Multi-Day Stochastic Dispatch for Power Systems With Long-Duration Storage Using Diffusion-Generated Weather Forecasts

Abstract

As power systems shift toward high shares of wind and solar, long-duration storage becomes pivotal for balancing multi-day deficits and surpluses of uncertain supply and demand. Current dispatch strategies for long-duration storage either assign a fixed value to stored energy or extend the modeled horizon using a single forecast. Stochastic system dispatch with a multi-day horizon for long-duration storage remains unexplored, and existing stochastic dispatch only considers day-ahead uncertainty or Markovian uncertainty beyond one week of perfect foresight. Therefore, we benchmark stochastic multi-day dispatch against deterministic alternatives in a rolling-horizon setup. To emulate probabilistic multi-day forecasts, we combine diffusion with a semi-Markov model of weather regimes and a logistic growth model for meteorological forecast errors. At high renewable shares, stochastic operation reduces thermal backup generation and consistently eliminates loss of load. The benefits decrease with smaller shares of renewables, less long-duration storage, or higher forecast skill. Overall, our results suggest that systems with high shares of variable renewables can substantially benefit from probabilistic multi-day forecasts and stochastic dispatch.