Feedstock Allocation System for Power Producers
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Solution Overview
Problem
Energy feedstock producers face challenges in optimizing their netback due to loss of control over feedstocks as they flow downstream, with fluctuations in market prices and inefficiencies in converting raw materials into electricity for peak demand, leading to reduced returns and increased risks in shale gas exploration.
Innovation Solution
A system and method utilizing grid-scale electric power storage devices to allocate energy feedstocks in real-time, integrating battery storage and independent generators to dynamically assess and control the conversion of feedstocks into electricity for storage and sale during peak prices, allowing for automated decision-making and optimization of netback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If feedstock is sold directly downstream, then producers lose control over feedstock allocation and pricing, but maintaining direct sale simplifies operations
Solution Approach 1:
The system segments feedstock allocation decisions into discrete controllable units at the production source, allowing producers to divide feedstock into different streams (power generation vs. direct sale) based on real-time market conditions, thereby regaining control without overwhelming system complexity
Solution Approach 2:
The system performs preliminary feedstock allocation decisions at the production site before feedstock enters the downstream market, allowing producers to pre-determine the fate of each unit of feedstock based on current market conditions, thus maintaining control upstream rather than losing it downstream
2Productivity
If feedstock is converted to electricity for peak demand, then additional revenue opportunities arise, but conversion inefficiencies and timing risks reduce overall returns
Solution Approach 1:
The system continuously monitors real-time market prices for both feedstock and electricity, using this feedback to dynamically adjust conversion decisions, ensuring that feedstock is converted to electricity only when market conditions favorably support the additional revenue while accounting for conversion inefficiencies
Solution Approach 2:
The system implements dynamic, real-time decision-making for feedstock conversion rather than static pre-determined allocation, allowing the conversion rate to flexibly adapt to changing market conditions, thereby optimizing revenue while minimizing losses from inefficient timing or pricing
3Extent of automation
If real-time market data integration is implemented, then automated optimization of netback is achieved, but system complexity and data processing requirements increase
Solution Approach 1:
The system implements self-service automation where the feedstock allocation and conversion decisions are automatically made by the system itself based on real-time market data, eliminating the need for manual intervention while the automation handles the complexity of data processing and decision optimization
Solution Approach 2:
The system changes operational parameters (feedstock allocation rates, conversion timing) dynamically based on real-time market data, using automated algorithms to adjust these parameters optimally without requiring complex manual system redesign, thus achieving automation with manageable complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables energy producers to maximize their netback by dynamically managing feedstock allocation and electricity generation, storage, and sale, reducing costs and risks through real-time market data integration and automated operations, while generating additional revenue from carbon credits.
Implementation Method 1
grid-scale electric power storage devices enables a feedstock supplier to utilize real-time market data to allocate a feedstock between its sale 'as is' and use of the feedstock to produce electric power which may be stored for later sale
Implementation Method 2
use of the feedstock to produce electric power
Data Source
AI summary
A system and method for optimizing the returns of power feedstock producers includes automated selection between selling raw feedstock or generating power and storing it in a storage device for subsequent sale under more favorable market conditions. The system may comprise grid-scale electric power storage devices which enable a feedstock supplier—e.g., a natural gas producer—to utilize real-time market data to allocate a feedstock between its sale “as is”—i.e., as fuel or chemical feedstock—and use of the feedstock to produce electric power which may be stored for later sale when the market price for peaking electric power is favorable. In certain embodiments, the system may be entirely automated and the collection of market data, the allocation of feedstock and the generation and subsequent supplying of electric power to a grid may be entirely performed by the system without operator intervention.