Analysis of grid flexibility in 100% electrified urban energy community: A year-long empirical study.

• Demonstrated the first real-world energy sharing in an energy community. • Fully electrified a community with a 107 MWh scale using high renewable energy. • Achieved 57.6% self-consumption, 38.4% self-sufficiency, 126.3% energy independence. • Used machine learning to uncover complex grid and building energy interactions. • Reduced annual electricity costs by 18.3% through demand side management. Achieving 'carbon neutrality' in the building sector involves high penetration of renewables, electrification of energy use, and improved flexible interactions with the grid to balance energy supply and demand. This study analyzed one year in an urban energy community powered entirely by electricity, using high levels of renewable sources. By facilitating energy sharing between residential and non-residential buildings, the community redirected surplus energy to meet demand, achieving 57.6% self-consumption, 38.4% self-sufficiency, and 126.3% energy independence rate. This achievement involved an annual electricity consumption of 107 MWh, sevenfold that of previous simulation-based studies, marking the first real-world demonstration. Using machine learning clustering with domain-based interpretation, the study unraveled complex interactions between building operations and the power grid, influenced by external factors (weather, occupant behavior), system design, and controls. Managing low-probability high-impact (LPHI) events of severe demand or production peaks, accounting for only 0.5% of the time and 1.8% of net energy annually, was crucial to grid stability and cost reduction. The study confirmed the limitations of increasing battery energy storage capacity in certain environments, highlighting the importance of demand-side management (DSM) with thermal systems. The DSM effectively handled LPHI events, reducing grid stress with an 18.3% decrease in annual electricity cost. [ABSTRACT FROM AUTHOR]