Multi-agent system for optimized energy management in multi-smart buildings via deregulated market system

Abstract

A key focus in smart grid research, especially within emerging distribution frameworks, is the effective management of energy resources. The proposed approach employs a multi-agent system to manage energy resources in interconnected smart buildings. The goal is to enhance resilience by determining the energy available for consumption or storage from the source by generating agent, adjusting energy usage in response to grid cost by load agent, and optimizing the supply-and-demand dynamic by storing surplus power during peak hours and discharging it during off-peak hours by storage agent. A MATLAB simulation-based model was developed to analyze five different cases, resulting in the proposed scheme. During off-peak hours, grid dependence decreases by 65.5 %, by 73.49 % during mid-peak hours, and by 85.9 % during peak hours, compared to the base case. When comparing case 3 to the base scenario, the share of energy imported by buildings from the main grid during off-peak, mid-peak, and peak hours is reduced to 69.8 %, 76.51 %, and 89.39 %, respectively, following the implementation of the deregulated market system. This paper presented a genetic algorithm in case 4 to optimize consumption patterns to minimize costs and achieve a 30 % reduction in consumption during peak hours. The resulting consumption decreases peak-hour grid imports by 85.4 % and 96.1 % for building 1 and building 3, respectively, compared to the base case. The findings of this study are useful in understanding the prospects of transforming the energy landscape through proper energy management and enabling a competitive energy market structure.