Multi-objective optimization for wind energy integration

Due to the diverse and conflicting challenges associated with wind integration, multi-objective optimization is an effective way to address these issues. In this paper the conflicting objectives of cost and reliability are formulated into an economic/reliability dispatch. The first objective function includes wind and thermal unit costs, wind imbalance charges, and reserve capacity costs. The second objective function is the control performance standard (CPS2) score corresponding to a given reserve level. The dispatch problem is solved using a modified multi-objective particle swarm optimization (MO-PSO). Wind and load forecast errors are analyzed to find best fitting distributions to use in the dispatch. A simple test system is used to both validate the dispatch method and perform a wind integration study using a modeled wind facility. Results from the integration study are compared with those obtained using the actual wind data. The phenomenon of artificial wind diversity is discovered to lower the required reserve capacity by strategically manipulating the schedules.