Release Notes

Release Notes#

Upcoming Release#

Warning

The features in this section are not released yet, but will be part of the next release! To use the features already you have to install the main branch, e.g. pip install git+https://github.com/assume-framework/assume

v0.5.0 - (10th December 2024)#

New Features: - Learning rate and noise scheduling: Added the possibility to schedule the learning rate and action noise in the learning process. This feature

enables streamlining the learning progress. Currently, only “linear” decay available by setting the learning_rate_schedule and action_noise_schedule in the learning config to “linear”. Defaults to no decay if not provided. It decays learning_rate/ noise_dt linearly from starting value to 0 over given training_episodes which can be adjusted by the user. The schedule parameters (e.g. end value and end fraction) are not adjustable in the config file, but can be set in the code.

  • Hydrogen Plant: A new demand side unit representing a hydrogen plant has been added. The hydrogen plant consists of an electrolyzer and a seasonal hydrogen storage unit. The electrolyzer converts electricity into hydrogen, which can be stored in the hydrogen storage unit and later used.

  • Seasonal Hydrogen Storage: A new storage unit representing a seasonal hydrogen storage has been added. The seasonal hydrogen storage unit can store hydrogen over long periods and release it when needed. It has specific constraints to avoid charging or discharging during off-season or on-season time as well as a target level to be reached at the end of the season.

Improvements: - Timeseries Performance Optimization: Switched to a custom FastIndex and FastSeries class, which is based on the pandas Series

but utilizes NumPy arrays for internal data storage and indexing. This change significantly improves the performance of read and write operations, achieving an average speedup of 2x to 3x compared to standard pandas Series. The FastSeries class retains a close resemblance to the pandas Series, including core functionalities like indexing, slicing, and arithmetic operations. This ensures seamless integration, allowing users to work with the new class without requiring significant code adaptation.

  • Outputs Role Performance Optimization: Output role handles dict data directly and only converts to DataFrame on Database write.

  • Overall Performance Optimization: The overall performance of the framework has been improved by a factor of 5x to 12x depending on the size of the simulation (number of units, markets, and time steps).

Bugfixes:

  • Tutorials: General fixes of the tutorials, to align with updated functionalitites of Assume

  • Tutorial 07: Aligned Amiris loader with changes in format in Amiris compare (fame-framework/fame-io#203 and fame-framework/fame-io#208)

  • Powerplant: Remove duplicate Powerplant.set_dispatch_plan() which broke multi-market bidding

  • CSV scenario loader: Fixed issue when one extra day was being added to the index, which lead to an error in the simulation when additional data was not available in the input data.

  • Market opening schedule: Fixed issue where the market opening was scheduled even though the simulation was ending before the required products. Now the market opening is only scheduled if the total duration of the market products plus first delivery time fits before the simulation end.

  • Loader fixes: Fixes for PyPSA, OEDS and AMIRIS loaders

v0.4.3 - (11th November 2024)#

Improvements:

  • Documentation: added codespell hook to pre-commit which checks for spelling errors in documentation and code

Bugfixes:

  • Simulation: Delete simulation results for same simulation prior to run (as before v0.4.2)

Full Changelog: [v0.4.2…v0.4.3](https://github.com/assume-framework/assume/compare/v0.4.2…v0.4.3)

v0.4.2 - (5th November 2024)#

New Features:

  • Residential Components: Added new residential DST components including PV, EV, Heat Pump, and Boiler, now with enhanced docstrings for better usability.

  • Modular DST Components: DST components have been converted from functions to classes, improving modularity and reusability.

  • Generic Storage Class: Introduced a GenericStorage class for storage components. Specific classes, such as EV and Hydrogen Storage, now inherit from it.

  • Storage Learning Strategy: Added a new DRL-based learning strategy for storage units. To use it, set storage_learning in the bidding_EOM column of storage_units.csv. Refer to the StorageRLStrategy documentation for more details.

  • Mango 2.x Update: Upgraded to mango 2.x, enabling synchronous world creation. To upgrade an existing environment, run: ` pip uninstall -y mango-agents mango-agents-assume && pip install assume-framework --upgrade `

  • Distributed Simulation Enhancements: Improved distributed simulation for TCP and MQTT, allowing containers to wait for each other during simulations.

  • Integrated Optimization with Pyomo and HIGHS Solver: The Pyomo library and HIGHS solver are now installed by default, removing the need to install assume-framework[optimization] separately. The HIGHS solver is used as the default, replacing the older GLPK solver for improved optimization performance and efficiency.

Improvements:

  • Documentation: Refined tutorial notebooks and added bug fixes.

