Industrial Gas Plant Power Forecasting With Renewable Storage Control
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Solution Overview
Problem
The variability and intermittency of renewable energy sources like wind, solar, and tidal power pose challenges for industrial gas plants, which require a constant power supply to efficiently produce gases such as ammonia, making it difficult to maximize utilization and production.
Innovation Solution
A method using machine learning models to predict available power resources from renewable sources, incorporating historical environmental and operational data, and controlling industrial gas plants and storage resources to optimize power utilization, including energy storage systems like battery, compressed air, and pumped hydroelectric storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If renewable energy sources (wind, solar, tidal) are used to power industrial gas plants, then environmental sustainability and green energy utilization are improved, but the variability and intermittency of power supply worsen, making it difficult to maintain constant power supply required for efficient gas production
Solution Approach 1:
The system performs preliminary actions by predicting future power availability from renewable sources using machine learning models, and pre-positioning industrial gas in storage facilities before power shortages occur. This allows the plant to maintain production continuity despite the intermittent nature of renewable energy, resolving the contradiction between using variable renewable power and maintaining stable gas production.
2Adaptability or versatility
If renewable energy sources are utilized, then green energy adoption is improved, but the natural variability and transient nature of such sources worsen, rendering it difficult to ensure maximum utilization of the industrial gas plant
Solution Approach 1:
The system dynamically adjusts plant operation and storage utilization based on real-time and predicted renewable power availability. The machine learning model continuously forecasts power resources, and the control system adapts production rates and storage drawdown accordingly, enabling maximum plant utilization despite the variable nature of renewable energy sources.
Solution Approach 2:
The system implements feedback mechanisms where actual power consumption and production data are continuously monitored and fed back to the machine learning model. This improves prediction accuracy over time and enables dynamic optimization of plant utilization, allowing the system to adapt to the transient nature of renewable energy while maximizing productivity.
3Productivity
If constant power supply is maintained for industrial gas plants, then production efficiency is improved, but the ability to accommodate variable renewable energy sources worsens, creating difficulty in transitioning to green energy
Solution Approach 1:
The system introduces storage facilities as an intermediary between renewable power sources and the industrial gas plant. During periods of high renewable power availability, excess energy is used to produce and store industrial gas. During periods of low power availability, stored gas is released to maintain constant production, thus decoupling the plant's constant production requirement from the variable renewable power input.
Data Source
AI summary
There is provided a method of determining and utilizing predicted available power resources from one or more renewable power sources for one or more industrial gas plants comprising one or more storage resources. The method is executed by at least one hardware processor and comprises: obtaining historical time-dependent environmental data associated with the one or more renewable power sources; obtaining historical time-dependent operational characteristic data associated with the one or more renewable power sources; training a machine learning model based on the historical time-dependent environmental data and the historical time-dependent operational characteristic data; executing the trained machine learning model to predict available power resources for the one or more industrial gas plants for a pre-determined future time period; and controlling the one or more industrial gas plants in response to the predicted available power resources for the pre-determined future time period.