Scheduling and assessment of multi-area virtual power plant including flexible resources using swarm intelligence technique

A virtual power plant (VPP) makes the distributed energy resources (DERs) visible to support grid services, promoting clean energy use by offering an alternative to reduce reliance on conventional sources. This paper proposes optimized operation and scheduling of VPP by forming a group of flexible resources that includes renewables i.e., solar, wind power, fuel cell, and co-generation units along with storage facilities to form a holistic network in a multi-area interconnected system framework. Initially, the scheduling is performed for a single area system in which a comparison is made with the available work relevant to the proposed formulation. In addition, the scheduling is further extended to the proposed multi-area based virtual power plant (MAVPP) performed on a day-ahead (DA) basis and the target objective(s) i.e., net profit and emission are optimized. Furthermore, a recently developed marine predator optimization algorithm (MPOA) is employed to solve the proposed problem formulation and the results are compared and discussed extensively with conventional techniques. Also, the analysis pertaining to MAVPP is carried out which is beneficial in terms of economic and environmental perspective. The result reveals that the maximum value of net profit for MAVPP is converging to an optimum value of 59,351.88 $ followed by the attained value of minimum emission of 51,700.73 Kg by consuming an elapsed time of 92.96 and 104.76 s., respectively in 200 iterations. The outcomes are promising and upon comparison with the available literature work, the superiority of the selected technique is evident in terms of computational time, improvement of net profit and reduction in emission followed by the ease of convergence behaviour in fewer iterations.