FAME-Core: An open Framework for distributed
Agent-based Modelling of Energy systems

Schimeczek, Christoph; Deissenroth-Uhrig, Marc; Frey, Ulrich; Fuchs, B.; Ghazi, Aboubakr Achraf El; Wetzel, Manuel; Nienhaus, Kristina

doi:10.21105/joss.05087

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…NetLogo (Gooding & Gooding, 2019) x x (own) Generic LSD (Valente, 2008) x x C++ Economics, Generic FLAME (Chin et al, 2012) x x Java Generic FAME (Schimeczek et al, 2023) x…”

Section: Statement Of Needmentioning

confidence: 99%

sfctools - A toolbox for stock-flow consistent, agent-based models

Baldauf¹

2023

JOSS

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One of the most challenging tasks in macroeconomic models is to describe the macro-level effects from the behavior of meso-or micro-level actors. Whereas in 1759, Adam Smith was still making use of the concept of an 'invisible hand' ensuring market stability and economic welfare (Rothschild, 1994), a more and more popular approach is to make the 'invisible' visible and to accurately model each actor individually by defining its behavioral rules and myopic knowledge domain (Castelfranchi, 2014;Cincotti et al., 2022). In agent-based computational economics (ACE), economic actors correspond to dynamically interacting entities, implemented as agents in a computer software (Axtell & Farmer, 2022;Klein et al., 2018;Tesfatsion, 2002). Such agent-based modeling (ABM) is a powerful approach utilized in economic simulations to generate complex dynamics, endogenous business cycles and market disequilibria. For many research topics, it is useful to combine agent-based modeling with the stock-flow consistent (SFC) paradigm (Caiani et al., 2016;Caverzasi & Godin, 2015;Nikiforos & Zezza, 2018). This architecture ensures there are no 'black holes', i.e. inconsistent sources or sinks, in an economic model. SFC-ABM models, however, are often intransparent and rely on very peculiar, custom-built data structures, thus hampering accessibility (Bandini et al., 2009;Hansen et al., 2019). A tedious task is to generate, maintain and distribute code for ABM, as well as to check for the inner consistency and logic of such models.

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“…NetLogo (Gooding & Gooding, 2019) x x (own) Generic LSD (Valente, 2008) x x C++ Economics, Generic FLAME (Chin et al, 2012) x x Java Generic FAME (Schimeczek et al, 2023) x…”

Section: Statement Of Needmentioning

confidence: 99%

sfctools - A toolbox for stock-flow consistent, agent-based models

Baldauf¹

2023

JOSS

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“…It supports configuring complex simulations and managing of files associated with FAME. • FAME-Core (Schimeczek, Deissenroth-Uhrig, et al, 2023) is a Java library for ABM modellers. It supports developing and executing ABM simulations.…”

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confidence: 99%

FAME-Io: Configuration tools for complex agent-based simulations

Nitsch,

Schimeczek,

Frey

et al. 2023

JOSS

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We present FAME-Io, a Python package designed to help users and creators of agent-based simulation models (ABM) better manage the preparation and processing of their input and output datasets. The package was built with the needs of researchers in mind. FAME-Io was specifically developed to interface with the open framework FAME 1 and is published under the open Apache-2.0 licence. The software offers various logging capabilities, shell-integrated help and documentation, as well as extensive pre-run integrity checks and helpful warning messages. It also allows individual data components to be easily extracted and used in secondary workflows. The code itself is operating system independent and follows best practices in software development. Test coverage, at the time of writing, is 92% and the project uses continuous integration and offers frequent releases. FAME-Io is designed as one of two main components of FAME, each addressing a particular aspect and user group within ABM development, see Figure 1:

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“…AMIRIS is developed in Java using the FAME-Core framework (Schimeczek et al, 2023) and is available on Gitlab 1 . One important design goal was to make assumptions and calculations as transparent as possible in order to improve reproducibility.…”

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confidence: 99%

AMIRIS: Agent-based Market model for the Investigation of Renewable and Integrated energy Systems

Schimeczek¹,

Nienhaus²,

Frey³

et al. 2023

JOSS

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AMIRIS is an agent-based model (ABM) to simulate electricity markets. The focus of this bottom-up model is on the business-oriented decisions of actors in the energy system. These actors are represented as prototypical agents in the model, each with own complex decisionmaking strategies. Inter alia, the bidding decisions are based on the assessment of electricity market prices and generation forecasts (Nitsch, Deissenroth-Uhrig, et al., 2021), and diverse actors deciding on different time scales may be modelled. In particular, the agents' behavior does not only reflect marginal prices, but can also consider effects of support instruments like market premia, uncertainties and limited information, or market power (Frey et al., 2020). This allows assessing which policy or market design is best suited to an economic and effective energy system (Torralba-Díaz et al., 2020). The simulations generate results on the dispatch of power plants and flexibility options, technology-specific market values, development of system costs or CO2 emissions. One important output of the model are simulated market prices (Deissenroth et al., 2017).

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FAME-Core: An open Framework for distributed Agent-based Modelling of Energy systems

Cited by 10 publications

References 10 publications

sfctools - A toolbox for stock-flow consistent, agent-based models

sfctools - A toolbox for stock-flow consistent, agent-based models

FAME-Io: Configuration tools for complex agent-based simulations

AMIRIS: Agent-based Market model for the Investigation of Renewable and Integrated energy Systems

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