Customer Installation Control Using Carbon Intensity Forecasts
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Solution Overview
Problem
Existing distribution networks lack the ability to efficiently manage resource consumption based on carbon intensity, leading to suboptimal energy use and environmental impact.
Innovation Solution
Implementing a method and system that determines a carbon intensity forecast in a distribution network, allowing for load-shedding commands to be sent to customer installations to reduce or stop high-carbon consumption, using an information server, meters, and home automation devices to manage equipment operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If load-shedding commands are implemented to reduce consumption during high-carbon periods, then carbon intensity is reduced, but customer convenience and equipment operation are worsened
Solution Approach 1:
The system performs preliminary actions by forecasting carbon intensity values for future time periods and proactively sending load-shedding commands to customer installations before high-carbon periods occur. This allows equipment to be adjusted in advance, reducing carbon intensity while maintaining customer convenience through automated pre-planned adjustments rather than reactive disruptions.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual consumption data from meters, comparing it against forecasted carbon intensity values, and automatically adjusting load-shedding commands accordingly. This closed-loop feedback ensures that carbon intensity reduction goals are met while adapting to actual customer needs and consumption patterns, thereby maintaining ease of operation.
2Object-affected harmful factors
If carbon intensity forecasting is implemented across the distribution network, then environmental impact is minimized, but system complexity increases
Solution Approach 1:
The system segments the distribution network into manageable components: information servers that generate carbon intensity forecasts, meters that measure consumption at individual customer installations, and home automation devices that execute load-shedding commands. This segmentation allows complex carbon intensity forecasting to be distributed across multiple simpler components, reducing overall system complexity while achieving environmental goals.
Solution Approach 2:
The patent introduces an information server as an intermediary that acts as a mediator between the complex carbon intensity forecasting algorithm and the simple metering devices. The information server handles the computational complexity of forecasting and communicates only simple load-shedding commands to customer installations, thereby minimizing system complexity at the device level while still achieving environmental impact reduction.
Data Source
AI summary
The method described relates to a resource distribution network comprising an information server and customer installations. It applies, for example, to an electricity distribution network. The method enables customer installations on the distribution network to be controlled according to a resource production carbon intensity forecast. For example, it may comprise sending a load-shedding command to a home automation device configured to manage the customer installation.