MEAN-VARIANCE MAPPING OPTIMIZATION TECHNIQUES FOR NONCONVEX ECONOMIC DISPATCH PROBLEMS

Economic dispatch (ED) is the determination of optimized real power output from
a number of electricity generators needed to meet load requirements at lowest possible
cost. Real-world ED problems have non-convex objective functions with complex
constraints due to the generators characteristics such as valve point loading effects,
usage of multiple fuel options and the existence of prohibited operating zones. This
leads to the difficulty in finding the global optimal solution. This thesis presents a
new application for solving non-convex ED problems by using mean-variance
mapping optimization (MVMO) techniques. MVMO algorithm has conceptual
similarities with other known metaheuristic algorithms which use three evolutionary
operators: selection, mutation and crossover. However, the special feature ofMVMO
is the mapping function applied for the mutation based on the mean and variance of nbest
archived population. The original MVMO utilizes a single particle to start the
search process.