Power Supply Base Siting Using Spatial Demand Convolution
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Solution Overview
Problem
Existing electric power planning methods struggle to accurately estimate a suitable location and scale for installing an electric power supply base due to temporal and spatial fluctuations in demand, particularly in regions with dispersed power demand points and time lags in electric power distribution using electric vehicles.
Innovation Solution
An information processing apparatus transforms electric power demand information into a spatial demand function, generates an integral kernel for evaluating supply requirements, and estimates the installation location and scale of an electric power supply base through convolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If electric power distribution using electric vehicles is implemented, then electric power can be supplied to dispersed demand points, but time lag occurs until electric power is supplied and the vehicle itself consumes electric power
Solution Approach 1:
The patent pre-calculates optimal supply base locations and scales by convolving spatial demand functions with supply capability distributions before actual power distribution occurs. This preliminary planning eliminates time delays during operational decision-making, as the system already knows the optimal configuration in advance.
Solution Approach 2:
The patent creates a virtual model of the power distribution system using demand functions and supply capability distributions that mirror the physical system. By performing calculations on this virtual model, the system determines optimal configurations without requiring real-time trial and error in the actual physical system.
2Device complexity
If conventional electric power planning methods are used, then planning process is simple, but accurate estimation of supply base location and scale is difficult due to temporal and spatial fluctuations in demand
Solution Approach 1:
The patent replaces traditional mechanical/planning methods with mathematical convolution operations. Instead of using complex iterative optimization algorithms, the system uses convolution of demand functions with supply capability distributions, which is computationally more efficient and provides accurate results for determining optimal supply base locations and scales.
Solution Approach 2:
The patent transforms discrete demand data into continuous demand functions and uses convolution with supply capability distributions. This parameter transformation from discrete to continuous domain enables precise estimation of supply base locations and scales while maintaining computational tractability.
3Reliability
If nanogrids are installed to supply renewable energy, then local power supply capability is improved, but appropriate determination of installation location and scale is difficult without accurate demand assessment
Solution Approach 1:
The patent creates virtual representations of both demand patterns and supply capabilities that mirror the physical system. By performing convolution operations on these virtual models, the system determines optimal nanogrid installation locations and scales without requiring complex physical surveys or trial installations.
Solution Approach 2:
The patent performs all necessary calculations to determine optimal nanogrid locations and scales before actual installation. The convolution of demand functions with supply capability distributions provides advance planning information, eliminating the need for complex on-site decision-making during installation.
Data Source
AI summary
An object of the present invention is to appropriately evaluate a temporally and spatially distributed and fluctuating electric power demand pattern, and accurately estimate a location and scale at which an electric power supply base needs to be installed. An information processing apparatus according to the present invention transforms electric power demand information into a demand function on a spatial axis, generates an integral kernel for evaluating a required supply amount, and estimates an installation location or required scale of an electric power supply point from a result of performing convolution on the demand function and the integral kernel.