An algorithmic-based energy sharing model with peer-to-peer energy trading for clustered residential buildings

Abstract

This paper presents a novel algorithmic-based method for energy sharing among consumers in a microgrid. The proposed method uses a greedy algorithm, which allows for the efficient selection of energy providers for users within the microgrid. In addition, the paper provides an overview of conventional control strategies used in microgrids, including centralized control, decentralized control, and distributed control. The purpose of this overview is to provide a better understanding of the energy management system and its control strategies. As renewable energy resources continue to be integrated into power systems, the need for efficient and reliable energy sharing models is becoming increasingly important. The proposed algorithmic-based model aims to increase the reliability of the microgrid system by providing a fast-sorting mechanism of promising prosumers with the largest charge capacity of their battery bank (BB) in the microgrid. This helps to ensure that the required energy demand of users is met with minimum switching between prosumers. The paper also addresses the peer-to-peer trade of electricity among prosumers with dynamic rates, which is monitored through the central controller (CC). The algorithm sorts the buildings in non-increasing order of the ratio (1-SoC)/capacity of the battery (Ah) and selects the buildings with battery capacity less than or equal to the remaining demand. The output of the algorithm is the set of selected buildings and their respective energy providers. By identifying optimal energy providers for microgrids, the algorithm provides a heuristic approach for improving the efficiency of energy sharing in microgrid systems. Finally, the paper provides a comparative study of the techniques used in microgrids, including the proposed model. This study provides insights into the potential benefits of the algorithmic-based energy-sharing model and its ability to contribute to the development of better future grids. Overall, the paper provides valuable insights into the field of microgrids and highlights the importance of efficient and reliable energy sharing models as renewable energy resources continue to be integrated into power systems.