Load Classification Power Allocation for Scalable Demand Response
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Solution Overview
Problem
Existing power distribution systems face challenges in managing demand response events due to scalability issues and inefficiencies in load forecasting, leading to potential outages and inefficiencies in power consumption management.
Innovation Solution
A power allocation system that classifies loads based on factors such as function, ownership, and location, allowing strategic allocation of power to different load types, enabling proactive demand management and reducing the need for reactive demand response events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If aggregate power consumption is metered using simple or smart meters, then billing simplicity is maintained, but demand response management scalability deteriorates
Solution Approach 1:
The patent segments the aggregate load into multiple individual load circuits, each equipped with its own smart meter and controller. This segmentation enables independent monitoring and control of each load, allowing scalable demand response management while maintaining simplified billing through centralized aggregation of the segmented data.
2Adaptability or versatility
If iterative negotiation is used to manage demand response events, then consumer agreement is obtained, but response time deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-negotiating and establishing demand response agreements and preferences with consumers before actual demand response events occur. Load controllers are pre-configured with consumer preferences, allowing automated real-time enforcement without iterative negotiation during actual events, thus reducing response time while maintaining consumer adaptability.
Solution Approach 2:
The load controllers autonomously enforce demand response agreements based on pre-negotiated consumer preferences, eliminating the need for continuous iterative negotiation. The system serves itself by automatically adjusting loads according to established agreements, significantly reducing response time while preserving consumer adaptability through the initial agreement process.
3Force
If load forecasts are used to anticipate demand response events, then proactive management is enabled, but forecast uncertainty deteriorates reliability
Solution Approach 1:
The system implements feedback mechanisms where actual load consumption data from smart meters is continuously monitored and compared against forecasted demand. This feedback loop allows the system to learn from forecast errors and adjust future forecasts, maintaining proactive management capability while improving reliability over time through data-driven corrections.
Data Source
AI summary
Systems and methods are provided for allocating and providing power to electric loads based on load classification information. A power system, such as a local electrical utility, may receive load demand requests from customers or loads wanting to consume power, and the system may allocate power to the loads based on load classifications of each of the loads. The system may then communicate the power allocations to the loads, and the loads may then consume power based on the power allocated to each of the loads. These improvements may be used to, for example, reduce the likelihood or frequency of demand response events in which power demand exceeds supply since the system may limit the total amount of power that is allocated at any given time.