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Optimal Observer-Based Power Imbalance Allocation for Frequency Regulation in Shipboard Microgrids
by
Menon, Prathyush P.
, Baby, Devika K.
, Rinaldi, Gianmario
in
Algorithms
/ control systems
/ Controllers
/ electrical power systems
/ Emissions
/ Energy consumption
/ Energy management systems
/ Energy storage
/ energy systems
/ Fuel cells
/ Hydrogen as fuel
/ Laws, regulations and rules
/ Linear programming
/ Load
/ microgrids
/ Numerical analysis
/ observers
/ optimisation
/ Power
2024
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Optimal Observer-Based Power Imbalance Allocation for Frequency Regulation in Shipboard Microgrids
by
Menon, Prathyush P.
, Baby, Devika K.
, Rinaldi, Gianmario
in
Algorithms
/ control systems
/ Controllers
/ electrical power systems
/ Emissions
/ Energy consumption
/ Energy management systems
/ Energy storage
/ energy systems
/ Fuel cells
/ Hydrogen as fuel
/ Laws, regulations and rules
/ Linear programming
/ Load
/ microgrids
/ Numerical analysis
/ observers
/ optimisation
/ Power
2024
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Do you wish to request the book?
Optimal Observer-Based Power Imbalance Allocation for Frequency Regulation in Shipboard Microgrids
by
Menon, Prathyush P.
, Baby, Devika K.
, Rinaldi, Gianmario
in
Algorithms
/ control systems
/ Controllers
/ electrical power systems
/ Emissions
/ Energy consumption
/ Energy management systems
/ Energy storage
/ energy systems
/ Fuel cells
/ Hydrogen as fuel
/ Laws, regulations and rules
/ Linear programming
/ Load
/ microgrids
/ Numerical analysis
/ observers
/ optimisation
/ Power
2024
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Optimal Observer-Based Power Imbalance Allocation for Frequency Regulation in Shipboard Microgrids
Journal Article
Optimal Observer-Based Power Imbalance Allocation for Frequency Regulation in Shipboard Microgrids
2024
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Overview
This paper proposes a two-level control strategy based on a super-twisting sliding-mode algorithm (STA) to optimally allocate power imbalances in shipboard microgrids (SMGs) while achieving frequency regulation. The strategy employs an STA observer to estimate the unknown power load demand imbalances in finite time. This estimate is then passed to an online high-level optimal control framework to periodically determine the optimal sequence of power reference values for each energy storage device (ESS), minimising the operational cost of the SMG. The online optimised power reference values are interpolated and passed to the low-level STA control strategy to control the output power of each ESS. The efficacy of the proposed methods is demonstrated through numerical simulations conducted on a prototypical model of an SMG equipped with two ESSs, namely batteries and fuel cells with associated hydrogen storage.
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