scholarly journals Tool-assisted Surrogate Selection for Simulation Models in Energy Systems

2019 ◽  
Author(s):  
Stephan Balduin ◽  
Frauke Oest ◽  
Marita Blank-Babazadeh ◽  
Astrid Nieße ◽  
Sebastian Lehnhoff
Keyword(s):  
Energy Systems ◽  
Simulation Models ◽  
Selection For ◽  
Surrogate Selection

scholarly journals The Potential Of Simulating Energy Systems: The Multi Energy Systems Simulator Model

2021 ◽  
Author(s):  
Luigi Bottecchia ◽  
Pietro Lubello ◽  
Pietro Zambelli ◽  
Carlo Carcasci ◽  
Lukas Kranzl
Keyword(s):  
Open Source ◽  
Spatial Scales ◽  
Optimal Solution ◽  
Energy Systems ◽  
Energy Transition ◽  
Simulation Models ◽  
Energy System ◽  
System Modelling ◽  
Energy System Model ◽  
Urban Level

Energy system modelling is an essential practice to assist a set of heterogeneous stakeholders in the process of defining an effective and efficient energy transition. From the analysis of a set of open source energy system models, it has emerged that most models employ an approach directed at finding the optimal solution for a given set of constraints. On the contrary, a simulation model is a representation of a system that is used to reproduce and understand its behaviour under given conditions, without seeking an optimal solution. Given the lack of simulation models that are also fully open source, in this paper a new open source energy system model is presented. The developed tool, called Multi Energy Systems Simulator (MESS), is a modular, multi-node model that allows to investigate non optimal solutions by simulating the energy system. The model has been built having in mind urban level analyses. However, each node can represent larger regions allowing wider spatial scales to be be represented as well. MESS is capable of performing analysis on systems composed by multiple energy carriers (e.g. electricity, heat, fuels). In this work, the tool’s features will be presented by a comparison between MESS itself and an optimization model, in order to analyze and highlight the differences between the two approaches, the potentialities of a simulation tool and possible areas for further development.

Multi-Scale, Multi-Dimensional Modelling of Future Energy Systems

2015 ◽  
pp. 113-135 ◽  
Author(s):  
Catalina Spataru ◽  
Andreas Koch ◽  
Pierrick Bouffaron
Keyword(s):  
Material Science ◽  
Control Strategies ◽  
Energy Systems ◽  
Simulation Models ◽  
Energy System ◽  
System Modelling ◽  
Multi Scale ◽  
Future Challenges ◽  
Urban Energy ◽  
Power System Engineering

This chapter provides a discussion of current multi-scale energy systems expressed by a multitude of data and simulation models, and how these modelling approaches can be (re)designed or combined to improve the representation of such system. It aims to address the knowledge gap in energy system modelling in order to better understand its existing and future challenges. The frontiers between operational algorithms embedded in hardware and modelling control strategies are becoming fuzzier: therefore the paradigm of modelling intelligent urban energy systems for the future has to be constantly evolving. The chapter concludes on the need to build a holistic, multi-dimensional and multi-scale framework in order to address tomorrow's urban energy challenges. Advances in multi-scale methods applied to material science, chemistry, fluid dynamics, and biology have not been transferred to the full extend to power system engineering. New tools are therefore necessary to describe dynamics of coupled energy systems with optimal control.

scholarly journals Bet-hedging across generations can affect the evolution of variance-sensitive strategies within generations

2019 ◽  
Vol 286 (1916) ◽  
pp. 20192070 ◽  
Author(s):  
Thomas R. Haaland ◽  
Jonathan Wright ◽  
Irja I. Ratikainen
Keyword(s):  
Life Histories ◽  
Environmental Variability ◽  
Geometric Mean ◽  
Simulation Models ◽  
Arithmetic Mean ◽  
Bet Hedging ◽  
Individual Fitness ◽  
Mean Fitness ◽  
Selection For ◽  
Independent Decision

In order to understand how organisms cope with ongoing changes in environmental variability, it is necessary to consider multiple adaptations to environmental uncertainty on different time scales. Conservative bet-hedging (CBH) represents a long-term genotype-level strategy maximizing lineage geometric mean fitness in stochastic environments by decreasing individual fitness variance, despite also lowering arithmetic mean fitness. Meanwhile, variance-prone (aka risk-prone) strategies produce greater variance in short-term payoffs, because this increases expected arithmetic mean fitness if the relationship between payoffs and fitness is accelerating. Using evolutionary simulation models, we investigate whether selection for such variance-prone strategies is counteracted by selection for bet-hedging that works to adaptively reduce fitness variance. In our model, variance proneness evolves in fine-grained environments (lower correlations among individuals in energetic state and/or payoffs), and with larger numbers of independent decision events over which resources accumulate prior to selection. Conversely, multiplicative fitness accumulation, caused by coarser environmental grain and fewer decision events selection, favours CBH via greater variance aversion. We discuss examples of variance-sensitive strategies in optimal foraging, migration, life histories and cooperative breeding using this bet-hedging perspective. By linking disparate fields of research studying adaptations to variable environments, we should be better able to understand effects of human-induced rapid environmental change.

