Customer Facility Load Shedding Using Carbon Intensity Forecasts
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Solution Overview
Problem
Existing energy distribution networks lack effective methods to manage carbon intensity variations, leading to inefficiencies and environmental impacts, particularly in the use of fossil fuels.
Innovation Solution
Implementing a system that includes a metering data management module and a network headend for communication with a subsystem for managing a resource distribution network comprising a meter and a network headend for managing a control of a customer installation with a meter and a tele-information device for controlling load shedding based on carbon intensity forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If load shedding commands are implemented to reduce carbon intensity, then environmental sustainability is improved, but device complexity increases due to additional control systems and communication infrastructure
Solution Approach 1:
The patent introduces a network headend and information server as intermediary components that mediate between the distribution network and customer facilities. These intermediaries process carbon intensity forecasts and generate load shedding commands, thereby managing the complexity centrally rather than distributing it across all customer devices.
Solution Approach 2:
The system segments the complexity management by separating functions into different modules: the metering data management module handles data collection, the information server processes carbon intensity forecasts, and the network headend distributes commands. This segmentation allows each component to handle specific tasks, reducing overall system complexity.
2Productivity
If real-time carbon intensity monitoring and load shedding control are implemented, then operational efficiency is improved, but device complexity and infrastructure requirements increase
Solution Approach 1:
The information server performs multiple functions: it receives carbon intensity data, forecasts future carbon intensity, generates load shedding commands, and communicates with both the network headend and customer facilities. This multi-functionality reduces the need for separate dedicated systems, thereby improving operational efficiency without proportionally increasing infrastructure complexity.
Solution Approach 2:
The system implements feedback loops where meter data is collected, carbon intensity is forecasted, and load shedding commands are issued based on this feedback. This closed-loop control enables real-time optimization of operational efficiency while managing complexity through systematic feedback processing.
3Measurement precision
If forecast load curves are calculated for all customers to predict carbon intensity, then carbon intensity forecasting accuracy is improved, but loss of time and computational resources increase
Solution Approach 1:
The system performs preliminary calculations by forecasting carbon intensity for future time periods based on historical and current load data. By preparing these forecasts in advance, the system can quickly issue load shedding commands when needed, improving forecast accuracy without incurring excessive calculation delays at the moment of decision-making.
Data Source
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AI summary
The described method concerns a resource distribution network comprising an information server and customer facilities. It applies, for example, to an electricity distribution network. The method allows for the control of customer facilities on the distribution network based on a forecast of the carbon intensity of resource production. For example, it could involve sending a load shedding command to a home automation device configured to manage the customer facility.