Load reduction optimization
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Solution Overview
Problem
Grid operators face challenges in managing electrical load during peak energy consumption periods, as existing methods for load reduction, such as thermostat control, lack precision and effectiveness in achieving optimal load reduction while maintaining customer comfort.
Innovation Solution
A method of load reduction optimization that assigns control parameters to a population of energy consumption devices, using simulations to iterate through various control parameters and optimize them against a specific load reduction curve, incorporating energy consumption models and grid operator data to achieve an optimal load reduction event.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If thermostat control is used for load reduction, then energy consumption is reduced, but load reduction precision and effectiveness deteriorate
Solution Approach 1:
The patent applies parameter changes by using simulations to iteratively modify control parameters (such as temperature setpoints, cycling rates, and offset values) to optimize load reduction effectiveness. The system evaluates multiple parameter combinations against a target load reduction curve and selects the optimal parameters that achieve both energy reduction and precise load control, thereby resolving the contradiction between energy consumption reduction and load reduction precision.
2Loss of energy
If thermostat control is used for load reduction, then energy consumption is reduced, but customer comfort deteriorates
Solution Approach 1:
The system dynamically adjusts thermostat control parameters within optimized ranges that balance energy reduction with comfort maintenance. By simulating various control scenarios and selecting parameters that achieve the target load reduction curve, the system ensures energy savings are realized while maintaining customer comfort within acceptable thresholds.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system evaluates the effectiveness of control parameters against the target load reduction curve and customer comfort requirements. The simulation results provide feedback on whether the control parameters achieve both energy reduction and comfort maintenance, allowing for iterative optimization of the control strategy.
3Productivity
If multiple simulations are performed to optimize control parameters, then load reduction effectiveness is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary simulations offline to determine optimal control parameters before actual load reduction events. By pre-computing the optimal parameters through multiple simulations and storing them for future use, the system achieves high load reduction effectiveness without requiring complex real-time computational resources during actual peak demand events.
Solution Approach 2:
The patent uses simulation models that replicate the behavior of actual HVAC systems and thermal dynamics. These virtual copies allow for extensive parameter testing and optimization in a computational environment without affecting real systems, enabling thorough evaluation of control strategies while minimizing the computational burden on the actual control infrastructure.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing load reduction optimization. In one aspect, a method includes accessing load reduction parameters for a load reduction event, accessing energy consumption models for multiple systems involved in the load reduction event, and performing, based on the load reduction parameters and the energy consumption models, a plurality of simulations of load reduction events that simulate variations in control parameters used to control the multiple systems. The method also includes optimizing, against a load reduction curve, the load reduction event by iteratively modifying the control parameters used in the plurality of simulations of load reduction events, and outputting the optimal load reduction event with optimized control parameters.