A gain–loss framework based on ensemble flow forecasts to switch the urban drainage–wastewater system management towards energy optimization during dry periods

Autor: Morten Grum, Peter Steen Mikkelsen, Thomas Munk-Nielsen, Vianney Augustin Thomas Courdent
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Energy utilization
Technology
010504 meteorology & atmospheric sciences
Operations research
Computer science
0208 environmental biotechnology
Social Sciences
02 engineering and technology
computer.software_genre
Environmental technology. Sanitary engineering
01 natural sciences
lcsh:Technology
Probability forecasts
Electric power transmission networks
Smart power grids
Mitigation measures
Geography. Anthropology. Recreation
GE1-350
lcsh:Environmental technology. Sanitary engineering
Decision making process
TD1-1066
lcsh:Environmental sciences
lcsh:GE1-350
lcsh:Geography. Anthropology. Recreation
Energy consumption
Waste water systems
Management
Catchments
Behavioral research
Spatial and temporal resolutions
Electric Power Transmission
Meteorology
Flood Control
Runoff
Weather forecasting
Forecast skill
lcsh:TD1-1066
Electric power system
SDG 7 - Affordable and Clean Energy
0105 earth and related environmental sciences
lcsh:T
Ensemble prediction systems
Numerical weather prediction models
Numerical weather prediction
020801 environmental engineering
Runoff model
Environmental sciences
Smart grid
lcsh:G
Electric Power Systems
Decision making
Postprocessing methods
computer
Lead time
Forecasting
Zdroj: Hydrology and Earth System Sciences, Vol 21, Iss 5, Pp 2531-2544 (2017)
Courdent, V, Grum, M, Munk-Nielsen, T & Mikkelsen, P S 2017, ' A gain-loss framework based on ensemble flow forecasts to switch the urban drainage-wastewater system management towards energy optimization during dry periods ', Hydrology and Earth System Sciences, vol. 21, no. 5, pp. 2531-2544 . https://doi.org/10.5194/hess-21-2531-2017
ISSN: 1607-7938
1027-5606
Popis: 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). 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.
Databáze: OpenAIRE
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