The single-unit commitment problem (1UC) is the problem of finding a cost optimal schedule for a single generator given a time series of electricity prices subject to generation limits, minimum up- and downtime and ramping limits. In this paper we present two efficient dynamic programming algorithms. For each time step we keep track of a set of functions that represent the cost of optimal schedules until that time step. We show that we can combine a subset of these functions by only considering their minimum. We can construct this minimum either implicitly or explicitly. Experiments show both methods scale linear in the amount of time steps and result in a significant speedup compared to the state-of-the-art for piece-wise linear as well as quadratic generation cost. Therefore using these methods could lead to significant improvements for solving large scale unit commitment problems with Lagrangian relaxation or related methods that use 1UC as subproblem.
- Dynamic programming
- Polynomial-time algorithm,
- Single-unit commitment problem