The integration of renewable energy into energy systems is advancing, yet the inherent uncertainty of wind and solar generation poses challenges to maintaining supply-demand balance. Existing research often optimizes individual energy sources or loads in isolation, lacking a holistic methodology to address supply-demand uncertainties and incorporate intraday response mechanisms. This paper proposes an intraday optimization approach that integrates carbon emissions, storage costs, unit costs, and interconnection fluctuations into a robust model. It ensures a power-heat equilibrium and establishes a source-load uncertainty framework. Real-time adjustments of loads, storage, and diesel generation are achieved through rolling optimization. A genetic algorithm optimizes storage output, while a dual-side unbalanced power model enables bilateral balancing control. The results demonstrate a source-load deviation of 0.2%, a wind-solar fluctuation of 1% per minute, a 1.59% increase in solar utilization, and a reduction in emissions by 0.75 t.
Rongfei Lu (Sat,) studied this question.