Multi-objective generation expansion and retirement planning using chaotic grasshopper optimisation algorithm

摘要

An efficient, cost-effective multi-objective generation expansion planning (GEP) with Load Forecast (LF) uncertainty is described and evaluated in this article. The aspects that are considered in this multi-objective GEP model are cost, gas emission, energy price risk, fuel consumption and reliability of the generating units. The retirement of existing generating units is included in the projected model as these units reach their maximumLlifetime. To solve the projected generation expansion and retirement planning model, a Chaotic Grasshopper Optimisation Algorithm (CGOA) is proposed and it is applied into 6-, 14- and 24-year planning periods, and numerical result shows that GEP provides a cost-effective solution with less emission.