# Unit Commitment Operating Rules

> Use for day-ahead or multi-period unit commitment problems, including thermal on/off schedules, dispatch, startup/shutdown logic, minimum up/down time, ramping, spinning reserve deliverability, renewable curtailment, operating-cost accounting, and independent feasibility checks for power-system operations schedules.

- **Type:** Skill
- **Install:** `agentstack add skill-xuansenpa1-skillrevise-unit-commitment-operating-rules`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [xuansenpa1](https://agentstack.voostack.com/s/xuansenpa1)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [xuansenpa1](https://github.com/xuansenpa1)
- **Source:** https://github.com/xuansenpa1/skillrevise/tree/main/data/skillsbench/tasks/energy-unit-commitment/environment/skills/unit-commitment-operating-rules
- **Website:** https://arxiv.org/abs/2606.01139

## Install

```sh
agentstack add skill-xuansenpa1-skillrevise-unit-commitment-operating-rules
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Unit Commitment Operating Rules

Use this skill when a task asks for a day-ahead or multi-period unit commitment schedule with generators, load, reserves, operating constraints, and cost tradeoffs.

This is a reusable UC operating guide. It gives formulation and validation patterns, not a complete task-specific mathematical model.

## UC In One Paragraph

Unit commitment decides which generators are online over time, when they start or stop, how much they produce, and how much reserve they can physically provide. It is harder than hourly economic dispatch because startup/shutdown decisions, ramping, minimum up/down time, reserve deliverability, initial conditions, and cost curves couple one period to the next.

## Keep These Concepts Separate

For each thermal unit `g` and period `t`:

```python
u[g, t]      # commitment/on status, binary
start[g, t]  # startup transition, binary
stop[g, t]   # shutdown transition, binary
p[g, t]      # production variable: know whether actual MW or above-minimum MW
r[g, t]      # scheduled reserve
```

Reports often require actual MW output. Many UC models internally use output above minimum:

```python
actual_output = pmin[g] * u[g, t] + p_above_min[g, t]
p_above_min = actual_output - pmin[g] * u[g, t]
```

Do not mix these conventions in ramping, reserve, cost, or reporting.

## Core Feasibility Checks

Before reporting a schedule, independently verify:

- every resource appears exactly once and all time-series have length `T`;
- commitment, startup, and shutdown are binary;
- offline thermal units have zero production and zero reserve;
- online thermal units respect min/max output;
- must-run units are online when required;
- startup/shutdown indicators match commitment transitions;
- demand balance holds in every period;
- renewable output stays within period-specific bounds;
- scheduled reserve meets the system requirement;
- reserve is deliverable under headroom, startup/shutdown capability, and ramp limits;
- minimum up/down time and initial conditions are respected;
- cost and summary fields are recomputed from arrays.

## Transition Logic

Link startup/shutdown to commitment and the initial state:

```python
prev_u = initial_on[g] if t == 0 else u[g, t - 1]
u[g, t] - prev_u == start[g, t] - stop[g, t]
start[g, t] + stop[g, t] = reserve_requirement[t] - tol
```

Renewables:

```python
renewable_min[r, t] = pts[-1][0]:
        return pts[-1][1]
    for (x0, y0), (x1, y1) in zip(pts, pts[1:]):
        if x0 <= output_mw <= x1:
            return y0 + (output_mw - x0) * (y1 - y0) / (x1 - x0)
    raise ValueError("output outside curve")
```

If the first point is at minimum output, its cost may represent online minimum-output cost. Do not add another fixed online cost unless the data says so.

## Implementation Workflow

1. Parse and normalize resource/time arrays.
2. Choose actual-output or above-minimum internal variables and stick to it.
3. Include hard feasibility constraints before optimizing cost.
4. Extract the solution into the report convention.
5. Run independent validation on extracted arrays.
6. Recompute costs and summaries from arrays.
7. Write `"pass"` checks only after validation passes.

## Common Pitfalls

- Treating UC as independent hourly dispatch.
- Counting reserve from offline units or renewable headroom.
- Checking reserve headroom but forgetting startup or ramp deliverability.
- Ignoring initial output in first-period ramping.
- Ignoring initial on/off duration in minimum up/down constraints.
- Trusting a repair LP that omits a constraint family.
- Hard-coding `"pass"` fields before validation.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [xuansenpa1](https://github.com/xuansenpa1)
- **Source:** [xuansenpa1/skillrevise](https://github.com/xuansenpa1/skillrevise)
- **License:** MIT
- **Homepage:** https://arxiv.org/abs/2606.01139

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-xuansenpa1-skillrevise-unit-commitment-operating-rules
- Seller: https://agentstack.voostack.com/s/xuansenpa1
- Browse the marketplace: https://agentstack.voostack.com/browse

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