  • Saving Frequency Logic: Refactored the saving frequency in the WriteOutput class for improved efficiency.

Bug Fixes:

  • Solver Compatibility: Addressed undefined solver_options when using solvers other than Gurobi or HIGHS.

  • Cashflow Calculation: Corrected cashflow calculations for single-digit orders.

  • Simulation Execution: Enabled simulations to synchronize and wait for each other.

  • Edge Case Handling: Fixed edge cases in pay_as_clear and pay_as_bid.

New Contributor:

  • @HafnerMichael made their first contribution with improvements to cashflow calculations and development of residential DST components.

Full Changelog: [v0.4.1…v0.4.2](https://github.com/assume-framework/assume/compare/v0.4.1…v0.4.2)

v0.4.1 (8th October 2024)#

New Features:

  • improve LSTM learning strategy (#382)

  • add python 3.12 compatibility (#334)

  • manual strategy for interactive market simulation (#403)

Improvements:

  • add the ability to define the solver for the optimization-based market clearing inside the param_dict of the config file (#432)

  • shallow clone in Jupyter notebooks so that cloning is faster (#433)

  • fixes in storage operation bidding (#417)

  • update GitHub Actions versions (#402)

Bug Fixes: - add compatibility with pyyaml-include (#421) - make complex clearing compatible to RL (#430) - pin PyPSA to remove DeprecationWarnings for now (#431)

v0.4.0 (8th August 2024)#

New Features:

  • Market Coupling: Users can now perform market clearing for different market zones with given transmission capacities. This feature allows for more realistic simulation of market conditions across multiple interconnected regions, enhancing the accuracy of market analysis and decision-making processes. A tutorial on how to use this feature is coming soon.

  • Adjust the Framework to Schedule Storing to the Learning Role: This enhancement enables Learning agents to participate in sequential markets, such as day-ahead and intraday markets. The rewards are now written after the last market, ensuring that the learning process accurately reflects the outcomes of all market interactions. This improvement supports more sophisticated and realistic agent training scenarios. A tutorial on how to use this feature is coming soon.

  • Multiprocessing: Using a command line option, it is now possible to use run each simulation agent in its own process to speed up larger simulations. You can read more about it in Distributed Simulation

  • Steel Plant Demand Side Management Unit: A new unit type has been added to the framework, enabling users to model the demand side management of a steel plant. This feature allows for more detailed and accurate simulations of industrial energy consumption patterns and market interactions. This unit can be configured with different components, such as the electric arc furnace, electrolyzer, and hot storage, to reflect the specific characteristics of steel production processes. The process can be optimized to minimize costs or to maximize the available flexibility, depending on the user’s requirements. A tutorial and detailed documentation on how to use this feature are coming soon.

  • LSTM Actor Architectures: The framework now supports long short-term memory (LSTM) networks as actor architectures for reinforcement learning. This feature enables users to apply more advanced neural network architectures to their learning agents, enhancing the learning process and enabling more accurate and efficient decision-making especially with time series data.

Improvements:

  • Significant speed up of the framework and especially of the learning process

  • Separated scenario loader function to improve speed and reduce unrequired operations

  • Refactored unit operator by adding a separate unit operator for learning units

  • Enhanced learning output and path handling

  • Updated dashboard for better storage view

  • Improved clearing with shuffling of bids, to avoid bias in clearing of units early in order book

  • Introduced a mechanism to clear the market according to defined market zones while maintaining information about individual nodes, enabling the establishment of specific market zones within the energy market and subsequent nodal-based markets such as redispatch.

  • Added zones_identifier to the configuration file and zone_id to the buses.csv, and refactored the complex market clearing algorithm to incorporate zone information, ensuring that bids submitted with a specific node are matched to the corresponding market zone.

  • If any values in the availability_df.csv file are larger than 1, the framework will now warn the user and run a method to normalize the values to [0, 1].

  • Examples have been restructured to easier orientation and understanding: example_01.. cover all feature demonstration examples, example_02.. cover all learning examples, example_03.. cover all full year examples

  • Added the option of integrating different actor network architectures to the reinforcement learning algorithm, currently a multilayer perceptron (mlp) and long short-term memory (lstm) are implemented

  • Added storing of network flows for complex clearing

Bug Fixes:

  • Fix learning when action dimension equals one

  • Fixed Tutorial 5

  • Correctly calculated timezone offsets

  • Improved handling of rejected bids

  • Fix the error that exploration mode is used during evaluation

  • Fix double dispatch writing

  • Fixed complex clearing with pyomo>=6.7

  • Resolved various issues with learning and policy saving

  • Fixed missing market dispatch values in day-ahead markets

  • Added a check for availability_df.csv file to check for any values larger than 1

