Approximate Dynamic Programming for Large-scale Unit Commitment Problems

Abstract

Unit commitment (UC) is crucial for reducing the generation cost of short-term power system scheduling. This study proposes a new approximate dynamic programming (ADP) method to solve large-scale UC considering ramp rate limits of thermal units. To solve the “curse of dimensionality,” a policy iteration algorithm using post-decision state variables is utilized for value function approximation. The post-decision state variables dramatically simplify the algorithm by avoiding the need to compute the value of all feasible states within Bellman’s equation. The proposed method is applied to systems ranging in size from 10 to 1000 units to optimize the generation scheduling of the units in a day. And the time horizon of the UC problem is split into 96 time periods. It is shown that ADP can converge to promising results in polynomial time complexity for large-scale UC.