Workload Scheduling for Renewable Energy Cost-Aware Power Control
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Solution Overview
Problem
Existing workload control methods do not consider the cost effectiveness of renewable energy procurement and assume known future power consumption and workload plans, which are often unrealistic.
Innovation Solution
A workload control support device that predicts the executable period and power consumption of each workload, calculates a target power consumption value to meet renewable energy utilization and cost conditions, and determines the execution timing of each workload to achieve these targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If workload execution timing is shifted to maximize renewable energy utilization, then renewable energy utilization rate is improved, but cost effectiveness deteriorates
Solution Approach 1:
The invention changes the parameters of workload execution by introducing flexible timing adjustments based on predicted renewable energy generation and cost conditions. The workload control support device dynamically modifies execution parameters (timing, duration) to optimize both renewable energy utilization and cost effectiveness simultaneously, rather than fixing one parameter at the expense of the other.
Solution Approach 2:
The system implements dynamic workload scheduling where execution timing is not fixed but adapts continuously based on real-time predictions of renewable energy availability and cost conditions. This dynamic approach allows the system to respond to changing conditions and balance multiple objectives (renewable energy utilization and cost effectiveness) rather than relying on static schedules.
2Device complexity
If future power consumption and workload plans are assumed known, then workload scheduling is simplified, but reliability deteriorates due to unrealistic assumptions
Solution Approach 1:
The system performs preliminary predictions of power consumption and workload characteristics before actual execution, but these are presented as probabilistic forecasts rather than fixed assumptions. The workload control support device uses machine learning models to predict future states in advance, allowing the system to prepare scheduling decisions while accounting for uncertainty through continuous prediction updates.
Solution Approach 2:
The invention implements continuous feedback loops where actual power consumption and workload data are fed back into the prediction models to improve future predictions. This feedback mechanism allows the system to learn from deviations between predicted and actual values, progressively improving reliability without requiring complex manual adjustments to scheduling assumptions.
Data Source
AI summary
A workload control support device includes: a parameter determination unit configured to acquire a prediction value of an executable period and a power consumption amount of each of a plurality of workloads that consume power scheduled to be executed in a future time period, and calculate, based on each acquired prediction value of the executable period and the power consumption amount, a target value of the power consumption amount in the future time period to satisfy a condition for a utilization rate of renewable energy in the power consumption in the future time period and a condition for cost related to utilization of the renewable energy; and a workload control unit configured to determine a timing of each workload to be executed in the future time period based on the calculated target value of the power consumption amount, and execute each workload at the determined timing.