Short-term management of hydropower assets of the Federal Columbia River Power System

We focus on the short-term optimization of large-scale hydropower systems with a mixture of storage reservoirs and run-of-the-river projects. If sufficient operational flexibility is available within the operational constraints, the system is capable of balancing the transmission network by compensating for load fluctuations and power production of other renewables such as solar and wind resources. The proposed optimization model is based on a nonlinear system representation of the hydropower system for forecasting state trajectories over a forecast horizon in combination with nonlinear programming for computing optimal release trajectories for specific hydropower projects. We present the application of a deterministic version of the approach to the short-term management of the Federal Columbia River Power System in the Pacific Northwest of the USA during the chum spawning season. An assessment of the computational performance of the approach for different optimization algorithms shows a superior performance of the Interior Point OPTimizer (IPOPT) in combination with the HSL/MA27 linear equation solver. In particular, the scaling properties are promising and will enable an extension of the deterministic approach towards a multi-stage stochastic optimization for taking into account the forecast uncertainty.