Advanced Energy Management Strategies for Multi-Energy Community Microgrids

The increasing demand for sustainable and resilient energy solutions has accelerated the development of multi-energy community microgrids (MECMGs). This study presents an advanced energy-management framework that integrates optimization techniques with artificial intelligence to enhance microgrid efficiency, cost-effectiveness, and environmental sustainability. This study proposes several approaches to minimize the operational cost of MECMGs. Initially, a techno-economic assessment was conducted, focusing on a grid-connected microgrid for regional Australian communities and analyzing various configurations of solar, wind, diesel, battery storage, and grid interconnection. Sensitivity analysis revealed that factors such as solar radiation, wind speed, interest rates, and battery lifespan significantly influence microgrid design decisions. Subsequently, a cost-effective design method that incorporates objective functions related to cost, reliability, efficiency, and emission reduction is proposed. For real-time energy management, rule-based control (RBC) and deep reinforcement learning (DRL) techniques were deployed. The DRL-based approach enables adaptive decision making and improves the response of the system to variable load demands, renewable fluctuations, and grid interactions. The proposed energy management strategies were validated through extensive simulations and real-world case studies, demonstrating a significant reduction in operational costs and emissions while ensuring high reliability. The results indicate that the DRL approach outperforms the traditional RBC methods, offering enhanced adaptability and improved cost savings. The proposed framework significantly reduced battery cycling losses, optimized grid import/export strategies, and minimized diesel generator usage, leading to enhanced cost savings and operational resilience. This research has critical implications for policymakers, energy planners, and microgrid operators, offering a cost-effective, resilient, and environmentally sustainable energy management solution for MECMGs.

Grantor dc:publisher

UNSW, Sydney

Year dc:date

2025

Author dc:creator

• Uddin, Moslem ; https://orcid.org/0000-0002-1693-011X

• Community Microgrid
• Artificial Intelligence
• Energy Management System (EMS)
• Distributed Energy Resources (DER)
• Renewable Energy Integration
• Microgrid Control Strategy
• Power System Optimization
• Deep Reinforcement Learning (DRL)
• Proximal Policy Optimization (PPO)
• Cost-Effective Design
• Microgrid
• Real-Time Energy Management
• anzsrc-for: 4008 Electrical engineering

Statement dc:rights

• embargoed access
• CC BY 4.0

Licence

• http://purl.org/coar/access_right/c_f1cf
• https://creativecommons.org/licenses/by/4.0/

Language dc:language

en

Identifier

https://doi.org/10.26190/unsworks/31536

OAI identifier oai:identifier

oai:unsworks.library.unsw.edu.au:1959.4/105674