Work

Soft-switching predictive Type-3 fuzzy control for microgrid energy management

Year: 2025

Type: article

Abstract: Because each mode has distinct optimization requirements, optimizing the economic performance of microgrids (MGs) in grid-connected and islanded modes presents unique issues. This research offers a novel methodology to overcome this difficulty and better use renewable resources while satisfying the growing needs of the energy market. To maximize the MG’s performance, this approach combines fuzzy control, machine learning, and artificial intelligence with soft switching technologies. Two predictive rules are used in the design of the control system to handle the particular requirements of either the grid-connected or islanded mode depending on the MG’s operational condition. The study’s findings demonstrate that, in addition to lowering energy expenses in large commercial buildings, the suggested strategy optimizes the use of renewable energy sources and storage capacity in a range of network scenarios. This method offers more flexibility in response to network changes and greatly improves energy efficiency.

Source: Energy Informatics

DOI: 10.1186/s42162-025-00508-6

Authors: Walid Ayadi, Jafar Tavoosi, Amirhossein Khosravi Sarvenoee, Ardashir Mohammadzadeh

Institutions: Ilam University, University of Bonab

Language: English

Topic: Microgrid Control and Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $1,490