  • Fixed compatibility issues between new pyomo and RL due to tensor handling

Other Changes:

  • Added closing word and final dashboard link to interoperability tutorial

v0.3.7 (21st March 2024)#

New Features:

  • Added Contract Market with feed-in policy and market premium (#248)

  • Introduced basic grid visualization (#305)

  • Added PyPSA loader (#311)

  • Implemented interoperability tutorial (#323)

Improvements:

  • Updated how Pyomo markets are imported (#310)

  • Added ARM docker platform support (#312)

  • Updated Grafana docker version to latest (#316)

  • Adjusted scenario loaders (#317)

  • Prepared ASSUME for proper nodal pricing integration (#304)

Bug Fixes:

  • Fixed bugs in tutorial 6 (#324)

  • Set correct compose.yml mount for docker (#320)

Other Changes:

  • Added Code of Conduct (#313)

  • Added fixed Pyomo version to avoid warnings (#325)

  • Increased version to 0.3.7 for latest release (#327)

v0.3.6 (22nd February 2024)#

Improvements:

  • Updated GitHub actions (#296, #297)

  • Silenced output of Gurobi by specifying a non-logging environment (#300)

  • Fixed writing of market_dispatch and dispatch for other product types (#301)

  • Fixed datetime warning (#302)

Bug Fixes:

  • Fixed Tutorial 2 (#299)

  • Fixed string conversion of paths (#307)

Documentation:

  • Added a tutorial for advanced order types and documentation for complex clearing (#303)

Other Changes:

  • Moved DMAS bidding strategies into try-except block since Pyomo is not a required dependency (#308)

v0.3.5 (14th February 2024)#

New Features:

  • Introduced the redispatch module for congestion management

  • Implemented cost-based and market-based redispatch strategies

  • Added support for “pay as bid” and “pay as clear” market methods in redispatch

Improvements:

  • Changed strategy allocation to use market names instead of product types (#289)

  • Implemented overall scenario loading improvements

Bug Fixes:

  • Fixed issues with storage operations (#291)

  • Removed empty bid as a method of bidding strategy (#293)

  • Cleaned up hard-coded EOM references (#294)

v0.3 (6th February 2024)#

New Features:

  • Added Data Request mechanism (#247)

  • Implemented block order and linked order with respective market clearing mechanism (#269)

  • Added MASTR based OEDS loader

  • Introduced AMIRIS Scenario loader

Improvements:

  • Added “Open in Colab” to notebooks (#258)

  • Improved data_dict usage (#274)

Bug Fixes:

  • Fixed calculation of marginal cost and output_before (#250)

  • Adjusted query of reward during training (#256)

  • Fixed calculation of flexible storage bids (#260)

  • Fixed RL evaluations (#280)

Documentation:

  • Added basic tutorials 01 and 02 (#257)

  • Created Custom Unit and Custom Strategy tutorial (#262)

  • Added tutorial for EOM and LTM comparison (#265)

  • Updated dependencies and installation instructions (#282)

  • Added additional clearing and strategy docs (#283)

Other Changes:

  • Added reuse compliance

  • Moved scenario loaders to separate folder (#264)

  • Added automatic assignment of RL units to one RL unit operator (#276)

v0.2.1 (3rd November 2023)#

Improvements:

  • Improved distribution of current time to agents running in shadow container in different processes (#199)

Bug Fixes:

  • Fixed loading of learned strategies (#219)

Documentation:

  • Added RL Documentation (#221)

Other Changes:

  • Added AMIRIS scenario loader (#224)

  • Added shields badges to README (#223)

  • Fixed issues for running distributed scenario with MQTT (#222)

v0.2.0 (30th September 2023)#

New Features:

  • Added support for CUDA-enabled devices for learning

  • Implemented tracking of evaluation periods for better learning performance evaluation

  • Added capability to start several simulations in parallel

Improvements:

  • Enhanced learning performance

  • Addressed storage units behavior bugs

Other Changes:

  • Added new Grafana dashboard definitions for easier analysis

  • Updated Docker compose file to include Renderer for saving plots directly from Grafana dashboards

v0.1.0 - Initial Release (12th September 2023)#

This is the initial release of the ASSUME Framework, published to PyPi.

Key Features:

  • Ability to define different energy market designs

  • Includes reinforcement learning capabilities

The ASSUME Framework allows users to model and simulate various energy market designs while incorporating reinforcement learning techniques for advanced analysis and optimization.