Real-time reservoir flood control operation enhanced by data assimilation

Abstract. Real world reservoir operations are usually not fully automatic based on computer models; instead, reservoir operators conduct the operations based on their experiences, professional justification, as well as modeling support for some cases due to unavoidable gap between computer modeling and real world reservoir operation conditions. In this paper, we propose a human-machine interactive method, namely Real-time Optimization Model Enhanced by Data Assimilation (ROMEDA) for reservoirs which have complex storage and stage relations (e.g. long and narrow reservoirs). The system is composed of 1) an optimization model to search for optimal releases, 2) reservoir operators’ choices based on their experiences, knowledge, and behaviors, and 3) a reservoir storage-stage simulation and data assimilation schedule to update the storage based on real-time reservoir stage observations. For every time period and based on the updated storage, ROMEDA provides optimal releases as recommendations, actual releases made by operators, as well as a warning of flood risk when the storage exceeds a threshold level. ROMEDA does not assume that operators strictly accept the recommendations, and storage will be updated based on actual release at each time period. Via a case study on-channel reservoir, it is found that for both small and large flood events, ROMEDA, which integrates the advantages of both machine and human, shows better performance on flood risk mitigation and water use (hydropower) benefit than the case with historical operation records (HOR) or optimization with single/multi-objective. ROMEDA is one of the first attempts of a human-machine interactive method for online use of an optimization model for real-time reservoir operation based on integrated modeling, observation, and operators’ choice.