Mobile Battery Grid Control for Predictive Charge-Discharge Allocation
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Solution Overview
Problem
Current electrical grid management systems face challenges in efficiently adjusting supply and demand due to the unpredictable nature of electrical power consumption and generation from mobile battery sources, leading to difficulties in accurately predicting and allocating electrical power resources.
Innovation Solution
A system that includes a network of stations and vehicles equipped with batteries, connected through a communication network, which uses predictive algorithms to manage charging and discharging operations based on demand, allocating control strategies such as first and second controls to optimize power supply and demand matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mobile batteries are used for electrical power supply, then power supply flexibility and accessibility are improved, but prediction accuracy of power availability deteriorates
Solution Approach 1:
The system performs preliminary actions by predicting the movement trajectories of mobile batteries in advance, calculating expected power availability at different locations and times before actual power supply occurs. This allows the system to prepare power allocation plans proactively rather than reactively.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual power consumption data and comparing it with predicted values, then using this information to refine future predictions and adjust power supply strategies dynamically based on observed patterns.
2Reliability
If more mobile batteries are deployed to meet grid demands, then power supply reliability is improved, but system complexity and allocation difficulty worsen
Solution Approach 1:
The system segments the power supply problem into discrete location-based units, dividing the service area into grid cells and allocating batteries to specific segments based on predicted demand patterns. This transforms a complex system-wide problem into manageable localized decisions.
Solution Approach 2:
The system changes parameters by dynamically adjusting power availability predictions based on battery location, movement patterns, and consumption data. By varying these parameters in real-time, the system optimizes power allocation without requiring additional infrastructure complexity.
3Productivity
If real-time power allocation is implemented, then power distribution efficiency is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary calculations of power availability and demand patterns in advance, creating prediction models that can be quickly applied in real-time scenarios. This reduces the computational burden during actual power allocation by pre-processing complex calculations.
Solution Approach 2:
The system implements partial real-time allocation by focusing computational resources on the most critical power supply decisions and locations, rather than attempting to optimize every aspect simultaneously. This selective approach maintains efficiency while reducing overall processing requirements.
Data Source
AI summary
A system comprising: a controlling module that provides an electric power grid with an electrical power resource by performing at least one of a first control for reducing power charging amounts for the multiple movable batteries, or a second control for increasing power supplying amounts from the multiple movable batteries to an outside, in response to a first request for requesting power consumption to be reduced; and a classification module that classifies, based on at least one of an integrated value of the discharging power or soundness of each of the multiple movable batteries, each of the multiple movable batteries into a battery that can be used for both the first control and the second control, and a battery that can be used for the first control but not used for the second control.