add joss paper

README.md

@@ -16,6 +16,7 @@ SPDX-License-Identifier: AGPL-3.0-or-later
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codemeta.json

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+{
+  "@context": "https://raw.githubusercontent.com/codemeta/codemeta/master/codemeta.jsonld",
+  "@type": "Code",
+  "author": [
+    {
+      "@id": "https://orcid.org/0000-0001-8345-3889",
+      "@type": "Person",
+      "email": "[email protected]",
+      "name": "Florian Maurer",
+      "affiliation": "FH Aachen"
+    },
+    {
+      "@id": "https://orcid.org/0000-0003-1897-3671",
+      "@type": "Person",
+      "email": "[email protected]",
+      "name": "Nick Harder",
+      "affiliation": "Universität Freiburg"
+    },
+    {
+      "@id": "https://orcid.org/0009-0009-1389-4844",
+      "@type": "Person",
+      "email": "[email protected]",
+      "name": "Kim K. Miskiw",
+      "affiliation": "Karlsruhe Institute of Technology"
+    },
+    {
+      "@id": "https://orcid.org/0000-0002-3347-9922",
+      "@type": "Person",
+      "email": "[email protected]",
+      "name": "Manish Khanra",
+      "affiliation": "Fraunhofer ISI"
+    }
+  ],
+  "identifier": "https://doi.org/10.5281/zenodo.8088760",
+  "codeRepository": "https://github.com/assume-framework/assume/",
+  "datePublished": "2024-11-15",
+  "dateModified": "2024-11-13",
+  "dateCreated": "2024-11-13",
+  "description": "ASSUME - Agent-based Simulation for Studying and Understanding Market Evolution",
+  "keywords": "agent-based, energy market, simulation, reinforcement learning, bidding",
+  "license": "AGPL-3.0-or-later",
+  "title": "ASSUME",
+  "version": "v0.4.3"
+}

codemeta.json.license

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+SPDX-FileCopyrightText: ASSUME Developers
+
+SPDX-License-Identifier: AGPL-3.0-or-later

