Unravelling the opposing effects of network clustering on electricity system models with high shares of renewables

2021 ◽  
Author(s):  
Martha Frysztacki ◽  
Jonas Hörsch ◽  
Veit Hagenmeyer ◽  
Tom Brown
Keyword(s):  
Spatial Resolution ◽  
Social Acceptance ◽  
Optimal Solution ◽  
Renewable Resource ◽  
Optimal Investment ◽  
Data Sets ◽  
Network Clustering ◽  
High Resolution Data ◽  
Transmission Expansion ◽  
Expansion Model

<p>Energy systems are typically modeled with a low spatial resolution that is based on administrative boundaries such as countries, which eases data collection and reduces computation times. However, a low spatial resolution can lead to sub-optimal investment decisions for renewable generation, transmission expansion or both. Ignoring power grid bottlenecks within regions tends to underestimate system costs, while combining locations with different renewable capacity factors tends to overestimate costs. We investigate these two competing effects in a capacity expansion model for Europe’s future power system that reduces carbon emissions by 95% compared to 1990s levels, taking advantage of newly-available high-resolution data sets and computational advances. We vary the model resolution by changing the number of substations, interpolating between a 37-node model where every country and synchronous zone is modeled with one node respectively, and a 512-node model based on the location of electricity substations. If we focus on the effect of renewable resource resolution and ignore network restrictions, we find that a higher resolution allows the optimal solution to concentrate wind and solar capacity at sites with higher capacity factors and thus reduces system costs by up to 10.5% compared to a low resolution model. This results in a big swing from offshore to onshore wind investment. However, if we introduce grid bottlenecks by raising the network resolution, costs increase by up to 19% as generation has to be sourced more locally where demand is high, typically at sites with worse capacity factors. These effects are most pronounced in scenarios where transmission expansion is limited, for example, by low social acceptance.</p>

A Least Cost Transmission Planning Model Considering Operation Cost

2014 ◽  
Vol 15 (2) ◽  
pp. 121-128
Author(s):  
Jorge Hans Alayo
Keyword(s):  
Test System ◽  
Mixed Integer ◽  
Transmission Planning ◽  
Integer Linear Programming Problem ◽  
Transmission Expansion ◽  
Planning Models ◽  
Proposed Model ◽  
Expansion Model ◽  
Operation Cost ◽  
Linearization Techniques

Abstract Existing transmission planning models consider basic aspects of the problem. In practice, a transmission utility needs to model other important details such as operation cost of the power system. In this article, a least cost transmission expansion model is proposed considering the operation cost in order to model the trade-off between building new transmission capacity and increasing the power system’s operation cost. The proposed model is transformed into a mixed integer linear programming problem using linearization techniques and solved with CPLEX. Finally, results of the model for the Garver test system and IEEE 24-bus test system are shown.

scholarly journals Land cover change analysis in Mexico using 30m Landsat and 250m MODIS data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 367-374 ◽  
Author(s):  
R. R. Colditz ◽  
R. M. Llamas ◽  
R. A. Ressl
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Time Interval ◽  
Data Sets ◽  
Change Analysis ◽  
Dimensional Error ◽  
Analysis Technique ◽  
Coarse Spatial Resolution

Change detection is one of the most important and widely requested applications of terrestrial remote sensing. Despite a wealth of techniques and successful studies, there is still a need for research in remote sensing science. This paper addresses two important issues: the temporal and spatial scales of change maps. Temporal scales relate to the time interval between observations for successful change detection. We compare annual change detection maps accumulated over five years against direct change detection over that period. Spatial scales relate to the spatial resolution of remote sensing products. We compare fractions from 30m Landsat change maps to 250m grid cells that match MODIS change products. Results suggest that change detection at annual scales better detect abrupt changes, in particular those that do not persist over a longer period. The analysis across spatial scales strongly recommends the use of an appropriate analysis technique, such as change fractions from fine spatial resolution data for comparison with coarse spatial resolution maps. Plotting those results in bi-dimensional error space and analyzing various criteria, the “lowest cost”, according to a user defined (here hyperbolic) cost function, was found most useful. In general, we found a poor match between Landsat and MODIS-based change maps which, besides obvious differences in the capabilities to detect change, is likely related to change detection errors in both data sets.

