In power markets featuring hydro generation such as the Nordic market, the optimal use of the available hydro power resource is essential to a well-functioning power system, in order to achieve desired environmental goals and to maximise the socio-economic benefit of the power system. Over many decades the industry has developed the concept of “water values” as the basis for conducting this optimisation. A reservoir’s water value is defined as the marginal expected benefit (e.g. market income, lower costs) of keeping the water in storage and saving it to generate electricity or provide power system services at some point in the future. Put simply, if the water value is higher than the benefit of using the water now it is optimal to keep it in storage, and if it is not the water should be used now. All actors - producers, grid operators, regulators, use and calculate water values to make bidding, operational and investment decisions. The methods used today to calculate these suffer from known computational limitations that reduce their accuracy and increase time-to-solve. The increase in renewable generation and market complexity (e.g. via new reserve markets) additionally challenge existing methods. There is an unmet need in the power industry for better methods to calculate water values that better capture realistic market, geographical and time detail, a full range of uncertainties and their impact, and can calculate these quickly. Our proposed project will research and develop innovative machine learning (ML) models to calculate water values for use in both operative applications and market simulation models. Our approach will build on recent developments in reinforcement and adversarial learning, to develop a system that “learns” how to optimally value (operate) storage hydro, and that uses the computational advantages of machine learning to do so at an increased level of detail and accuracy.
Project leader: Gavin Bell
Institution: OPTIMEERING AS