Distributed Energy Router Control for Peak Demand Smoothing
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Solution Overview
Problem
Current electrical power management systems face challenges in efficiently managing peak demand, grid stability, and energy distribution due to intermittency in clean energy sources, leading to increased costs and infrastructure inefficiencies, as well as limitations in utilizing local electricity production capacity.
Innovation Solution
A power distribution control system that balances energy sources, loads, and storage elements within a 'balanced string' to maintain a controlled rate of change, using energy routers to dynamically adjust power transfer and prioritize energy usage based on anticipated demand and supply, thereby reducing peak demand and stabilizing the grid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If pumped-water energy storage facilities are used to meet peak demand, then power generation capacity is improved, but response speed is insufficient and infrastructure cost increases
Solution Approach 1:
The system segments the power storage and delivery function into distributed battery assets located at various points in the network, rather than relying on a single centralized pumped-storage facility. This segmentation enables faster local response to frequency changes while reducing infrastructure costs and environmental impact.
Solution Approach 2:
The invention replaces the mechanical pumped-water storage system with electrochemical battery storage systems. This substitution enables much faster response times (seconds vs. minutes) while reducing infrastructure complexity and environmental footprint.
2Loss of energy
If clean energy sources like wind and solar are used, then energy efficiency is improved, but grid stability deteriorates due to intermittency
Solution Approach 1:
The system implements real-time frequency measurement and feedback control, where battery assets automatically respond to frequency deviations by charging or discharging. This closed-loop feedback mechanism stabilizes the grid by compensating for intermittency in clean energy sources while maintaining high energy efficiency.
Solution Approach 2:
The invention changes the operational parameters of battery assets from static to dynamic, allowing them to adjust their charge/discharge rates in real-time based on grid frequency conditions. This enables clean energy sources to maintain stability while preserving their efficiency advantages.
3Loss of energy
If local electricity production capacity is utilized, then energy waste is reduced, but hardware constraints limit the ability to feed into the grid
Solution Approach 1:
The system introduces battery assets as intermediary elements between local production sources and the grid. These batteries buffer the hardware constraints by absorbing excess local generation and releasing it when needed, enabling full utilization of local capacity without exceeding grid connection limits.
Solution Approach 2:
The invention implements preliminary charging of battery assets during periods of low demand or high local generation, before peak demand occurs. This preliminary action allows local production capacity to be fully utilized without immediate grid injection, avoiding hardware constraints while preventing energy waste.
4Loss of energy
If responsive loads are used to manage peak demand, then operational cost is reduced, but the ability to respond to rapid frequency changes is insufficient
Solution Approach 1:
The system merges responsive load management with battery storage assets, combining the cost-saving benefits of load shifting with the rapid response capability of batteries. This hybrid approach maintains low operational costs while achieving the fast response needed for frequency regulation.
Data Source
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AI summary
A power distribution control system having a string of power assets comprising at least two different power assets selected from sources, stores and responsive loads is disclosed. The assets and associated local routers communicate with a central server and attempt to fulfil high level aims of the server by negotiating times and quantities of power transfer between themselves. Preferably a database stores parameters in relation to the power assets. Preferably a control system at the server anticipates future activity, such as future peaks in demand or supply, in the grid, and local power assets prepare in response. Preferably the power assets communicate between themselves on a peer-to-peer basis and collectively confirm to the server their ability to modify their collective behaviour in response to an event in the grid.