Aggregation of building predictive energy flexibility in smart microgrid

• Published in: International journal of electrical power & energy systems 2024-09, Vol.160, p.110073, Article 110073
• Main Authors: Zhang, Zhigang, Guéguen, Hervé
• Format: Article
• Language: English
• Subjects: Aggregation; Approximation; Building; Demand response; Engineering Sciences; Flexibility
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2024.110073

Numerous demand-side resources possess significant potential to offer substantial flexible reserves and ancillary services, thereby enhancing the economic and technical performance of power systems within the demand response domain. A novel approach to aggregating the energy flexibility of buildings based on the virtual battery model is proposed in the smart microgrid, employing predictive control techniques while ensuring consumer privacy. The individual approximation process is implemented to extract the flexible capacity of local resources while protecting information privacy. Subsequently, the aggregator collects the flexibility information to develop an optimization strategy for maximum flexibility aggregation at the upper level, enabling the broader exploitation of global flexibility. The feasibility of power disaggregation is guaranteed according to the aggregation procedure. Furthermore, the comprehensive framework accommodates temporal preferences for flexibility requests from the aggregator. Numerical simulations are conducted to perform a comparative analysis with existing state-of-the-art methods. The results demonstrate that the proposed approach exhibits improved adaptability in characterizing the aggregate flexibility from buildings, effectively managing the heterogeneity of load characteristics and the variability in the collected flexibility information. Microgrids can realize cost savings and achieve dynamic flexibility utilization. This methodology can be extended and applied to diverse types of flexible resources. [Display omitted] •The approach enhances flexibility capacity at individual and aggregation levels.•The framework prioritizes the protection of local building privacy.•Temporal preferences in optimizations boost flexibility and reduce energy costs.