AI Energy Edge Platform With RPA for Distributed Resource Control
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Solution Overview
Problem
The energy market is transitioning from a centralized model to a decentralized one, requiring a platform that can manage and improve legacy infrastructure while coordinating with distributed energy systems.
Innovation Solution
An AI-based energy edge platform that incorporates emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability, using AI, IoT, and data processing technologies to manage energy generation, storage, delivery, and consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a centralized energy management model is used, then infrastructure control is simplified, but adaptability to distributed energy systems deteriorates
Solution Approach 1:
The patent segments the energy management system into multiple hierarchical levels: centralized cloud platform for high-level optimization, edge computing nodes for local processing, and distributed energy resources. This segmentation allows simplified centralized control while simultaneously enabling adaptability to distributed systems through autonomous edge intelligence.
Solution Approach 2:
The patent introduces a spatial dimension to energy management by deploying edge computing nodes at multiple geographic locations across the energy infrastructure. This multi-dimensional architecture enables localized decision-making at edge nodes while maintaining centralized coordination, thus resolving the contradiction between simplified control and distributed adaptability.
2Reliability
If legacy infrastructure is maintained, then existing energy delivery capabilities are preserved, but optimization of energy generation and consumption deteriorates
Solution Approach 1:
The patent introduces an AI-based edge computing platform as an intermediary layer between legacy infrastructure and modern distributed energy resources. This intermediary enables advanced optimization of energy generation and consumption through machine learning algorithms while maintaining compatibility with existing infrastructure through standardized interfaces and protocols.
Solution Approach 2:
The patent implements predictive analytics and demand forecasting capabilities at the edge computing nodes, allowing the system to perform preliminary optimization actions before peak demand periods. This enables proactive energy management that optimizes generation and consumption patterns while ensuring reliable delivery during critical periods.
3Adaptability or versatility
If distributed energy systems are integrated, then adaptability and efficiency are improved, but system complexity increases
Solution Approach 1:
The patent implements a universal edge computing platform that can interface with multiple types of distributed energy resources (solar panels, wind turbines, battery storage, electric vehicles) through standardized protocols. This multi-functional architecture enables broad ecosystem coordination without proportionally increasing system complexity, as the same platform infrastructure serves diverse energy assets.
4Productivity
If AI and data processing technologies are deployed, then energy optimization is improved, but data processing requirements and computational load increase
Solution Approach 1:
The patent implements edge computing nodes that perform preliminary data processing, filtering, and feature extraction locally at the source. This preliminary action reduces the volume and complexity of data that needs to be transmitted and processed centrally, thereby improving energy management efficiency while reducing overall computational energy consumption across the system.
Data Source
AI summary
An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a set of adaptive, autonomous data handling systems, wherein each of the adaptive, autonomous data handling systems is configured to collect data relating to energy generation, energy storage, energy delivery, and/or energy consumption, wherein the data is collected from a set of edge devices that are in operational control of a set of distributed energy resources; and a set of intelligent agents configured to, by robotic process automation, autonomously adjust, based on the collected data, a set of operational parameters for such operational control.