Automatic generation control of ubiquitous power Internet of Things integrated energy system based on deep reinforcement learning

Abstract

<p indent=0mm>The integrated energy systems are developing in the direction of ubiquitous power Internet of Things (IoT). The main feature is the large-scale integration of distributed energies, loads, and cogenerations, which usually brings random disturbances to the systems, thus causing frequency stability control problems, where cannot be effectively addressed by the traditional automatic generation control methods. The recently developed machine learning approach provides potential solutions for complex systems with random disturbances. However, when this approach is applied to the ultra-large-scale ubiquitous power IoT systems, the dimensionality related problem arises, and it should be solved. In this paper, a deep reinforcement learning algorithm is developed for the frequency stability control of the ultra-large-scale ubiquitous power IoT systems with random disturbances. The developed algorithm is based on the idea of a proportional priority sampling mechanism and the prioritized replay DDQN-AD (PRDDQN-AD) strategy. In this work, both the two-region integrated energy system model and the multi-regional ubiquitous power IoT integrated energy system model are adopted in simulation and analysis; these models include a large number of sources, loads, energy-storage units, and grids. Simulation and comparison results show that the training quality of samples, learning efficiency, and generalization performance of the strategy are improved by using PRDDQN-AD. The strategy has a fast convergence speed, and thus can successfully solve the dimensionality problem.