Optimal day-ahead scheduling of renewable energy-based virtual power plant considering electrical, thermal and cooling energy

Abstract

For synchronized functioning of distributed renewable energy resources, virtual power plant (VPP) is manifested as an upshot for maximizing net profit of the partakers taking into account uncertainties. Here, optimal day-ahead scheduling for VPP has been performed for meeting electrical, thermal and cooling demands. VPP comprises small hydro power plants, wind turbine generators, solar micro-cogeneration units, biomass-fuel-fired micro-cogeneration units, battery energy storage system, plug-in electric vehicles, electric chiller, absorption chiller, ice storage conditioner and thermal energy storage system. SMC units and BMC units are incorporated alternately. Demand response program is applied for electric, thermal and cooling demands to flatten demand curves and to improve system flexibility and decrease cost. The scheduling problem is solved by utilizing bottlenose dolphin optimizer (BDO), self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf (GWO) optimization so as to maximize net profit of VPP operator. The maximum profit obtained from BDO is about 5.25 % and 9.82 % more than the maximum profit obtained from HPSO-TVAC and GWO respectively.