Optimized battery energy management using an improved type-2 fuzzy logic approach

Abstract

Energy management systems (EMSs) designed for sustainable buildings can also support smart grids by implementing efficient control algorithms. This role is particularly important when integrating renewable energy sources (RES), including photovoltaic (PV) and wind systems, with electric energy storage systems (ESSs). However, advanced EMS strategies for real-time operation and storage management often demand substantial computational resources, which conventional EMSs may lack. This paper presents and validates an improved type-2 fuzzy logic (T2FL)-based EMS for optimizing PV systems with a battery pack, implemented through edge computing technology. This method is improved by adding 3 main inputs, respectively, Price, SOC, and the energy difference to the T2FL Control, and increasing the rule base to 45 rules to reduce the range of uncertainties. The proposed control system was developed on the MATLAB/Simulink platform and compared with a conventional rule-based EMS. Its effectiveness was further confirmed using data collected from a real system. The findings illustrate the viability and enhanced efficacy of the T2FL algorithm in optimizing energy storage management under various operating conditions. Moreover, the study highlights the advantages and limitations of each control approach, emphasizing the importance of selecting an appropriate EMS to enhance storage effectiveness and deliver energy at the lowest possible cost.