Function Optimization by Reinforcement Learning for power system dispatch and voltage stability

This paper presents a new algorithm, Function Optimisation by Reinforcement Learning (FORL), to solve large-scale and complex function optimisation problems, in particular for those in a high-dimensional space. FORL undertakes the dimensional search in sequence, in contrast to evolutionary algorithms (EAs) which are based on the population-based search, and has the ability of memory of history incorporated via estimating and updating of the values of states that have been visited, which is different from EAs that aggregate the individuals of a population towards the best selected in a current population. FORL is applied to solve the optimal power system dispatch and voltage stability problem. Simulation studies are carried out on the standard IEEE 30-bus and 57-bus power systems respectively. Advantages of FORL have been demonstrated by comparing its performance with that of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The results have shown that FORL outperforms PSO and GA by reducing the fuel cost while enhancing the voltage stability of the power system significantly.