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Thermal Overload Mitigation

Organization
Power-Agent
thermal-overload-mitigation

Senior power-engineer playbook for overloaded lines and transformers. Use whenever power-flow or contingency studies show loading above ratings — including escalations from a tool skill where branch loading exceeds 100% of its rating. Triggers on "line overloaded", "transformer above rating", "relieve congestion", or "redispatch to unload". Gives an operationally credible sequence of redispatch, topology, phase-shifter, and reinforcement actions rather than only flagging the overload.

Overview

PublisherPower-Agent
RepositoryPowerSkills
Skill namethermal-overload-mitigation
Stars
77
Forks
23
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Power-Agent on GitHub. Read the source before you install it.

Installation

Install the Thermal Overload Mitigation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Power-Agent/PowerSkills/tree/main/powerskills-engineering/skills/thermal-overload-mitigation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Power-Agent/PowerSkills.git /tmp/PowerSkills
mkdir -p .claude/skills
cp -r /tmp/PowerSkills/powerskills-engineering/skills/thermal-overload-mitigation .claude/skills/thermal-overload-mitigation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thermal Overload Mitigation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Thermal Overload Mitigation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Thermal Overload Mitigation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Thermal overload mitigation

Start with the monitored element, the applicable rating, and the driving condition.

Preferred action order

  1. Confirm the overload is real: correct rating set, correct outage state, and solved base case or contingency.
  2. Redispatch generation or transfer patterns to unload the corridor.
  3. Reconfigure topology or switching where operations permit.
  4. Use phase shifters, HVDC schedules, or transformer controls only when sensitivity evidence supports them.
  5. If the issue is persistent, move to reconductoring, uprating, or new facilities.
  6. After each action, re-solve and confirm the overload is relieved. If it is not, continue to the next step.

Worked example

Line A-B sits at 105% of its normal rating after the N-1 loss of Line C-D. The PTDF for a G3-to-G5 transfer on Line A-B is -0.18 MW/MW, so shifting 40 MW off G3 onto G5 unloads A-B by about 7 MW, bringing it to 94%. Re-solving the full monitored contingency set confirms the redispatch creates no new violations elsewhere.

Working rules

  • Separate normal-rating issues from emergency-rating issues.
  • Fix the smallest set of elements that relieve the monitored overload cluster.
  • Validate a candidate fix on all critical monitored contingencies, not just the worst case.
  • Use PTDF or LODF style sensitivities when the software provides them.

Deliver

  • The overloaded elements and rating basis.
  • The dispatch or topology changes that help most.
  • The recommended corrective action and the residual risk.

Frequently asked questions

What does the Thermal Overload Mitigation AI skill do?

Senior power-engineer playbook for overloaded lines and transformers. Use whenever power-flow or contingency studies show loading above ratings — including escalations from a tool skill where branch loading exceeds 100% of its rating. Triggers on "line overloaded", "transformer above rating", "relieve congestion", or "redispatch to unload". Gives an operationally credible sequence of redispatch, topology, phase-shifter, and reinforcement actions rather than only flagging the overload.

Why use Thermal Overload Mitigation on TypingMind?

Because you install it once and use it with any model. Thermal Overload Mitigation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Thermal Overload Mitigation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Power-Agent/PowerSkills/tree/main/powerskills-engineering/skills/thermal-overload-mitigation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Thermal Overload Mitigation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Thermal Overload Mitigation?

As many as you like. As long as a model supports skills, you can use Thermal Overload Mitigation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Thermal Overload Mitigation AI skill free?

Yes. It is published on GitHub by Power-Agent under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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