Site Energy Optimizer Sensitivity Analysis for Parameter Impact
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Solution Overview
Problem
Complex energy management systems in large sites with multiple energy streams and consumers struggle to assess the impact of various conditions and parameters on overall energy management due to their complexity and interplay of entities.
Innovation Solution
A system comprising an energy optimizer and a data variator that determines optimization goals using an optimization algorithm, receives energy data inputs, and performs sensitivity analysis to assess how energy data outputs are altered by changes in energy inputs, allowing for informed improvements to the optimization algorithm and cost reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex energy management systems are used to manage multiple energy streams and consumers, then energy management capability is improved, but system transparency and comprehensibility deteriorate
Solution Approach 1:
The patent segments the complex energy management system into multiple virtual power plants (VPPs), each managing specific energy streams or consumers. This segmentation allows the overall complex system to be broken down into manageable, transparent components while maintaining comprehensive energy management capability across all segments.
Solution Approach 2:
The patent introduces an intermediary optimization system that acts as a mediator between the complex energy management requirements and the need for transparency. This intermediary layer provides sensitivity analysis and scenario evaluation that makes the complex system's behavior understandable and transparent to users.
2Loss of information
If sensitivity analysis is performed to assess parameter impact, then system transparency is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial sensitivity analysis by focusing on key parameters and scenarios rather than performing exhaustive analysis on all possible parameters. The system identifies and analyzes only the most relevant parameters for each VPP and scenario, providing sufficient transparency without requiring complete computational analysis of every possible variable.
Solution Approach 2:
The patent uses parameter changes to simplify sensitivity analysis by varying only critical parameters while holding others constant. This approach reduces computational complexity by focusing on the most influential parameters that drive system behavior, rather than analyzing all parameters simultaneously.
3Productivity
If optimization algorithms are made more complex to handle multiple energy streams, then energy management performance is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements self-service through automated sensitivity analysis and scenario evaluation that requires minimal user input. The system automatically identifies relevant parameters, performs sensitivity analysis, and presents results in an easily interpretable format, allowing operators to understand and control complex energy management without requiring deep expertise in the underlying algorithms.
Solution Approach 2:
The patent incorporates feedback mechanisms that provide operators with actionable insights from sensitivity analysis and scenario evaluation. The system feeds back information about which parameters have the greatest impact on performance, enabling operators to easily adjust and optimize energy management strategies without needing to understand the complex optimization algorithms themselves.
Data Source
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AI summary
A system (100) for optimizing an energy management of a site, the system (100) comprising: an energy optimizer (10), wherein the energy optimizer (10) is adapted to determine one or more optimization goals (12) based on an optimization algorithm (14), a data variator (20), wherein the data variator (20) is adapted to provide at least one first energy data input and at least one second energy data input, different to the first energy data input, to the energy optimizer (10), wherein the energy optimizer (10) is adapted to receive the first and second energy data inputs from the data variator (20), and to determine, based the first and second energy data inputs, respective at least one first and second energy data outputs via the optimization algorithm (14), wherein a data sensitivity of the energy optimizer (10) can be determined with respect to the first and second energy data inputs and the first and second energy data outputs.