.
AB - The growing adoption of Plug-in Electric Vehicles (PEVs) is expected to challenge the stability of electricity networks due to increased demand. However, Vehicle-to-Grid (V2G) technology offers a solution by enabling PEVs to provide ancillary services that enhance grid stability and reliability. This paper introduces an optimal power management system with built-in frequency support capabilities, leveraging the Squid Game Optimizer (SGO) algorithm to determine the optimal power exchange between PEV batteries and the grid throughout the day. The study further explores the potential of PEVs to provide frequency support through a frequency-PEV load droop control approach, which dynamically adjusts the PEV's power output in response to frequency deviations. After the need for frequency support is met, the SGO algorithm reschedules the PEV's operations, ensuring that all pre-set objectives and constraints are satisfied. This dual-purpose approach optimizes energy management within a renewable energy-based microgrid and contributes to maintaining grid frequency stability, thereby enhancing the resilience and efficiency of future power systems.
KW - and Squid game optimizer (SGO)
KW - Frequency support
KW - Optimal energy management
KW - Plug-in electric vehicles (PEVs)
KW - Vehicle-to-grid (V2G)
UR - https://www.scopus.com/pages/publications/85216928522
U2 - 10.1109/ICAMechS63130.2024.10818799
DO - 10.1109/ICAMechS63130.2024.10818799
M3 - Conference contribution
AN - SCOPUS:85216928522
T3 - International Conference on Advanced Mechatronic Systems, ICAMechS
SP - 19
EP - 24
BT - 2024 International Conference on Advanced Mechatronic Systems, ICAMechS 2024
PB - IEEE Computer Society
T2 - 2024 International Conference on Advanced Mechatronic Systems, ICAMechS 2024
Y2 - 26 November 2024 through 30 November 2024
ER -