Bilateral balance optimization control method of wind-solar IES energy storage load based on intraday response model

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.