期刊
ENVIRONMENTAL MODELLING & SOFTWARE
卷 126, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2020.104635
关键词
Water resource simulation; Network optimisation; Open source; Python; Multi-reservoir operations; Decision making under deep uncertainty
类别
资金
- UK Research and Innovation (UKRI) through Global Challenge Research Fund (GCRF) FutureDAMS project [ES/P011373/1]
- University of Manchester, United Kingdom
- EPSRC [EP/J005274/1, EP/G060460/1] Funding Source: UKRI
- ESRC [ES/P011373/1] Funding Source: UKRI
- NERC [NE/M020142/1, NE/P017002/1, NE/S014098/1, NE/S017305/1, NE/L009285/2] Funding Source: UKRI
A new generalised water resource network modelling Python library, Pywr, is presented. Given hydrological inflows, Pywr simulates customisable water allocation and operation rules throughout complex multi-purpose managed water systems at each user-defined time-step. The model uses a low-level interface to existing linear programming solvers for fast priority-based optimisation-driven simulation. The library uses an object based system for users to provide input data and record simulation outputs. A novel multi-scenario simulation method provides an almost 4-fold improvement in model run-times and supports calculating robustness metrics across scenarios. A flexible interface to specify multi-objective optimisation formulations as part of a model's input file is included. These features enable analysts to apply advanced water planning approaches, such as robust decision making and robust optimisation, to real systems. The library is available under the GPLv3 open source licence, includes several examples and a regression test suite.
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