Dynamic Contingency Avoidance System for Smart Grid Resource Allocation
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Solution Overview
Problem
Infrastructure-based businesses such as water, electric, and gas companies face increased complexity in allocating resources due to shifting supply and consumption patterns, technological advancements, and the integration of distributed energy sources, leading to a need for more intelligent management systems to prevent disruptions and ensure efficient distribution.
Innovation Solution
The Dynamic Contingency Avoidance and Mitigation System (DCAMS) combines reliability analysis and machine learning to predict potential disruptions, manage resource allocation, and implement preventative measures, using predictive models and real-time data to optimize the distribution of resources like electricity from various sources, including alternative energy, and curtailable loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional centralized power generation and distribution is used, then infrastructure simplicity is maintained, but system reliability and adaptability to distributed energy sources deteriorate
Solution Approach 1:
The patent segments the monolithic centralized control system into distributed intelligence units embedded throughout the grid. Each unit independently analyzes local conditions and makes decisions, while contributing to overall system coordination. This segmentation enables the system to handle distributed energy sources while maintaining manageable complexity at each node.
Solution Approach 2:
The control system transitions from static, pre-programmed responses to dynamic, adaptive decision-making using real-time data from sensors and machine learning algorithms. The system continuously learns from new data patterns and adjusts its behavior accordingly, enabling it to adapt to the variable nature of distributed energy sources and changing grid conditions.
2Reliability
If real-time monitoring and prediction systems are implemented, then disruption prediction capability is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary analysis of historical data and established patterns to create baseline models of normal operations and known failure modes. By pre-processing and pre-categorizing data during off-peak periods, the system reduces the computational burden during real-time operation, focusing only on detecting deviations from established patterns rather than analyzing all raw data from scratch.
Solution Approach 2:
The patent replaces traditional mechanical and rule-based monitoring systems with machine learning algorithms that automatically identify patterns and anomalies. This substitution enables the system to handle complex, multi-dimensional data from numerous sensors without requiring proportional increases in computational infrastructure, as the algorithms efficiently learn and adapt to data patterns.
3Productivity
If granular resource allocation based on multiple sources and sinks is implemented, then resource distribution efficiency is improved, but control system complexity increases
Solution Approach 1:
The control system is designed with universal, modular components that can handle multiple types of energy sources and sinks through standardized interfaces and protocols. This multi-functionality allows the same control architecture to manage diverse resources (solar, wind, traditional generation) and various sinks (residential, commercial, industrial) without requiring separate specialized systems for each, thereby improving efficiency while containing complexity.
Data Source
AI summary
The disclosed subject matter provides systems and methods for allocating resources within an infrastructure, such as an electrical grid, in response to changes to inputs and output demands on the infrastructure, such as energy sources and sinks. A disclosed system includes one or more processors, each having respective communication interfaces to receive data from the infrastructure, the data comprising infrastructure network data, one or more software applications, operatively coupled to and at least partially controlling the one or more processors, to process and characterize the infrastructure network data; and a display, coupled to the one or more processors, for visually presenting a depiction of at least a portion of the infrastructure including any changes in condition thereof, and one or more controllers in communication with the one or more processors, to manage processing of the resource, wherein the resource is obtained and/or distributed based on the characterization of the real time infrastructure data.