Hierarchical Energy Allocation System for Smart Grid Optimization
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Solution Overview
Problem
In smart grid environments, the integration of renewable energy sources creates fluctuations in power supply and demand, leading to inefficiencies in energy management, as existing systems struggle to optimize energy allocation across devices with varying consumption and generation capabilities, increasing computational and communicational resources required.
Innovation Solution
A method and system that assign devices to groups with aggregation nodes to collect local power cost functions and usage profiles, transmitting aggregated data to a central processing device for optimizing a global cost function, reducing communicational load and calculational effort through hierarchical and distributed calculation, and redistributing optimization parameters back to devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a centralized system optimizes energy allocation for all devices directly, then optimization precision is improved, but communicational load and computational resources increase significantly
Solution Approach 1:
The system divides devices into multiple groups, each managed by a local aggregation node. This segmentation reduces the communicational load on the central controller by localizing data aggregation and preliminary optimization tasks at the group level, while maintaining centralized coordination for global optimization.
Solution Approach 2:
Aggregation nodes serve as intermediaries between individual devices and the central controller. These intermediaries collect and aggregate data from multiple devices, perform local preprocessing, and forward consolidated information to the central controller, thereby reducing the overall communicational burden while preserving optimization accuracy.
2Productivity
If device properties and constraints are taken into account for optimization, then energy allocation efficiency is improved, but computational complexity increases
Solution Approach 1:
The computational task is segmented between aggregation nodes and the central controller. Aggregation nodes perform local computations on grouped device data, reducing the computational burden on the central controller while still considering individual device properties and constraints through the aggregated information.
Solution Approach 2:
The system performs optimization in stages: first aggregating device properties at the local level, then performing global optimization based on aggregated data. This partial action approach allows the system to consider detailed device properties without requiring all computations to be performed simultaneously at the central level, thereby managing computational complexity.
3Measurement precision
If all devices communicate directly with the central controller, then data accuracy is improved, but system scalability is limited
Solution Approach 1:
The system is segmented into hierarchical levels with aggregation nodes managing groups of devices. This structure allows the system to scale by adding more aggregation nodes and groups without requiring each device to communicate directly with the central controller, thus maintaining data accuracy while improving scalability.
Solution Approach 2:
The system introduces a hierarchical dimension to the communication structure, moving from a flat one-to-many communication model to a multi-level hierarchy. This dimensional change allows the system to maintain data accuracy through multiple aggregation layers while significantly improving scalability and reducing the communication burden on the central controller.
Data Source
AI summary
A method for allocating energy to a plurality of devices, wherein each device is configured to consume, store and/or supply energy, the method includes the steps of: assigning each device to a group of devices; assigning an aggregation node device to each group of devices; for a selection of devices transmitting local power cost functions of the devices and/or power usage profiles of the devices with respect to a predetermined time slot to the assigned aggregation node device; at the aggregation node device, generating aggregated data as a function of the received local power cost functions and/or power usage profiles; transmitting the aggregated data to a central processing device; at the central processing device, optimizing a global cost function for allocating power to the devices as a function of the aggregated data.