UBC Theses and Dissertations
Incorporating flood control rule curves of the Columbia River hydroelectric system in a multireservoir reinforcement learning optimization model Shabani, Nazanin
The main objective of reservoir operation planning is to determine the amount of water released from a reservoir and the amount of energy traded in each time step to make the best use of available resources. This is done by evaluating the trade-off between the immediate and the future profit of power generation. A set of constraints have to be met in operating a reservoir system such as the continuity equations, transmission limits, generation and reservoir limits, flood control limits and load resource balance. Another important issue that needs to be addressed in these problems is uncertainty, which comes from lack of knowledge or certainty about the exact amount of an input parameter because of its spatial and temporal variability, inherent nature of a problem or parameter, errors in measurement due to human or technology inaccuracy and other errors in modeling due to simplification or ignorance. This research successfully implements a Reinforcement Learning (RL) optimization algorithm incorporating some of the operating rules and flood control constraints of the Columbia River Treaty. It considers the main sources of uncertainty in operating a large scale hydropower system: market prices and inflows by using a number of scenarios of historical data on inflow and energy prices in the learning process. The RL method reduces the time and computational effort needed to solve the operational planning problem and can be used to determine the value of water and marginal value of water for the BC Hydro system. The results suggest that the RL algorithm can incorporate a typical flood control rules for multireservoir optimization problems of this type.
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