ltbams

LTB AMS

Python Software for Power System Scheduling Modeling and Co-Simulation with Dynamics, serving as the market simulator for the CURENT Largescale Testbed.

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Why AMS

AMS facilitates Dynamics Incorporated Scheduling and Scheduling-Dynamics Co-Simulation through an integrated interface with ANDES.

This package is helpful for power system engineers, researchers, and students conducting scheduling and transient stability studies at specific operating points. It also benefits those interested in developing new scheduling formulations and algorithms, particularly by extending existing formulations to include new decision variables, constraints, and objective functions.

AMS is a Modeling Framework that provides a descriptive way to formulate scheduling problems. The optimization problems are then handled by CVXPY and solved with third-party solvers.

AMS produces credible scheduling results and competitive performance. The following results show the comparison of DCOPF between AMS and other tools.

Cost [$]	AMS	pandapower	MATPOWER
IEEE 14-Bus	7,642.59	7,642.59	7,642.59
IEEE 39-Bus	41,263.94	41,263.94	41,263.94
PEGASE 89-Bus	5,733.37	5,733.37	5,733.37
IEEE 118-Bus	125,947.88	125,947.88	125,947.88
NPCC 140-Bus	810,033.37	810,016.06	810,033.37
WECC 179-Bus	411,706.13	411,706.13	411,706.13
IEEE 300-Bus	706,292.32	706,292.32	706,292.32
PEGASE 1354-Bus	1,218,096.86	1,218,096.86	1,218,096.86
PEGASE 2869-Bus	2,386,235.33	2,386,235.33	2,386,235.33
GOC 4020-Bus	793,634.11	793,634.11	793,634.11
EPIGRIDS 5658-Bus	1,195,466.12	1,195,466.12	1,195,466.12
EPIGRIDS 7336-Bus	1,855,870.94	1,855,870.94	1,855,870.94

Figure: Computation time of OPF on small-scale cases.

In the bar chart, the gray bar labeled "AMS Symbolic Processing" represents the time spent on symbolic processing, while the wheat-colored bar "AMS Numeric Evaluation" represents the time spent on system matrices calculation and optimization model construction. The orange bar labeled "AMS GUROBI" represents the optimization-solving time using the GUROBI solver. Similarly, the red bar labeled "AMS MOSEK" and the pink bar labeled "AMS PIQP" represent the time used by the solvers MOSEK and PIQP, respectively. Regarding the baselines, the blue and green bars represent the running time of MATPOWER using solver MIPS and pandapower using solver PIPS, respectively. The results for AMS, pandapower, and matpower are the average time consumed over ten repeat tests.

AMS is currently under active development. Use the following resources to get involved.

• Start from the documentation for installation and tutorial.
• Check out examples in the examples folder
• Read the model verification results in the examples/verification folder
• Ask a question in the GitHub Discussions
• Report bugs or issues by submitting a GitHub issue
• Submit contributions using pull requests
• Read release notes highlighted here
• Try in Jupyter Notebook on Binder
• Check out the source code used for benchmark
• Check out and and cite our paper

Installation

AMS is released as ltbams on PyPI and conda-forge. Install from PyPI using pip:

pip install ltbams

Install from conda-forge using conda:

conda install conda-forge::ltbams

Install from GitHub source:

pip install git+https://github.com/CURENT/ams.git

NOTE:

• cvxpy is distributed with the open source solvers CLARABEL, OSQP, and SCS, but MIP-capable solvers need separate installation
• cvxpy versions below 1.5 are incompatible with numpy versions 2.0 and above
• If the solver SCIP encounters an import error caused by a missing libscip.9.1.dylib, try reinstalling its Python interface by running pip install pyscipopt --no-binary scip --force
• kvxopt is recommended to install via conda as sometimes pip struggles to set the correct path for compiled libraries, more detailes can be found in this closed issue Bug with dependency kvxopt 1.3.2.0
• Versions 1.0.0 and 1.0.1 are only available on PyPI
• Version 0.9.9 has known issues and has been yanked from PyPI

Example Usage

import ams
import andes

ss = ams.load(ams.get_case('ieee14/ieee14_uced.xlsx'))

# solve RTED
ss.RTED.run(solver='CLARABEL')

ss.RTED.pg.v
>>> array([1.8743862, 0.3226138, 0.01     , 0.02     , 0.01     ])

# convert to ANDES case
sa = ss.to_andes(addfile=andes.get_case('ieee14/ieee14_full.xlsx'),
                 setup=True, verify=False)
sa
>>> <andes.system.System at 0x14bd98190>

Citing AMS

If you use AMS for research or consulting, please cite the following paper in your publication that uses AMS:

J. Wang et al., "Dynamics-incorporated Modeling Framework for Stability Constrained Scheduling Under High-penetration of Renewable Energy," in IEEE Transactions on Sustainable Energy, doi: 10.1109/TSTE.2025.3528027.

Sponsors and Contributors

AMS is the scheduling simulation engine for the CURENT Largescale Testbed (LTB). More information about CURENT LTB can be found at the LTB Repository.

This work was supported in part by the Engineering Research Center Program of the National Science Foundation and the Department of Energy under NSF Award Number EEC-1041877 and the CURENT Industry Partnership Program.

This work was supported in part by the Advanced Grid Research and Development Program in the Office of Electricity at the U.S. Department of Energy.

See GitHub contributors for the contributor list.

License

AMS is licensed under the GPL v3 License.

Related Projects

• Popular Open Source Libraries for Power System Analysis
• G-PST Tools Portal: An open tools portal with a classification approach
• Open Source Software (OSS) for Electricity Market Research, Teaching, and Training

Some commercial solvers provide academic licenses, such as COPT, GUROBI, CPLEX, and MOSEK.