COMBINED REAL AND REACTIVE DISPATCH OF POWER USING REINFORCEMENT LEARNING

Most economic dispatch problems involve real power only. With the integration of renewable energy into the grid, reactive power dispatch cannot be ig nored any longer. This project shows how reactive power dispatch and real power dispatch are combined. This project proposes an effective algorithm that uses Reinforcement Learning (RL) for

optimum generation dispatch to minimize the fuel cost.

Various methods have been used to solve the Economic Dispatch (ED) problem. These include conventional methods such as linear programming, non -linear programming, mixed integer programming, interior points and quadratic programming. The non - conventional methods are Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Tabu Search (TS), Ant-Colony, simulated annealing, neural networks and hybrid techniques. In this project, Reinforcement Learning (RL) method has been used to develop an algorithm for economic dispatch.

The developed algorithm has been tested on IEEE 14 -bus, five generator network. The allocation schedule for the five generating units was found for the following sets of real and reactive power demands: 800MW & 370MVAR, 900MW & 470MVAR and 1000MW &

570MVAR. The optimal fuel costs for real power, reactive power and combined real and

reactive power generation was also computed.