Deep Reinforcement Learning-Based Smart Grid Resource Allocation System

The existing power grid scheduling systems often use a distributed generation model. Because they need to manage multiple independent generators simultaneously, this system may be more complex in terms of coordination, scheduling, and maintenance. In addition, the variability and intermittency of renewable energy resources can lead to reliability issues, causing grid instability. To address issues such as resource wastage, high costs, and grid instability that occur during the power grid resource scheduling process, this paper proposes an intelligent power distribution system to achieve a rational allocation of electrical resources throughout the network. Firstly, the algorithm introduces a deep learning-based node fault detection module to address the problem of the lack of real-time monitoring and fault detection capabilities in traditional distribution networks. Secondly, by modeling it as a Markov decision process (MDP), it constructs state, action, and reward functions and uses a deep reinforcement learning module based on double deep Q-network (DDQN) to optimize the objective function. This ensures the allocation of power resources during peak periods, reduces energy waste, and avoids overloads. Experiments show that this algorithm has excellent fault localization capabilities, improving the stability of the grid during peak electricity demand periods. Additionally, it offers more flexibility in resource scheduling, enabling more precise resource allocation.