From a8d4eda8c9bce1b3f7d168ca9349486fc8d0a7d0 Mon Sep 17 00:00:00 2001
From: Thomas Wolgast <thomas.wolgast@uni-oldenburg.de>
Date: Wed, 9 Oct 2024 14:28:05 +0200
Subject: [PATCH] Update to version 0.1.0

---
 CITATION.cff      | 41 +++++++++++++++++++++++++++++++++++++++++
 README.md         |  3 ++-
 opfgym/opf_env.py |  1 +
 setup.py          |  6 +++---
 4 files changed, 47 insertions(+), 4 deletions(-)
 create mode 100644 CITATION.cff

diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..a929de6
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,41 @@
+# This CITATION.cff file was generated with cffinit.
+# Visit https://bit.ly/cffinit to generate yours today!
+
+cff-version: 1.2.0
+title: OPF-Gym
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file. Additionally, please check out the publication
+  "Learning the optimal power flow: Environment design matters", where the 
+  library was first introduced. 
+  (https://www.sciencedirect.com/science/article/pii/S2666546824000764) 
+type: software
+authors:
+  - given-names: Thomas
+    family-names: Wolgast
+    email: thomas.wolgast@uni-oldenburg.de
+    affiliation: Carl von Ossietzky Universität Oldenburg
+    orcid: 'https://orcid.org/0000-0002-9042-9964'
+repository-code: 'https://github.com/Digitalized-Energy-Systems/opfgym'
+abstract: >-
+  The OPF-Gym library allows for easy creation of
+  reinforcement learning (RL) environments for solving the
+  optimal power flow (OPF) problem. OPF-Gym also provides
+  five benchmark environments to ensure comparability of
+  future RL-OPF research. Various kinds of OPF problems are
+  supported, for example, multi-stage OPF, discrete actions,
+  stochastic OPF, etc. Further, it is possible to generate
+  labeled training data for supervised learning, which again
+  improves comparability of research advances. 
+keywords:
+  - Optimal Power Flow
+  - Reinforcement Learning
+  - Gymnasium
+  - Supervised Learning
+  - Voltage Control
+  - Economic Dispatch
+  - Reactive Power Market
+  - Load Shedding
+  - Benchmark
+license: MIT
+version: 0.1.0
diff --git a/README.md b/README.md
index d9239dc..b7b19b8 100644
--- a/README.md
+++ b/README.md
@@ -24,7 +24,8 @@ Tested for python 3.10.
 
 
 ### Environments
-Currently, five OPF benchmark environments are available:
+Currently, five OPF benchmark environments are available. Additionally, some 
+example environments for more advanced features can be found in `opfgym/examples`. 
 
 #### Maximize Renewable Feed-In (MaxRenewable)
 Use `from opfgym.envs import MaxRenewable` to import this env.
diff --git a/opfgym/opf_env.py b/opfgym/opf_env.py
index e9ad15d..ba365de 100644
--- a/opfgym/opf_env.py
+++ b/opfgym/opf_env.py
@@ -21,6 +21,7 @@
 
 warnings.simplefilter('once')
 
+
 class PowerFlowNotAvailable(Exception):
     pass
 
diff --git a/setup.py b/setup.py
index 7f0eaef..7e8784d 100644
--- a/setup.py
+++ b/setup.py
@@ -7,14 +7,14 @@
 
 setup(
     name='opfgym',
-    version='0.0.1',
+    version='0.1.0',
     author='Thomas Wolgast',
     author_email='thomas.wolgast@uol.de',
-    description='Some environments to learn the Optimal Power Flow with Reinforcement Learning',
+    description='Environment framework to learn the Optimal Power Flow with Reinforcement Learning, including multiple benchmark environments.',
     long_description=long_description,
     long_description_content_type="text/markdown",
     packages=find_packages(include=['opfgym', 'opfgym.*']),
-    url='https://gitlab.com/thomaswolgast/opfgym',
+    url='https://github.com/Digitalized-Energy-Systems/opfgym',
     license='MIT',
     install_requires=[
         'numpy==1.22.4',