Smart Grid Deployment Simulator for Cost and Bandwidth Estimation
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Solution Overview
Problem
The management of power grids is inefficient and costly due to challenges in upgrading to a smart grid, including uncertainties about network infrastructure requirements and costs, leading to potential budget overruns and insufficient or over-provisioned infrastructure.
Innovation Solution
A decision management system that simulates smart grid communications network service deployments using business and technology changeable parameters, models describing traffic profiles and cost models to determine optimal candidate solutions for deploying smart grid services, including estimated bandwidth and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If power companies upgrade to a smart grid using digital technology, then management efficiency and cost reduction are improved, but the challenge and costs of upgrading are not trivial, leading to potential budget overruns
Solution Approach 1:
The patent applies preliminary action by using simulation tools to model and predict smart grid deployment scenarios before actual implementation. The system pre-calculates infrastructure requirements, bandwidth needs, and cost estimates based on projected traffic patterns and business objectives, allowing power companies to prepare budgets and infrastructure plans in advance, thereby reducing the risk of budget overruns during actual deployment
Solution Approach 2:
The patent implements feedback mechanisms through continuous monitoring and adjustment of deployment parameters. The simulation system provides feedback loops that allow power companies to adjust their infrastructure provisioning based on actual traffic patterns, business performance, and cost outcomes, enabling iterative optimization of the smart grid deployment to achieve the desired balance between efficiency gains and cost control
2Measurement precision
If power companies estimate infrastructure capacity for smart grid deployment, then budget planning is improved, but inaccurate estimation leads to insufficient or over-provisioned infrastructure
Solution Approach 1:
The patent applies parameter changes by allowing power companies to input multiple variable parameters into the simulation system, including traffic growth rates, business objectives, service level requirements, and existing infrastructure characteristics. The system dynamically adjusts infrastructure capacity estimates based on these parameter changes, providing precise measurements tailored to specific deployment scenarios while avoiding the complexity of manual calculation
Solution Approach 2:
The patent uses copying by creating virtual models of the smart grid infrastructure and traffic patterns through simulation. Instead of physically testing every possible deployment configuration, the system creates digital copies of the network and runs multiple scenarios through the simulator, allowing power companies to analyze infrastructure capacity requirements for various future states without the complexity of physical prototyping
3Ease of operation
If power companies use conventional management methods relying on telephone calls and field workers, then operational simplicity is maintained, but management efficiency is poor and costs are high
Solution Approach 1:
The patent applies mechanics substitution by replacing the mechanical system of manual telephone calls and field worker inspections with an automated simulation and monitoring system. The simulation tool automatically models network behavior, predicts faults, and optimizes management decisions, substituting the manual mechanical processes with computational automation that maintains operational simplicity while dramatically improving management efficiency and reducing costs
Data Source
AI summary
A decision management system simulates a smart grid communications network service deployment using business and technology changeable parameters, models describing traffic profiles for smart grid domain devices and smart grid applications, smart grid infrastructure and a cost model. Candidate solutions for deploying the smart grid service are determined for different sets of changeable parameters through the simulations. These solutions are analyzed to identify a solution for deploying the smart grid service.