Hydropower Energy Simulation Using Mike 11 Model; A Case Study In South Germany\u27s 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

Survey and identification of termites (Insecta, Isoptera) using morphological and molecular methods from eastern, central and western Ethiopia.

The subfamily Macrotermitinae are the largest members among the Family Termitidae which are the fungus growing sub-family and Odontotermes are the most abundant genus from the subfamily.  The taxonomy of termites is poorly described in Ethiopia. In the present study 168 termite samples were collected from eight locations of Eastern, Western and Central Ethiopia. The collected samples were identified based on morphological and molecular characteristics. Molecular identification was done based on the dna sequence of a portion of the mitochondrial 16S rrna gene. A phylogenetic analysis of the collected samples and the outgroup resulted in a consensus tree with four distinct groups. Geographical distribution of the samples also supported the resulting clades. Odontotermes were the most widely distributed termites from the collected samples. The genetic distance between the sample showed that Odontotermes zambesiensis, Babile 33 is more distantly related with the rest of the samples

Event-based model calibration approaches for selecting representative distributed parameters in semi-urban watersheds

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.advwatres.2018.05.013 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The objective of this study is to propose an event-based calibration approach for selecting representative semi-distributed hydrologic model parameters and to enhance peak flow prediction at multiple sites of a semi-urban catchment. The performance of three multi-site calibration approaches (multi-site simultaneous (MS-S), multi-site average objective function (MS-A) and multi-event multi-site (ME-MS)) and a benchmark at-catchment outlet (OU) calibration method, are compared in this study. Additional insightful contributions include assessing the nature of the spatio-temporal parameter variability among calibration events and developing an advanced event-based calibration approach to identify skillful model parameter-sets. This study used a SWMM5 hydrologic model in the Humber River Watershed located in Southern Ontario, Canada. For MS-S and OU calibration methods, the multi-objective calibration formulation is solved with the Pareto Archived Dynamically Dimensioned Search (PA-DDS) algorithm. For the MS-A and ME-MS methods, the single objective calibration formulation is solved with the Dynamically Dimensioned Search (DDS) algorithm. The results indicate that the MS-A calibration approach achieved better performance than other considered methods. Comparison between optimized model parameter sets showed that the DDS optimization in MS-A approach improved the model performance at multiple sites. The spatial and temporal variability analysis indicates a presence of uncertainty on sensitive parameters and most importantly on peak flow responses in an event-based calibration process. This finding implied the need to evaluate potential model parameters sets with a series of calibration steps as proposed herein. The proposed calibration and optimization formulation successfully identified representative model parameter set, which is more skillful than what is attainable when using simultaneous multi-site (MS-S), multi-event multi-site (MS-ME) or at basin outlet (OU) approach.Natural Sciences and Engineering Research Council of Canada [NETGP 451456

Investigating Market Diffusion of Electric Vehicles with Experimental Design of Agent-Based Modeling Simulation

The transportation sector is recognized as one of the largest contributors to the problems of global warming and environmental pollution, and is responsible for a great deal of global energy consumption, which is heavily dependent upon scarce crude oil reserves. Different countries have adopted promotional policies to replace conventional internal combustion engine vehicles with electric vehicles as a means of mitigating global warming. Nevertheless, the current market share of eco-friendly vehicles remains stagnant in many parts of the world. This study aims to investigate the impact and relative importance of financial, technical, and political measures on the market penetration of electric vehicles using an agent-based simulation. More specifically, a series of agent-based simulation experiments is carried out following the statistical experimental design scheme to systematically assess the diffusion of electric vehicles. Affected by various factors and measures, the choice behavior of individual agents is modeled with a multinomial logit utility function of experimental factors. The simulated data are analyzed using different analysis methods, including full factorial analysis, response surface methodology, and support vector machine, in order to scrutinize the effects of different measures. It is advocated that factors affecting the choice of vehicle by individuals, including two-way interactions among various measures as well as policy measures such as purchase subsidies and tax breaks, have more significant effects on the widespread adoption of electric vehicles than do technical improvements in terms of battery charging times and driving mileage. This implies that the adoption of such measures needs to be carefully designed in order to account for potential interactions among individual measures as well as their main effects on the diffusion of electric vehicles

Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed

Accurate and reliable flow forecasting in complex Canadian prairie watersheds has been one of the major challenges faced by hydrologists. In an attempt to improve the accuracy and reliability of a reservoir inflow forecast, this study investigates structurally different hydrological models along with ensemble precipitation forecasts to identify the most skillful and reliable model. The key goal is to assess whether short- and medium-range ensemble flood forecasting in large complex basins can be accurately achieved by simple conceptual lumped models (e.g., SACSMA with SNOW17 and MACHBV with SNOW17) or it requires a medium level distributed model (e.g., WATFLOOD) or an advanced macroscale land-surface based model (VIC coupled with routing module (RVIC)). Eleven (11)-member precipitation forecasts from second-generation Global Ensemble Forecast System reforecast (GEFSv2) were used as inputs. Each of the ensemble members was bias-corrected by Empirical Quantile Mapping method using the Canadian Precipitation Analysis (CaPA) as a training/verification dataset. Forecast evaluation is performed for 1-day up to 8-days forecast lead times in a 6-month hindcast period. Results indicate that bias-correcting precipitation forecasts using verifying datasets (such as CaPA) for a training period of at least two years before the forecast time, produces skillful ensemble hydrological forecasts. A comparison of models in forecast mode shows that the two lumped models (SACSMA and MACHBV) can provide better overall forecast performance than the benchmark WATFLOOD and the macroscale Variable Infiltration Capacity (VIC) model. However, for shorter lead-times, particularly up to day 3, the benchmark distributed model provides competitive reliability, as compared to the lumped models. In general, the SACSMA model provided better forecast quality, reliability and differentiation skill than other considered models at all lead times

Investigating Market Diffusion of Electric Vehicles with Experimental Design of Agent-Based Modeling Simulation

The transportation sector is recognized as one of the largest contributors to the problems of global warming and environmental pollution, and is responsible for a great deal of global energy consumption, which is heavily dependent upon scarce crude oil reserves. Different countries have adopted promotional policies to replace conventional internal combustion engine vehicles with electric vehicles as a means of mitigating global warming. Nevertheless, the current market share of eco-friendly vehicles remains stagnant in many parts of the world. This study aims to investigate the impact and relative importance of financial, technical, and political measures on the market penetration of electric vehicles using an agent-based simulation. More specifically, a series of agent-based simulation experiments is carried out following the statistical experimental design scheme to systematically assess the diffusion of electric vehicles. Affected by various factors and measures, the choice behavior of individual agents is modeled with a multinomial logit utility function of experimental factors. The simulated data are analyzed using different analysis methods, including full factorial analysis, response surface methodology, and support vector machine, in order to scrutinize the effects of different measures. It is advocated that factors affecting the choice of vehicle by individuals, including two-way interactions among various measures as well as policy measures such as purchase subsidies and tax breaks, have more significant effects on the widespread adoption of electric vehicles than do technical improvements in terms of battery charging times and driving mileage. This implies that the adoption of such measures needs to be carefully designed in order to account for potential interactions among individual measures as well as their main effects on the diffusion of electric vehicles

Hydrological Analysis of Extreme Rain Events in a Medium-Sized Basin

The hydrological response of a medium-sized watershed with both rural and urban characteristics was investigated through event-based modeling. Different meteorological event conditions were examined, such as events of high precipitation intensity, double hydrological peak, and mainly normal to wet antecedent moisture conditions. Analysis of the hydrometric features of the precipitation events was conducted by comparing the different rainfall time intervals, the total volume of water, and the precedent soil moisture. Parameter model calibration and validation were performed for rainfall events under similar conditions, examined in pairs, in order to verify two hydrological models, the lumped HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System model) and the semi-distributed HBV-light (a recent version of Hydrologiska Byråns Vattenbalansavdelning model), at the exit of six individual gauged sub-basins. Model verification was achieved by using the Nash–Sutcliffe efficiency and volume error index. Different time of concentration (Tc) formulas are better applied to the sub-watersheds with respect to the dominant land uses, classifying the Tc among the most sensitive parameters that influence the time of appearance and the magnitude of the peak modeled flow through the HEC-HMS model. The maximum water content of the soil box (FC) affects most the peak flow via the HBV-light model, whereas the MAXBAS parameter has the greatest effect on the displayed time of peak discharge. The modeling results show that the HBV-light performed better in the events that had less precipitation volume compared to their pairs. The event with the higher total precipitated water produced better results with the HEC-HMS model, whereas the rest of the two high precipitation events performed satisfactorily with both models. April to July is a flood hazard period that will be worsened with the effect of climate change. The suggested calibrated parameters for severe precipitation events can be used for the prediction of future events with similar features. The above results can be used in the water resources management of the basin