This skill enables AI agents to minimize total power generation costs while strictly adhering to load demands and generator constraints.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install grid-dispatch-operator-economic-dispatch
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install grid-dispatch-operator-economic-dispatch using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Economic Dispatch?
The Economic Dispatch skill is a technical framework designed for power system optimization within the Openclaw Skills ecosystem. It provides the logic required to minimize the total cost of generation by calculating optimal setpoints for various generators. By utilizing MATPOWER data formats and the CVXPY optimization library, the skill handles complex quadratic cost functions and ensures that all generation limits and power balance constraints are satisfied.
Built for developers and engineers working with grid simulations, this skill bridges the gap between raw power system data and actionable dispatch instructions. It allows agents to process generator indices, cost coefficients, and reserve requirements to produce high-precision operational plans. Integrating this into your Openclaw Skills workflow ensures robust handling of electrical load sharing and cost-efficient grid management.
Economic Dispatch Use Cases
- Minimizing total hourly generation costs for complex power networks.
- Calculating optimal real power setpoints (MW) for individual generators.
- Managing spinning reserve requirements alongside energy generation.
- Determining system operating margins and uncommitted capacity.
- Solving constrained optimization problems for utility-scale load balancing.
How Economic Dispatch Works
- The skill parses MATPOWER-formatted generator and bus data to map technical limits and connectivity.
- It interprets polynomial cost coefficients (quadratic or linear) to build a mathematical objective function.
- A CVXPY optimization model is initialized, defining variables for generator output and operating reserves.
- System-wide constraints are applied, including total power balance (generation must equal load) and individual generator Pmin/Pmax limits.
- The CLARABEL interior-point solver is utilized to solve the optimization problem, ensuring stability even for ill-conditioned grid data.
- The results are post-processed to convert per-unit values back to MW and generate a comprehensive dispatch report.
Economic Dispatch Setup
To implement this Openclaw Skills module, you need a Python environment with optimization libraries. Install the dependencies using the following command:
pip install cvxpy clarabel numpy
Ensure your input data provides the MATPOWER gen and gencost array structures, specifically focusing on polynomial type 2 models for cost calculations.
Economic Dispatch Data Schema & Taxonomy
The skill organizes data using standard power system indices for seamless integration.
| Data Group | Key Indices | Description |
|---|---|---|
| Generator Data | 0, 8, 9 | Bus mapping, Maximum (PMAX), and Minimum (PMIN) real power limits. |
| Cost Functions | 0, 3, 4+ | Model type (Polynomial), number of coefficients, and actual cost values [c2, c1, c0]. |
| System Metrics | N/A | Total system load, base MVA for per-unit conversion, and reserve requirements. |
| Output JSON | id, output_MW, reserve_MW | Detailed generator-level dispatch results and system-wide cost totals. |
Economic Dispatch Advanced Features
- Reserve Co-optimization: Logic for simultaneously scheduling energy and reserves to meet system reliability standards.
- Polynomial Cost Support: Full support for both quadratic (NCOST=3) and linear (NCOST=2) generator cost models.
- CLARABEL Integration: Leverages robust interior-point solvers to handle large-scale DC-OPF problems efficiently.
- Operating Margin Calculation: Built-in functions to track uncommitted headroom beyond scheduled energy and reserves.
- Non-Contiguous Bus Mapping: Automated mapping of bus numbers to array indices for complex, non-sequential network topologies.
SKILL.md
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