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A parallelized, Adam-based solver for reserve and security constrained AC unit commitment
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DOI：10.1016/j.epsr.2024.110685.png)
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Abstract
En 中文
Power system optimization problems which include the nonlinear AC power flow equations require powerful and robust numerical solution algorithms. Within this sub -field of nonlinear optimization, interior point methods have come to dominate the solver landscape. Over the last decade, however, a number of efficient numerical optimizers have emerged from the field of Machine Learning (ML). One algorithm in particular, Adam, has become the optimizer -of -choice for a massive percentage of ML training problems (including, e.g., the training of GPT-3), solving some of the largest unconstrained optimization problems ever conceived of. Inspired by such progress, this paper designs a parallelized Adam -based numerical solver to overcome one of the most challenging power system optimization problems: security and reserve constrained AC Unit Commitment. The resulting solver, termed QuasiGrad, recently competed in the third ARPA-E Grid Optimization (GO3) competition. In the day -ahead market clearing category (with systems ranging from 3 to 23,643 buses over 48 time periods), QuasiGrad's aggregated market surplus scores were within 5% of the winningest market surplus scores. The QuasiGrad solver is now released as an open -source Julia package: QuasiGrad.jl. The internal gradient -based solver (Adam) can easily be substituted for other ML -inspired solvers (e.g., AdaGrad, AdaDelta, RMSProp, etc.). Test results from large experiments are provided.
Keywords:
AC unit commitment
Adam
Optimal power flow
Market surplus
Mixed-integer
Security constraints
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