evo-grid-dispatch-operator

evo-grid-dispatch-operator

Solves DC Optimal Power Flow with spinning reserve co-optimization from MATPOWER JSON data and produces a structured dispatch report.

Quick Start — Full Pipeline

import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "cvxpy", "clarabel", "-q"])

import sys as _sys
_sys.path.insert(0, '/app/environment/skills/evo-grid-dispatch-operator/scripts')
from solve import main
main("/root/network.json", "/root/report.json")

This single call handles everything: parse → optimize → report.

Formulation Details

DC Power Flow Model

• Bus voltage angle variables theta (radians), slack bus angle fixed to 0
• Branch power flow: flow_MW = (1/X) * (theta_f - theta_t) * baseMVA
• Transformer tap ratios handled (tap=0 treated as 1.0)
• Nodal power balance: sum(Pg at bus) - Pd = B_row @ theta (per-unit)

Generator Cost

• Quadratic polynomial: cost = c2 * Pg_MW^2 + c1 * Pg_MW + c0
• Coefficients from gencost array columns [4,5,6] for ncost=3

Constraints

• Generator limits: Pmin <= Pg <= Pmax (per-unit internally)
• Line flow limits: |flow_MW| <= RATE_A (skip if RATE_A=0)
• Reserve non-negativity: Rg >= 0
• Reserve capacity: Rg <= reserve_capacity[g]
• Capacity coupling: Pg_MW + Rg <= Pmax_MW
• System reserve: sum(Rg) >= reserve_requirement

Solver

• cvxpy with CLARABEL (interior-point, handles QP and LP)

Report Structure

{
  "generator_dispatch": [
    {"id": 1, "bus": 1, "output_MW": 100.0, "reserve_MW": 30.0, "pmax_MW": 150.0}
  ],
  "totals": {
    "cost_dollars_per_hour": 8000.0,
    "load_MW": 259.0,
    "generation_MW": 259.0,
    "reserve_MW": 500.0
  },
  "most_loaded_lines": [{"from": 1, "to": 2, "loading_pct": 85.0}],
  "operating_margin_MW": 50.0
}

Key Definitions

• operating_margin_MW = sum(Pmax - output_MW - reserve_MW) over all generators
• most_loaded_lines: top 3 lines sorted descending by loading_pct
• loading_pct = |flow_MW| / RATE_A * 100 (only for lines with RATE_A > 0)