Micro-Grid Energy Planning Using Mobility-Aware Connected Lighting
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Solution Overview
Problem
Cities face unpredictable energy demand spikes leading to increased costs due to inefficient power source switching capabilities, as their infrastructure often relies on a single power source, resulting in wasted energy and higher costs.
Innovation Solution
A system that predicts mobility patterns of people within micro-grids to estimate energy requirements, identifies suitable energy resources, and dynamically switches between power sources to meet demand, utilizing data from luminaires and telecommunications providers to optimize energy resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a city relies on a single power source with fixed infrastructure, then the infrastructure complexity is reduced, but the ability to switch between power sources is lost, leading to higher energy costs during demand spikes
Solution Approach 1:
The patent divides the city's power infrastructure into multiple independent micro-grids, each capable of autonomous operation and switching between different power sources. This segmentation allows each micro-grid to adapt locally without requiring complex city-wide infrastructure changes, resolving the contradiction between adaptability and infrastructure complexity.
Solution Approach 2:
The system implements dynamic switching capabilities that allow micro-grids to transition between different power sources (renewable, fossil fuel, grid power) based on real-time demand conditions. This dynamic adaptability enables the infrastructure to respond flexibly to changing energy requirements without requiring permanent complex multi-source infrastructure.
2Reliability
If energy demand spikes are met by ramping up power sources, then energy availability is ensured, but energy waste increases due to inefficient ramping processes
Solution Approach 1:
The system uses mobility pattern prediction to anticipate energy demand spikes before they occur. By predicting when and where demand will increase based on planned events or typical patterns, the system can pre-position energy resources and prepare appropriate power sources, avoiding the need for inefficient last-minute ramping operations.
Solution Approach 2:
The system continuously monitors actual energy consumption against predicted patterns and uses this feedback to refine future predictions and adjust power source selection. This closed-loop control ensures that energy availability is maintained while minimizing waste by learning from past performance and optimizing future decisions.
3Loss of energy
If renewable energy sources are used during predictable mobility patterns, then energy costs are reduced, but the system must accurately predict mobility patterns to optimize resource allocation
Solution Approach 1:
The system uses mobile devices to serve multiple functions: they act as both mobility pattern sensors (tracking location and movement) and communication nodes (transmitting data to the prediction system). This multi-functionality reduces the need for dedicated prediction infrastructure while improving prediction accuracy through comprehensive mobile device data.
Solution Approach 2:
The system leverages data that mobile devices already collect and transmit for other purposes (location tracking, communication metadata) to infer mobility patterns. By repurposing existing device capabilities and data streams, the system obtains prediction inputs without requiring additional specialized sensing infrastructure, reducing costs while maintaining accuracy.
Data Source
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AI summary
The described embodiments relate to systems, methods, and apparatuses for controlling energy resources available to micro-grids of a city based on mobility patterns of people moving within the micro-grids. The mobility patterns can be identified using a network of sensors within each micro-grid for collecting data related to the movement of people within the micro-grids. The mobility patterns can be used to estimate energy demand for each micro-grid and prioritize the energy demands to determine the energy resources that would be suitable for supplying power to each micro-grid. This allows for micro-grids to dynamically and efficiently change their power sources according to predictions about the movement of people within the micro-grids.