Automated TCO-Based Resource Allocation for Electrical Infrastructure
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Solution Overview
Problem
Optimizing the replacement and maintenance of electrical infrastructure components in power grids to minimize the total cost of ownership (TCO) is computationally challenging due to the large number of variables and operational constraints, often requiring subjective human decision-making.
Innovation Solution
A method involving a computing device that generates and stores total cost of ownership (TCO) values for each component over various intervention time periods, ranks components based on these values, and allocates resources automatically to minimize TCO while adhering to operational constraints, such as budget and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual optimization methods are used to determine replacement timing for electrical infrastructure components, then subjectivity and complexity of decision-making increases, but computational resources required decrease
Solution Approach 1:
The patent segments the optimization problem by pre-calculating TCO values for individual components separately, then combining results through a systematic allocation process. This divides the complex system-wide optimization into manageable component-level calculations, reducing overall computational burden while maintaining automation.
Solution Approach 2:
The patent performs preliminary calculations of TCO values for multiple intervention time periods for each component before final allocation decisions are made. This pre-computation of cost data enables the automated allocation system to make informed decisions without requiring complex real-time optimization calculations.
2Reliability
If comprehensive optimization of intervention timing is performed for all components, then total cost minimization improves, but computational intractability increases
Solution Approach 1:
The patent extracts the essential optimization criterion (TCO minimization) from the complex system and applies it independently to each component. By taking out the optimization logic and applying it component-by-component with pre-calculated TCO values, the system achieves comprehensive optimization without confronting computationally intractable system-wide optimization problems.
Solution Approach 2:
The patent changes the approach from optimizing intervention timing directly to using pre-calculated TCO values as parameters for allocation decisions. This parameter transformation simplifies the optimization problem by replacing complex timing optimization with a simpler allocation problem based on ready-made cost data.
3Measurement precision
If detailed TCO calculations are performed for multiple time periods for each component, then allocation accuracy improves, but data processing requirements increase
Solution Approach 1:
The patent performs preliminary TCO calculations for multiple time periods in advance, storing these pre-computed values for later allocation decisions. This pre-computation approach enables accurate allocation based on comprehensive cost data while avoiding the need to perform complex calculations in real-time during the allocation process.
Solution Approach 2:
The patent creates and stores copies of TCO values for different time periods as reference data. These pre-calculated cost profiles serve as templates that guide the allocation process, allowing accurate component selection without repeatedly performing full optimization calculations.
Data Source
AI summary
A method includes: storing, for each of a sequence of candidate intervention time periods, respective total cost of ownership (TCO) values for a plurality of electrical infrastructure components; obtaining a planning horizon value corresponding to a set of the candidate intervention time periods; obtaining an operational constraint; for each of the set of candidate intervention time periods corresponding to the planning horizon value, generating an intervention resource allocation segment by: (i) retrieving the TCO values corresponding to the candidate intervention time period, (ii) ranking the retrieved TCO values, and (iii) adding electrical infrastructure components to an intervention pool by traversing the ranked TCO values until the operational constraint is reached; combining the intervention resource allocation segments; and outputting the combined intervention resource allocation segments.