Modeling and Optimized Scheduling of Virtual Power Plant using Particle Swarm Optimization

Due to the increase in electrical energy consumption and the fact that most of the fuels being used now are CO2 pollutant, utilizing renewable energy sources such as Photovoltaic and wind turbines for electrical energy generation is unavoidable. Renewable energy has been used as sustainable and clean source of energy in the last decade. The main barrier in using renewable energy sources is that their intermittent nature prohibits them to supply the loads. It becomes important when there are critical loads that need to be supplied at all time. Virtual Power Plant (VPP) is a proper energy management for the control of the energy storage, power grid, non-critical loads, and renewable energy resources and can be used to overcome the barrier. The presented study implements a methodology for VPP that aggregates and controls the renewable energy generation units, electric vehicles as storage, and non-critical loads based on the Particle Swarm Optimization (PSO) algorithm that minimizes the operational cost. The results indicate that PSO algorithm ensures continuity in power for the critical loads while keeping the cost at minimum