Smart Grid Infrastructure Assessment via Cross-Layer Data Analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The smart grid infrastructure faces challenges in anticipating and modifying requirements for its layers, especially in hardware and software, to ensure performance and quality of service, due to increased complexity with prosumer devices that can both consume and produce energy, and the lack of integrated real-time data dissemination across distributed systems.
Innovation Solution
An infrastructure assessment system is integrated into the smart grid, collecting real-time data from prosumer devices, information processing layers, and enterprise services to assess the grid's health and deploy maintenance, using a receiver module, cockpit module, KPI monitor, historian, analytics module, simulator, decision support module, and management engine to manage and optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the smart grid integrates prosumer devices with dual roles (energy consumer and producer), then the system becomes more versatile and intelligent, but the complexity of the infrastructure increases
Solution Approach 1:
The patent segments the smart grid infrastructure into multiple hierarchical layers (physical layer, data link layer, network layer, application layer) to manage complexity. Each layer handles specific functions independently, allowing the system to accommodate versatile prosumer devices without overwhelming overall system complexity.
Solution Approach 2:
The patent creates universal communication protocols and data formats that work across different device types (consumers, producers, prosumers). This universal approach allows the infrastructure to handle diverse devices through standardized interfaces, maintaining versatility while simplifying integration complexity.
2Loss of information
If real-time monitoring of distributed smart metering points is implemented, then near real-time data becomes available for decision making, but the difficulty of detecting and measuring system state increases
Solution Approach 1:
The patent introduces intermediary components such as gateway devices and aggregation points that collect data from distributed smart metering points and translate it into standardized formats. These intermediaries simplify the detection and measurement processes by handling data normalization and preliminary processing before data reaches central monitoring systems.
Solution Approach 2:
The patent creates virtual representations and models of the physical grid infrastructure that mirror the actual system state. These digital twins or virtual models allow monitoring and analysis without directly complicating the physical detection and measurement processes, as changes in the physical system are replicated in the virtual model for easier analysis.
3Reliability
If the infrastructure is modified on the fly to guarantee performance constraints, then quality of service is maintained, but the device complexity and difficulty of management increase
Solution Approach 1:
The patent implements preliminary configuration and planning phases where performance constraints and service level agreements are defined before deployment. This allows the system to pre-calculate resource allocation and modification strategies, enabling on-the-fly adjustments without requiring complex real-time decision-making processes.
Solution Approach 2:
The patent establishes feedback loops that continuously monitor system performance against predefined constraints. When performance degradation is detected, automated feedback mechanisms trigger pre-planned modification strategies, reducing the need for complex real-time human intervention and simplifying management while maintaining quality of service.
Data Source
AI summary
An infrastructure assessment system integrates with a smart grid infrastructure at all layers of the infrastructure. Data may be collected across layers. Performance metrics may be monitored and simulations may be performed. Action items may be decided upon based on actual behavior of the infrastructure determined from the collected data and on predicted behavior from simulations of the infrastructure. The action items may then be dispatched to be performed on the infrastructure. The effect of the management actions can then be “acquired” by the system via detailed monitoring and can be used, for example, to measure the effectiveness of the decisions or recalibration of the whole system.