Enterprise Energy Balancing for Renewable Grid Reliability
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Solution Overview
Problem
Enterprise information technology providers and their clients face challenges in reducing their carbon footprints and energy costs due to insufficient knowledge, visibility, and control over their infrastructure's energy consumption, exacerbated by regulatory pressures and unpredictable renewable energy sources, which can lead to critical infrastructure failures during high demand or supply shortages.
Innovation Solution
A sustainable enterprise energy balancing solution (SEEBS) utilizing a reactive and proactive energy balancing engine that integrates machine-learning and data mining to optimize energy consumption, allowing customers to opt-in or opt-out of energy reduction levels, prioritize critical systems, and manage energy sources through 'as a service' models, with features like a sustainable job scheduler and catastrophe grid relief engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional energy management approaches are used, then enterprises have simple operational control, but they lack sufficient knowledge, visibility, and control over infrastructure energy consumption
Solution Approach 1:
The patent introduces an energy balancing engine as an intermediary system that sits between the power grid and enterprise IT infrastructure. This engine aggregates energy consumption data from multiple sources, analyzes it using machine learning algorithms, and provides actionable insights to stakeholders. The intermediary layer transforms raw energy data into meaningful information without requiring enterprises to build complex monitoring systems themselves.
Solution Approach 2:
The system enables enterprises to self-monitor and self-optimize their energy consumption through automated machine learning models that continuously analyze consumption patterns and provide recommendations. The energy balancing engine automatically adjusts workloads and scheduling to optimize energy usage, reducing the need for manual intervention while providing full visibility into consumption patterns.
2Object-generated harmful factors
If renewable energy sources are used, then carbon footprint is reduced, but energy supply becomes unpredictable during high demand periods
Solution Approach 1:
The energy balancing engine performs preliminary analysis of energy consumption patterns and renewable energy availability to predict future energy needs. By analyzing historical data and current trends, the system proactively schedules energy-intensive tasks during periods when renewable energy availability is high, preventing energy shortages before they occur and ensuring continuous operation of critical infrastructure.
Solution Approach 2:
The system dynamically adjusts energy management strategies based on real-time conditions. Machine learning models continuously adapt to changing renewable energy availability and demand patterns, optimizing workload scheduling and resource allocation. This dynamic approach allows the system to respond flexibly to unpredictable renewable energy supply while maintaining reliable operation of critical systems.
3Use of energy by moving object
If energy reduction measures are implemented, then energy consumption is lowered, but control over critical infrastructure operations may be compromised
Solution Approach 1:
The energy balancing engine applies different energy management strategies to different parts of the IT infrastructure based on their criticality. Critical systems receive prioritized energy allocation and enhanced monitoring to ensure continuous operation, while non-critical systems undergo more aggressive energy reduction measures. This localized approach reduces overall energy consumption while maintaining control and operation of essential infrastructure.
Solution Approach 2:
The system implements continuous feedback loops that monitor energy consumption, system performance, and operational status. Machine learning models analyze this feedback to dynamically adjust energy management decisions, ensuring that energy reduction measures do not compromise critical operations. The feedback mechanism provides real-time visibility into the impact of energy reduction actions, allowing operators to maintain control over infrastructure operations while achieving energy savings.
Data Source
AI summary
Sustainable enterprise-wide energy balancing is presented herein, which describes a series of processes to make choices in favor of a sustainably sourced energy mix, grid reliability, and/or to prioritize critical systems in times of a catastrophe. An example method comprises receiving a group of input data representing a standardized phrase, wherein the group of input data is received from first equipment associated with an electric power generation and transmission operator entity; parsing the standardized phrase to identify an initiating event; in response to identifying the initiating event, determining that an initiating threshold value associated with the initiating event has fallen below a defined risk threshold value; and executing, on remote second equipment, a flow of customer defined processes representing a group of runbooks.