Renewable Energy Orchestration for Surplus-Powered Computing Pools
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Solution Overview
Problem
There is a need to manage and maximize the effective and efficient use of excess energy produced by local renewable energy sources, as existing systems often discard or dissipate this energy when it is not immediately consumed by connected appliances.
Innovation Solution
A method that enables computing resources to be powered and utilized when there is a surplus of renewable energy, by tracking energy consumption, determining computation demand, and comparing the efficiency of using the energy for computing versus injecting it into the grid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If excess renewable energy is injected into the electrical grid, then the energy can be utilized by the broader grid system, but the energy may be discarded or dissipated when not forecasted to be consumed
Solution Approach 1:
The system dynamically switches between different energy utilization modes (grid injection vs. local computing power supply) based on real-time conditions. The orchestrator continuously monitors energy surplus, computation demand, and efficiency metrics to adaptively decide whether to enable computing resources or inject energy into the grid, thereby minimizing energy loss while maintaining operational flexibility.
Solution Approach 2:
The system changes the operational state of computing resources (enabled/disabled) based on varying parameters such as energy surplus levels, computation demand, and efficiency comparisons. This parameter-driven approach allows the system to optimize energy utilization by transitioning between different operational modes to match changing conditions.
2Loss of energy
If computing resources are enabled to consume excess energy, then energy efficiency is maximized, but the system complexity increases due to tracking and decision-making mechanisms
Solution Approach 1:
The orchestrator serves multiple functions within a single component: it tracks electrical consumption, determines energy surplus, monitors computation demand, compares efficiency metrics, and makes enabling decisions. This multi-functional approach consolidates what could be separate complex systems into one coordinated component, reducing overall system complexity while maintaining comprehensive energy management.
Solution Approach 2:
The system uses readily available data from existing components (consumption tracking from the converter, grid status information) to make autonomous decisions about computing resource enablement. The orchestrator self-determines whether to enable computing resources based on predefined efficiency criteria, eliminating the need for external complex control systems.
3Loss of energy
If computing resources are dynamically enabled based on energy surplus, then energy efficiency improves, but the reliability of computing service delivery may be affected
Solution Approach 1:
The system continuously monitors multiple parameters (energy surplus, computation demand, efficiency metrics) and uses this feedback to make real-time decisions about computing resource enablement. This closed-loop feedback mechanism ensures that computing resources are enabled only when conditions favor energy efficiency, while continuously adapting to maintain optimal performance and reliability.
Data Source
AI summary
There is provided a method, a system, a storage medium and an orchestrator to enable a computer resource of a computing pool. In particular, the methods and apparatuses of the present disclosure are configured to enable a computing resource (16) of one or more computing pools (17), the computing resource (16) being in communication with a converter (13) of a renewable energy source (12). The methods and apparatuses may be configured to:tracking an electrical consumption of one or more electrical appliances (14) in communication with the converter;enabling the computing resource (16) in response to determining a surplus of renewable electrical energy provided by the renewable energy source (12); and to determining that there is computation demand from the one or more computing pools (17); and to determining that an estimated efficiency of use of the renewable energy is greater when responding to the demand of the one or more computing pools (17) than when injecting the renewable energy into an electrical grid (11) in communication with the converter (13).