PowerMCP
PowerMCP is an open-source collection of MCP servers for power system software like PowerWorld, PSSE and OpenDSS. These tools enable LLMs to directly interact with power system applications, facilitating intelligent coordination, simulation, and control in the energy domain.
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PowerMCP ⚡
PowerMCP is an open-source collection of MCP servers for power system software like PowerWorld and OpenDSS. These tools enable LLMs to directly interact with power system applications, facilitating intelligent coordination, simulation, and control in the energy domain.
🌟 What is MCP?
The Model Context Protocol (MCP) is a revolutionary standard that enables AI applications to seamlessly connect with various external tools. Think of MCP as a universal adapter for AI applications, similar to what USB-C is for physical devices. It provides:
- Standardized connections to power system software and data sources
- Secure and efficient data exchange between AI agents and power systems
- Reusable components for building intelligent power system applications
- Interoperability between different AI models and power system tools
🤝 Our Community Vision
We're building an open-source community focused on accelerating AI adoption in the power domain through MCP. Our goals are:
- Collaboration: Bring together power system experts, AI researchers, and software developers
- Innovation: Create and share MCP servers for various power system software and tools
- Education: Provide resources and examples for implementing AI in power systems
- Standardization: Develop best practices for AI integration in the energy sector
🚀 Getting Started with MCP
📖 Quick Tutorial
🚀 New to PowerMCP? Start here!
📋 PowerMCP Tutorial PDF - Your complete guide to getting started with PowerMCP
This comprehensive tutorial will walk you through everything you need to know to begin using PowerMCP effectively.
Video Demos
Check out these demos showcasing PowerMCP in action:
-
Contingency Evaluation Demo: An LLM automatically operates power system software, such as PowerWorld and pandapower, to perform contingency analysis and generate professional reports.
-
Loadgrowth Evaluation Demo: An LLM automatically operates power system software, such as PowerWorld, to evaluate different load growth scenarios and generate professional reports with recommendations.
Useful MCP Tutorials
MCP follows a client-server architecture where:
- Hosts are LLM applications (like Claude Desktop or IDEs) that initiate connections
- Clients maintain 1:1 connections with servers, inside the host application
- Servers provide context, tools, and prompts to clients
Check out these helpful tutorials to get started with MCP:
- Getting Started with MCP: Official introduction to the Model Context Protocol fundamentals.
- Core Architecture: Detailed explanation of MCP's client-server architecture.
- Building Your First MCP Server: Step-by-step guide to creating a basic MCP server.
- Anthropic MCP Tutorial: Learn how to use MCP with Claude models.
- Cursor MCP Tutorial: Learn how to use MCP with Cursor.
- Other Protocol: Open AI Function Calling Tool
Testing with your LLMs
Note: All MCPs should be tested via Claude Desktop before submitting a PR to ensure consistency.
Open config.json and add the MCP servers for the power system tools you have installed. For example:
{
"mcpServers": {
"pandapower": {
"command": "python",
"args": ["pandapower/panda_mcp.py"]
},
"powerworld": {
"command": "python",
"args": ["PowerWorld/powerworld_mcp.py"]
}
}
}
Then, follow the setup instructions and configuration details in PowerMCP Tutorial PDF.
📚 Documentation
For detailed documentation about MCP, please visit:
🤝 Contributing
We welcome contributions! Please see our Contributing Guidelines for details.
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🙏 Acknowledgments
Core Team
- Qian Zhang, Muhy Eddin Za’ter, Steven Black, Luan Lopes dos Santos, Paulo Radatz, Stephen Jenkins, Maanas Goel
Special Thanks
- All contributors who help make this project better
- The Power and AI Initiative (PAI) at Harvard SEAS
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