Hydropower Energy Simulation Using Mike 11 Model; A Case Study In South Germany's Small Run-Of-River Hydropower Plants

Renewable energy production is a basic supplement to stabilize rapidly increasing global energy demand and skyrocketing energy price as well as to balance the fluctuation of supply from non-renewable energy sources at electrical grid hubs. The European energy traders, government and private company energy providers and other stakeholders have been, since recently, a major beneficiary, customer and clients of Hydropower simulation solutions. The relationship between rainfall-runoff model outputs and energy productions of hydropower plants has not been clearly studied. In this research, association of rainfall, catchment characteristics, river network and runoff with energy production of a particular hydropower station is examined. The essence of this study is to justify the correspondence between runoff extracted from calibrated catchment and energy production of hydropower plant located at a catchment outlet; to employ a unique technique to convert runoff to energy based on statistical and graphical trend analysis of the two, and to provide environment for energy forecast. For rainfall-runoff model setup and calibration, MIKE 11 NAM model is applied, meanwhile MIKE 11 SO model is used to track, adopt and set a control strategy at hydropower location for runoff-energy correlation. The model is tested at two selected micro run-of-river hydropower plants located in South Germany. Two consecutive calibration is compromised to test the model; one for rainfall-runoff model and other for energy simulation. Calibration results and supporting verification plots of two case studies indicated that simulated discharge and energy production is comparable with the measured discharge and energy production respectively.

Uncertainty Characterization Of A Conceptual Rainfall-Runoff Model By Using Grey Numbers

A procedure for characterizing global uncertainty of a rainfall-runoff simulation model based on using grey numbers is presented. By using the grey numbers technique the uncertainty is characterized by an interval; once the parameters of the rainfall-runoff model have been properly defined as grey numbers, by using the grey mathematics and functions it is possible to obtain simulated discharges in the form of grey numbers whose envelope defines a band which represents the vagueness/uncertainty associated with the simulated variable. The grey numbers representing the model parameters are estimated in such a way that the band obtained from the envelope of simulated grey discharges includes an assigned percentage of observed discharge values and is at the same time as narrow as possible. The approach is applied to a real case study highlighting that a rigorous application of the procedure for direct simulation through the rainfall-runoff model with grey parameters involves long computational times. However, these times can be significantly reduced using a simplified computing procedure with minimal approximations in the quantification of the grey numbers representing the simulated discharges. Relying on this simplified procedure, the conceptual rainfall-runoff grey model is thus calibrated and the uncertainty bands obtained both downstream of the calibration process and downstream of the validation process are compared with those obtained by using a well-established approach, like the GLUE approach, for characterizing uncertainty. The results of the comparison show that the proposed approach may represent a valid tool for characterizing the global uncertainty associable with the output of a rainfall-runoff simulation model.

Model Configuration And Data Management In The Short-Term Water Information Forecasting Tools

The Short-term Water Information and Forecasting Tools (SWIFT) is a suite of tools for flood and short-term streamflow forecasting, consisting of a collection of hydrologic model components and utilities. Catchments are modeled using conceptual subareas and a node-link structure for channel routing. The tools comprise modules for calibration, model state updating, output error correction, ensemble runs and data assimilation. Given the combinatorial nature of the modelling experiments and the sub-daily time steps typically used for simulations, the volume of model configurations and time series data is substantial and its management is not trivial. SWIFT is currently used mostly for research purposes but has also been used operationally, with intersecting but significantly different requirements. Early versions of SWIFT used mostly ad-hoc text files handled via Fortran code, with limited use of netCDF for time series data. The configuration and data handling modules have since been redesigned. The model configuration now follows a design where the data model is decoupled from the on-disk persistence mechanism. For research purposes the preferred on-disk format is JSON, to leverage numerous software libraries in a variety of languages, while retaining the legacy option of custom tab-separated text formats when it is a preferred access arrangement for the researcher. By decoupling data model and data persistence, it is much easier to interchangeably use for instance relational databases to provide stricter provenance and audit trail capabilities in an operational flood forecasting context. For the time series data, given the volume and required throughput, text based formats are usually inadequate. A schema derived from CF conventions has been designed to efficiently handle time series for SWIFT.