Grid Control Optimization via Dynamic Load Simulation
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Solution Overview
Problem
Grid operators face challenges in managing electrical load effectively, particularly during periods of high energy consumption or intermittent renewable energy generation, as existing control strategies are not adaptive enough to real-time variations and may not efficiently allocate resources.
Innovation Solution
A grid management system that continuously updates control strategies for devices connected to the electrical grid using monitoring data, simulates multiple control scenarios, and optimizes device operations to achieve target load curves through an optimization procedure, ensuring efficient resource allocation and adaptability to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing control strategies are used for managing electrical load, then basic load management is maintained, but the system is not adaptive enough to real-time variations and cannot efficiently allocate resources
Solution Approach 1:
The control strategy is transformed from a static, pre-defined set of rules to a dynamic, continuously updated optimization-based strategy. The system periodically re-solves the optimization problem using current monitoring data to generate updated control strategies that adapt to real-time grid conditions, including varying renewable energy generation and load demands.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where monitoring data from the grid is continuously collected, fed into the optimization system, and used to update control strategies. The updated strategies are then applied to devices and their effectiveness is monitored, creating a continuous improvement cycle that enhances both adaptability and resource allocation efficiency.
2Productivity
If control strategies are updated continuously based on monitoring data, then adaptability and resource allocation efficiency are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system architecture is segmented into distinct functional modules: a monitoring data collection module, an optimization system module, and a control strategy implementation module. This segmentation allows each component to be developed, tested, and maintained independently, reducing overall system complexity despite the sophisticated optimization algorithms employed.
Solution Approach 2:
The optimization system acts as an intermediary layer between the monitoring data collection and the control strategy implementation. This intermediary processes the raw monitoring data, performs the complex optimization calculations, and translates the results into actionable control strategies, thereby managing computational complexity in a structured manner.
3Manufacturing precision
If multiple control scenarios are simulated and optimized, then the precision of control actions is improved, but the time and computational resources required increase
Solution Approach 1:
The optimization system evaluates multiple control scenarios and selects the optimal control strategy without requiring exhaustive simulation of all possible scenarios. By using optimization algorithms that can identify the best solution among many possibilities without evaluating each one completely, the system achieves high control action precision while limiting the time and computational resources required.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining controls to be applied to a population of devices. In one aspect, a method comprises: determining a plurality of simulated load curves, wherein each simulated load curve simulates load generated by a respective device from a population of devices based on application of a respective control from a set of controls; adjusting values of a plurality of weights during a plurality of optimization iterations using an optimization technique to optimize a loss function, wherein each weight corresponds to a respective simulated load curve, wherein the loss function measures: a sparsity of the values of the plurality of weights, and an error between: (i) an aggregate load curve that is defined by combining the simulated load curves in accordance with the values of the plurality of weights, and (ii) a target load curve.