Orthogonal PSO algorithm for solving ramp rate constraints and prohibited operating zones in smart grid applications

Recently Particle Swarm Optimization (PSO) algorithms have been used to solve several engineering optimization problems. In this paper, we propose a novel Orthogonal PSO Learning Algorithm (OPSOLA) to solve Economic Dispatch (ED) problems in a Smart Electric Power Grid (SEPG) application. Thermal power 'turbine-generator' has several nonlinear characteristics, e.g., the ramp-rate limits, prohibited operating zones and non-smooth cost functions. The proposed OPSOLA has ability to solve such complex problems in SEPG. The OPSOLA utilizes a combined method by adding orthogonality to PSO algorithm. In this combined learning strategy, the particles move and construct a new exemplar to guide the particles to fly more steadily toward the optimum solution. This is accomplished by determining the promising movements of the candidate particle in subsequent iterations based on orthogonality. The OPSOLA is evaluated and tested through a IEEE 6-unit thermal power plant and by comparing the results with several other optimization methods. We found that OPSOLA provides better performance in solving the ED problems in terms of convergence characteristics, quality of solution and execution time.