power-agent potpourri workflow
potpourri builds Pyomo AC/DC OPF models over pandapower networks. There is no PowerMCP server for it, so drive it through its Python API using the bundled scripts. Establish a credible network and base case before optimising, and a credible single period before a multi-period horizon.
Default ladder
scripts/inspect_case.py --case <simbench:CODE|path.json>— load, validate, and solve the pandapower base case. Do not skip this: an OPF that fails later is usually a data problem, and this is where data problems are cheap to find.scripts/solve_opf.py --list-solvers— confirm what is installed. AC needs an NLP solver (IPOPT); DC needs an LP/MIP solver (GLPK, CBC, HiGHS).scripts/solve_opf.py --formulation dc --objective ext_grid_import— linear screening for congestion and active-power headroom.scripts/solve_opf.py --formulation ac --objective <objective>— the real answer whenever voltage, reactive power, losses, or inverter capability matters.scripts/solve_opf.py --horizon <N> --battery-penetration <pct> --seed <n>— multi-period, only once a single period is credible.
Both scripts print an engineering summary, take --json for a machine-readable
payload, and exit non-zero on genuine failure (1 = solved but not optimal,
2 = bad input, 3 = no compatible solver).
Working rules
- Never read
net.res_*without checking termination. The model constructor runspp.runpp, sonet.res_*is always populated; when a solve is not optimal potpourri skips its result mapping and those tables still hold the base case.solve_opf.pyreports no results at all in that situation. - Multi-period
solve(to_net=True)writes nothing. It only logs that it did. Map each step withpyo_to_net_multi_period.pyo_sol_to_net_res(net, model, t). - DC gives no voltage answer. After a DC OPF
net.res_bus.vm_puis a flat 1.0 placeholder. DC feasibility never establishes AC feasibility, least of all in distribution networks where voltage and reactive power are the binding physics. - A local NLP optimum is not a global one. IPOPT on a nonconvex AC OPF gives a locally optimal point; say "locally optimal", and never claim global optimality without a global solver reporting it.
- Quote numbers, units, and the limit that was configured — bus and
vm_pu, element andloading_percentagainst its rating, MW/Mvar/MWh, and the solver's own termination condition. - Match the objective to the question and state its side effects: voltage- deviation minimisation puts no value on energy, so where curtailment is free it will curtail everything. Check the reported curtailment before accepting a dispatch.
Reference material
Read the file that matches what you are doing; do not load them all.
references/API_REFERENCE.md— verified classes,add_OPFoptions, objectives, devices, result extraction, and what is not supported.references/FORMULATION_GUIDE.md— AC vs DC, snapshot vs multi-period, LP/NLP/MINLP consequences, and single- vs multi-period model differences.references/SOLVER_GUIDE.md— solver compatibility, termination interpretation, and the ordered infeasibility diagnosis.references/VALIDATION_GUIDE.md— power balance, constraint checks, binding constraints, cross-validation against pandapower, and the reporting checklist.
Local assets in this skill
scripts/inspect_case.py— load, validate, base power flow.scripts/solve_opf.py— build, solve, validate, and escalate.scripts/test_scripts.py— smoke tests; skips cleanly without solvers.requirements.txt— including thepandapower<3.5pin potpourri needs.
Escalation triggers
Quote the element, value, unit, and configured limit that tripped each row.
solve_opf.py emits these in its escalation payload.
| Observation | Escalate to |
|---|---|
Bus vm_pu outside the configured [min_vm_pu, max_vm_pu] band (AC only) |
voltage-violation-mitigation |
res_line / res_trafo loading_percent above its configured max_loading_percent |
thermal-overload-mitigation |
termination_condition is infeasible or unbounded, or curtailment exceeds 5 % of available renewable energy |
operations-planning-mitigation |
Solve ends in any other non-optimal condition (other, maxIterations, maxTimeLimit), or the base pp.runpp fails |
convergence-failure-mitigation |
| Net export through the external grid, or PV-driven voltage rise on a distribution feeder | der-hosting-capacity-mitigation |
| A newly added generator, storage unit, or large load causes any of the above | interconnection-impact-mitigation |
Deliver
- Network source and size, study type and formulation, horizon and resolution.
- Objective, what it means in units, and the solver plus its termination condition.
- Power balance, voltage extrema with buses, worst line and transformer loading, dispatch by group, curtailment, and storage SOC extrema where relevant.
- Binding constraints, and limitations (local optimum, DC assumptions, storage modelling caveats).
- Any justified escalation, with the numbers behind it.