A Novel Multi-Agent DDQN-AD Method-Based Distributed Strategy for Automatic Generation Control of Integrated Energy Systems

TLDR

Distributed renewable energy adoption cuts emissions but introduces random disturbances that challenge traditional centralized AGC. The study proposes a multi‑agent distributed AGC strategy based on deep reinforcement learning and action‑discovery techniques. The strategy incorporates area control error and carbon‑emission metrics into its reward function to guide optimal control decisions. Simulations demonstrate the strategy’s effectiveness and superiority over two other intelligent AGC algorithms.

Abstract

The widely adoption of distributed renewable energy sources (DREs) effectively reduces carbon emission and beat atmospheric haze in developing countries. However, random disturbance issues emerge in power grids with DREs when applying traditional centralized automatic generation control (AGC) strategies. Therefore, a multi-agent distributed control strategy is proposed for AGC in this article, which is mainly based on the concept of deep reinforcement learning, and developed by the strategy of action discovery. Moreover, area control error and the amount of carbon emission are employed in reward functions to obtain optimal solutions in the implementing process of the proposed strategy. Simulations are provided in the work to show the effectiveness of the strategy, while comparisons are also offered, where the simulating results obtained by two other intelligent AGC algorithms are used as references, according to which, the superiority of the proposed strategy is confirmed.