Bakgrunn og aktiviteter

Background & Objective for PhD 

The main topic in the PhD is automation of short-term production planning for Hydro- and wind power

Every day power is traded based on the estimated consumption, and the scheduled production on the Nord Pool Spot power exchange. On this exchange, power produced from different sources is bought and sold and is ready for delivery the next day. This is called the Day-ahead market.

Even though the marked is planned to be in balance, the system is continuously influenced by factors that could lead to imbalances. This could be changes in consumption as result of colder weather or unplanned outage in a Power Station. During the last decades Statnett have introduced market solutions to ensure sufficient supply of reserves.

To manage and plan for sales in an increasing number of markets, most power producers have engaged production planners. In production planning, the power producer attempts to optimize the value of the resources in a long and short-term perspective. This is done by applying a wide range of models and commercial competence

 

The complexity in the planning and nomination process is increasing. 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.

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.

Published research

Optimal pricing of production changes in cascaded river systems with limited storage: A new method, using marginal cost curves for individual powerplants to generate an overall marginal cost curve for interlinked power stations has been developed. Results based on a real-world case study demonstrate the advantage of the proposed method in terms of solution quality, in addition to significant insight into how optimal load distribution should be executed in daily operations.

Rolling Horizon Simulator for Evaluation of Bidding Strategies for Reservoir Hydro: A rolling horizon simulation framework is developed and closely integrated in the daily operations of a hydropower producer. The power producer’s existing framework of decision support models and data for prices and inflow has been used to simulate the use of alternative strategies on a real-life case. Results from the case study show that one single strategy not necessarily will be the optimal one under all conditions, because the optimal strategy will depend on the state of the system.

Ongoing research

Applied machined learning for optimal selection of bidding strategies in reservoir hydro: Access to an increasing amount of data opens up for applying machine learning for predicting the best combination of models and strategy for any given day. Historic performance of two given bidding strategies over several years have been analyzed with a combination of domain knowledge and machine learning. A wide range of parameters accessible to the models prior to bidding have been evaluated to predict the optimal strategy for a given day. Results indicate that a prediction model will outperform a static strategy where one bidding method is chosen based on overall historic performance.

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