Building Energy Optimization System for Capacity Market Planning
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Solution Overview
Problem
Current systems lack an efficient method to determine the optimal nominated capacity value for participating in capacity market programs, which are essential for energy load reduction and grid management, often relying on outdated or inefficient optimization techniques.
Innovation Solution
A method and system that utilize a processing circuit to generate an objective function with a nominated capacity term, optimizing it to determine the optimal curtailment value for load reduction, considering various constraints, scenarios, and probabilities to ensure effective participation in capacity market programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optimization methods are used to determine nominated capacity value, then the system can participate in capacity market programs, but the optimization efficiency is low and accuracy is insufficient
Solution Approach 1:
The system transforms the optimization problem by changing parameters: it converts the traditional approach of optimizing for maximum revenue into a constrained optimization problem where the nominated capacity value is set as a fixed parameter, and the system optimizes for minimum cost to achieve that capacity reduction. This parameter transformation enables more accurate and efficient determination of the optimal nominated capacity value.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing baseline energy consumption data, equipment operational characteristics, and cost parameters before the optimization process. This preliminary data preparation and system configuration enables faster real-time optimization computations while maintaining high accuracy in determining the optimal nominated capacity value.
2Productivity
If the system reduces load by optimal nominated capacity value to participate in CMP, then revenue is generated and utility demands are met, but energy management complexity increases
Solution Approach 1:
The system introduces an intermediary optimization module that acts as a mediator between the utility company's demand signals and the building's energy management systems. This intermediary layer translates utility requirements into optimized load reduction strategies, automatically determining which equipment to adjust and by how much, thereby generating CMP revenue while managing system complexity through centralized intelligent coordination.
Solution Approach 2:
The system implements feedback mechanisms where the optimization results, equipment responses, and actual load reduction outcomes are continuously monitored and fed back into the optimization model. This feedback loop enables the system to learn from past performance, refine its optimization strategies, and improve revenue generation while adapting to changing building conditions and utility requirements.
Data Source
AI summary
A method for generating an optimal nominated capacity value for participation in a capacity market program (CMP) includes generating, by a processing circuit, an objective function comprising a nominated capacity term, wherein the nominated capacity term indicates a nominated capacity value, wherein the nominated capacity value is a curtailment value that a facility is on standby to reduce its load by in response to receiving a dispatch from a utility. The method includes optimizing, by the processing circuit, the objective function to determine the optimal nominated capacity value for a program operating period and transmitting, by the processing circuit, the optimal nominated capacity value to one or more systems associated with the CMP to participate in the CMP.