scholarly journals Simulation models to study development of the coal industry as energy sector component

2019 ◽  
Vol 77 ◽  
pp. 02004
Author(s):  
Liudmila Takaishvili
Keyword(s):  
Energy Systems ◽  
Simulation Models ◽  
Energy Sector ◽  
Coal Industry ◽  
Level Of Detail ◽  
Optimization Models ◽  
Industry Development ◽  
Investment Projects ◽  
Its Regions ◽  
Study Development

This paper presents simulation models constructed at Melentiev Energy Systems Institute of SB RAS to study prospects of the coal industry development in Russia and its regions. The models are implemented within the information and model software COAL, which includes four types of models: three types of balance models and a model intended to assess investment projects of enterprises and their groups. The balance models differ in the level of detail of representing the coal industry in regions. The presented models may be applied both jointly with the existing optimization models and independently.

Surrogate Selection for Escherichia coli O157:H7 Based on Cryotolerance and Attachment to Romaine Lettuce

2009 ◽  
Vol 72 (7) ◽  
pp. 1385-1391 ◽  
Author(s):  
JIN KYUNG KIM ◽  
MARK A. HARRISON
Keyword(s):  
Escherichia Coli ◽  
Zeta Potential ◽  
Cell Surface ◽  
Surface Characteristics ◽  
Escherichia Coli O157 ◽  
Romaine Lettuce ◽  
E Coli ◽  
Zeta Potentials ◽  
Selection For ◽  
Surrogate Selection

Using nonpathogenic surrogates in place of pathogens when evaluating commercial food processing operations offers safety advantages, but their usefulness may be limited if they do not behave in the same manner in challenge situations. Nonpathogenic Escherichia coli strains were compared with E. coli O157:H7 based on cryotolerance, cell surface characteristics (hydrophobicity, zeta potential, and morphology), and attachment to lettuce. Populations for all strains were reduced less than 1 log CFU/ml over 7 days of storage at −18°C. After 1 day of storage, the survival rate for E. coli ATCC 25922 was 44.3%, similar to that of E. coli O157:H7 (49%). No capsule was produced by any of the strains. E. coli O157:H7 expressed curli at both 20 and 37°C, whereas E. coli ATCC 25922 expressed curli only when grown at 20°C. Hydrophobicity of E. coli ATCC 25922 was 53.5%, similar to that of E. coli O157:H7 (56.2%). The zeta potentials of nonpathogenic E. coli and E. coli O157: H7 cells were −4.95 to −10.92 mV. The zeta potential of E. coli ATCC 25922 was not significantly different (P > 0.05) from that of E. coli O157:H7 at 37°C and was the closest value to that of E. coli O157:H7 at 20°C. E. coli ATCC 25922 exhibited the greatest attachment to lettuce among the surrogates and was not significantly different from E. coli O157:H7 (P > 0.05). Based on cryotolerance and cell surface characteristics, E. coli ATCC 25922 is a useful surrogate for E. coli O157: H7 for studies involving attachment to fresh produce.

scholarly journals Bet-hedging across generations can affect the evolution of variance-sensitive strategies within generations

2019 ◽  
Author(s):  
Thomas R. Haaland ◽  
Jonathan Wright ◽  
Irja I. Ratikainen
Keyword(s):  
Life Histories ◽  
Environmental Variability ◽  
Geometric Mean ◽  
Simulation Models ◽  
Arithmetic Mean ◽  
Bet Hedging ◽  
Individual Fitness ◽  
Mean Fitness ◽  
Selection For ◽  
Independent Decision

AbstractIn order to understand how organisms cope with ongoing changes in environmental variability it is important to consider all types of adaptations to environmental uncertainty on different time-scales. Conservative bet-hedging represents a long-term genotype-level strategy that maximizes lineage geometric mean fitness in stochastic environments by decreasing individual fitness variance, despite also lowering arithmetic mean fitness. Meanwhile, variance-prone (aka risk-prone) strategies produce greater variance in short-term payoffs because this increases expected arithmetic mean fitness if the relationship between payoffs and fitness is accelerating. Using two evolutionary simulation models, we investigate whether selection for such variance-prone strategies are counteracted by selection for bet-hedging that works to adaptively reduce fitness variance. We predict that variance-prone strategies will be favored in scenarios with more decision events per lifetime and when fitness accumulates additively rather than multiplicatively. In our model variance-proneness evolved in fine-grained environments (with lower correlations among individuals in energetic state and/or in payoffs when choosing the variable decision), and with larger numbers of independent decision events over which resources accumulate prior to selection. In contrast, geometric fitness accumulation caused by coarser environmental grain and fewer decision events prior to selection favors conservative bet-hedging via greater variance-aversion. We discuss examples of variance-sensitive strategies in optimal foraging, migration, life histories and cooperative breeding in light of these results concerning bet-hedging. By linking disparate fields of research studying adaptations to variable environments we should be more able to understand the effects in nature of human-induced rapid environmental change.Data depositionR code is available upon request.

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