A gain-loss framework based on ensemble flow forecasts to switch the urban drainage-wastewater system management towards energy optimization during dry periods
Precipitation is the cause of major perturbation to the flow in urban
drainage and wastewater systems. Flow forecasts, generated by coupling
rainfall predictions with a hydrologic runoff model, can potentially be used
to optimize the operation of integrated urban drainage–wastewater systems
(IUDWSs) during both wet and dry weather periods. Numerical weather prediction
(NWP) models have significantly improved in recent years, having increased their
spatial and temporal resolution. Finer resolution NWP are suitable for urban-catchment-scale applications, providing longer lead time than radar
extrapolation. However, forecasts are inevitably uncertain, and fine
resolution is especially challenging for NWP. This uncertainty is commonly
addressed in meteorology with ensemble prediction systems (EPSs). Handling
uncertainty is challenging for decision makers and hence tools are necessary
to provide insight on ensemble forecast usage and to support the rationality
of decisions (i.e. forecasts are uncertain and therefore errors will be made;
decision makers need tools to justify their choices, demonstrating that these
choices are beneficial in the long run).
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This study presents an economic framework to support the decision-making
process by providing information on when acting on the forecast is
beneficial and how to handle the EPS. The relative economic value (REV)
approach associates economic values with the potential outcomes and determines
the preferential use of the EPS forecast. The envelope curve of the REV diagram
combines the results from each probability forecast to provide the highest
relative economic value for a given gain–loss ratio. This approach is
traditionally used at larger scales to assess mitigation measures for
adverse events (i.e. the actions are taken when events are forecast). The
specificity of this study is to optimize the energy consumption in IUDWS
during low-flow periods by exploiting the electrical smart grid market (i.e.
the actions are taken when no events are forecast). Furthermore, the results
demonstrate the benefit of NWP neighbourhood post-processing methods to
enhance the forecast skill and increase the range of beneficial uses