scholarly journals Analysis of the effects of biases in ESP forecasts on electricity production in hydropower reservoir management

2018 ◽  
Author(s):  
Richard Arsenault ◽  
Pascal Côté
Keyword(s):  
Hydrological Modeling ◽  
Reservoir Management ◽  
Optimization Techniques ◽  
Electricity Production ◽  
Weather Data ◽  
Climate Data ◽  
Streamflow Prediction ◽  
Test Bed ◽  
Time Step ◽  
Deterministic Optimization

Abstract. This paper presents an analysis of the effects of biased Extended Streamflow Prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon testbed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data was fed to the hydrological model and the synthetic streamflow thus generated was considered as a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method’s response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test-bed in which the entire system is precisely and accurately modeled. The ensemble climate data forecasts, the hydrological modeling and ESP forecast generation, optimization model and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focused on one hydropower system both with and without minimum base load constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore increases the odds of forced spillage by attempting to maximize short-term profit by keeping a higher net head. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider optimal trajectories.

scholarly journals Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management

2019 ◽  
Vol 23 (6) ◽  
pp. 2735-2750 ◽  
Author(s):  
Richard Arsenault ◽  
Pascal Côté
Keyword(s):  
Reservoir Management ◽  
Optimization Techniques ◽  
Electricity Production ◽  
Weather Data ◽  
Climate Data ◽  
Streamflow Prediction ◽  
Test Bed ◽  
Time Step ◽  
Deterministic Optimization ◽  
Management Optimization

Abstract. This paper presents an analysis of the effects of biased extended streamflow prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon test bed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data were fed to the hydrological model, and the synthetic streamflow subsequently generated was considered to be a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method's response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test bed in which the entire system is precisely and accurately modelled. The ensemble climate data forecasts, the hydrological modelling and ESP forecast generation, optimization model, and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focussed on one hydropower system both with and without minimum baseload constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore place the reservoir levels at greater risk to maximize short-term profit. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider to be optimal trajectories.

scholarly journals A method for simulating risk profiles of wheat yield in data-sparse conditions

2021 ◽  
pp. 1-12
Author(s):  
G. Bracho-Mujica ◽  
P.T. Hayman ◽  
V.O. Sadras ◽  
B. Ostendorf
Keyword(s):  
Reference Site ◽  
Climate Impacts ◽  
Weather Data ◽  
Climate Data ◽  
High Quality ◽  
Risk Profiles ◽  
Record Length ◽  
Daily Weather Data ◽  
Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

Determination of the Minimum Distance Between Moving Objects Including Velocity Information

1993 ◽  
Author(s):  
Meyer Nahon
Keyword(s):  
Minimum Distance ◽  
Moving Objects ◽  
Rapid Determination ◽  
Computation Time ◽  
Optimization Techniques ◽  
Real Time Control ◽  
Time Step ◽  
Time Control ◽  
Velocity Information

Abstract The rapid determination of the minimum distance between objects is of importance in collision avoidance for a robot maneuvering among obstacles. Currently, the fastest algorithms for the solution of this problem are based on the use of optimization techniques to minimize a distance function. Furthermore, to date this problem has been approached purely through the position kinematics of the two objects. However, although the minimum distance between two objects can be found quickly on state-of-the-art hardware, the modelling of realistic scenes entails the determination of the minimum distances between large numbers of pairs of objects, and the computation time to calculate the overall minimum distance between any two objects is significant, and introduces a delay which has serious repercussions on the real-time control of the robot. This paper presents a technique to modify the original optimization problem in order to include velocity information. In effect, the minimum distance calculation is performed at a future time step by projecting the effect of present velocity. This method has proven to give good results on a 6-dof robot maneuvering among obstacles, and has allowed a complete compensation of the lags incurred due to computational delays.

