Opti-VGI is an EV smart charging management application designed to optimize electric vehicle charging based on power or pricing constraints. It provides a flexible framework whose scheduling logic can integrate with Charge Station Management Systems (CSMS). The specific integration method, potentially supporting protocols like OCPP 1.6 or OCPP 2.0.1 / ISO 15118, depends on the implementation of its communication layer.
Documentation: https://argonne-vci.github.io/Opti-VGI
- Constraint-Based Optimization: Schedules EV charging considering factors like overall site power limits or dynamic pricing signals.
- Modular Architecture: Clearly separates communication logic (
Translation
layer) from optimization strategies (Algorithm
layer). - Pluggable Algorithms: Supports different optimization approaches. Includes implementations like:
PulpNumericalAlgorithm
: Uses linear programming via the PuLP library.GoAlgorithm
: A custom heuristic-based algorithm.
- CSMS Integration Interface: The
Translation
abstract class defines the necessary methods to fetch data (EVs, constraints) and send charging commands, allowing integration with various external systems. The implementation of this layer determines how Opti-VGI communicates (e.g., via API calls, database interaction, or specific protocols like OCPP). - Handles Active & Future EVs: Considers both currently connected EVs and planned future reservations in its scheduling.
- Asynchronous Operation: Designed to run scheduling logic periodically or in response to events using background worker threads.
Opti-VGI follows a modular design:
- Translation Layer (
optivgi.translation
): An abstract interface (Translation
) for communication with external systems (e.g., a CSMS). Implementations handle fetching EV data, power constraints, and sending charging profiles. This is where specific communication protocols (like OCPP methods via a library, or custom API calls) would be implemented. - SCM Runner (
optivgi.scm_runner
): Orchestrates the scheduling cycle, using aTranslation
instance to get inputs, anAlgorithm
instance to calculate schedules, and theTranslation
instance again to dispatch results. - SCM Algorithm (
optivgi.scm.algorithm
): An abstract interface (Algorithm
) for charging strategies. Concrete implementations contain the core optimization logic (e.g.,GoAlgorithm
,PulpNumericalAlgorithm
). - EV Data Structure (
optivgi.scm.ev
): A standard dataclass (EV
) representing vehicles and their charging parameters. - Worker Threads (
optivgi.threads
): Helpers to run the SCM logic periodically or on events without blocking the main application.
The diagram shows the flow of data and commands between the EVSEs, Opti-VGI, and the CSMS, highlighting the roles of the Translation
and Algorithm
layers in this interaction.
It illustrates how Opti-VGI fetches data from the CSMS, processes it using the selected algorithm, and sends back charging commands.
You can install the latest stable release from PyPI:
pip install optivgi
For development, clone the repository and install with development dependencies:
git clone https://github.com/argonne-vci/Opti-VGI.git
cd Opti-VGI
pip install -e ".[dev]"
Opti-VGI provides the core scheduling framework. To use it, you need to:
- Implement the
Translation
interface: Create a concrete class that inherits fromoptivgi.translation.Translation
and implements theget_evs
,get_peak_power_demand
, andsend_power_to_evs
methods to communicate with your specific CSMS or data source. - Choose an
Algorithm
: Select one of the provided algorithms (e.g.,GoAlgorithm
,PulpNumericalAlgorithm
) or implement your own inheriting fromoptivgi.scm.algorithm.Algorithm
. - Run the
scm_worker
: Use theoptivgi.threads.scm_worker
function in a separate thread, providing yourTranslation
implementation and chosenAlgorithm
class. Trigger the worker using an event queue.
An example demonstrating integration via a simple HTTP API and WebSocket notifications can be found in the ./examples/http-api/
directory. See the Examples Documentation for more details.
Contributions are welcome! Please feel free to open an issue on the GitHub Issues page to report bugs, suggest features, or discuss potential improvements.
This project is licensed under the Apache License, Version 2.0. See the LICENSE file for details.
- Documentation: https://argonne-vci.github.io/Opti-VGI
- PyPI Package: https://pypi.org/project/optivgi/
- GitHub Repository: https://github.com/argonne-vci/Opti-VGI
- Issue Tracker: https://github.com/argonne-vci/Opti-VGI/issues