Deep reinforcement learning-enhanced framework for precision investment decision and dynamic optimization in power grid systems

Abstract

The digital transformation of modern power grids demands intelligent, data-driven strategies for long-term investment decision-making under uncertainty. Traditional deterministic or rule-based optimization approaches struggle to adapt to stochastic market dynamics, renewable intermittency, and evolving operational constraints. This paper presents a Deep Reinforcement Learning–Enhanced Dynamic Optimization Framework (DRL-DOF) that integrates uncertainty-aware policy optimization, temporal-attention actor–critic networks, and digital twin simulation for precision investment management. The proposed method formulates grid investment as a constrained Markov Decision Process, balancing return, cost, and risk via a Conditional Value-at-Risk (CVaR)-based reward function. A federated learning mechanism further enables decentralized coordination across regional grids without compromising data privacy. Experimental results across synthetic and real datasets demonstrate that DRL-DOF achieves up to 15% higher cost efficiency, enhanced reliability, and faster convergence than state-of-the-art optimization and baseline DRL methods. This work establishes a scalable and interpretable foundation for intelligent investment decision-making in sustainable and resilient power systems.