Generalized stochastic Petri nets for uncertain renewable-based hybrid generation and load in a microgrid system

Multiple strategy-based microgrid systems, consisting of several renewable energy sources (RES) with distributed storages are becoming well accepted in the context of smart grid framework. While such kind of microgrid seems to be pivotal in future energy structure, they impose numerous setbacks in the power network due to stochastic variability of distributed generations and uncertainties in load. In this paper, it has been emphasized on major challenges of microgrid for secure, automated, and competent operation. To deal with such kind of encounters, the smart modeling of microgrid assets refer to propose an efficient stochastic scaffold of using Petri nets (PNs). PNs are comprehensively used to demonstrate and investigate industrial systems activities under both discrete and continuous-time occurrence. Hence, time-based PNs develop more reliable option for grid penetration strategies in the realm of uncertain function for loads and RES-integrated microgrid. This work intends to uphold such microgrid model that features unpredictability in distributed energy resources and load. The proposed model using PNs produces effective results that are described as in the category of parallel computing. With the awareness of economic green energy platform, a two-stage microgrid model has been designed using generalized stochastic PNs (GSPN). In the first phase, scenario generations have been configured for each uncertain variable, and then modified real-coded genetic algorithm (MRCGA) is utilized to resolve each deterministic dilemma populated from first phase. In order to replicate deliverable attributes of microgrid, reachability graph is stimulated also by useful marking to examine the complete system.