A distributed asynchronous algorithm for the two-stage stochastic unit commitment problem

We present a distributed asynchronous algorithm for solving the two-stage stochastic unit commitment problem. The algorithm uses Lagrangian relaxation to decompose the problem by scenarios and applies an incremental method to solve the dual problem. At each incremental dual iteration, the algorithm evaluates the dual function, providing a lower bound, and recovers a feasible commitment for first stage units, which (through a feasibility recovery process) results in an upper bound. Both the incremental dual iterations as well as the feasibility recovery are executed asynchronously, resulting in more efficient utilization of parallel processors. The method is tested on a model of the Central Western European system, for which it achieved convergence three times faster than an equivalent distributed synchronous algorithm.