IoT Demand Management for Natural Gas Distribution Pipelines
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Solution Overview
Problem
There is a need for an effective method to manage natural gas demand in remote areas with distributed energy pipelines, ensuring timely delivery and adequate storage based on changing demand patterns.
Innovation Solution
A method and IoT system for demand management of natural gas in distributed energy pipelines, which involves determining consumption change sequences, obtaining gas flow data, constructing micro-pipeline network maps, and adjusting gas storage parameters to optimize supply and demand alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If gas supply is increased to meet peak demand, then gas availability is improved, but storage capacity is exceeded and waste occurs
Solution Approach 1:
The system performs preliminary actions by predicting future gas demand using historical consumption data and seasonal patterns before the demand actually occurs. This allows gas field stations to pre-position storage and plan supply routes in advance, ensuring reliable gas availability during peak periods without excessive storage accumulation that would lead to waste.
Solution Approach 2:
The system implements continuous feedback mechanisms by monitoring real-time gas consumption data from multiple gas field stations and comparing it against predicted demand patterns. This feedback loop enables dynamic adjustment of storage levels and supply rates, maintaining optimal gas availability while preventing both shortages and wasteful over-supply.
2Productivity
If multiple gas field stations are coordinated centrally, then supply efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the gas supply network into multiple independent gas field stations, each equipped with local monitoring and control capabilities. This segmentation allows each station to operate semi-autonomously based on local conditions while still contributing to the overall coordinated supply, reducing the complexity burden on any single central control point while maintaining high supply efficiency.
Solution Approach 2:
The central management platform performs multiple functions including demand prediction, storage optimization, route planning, and real-time monitoring across all gas field stations. This multi-functionality consolidates complex control logic into a single universal system that coordinates all stations efficiently without requiring complex point-to-point control mechanisms between individual stations.
3Measurement precision
If real-time monitoring is implemented across all nodes, then demand prediction accuracy is improved, but data processing load increases
Solution Approach 1:
The system extracts and focuses monitoring efforts on the most critical nodes and parameters that have the greatest impact on demand prediction accuracy. Rather than uniformly monitoring all possible variables at all nodes, the system identifies and prioritizes key measurement points, reducing overall data processing load while maintaining high prediction accuracy through strategic selective monitoring.
Solution Approach 2:
The system implements monitoring at a level that is sufficient for accurate prediction without being excessive. By applying monitoring selectively to critical parameters and nodes rather than all possible measurements, the system achieves the necessary measurement precision for reliable demand forecasting while keeping data processing energy consumption at optimal levels.
Data Source
AI summary
Provided are a method, an IoT system, and a storage medium for demand management of natural gas in distributed energy pipelines. The method includes: determining a commercial gas consumption change sequence; obtaining gas flow data; obtaining historical gas consumption data based on the gas flow data; determining a residential gas consumption change sequence; determining a demand volume sequence based on the residential gas consumption change sequence, the commercial gas consumption change sequence, and the historical gas consumption data; constructing a micro-pipeline network map based on a low-pressure transportation network, a current gas storage amount and a storage capacity of a gas field station, and the demand volume sequence; determining a gas storage coverage rate and a gas supply priority; determining a gas storage adjustment parameter based on the gas storage coverage rate and the gas supply priority; and generating a storage adjustment instruction based on the gas storage adjustment parameter.