Inspired lightweight robust quantum Q-learning for smart generation control of power systems

Abstract

With the continuous development of zero-carbon power systems, more and more models need to be considered in the system, and the dimension of data provided in the system will be higher and higher. The grid frequency will be difficult to be controlled stably. The existing smart generation control (SGC) methods like proportional–integral–derivative (PID), traditional Q-learning (QL), state–action–reward–state–action (SARSA), sliding mode control (SMC), and fuzzy logic control (FLC) have low-performance problems, the curse of dimensionality, and a slow convergence rate in the zero-carbon power system. This work proposes lightweight robust quantum Q-learning (LRQQL) methods to improve the performance of SGC methods and solve these problems. The LRQQL methods are divided into four methods. Two methods apply the idea of quantum normalization to update the action probability and different action selection methods to provide the action with higher performance. Besides, two lighter methods based on the idea of Grover iteration with the less curse of dimensionality and a faster convergence rate are proposed. The LRQQL methods are simulated in the two-area, four-area, and complex four-area systems. The evaluation indexes verify the feasibility of LRQQL algorithms. The LRQQL methods are more than 25% smaller than traditional QL in frequency error, more than 40% smaller than traditional QL in area control error, and more than 40% faster than the QL and SARSA in convergence rate. Meanwhile, the LRQQL methods possess higher adaptivity than PID, QL, SARSA, SMC, and FLC.