Demand Response Assessment Using Real-Time LMP Data
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Solution Overview
Problem
Current demand response systems rely on outdated data for issuing demand response events, often issued a day or more in advance, which may not accurately reflect real-time energy demands, leading to inefficiencies in energy curtailment and profitability optimization.
Innovation Solution
A method for real-time demand response assessment that reads energy economics data, estimates real-time energy demand, determines demand imbalances, and calculates the cost of demand response events to optimize energy curtailment requests, using day-ahead and real-time locational marginal pricing data to select an optimal curtailment amount.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If demand response events are issued a day or more in advance based on available data, then customers have sufficient time to coordinate curtailment, but the accuracy of real-time energy demand assessment deteriorates
Solution Approach 1:
The system performs preliminary actions by issuing DR events in advance to give customers sufficient time to coordinate curtailment, while simultaneously performing real-time assessment to maintain accuracy. This resolves the contradiction by layering preliminary scheduling with real-time validation.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring real-time energy demand and comparing it against forecasted demand. This feedback loop allows the system to assess the accuracy of preliminary DR event planning and make real-time adjustments, thus maintaining measurement precision even when events are issued in advance.
2Ease of manufacture
If demand response events are based on forecasted data available before issuance, then event planning can be done in advance, but the reliability of energy curtailment optimization deteriorates
Solution Approach 1:
The system transitions from static, forecast-only planning to a dynamic approach that continuously updates assessment based on real-time data. The DR event planning starts with forecasts but is dynamically adjusted using real-time energy demand measurements, ensuring both advance planning capability and optimization reliability.
Solution Approach 2:
The system changes the temporal parameter of data usage by incorporating both historical/forecasted parameters and real-time parameters. This allows the system to maintain ease of advance planning while improving reliability through real-time parameter updates that reflect actual energy demand conditions.
3Measurement precision
If real-time data is used for demand response assessment, then energy curtailment accuracy is improved, but the complexity of data processing increases
Solution Approach 1:
The system introduces an intermediary assessment layer that processes real-time data between the raw data sources and the DR event execution. This intermediary component aggregates and analyzes real-time energy demand data, simplifying the overall processing complexity while maintaining high measurement precision through structured data intermediation.
4Ease of operation
If demand response events are issued in advance, then customer coordination is facilitated, but the ability to respond to actual peak demand conditions deteriorates
Solution Approach 1:
The system maintains continuity by combining advance DR event issuance with continuous real-time assessment. Customers receive advance notice for easy coordination, while the system continuously monitors actual energy demand to ensure peak demand response effectiveness, creating an unbroken chain from planning to execution.
Data Source
AI summary
A method of demand response (DR) assessment may include reading energy economics data from a third party for an interval of a DR event period. The energy economics data may include a day-ahead demand, a day-ahead locational marginal pricing (LMP), and a real-time LMP. The method may also include estimating a real-time energy demand for the interval. Based on the energy economics data and the estimated real-time energy demand, the method may also include determining a demand imbalance for the interval. The method may also include calculating a cost of a DR event that exploits the demand imbalance. The method may also include selecting an energy curtailment request amount for the interval that optimizes profitability of the DR event.