paper/paper.bib

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+@Comment{
+SPDX-FileCopyrightText: ASSUME Developers
+
+SPDX-License-Identifier: AGPL-3.0-or-later
+}
+
+@incollection{maurerMarketAbstractionEnergy2023,
+  title = {Market {{Abstraction}} of {{Energy Markets}} and {{Policies}} - {{Application}} in an {{Agent-Based Modeling Toolbox}}},
+  booktitle = {Energy {{Informatics}}},
+  author = {Maurer, Florian and Miskiw, Kim K. and Acosta, Rebeca Ramirez and Harder, Nick and Sander, Volker and Lehnhoff, Sebastian},
+  editor = {J{\o}rgensen, Bo N{\o}rregaard and Da Silva, Luiz Carlos Pereira and Ma, Zheng},
+  year = {2023},
+  month = dec,
+  volume = {14468},
+  pages = {139--157},
+  publisher = {Springer Nature Switzerland},
+  address = {Cham},
+  doi = {10.1007/978-3-031-48652-4_10},
+  isbn = {978-3-031-48652-4},
+}
+
+@inproceedings{maurerKnowYourTools2024,
+  title = {Know {{Your Tools}} - {{A Comparison}} of {{Two Open Agent-Based Energy Market Models}}},
+  booktitle = {2024 20th {{International Conference}} on the {{European Energy Market}} ({{EEM}})},
+  author = {Maurer, Florian and Nitsch, Felix and Kochems, Johannes and Schimeczek, Christoph and Sander, Volker and Lehnhoff, Sebastian},
+  year = {2024},
+  month = jun,
+  pages = {1--8},
+  issn = {2165-4093},
+  doi = {10.1109/EEM60825.2024.10609021},
+}
+
+@inproceedings{miskiwExplainableDeepReinforcement2024,
+  title = {Explainable {{Deep Reinforcement Learning}} for {{Multi-Agent Electricity Market Simulations}}},
+  booktitle = {2024 20th {{International Conference}} on the {{European Energy Market}} ({{EEM}})},
+  author = {Miskiw, Kim K. and Staudt, Philipp},
+  year = {2024},
+  month = jun,
+  pages = {1--9},
+  issn = {2165-4093},
+  doi = {10.1109/EEM60825.2024.10608907},
+}
+
+
+@inproceedings{khanraEconomicEvaluationElectricity2024,
+  title = {Economic {{Evaluation}} of {{Electricity}} and {{Hydrogen-Based Steel Production Pathways}}: {{Leveraging Market Dynamics}} and {{Grid Congestion Mitigation}} through {{Demand Side Flexibility}}},
+  shorttitle = {Economic {{Evaluation}} of {{Electricity}} and {{Hydrogen-Based Steel Production Pathways}}},
+  booktitle = {2024 20th {{International Conference}} on the {{European Energy Market}} ({{EEM}})},
+  author = {Khanra, Manish and Klobasa, Marian and Patil, Parag and Scholz, Daniel},
+  year = {2024},
+  month = jun,
+  pages = {1--6},
+  issn = {2165-4093},
+  doi = {10.1109/EEM60825.2024.10608890},
+}
+
+@inproceedings{adamsBlockOrdersMatter2024,
+  title = {Do {{Block Orders Matter}}? {{Impact}} of {{Regular Block}} and {{Linked Orders}} on {{Electricity Market Simulation Outcomes}}},
+  shorttitle = {Do {{Block Orders Matter}}?},
+  booktitle = {2024 20th {{International Conference}} on the {{European Energy Market}} ({{EEM}})},
+  author = {Adams, Johanna and Harder, Nick and Weidlich, Anke},
+  date = {2024-06},
+  pages = {1--7},
+  issn = {2165-4093},
+  doi = {10.1109/EEM60825.2024.10608956},
+}
+
+
+@article{harderHowSatisfactoryCan2024,
+  title = {How {{Satisfactory Can Deep Reinforcement Learning Methods Simulate Electricity Market Dynamics}}? {{Bechmarking}} via {{Bi-level Optimization}}},
+  shorttitle = {How {{Satisfactory Can Deep Reinforcement Learning Methods Simulate Electricity Market Dynamics}}?},
+  author = {Nick Harder and Lesia Mitridati and Farzaneh Pourahmadi and Anke Weidlich and Jalal Kazempour},
