Game Theoretical Energy Allocation System
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Solution Overview
Problem
Existing energy management systems fail to optimize energy allocation among devices, often leading to inefficiencies such as energy shortages or excessive consumption, as they lack cooperative comparison techniques to adjust device operations based on relative priority levels.
Innovation Solution
Implementing a cooperative comparison method using a game theoretical model among agents representing devices, where energy allocation is determined by comparing priority levels and energy requirements, with agents being allocated energy based on aggregated outcomes and threshold values, and instructed to enter low-power modes if not meeting the threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a competitive prioritization technique is used to allocate energy to agents with higher priority levels, then energy allocation efficiency is improved, but lower priority agents are starved of energy resulting in consumer frustration
Solution Approach 1:
The system implements a feedback mechanism where agents receive feedback about their priority level comparisons and energy allocation outcomes. This allows the system to adjust future allocations based on past performance, ensuring both high-priority agents receive timely energy and lower-priority agents receive adequate energy to prevent frustration, thus resolving the contradiction between allocation efficiency and distribution equity.
Solution Approach 2:
The patent applies dynamic prioritization where agent priority levels are not fixed but can change based on current system conditions, energy availability, and historical performance. This dynamic approach allows the system to optimize energy allocation efficiency in real-time while ensuring equitable distribution across different priority levels, as agents can transition between priority states based on changing conditions.
2Device complexity
If energy is allocated based on fixed priority levels, then allocation simplicity is maintained, but adaptability to changing energy availability and device needs is reduced
Solution Approach 1:
The system maintains relative simplicity by using a structured priority level framework while introducing dynamics through automated adjustments based on energy availability and device performance. The priority levels serve as a simple foundation, but the system dynamically adapts allocations within this framework, balancing simplicity with adaptability without requiring complex real-time renegotiation of priorities.
Solution Approach 2:
The patent changes key parameters such as energy allocation thresholds, priority weightings, and availability coefficients based on system conditions. This allows the system to adapt to changing energy availability and device needs while maintaining the overall simplicity of the priority-based allocation structure, as parameter adjustments are automated and occur within the existing framework rather than requiring structural changes.
3Reliability
If cooperative comparison techniques are implemented among agents, then energy distribution equity is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The cooperative comparison process is segmented into discrete, manageable steps: priority level determination, pairwise comparison execution, outcome aggregation, and threshold evaluation. This segmentation reduces computational complexity by breaking down the complex cooperative comparison into simpler, sequential operations that can be efficiently processed while still achieving equitable energy distribution across all agents.
Solution Approach 2:
The system uses parameter changes to simplify cooperative comparison by introducing standardized priority levels and threshold values that agents can compare using simple arithmetic operations. This approach maintains equity through cooperative comparison while reducing computational complexity, as agents compare predefined parameters rather than performing complex multi-dimensional evaluations.
Data Source
AI summary
An energy allocation system may include a group of agents, each agent corresponding to a device requesting an amount of energy. The energy allocation system may perform a comparison, such as a hybridized comparison, between a selected agent and each other agent included in the group. Based on the outcome of each comparison for the selected agent, an aggregated outcome for the selected agent is determined. The aggregated outcome for the selected agent is compared to a threshold for the energy allocation system. Based on the comparison of the aggregated outcome to the threshold, the selected agent either receives the requested amount of energy or receives an instruction to enter a low-power state.