Model Predictive Control of Water Resources Systems: A Review and Research Agenda
The eiLab led a study in collaboration with Technische Universität Berlin, Einstein Center Digital Future, Delft University of Technology, University of Melbourne, TU Dortmund University, Universitat Politècnica de Catalunya – BarcelonaTECH, and University of Seville, to carried out a systematic review on Model Predictive Control (MPC) and its recently gained increasing interest in the adaptive management of water
resources systems due to its capability of incorporating disturbance forecasts into real-time optimal control
problems.
The study focus on 149 peer-reviewed journal articles published over the last 25 years on MPC applied to water reservoirs, open channels, and urban water networks to identify common trends and open challenges in research and practice. The three water systems considered were inter-connected, multi-purpose and multi-scale dynamical systems affected by multiple hydro-climatic uncertainties and evolving socioeconomic factors. Results highlighted four main challenges currently limiting most MPC applications in the water domain: (i) lack of
systematic benchmarking of MPC with respect to other control methods; (ii) lack of assessment of the impact of uncertainties on the model-based control; (iii) limited analysis of the impact of diverse forecast types, resolutions, and prediction horizons; (iv) under-consideration of the multi-objective nature of most water resources systems.
In the study published in Annual Reviews in Control, the authors argue that future MPC applications in water resources systems should focus on addressing these four challenges as key priorities for future developments
Read more here: Castelletti A., Ficchì A., Cominola A., Segovia P., Giuliani M., Wu W., Lucia S., Ocampo-Martinez C., De Schutter B., Maestre J.M. (2023). Model Predictive Control of water resources systems: A review and research agenda, Annual Reviews in Control, 55, 442-465. https://doi.org/10.1016/j.arcontrol.2023.03.013
