AI Edge Energy Orchestration for Distributed Power Coordination
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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 strategic decision-making, edge computing devices for local optimization, and individual energy assets. This segmentation allows simplified centralized control while simultaneously enabling adaptability to distributed systems through autonomous edge intelligence.
Solution Approach 2:
The patent introduces a new dimensional layer (edge computing layer) between the centralized cloud and distributed energy assets. This additional dimension enables the system to maintain centralized oversight while simultaneously adapting to distributed configurations, resolving the contradiction by operating effectively in both centralized and distributed dimensions.
2Reliability
If legacy infrastructure is maintained, then existing energy delivery capability is preserved, but optimization of energy generation and consumption deteriorates
Solution Approach 1:
The patent introduces an AI-based edge computing intermediary that sits between legacy infrastructure and modern optimization requirements. This intermediary translates and coordinates between old and new systems, enabling legacy infrastructure to deliver energy reliably while simultaneously optimizing generation and consumption through intelligent intermediation.
Solution Approach 2:
The patent replaces mechanical/manual optimization processes in legacy systems with AI-driven automated optimization. Machine learning models and intelligent algorithms substitute for traditional manual control methods, enabling continuous optimization of energy generation and consumption while preserving the physical legacy infrastructure.
3Adaptability or versatility
If distributed energy systems are integrated, then adaptability and efficiency are improved, but system complexity increases
Solution Approach 1:
The patent creates a universal energy management platform that can operate in multiple modes (centralized, distributed, hybrid) and support diverse energy assets (generation, storage, consumption). This multi-functional design enables the system to integrate distributed energy systems with improved adaptability while managing complexity through a unified universal architecture rather than separate specialized systems.
Data Source
AI summary
Disclosed herein are AI-based platforms for enabling intelligent orchestration and management of power and energy. In various embodiments, a machine learning system is trained on a set of energy intelligence data and deployed on an edge device, wherein the machine learning system is configured to receive additional training by the edge device to improve energy management. In some embodiments, the energy management includes management of generation of energy by a set of distributed energy generation resources, management of storage of energy by a set of distributed energy storage resources management of delivery of energy by a set of distributed energy delivery resources, management of delivery of energy by a set of distributed energy delivery resources, and/or management of consumption of energy by a set of distributed energy consumption resources.