Distributed learning for optimal allocation of synchronous and converter-based generation
(2021) 29th Mediterranean Conference on Control and Automation (MED 2021) p.386-391- Abstract
- Motivated by the penetration of converter-based generation into the electrical grid, we revisit the classical log-linear learning algorithm for optimal allocation {of synchronous machines and converters} for mixed power generation. The objective is to assign to each generator unit a type (either synchronous machine or DC/AC converter in closed-loop with droop control), while minimizing the steady state angle deviation relative to an optimum induced by unknown optimal configuration of synchronous and DC/AC converter-based generation. Additionally, we study the robustness of the learning algorithm against a uniform drop in the line susceptances and with respect to a well-defined feasibility region describing admissible power deviations. We... (More)
- Motivated by the penetration of converter-based generation into the electrical grid, we revisit the classical log-linear learning algorithm for optimal allocation {of synchronous machines and converters} for mixed power generation. The objective is to assign to each generator unit a type (either synchronous machine or DC/AC converter in closed-loop with droop control), while minimizing the steady state angle deviation relative to an optimum induced by unknown optimal configuration of synchronous and DC/AC converter-based generation. Additionally, we study the robustness of the learning algorithm against a uniform drop in the line susceptances and with respect to a well-defined feasibility region describing admissible power deviations. We show guaranteed probabilistic convergence to maximizers of the perturbed potential function with feasible power flows and demonstrate our theoretical findings via simulative examples of a power network with six generation units. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/d5a89f37-fa11-4706-b858-d11240de2cf0
- author
- Jouini, Taouba LU and Sun, Zhiyong LU
- organization
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 29th Mediterranean Conference on Control and Automation
- article number
- 9480195
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 29th Mediterranean Conference on Control and Automation (MED 2021)
- conference location
- Puglia, Italy
- conference dates
- 2021-06-22 - 2021-06-25
- external identifiers
-
- scopus:85113633638
- ISBN
- 978-166542258-1
- DOI
- 10.1109/MED51440.2021.9480195
- project
- Scalable Control of Interconnected Systems
- language
- English
- LU publication?
- yes
- id
- d5a89f37-fa11-4706-b858-d11240de2cf0
- date added to LUP
- 2020-10-11 10:39:21
- date last changed
- 2025-04-04 14:56:36
@inproceedings{d5a89f37-fa11-4706-b858-d11240de2cf0,
abstract = {{Motivated by the penetration of converter-based generation into the electrical grid, we revisit the classical log-linear learning algorithm for optimal allocation {of synchronous machines and converters} for mixed power generation. The objective is to assign to each generator unit a type (either synchronous machine or DC/AC converter in closed-loop with droop control), while minimizing the steady state angle deviation relative to an optimum induced by unknown optimal configuration of synchronous and DC/AC converter-based generation. Additionally, we study the robustness of the learning algorithm against a uniform drop in the line susceptances and with respect to a well-defined feasibility region describing admissible power deviations. We show guaranteed probabilistic convergence to maximizers of the perturbed potential function with feasible power flows and demonstrate our theoretical findings via simulative examples of a power network with six generation units.}},
author = {{Jouini, Taouba and Sun, Zhiyong}},
booktitle = {{29th Mediterranean Conference on Control and Automation}},
isbn = {{978-166542258-1}},
language = {{eng}},
pages = {{386--391}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Distributed learning for optimal allocation of synchronous and converter-based generation}},
url = {{https://lup.lub.lu.se/search/files/97548352/Optimal_allocation_in_power_systems.pdf}},
doi = {{10.1109/MED51440.2021.9480195}},
year = {{2021}},
}