+  year = {2024},
+  month = oct,
+  journal = {Energy Informatics Review},
+}
+
+
+@inproceedings{bublitzAgentbasedSimulationGerman2014,
+  title = {Agent-Based {{Simulation}} of the {{German}} and {{French Wholesale Electricity Markets}}},
+  booktitle = {Proceedings of the 6th {{International Conference}} on {{Agents}} and {{Artificial Intelligence}} - {{Volume}} 2},
+  author = {Bublitz, Andreas and Ringler, Philipp and Genoese, Massimo and Fichtner, Wolf},
+  year = {2014},
+  month = mar,
+  series = {{{ICAART}} 2014},
+  pages = {40--49},
+  publisher = {{SCITEPRESS - Science and Technology Publications, Lda}},
+  address = {Setubal, PRT},
+  doi = {10.5220/0004760000400049},
+  urldate = {2023-05-15},
+  isbn = {978-989-758-016-1},
+}
+
+@article{schimeczekAMIRISAgentbasedMarket2023,
+  title = {{{AMIRIS}}: {{Agent-based Market}} Model for the {{Investigationof Renewable}} and {{Integrated}} Energy {{Systems}}},
+  shorttitle = {{{AMIRIS}}},
+  author = {Schimeczek, Christoph and Nienhaus, Kristina and Frey, Ulrich and Sperber, Evelyn and Sarfarazi, Seyedfarzad and Nitsch, Felix and Kochems, Johannes and Ghazi, A. Achraf El},
+  year = {2023},
+  month = apr,
+  journal = {Journal of Open Source Software},
+  volume = {8},
+  number = {84},
+  issn = {2475-9066},
+  doi = {10.21105/joss.05041},
+}
+
+@article{harderFitPurposeModeling2023,
+  title = {Fit for Purpose: {{Modeling}} Wholesale Electricity Markets Realistically with Multi-Agent Deep Reinforcement Learning},
+  shorttitle = {Fit for Purpose},
+  author = {Harder, Nick and Qussous, Ramiz and Weidlich, Anke},
+  date = {2023-10-01},
+  journaltitle = {Energy and AI},
+  shortjournal = {Energy and AI},
+  volume = {14},
+  pages = {100295},
+  issn = {2666-5468},
+  doi = {10.1016/j.egyai.2023.100295},
+  url = {https://www.sciencedirect.com/science/article/pii/S2666546823000678},
+  urldate = {2024-03-03},
+  abstract = {Electricity markets need to continuously evolve to address the growing complexity of a predominantly renewable energy-driven, highly interconnected, and sector-integrated energy system. Simulation models allow testing market designs before implementation, which offers advantages for market robustness and efficiency. This work presents a novel approach to simulate the electricity market by using multi-agent deep reinforcement learning for representing revenue-maximizing market participants. The learning capability makes the agents highly adaptive, thereby facilitating a rigorous performance evaluation of market mechanisms under challenging yet practical conditions. Through distinct test cases that vary the number and size of learning agents in an energy-only market, we demonstrate the ability of the proposed method to diagnose market manipulation and reflect market liquidity. Our method is highly scalable, as demonstrated by a case study of the German wholesale energy market with 145 learning agents. This makes the model well-suited for analyzing large and complex electricity markets. The capability of the presented simulation approach facilitates market design analysis, thereby contributing to the establishment future-proof electricity markets to support the energy transition.},
+  keywords = {Agent-based modeling,Electricity markets,Machine learning,Multi-agent reinforcement learning,Reinforcement learning},
+  file = {/home/maurer/Zotero/storage/88ED8A4Z/S2666546823000678.html}
+}