Advances in the data rescue and digitization of historical wind speed observations in Sweden: the WINDGUST project

2021 ◽  
Author(s):  
Erik Engström ◽  
Cesar Azorin-Molina ◽  
Lennart Wern ◽  
Sverker Hellström ◽  
Christophe Sturm ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Global Climate ◽  
Current Status ◽  
Weather Data ◽  
Climate Data ◽  
Intergovernmental Panel ◽  
Staff Members ◽  
The 1960S

<p>Here we present the progress of the first work package (WP1) of the project “Assessing centennial wind speed variability from a historical weather data rescue project in Sweden” (WINDGUST), funded by FORMAS – A Swedish Research Council for Sustainable Development (ref. 2019-00509); previously introduced in EGU2019-17792-1 and EGU2020-3491. In a global climate change, one of the major uncertainties on the causes driving the climate variability of winds (i.e., the “stilling” phenomenon and the recent “recovery” since the 2010s) is mainly due to short availability (i.e., since the 1960s) and low quality of observed wind records as stated by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).</p><p>The WINDGUST is a joint initiative between the Swedish Meteorological and Hydrological Institute (SMHI) and the University of Gothenburg aimed at filling the key gap of short availability and low quality of wind datasets, and improve the limited knowledge on the causes driving wind speed variability in a changing climate across Sweden.</p><p>During 2020, we worked in WP1 to rescue historical wind speed series available in the old weather archives at SMHI for the 1920s-1930s. In the process we followed the “Guidelines on Best Practices for Climate Data Rescue” of the World Meteorological Organization. Our protocol consisted on: (i) designing a template for digitization; (ii) digitizing papers by an imaging process based on scanning and photographs; and (iii) typing numbers of wind speed data into the template. We will report the advances and current status, challenges and experiences learned during the development of WP1. Until new year 2020/2021 eight out of thirteen selected stations spanning over the years 1925 to 1948 have been scanned and digitized by three staff members of SMHI during 1,660 manhours.</p>

scholarly journals A Direct-Steam Linear Fresnel Performance Model for NREL’S System Advisor Model

2012 ◽  
Author(s):  
Michael J. Wagner ◽  
Guangdong Zhu
Keyword(s):  
Heat Loss ◽  
Mathematical Formulation ◽  
Model Performance ◽  
Performance Model ◽  
Electricity Production ◽  
Steam Generation ◽  
Time Step ◽  
Wide Range ◽  
Direct Steam ◽  
Solar Field

This paper presents the technical formulation and demonstrated model performance results of a new direct-steam-generation (DSG) model in NREL’s System Advisor Model (SAM). The model predicts the annual electricity production of a wide range of system configurations within the DSG Linear Fresnel technology by modeling hourly performance of the plant in detail. The quasi-steady-state formulation allows users to investigate energy and mass flows, operating temperatures, and pressure drops for geometries and solar field configurations of interest. The model includes tools for heat loss calculation using either empirical polynomial heat loss curves as a function of steam temperature, ambient temperature, and wind velocity, or a detailed evacuated tube receiver heat loss model. Thermal losses are evaluated using a computationally efficient nodal approach, where the solar field and headers are discretized into multiple nodes where heat losses, thermal inertia, steam conditions (including pressure, temperature, enthalpy, etc.) are individually evaluated during each time step of the simulation. This paper discusses the mathematical formulation for the solar field model and describes how the solar field is integrated with the other subsystem models, including the power cycle and optional auxiliary fossil system. Model results are also presented to demonstrate plant behavior in the various operating modes.

