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 efficiency in achieving optimal load reduction curves while maintaining customer comfort.
Innovation Solution
A method of load reduction optimization that assigns control parameters to a population of energy consumption devices through simulations, optimizing control strategies against a specific load reduction curve by iteratively modifying parameters, including weighted values for load objectives like total reduction, shed drift, and noise, using techniques like parallel interacting simulations and annealing optimizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If thermostat control is deployed using methods of thermostat cycling and/or temperature offset, then load reduction can be achieved during peak energy consumption periods, but the control lacks precision and efficiency in achieving optimal load reduction curves
Solution Approach 1:
The system performs multiple simulations before actual load reduction events to pre-determine optimal control parameters. By conducting simulations in advance that model various thermostat control strategies against target load reduction curves, the system identifies the most effective control settings before peak demand occurs, enabling precise control when needed without trial-and-error during actual events.
Solution Approach 2:
The system uses simulation results as feedback to iteratively refine control parameter selections. By comparing simulated load reduction outcomes against target curves and using this information to adjust parameters for subsequent simulations, the system converges on optimal control settings that achieve precise load reduction while maintaining customer comfort constraints.
2Measurement precision
If multiple simulations are performed to iterate through various control parameters, then optimal control strategy can be achieved, but computational complexity and time increase
Solution Approach 1:
The simulation process is divided into separate, independent simulations that can be executed in parallel. Rather than performing one comprehensive sequential simulation, the system segments the analysis into multiple focused simulations, each evaluating specific control parameter combinations, allowing computational tasks to be distributed and executed concurrently to reduce total computation time.
Solution Approach 2:
The system systematically varies control parameters across simulations to efficiently explore the parameter space. By changing parameters methodically between simulations rather than exhaustively testing all combinations, the system identifies optimal settings with fewer simulation runs, reducing computational time while maintaining optimization accuracy.
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.