Building Energy Optimization with Dynamic ELDR Participation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current energy cost optimization systems for buildings lack effective methods to maximize revenue from incentive-based demand response programs, particularly in economic load demand response (ELDR), as they fail to optimally determine participation hours and adjust electric load setpoints to balance costs and incentives.
Innovation Solution
An energy cost optimization system that includes a controller configured to generate a cost function incorporating ELDR terms, determining optimal electric load setpoints, and generating participation hours to maximize revenue by participating in ELDR programs, with features such as bid generation for incentive programs and adjustment based on locational marginal prices (LMP) and customer baseline loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the HVAC equipment operates at baseline load during ELDR participation hours, then the customer receives full compensation from the RTO/ISO, but the customer fails to maximize revenue opportunities when electricity prices are low
Solution Approach 1:
The system dynamically adjusts the HVAC equipment operation schedule based on real-time electricity price signals (LMPs) and thermal comfort constraints, transitioning from static baseline operation to adaptive optimization. The controller modifies setpoints and operation timing to capture revenue opportunities when prices are low while maintaining comfort, thereby resolving the contradiction between reliable participation and revenue maximization.
Solution Approach 2:
The system changes operational parameters (temperature setpoints, operation timing, load levels) based on economic conditions and thermal constraints. By adjusting these parameters dynamically, the system achieves both reliable ELDR participation and optimized revenue generation, overcoming the fixed baseline operation limitation.
2Productivity
If the customer reduces electric load below baseline during ELDR hours, then the customer increases revenue potential, but the customer risks thermal comfort violations and equipment performance degradation
Solution Approach 1:
The system performs preliminary thermal energy storage or pre-cooling/pre-heating actions before ELDR participation hours begin. By storing thermal energy in advance, the system can reduce load during ELDR hours without violating comfort constraints, thus achieving both revenue increase and comfort maintenance.
Solution Approach 2:
The system establishes thermal cushions (energy storage buffers) before ELDR events to protect against comfort violations. This cushioning allows aggressive load reduction during ELDR hours while maintaining comfort, resolving the contradiction between revenue maximization and comfort reliability.
3Productivity
If the system optimizes for maximum revenue by adjusting load setpoints, then the ELDR revenue increases, but the system complexity and computational requirements increase
Solution Approach 1:
The system implements feedback loops that monitor electricity prices, thermal conditions, and equipment status in real-time, continuously adjusting operations to maximize revenue. This feedback mechanism automates the optimization process, managing complexity through closed-loop control rather than requiring overly sophisticated open-loop algorithms.
Solution Approach 2:
The optimization system performs self-adjustment based on pre-programmed algorithms and real-time data, automatically determining optimal setpoints and schedules without extensive external intervention. This self-service capability reduces operational complexity while maintaining high revenue optimization performance.
4Productivity
If the HVAC equipment operates flexibly to capture low-price electricity opportunities, then the revenue optimization improves, but the control and coordination requirements increase
Solution Approach 1:
The controller is designed as a multi-functional device that simultaneously handles ELDR participation, real-time optimization, thermal comfort monitoring, and equipment coordination. By consolidating these functions into a single universal controller, the system achieves flexible operation without proportionally increasing control complexity.
Data Source
AI summary
An energy optimization system for a building includes a processing circuit configured to provide a first bid including one or more first participation hours and a first load reduction amount for each of the one or more first participation hours to a computing system. The processing circuit is configured to operate one or more pieces of building equipment based on one or more first equipment loads and receive one or more awarded or rejected participation hours from the computing system responsive to the first bid. The processing circuit is configured to generate one or more second participation hours, a second load reduction amount for each of the one or more second participation hours, and one or more second equipment loads based on the one or more awarded or rejected participation hours and operate the one or more pieces of building equipment based on the one or more second equipment loads.