scholarly journals Optimizing preservation strategies for deteriorating items with time-varying holding cost and stock-dependent demand

2020 ◽  
Vol 30 (2) ◽  
pp. 237-250
Author(s):  
Aditi Khanna ◽  
P Priyamvada ◽  
Chandra Jaggi
Keyword(s):  
Optimal Solution ◽  
Optimal Investment ◽  
Deteriorating Items ◽  
Time Varying ◽  
Preservation Technology ◽  
Special Cases ◽  
Stock Dependent Demand ◽  
Managerial Implications ◽  
Holding Cost ◽  
Dependent Demand

Organizations are keen on rethinking and optimizing their existing inventory strategies so as to attain profitability. The phenomenon of deterioration is a common phenomenon while managing any inventory system. However, it could become a major challenge for the business if not dealt carefully. An investment in preservation technology is by far the most inuential move towards dealing with deterioration proficiently. Additionally, it is noticed that the demand pattern of many products is reliant on its availability and usability. Thus, considering demand of the product to be ?stock-dependent" is a more practical approach. Further, in case of deteriorating items, it is observed that the longer an item stays in the system the higher is its holding cost. Therefore, the model assumes the holding cost to be time varying. Hence, the proposed framework aims to develop an inventory model for deteriorating items with stock-dependent demand and time-varying holding cost under an investment in preservation technology. The objective is to determine the optimal investment in preservation technology and the optimal cycle length so as to minimize the total cost. Numerical example with various special cases have been discussed which signifies the effect of preservation technology investment in controlling the loss due to deterioration. Finally, the effect of key model features on the optimal solution is studied through sensitivity analysis which provides some important managerial implications.

The multi-scale generation and transmission expansion model

Energy ◽  
2018 ◽  
Vol 148 ◽  
pp. 977-991 ◽  
Author(s):  
A. Sarid ◽  
M. Tzur
Keyword(s):  
Multi Scale ◽  
Transmission Expansion ◽  
Expansion Model

Computing Constrained Shortest-Paths at Scale

2021 ◽  
Author(s):  
Alberto Vera ◽  
Siddhartha Banerjee ◽  
Samitha Samaranayake
Keyword(s):  
Shortest Paths ◽  
Data Sets ◽  
Network Clustering ◽  
Real World Data ◽  
Scalable Algorithm ◽  
Theoretical Contribution ◽  
Practical Algorithms ◽  
Transportation Service ◽  
Constrained Shortest Paths

Motivated by the needs of modern transportation service platforms, we study the problem of computing constrained shortest paths (CSP) at scale via preprocessing techniques. Our work makes two contributions in this regard: 1) We propose a scalable algorithm for CSP queries and show how its performance can be parametrized in terms of a new network primitive, the constrained highway dimension. This development extends recent work that established the highway dimension as the appropriate primitive for characterizing the performance of unconstrained shortest-path (SP) algorithms. Our main theoretical contribution is deriving conditions relating the two notions, thereby providing a characterization of networks where CSP and SP queries are of comparable hardness. 2) We develop practical algorithms for scalable CSP computation, augmenting our theory with additional network clustering heuristics. We evaluate these algorithms on real-world data sets to validate our theoretical findings. Our techniques are orders of magnitude faster than existing approaches while requiring only limited additional storage and preprocessing.

scholarly journals SVM-Based Deep Stacking Networks

2019 ◽  
Vol 33 ◽  
pp. 5273-5280 ◽  
Author(s):  
Jingyuan Wang ◽  
Kai Feng ◽  
Junjie Wu
Keyword(s):  
Neural Networks ◽  
High Performance ◽  
Optimal Solution ◽  
Data Sets ◽  
Complex Data ◽  
Layer By Layer ◽  
Deep Network ◽  
Local View ◽  
Linear Svm ◽  
Network Approaches

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models.

