Uncertainty-aware demand management of water distribution networks in deregulated energy markets. (March 2018)

• Record Type: Journal Article
• Authors: Sopasakis, P.; Sampathirao, A.K.; Bemporad, A.; Patrinos, P.
• Is Part Of: Environmental modelling & software. Volume 101(2018)
• Page Start: 10
• Page End: 22
• Publication Date: 2018-03
• Subjects: Drinking water networks -- Bid-based energy market -- Stochastic model predictive control -- Graphics processing units -- Scenario trees -- Open-source software; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.11.035
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We present an open-source solution for the operational control of drinking water distribution networks which accounts for the inherent uncertainty in water demand and electricity prices in the day-ahead market of a volatile deregulated economy. As increasingly more energy markets adopt this trading scheme, the operation of drinking water networks requires uncertainty-aware control approaches that mitigate the effect of volatility and result in an economic and safe operation of the network that meets the consumers' need for uninterrupted water supply. We propose the use of scenario-based stochastic model predictive control: an advanced control methodology which comes at a considerable computation cost which is overcome by harnessing the parallelization capabilities of graphics processing units (GPUs) and using a massively parallelizable algorithm based on the accelerated proximal gradient method. Highlights: Operational management of drinking water transportation networks in highly uncertain contexts. Mitigation of the effect of the volatility of energy markets on the operation of a water network. GPU implementation of fast solver for large-scale scenario-based model predictive control. Easy-to-use, open-source, interoperable, fully-documented, thoroughly-tested software.