Self-Configurable Node Controllers for Micro-Grid Energy Management
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Solution Overview
Problem
Conventional smart grid solutions fail to create a fully integrated, dynamic, and self-configurable local energy grid where nodes can simultaneously act as energy generators, storage units, and consumers, leading to inefficiencies in energy management and transfer, particularly with renewable energy sources like photovoltaic solar power.
Innovation Solution
A local grid architecture with self-configurable node controllers that optimize energy transfers and manage generation, storage, and consumption dynamically, enabling bidirectional communication and adaptive configuration to balance energy demands and supply, reducing reliance on external grids and enhancing energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional smart grid solutions are implemented, then energy management and control are improved, but full integration and self-configuration of nodes as simultaneous generators, storers, and consumers is not achieved
Solution Approach 1:
The node controller is designed to dynamically adapt its configuration and operation mode based on real-time conditions. Nodes can switch between different roles (generator, storer, consumer, or any combination) and automatically reconfigure their internal components according to system needs and available resources, enabling full versatility while maintaining automated management.
Solution Approach 2:
Each node is equipped with an intelligent controller that autonomously manages its own operation, configuration, and role assignment without requiring centralized micromanagement. The controller self-adjusts parameters, monitors component status, and makes real-time decisions about energy generation, storage, and consumption based on local and grid-wide conditions.
2Loss of energy
If energy transfer distances are reduced to optimize efficiency, then energy waste is minimized, but grid connectivity and resource sharing between nodes are limited
Solution Approach 1:
The grid is segmented into multiple autonomous nodes that can operate independently or in coordination. Each node manages its own energy resources and can share excess energy with neighboring nodes through direct peer-to-peer transfers, minimizing transmission distances and associated losses while maintaining overall grid productivity through distributed resource sharing.
3Adaptability or versatility
If nodes are made self-configurable to enhance adaptability, then system versatility is improved, but controller complexity increases
Solution Approach 1:
A universal node controller architecture is implemented that can perform multiple functions through software configuration rather than hardware complexity. The same controller hardware can manage different node types (PV, wind, storage, load) and adapt to various operational modes, achieving high versatility without proportionally increasing physical complexity.
4Productivity
If bidirectional communication is implemented for optimized energy management, then energy transfer optimization is improved, but system complexity and communication requirements increase
Solution Approach 1:
Bidirectional communication channels enable real-time feedback loops between node controllers and the central management system. Controllers continuously exchange information about energy production, consumption, storage status, and component health, allowing for dynamic optimization of energy transfers and automatic adjustment of operational parameters based on current system state.
Data Source
Figure 1a~1b
Figure 1c~2
Figure 3~5
AI summary
Architecture system of a local grid (10) made up of at least two single nodes (11) constituting micro-grids, each managed by a self-configurable node controller (200) that is also connected to the controllers of the other nodes and to the single energy generation, storage and consumption elements of its own node, said elements being variable in their configuration and dynamic in their behaviour; said controller (200) also optimizing the energy transfers according to specific management logics of the routine and sub-routine type.