AI Energy Edge Orchestration for Distributed Grid 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 facilitates management and improvement of legacy infrastructure while coordinating with distributed systems, including energy generation, storage, and consumption.
Innovation Solution
An AI-based energy edge platform that integrates advanced energy resources, systems, and technologies to enable intelligent orchestration, optimization, and automation, utilizing AI, IoT, blockchain, and smart contracts for efficient energy management and transaction enablement across decentralized networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a centralized energy management model is used, then infrastructure control is simplified, but system flexibility and adaptability to decentralized energy resources deteriorates
Solution Approach 1:
The patent segments the energy management system into multiple hierarchical levels: centralized cloud platform for high-level coordination, edge computing nodes for local processing, and distributed energy resources. This segmentation allows simplified centralized control while maintaining flexibility at the edge through autonomous decision-making capabilities at each level.
Solution Approach 2:
The patent introduces a new dimensional approach by implementing edge computing capabilities that operate independently from the centralized cloud. This creates a multi-dimensional architecture where decisions can be made at different levels (cloud vs. edge) based on requirements, thereby maintaining centralized simplicity while adding decentralized flexibility.
2Loss of energy
If legacy energy infrastructure is maintained, then infrastructure investment is protected, but integration with new distributed energy systems becomes more difficult
Solution Approach 1:
The patent introduces an edge computing intermediary layer that sits between legacy infrastructure and new distributed energy systems. This intermediary translates and coordinates communications between old and new systems, enabling integration without requiring replacement of existing infrastructure while maintaining investment protection.
Solution Approach 2:
The edge computing platform provides universal interoperability capabilities that work with multiple types of energy systems simultaneously - both legacy centralized infrastructure and new distributed resources. This multi-functionality allows a single platform to handle diverse integration requirements without specialized systems for each scenario.
3Device complexity
If manual processes are used for energy transactions, then system simplicity is maintained, but transaction efficiency and automation capability deteriorates
Solution Approach 1:
The patent implements self-service automation through smart contracts and autonomous agents that automatically execute energy transactions without manual intervention. The system autonomously matches energy supply and demand, negotiates terms, and executes transactions, thereby dramatically improving efficiency while the underlying platform maintains simplicity through standardized protocols.
Solution Approach 2:
The patent replaces manual mechanical processes with automated digital systems. Smart contracts on blockchain networks automatically execute transactions based on predefined conditions, substituting human-operated mechanical processes with automated computational systems that operate faster and without error.
4Adaptability or versatility
If distributed energy resources are integrated, then system versatility and renewable energy adoption improve, but grid stability and control complexity worsens
Solution Approach 1:
The patent implements real-time feedback loops where edge computing nodes continuously monitor grid conditions and automatically adjust distributed energy resource operations. This feedback mechanism maintains grid stability by detecting and responding to fluctuations in frequency, voltage, and load, thereby managing control complexity through automated closed-loop control.
Solution Approach 2:
The system performs preliminary actions by pre-coordinating distributed energy resources through the edge platform before grid disturbances occur. This includes pre-scheduling energy generation, storage charging/discharging, and load management to prevent instability, thereby maintaining reliability while supporting high levels of distributed resource integration.
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.