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. In this paper, we propose a human-machine interactive method, namely Real-time Optimization Model Enhanced by Data Assimilation (ROMEDA) tested with simple but reasonable rules for direct interaction between operators and a computer model for reservoirs which have complex storage and stage relations (e.g. long and narrow reservoirs). ROMEDA couples 1) an optimization model to search for optimal releases, 2) a reservoir storage-stage simulation and data assimilation schedule to update the storage based on real-time reservoir stage observations, and 3) reservoir operators’ choices based on the optimization model solutions, as well as their experiences, knowledge, and behaviors. For every time period and based on the updated storage, ROMEDA provides optimal releases as recommendations and adopts actual releases made by operators. ROMEDA does not assume that operators strictly accept the recommendations, and the storage will be updated based on the actual release at each time period via a data assimilation procedure, which plays a key linkage between human and machine in ROMEDA. 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.