Enhanced load frequency control in multi-source power systems with stochastic optimization algorithms and SMES integration via AC-DC parallel tie-lines

Sarker, S; Ahmed, I; Hossain, A; Islam, R; Hossain, A; Sheikh, RI

Enhanced load frequency control in multi-source power systems with stochastic optimization algorithms and SMES integration via AC-DC parallel tie-lines

Sarker, S Ahmed, I Hossain, A Islam, R Hossain, A Sheikh, RI

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Next Research, 2025, 2, (2), pp. 100288
Issue Date:: 2025-06

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Field	Value	Language
dc.contributor.author	Sarker, S
dc.contributor.author	Ahmed, I
dc.contributor.author	Hossain, A
dc.contributor.author	Islam, R
dc.contributor.author	Hossain, A
dc.contributor.author	Sheikh, RI
dc.date.accessioned	2026-05-14T04:26:26Z
dc.date.available	2026-05-14T04:26:26Z
dc.date.issued	2025-06
dc.identifier.citation	Next Research, 2025, 2, (2), pp. 100288
dc.identifier.issn	3050-4759
dc.identifier.uri	http://hdl.handle.net/10453/194979
dc.description.abstract	The integration of renewable energy sources alongside conventional generators introduces challenges in maintaining frequency and power exchange between interconnected regions. To address these complexities and adjust the control parameters, this study investigates the application of three different stochastic optimization algorithms-particle swarm optimization (PSO), genetic algorithm (GA) and sine cosine algorithm (SCA)-combined with superconducting magnetic energy storage (SMES) technology for improved load frequency control (LFC). The results of integrating SMES technology are evaluated and compared with traditional control approaches in various operating scenarios. For the specific system with SMES, a single Proportional-Integral-Derivative (PID) controller was switched out for three PID controllers, and the results were noted for comparison. The expanded LFC technique effectively maintains optimal power exchange and a steady frequency throughout the linked regions, as demonstrated by the simulation results. This study bridges research gaps by combining advanced optimization algorithms and SMES technology to advance LFC, which is often overlooked in the literature. A comparative analysis under varying conditions confirms the effectiveness of the proposed system. The robustness of the system was validated with parameter variations of 25 and 50 , demonstrating its adaptability and reliability under dynamic conditions. When SMES was integrated, the deviation of the power of the tie line decreased by 13.59 , the frequency deviation in area-1 improved by 34.13 , and the frequency deviation of area-2 improved by 12.57 . Therefore, the integration of SMES ensures dependable and sturdy performance in multiple power source systems linked by AC-DC parallel tie-lines. The results highlight the efficiency and resilience of the system, confirming its suitability for diverse operating scenarios.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Next Research
dc.relation.isbasedon	10.1016/j.nexres.2025.100288
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Enhanced load frequency control in multi-source power systems with stochastic optimization algorithms and SMES integration via AC-DC parallel tie-lines
dc.type	Journal Article
utslib.citation.volume	2
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-05-14T04:26:24Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	2
utslib.citation.issue	2

Abstract:

The integration of renewable energy sources alongside conventional generators introduces challenges in maintaining frequency and power exchange between interconnected regions. To address these complexities and adjust the control parameters, this study investigates the application of three different stochastic optimization algorithms-particle swarm optimization (PSO), genetic algorithm (GA) and sine cosine algorithm (SCA)-combined with superconducting magnetic energy storage (SMES) technology for improved load frequency control (LFC). The results of integrating SMES technology are evaluated and compared with traditional control approaches in various operating scenarios. For the specific system with SMES, a single Proportional-Integral-Derivative (PID) controller was switched out for three PID controllers, and the results were noted for comparison. The expanded LFC technique effectively maintains optimal power exchange and a steady frequency throughout the linked regions, as demonstrated by the simulation results. This study bridges research gaps by combining advanced optimization algorithms and SMES technology to advance LFC, which is often overlooked in the literature. A comparative analysis under varying conditions confirms the effectiveness of the proposed system. The robustness of the system was validated with parameter variations of 25 and 50 , demonstrating its adaptability and reliability under dynamic conditions. When SMES was integrated, the deviation of the power of the tie line decreased by 13.59 , the frequency deviation in area-1 improved by 34.13 , and the frequency deviation of area-2 improved by 12.57 . Therefore, the integration of SMES ensures dependable and sturdy performance in multiple power source systems linked by AC-DC parallel tie-lines. The results highlight the efficiency and resilience of the system, confirming its suitability for diverse operating scenarios.

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http://hdl.handle.net/10453/194979