Rings of Uranus: A Review of Occultation Results

1982 ◽  
Vol 60 ◽  
pp. 237-256 ◽  
Author(s):  
James L. Elliot
Keyword(s):  
Spatial Resolution ◽  
Particle Collisions ◽  
Dynamical Models ◽  
High Resolution Data ◽  
Unique System ◽  
Structural Details ◽  
The Mean ◽  
Geometric Albedo ◽  
Resolution Data ◽  
Better Than

Since their discovery in 1977 (Elliot 1979), the dark, narrow rings of Uranus have intrigued dynamicists. The main enigma has been how the rings can remain so narrow - only a few km wide - when particle collisions and the Poynting-Robertson effect should cause the particles to disperse. The Uranian rings have posed other problems as well, and have proved to be a unique system for developing dynamical models of rings. The reason for this theoretical interest is the high precision and time coverage of the data available from occultation observations. With occultations we obtain a spatial resolution of 1 km in the position of ring segments and a resolution of 4 km in their structural details. These high-resolution data are available sufficiently often to be useful for dynamical purposes - at the rate of 1-2 events per year. This spatial resolution is somewhat better than that obtained by Voyager imaging of Jupiter’s and Saturn’s rings (Owen et al. 1979; Smith et al. 1981). Ground-based images of the Uranian rings, obtained by Matthews, Nicholson, and Neugebauer (1981), have a spatial resolution of ~50,000 km. Although unable to resolve individual rings, these data have established the mean geometric albedo of the rings at 0.030 ± 0.005.

scholarly journals High-resolution mapping of the NO<sub>2</sub> spatial distribution over Belgian urban areas based on airborne APEX remote sensing

2017 ◽  
Vol 10 (5) ◽  
pp. 1665-1688 ◽  
Author(s):  
Frederik Tack ◽  
Alexis Merlaud ◽  
Marian-Daniel Iordache ◽  
Thomas Danckaert ◽  
Huan Yu ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Urban Areas ◽  
High Spatial Resolution ◽  
European Space Agency ◽  
Correlation Coefficients ◽  
Time Frame ◽  
Quantitative Information ◽  
Data Sets ◽  
Vertical Column

Abstract. We present retrieval results of tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs), mapped at high spatial resolution over three Belgian cities, based on the DOAS analysis of Airborne Prism EXperiment (APEX) observations. APEX, developed by a Swiss-Belgian consortium on behalf of ESA (European Space Agency), is a pushbroom hyperspectral imager characterised by a high spatial resolution and high spectral performance. APEX data have been acquired under clear-sky conditions over the two largest and most heavily polluted Belgian cities, i.e. Antwerp and Brussels on 15 April and 30 June 2015. Additionally, a number of background sites have been covered for the reference spectra. The APEX instrument was mounted in a Dornier DO-228 aeroplane, operated by Deutsches Zentrum für Luft- und Raumfahrt (DLR). NO2 VCDs were retrieved from spatially aggregated radiance spectra allowing urban plumes to be resolved at the resolution of 60  ×  80 m2. The main sources in the Antwerp area appear to be related to the (petro)chemical industry while traffic-related emissions dominate in Brussels. The NO2 levels observed in Antwerp range between 3 and 35  ×  1015 molec cm−2, with a mean VCD of 17.4 ± 3.7  ×  1015 molec cm−2. In the Brussels area, smaller levels are found, ranging between 1 and 20  ×  1015 molec cm−2 and a mean VCD of 7.7 ± 2.1  ×  1015 molec cm−2. The overall errors on the retrieved NO2 VCDs are on average 21 and 28 % for the Antwerp and Brussels data sets. Low VCD retrievals are mainly limited by noise (1σ slant error), while high retrievals are mainly limited by systematic errors. Compared to coincident car mobile-DOAS measurements taken in Antwerp and Brussels, both data sets are in good agreement with correlation coefficients around 0.85 and slopes close to unity. APEX retrievals tend to be, on average, 12 and 6 % higher for Antwerp and Brussels, respectively. Results demonstrate that the NO2 distribution in an urban environment, and its fine-scale variability, can be mapped accurately with high spatial resolution and in a relatively short time frame, and the contributing emission sources can be resolved. High-resolution quantitative information about the atmospheric NO2 horizontal variability is currently rare, but can be very valuable for (air quality) studies at the urban scale.

