Adaptive Corridor-Based Control of a Lithium-Ion Battery Energy Storage System for Wind-Turbine Power Stabilisation and Reliability Improvement in Industrial Microgrids

The increasing penetration of wind generation into autonomous and weakly coupled industrial microgrids requires control strategies that can maintain power-supply reliability under stochastic generation and sharply variable loads. This paper proposes an adaptive corridor-based supervisory control algorithm for a lithium-ion battery energy storage system (BESS) integrated with a wind-turbine generator. The novelty of the method is not the general use of battery storage for power smoothing but a control law that maintains the generator within a predefined active-power corridor while transferring fast and medium-duration imbalances to the battery under state-of-charge, power-limit, and response-delay constraints. Unlike PI-based smoothing, model predictive control, or fixed rule-based switching, the proposed approach uses corridor retention as the primary operating criterion and relies only on directly measurable variables. The model was implemented in MATLAB/Simulink for a 2 MW wind-turbine generator coupled with a 444 kWh/1776 kW lithium-ion battery energy storage system. Field-measurement-based simulation validation was performed in MATLAB/Simulink using industrial load data measured at an autonomous oilfield power plant; the validation scenarios included extracted step disturbances, a real multi-peak load profile, prolonged deficit operation, and a scaled configuration scenario. The algorithm compensated for 86.3–87.4% of short-term load peaks, reduced the standard deviation of generator power from 467 to 98 kW, and decreased recovery time from 6.8 to 1.6 s.

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Publication Details

Journal
Electricity
Published
2026-06-17
DOI
https://doi.org/10.3390/electricity7020056
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
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article

Adaptive Corridor-Based Control of a Lithium-Ion Battery Energy Storage System for Wind-Turbine Power Stabilisation and Reliability Improvement in Industrial Microgrids

Yadviga Aleksandrovna Tynchenko, Nikita V. Martyushev, В С Тынченко, Boris V. Malozyomov et al.
Electricity
Microgrid Control and Optimization
article

Adaptive Corridor-Based Control of a Lithium-Ion Battery Energy Storage System for Wind-Turbine Power Stabilisation and Reliability Improvement in Industrial Microgrids

Yadviga Aleksandrovna Tynchenko, Nikita V. Martyushev, В С Тынченко, Boris V. Malozyomov, Rollan Nussipali, Viktor A. Kukartsev, Tatyana A. Panfilova
article en

Abstract

The increasing penetration of wind generation into autonomous and weakly coupled industrial microgrids requires control strategies that can maintain power-supply reliability under stochastic generation and sharply variable loads. This paper proposes an adaptive corridor-based supervisory control algorithm for a lithium-ion battery energy storage system (BESS) integrated with a wind-turbine generator. The novelty of the method is not the general use of battery storage for power smoothing but a control law that maintains the generator within a predefined active-power corridor while transferring fast and medium-duration imbalances to the battery under state-of-charge, power-limit, and response-delay constraints. Unlike PI-based smoothing, model predictive control, or fixed rule-based switching, the proposed approach uses corridor retention as the primary operating criterion and relies only on directly measurable variables. The model was implemented in MATLAB/Simulink for a 2 MW wind-turbine generator coupled with a 444 kWh/1776 kW lithium-ion battery energy storage system. Field-measurement-based simulation validation was performed in MATLAB/Simulink using industrial load data measured at an autonomous oilfield power plant; the validation scenarios included extracted step disturbances, a real multi-peak load profile, prolonged deficit operation, and a scaled configuration scenario. The algorithm compensated for 86.3–87.4% of short-term load peaks, reduced the standard deviation of generator power from 467 to 98 kW, and decreased recovery time from 6.8 to 1.6 s.

ElectricityVol. 7(2)
Bauman Moscow State Technical University (RU), Ministry of Water Resources and Irrigation (EG), National Research Tomsk State University (RU), Tomsk Polytechnic University (RU), Siberian Federal University (RU), Novosibirsk State Technical University (RU)
Affordable and clean energy
Openalex Percentile: Top 8%
Microgrid Control and Optimization
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