Stochastic model predictive control approaches applied to drinking water networks

Abstract

Control of drinking water networks is an arduous task given their size and the presence of uncertainty in water demand. It is necessary to impose different constraints for ensuring a reliable water supply in the most economic and safe ways. To cope with uncertainty in system disturbances due to the stochastic water demand/consumption, and optimize operational costs, this paper proposes three stochastic model predictive control (MPC) approaches, namely: chance-constrained MPC, tree-based MPC, and multiplescenarios MPC. A comparative assessment of these approaches is performed when they are applied to real case studies, specifically, a sector and an aggregate version of the Barcelona drinking water network in Spain.

Uncertainty-aware demand management of water distribution networks in deregulated energy markets

“…This has rendered the use of SSMPC prohibitive and has hindered its applicability. Indeed, hitherto there have been used only conventional model predictive control approaches [14,15], robust worst-case formulations [5,16,17] and stochastic formulations where the underlying uncertainty is assumed to be normally identically independently distributed [18,19]. Note that it has been observed that demand prediction errors are typically follow heavy-tail distributions which cannot be well approximated by normal ones [20].…”

“…It is common in stochastic control-oriented modeling to assume that the errors j|k are independently distributed [18,19]. This assumption however neglects the covariance across the times stages -indeed, if at the future time j = 1 the model has a large prediction error we would rather expect that the prediction error at time j = 2 is likely to be large too.…”

“…The scenario-based optimization problem (19) is now in the form (22) with f and g given by (24) and (28) respectively and H given by (27).…”

Regulador cuadrático lineal y control predictivo aplicados en un sistema de cuatro tanques: una comparación de rendimiento

“…El controlador MPC puede considerar retardos en las entradas y salidas, no linealidades, incertidumbres, naturaleza estocástica, por nombrar algunos (Grosso, et al, 2016); (Camacho & Bordons, 2004); (Velarde, et al, 2017). La idea detrás de este esquema de control es minimizar una función objetivo mediante el cálculo de una secuencia de acciones de control a lo largo de un horizonte de predicción; solo el primer componente de esta secuencia de control se implementa en el paso de tiempo actual; las acciones de control restantes se descartan.…”

On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid

“…An application of this technique in the context of the drinking water network of the city of Barcelona is reported in [28]. In addition, [29] shows a comparison between MS-MPC, TB-MPC, and CC-MPC approaches applied to drinking water networks via simulation. Further, this subject has drawn significant interest; a stochastic optimization model implemented in the context of the control of microgrids can be seen in [30][31][32][33] and references therein.…”