Design a neural network gray wolf optimization-based approach for load distribution forecast in virtual power plant

A novel and effective way to oversee the implementation of distributed energy resources (DERs) through the electrical grid is the virtual power plant (VPP). Conventional scheduling systems always set aside a large amount of reserve in order to ensure the reliability of execution, and this is too conservative to reap the full benefits of stochastic distributed energy resources (DERs). This research proposes an energy blockchain network structure that addresses the shortcomings of the existing VPP models. It is built on the combination of gray wolf optimization (GWO) alongside neural network (NN). The suggested method is centered on accomplishing the goals of cost minimization, efficient energy usage, and acceptable load distribution across units, optimal load scheduling, and overall customer comfort maximization. With a friendly atmosphere and clear information, VPP’s consistent scheduling is enhanced by this model’s increased ability to acquire demand-side real-time data. The mean square error and average absolute error of the neural network model-based gray wolf optimization (NNGWO) and particle swarm optimization (NNPSO) are examined in a simulation comparison analysis.