The project is motivated by the fact that the Nordic electricity market for the last two years has experienced a huge fall in energy prices. The reasons for this fall are many, but the undisputable consequence for the Norwegian hydropower sector is that income is going down and there is an urgent need to lower costs and run the operations more efficiently. To this end, there is a need for more practical applicable simulation software. However, traditional solvers based on linear and dynamic programming techniques have significant shortcomings in operational use. The underlying idea in this research project is to apply and adapt recent breakthroughs in so-called Deep Reinforcement Learning (DRL) to the hydropower scheduling problem. To our knowledge, this has never been done before. We will develop DRL based models, algorithms and an accompanying hydraulic-economic model that can utilize inexpensive and massive parallel computing platforms offered by Graphical Processing Units (GPUs). The new software will be tested in an operational setting at Agder Energi and compared to traditional optimization techniques based on linear- and dynamic programming methods (LP, DP). Given the success in this project the value creation will be more efficient utilization of the water resources available at Agder Energi. Today this energy resource is about 4 percent of the Norwegian hydropower production and represents a huge part of Agder Energi's income. It is clear that even small incremental improvements in how water resources are used (1-2 %), will create huge increase in income for the Norwegian hydropower sector. Furthermore, societal costs related to flooding may be reduced with improved techniques for hydropower optimization.
Project leader: Ole-Christoffer Granmo
Institution: AGDER ENERGI KRAFTFORVALTNING AS