Building HVAC Energy Cost Optimization with ELDR Program Participation
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Solution Overview
Problem
Current energy cost optimization systems for buildings lack efficient methods to participate in economic load demand response (ELDR) programs, failing to maximize revenue and minimize penalties by not optimally determining participation hours and electric load setpoints based on dynamic pricing and customer baseline loads.
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 participation hours by comparing predicted locational marginal prices and net benefit tests, and submitting bids to ELDR programs to maximize revenue and minimize costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the HVAC equipment operates without ELDR program participation optimization, then the operation is simple and straightforward, but the revenue from ELDR programs is not maximized and penalties are not minimized
Solution Approach 1:
The system enables the HVAC equipment to automatically participate in ELDR programs by self-generating cost functions, determining optimal electric load setpoints, and submitting bids without manual intervention. The controller autonomously optimizes participation hours and load adjustments to maximize revenue and minimize penalties, making the system self-sufficient in navigating complex demand response programs.
Solution Approach 2:
The system dynamically adjusts electric load setpoints as optimization parameters based on predicted locational marginal prices and customer baseline loads. By treating load setpoints as decision variables in the cost function, the system continuously optimizes operational parameters to achieve economic benefits from ELDR program participation.
2Productivity
If the system determines optimal electric load setpoints and participation hours using dynamic pricing and baseline loads, then revenue and penalties are optimized, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by predicting locational marginal prices and determining customer baseline loads before the actual ELDR program execution. The controller generates cost functions and determines optimal electric load setpoints in advance, allowing the HVAC equipment to be pre-configured for optimal ELDR participation, thereby simplifying real-time decision-making.
3Productivity
If the HVAC equipment adjusts electric load setpoints dynamically, then ELDR program revenue is maximized, but the control system complexity and automation requirements increase
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring predicted locational marginal prices, customer baseline loads, and actual HVAC equipment performance. The controller uses this feedback to dynamically adjust electric load setpoints and optimize cost functions, creating a closed-loop control system that automatically adapts to changing ELDR program conditions and maximizes revenue.
Data Source
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AI summary
An energy cost optimization system for a building includes HVAC equipment configured to operate in the building and a controller. The controller is configure to generate a cost function defining a cost of operating the HVAC equipment over an optimization period as a function of one or more electric loads for the HVAC equipment. The controller is further configured to generate participation hours. The participation hours indicate one or more hours that the HVAC equipment will participate in an economic load demand response (ELDR) program. The controller is further configured to generate an ELDR term based on the participation hours, the ELDR term indicating revenue generated by participating in the ELDR program. The controller is further configured to modify the cost function to include the ELDR term and perform an optimization using the modified cost function to determine an optimal electric load for each hour of the participation hours.