Demand Response Threshold Optimization for Utility Load Shedding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Utilities face challenges in accurately forecasting long-term electricity demand, leading to suboptimal invocation of demand response or curtailment events, as current methods rely on simple heuristic triggers that do not maximize economic load shedding or cost savings.
Innovation Solution
A method and system that calculate a demand response parameter threshold based on available events and opportunities, comparing current values to determine whether to invoke a demand response event, optimizing utility objectives such as cost savings and reliability by considering probability distributions of future variables like demand and market prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple heuristic triggers (temperature or reserve margin) are used to determine demand response events, then the system is easy to operate, but the economic gains and savings are not optimized
Solution Approach 1:
The patent transforms the demand response decision system from using simple heuristic parameters (temperature, reserve margin) to using dynamic threshold parameters calculated through stochastic optimization. The threshold is updated based on remaining events and opportunities, changing the parameter values adaptively to optimize economic outcomes while maintaining operational clarity through automated calculations.
Solution Approach 2:
The patent introduces an intermediary calculation layer (the stochastic optimization model and threshold calculation mechanism) between the simple triggers and the demand response decisions. This intermediary processes multiple variables including probability distributions of future variables, remaining events, and opportunities to produce optimized threshold values that balance ease of operation with economic optimization.
2Loss of energy
If demand response events are invoked frequently to maximize immediate savings, then cost savings increase, but the limited number of events is depleted faster, reducing future flexibility
Solution Approach 1:
The patent applies preliminary action by calculating and setting thresholds in advance that account for future opportunities and remaining events. The stochastic optimization model预先 determines the threshold values that balance immediate savings with future flexibility, so decisions are made based on pre-computed optimal thresholds rather than reactive frequent invocation.
Solution Approach 2:
The patent implements feedback by updating the threshold calculation based on the number of remaining events and remaining opportunities. As events are consumed or opportunities pass, the threshold dynamically adjusts to reflect the changed state, providing feedback that prevents over-depletion of events while maximizing savings across the entire planning horizon.
3Adaptability or versatility
If demand response events are invoked conservatively to preserve future opportunities, then future flexibility is maintained, but immediate cost savings are reduced
Solution Approach 1:
The patent uses parameter changes by dynamically adjusting the threshold based on the ratio of remaining events to remaining opportunities. When events are abundant relative to opportunities, the threshold is lower allowing more invocation. When events are scarce, the threshold rises to preserve flexibility, automatically balancing immediate savings against future adaptability.
Solution Approach 2:
The patent applies dynamics by making the threshold flexible and adaptive rather than fixed. The stochastic optimization model continuously updates thresholds based on the evolving state of remaining events and opportunities, allowing the system to dynamically adjust between conservative and aggressive invocation strategies based on current conditions.
Data Source
AI summary
A method and system for controlling demand events in a utility network with multiple customer sites. The value of a demand response parameter threshold for invoking a demand response event is calculated based on the number of available demand response events and the number of opportunities remaining to issue the available demand response events. This parameter represents the utility objectives for using the demand response program (e.g., cost savings, reliability, avoided costs). A current value of the demand response parameter is compared to the threshold value, and a determination is made whether or not to call a demand response event for the current opportunity, or to save the event for a future opportunity based upon this comparison.