Energy Allocation Control for Cost-Aware Low-Carbon Power Mix
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Solution Overview
Problem
Current energy allocation methods for companies are manual and prone to errors, failing to achieve energy conservation and carbon reduction goals due to reliance on human expertise and unpredictable conditions.
Innovation Solution
An energy allocation method and computing apparatus that determines demand and supply differences for target energy sources using machine learning algorithms to optimize energy allocation, recommending appropriate energy usage based on limit ratios and electricity consumption, and generating energy-saving commands for equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual energy allocation methods are used, then human expertise can be applied to energy planning, but the method is subject to unexpected conditions and errors and cannot achieve energy conservation and carbon reduction goals
Solution Approach 1:
The system enables self-service through automated energy allocation where the computing apparatus independently analyzes energy consumption data, compares supply differences, determines target conditions, and generates allocation recommendations without human intervention, eliminating manual errors while maintaining expertise through algorithmic processing
Solution Approach 2:
The patent replaces the mechanical manual planning process with an automated computing system that processes energy data, calculates demands and supply differences, and generates allocation schemes through electronic computation, substituting human manual operations with automated mechanical/electronic systems to improve reliability
2Reliability
If green electricity is purchased with high recognition level, then carbon reduction goals can be achieved, but the price is significantly higher and supply may be insufficient
Solution Approach 1:
The system changes the parameter of energy source selection by dynamically evaluating multiple factors including carbon reduction effectiveness, cost, and supply availability. It adjusts the mix of green electricity and other energy sources based on target conditions derived from supply differences, optimizing both environmental and economic parameters simultaneously
Solution Approach 2:
The patent applies partial action by purchasing the optimal amount of green electricity based on supply differences and target conditions rather than maximizing green electricity purchase. It achieves sufficient carbon reduction through a balanced mix of energy sources, avoiding excessive spending on premium green electricity while meeting carbon reduction goals
Data Source
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AI summary
An energy allocation method and computing apparatus (10). In the method, the demand for target energy is determined based on the limit ratio and electricity consumption, where the limit ratio is the proportion of target energy to all energy, and the electricity consumption is the statistic of all energy used. The supply difference between the target energy and other energy sources in all energy sources is compared, where all energy sources include the target energy source and other energy sources, and the supply difference is the difference in the payment amount to obtain energy. A target condition corresponding to the target energy is determined based on the demand and supply differences, and the recommended amount of target energy is determined based on the target condition.