Multi-site Energy System Optimization via Data Reconciliation
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Solution Overview
Problem
Industrial steam and power utility systems face inefficiencies due to inaccurate and incomplete measurement data, leading to suboptimal operation and increased greenhouse gas emissions.
Innovation Solution
A computer-implemented method for optimizing equipment operation across multiple facilities by performing equipment-level data validation and reconciliation, determining site-level and multi-site constraints, and adjusting operating parameters using a global optimization matrix to improve energy efficiency and reduce emissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If equipment operation is optimized using traditional single-site methods, then local operational efficiency is improved, but multi-site energy efficiency and emission reduction targets are not achieved
Solution Approach 1:
The patent combines multiple single-site optimization systems into a unified multi-site optimization platform. The central server aggregates operational data from multiple facilities and coordinates optimization across all sites simultaneously, enabling system-wide energy efficiency improvements while meeting multi-site emission reduction targets that individual sites cannot achieve alone.
Solution Approach 2:
The optimization platform is designed to handle diverse energy systems (steam, power, gas) across different facility types (refineries, petrochemical plants, power generation facilities). The system provides universal optimization capabilities that adapt to various site-specific constraints while achieving coordinated multi-site performance improvement.
2Measurement precision
If comprehensive data validation and reconciliation are performed across all equipment, then measurement accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The data validation and reconciliation process is segmented into hierarchical levels: equipment-level validation, site-level reconciliation, and multi-site optimization. This segmentation allows comprehensive data processing to be broken down into manageable stages, improving measurement accuracy at each level while preventing computational overload through progressive refinement.
Solution Approach 2:
Data validation and reconciliation are performed as preliminary steps before optimization calculations. By pre-processing and validating operational data at equipment and site levels beforehand, the system ensures high measurement accuracy for subsequent optimization while reducing the computational burden during the actual optimization phase.
3Loss of energy
If real-time optimization is implemented across multiple sites, then operational costs and emissions are reduced, but system reliability requirements become more difficult to maintain
Solution Approach 1:
The optimization system incorporates site-specific constraints and local quality requirements for each facility while coordinating multi-site operations. Each site maintains its own reliability standards and operational constraints, allowing real-time optimization to reduce emissions without compromising the reliability requirements of individual critical systems.
Solution Approach 2:
The system implements continuous feedback loops that monitor both emission reduction performance and system reliability metrics in real-time. This feedback mechanism allows the optimization algorithm to adjust operational parameters to achieve emission targets while automatically maintaining system reliability by detecting and responding to conditions that could compromise operational stability.
Data Source
AI summary
This disclosure describes methods and systems for optimizing operation of industrial steam and power utility systems across multiple facilities. A method involves: for each energy system at each facility: (a) performing equipment level data validation for the plurality of respective power generation equipment, (b) performing equipment level data reconciliation for the plurality of respective power generation equipment, (c) performing site-level optimization to determine equipment operating parameters for the plurality of respective power generation equipment; determining: (i) site-level constraints for the plurality of energy systems, and (ii) multi-site constraints across the plurality of energy systems; optimizing, based on the site-level constraints and the multi-site constraints, the equipment operating parameters for the plurality of respective power generation equipment across the plurality of energy systems.