Aspen: A microsimulation model of the economy

Basu, N.; Pryor, Richard J.; Quint, Thomas; Arnold, Thomas Clay

doi:10.2172/399684

Cited by 48 publications

(44 citation statements)

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“…An overview of the reviewed literature is given in Figure 5. • Conzelmann et al 2004;North et al 2002a;•North et al 2002b;Veselka et al 2002 4.2 Integration green house gas emissions Batten et al 2005;Graham 2004 4.3 Integration of emission trading and renewable support schemes , •Czernohous et al 2003 [PowerACE] •Sensfuß and Genoese 2006 4.4 Integration of macroeconomic aspects et al, 1996et al, •Barton et al, 2000 Source: (own visualisation)…”

Section: Models For the Coupling Of Long-term And Short-term Simulationsmentioning

confidence: 99%

Agent-based Simulation of Electricity Markets -A Literature Review-

Sensfuß

Ragwitz

Genoese

et al. 2007

Energy Studies Review

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractLiberalisation, climate policy and promotion of renewable energy are challenges to players of the electricity sector in many countries. Policy makers have to consider issues like market power, bounded rationality of players and the appearance of fluctuating energy sources in order to provide adequate legislation. Furthermore the interactions between markets and environmental policy instruments become an issue of increasing importance. A promising approach for the scientific analysis of these developments is the field of agent-based simulation. The goal of this article is to provide an overview of the current work applying this methodology to the analysis of electricity markets.

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Section: Models For the Coupling Of Long-term And Short-term Simulationsmentioning

confidence: 99%

Agent-based Simulation of Electricity Markets -A Literature Review-

Sensfuß

Ragwitz

Genoese

et al. 2007

Energy Studies Review

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“…The major advantage of AIMS over similar existing simulation suites such as CISIA [16] and ASPEN [5] is the following:…”

Section: Critical Infrastructure Systems Behavior Analysismentioning

confidence: 99%

“…Usually such a process is supported by agent-based simulations or differential and/or algebraic-differential equations modeling. Some of the wellknown examples of such approaches are the Agent-based Interdependency Model-ing (AIMS) suite [4], Aspen [5], the inoperability input-output model, which has been based upon Leontief's I/O model [6].…”

Section: Introductionmentioning

confidence: 99%

A Framework for the Manifestation of Tacit Critical Infrastructure Knowledge

Bagheri¹,

Ghorbani²

2010

Sustainable and Resilient Critical Infrastructure Systems

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Critical infrastructure systems are tightly-coupled socio-technical systems with complicated behavior. They have emerged as an important focal point of research due to both their vital role in the normal conduct of societal activities as well as their inherent appealing complications for researchers. In this chapter, we will report on our experience in developing techniques, tools and algorithms for revealing and interpreting the hidden intricacies of such systems. The chapter will include the description of several of our technologies that allow for the guided understanding of the current status quo of infrastructure systems through the Astrolabe methodology, the formal profiling of infrastructure systems using the UML-CI meta-modeling mechanism, and also observing the emergent behavior of these complex systems through the application of the agent-based AIMS simulation suite.

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“…Importantly, however, Aspen and Aspen-EE agents can easily be modified and used in CommAspen for user problems in which the government, for example, is of interest. For a description of agents in their original form, see Aspen: A Microsimulation Model of the Economy [6]. Fifth, CommAspen features a communications network not found in Aspen-EE.…”

Section: Summarizing Differences Between Aspen-ee and Commaspenmentioning

confidence: 99%

“…For more details on GALCSs and GALCS results from other Sandia runs, see Aspen: A Microsimulation Model of the Economy [6]. probability through the input parameter named sampling_prob.…”

Section: Input File Examplementioning

confidence: 99%

Simulating economic effects of disruptions in the telecommunications infrastructure.

Cox¹,

Barton²,

Reinert³

et al. 2004

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CommAspen is a new agent-based model for simulating the interdependent effects of market decisions and disruptions in the telecommunications infrastructure on other critical infrastructures in the U.S. economy such as banking and finance, and electric power. CommAspen extends and modifies the capabilities of Aspen-EE, an agent-based model previously developed by Sandia National Laboratories to analyze the interdependencies between the electric power system and other critical infrastructures. CommAspen has been tested on a series of scenarios in which the communications network has been disrupted, due to congestion and outages. Analysis of the scenario results indicates that communications networks simulated by the model behave as their counterparts do in the real world. Results also show that the model could be used to analyze the economic impact of communications congestion and outages.4

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Aspen: A microsimulation model of the economy

Cited by 48 publications

References 1 publication

Agent-based Simulation of Electricity Markets -A Literature Review-

Agent-based Simulation of Electricity Markets -A Literature Review-

A Framework for the Manifestation of Tacit Critical Infrastructure Knowledge

Simulating economic effects of disruptions in the telecommunications infrastructure.

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