Mixed-Integer Nonlinear Differentiable Predictive Control for Underground Pumped Hydro Energy Storage Systems
Read the original on arXiv Machine Learning →The paper extends Mixed-Integer Differentiable Predictive Control (MI‑DPC) to handle multi‑modal discrete decisions and nonconvex polynomial dynamics in Underground Pumped Hydro Energy Storage Systems (UPHES). It introduces a neural policy that maps problem parameters to continuous setpoints and integer mode selections using a Gumbel‑Softmax layer, trained via self‑supervised differentiation of the finite‑horizon control objective. Key methodological advances include a parallel differentiable simulator, a Transformer encoder for long‑range temporal dependencies, and a Gumbel‑Softmax temperature annealing schedule, enabling the framework to achieve only 1.6% suboptimality compared to a mixed‑integer quadratic programming baseline while providing a five‑order‑of‑magnitude speedup in online scheduling.
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