Lexicographic Goal Programming and Assessment Tools for a Combinatorial Production Problem

2011 ◽  
pp. 148-184 ◽  
Author(s):  
Seamus M. McGovern ◽  
Surendra M. Gupta
Keyword(s):  
Goal Programming ◽  
Optimal Solution ◽  
General Purpose ◽  
Combinatorial Problems ◽  
Assessment Tools ◽  
Data Sets ◽  
Combinatorial Search ◽  
Benchmark Data ◽  
Lexicographic Goal Programming ◽  
Solution Techniques

NP-complete combinatorial problems often necessitate the use of near-optimal solution techniques including heuristics and metaheuristics. The addition of multiple optimization criteria can further complicate comparison of these solution techniques due to the decision-maker’s weighting schema potentially masking search limitations. In addition, many contemporary problems lack quantitative assessment tools, including benchmark data sets. This chapter proposes the use of lexicographic goal programming for use in comparing combinatorial search techniques. These techniques are implemented here using a recently formulated problem from the area of production analysis. The development of a benchmark data set and other assessment tools is demonstrated, and these are then used to compare the performance of a genetic algorithm and an H-K general-purpose heuristic as applied to the production-related application.

scholarly journals Time Series High-Resolution Land Surface Albedo Estimation Based on the Ensemble Kalman Filter Algorithm

2019 ◽  
Vol 11 (7) ◽  
pp. 753 ◽  
Author(s):  
Guodong Zhang ◽  
Hongmin Zhou ◽  
Changjing Wang ◽  
Huazhu Xue ◽  
Jindi Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
High Resolution ◽  
Spatial Resolution ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Surface Albedo ◽  
Data Sets ◽  
Broadleaf Forest ◽  
Kalman Filter Algorithm

Continuous, long-term sequence, land surface albedo data have crucial significance for climate simulations and land surface process research. Sensors such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) provide global albedo product data sets with a spatial resolution of 500 m over long time periods. There is demand for new high-resolution albedo data for regional applications. High-resolution observations are often unavailable due to cloud contamination, which makes it difficult to obtain time series albedo estimations. This paper proposes an “amalgamation albedo“ approach to generate daily land surface shortwave albedo with 30 m spatial resolution using Landsat data and the MODIS Bidirectional Reflectance Distribution Functions (BRDF)/Albedo product MCD43A3 (V006). Historical MODIS land surface albedo products were averaged to obtain an albedo estimation background, which was used to construct the albedo dynamic model . The Thematic Mapper (TM) albedo derived via direct estimation approach was then introduced to generate high spatial-temporal resolution albedo data based on the Ensemble Kalman Filter algorithm (EnKF). Estimation results were compared to field observations for cropland, deciduous broadleaf forest, evergreen needleleaf forest, grassland, and evergreen broadleaf forest domains. The results indicated that for all land cover types, the estimated albedos coincided with ground measurements at a root mean squared error (RMSE) of 0.0085–0.0152. The proposed algorithm was then applied to regional time series albedo estimation; the results indicated that it captured spatial and temporal variation patterns for each site. Taken together, our results suggest that the amalgamation albedo approach is a feasible solution to generate albedo data sets with high spatio-temporal resolution.

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