Optimization framework for multi-vector energy communities with uncertainty-aware energy management

This paper presents a robust optimization framework for multi-vector energy communities that addresses key limitations of existing models by integrating electrical and thermal energy systems to enhance resource utilization and reduce grid dependency. The framework employs Mixed-Integer Linear Programming (MILP) to optimize energy flows under stochastic uncertainties and dynamic tariff structures, overcoming the scalability and adaptability challenges faced by prior approaches. A novel aspect of this work is the strategic use of Power-Type Batteries (PTBs) for short-term demand fluctuations and Energy-Type Batteries (ETBs) for long-term energy storage, enabling balanced and efficient energy management. Case studies in New South Wales and Tasmania demonstrate the framework’s scalability, effectively managing scenarios with 10, 50, and 100 feeders. Results show PTBs reduce grid reliance by up to 30 % during peak hours, while ETBs meet both electrical and thermal demands, reducing gas usage by up to 20 %. Additionally, capability-index analysis further highlights the framework’s robustness in maintaining operational efficiency across varying uncertainty levels. By integrating tailored incentive mechanisms, the framework achieves up to 30 % cost reductions, establishing a scalable, low-carbon energy management solution that significantly advances the integration and operational robustness of multi-vector energy systems.