Parametric Hybrid Model for Energy System Optimization
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Solution Overview
Problem
Current energy management systems in commercial and industrial sectors face challenges in achieving broad energy-saving and efficiency improvements due to limited targeting of opportunities and a lack of holistic views in energy system operation, leading to inefficiencies and high implementation costs.
Innovation Solution
The implementation of parametric hybrid models that combine fundamental and empirical modeling techniques to create a holistic view of energy systems, allowing for real-time optimization and control of energy components, such as boilers and chillers, through dynamic optimization methods and online parameter modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional energy management systems are used, then implementation costs are reduced, but energy-saving opportunities are limited and holistic system optimization is not achieved
Solution Approach 1:
The energy management system is segmented into multiple functional modules including data acquisition module, parametric hybrid model module, optimization module, and control module. Each module handles specific tasks independently, allowing the complex system to be managed through modular components that can be developed and maintained separately while achieving comprehensive energy optimization.
Solution Approach 2:
A parametric hybrid model serves as an intermediary between physical energy systems and control decisions. This model layer translates complex physical relationships into mathematical representations that can be optimized, acting as a mediator that simplifies the interface between physical complexity and control simplicity.
2Reliability
If parametric hybrid models with online parameter modification are implemented, then continuous optimal operation is achieved, but model complexity and computational requirements increase
Solution Approach 1:
The system implements dynamic parameter modification where model parameters are continuously updated based on real-time operating conditions. The parametric hybrid model adapts its parameters online to reflect changing system states, enabling continuous optimal operation while the modular architecture manages the complexity of dynamic adjustments.
Solution Approach 2:
Online parameter modification incorporates feedback loops where system performance is continuously monitored and fed back to adjust model parameters. This feedback mechanism ensures the model remains accurate under varying conditions while the structured feedback architecture prevents complexity from becoming unmanageable.
3Loss of energy
If holistic energy system optimization is pursued, then energy efficiency improves, but implementation costs and system complexity increase
Solution Approach 1:
The system merges multiple energy systems and components into a unified parametric hybrid model framework. By combining generation, storage, conversion, and consumption elements into a single optimized system model, holistic energy efficiency is achieved while the modular structure manages integration complexity through standardized interfaces.
Solution Approach 2:
The parametric hybrid model framework provides universal applicability across different energy system configurations. The same modular architecture and optimization approach can be applied to various system types, reducing implementation complexity through standardized methods while achieving comprehensive energy optimization.
Data Source
AI summary
The present invention provides novel techniques for controlling energy systems. In particular, parametric hybrid models may be used to parameterize inputs and outputs of groups of equipment of energy systems. Each parametric hybrid model may include an empirical model, a parameter model, and a dynamic model. Critical parameters for groups of equipment modeled by the parametric hybrid models, which are correlated with, but not the same as, input and output variables of the groups of equipment may be monitored during operation of the energy system. The critical parameters may be used to generate optimal trajectories for the energy system, which may be used to control the energy system.