SmartGrid Optimizer

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  Architecture_Diagram

Inspiration

Nigeria (where I'm situated) has been experiencing recurring cases of Power Grid Collapse which got me curious about the causes of the collapse. In my research, I found out this is largely due to an imbalance of 3 forces - demand, supply(distribution) and storage. Fast forward to when I got a notification about this hackathon, I thought it'd be a great idea to present a solution that addresses this problem, leveraging Gemini API.

What it does

SmartGrid Optimizer accepts demand, supply and storage data, for analysis and generates recommendations that create a balance such that the grid is in optimal operational capacity. In addition, it is capable of integrating to real time data, demand profiling/segmentation, demand prediction and scalable for full automation.

How we built it

This was built with Python in Google Colab. The app requests for Energy supply, demand and storage capacity as inputs, analyzes the input data and provides a recommendation that guarantees sustainability. One recommendation is based on an programmatic analysis while the second recommendation is from Gemini.

Challenges we ran into

1. Building a web interface was quite difficult to achieve, so I made do with Widgets.
2. Got into some last minute errors/bugs that made the Gemini recommendation bit stop functioning

Accomplishments that we're proud of

1. Able to deliver a hackathon-ready solution that works within the short timeframe

What we learned

Start, no matter how imperfect it may sound or look Push yourself, you never know your capabilities till you go beyond your comfort zone

What's next for SmartGrid Optimizer

1. Integrate this to real-life data/grids
2. Experiment with advanced metrics & optimization techniques
3. POC with a power/energy supplier
4. Integrate with the control framework of the grid for full automation
5. Modify based on customer feedback

Built With

• colab
• gemini
• python