Hydropower Aggregation by Spatial Decomposition-An SDDP Approach

The balance between detailed technical description, representation of uncertainty and computational complexity is central in long-term scheduling models applied to hydro-dominated power system. The aggregation of complex hydropower systems into equivalent energy representations (EER) is a commonly used technique to reduce dimensionality and computation time in scheduling models. This work presents a method for coordinating the EERs with their detailed hydropower system representation within a model based on stochastic dual dynamic programming (SDDP). SDDP is applied to an EER representation of the hydropower system, where feasibility cuts derived from optimization of the detailed hydropower are used to constrain the flexibility of the EERs. These cuts can be computed either before or during the execution of the SDDP algorithm and allow system details to be captured within the SDDP strategies without compromising the convergence rate and state-space dimensionality. Results in terms of computational performance and system operation are reported from a test system comprising realistic hydropower watercourses.

Automated summary

The balance between technical description, uncertainty, and computational complexity is crucial in long-term scheduling models for hydro-dominated power systems. The aggregation of hydropower systems into equivalent energy representations (EERs) is commonly used to reduce dimensionality and computation time. This paper proposes a method for coordinating EERs with their detailed representations using stochastic dual dynamic programming (SDDP). Feasibility cuts derived from detailed hydropower optimization are used to constrain the flexibility of EERs in the SDDP algorithm. Results from a test system with realistic hydropower watercourses demonstrate the computational performance and system operation.