Random Number Generation Using Surplus Renewable Grid Loads
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The growing energy consumption of computational loads, particularly proof-of-work computations for blockchain systems and machine learning computations, poses significant challenges for power grid stability and environmental sustainability.
Innovation Solution
The proposed solution involves configuring auxiliary computational loads to provide on-demand grid stabilization by leveraging excess renewable energy for performing computations that generate random numbers, which can then be used in other computationally intensive tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If proof-of-work computations and machine learning computations are performed separately using traditional methods, then each computation can be completed independently, but the combined energy cost is extremely high and contributes to power grid instability
Solution Approach 1:
The patent combines proof-of-work computations and machine learning computations into a single integrated computational process. The proof-of-work hash calculations are performed simultaneously with the machine learning training process, allowing both computations to share the same computational resources and energy input, thereby reducing the combined energy cost while providing grid stabilization services
Solution Approach 2:
The computational system performs multiple functions simultaneously: it conducts proof-of-work computations for cryptocurrency mining, trains machine learning models for artificial intelligence applications, and provides grid stabilization services by consuming excess renewable energy. This multi-functionality allows the system to address multiple computational needs while reducing overall energy costs and improving power grid reliability
2Productivity
If computational loads are increased to meet growing AI and blockchain demands, then processing capacity and productivity improve, but energy consumption increases significantly
Solution Approach 1:
The patent merges proof-of-work computations and machine learning computations into a single integrated process that shares computational resources and energy input, thereby increasing productivity without proportionally increasing energy consumption
Solution Approach 2:
The system uses excess renewable energy from the power grid that would otherwise be wasted, converting it into useful computational work. This self-service approach allows the system to increase its computational capacity while utilizing already-generated energy that would otherwise be curtailed, thereby improving productivity without additional energy consumption
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Systems and methods are provided. A method includes obtaining, by one or more computing devices, one or more work instructions associated with a proof-of-work protocol. The method includes performing, by the one or more computing devices, one or more first tasks based at least in part on the work instructions. The method includes determining, by the one or more computing devices and based on one or more values generated by the one or more computing devices during the one or more first tasks, one or more random or pseudorandom values. The method includes performing, by the one or more computing devices and based on the one or more random or pseudorandom values, one or more second tasks different from the one or more first tasks.