An open-source Python implementation of California's hydroeconomic optimization model. (October 2018)

• Record Type: Journal Article
• Authors: Dogan, Mustafa S.; Fefer, Max A.; Herman, Jonathan D.; Hart, Quinn J.; Merz, Justin R.; Medellín-Azuara, Josue; Lund, Jay R.
• Is Part Of: Environmental modelling & software. Volume 108(2018)
• Page Start: 8
• Page End: 13
• Publication Date: 2018-10
• Subjects: Hydroeconomic modeling -- Network flow optimization -- Multi-reservoir operations; 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.2018.07.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This short communication describes a new open-source implementation of the CALVIN model (CALifornia Value Integrated Network), a large-scale network flow optimization model of California's water supply system. The model is cross-platform, uses common data formats, and connects to several freely available linear programming solvers. Given inputs including hydrology, urban/agricultural demand curves, and variable operating costs, the model minimizes the systemwide cost of water scarcity and operations including surface and groundwater reservoirs, wastewater reuse, desalination, environmental flow requirements, and hydropower. Key outputs include water shortage costs and marginal economic values of water and infrastructure capacity. We benchmark the scalability of different solvers up to roughly 5 million decision variables, using shared-memory parallelization on a high performance computing cluster. Runtimes are reduced by two orders of magnitude relative to the original model when no initial solution is provided, in addition to the benefits such as accessibility and transparency that come with an open-source platform. While this model is specific to California, the data and model structure are separated, so a similar framework could be used in any system where water allocation has been formulated as a network flow problem.