Quantum Power Optimization for Section-Level Demand Imbalance
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Solution Overview
Problem
Current power distribution techniques fail to accurately meet growing power demands, lead to unreliable supplies, and inefficient utilization of power sources due to unplanned and haphazard distribution, under/overutilization, and inaccurate calculations of power supply and demand.
Innovation Solution
A power optimizer system utilizing quantum computing and a power optimizer model processes demographic, power demand, power source, route, technology, and industry data to determine optimized power insights, prioritizing sections with high demand and implementing actions to balance supply and demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional power distribution methods are used, then implementation is simple, but power demand accuracy and supply reliability deteriorate
Solution Approach 1:
The patent introduces a quantum computer as an intermediary computing device that processes power distribution optimization problems. The quantum computer receives power distribution data from classical systems, performs quantum optimization calculations, and returns optimized solutions. This intermediary approach enables advanced optimization capabilities while maintaining compatibility with existing power distribution infrastructure.
Solution Approach 2:
The patent replaces traditional classical computing mechanisms with quantum computing mechanisms for solving power distribution optimization problems. Quantum algorithms such as QAOA (Quantum Approximate Optimization Algorithm) and QMCS (Quantum Monte Carlo Simulation) substitute for classical optimization methods, providing superior accuracy in calculating power demands and optimizing distribution strategies.
2Productivity
If quantum computing is used for power optimization, then power distribution efficiency improves, but computing resource consumption increases
Solution Approach 1:
The patent implements a hybrid computing approach where only critical and complex power distribution optimization problems are solved using quantum computing, while less complex problems are handled by classical systems. This partial application of quantum computing resources optimizes power distribution efficiency for high-impact scenarios while avoiding unnecessary quantum computing overhead for simpler cases.
3Measurement precision
If detailed section-level analysis is performed, then power allocation accuracy improves, but processing time increases
Solution Approach 1:
The patent divides the power distribution system into discrete sections or regions, each analyzed independently using quantum optimization algorithms. By segmenting the overall problem into smaller sub-problems at the section level, the system achieves high power allocation accuracy for each segment while the modular structure enables parallel processing that reduces total computation time.
Solution Approach 2:
The patent performs preliminary data processing and segmentation of power distribution data before quantum optimization. Power distribution data is pre-processed, categorized by geographic sections, and prepared in appropriate formats for quantum algorithms. This preliminary action reduces the computational burden during the actual quantum optimization phase, decreasing overall processing time while maintaining detailed section-level analysis accuracy.
Data Source
AI summary
A device may receive input data that includes demographic data, power demand data, power source data, power route data, technology data, industry data, and problem data associated with a geographic location, and may identify a section of the geographic location from the demographic data. The device may identify power sources of the section, and may estimate power generation and power demand for the section. The device may determine whether the power demand is greater than the power generation for the section. The device may utilize a quantum computer and a power optimizer model with the input data associated with the section to determine optimized power insights for the section based on determining that the power demand is greater than the power generation for the section, and may perform actions based on the optimized power insights for the section.