v0.3.7

What's Changed

• Edit how pyomo markets are imported by @nick-harder in #310
• Add ARM docker platform by @maurerle in #312
• Contract Market with feed in policy and market premium by @maurerle in #248
• Add basic grid visualization by @maurerle in #305
• Pypsa loader by @maurerle in #311
• update grafana docker version to latest by @maurerle in #316
• adjustments to scenario loaders by @maurerle in #317
• set correct compose.yml mount for docker by @maurerle in #320
• This commit prepares assume to integrate proper nodal pricing by @nick-harder in #304
• fix bugs in tutorial 6 by @adamsjohanna in #324
• Add fixed pyomo version to avoid warning by @adamsjohanna in #325
• Add Code of Conduct by @maurerle in #313
• Interoperability tutorial by @maurerle in #323
• increase version to 0.3.7 for latest release by @maurerle in #327

Full Changelog: v0.3.6...v0.3.7

v0.3.6

What's Changed

• update github actions by @maurerle in #296
• use latest github actions versions for codecov too by @maurerle in #297
• Fix tutorial 2 by @nick-harder in #299
• silence output of gurobipy by specifying an env which does not log by @maurerle in #300
• fixes writing market_dispatch and dispatch for other product_types by @maurerle in #301
• Fix datetime warning by @maurerle in #302
• Add a tutorial for the advanced order types and documentation for the complex clearing by @adamsjohanna in #303
• Fixes string conversion of paths by @kim-mskw in #307
• move dmas bidding strategies into try since pyomo is not a required d… by @nick-harder in #308

Full Changelog: v0.3.5...v0.3.6

v0.3.5

Release Notes - v0.3.5

We are thrilled to announce the release of v0.3.5 of ASSUME Framework. This release marks the introduction of the redispatch module, a tool for congestion management, alongside several bug fixes and improvements. Let's delve into the details of the changes:

Redispatch Module Introduction

Congestion Management

In v0.3.5, the introduction of the redispatch module significantly enhances the framework's capabilities in addressing congestion management challenges. This module is equipped to support both cost-based and market-based redispatch strategies, leveraging the PyPSA network to detect and resolve congestion effectively.

To explore its functionality, users can engage with the Example 01d, wherein a Day-Ahead Energy Market and a subsequent Redispatch Market are employed. Initially, the market is cleared using a single bidding zone, followed by a congestion management process. Furthermore, a detailed Jupyter-based tutorial will be made available to facilitate a deeper understanding of the module's application.

Cost-Based and Market-Based Redispatch

The redispatch module offers support for both cost-based and market-based redispatch strategies. This includes the implementation of "pay as bid" and "pay as clear" market methods, empowering users with versatile tools for congestion management.

Detailed Changes

Redispatch v1

Implemented by @nick-harder. @paragpatil39 and @rqussous in PR #279, this significant update introduces the initial version of the redispatch feature, laying the foundation for advanced congestion management.

New Strategies Allocation

@nick-harder's contribution in PR #289 brings about a crucial change in strategy allocation, now utilizing market names instead of product types, enhancing the overall clarity and usability of the framework.

Bug Fixes and Refinements

• Storage Operation Fixes: @adamsjohanna addressed some bugs in storage operations, ensuring smoother functionality (PR #291).
• Removal of Empty Bid Method: In PR #293, @nick-harder eliminated the use of empty bid as a method of bidding strategy, streamlining the bidding process.
• EOM References Cleanup: @nick-harder's contribution in PR #294 involved the removal of hard-coded EOM references from the code base, ensuring a more flexible and maintainable code structure.
• Overall scenario loading and other quality improvements by @maurerle

For a comprehensive list of changes, please refer to the Full Changelog.

We encourage all users to upgrade to v0.3.5 to leverage the latest enhancements and bug fixes. Your feedback is invaluable, and we look forward to hearing about your experiences with these new features.

v0.3

Release Notes - Version 0.3

What's Changed

Features

• Added reuse compliance by @maurerle
• Added Data Request mechanism by @maurerle (#247)
• Added "Open in Collab" to notebooks by @nick-harder (#258)
• Added block order and linked order and respective market clearing mechanism by @adamsjohanna (#269)
• Added MASTR based OEDS loader by @maurerle
• Added AMIRIS Scenario loader by @maurerle

Fixes

• Fixed calculation of marginal cost and output_before by @maurerle (#250)
• Changed buffer add function call by @kim-mskw (#255)
• Adjusted query of reward during training by @nick-harder (#256)
• Fixed calculation of flexible storage bids by @maurerle (#260)
• Fixed RL evaluations by @kim-mskw (#280)
• Fixed usage of license references by @maurerle (#246)

Documentation

• [Tutorial] Added basic tutorial 01 and 02 by @nick-harder (#257)
• [Tutorial] Added Custom Unit and Custom Strategy tutorial by @Manish-Khanra (#262)
• [Tutorial] Added tutorial of EOM and LTM comparison by @maurerle (#265)
• Updated dependencies and installation instructions by @nick-harder (#282)
• Added additional clearing and strategy docs by @maurerle (#283)

Other

• Moved scenario loaders to separate folder by @maurerle (#264)
• Added an automatic assignment of RL units to one RL unit operator by @kim-mskw (#276)
• Improved data_dict usage by @maurerle (#274)

Full Changelog

Full Changelog

v0.2.1

What's Changed

• fix loading learned strategies by @nick-harder in #219
• Workshop dach by @kim-mskw in #218
• distribute current time correctly to agents running in shadow container in different process by @maurerle in #199
• RL Documentation by @kim-mskw in #221
• Add shields badges to readme by @maurerle in #223
• add amiris scenario loader by @maurerle in #224
• fixes for running distributed scenario with mqtt by @maurerle in #222

Full Changelog: v0.2.0...v0.2.1

v0.2.0

What has changed:

• The learning performance has been improved
• The learning can now be also performed on CUDA enabled devices
• Added tracking of evaluation periods for better learning performance evaluation
• Storage units behavior bugs have been adressed
• Now several simulation can be started in parallel
• New Grafana dashboard definitions for easier analysis
• Docker compose file now includes Renderer to save plots directly from Grafan dashboards

v0.1.0 - initial release

This is the initial release of assume-framework, which is published to PyPi.

The assume-framework allows to define different energy market designs and includes reinforcement learning capabilities.

EIA-2023 - Market Abstraction Paper

This tag is used to release a citable version of the code used for the conference paper:
"Market Abstraction of Energy Markets and Policies – Application in an Agent-Based Modeling Toolbox"