In all power markets, the best - or optimal - use of the generation resources available is essential in order to achieve the best outcomes for both actors in the market and as society as a whole. In markets featuring substantial amounts of hydropower, such as the Nordic market, such optimisation is particularly challenging, as water can be stored from one period to the next. Water used today cannot be used tomorrow; conversely forgoing generation (use of water) today enables its use tomorrow. To figure out what is the optimal use of generation resources today, we thus need to take into account the likely best use of generation resources in the coming days, months and years.
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 likely benefit (e.g. market income, lower costs) of keeping the water in storage and saving it to generate electricity 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.
There is a clear need for accurate, timely calculation of water values for a well-functioning electricity system. Today?s methods fall short of this however. They both take a long time to calculate and are overly-simplified. Our project if focused on developing novel machine learning (ML) models to calculate water values, building on recent developments in reinforcement and adversarial learning. Our project is broadly divided into three parts: the first, an investigative component to research the RL and related literature and algorithms, to determine our broad plan of attack for our project; the second to iteratively build and test candidate algorithms; and the third to test and assess the candidates against real-world water value calculations. Our focus thus far in 2021 has been on the first part. In particular, we have undertaken extensive literature reviews in the machine learning, water value, and inventory management literatures, and have tested a variety of alternative methodologies on simplified problem instances in order to decide the candidate methods for part 2. Part 2 will be undertaken in 2022, and part 3 in 2023.
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.