Demand Response Incentive Signal Modulation
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Solution Overview
Problem
Automated demand response programs face challenges in optimizing the selection of demand response resources during events, particularly in minimizing overall costs while ensuring capabilities are met, and in accurately predicting load responses due to the unpredictability of demand response resources.
Innovation Solution
The system employs a scoring function to rank demand response resources based on their attributes, including load consumption capabilities and costs, and uses a demand response management system to dynamically modulate incentive signals to retain participants in events, rather than adjusting the number of participants, and restricts DR signals to predefined finite values for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of participants in demand response events is increased to ensure capability fulfillment, then reliability is improved, but device complexity increases due to managing more participants and their attributes
Solution Approach 1:
The patent transforms the management approach from tracking individual participant attributes to using aggregated scoring values. Each resource is assigned a score based on multiple attributes (load consumption capability, cost, availability), and the system manages these scored resources rather than individual attributes, simplifying the complexity while ensuring capability fulfillment through score-based selection
Solution Approach 2:
The patent creates an automated scoring and selection system that replicates the manual resource evaluation process. The scoring function automatically evaluates and ranks resources based on predefined criteria, eliminating the need for manual assessment of each participant's attributes and reducing management complexity
2Ease of operation
If automated demand response programs are implemented to reduce operational complexity, then ease of operation is improved, but measurement precision deteriorates in predicting load responses
Solution Approach 1:
The patent incorporates feedback mechanisms where the scoring function is continuously refined based on actual load response data from demand response events. Historical performance data feeds back into the scoring model, improving prediction accuracy over time while maintaining automated operation. The system learns from past events to better predict future responses
Solution Approach 2:
The patent performs preliminary scoring and ranking of demand response resources before events occur. By pre-evaluating resources based on their attributes and historical performance, the system prepares optimized participant selections in advance, improving both automation efficiency and prediction accuracy for upcoming events
3Loss of energy
If demand response resources are optimized for cost minimization, then loss of energy is reduced, but reliability worsens due to selecting lower-cost resources with potentially inferior capabilities
Solution Approach 1:
The patent transforms the cost-capability trade-off into a multi-dimensional scoring problem. Instead of selecting resources based on single criteria (cost or capability), the system evaluates multiple parameters simultaneously (load consumption capability, cost, availability, historical performance) and assigns composite scores. This allows optimization of the overall objective function that balances both cost and capability requirements
Solution Approach 2:
The patent implements dynamic resource selection where the optimal participant set is determined based on real-time conditions and event-specific requirements. The scoring function can be adjusted to emphasize different parameters depending on the event objectives, allowing flexible optimization that adapts to changing conditions while balancing cost and capability
Data Source
AI summary
An approach for influencing demand response event performance through a variable incentive signal. Automated demand response programs may achieve an energy demand reduction by signaling participating consumers to curtail energy usage for a certain period of time, referred to as an “event”. Customers may be free to “opt-out” and withdraw their participation from DR events, on a per-event basis. When a participant opts out, the quantity of energy savings of the event may be reduced. Participating customers may be sent a message offering an incentive to tolerate an ongoing DR event. As the event progresses, the DR operator may dynamically monitor and modulate the rate of opt-outs. The present approach may be different in that instead of modulating the number of participants that are included in the event, it may modulate an incentive signal to keep already-included participants from opting out.