Short-term pumped storage hydrothermal generation scheduling considering uncertainty of load demand and renewable energy sources

Abstract

This manuscript suggests quasi-oppositional fast convergence real coded genetic algorithm for determining short-term optimum generation schedule of variable head and fixed head hydrothermal system comprising pumped storage hydro plants (PSHPs), solar PV plants and wind turbine generators with demand response program (DRP). PSHP is used for convenient operation. Here, uncertainty of load demand and renewable energy sources has been considered. The uncertainties are curtailed by enclosing richer estimations of variables within well defined confidence intervals. Self adaptive scenarios of uncertain parameters are generated using Tent chaos mapping within confidence intervals. DRP is applied to flatten the load demand curves and to augment system flexibility and decrease operational cost. Numerical results of typical test systems are matched with those attained by fast convergence real coded genetic algorithm and real coded genetic algorithm.