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Competition vs cooperation : application of game theory in the multi-agent coordination of a BC Hydropower system

University of British Columbia

Game theory has been gaining popularity as an innovative tool in the coordination of multi-reservoir systems for the optimal release and market policies through finding equilibrium. An equilibrium-based decision-making model (EDM) was developed to coordinate release and market decisions to meet demand and trade electricity from the Peace and Columbia systems in the US and Alberta Markets. The Williston and Kinbasket Reservoirs were taken as the main two agents to represent the Peace and Columbia rivers, respectively. Data on demand, inflows, prices, and release limits were provided by BC Hydro; and the Water Value Function for each reservoir was obtained from the Energy Studies Peace and Columbia Optimizers for the December 2019 study. The policies resulting from the game-theoretic model were compared to these of an existing iterative simulation and coordination model, the Energy Studies Models. The model showed reasonable results for the Peace system with low absolute error and mean absolute deviation for the drawdowns from Williston Reservoir, while the drawdowns from Kinbasket Reservoir showed larger error as compared to energy studies. Three different solution algorithms were investigated: social optimum, Nash Support Enumeration, and Mixed Integer Linear Programming. In this case study, the Mixed Integer Linear Programming algorithm to find Nash Equilibrium gave the best strategies and rewards. However, the Nash Support Enumeration algorithm is more adaptable to situations with more than two agents. The results suggest that Game Theory is a promising technique that should be further investigated and enhanced to aide Energy Studies in the coordination of reservoir release policies. To further develop the model results, inverse reinforcement learning algorithms in Stochastic Games were investigated and presented. An effective way to validate and compare this model and the different tools developed by the BC Hydro’s system optimization group is by following a model benchmarking framework detailed in this research.

Text

2020-08-25

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/75687

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/