Load Reduction Optimization Using Simulation-Based Thermostat Control
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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.
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, using energy consumption models and weighted objectives to achieve optimal load reduction while maintaining customer comfort and fairness.
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 and their impacts on load reduction curves, the system establishes optimized control settings before peak demand periods occur, thereby achieving both precision and efficiency without real-time trial and error
Solution Approach 2:
The system uses simulation results as feedback to iteratively refine control parameters. By analyzing simulated load reduction outcomes and using this information to adjust control strategies in subsequent simulations, the system converges on optimal parameters that precisely achieve target load reduction curves while maintaining high efficiency
2Measurement precision
If multiple simulations are performed to optimize control parameters, then precision and efficiency of load reduction are improved, but computational complexity and time requirements increase
Solution Approach 1:
The simulation process is divided into separate, independent simulation runs that can be executed in parallel or staged sequences. By segmenting the optimization process into discrete simulation tasks that evaluate specific control parameter sets, the system achieves comprehensive optimization precision while allowing flexible resource allocation and time management across multiple smaller computational units
Solution Approach 2:
The system performs a sufficient number of simulations to achieve the desired optimization precision without conducting exhaustive simulations of all possible parameter combinations. By identifying and executing only the critical simulations that provide the most significant optimization insights, the system achieves effective precision while minimizing unnecessary computational time expenditure
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.