title

Intelligent dispatching and optimal operation of cascade hydropower plants based on big spatiotemporal data (IntHydro)

description

In Norway, hydropower accounts for 96% of the electricity generation. China's hydropower sector has grown twenty-fold to a total capacity of 352 GW in the past 40 years. This represents over a quarter of the world's hydropower installed capacity.

Today fundamental stochastic market models are used. These models assume that all uncertainty is revealed in weekly steps and that all functional relationships are linear. In a power market with increasing shares of variable renewable energy and time resolutions, decision support tools that can swiftly adapt to changes in the power system is essential.

Our goal will be attained by achieving the following objectives:

  • What is the benefit of the digitalisation of hydropower scheduling?
  • Elaborate on the digital platform for the integration of artificial intelligence in different stages of hydropower scheduling models and define comprehensive coupling principles between the strategic and operational modelling.
  • Develop a prototype for fundamental hydropower modelling that allows modelling of RES on a detailed time scale.

The complete innovation chain in IntHydro consists of sub-innovations that can be assigned to intelligent monitoring systems, decision support for optimal operation and handle the real-time challenges. By implementing a practical demonstration tool, the research carried out within IntHydro will allow to accurately quantify the potential value of the scheduling strategies regarding real cases in terms of technical and economic issues.
 

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Our objective

Our objective

We will develop and demonstrate a new hydropower scheduling tool based on machine learning techniques, which manages the hydropower plants more efficiently and effectively through optimizing water resource management and multi-dispatch between hydropower and variable renewable energy sources.

The scheduling methodology will address shortcomings in existing hydropower scheduling models to deal with uncertainties brought by the high share of variable renewable energy resources in both Norwegian and Chinese power system. 

News

News

Chinese partners

Chinese partners

  • Hohai University
  • NanJing Nari Water Resources and Hydropower Technology Company, Ltd (NARI)
  • Yalong River Hydropower Development Company, Ltd (YLR)
     

Norwegian partners

Norwegian partners

  • Norwegian University of Science and Technology
  • Smart Innovation Norway
  • Østfold Energi AS
  • Lyse produksjon AS 
     

Contact

Contact

Research activity

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