Platform Power Configuration for Renewable-Aware Workload Scheduling

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Solution Overview

Problem

Computing centers face challenges in achieving carbon neutrality and energy neutrality due to fluctuations in renewable energy sources, leading to increased reliance on non-renewable energy sources, which are costly and environmentally impactful.

Innovation Solution

A system that dynamically adjusts the operating configurations of hardware, operating systems, and applications based on renewable energy availability, using learning agents to optimize power utilization and incorporate renewable energy credits, while deferring non-time-sensitive workloads to reduce reliance on non-renewable energy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If computing centers rely on non-renewable energy sources to ensure continuous operation, then operational reliability is improved, but environmental harm and operational costs increase

Engineering Contradiction:

Improveoperational reliabilityVSAvoidenvironmental harm

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The system dynamically adjusts workload execution timing based on real-time renewable energy availability. Learning agents continuously monitor renewable energy forecasts and grid conditions, then adaptively schedule workloads to execute when renewable energy is abundant, transforming the static energy consumption pattern into a dynamic one that responds to changing energy supply conditions.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements closed-loop feedback through learning agents that monitor renewable energy availability, workload completion status, and energy consumption patterns. This feedback enables continuous optimization of workload scheduling decisions, allowing the system to learn from past performance and improve its ability to leverage renewable energy sources while maintaining operational reliability.

Inventive Principle:

Principle #23Feedback

2Object-affected harmful factors

If computing centers increase renewable energy utilization, then environmental sustainability is improved, but operational stability deteriorates due to fluctuations in renewable energy availability

Engineering Contradiction:

Improveenvironmental sustainabilityVSAvoidoperational stability

Core Design Contradiction:

Object-affected harmful factorsVSStability of the object's composition

Solution Approach 1:

The system performs preliminary actions by pre-scheduling and pre-positioning workloads in queues based on predicted renewable energy availability. Learning agents forecast future renewable energy generation and proactively prepare workload schedules in advance, allowing the system to capitalize on upcoming renewable energy surges while maintaining operational stability through careful planning rather than reactive adjustments.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables self-service by allowing workloads to be automatically scheduled and executed based on renewable energy availability without requiring manual intervention. The learning agents autonomously manage the complex decisions of when to execute which workloads, handling the variability of renewable energy sources through automated adaptation rather than human control.

Inventive Principle:

Principle #25Self-service

3Use of energy by moving object

If computing centers dynamically adjust operating configurations based on renewable energy availability, then energy optimization is improved, but system complexity increases

Engineering Contradiction:

Improveenergy optimizationVSAvoidsystem complexity

Core Design Contradiction:

Use of energy by moving objectVSDevice complexity

Solution Approach 1:

The system introduces learning agents as intermediary components that mediate between the variable renewable energy supply and the computing workloads. These agents absorb and manage the complexity of real-time energy optimization decisions, translating fluctuating energy availability into standardized workload scheduling commands, thereby shielding the core computing infrastructure from direct exposure to energy variability while still achieving optimization.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12476462B2Platform configuration based on availability of sustainable energy

Publication Date: 2025.11.18 INTEL CORP

AI summary

Examples described herein relate to controlling power available to processes and hardware devices to control a monetary cost of utilized electricity and/or amount of energy utilized from non-renewable energy sources. The system can modify operating configurations of processes and/or hardware based on the available power. The system can control total power drawn to control a monetary cost of power and/or avoid drawing power from non-renewable sources (e.g., fossil fuel sources or grid including gas or coal-based energy sources).