The objective of this project is to develop new methods for applied decision support for hydro- and windpower production planning. The long-term target is automatization of the nomination process using a combination of fundamental models, and deep reinforced learning methods. Traditionally Nordic power producers have optimized production towards the Nordpool spot market. Closer interactions with European markets, large scale introduction of wind and unregulated power production, and implementation of markets solutions for secondary and tertiary reserves, have increased the complexity in the planning and nomination process. The time from when information is acquired to decisions are made is getting shorter, and the degree of details modelled in the power systems, and the amount of information processed, is continuously increasing. In addition restrictions given by local, state-dependent, concessional and environmental conditions tend to introduce additional requirements to models that are applied in the planning process. In production planning, the power producer attempt to optimize the value of the available resources in a long and short term perspective. This is done by applying a wide range of models and commercial competence. A common challenge for the models applied in the existing planning process is the time requirements associated with complex modelling. The following models are used in the production planning process: o Price models to model expected long- and short-term prices o Long-term fundamental models to generate water values and weekly production plans o Short-term models to create hourly production schedules and marginal-cost o Marginal-cost models to translate results from short-term models in the price-dependent nomination process. The main tasks will focus on solving the identified, and capturing further, arising factors in scheduling.
Project leader: Lisa Haukaas
Institution: HYDRO ENERGI AS