Researchers Jinzhou Xu, Yuanxin Zhuo, and Paola Tapia from the University of Technology Sydney have developed a new operational optimization strategy for energy storage systems (ESSs) in user-side microgrids. Their work, published in the journal Applied Energy, aims to address practical user-oriented application requirements and improve the efficiency of microgrid operations.
The team first established a fundamental ESS model to characterize system dynamics and operational constraints, providing a theoretical basis for optimization. They then formulated a multi-objective operational optimization framework designed to simultaneously minimize electricity cost, reduce carbon emissions, and enhance renewable energy utilization. To ensure computational efficiency and scalability, the researchers employed the commercial optimization solver Gurobi.
The proposed strategy was evaluated using actual microgrid operational data, demonstrating that the developed ESS model accurately represents real system constraints. Compared with existing user operational strategies, the new approach achieves an average reduction of 13.47% in electricity cost. By dynamically adjusting the weighting factors of the multi-objective formulation, the strategy enables flexible operational modes and significantly improves adaptability to varying operating scenarios.
One practical application of this research is in decision support for user-side microgrids participating in surplus electricity feed-in policies. The main contribution of this work lies in its user-centric optimization design, which enhances operational flexibility and scenario adaptability through multi-objective weight allocation. This offers a practical and scalable solution for real-world microgrid ESS operation, potentially benefiting energy providers and consumers alike.
For the energy industry, this research highlights the importance of optimizing energy storage systems to reduce costs, lower emissions, and better integrate renewable energy sources. The developed framework can be applied to various microgrid settings, providing a tool for more efficient and flexible energy management.
This article is based on research available at arXiv.