Demand Response Incentive Optimization Using Building Thermal Models
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Solution Overview
Problem
Utilities face challenges in identifying optimal incentives for demand response programs that balance their need to minimize costs with building constraints, as existing methods fail to account for individual building requirements and constraints, particularly in HVAC systems, leading to inefficiencies in demand reduction and revenue loss.
Innovation Solution
A processor-implemented method and system that estimate demand response potential (DRP) by modeling baseline and DR energy consumption using dynamic thermal and HVAC models, allowing for the determination of optimal incentives based on cost criteria and constraints, such as thermal comfort and systemic inertia, to achieve target demand reductions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If utilities offer higher incentives for demand response, then demand reduction is improved, but utility revenue is worsened
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting incentive levels based on building-specific characteristics (thermal mass, HVAC system properties, occupancy patterns) and external conditions (ambient temperature, humidity, outdoor weather). This allows the utility to optimize the incentive parameter for each building individually, achieving maximum demand reduction at minimum cost by tailoring incentives to the specific thermal and operational parameters of each facility.
Solution Approach 2:
The patent implements local quality by recognizing that different buildings have different demand response potentials and constraints. Instead of applying a uniform incentive structure, the system determines building-specific optimal incentives based on local characteristics such as thermal mass, HVAC system efficiency, and occupancy patterns. This localized approach ensures that each building receives the appropriate incentive level to achieve demand reduction without wasting utility revenue on buildings with low DR potential.
2Loss of energy
If utilities minimize incentive costs, then utility revenue is improved, but demand reduction is worsened
Solution Approach 1:
The patent employs feedback mechanisms by continuously monitoring building energy consumption, thermal conditions, and HVAC operations to verify that demand reduction is actually achieved. The system uses this feedback to refine future incentive determinations, ensuring that incentives are only awarded when they result in genuine demand reduction. This feedback loop prevents utility revenue loss by adjusting incentive levels based on actual performance data.
Solution Approach 2:
The patent replaces traditional mechanical approaches to demand response (simple load curtailment) with a thermal-based approach that leverages building thermal mass and HVAC system characteristics. By substituting the mechanical control of electricity consumption with a thermal management strategy, the system achieves demand reduction through natural thermal processes rather than direct electrical load control, thereby reducing the need for high incentive costs.
3Reliability
If building constraints such as thermal comfort are enforced, then building operation quality is improved, but demand response flexibility is worsened
Solution Approach 1:
The patent applies preliminary action by pre-cooling or pre-heating building spaces during off-peak hours when demand response participation is more flexible. The system uses the building's thermal mass to store cooling capacity before peak demand periods, allowing the HVAC system to reduce or suspend operation during critical periods while maintaining thermal comfort. This preliminary thermal conditioning creates a buffer that enables demand response participation without compromising building operation quality.
Solution Approach 2:
The patent implements dynamics by making the HVAC system operation adaptive and flexible rather than static. The system dynamically adjusts HVAC setpoints, runtime, and capacity modulation based on real-time conditions including outdoor weather, indoor thermal state, and demand response program requirements. This dynamic control allows the building to flexibly respond to utility incentives while maintaining thermal comfort through continuous adaptation to changing conditions.
Data Source
AI summary
Electrical utilities offer incentives to customers to reduce consumption during periods of demand-supply mismatch. A building's participation in demand response (DR) depends both on its ability (due to building constraints), and its willingness (a function of incentive) to reduce electricity. Customers prefer a large incentive whereas a utility would want to minimize the revenue outflow to achieve a target reduction. Systems and methods of the present disclosure identify optimal incentive from the utility's perspective reflecting this trade-off. A model is built to estimate the demand response potential (DRP) of a building for a given incentive offered by the utility. The models for individual buildings are used to characterize the behavior of an ensemble of buildings. The utility may then decide optimum incentives that should be offered to achieve a target DR, using the associated DRP.