Virtual Power Plant Hub Routing With Geolocation Queue Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current virtual power plant environments lack efficient optimization methods that consider multi-dimensional variance and covariance, leading to inefficiencies, increased wear and tear on devices, and higher costs for retail consumers, while existing systems fail to integrate geolocation data and social networks for improved power aggregation.
Innovation Solution
Implement a multi-dimensional mapping database integrated with social networks, CPU/GPU devices, and price-time priority queues to optimize power consumption and generation across various dimensions, including latitude, longitude, sound, and weather, using CPU/GPU form factors like mobile devices and augmented reality tools to enhance user utility and reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rule-based power aggregation systems are used, then system simplicity is maintained, but power optimization efficiency deteriorates and leads to increased device wear and tear
Solution Approach 1:
The patent transforms rule-based power aggregation into a data-driven optimization system by changing the fundamental parameters from static rules to dynamic multi-dimensional analysis. The system incorporates variance and covariance calculations across multiple dimensions (geolocation, social networks, power consumption patterns) to dynamically optimize power aggregation, replacing simple rule-based decisions with sophisticated statistical optimization algorithms that consider multiple variables simultaneously.
Solution Approach 2:
The patent introduces multiple new dimensions to the power aggregation problem: geolocation data (latitude, longitude), social network connections, temporal patterns, and environmental factors. By adding these dimensions, the system moves from a one-dimensional rule-based approach to a multi-dimensional optimization framework, enabling more comprehensive power optimization while managing complexity through structured data organization and processing.
2Productivity
If virtual power plant systems are implemented without multi-dimensional optimization, then implementation simplicity is maintained, but power aggregation efficiency deteriorates and retail costs increase
Solution Approach 1:
The patent segments the virtual power plant system into distinct functional modules: data collection layer (geolocation, social networks, power metrics), processing layer (variance and covariance calculations, optimization algorithms), and execution layer (power aggregation control). This segmentation allows the complex multi-dimensional optimization to be implemented as manageable, independent components that can be developed, tested, and maintained separately, reducing overall system complexity while enabling advanced optimization capabilities.
3Measurement precision
If geolocation data and social networks are integrated into power optimization, then power aggregation accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent creates a universal data processing framework that handles multiple data types (geolocation coordinates, social network graphs, power consumption metrics, environmental data) through unified statistical methods. The variance and covariance calculation engine serves as a multi-functional tool that processes diverse data sources using consistent mathematical operations, enabling accurate power optimization across different data types without requiring separate processing pipelines for each data source.
Data Source
AI summary
Various implementations directed to price time priority queue for virtual power plant and connected grid aggregation are provided. In one implementation, a method may include receiving origin location data and destination location data. The method may also include generating nodal power networks based on the optimized origin location data and the destination location data. The method may further include determining virtual power plant hubs along the transmission route and network, where the virtual hubs include a first virtual hub based on the origin location data and a second virtual hub based on the destination location data. The method may additionally include receiving power device data for the geolocation exchange units. In addition, the method may include receiving market depth data for a geolocation exchange for the geolocation exchange units based on the virtual power plant nodal sequences.