Optimized energy management for interconnected networked microgrids: A hybrid NEGCN-PFOA approach with demand response and marginal pricing

Abstract

This manuscript proposes a hybrid approach for operating cost reduction in networked microgrids. The proposed hybrid system is the combined performance of both the Neighbour Enhanced Graph Convolutional Networks (NEGCN) and the Piranha Foraging Optimization Algorithm (PFOA). Commonly it is named as NEGCN-PFOA technique. The primary goal of the proposed strategy is operating cost minimization in networked micro grids. The NEGCN is used for the demand prediction in networked microgrids and the PFOA is used for the operating cost optimization of networked microgrids. Moreover, the result of the system critic behaviour is researched, where the critic is a function of the control range. By then, the proposed method has been included in the working MATLAB platform, and its execution is calculated using the existing procedures. Compared to all current approaches, the proposed technique yields better outcomes like Deep Reinforcement Learning, Long Short-Term Memory and Deep Learning Artificial Neural Network. The proposed method is useful in reducing the operating cost in the networked micro grids. From the outcome, it is concluded that the proposed technique based costs is less contrasted with existing methods.