A Day-Ahead Optimal Battery Scheduling Considering the Grid Stability of Distribution Feeders

Umme Mumtahina (), Sanath Alahakoon and Peter Wolfs
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Umme Mumtahina: Central Queensland University, Rockhampton, QLD 4701, Australia
Sanath Alahakoon: Central Queensland University, Gladstone, QLD 4680, Australia
Peter Wolfs: Central Queensland University, Rockhampton, QLD 4701, Australia

Energies, 2025, vol. 18, issue 5, 1-20

Abstract: This study presents a comprehensive framework for optimizing energy management systems by integrating advanced methodologies for weather forecasting, energy cost analysis, and grid stability using a mixed-integer linear programming (MILP) algorithm. A novel approach is proposed for day-ahead weather forecasting, leveraging real-time data extraction from reliable weather websites and applying clear sky modeling to estimate photovoltaic (PV) generation with high accuracy. By automating weather data acquisition, the methodology bridges the gap between weather predictions and practical energy management, providing utilities with a reliable tool for operating and integrating renewable energy. The optimization framework focuses on minimizing the utility bill by analyzing a distribution feeder representative of Australia’s energy infrastructure, incorporating time-of-use (TOU) and flat tariff systems across eight Australian states to simulate realistic energy costs. Furthermore, voltage constraints are applied within the optimization framework to maintain system stability and improve voltage profiles, ensuring both technical reliability and economic efficiency. The proposed framework delivers actionable insights for utility industries, enhancing the scheduling of battery energy storage systems (BESS) and facilitating the integration of renewable energy into the grid.

Keywords: optimal scheduling; battery energy storage system; energy management system; mixed-integer linear programming; weather forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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