Alternative climate data sources for distributed hydrological modelling on a daily time step

2010 ◽  
Vol 25 (10) ◽  
pp. 1542-1557 ◽  
Author(s):  
Ashraf El-Sadek ◽  
Max Bleiweiss ◽  
Manoj Shukla ◽  
Steve Guldan ◽  
Alexander Fernald
Keyword(s):  
Hydrological Modelling ◽  
Data Sources ◽  
Climate Data ◽  
Time Step ◽  
Daily Time Step ◽  
Distributed Hydrological Modelling ◽  
Daily Time

scholarly journals Assessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UK

2020 ◽  
Vol 24 (12) ◽  
pp. 6059-6073
Author(s):  
Andres Peñuela ◽  
Christopher Hutton ◽  
Francesca Pianosi
Keyword(s):  
Decision Maker ◽  
Water Resource ◽  
Water Resource Management ◽  
Forecast Skill ◽  
Weather Data ◽  
Ensemble Size ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Hydrological Conditions ◽  
The Uk

Abstract. Improved skill of long-range weather forecasts has motivated an increasing effort towards developing seasonal hydrological forecasting systems across Europe. Among other purposes, such forecasting systems are expected to support better water management decisions. In this paper we evaluate the potential use of a real-time optimization system (RTOS) informed by seasonal forecasts in a water supply system in the UK. For this purpose, we simulate the performances of the RTOS fed by ECMWF seasonal forecasting systems (SEAS5) over the past 10 years, and we compare them to a benchmark operation that mimics the common practices for reservoir operation in the UK. We also attempt to link the improvement of system performances, i.e. the forecast value, to the forecast skill (measured by the mean error and the continuous ranked probability skill score) as well as to the bias correction of the meteorological forcing, the decision maker priorities, the hydrological conditions and the forecast ensemble size. We find that in particular the decision maker priorities and the hydrological conditions exert a strong influence on the forecast skill–value relationship. For the (realistic) scenario where the decision maker prioritizes the water resource availability over energy cost reductions, we identify clear operational benefits from using seasonal forecasts, provided that forecast uncertainty is explicitly considered by optimizing against an ensemble of 25 equiprobable forecasts. These operational benefits are also observed when the ensemble size is reduced up to a certain limit. However, when comparing the use of ECMWF-SEAS5 products to ensemble streamflow prediction (ESP), which is more easily derived from historical weather data, we find that ESP remains a hard-to-beat reference, not only in terms of skill but also in terms of value.

scholarly journals Version 2 of the EUMETSAT OSI SAF and ESA CCI Sea Ice Concentration Climate Data Records

2018 ◽  
Author(s):  
Thomas Lavergne ◽  
Atle Macdonald Sørensen ◽  
Stefan Kern ◽  
Rasmus Tonboe ◽  
Dirk Notz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Satellite Data ◽  
Grid Point ◽  
Ice Cover ◽  
Quantitative Information ◽  
Horizontal Resolution ◽  
Climate Data ◽  
Time Step ◽  
Sea Ice Concentration ◽  
Ice Concentration

Abstract. We introduce the OSI-450, the SICCI-25km and the SICCI-50km climate data records of gridded global sea-ice concentration. These three records are derived from passive microwave satellite data and offer three distinct advantages compared to existing records: First, all three records provide quantitative information on uncertainty and possibly applied filtering at every grid point and every time step. Second, they are based on dynamic tie points, which capture the time evolution of surface characteristics of the ice cover and accommodate potential calibration differences between satellite missions. Third, they are produced in the context of sustained services offering committed extension, documentation, traceability, and user support. The three records differ in the underlying satellite data (SMMR & SSM/I & SSMIS or AMSR-E & AMSR2), in the imaging frequency channels (37 GHz and either 6 GHz or 19 GHz), in their horizontal resolution (25 km or 50 km) and in the time period they cover. We introduce the underlying algorithms and provide an initial evaluation. We find that all three records compare well with independent estimates of sea-ice concentration both in regions with very high sea-ice concentration and in regions with very low sea-ice concentration. We hence trust that these records will prove helpful for a better understanding of the evolution of the Earth's sea-ice cover.

