AI as a power systems analysis resource.

This is the most basic interface that many users will be familiar with, through use of a simple Chat type interface. You ask the AI a question, and it responds. This could be a simple question-responses approach or a more detailed backwards and forwards analysis over many hours. This process can be a simple question and response approach, or a more detailed analysis where reference documents, results and models are uploaded and analysed to help with interpretation.

The use cases here are many and varied. There are a few key things to always remember.

• Do not expect the first answer to be the right one — AIs are fallible, and often don’t always fully understand your first question. You will often need to interact with a series of question-response cycles before it begins to converge on a correct understanding.
• Prompt crafting — AI cannot guess what you are thinking. You need to articulate the problem you are asking it to help with; a longer paragraph with detail helps significantly. Key words in prompts can trigger certain responses, such as Think Deeply, Check from First Principles, Indicate Confidence Levels of Answers, Validate and Provide References.
• Context is everything — all AIs are trained on publicly available data and often have limited specialist knowledge. In a power systems context this means they will often understand classical power systems analysis theory and control system theory well, but they will not be up to date with latest state-of-the-art thinking. They will also not have detailed knowledge of specific standards, codes or CIGRE / IEEE technical reports and brochures.
• Context window — all AIs have a context window length, this means how long they can sustain a conversation for. A sustained conversation is usually (not always) beneficial.
• All AI models are different and have their own strengths and weaknesses. Get to know them and their foibles. Some models are good at power systems, others are less so. Within your chosen AI, they also have different reasoning models. As a good point of principle power systems analysis usually needs the most powerful model the AI can provide.
• Recognise when the AI is getting lost and going down a rabbit hole. Stop and re-evaluate — some problems an AI cannot solve. Try a different reasoning model, or a different AI all together.

A more powerful approach is a direct interface between the AI and the engineering software. Anthropic’s Model Context Protocol, or MCP, is one example of this type of approach. In simple terms, MCP allows an AI system to connect to external tools and applications in a controlled way.

For power systems work, this could mean connecting an AI interface to software such as DIgSILENT PowerFactory, PSCAD, ETAP or similar tools. The AI does not just talk about the model; it can potentially interrogate it. It can call scripts, inspect objects, list study cases, read parameters, export results and help automate repetitive workflows.

This avoids one of the major weaknesses of the basic chat interface, which is the constant need to manually copy and paste information between the engineering software and the AI. Instead, the AI can be given a structured route into the model and can retrieve the information it needs.

There are limits. Direct interfaces can be fragile, time-consuming to set up, and heavily dependent on the software API. In some cases, it may still be quicker for an experienced engineer to drive the software directly. The value is not in replacing every manual action. The value is in reducing friction, improving repeatability, and allowing the AI to work with real model data rather than guessed context.