Voltage stability enhancement in IEEE 14-bus system using deep deterministic policy gradient for EV charging management

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Mousaei, Arash and Naderi, Yahya and Arif, Syed Muhammad and Bayram, I. Safak; (2025) Voltage stability enhancement in IEEE 14-bus system using deep deterministic policy gradient for EV charging management. In: 2025 10th IEEE Workshop on the Electronic Grid (eGRID). IEEE Electronic Power Grid (eGrid) . IEEE, GBR. ISBN 9798331593643 (https://doi.org/10.1109/eGRID63452.2025.11255115)

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Abstract

The increasing integration of Electric Vehicles (EVs) into power systems presents significant challenges for voltage stability due to their unpredictable charging behaviors and potential grid disturbances. This paper proposes a novel Deep Reinforcement Learning (DRL) framework to coordinate EV charging and voltage control in distribution networks using the Deep Deterministic Policy Gradient (DDPG) algorithm. A two-layer control architecture is introduced, where the upper layer handles charging scheduling while the lower layer focuses on voltage regulation. The framework is modeled as a Markov Decision Process (MDP) and trained using an Actor-Critic structure capable of managing mixed discrete-continuous action spaces. Simulation results on a modified IEEE 14-bus system validate the effectiveness of the proposed method. Compared with conventional methods like Deep Q-network (DQN), Proximal policy optimization (PPO), and Soft Actor Critic (SAC), our approach achieves faster convergence and superior voltage stability across varying levels of EV penetration, ensuring all nodal voltages remain within safe operational limits. The results demonstrate the potential of DDPG-based strategies in enhancing grid reliability and supporting smart EV integration.

ORCID iDs

Mousaei, Arash, Naderi, Yahya, Arif, Syed Muhammad and Bayram, I. Safak ORCID logoORCID: https://orcid.org/0000-0001-8130-5583;
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