paper/paper.md

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+---
+title: 'ASSUME - Agent-based Simulation for Studying and Understanding Market Evolution'
+tags:
+  - Python
+  - agent based modeling
+  - energy market
+  - reinforcement learning
+  - software simulation
+authors:
+  - name: Florian Maurer
+    orcid: 0000-0001-8345-3889
+    corresponding: true
+    equal-contrib: true
+    affiliation: 1
+  - name: Nick Harder
+    orcid: 0000-0003-1897-3671
+    equal-contrib: true
+    affiliation: 2
+  - name: Kim K. Miskiw
+    orcid: 0009-0009-1389-4844
+    equal-contrib: true
+    affiliation: 3
+  - name: Manish Khanra
+    orcid: 0000-0002-3347-9922
+    equal-contrib: true
+    affiliation: 4
+
+affiliations:
+ - name: University of Applied Sciences Aachen, Germany
+   index: 1
+ - name: Institute for Sustainable Systems Engineering, University of Freiburg, Freiburg, Germany
+   index: 2
+ - name: Institute of Information Systems and Marketing, Karlsruhe Institute of Technology, Karlsruhe, Germany
+   index: 3
+ - name: Fraunhofer Institute for Systems and Innovation Research, Karlsruhe, Germany
+   index: 4
+date: 13 November 2024
+bibliography: paper.bib
+---
+
+# Summary
+
+**ASSUME** is an open-source toolbox for agent-based simulations of various energy markets, with a primary focus on European electricity markets. Developed as an open-source model, its objectives are to ensure usability and customizability for a wide range of users and use cases in the energy system modeling community.
+
+A unique feature of the `ASSUME` toolbox is its integration of **Deep Reinforcement Learning** methods into the behavioral strategies of market agents. The model offers various predefined agent representations for both the demand and generation sides, which can be used as plug-and-play modules, simplifying the usage of reinforcement learning strategies. This setup enables research of new market designs and evolving dynamics in energy markets.
+
+# Statement of need
+
+Various open-source agent-based models have been developed for studying energy markets, such as PowerACE [@bublitzAgentbasedSimulationGerman2014] and AMIRIS [@schimeczekAMIRISAgentbasedMarket2023].
+Yet, the possible integration of reinforcement learning methods into the behavioral strategies of market agents is currently unique to `ASSUME` and is build upon prior research on multi-agent reinforcement learning [@harderFitPurposeModeling2023].
+Simulations which solely rely on rule-based bidding strategy representation, limit the ability to represent future markets or alternative markets designs, as in reality bidding agents would adapt to the new market design.
+Most notably, `ASSUME` enables the highest number of simultanelously learning market agents in literature [@miskiwExplainableDeepReinforcement2024].
+This feature allows for the exploration of new market designs and emergent dynamics in energy markets using a common open-source simulation framework.
+Further unique features of `ASSUME` are the extensive market abstraction which allows to define complex multi-market scenarios as shown in [@maurerMarketAbstractionEnergy2023].
+Even redispatch markets and nodal markets are supported, making it possible to represent network constraints and market coupling.
+`ASSUME` is designed to provide results which are easily comparable and interactable.
+This is set in contrast to other agent-based simulation frameworks, which are often designed to be used for a specific use case.
+Instead, various moving parts can be configured, such as the market abstraction, the agent properties and bidding strategies, or the reinforcement learning methods.
+
+# Architecture
+
+`ASSUME` builds on the open-source agent-based simulation library [mango-agents](https://pypi.org/project/mango-agents/) to model the simulation world and to give users continuous feedback during the simulation runs.
+Interaction with the results of the simulation is possible through the preconfigured provided Grafana dashboards, which do have direct access to the simulation database.
+Additionally, writing the output to CSV files is supported for scenarios in which a database is not available or needed.
+
+![Componental overview of the ASSUME simulation architecture\label{fig:architecture}](../docs/source/img/architecture.svg)
+
+New scenarios can be created by providing CSV files, accessing simulation data from a database or by using the object-oriented API.
+The object-oriented API makes it possible to integrate new classes, maket mechanisms and bidding strategies as well.
+
+Less technical users can adjust the examples from the yaml config and csv inputs directly, while new bidding strategies and methods can be implemented using the provided base classes.
+This is possible through the decoupled architecture which separates the declaration of simulation behavior from the input data, while allowing to define either externally without changes to the core.
+The overview of available classes and the interaction with the world is shown in \autoref{fig:architecture}.
+Extensive documentation about these features is available at [https://assume.readthedocs.io/en/latest/](https://assume.readthedocs.io/en/latest/examples_basic.html).
+This also includes a variety of notebooks which clarify the usage through examples.
+
+# Publications
+
+`ASSUME` has been used to investigate the usage of complex order types like block bids in wholesale markets [@adamsBlockOrdersMatter2024] as well as for the integration of demand-side flexibility and redispatch markets in [@khanraEconomicEvaluationElectricity2024].
+Studies of applicability of reinforcement learning methods in energy markets were tackled in [@harderHowSatisfactoryCan2024], while an analysis of explainable AI methods was applied to `ASSUME` in [@miskiwExplainableDeepReinforcement2024].
+For better interoperability with other energy system model data, adapters to interact from `ASSUME` with PyPSA and AMIRIS are available and make a comparison to other renomated market simulation tools possible [@maurerKnowYourTools2024].
+
+# Acknowledgements
+
+This work was conducted as part of the project "ASSUME: Agent-Based Electricity Markets Simulation Toolbox," funded by the German Federal Ministry for Economic Affairs and Energy under grant number BMWK 03EI1052A.
+We express our gratitude to all contributors to ASSUME.
+
+# References

paper/paper.md.license

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+SPDX-FileCopyrightText: ASSUME Developers
+
+SPDX-License-Identifier: AGPL-3.0-or-later