Work

Deep reinforcement learning based control for enhanced frequency response with multi-energy storage systems

Year: 2025

Type: article

Abstract: This paper proposes an advanced strategy for managing multiple battery energy storage systems (BESS) to enhance frequency support during contingencies. A novel deep reinforcement learning (DRL) framework, based on the guided surrogate-gradient-based evolutionary strategy (GSES), is developed to dynamically regulate BESS outputs for rapid power injection or absorption. This approach effectively mitigates the rate of change of frequency (ROCOF) and stabilizes system frequency under varying operating conditions. Parallel computing techniques are employed to accelerate training and ensure robust performance. In addition, a genetic algorithm is implemented to determine the placement of BESS within the grid network, strategically minimizing ROCOF during disturbances by accounting for active power injections and inertia contributions from multiple BESS units. The proposed GSES-DRL methodology is rigorously validated using extensive simulations on a modified IEEE 39-bus system, demonstrating effective performance in frequency response compared to existing strategies. In severe scenarios, the proposed method successfully prevents unnecessary load-shedding relay operations, thereby enhancing system stability.

Source: Electric Power Systems Research

DOI: 10.1016/j.epsr.2025.112432

Authors: Abu Shouaib Hasan, Rui Fan, Wei Gao, Di Wu

Institutions: University of Denver, Argonne National Laboratory, Pacific Northwest National Laboratory

Language: English

Topic: Frequency Control in Power Systems

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,810