No abstract

Hydropower producers estimate the opportunity value of their water, known as a water value, by comparing current prices to future opportunities. When hydropower dominates the energy mix, the system’s hydrological state predominantly governs supply and thus prices. Despite this intuitive relationship, industry practice is to assume that inflow to reservoirs and prices are independent when they establish operational policies 1–2 years ahead. To investigate the impact of this assumption, we formulate the hydropower scheduling problem as a Markov decision process and develop a novel price model that considers the joint dynamics of forward prices and inflows. We find that producers underestimate their water value when they ignore co-movements between price and inflow. The dependency makes producers more willing to postpone generation and tolerate slightly higher spillage risk. This is because high inflow periods tend to observe low prices and the reservoir capacity is limited. Nevertheless, a case study of a hydropower plant with industry data suggests modest economic losses in practice. Our numerical results suggest a potential gain of 0.17% in expected revenue and approximately unchanged revenue variance if producers consider the co-movements when establishing an operational policy.

We consider an operator of machinery with deteriorating efficiency, facing the problem of optimally timing of either a minor (maintenance) investment or a major (replacement) investment under price uncertainty. If a maintenance investment is chosen, the efficiency of the machinery will deteriorate more slowly, and replacing later is still possible. The optimal decision rule is expressed in the form of thresholds for long-run prices, indicating that it may be rational to wait to see which of the large and small investment is the better choice. We relate the setting to repowering of green energy facilities, such as hydropower plants and wind farms. Our analysis provides several managerial insights. We characterize the conditions that govern whether the smaller investment should be considered at all, and we quantify the effect of having a replacement option embedded in a maintenance option. Our analysis demonstrates that the large investment may get postponed significantly in expectation, which recognizes maintenance as a temporary alternative to replacement.

This paper contributes to forecasting of renewable infeed for use in dispatch scheduling and power systems analysis. Ensemble predictions are commonly used to assess the uncertainty of a future weather event, but they often are biased and have too small variance. Reliable forecasts for future inflow are important for hydropower operation, and the main purpose of this work is to develop methods to generate better calibrated and sharper probabilistic forecasts for inflow. We propose to extend Bayesian model averaging with a varying coefficient regression model to better respect changing weather patterns. We report on results from a case study from a catchment upstream of a Norwegian power plant during the period from 24 June 2014 to 22 June 2015.