Hybrid Adaptive Peak Load Threshold Controller for Battery Energy Storage System: An Industrial Case Study

Battery Energy Storage Systems (BESS) provide a flexible solution for peak load reductions in industrial power management. Industrial facilities face challenges in managing peak power demands due to unpredictable load variations and the limitations of traditional BESS control strategies. To address this, a Hybrid Adaptive Peak Load Threshold (HAPLT) controller is introduced, integrating day-ahead forecasting with real-time 30-minute updates to refine thresholds dynamically. This approach integrates advanced predictive modelling techniques to optimize peak load reduction, enhance energy savings, and ensure reliable operation under real-world conditions. Validation using Daikin R&D power network data showed an average maximum demand reduction factor (KMDR) of 0.89. Realtime analysis demonstrated effective power demand management and optimal State-of-Charge (SOC) control. The system successfully reduced peak loads while preventing early battery depletion. The HAPLT controller minimizes forecasting errors, optimizes battery utilization, and enhances energy savings, proving a robust solution for industrial applications.

Authors

Publication Details

Journal
International Journal of Electrical and Electronic Engineering & Telecommunications
Published
2025-01-01
DOI
https://doi.org/10.18178/ijeetc.14.4.188-198
Citations
3
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
3.64
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article

Hybrid Adaptive Peak Load Threshold Controller for Battery Energy Storage System: An Industrial Case Study

Huoy Lih Bong
3 citations
International Journal of Electrical and Electronic Engineering & Telecommunications
Microgrid Control and Optimization
3.64
article

Hybrid Adaptive Peak Load Threshold Controller for Battery Energy Storage System: An Industrial Case Study

Huoy Lih Bong
article en
3 citations

Abstract

Battery Energy Storage Systems (BESS) provide a flexible solution for peak load reductions in industrial power management. Industrial facilities face challenges in managing peak power demands due to unpredictable load variations and the limitations of traditional BESS control strategies. To address this, a Hybrid Adaptive Peak Load Threshold (HAPLT) controller is introduced, integrating day-ahead forecasting with real-time 30-minute updates to refine thresholds dynamically. This approach integrates advanced predictive modelling techniques to optimize peak load reduction, enhance energy savings, and ensure reliable operation under real-world conditions. Validation using Daikin R&D power network data showed an average maximum demand reduction factor (KMDR) of 0.89. Realtime analysis demonstrated effective power demand management and optimal State-of-Charge (SOC) control. The system successfully reduced peak loads while preventing early battery depletion. The HAPLT controller minimizes forecasting errors, optimizes battery utilization, and enhances energy savings, proving a robust solution for industrial applications.

International Journal of Electrical and Electronic Engineering & TelecommunicationsVol. 14(4)
Openalex Percentile: Top 7%
Microgrid Control and Optimization
3.64
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