Cereal yield forecasting combining satellite drought-based indices, regional climate and weather data using machine learning approaches in Morocco

2021 ◽  
Author(s):  
El houssaine Bouras ◽  
Lionel Jarlan ◽  
Salah Er-Raki ◽  
Riad Balaghi ◽  
Abdelhakim Amazirh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regional Climate ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Drought Indices ◽  
Support Vector ◽  
Learning Approaches ◽  
Climate Data ◽  
Yield Forecasting

<p>Cereals are the main crop in Morocco. Its production exhibits a high inter-annual due to uncertain rainfall and recurrent drought periods. Considering the importance of this resource to the country's economy, it is thus important for decision makers to have reliable forecasts of the annual cereal production in order to pre-empt importation needs. In this study, we assessed the joint use of satellite-based drought indices, weather (precipitation and temperature) and climate data (pseudo-oscillation indices including NAO and the leading modes of sea surface temperature -SST- in the mid-latitude and in the tropical area) to predict cereal yields at the level of the agricultural province using machine learning algorithms (Support Vector Machine -SVM-, Random forest -FR- and eXtreme Gradient Boost -XGBoost-) in addition to Multiple Linear Regression (MLR). Also, we evaluate the models for different lead times along the growing season from January (about 5 months before harvest) to March (2 months before harvest). The results show the combination of data from the different sources outperformed the use of a single dataset; the highest accuracy being obtained when the three data sources were all considered in the model development. In addition, the results show that the models can accurately predict yields in January (5 months before harvesting) with an R² = 0.90 and RMSE about 3.4 Qt.ha<sup>-1</sup>.  When comparing the model’s performance, XGBoost represents the best one for predicting yields. Also, considering specific models for each province separately improves the statistical metrics by approximately 10-50% depending on the province with regards to one global model applied to all the provinces. The results of this study pointed out that machine learning is a promising tool for cereal yield forecasting. Also, the proposed methodology can be extended to different crops and different regions for crop yield forecasting.</p>

scholarly journals Assessment of TMPA 3B42V7 and PERSIANN-CDR in Driving Hydrological Modeling in a Semi-Humid Watershed in Northeastern China

2020 ◽  
Vol 12 (19) ◽  
pp. 3133
Author(s):  
Lu Zhang ◽  
Zhuohang Xin ◽  
Huicheng Zhou
Keyword(s):  
Hydrological Modeling ◽  
Assessment Tool ◽  
Surface Flow ◽  
Northeastern China ◽  
Base Flow ◽  
Hydrological Processes ◽  
Superior Performance ◽  
Climate Data ◽  
Satellite Precipitation ◽  
Precipitation Estimation

Recent developments of satellite precipitation products provide an unprecedented opportunity for better precipitation estimation, and thus broaden hydrological application. However, due to the errors and uncertainties of satellite products, a thorough validation is usually required before putting into the real hydrological application. As such, this study aims to provide a comprehensive evaluation on the performances of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7 and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), as well as their adequacies in simulating hydrological processes in a semi-humid region in the northeastern China. It was found that TMPA 3B42V7 showed a superior performance at the daily and monthly time scales, and had a favorable capture of the rainfall-intensity distribution. Intra-annual comparisons indicated a better representation of TMPA 3B42V7 from January to September, whereas PERSIANN-CDR was more reliable from October to December. The Soil and Water Assessment Tool (SWAT) driven by gauge precipitation data performed excellently with NSE > 0.9, while the performances of TMPA 3B42V7- and PERSIANN-CDR-based models are satisfactory with NSE > 0.5. The performances varied under different flow levels and hydrological years. Water balance analysis indicated a better performance of TMPA 3B42V7 in simulating the hydrological processes, including evapotranspiration, groundwater recharge and total runoff. The runoff compositions (i.e., base flow, subsurface flow, and surface flow) driven by TMPA 3B42V7 were more accordant with the actual hydrological features. This study will not only help recognize the potential satellite precipitation products for local water resources management, but also be a reference for the poor-gauged regions with similar hydrologic and climatic conditions around the world, especially the northeastern China and western Russia.

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