Profit Maximization based Optimal Scheduling of a Virtual Power Plant using Red Fox Optimizer

The management of electricity consumption is necessary to tackle the growing demand of electricity in a supervised and efficient manner. A virtual power plant (VPP) is the best way to solve this crisis with the use of technologically advanced software systems to ensure the supply and demand by avoiding the additional cost to setup the necessary distribution networking infrastructure. In this paper, optimal scheduling of the VPP is performed in which the main intention is to enhance the overall net profit that can be beneficial for all the participants involved in the VPP system. The resources considered for the VPP system under study are solar PV, wind power, fuel cell, electric load followed by electricity price and energy market. A newly developed metaheuristic technique Red Fox optimizer (RFO) is utilized to carry out the optimization and the obtained results are compared with various other well-established techniques i.e., genetic algorithm (GA), teaching learning based algorithm (TLBO), ant colony optimization (ACO) and particle swarm optimization (PSO). Numerical results are obtained after 100 independent trials and the comparative analysis shows the effectiveness of the selected algorithm. The computation time required to obtain the optimum value for the net profit is also reduced and the convergence trend is also improved by reaching to the desired solution in lesser number of iterations.