Forward Energy Purchasing for Cost-Stable Machine Operation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing demand for efficient and flexible management of energy and compute resources in distributed markets, particularly in the context of blockchain and AI applications, is hindered by volatility in energy and compute costs, as well as uncertainty in resource availability and market fluctuations.
Innovation Solution
A transaction-enabling system that includes a smart contract wrapper to access distributed ledgers, a controller with resource requirement circuits for determining energy needs, and market forecasting using machine learning or AI to execute transactions on forward resource markets, optimizing energy and compute resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If forward market transactions are used to hedge against volatility, then cost stability is improved, but transaction complexity increases
Solution Approach 1:
The system performs preliminary actions by executing forward market transactions in advance to lock in energy prices before spot market volatility affects operations. The machine determines energy requirements and executes forward purchases ahead of time, thereby stabilizing costs while avoiding the complexity of real-time spot market trading during operational peaks.
Solution Approach 2:
The system introduces an intermediary layer between energy requirements and market transactions through automated agents that manage forward market hedging. These agents act as intermediaries that translate energy needs into optimized forward market strategies, reducing the complexity burden on the primary system while achieving cost stability.
2Speed
If automated agents execute transactions in distributed markets, then transaction speed is improved, but system reliability deteriorates due to market volatility
Solution Approach 1:
Automated agents execute forward market transactions in advance during periods of lower volatility, locking in prices before spot market fluctuations occur. This preliminary action allows high-speed automated trading to achieve cost stability rather than merely transaction speed, thereby improving reliability while maintaining automation benefits.
Solution Approach 2:
The system incorporates feedback mechanisms where automated agents continuously monitor market conditions, energy requirements, and portfolio performance. This feedback loop enables the agents to adjust forward market positions dynamically, maintaining reliability by responding to volatility patterns while preserving the speed advantages of automated execution.
3Productivity
If machine learning forecasting is used to predict market prices, then resource allocation efficiency is improved, but computational energy consumption increases
Solution Approach 1:
The system applies partial action by using simplified forecasting models for routine predictions and reserving complex machine learning approaches for critical decision points. This selective application of computational intensity maintains resource allocation efficiency for most transactions while reducing overall energy consumption by avoiding excessive computational overhead for every market prediction.
Data Source
AI summary
Systems and methods for machine forward energy and energy credit purchase are disclosed. An example transaction-enabling system may include a machine having an energy requirement for a task and a controller. The controller may include a resource requirement circuit to determine an amount of an energy resource for the machine to service the energy requirement, a forward resource market circuit to access a forward resource market, and a resource distribution circuit to execute a transaction of on the forward resource market in response to the determined amount of the energy resource.