Automatic Generation Control Strategy for Integrated Energy System Based on Ubiquitous Power Internet of Things

The integrated energy system based on ubiquitous power Internet of Things (IoT) has the characteristics of ubiquitous connection of everything, complex energy conversion mode, and unbalanced supply-demand relationship. It brings strong random disturbance to the power grid, which deteriorates the comprehensive control performance of automatic generation control. Therefore, a novel deep reinforcement learning algorithm, namely, collaborative learning actor-critic strategy, is proposed. It is oriented to different exploration horizons, has the advantage on experience sharing mechanism and can continuously coordinate the key behavioral strategies. Simulation tests are performed on the two-area integrated energy system and the four-area integrated energy system based on ubiquitous power IoT. Comparative analyses show that the proposed algorithm can efficiently solve the problem of strong random disturbance, and has better convergence characteristic and generalization performance. Besides, it can realize the optimal cooperative control of multiarea integrated energy system efficiently.

Automated summary

The integrated energy system based on the ubiquitous power Internet of Things (IoT) has the characteristics of ubiquitous connection, complex energy conversion, and unbalanced supply-demand relationship. In order to address the strong random disturbance problem and achieve optimal cooperative control, a novel deep reinforcement learning algorithm, the collaborative learning actor-critic strategy, is proposed. Simulation tests on the two-area and four-area integrated energy systems show that the algorithm efficiently solves the disturbance problem and demonstrates better convergence and generalization performance.