Energy System Curve for Dynamic Multi-Source Optimization
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Solution Overview
Problem
Existing energy management systems struggle to efficiently optimize energy consumption across multiple energy sources in heterogeneous energy environments, leading to suboptimal energy efficiency and increased carbon emissions.
Innovation Solution
A system configuration device that processes diverse energy data inputs to generate an energy system curve, allowing for operational adjustments and component reconfiguration to select the most efficient energy sources and optimize energy use based on objectives like minimizing carbon emissions or maximizing productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If existing energy management systems are used to manage multiple energy sources, then the system can operate with multiple energy types, but energy efficiency is suboptimal and carbon emissions increase
Solution Approach 1:
The energy management system dynamically adjusts operational parameters and reconfigures system components based on real-time energy source availability and efficiency metrics. The system continuously optimizes energy consumption by adapting to changing conditions, selecting the most efficient energy sources and operational modes, thereby resolving the contradiction between improving energy efficiency and managing system complexity through automated dynamic control.
Solution Approach 2:
The system changes operational parameters such as energy source selection, component configuration, and operational modes based on efficiency analysis. By dynamically adjusting these parameters, the system optimizes energy efficiency across multiple energy sources while using automated algorithms to manage the complexity of coordinating these changes across the entire energy consuming system.
2Object-generated harmful factors
If existing energy management systems manage multiple energy sources, then operational flexibility is maintained, but carbon emissions are not sufficiently reduced
Solution Approach 1:
The energy management system incorporates feedback mechanisms that continuously monitor carbon emissions, energy source availability, and operational requirements. This feedback loop enables the system to optimize energy source selection and operational configuration to minimize carbon emissions while maintaining operational flexibility, as the system adapts its decisions based on real-time data about emission levels and operational needs.
Solution Approach 2:
The system dynamically changes operational parameters including energy source selection and component activation based on carbon intensity metrics. By adjusting these parameters in response to emission data, the system reduces carbon emissions while preserving operational flexibility through automated adaptation to changing environmental and operational conditions.
3Productivity
If energy consuming systems operate without dynamic reconfiguration, then system operation is simple, but energy efficiency and productivity are suboptimal
Solution Approach 1:
The energy management system performs self-service by automatically analyzing energy data, determining optimal configurations, and reconfiguring system components without external intervention. This autonomous operation increases productivity by continuously optimizing energy usage while managing configuration complexity through automated decision-making algorithms that handle the complexity internally without requiring complex external control.
Solution Approach 2:
The system dynamically reconfigures itself based on real-time energy source availability and efficiency metrics. This dynamic self-adjustment enhances productivity by optimizing energy consumption patterns, while the automated nature of the reconfiguration process manages configuration complexity through algorithmic control rather than requiring complex manual or external management.
Data Source
AI summary
Example computer-implemented methods, media, and systems for configuring an energy consuming system are described. One example computer-implemented method includes receiving, for each type of energy of multiple types of energy, data defining a set of parameters related to a capacity of the type of energy for a particular region. The capacity for each type of energy is converted into a common unit of energy using the set of parameters for each type of energy. A determination is made, by evaluating a model using at least on the capacity of two o1r more of the types of energy and a required amount for each of the two or more types of energy, an operational adjustment to one or more components of an energy consuming system that adjusts energy consumption of the one or more components. The one or more components are reconfigured according to the operational adjustment.