| 摘要 |
In a renewable energy-based electricity grid infrastructure, distributed data analytics enable modeling and delivery of an appropriate, time-sensitive, dynamic demand response from multiple renewable energy resource components to an intelligent power distribution network. Distributed data analytics also enable the electricity grid infrastructure to virtually, optimally and adaptively make decisions about power production, distribution, and consumption so that a demand response is a dynamic reaction across the electricity grid infrastructure in a distributed energy generation from multiple renewable energy resources responsive to various types of grid demand situations, such as customer demand, direct current-specific demand, and security issues, and so that power production is substantially balanced with power consumption. |
| 主权项 |
1. A method of virtualizing dynamic demand response in a renewable energy-based electricity grid infrastructure, comprising:
identifying a power requirement over a specified power of time of one or more microgrids among a plurality of microgrids forming an intelligent power distribution network, each microgrid capable of being separately decoupled and separately generating and communicating data defining the power requirement; aggregating the data defining the power requirement and additional input data for a plurality of mathematical modeling functions from across different portions of a plurality of remote interconnected computing networks in a shared, secure and privately-hosted computing environment forming a distributed computing infrastructure, wherein at least a portion of the additional input data for the plurality of mathematical modeling functions is requested from one or more external computing networks; analyzing, across the different portions of the plurality of remote interconnected computing networks in the distributed computing infrastructure, the aggregated data defining the power requirement with the additional input data, within the plurality of mathematical modeling functions configured to continuously model a response to the power requirement with at least one of modeling commodity pricing of one or more renewable energy resources, modeling meteorological conditions, modeling power usage patterns of the one or more microgrids, modeling a power production capacity of multiple renewable energy resource components configured on a multi-resource offshore renewable energy installation, and modeling a load capacity of a power transmission system, and to generate one or more resulting data sets relative to the response to the power requirement; adapting the one or more resulting data sets relative to the response to the power requirement to data representative of real-time conditions within an electricity grid infrastructure communicated by the intelligent power distribution network, the multi-resource offshore renewable energy installation, and the power transmission system to develop a dynamic demand response that includes a production, transmission and distribution of power optimized to real-time conditions within the renewable energy-based electricity grid infrastructure; and delivering the dynamic demand response that includes the production, transmission, and distribution of power from the multiple renewable energy resource components configured on the multi-resource offshore renewable energy installation to the intelligent power distribution network, the distributed computing infrastructure configured to securely communicate, manage, store and process the data defining the power requirement and additional input data for the plurality of mathematical modeling functions so that the mathematical modeling functions are performed across the different portions of the plurality of remote interconnected computing networks to utilize an efficient allocation of available computing resources to minimize weakness points in the renewable energy-based electricity grid infrastructure, to enable real-time scalability and flexibility of a dynamic demand response to the power requirement in response to the real-time conditions, and to facilitate a secure delivery of the dynamic demand response to the power requirement from the multiple renewable energy resource components configured on the multi-resource offshore renewable energy installation to the intelligent power distribution network. |
