Fleet Forward Energy Purchasing for Adaptive Resource Allocation
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Solution Overview
Problem
The increasing complexity and energy intensity of distributed ledger systems and automated market transactions pose challenges in optimizing energy and compute resource management, particularly due to volatility in resource costs and availability, and the need for flexible and intelligent systems that can adapt to uncertainty and variability.
Innovation Solution
A transaction-enabling system that includes a smart contract wrapper to access distributed ledgers, a resource requirement circuit to aggregate needs, a forward resource market circuit to access energy markets, and a machine learning or AI component to adaptively improve resource allocation and cost management, using historical data and market forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed ledger systems and automated market transactions are used, then transaction execution speed and automation are improved, but energy consumption and system complexity increase
Solution Approach 1:
The system performs preliminary actions by aggregating resource requirements before executing transactions on forward markets. The resource requirement circuit collects and aggregates energy and compute resource needs in advance, allowing the system to secure resources at predetermined prices and conditions, thereby reducing the need for high-speed real-time transactions while maintaining productivity.
Solution Approach 2:
The patent introduces intermediary components including the resource requirement circuit, forward resource market circuit, and machine learning component that mediate between the distributed ledger system and energy/compute resources. These intermediaries manage resource allocation and transactions, reducing the direct energy burden on the core blockchain operations while maintaining system functionality.
2Reliability
If distributed ledger operations are increased, then market automation and trust are improved, but energy-intensive computing operations increase
Solution Approach 1:
The system extracts energy-intensive computing operations from the core distributed ledger validation process. By separating resource allocation and market transactions from the blockchain consensus mechanism, the system maintains trust and reliability through the ledger while reducing its energy burden. The resource requirement aggregation and forward market transactions occur outside the high-energy proof-of-work or proof-of-stake operations.
Solution Approach 2:
The system performs preliminary resource allocation and market transactions before they are recorded on the distributed ledger. The resource requirement circuit and forward market circuit handle energy-intensive computations in advance, allowing the blockchain to verify and record outcomes with minimal energy consumption, thereby maintaining reliability while reducing ongoing energy demands.
3Adaptability or versatility
If forward market transactions are used for resource allocation, then adaptability to volatility is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal resource acquisition system where the machine learning component and forward resource market circuit can handle multiple types of resources (energy, compute, storage) through a unified interface. This multi-functional approach allows the system to adapt to different resource types and market conditions without proportionally increasing complexity, as the same core mechanisms serve multiple purposes.
Solution Approach 2:
The system incorporates self-service capabilities through automated resource requirement aggregation and machine learning-driven decision-making. The resource requirement circuit automatically collects and aggregates needs, while the machine learning component autonomously determines optimal transaction strategies on forward markets, reducing the operational complexity burden on users and streamlining the forward market transaction process.
4Adaptability or versatility
If machine learning and AI components are added, then adaptive resource allocation is improved, but device complexity increases
Solution Approach 1:
The machine learning component serves as an intermediary between the resource requirement circuit and the forward resource market circuit. It processes aggregated resource needs and automatically determines optimal transaction strategies without requiring direct complex interactions between the other components. This intermediary role simplifies the overall system architecture while enabling adaptive resource allocation through intelligent decision-making.
Solution Approach 2:
The machine learning component provides self-service adaptive resource allocation by autonomously analyzing market conditions, resource requirements, and transaction opportunities. It automatically adjusts allocation strategies without human intervention or complex external coordination, thereby improving adaptability while containing complexity within the AI component itself rather than propagating it throughout the entire system.
Data Source
AI summary
Systems and methods for fleet forward energy and energy credits purchase are disclosed. An example transaction-enabling system may include a resource requirement circuit to aggregate a resource requirement for a fleet of machines to perform a task; a forward resource market circuit to access a forward market for energy; and a machine resource acquisition circuit to execute a transaction on the forward market for energy in response to the aggregated resource requirement.