Asynchronous parallel stochastic dual dynamic programming applied to hydrothermal generation planning

Optimizing power generation planning is very important to achieve the lowest possible costs while meeting the required power demand along time. High computational resources are required to solve this problem and, in some cases, the use of parallel schemes is imperative. A widely used method to solve long-term energy planning problems is an extension of Dual Dynamic Programming (DDP) called Stochastic Dual Dynamic Programming (SDDP) which makes use of sampling techniques to be able to deal with high-dimensional state-spaces. In this work, we propose an asynchronous SDDP parallel scheme (labeled ASDDP) capable of overcoming the intrinsic synchronism of traditional versions of the SDDP method, by dividing subproblems by time steps instead of forward samples. This parallelization strategy, which includes a variant called totally asynchronous ASDDP (TASDDP), allows us to better exploit the parallel resources and decreases the overall CPU time to solve the problem. Consistency and performance tests were applied to evaluate the application of the proposed ASDDP and TASDDP algorithms in the real Brazilian system.

Automated summary

Optimizing power generation planning is crucial for cost-effectiveness and meeting power demand, and the use of parallel computing methods can significantly enhance the efficiency of solving this problem.