Computing Power Management for Renewable Workload Scheduling
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Solution Overview
Problem
Cryptocurrency data centers face high power costs and inefficiencies in managing power supply for high computational workloads, particularly in utilizing renewable energy sources and energy storage systems to maintain continuous operation and reduce energy costs.
Innovation Solution
A method and system that selectively control power supply from renewable energy sources and energy storage devices to data processing systems using an electronic control system, continuously updating operational parameters based on analyzed energy usage and production data to optimize power distribution and reduce idle time and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If power is continuously supplied to mining nodes to maintain continuous operation, then reliability is improved, but energy cost increases
Solution Approach 1:
The system performs preliminary actions by charging energy storage devices during periods of low energy cost or high renewable energy availability. The power management system predicts energy production and usage patterns, and proactively stores energy in advance of when it will be needed, thereby ensuring continuous operation while avoiding high energy costs.
Solution Approach 2:
The system implements periodic action by cycling between drawing power from the grid, renewable energy sources, and energy storage devices based on real-time conditions and predicted patterns. The power management system continuously adjusts power sourcing in periodic cycles, optimizing the mix of power sources to maintain reliability while minimizing energy costs.
2Productivity
If mining nodes operate at high computational workload, then productivity is improved, but power consumption increases
Solution Approach 1:
The system applies dynamics by continuously adjusting operational parameters of mining nodes based on real-time power availability and cost conditions. The power management system dynamically modifies computational workload, power distribution, and node operation levels to maintain high productivity when power is abundant and affordable, while reducing consumption when power is scarce or expensive.
Solution Approach 2:
The system changes operational parameters including power consumption levels, computational workload intensity, and node operation states based on predicted energy production and usage patterns. By continuously updating these parameters in response to energy conditions, the system optimizes the balance between productivity and power consumption.
3Use of energy by moving object
If renewable energy sources are used to power mining operations, then energy cost is reduced, but power supply stability deteriorates
Solution Approach 1:
The system uses energy storage devices as intermediaries between renewable energy sources and mining operations. The storage devices buffer the intermittent nature of renewable energy, releasing stored energy when production is low and absorbing excess energy when production is high, thereby stabilizing the power supply to mining nodes while maintaining the use of low-cost renewable energy.
Solution Approach 2:
The power management system implements feedback by continuously monitoring renewable energy production, energy storage levels, and mining node power requirements. This feedback loop enables real-time adjustments to power distribution and storage charging/discharging rates, ensuring stable power supply to mining operations while maximizing the use of renewable energy sources.
Data Source
AI summary
A method includes selectably controlling a power supply from a renewable energy source based power system and an energy storage device charged thereby and/or an Alternating Current (AC) power system to a computing system including one or more data processing device(s) and a set of loads using an electronic control system, and continuously updating, through a computing power management system associated with the electronic control system, a parameter of operation of the one or more data processing device(s) and/or the set of loads in response to analyzing data pertinent to prior energy usage/production and/or predicted energy usage/production relevant to execution of a high computational workload through the one or more data processing device(s). The method also includes optimizing the power supply from the renewable energy source based power system and/or the energy storage device to the one or more data processing device(s) based on the continuously updated parameter of operation.