Receding horizon control for water resources management

作者:

Highlights:

摘要

Integrated water resources management (IWRM) is recognized worldwide as the reference paradigm to meet society’s long-term needs for water resources while maintaining essential ecological services and economic benefits. In previous publications [A. Castelletti, R. Soncini-Sessa, A procedural approach to strengthening integration and participation in water resource planning, Environmental Modelling & Software 21(10) (2006) 1455–1470; A. Castelletti, F. Pianosi, R. Soncini-Sessa, Integration, participation and optimal control in water resources planning and management, Applied Mathematics and Computation, (2007), doi:10.1016/j.amc.2007.09.069], the authors have already insisted on the need for a procedural approach to make the IWRM paradigm truly operational; they have emphasized the role played by dynamic optimization in rationalizing and facilitating the selection by the decision maker of a best compromise planning alternative. When planning alternatives also include management policies, as in the case of the water reservoir networks considered in this paper, the best compromise off-line policy resulting from the planning exercise has to be actually implemented in the daily management of the system. Here, again, dynamic optimization may play a central role, as it can be adopted on-line to improve the performance of the off-line policy by exploiting any new useful information available in real-time (e.g., inflow predictions, a power station being temporarily out of service, etc.). In this paper, this approach is explored through a real-world case study of a simple reservoir system. The off-line management policy computed in a previous planning process is refined on-line with a receding horizon control scheme combined with an inflow predictor. The results yield indications that the approach can provide significant advantages to cope with extreme events, particularly those occurring in unusual periods of the year.

论文关键词:Water resources management,Stochastic optimal control,Adaptive control

论文评审过程:Available online 15 May 2008.

论文官网地址:https://doi.org/10.1016/j.amc.2008.05.044