Power Network Management via Machine Learning Agents
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Solution Overview
Problem
Existing power grid management systems face inefficiencies in distributing electric energy due to component failures, unanticipated demand, and increasing load from modern electronics, particularly exacerbated by the rise of battery-operated cars, leading to peaks in power consumption.
Innovation Solution
A power network management system utilizing software agents and a convergence unit with machine-learning algorithms to estimate energy consumption needs of consumers and generate optimized energy distribution plans, which include instructions for when to consume energy, thereby reducing peak demand and optimizing energy distribution across the grid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static power scheduling is used in modern grids, then system simplicity is maintained, but energy distribution efficiency deteriorates
Solution Approach 1:
The patent transforms static power scheduling into dynamic scheduling by introducing software agents that continuously monitor consumer energy needs and grid conditions. The convergence unit dynamically adjusts energy distribution plans based on real-time data, allowing the system to adapt to changing conditions while maintaining manageable complexity through modular agent-based architecture.
Solution Approach 2:
The patent implements feedback mechanisms where software agents collect consume-related data from consumers, transmit it to the convergence unit, and receive optimized distribution plans in return. This closed-loop feedback system enables continuous improvement of energy distribution efficiency while keeping the overall system structure relatively simple through standardized agent-convergence unit interactions.
2Adaptability or versatility
If power distribution accommodates increasing consumer demand and battery-operated cars, then consumer service quality is improved, but peak power consumption increases
Solution Approach 1:
The patent applies preliminary action by having software agents estimate consumer energy needs in advance and having the convergence unit generate optimized distribution plans before peak demand occurs. This allows energy to be distributed more evenly over time, accommodating consumer needs including battery-operated cars while avoiding peak consumption spikes through proactive energy allocation.
Solution Approach 2:
The system dynamically adjusts energy distribution to accommodate varying consumer demands, including the increasing load from battery-operated cars and modern electronics. The convergence unit modifies distribution plans in real-time based on consumed data, allowing the system to adapt to new consumer types and demand patterns without creating excessive peak loads.
3Productivity
If machine-learning algorithms are used to optimize energy distribution, then energy distribution efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex optimization task into manageable components by distributing machine-learning algorithms across multiple independent software agents rather than concentrating them in a single central system. Each agent handles local energy estimation and planning, reducing the complexity burden on any single component while collectively achieving high distribution efficiency through coordinated operation.
Data Source
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AI summary
The invention pertains to a power network management system (10), for managing distribution of electric energy from a power network to a multitude of power consumers (40, 41) by means of a power grid, the system comprising a plurality of software agents (30, 31) and a convergence unit (20) having a computing unit (22), a memory unit (24) and at least one machine-learning algorithm, wherein each software agent is installable on a power consumer of the multitude of power consumers or installed on a communication module that is adapted to be connected to one of the power consumers and to exchange data with the power consumer connected to, wherein each software agent is adapted to exchange data with the power consumer it is installed on or connected to and with the convergence unit, and wherein the convergence unit is adapted to request and receive from the plurality of agents consume-related data of the consumers, to estimate energy consumption needs of each of the consumers within a defined time period, to generate an optimized energy distribution plan for distributing available electric energy among the consumers, to generate consumption plan data for each of the consumers, the consumption plan data comprising instructions for the consumers when to consume electric energy, and to provide the consumption plan data to the software agents of the consumers.