Autonomous Grid Management System for Dynamic Load Control
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Solution Overview
Problem
Grid operators face challenges in managing electrical load on their grids, particularly during periods of high energy consumption or intermittent renewable energy generation, as existing control strategies are not efficient in dynamically adjusting device operations to match target load curves.
Innovation Solution
A grid management system that continuously monitors and updates control strategies for devices connected to the grid by simulating multiple control scenarios and optimizing device operations to achieve target load curves, using an optimization system to determine the best controls to apply based on current monitoring data and objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed control strategies are used for devices connected to the grid, then device operations remain stable and predictable, but the system cannot dynamically adjust to match target load curves during periods of high energy consumption or intermittent renewable generation
Solution Approach 1:
The control strategy transitions from a fixed static approach to a dynamic adaptive system that continuously monitors grid conditions and automatically adjusts device operations. The system updates control strategies at regular intervals or in response to triggering events, enabling real-time adaptation to changing load curves and renewable generation patterns while maintaining manageable complexity through automated decision-making algorithms.
Solution Approach 2:
The system implements continuous feedback loops where monitoring data from grid operations is fed back into the control strategy optimization process. By simulating device operations under multiple possible control strategies and comparing outcomes against target load curves, the system identifies optimal control actions and updates strategies accordingly, creating a closed-loop adaptive control mechanism.
2Productivity
If control strategies are continuously updated based on monitoring data, then the system can efficiently match target load curves and allocate energy resources, but the computational complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary simulations of device operations under multiple possible control strategies before implementing actual control actions. By pre-evaluating potential outcomes and identifying optimal strategies through simulation, the system reduces the complexity of real-time decision-making and enables efficient resource allocation without requiring overly complex online optimization computations.
Solution Approach 2:
The system creates simulated copies of device operations and control scenarios to evaluate different control strategies without affecting actual grid operations. By working with simulated data and virtual models rather than directly manipulating complex real-time systems, the optimization process becomes more manageable while still producing effective control strategies for actual device operations.
3Measurement precision
If multiple control scenarios are simulated and optimized, then the best control strategy can be identified to satisfy objectives, but the computational time and processing resources required increase
Solution Approach 1:
The system evaluates multiple control strategies through simulation but implements only the single optimal strategy identified from these simulations. By performing comprehensive simulations of multiple scenarios to ensure accuracy, then selecting and implementing only the best strategy, the system achieves high optimization precision without requiring continuous updates of all simulated strategies, thus reducing the time loss associated with excessive computational processing.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing an electrical grid by controlling devices connected to the electrical grid. In one aspect, a method comprises: autonomously determining whether a control strategy update criterion is satisfied, and in response to determining that the control strategy update criterion is satisfied: identifying: (i) a plurality of devices connected to the electrical grid, and (ii) one or more objectives to be satisfied by the plurality of devices; generating, for the plurality of devices and for a plurality of controls, simulated data that simulates operation of the plurality of devices based on application of the plurality of controls; and identifying, by an optimization procedure and based on at least the simulated data, a respective control to be applied to each of the plurality of devices to satisfy the objectives.