Transmission-Distribution Grid Planning With Confidential Data Mediation
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Solution Overview
Problem
Existing power transmission and distribution utilities face challenges in collaborative planning due to confidentiality issues in data sharing, complexity of grid configurations, and computational burden, which hinder efficient integration and economic optimization.
Innovation Solution
An integrated planning system that includes a planning component for generating grid configurations, an analysis component for calculating optimal power flow, and an evaluation component for calculating investment and operation costs, while limiting shared information and reducing the number of analyzed configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If utilities develop grid plans independently to maximize economic gains, then each utility achieves its own economic optimization, but grid-wide cooperation and integration efficiency deteriorate
Solution Approach 1:
The system segments the grid planning process into distinct components: transmission grid planning and distribution grid planning. Each component can be optimized independently while maintaining standardized interfaces through the common data model, allowing economic optimization at each level without compromising overall integration.
Solution Approach 2:
A standardized common data model acts as an intermediary between transmission and distribution utilities. This mediator enables data exchange and coordination without requiring full integration of internal systems, facilitating grid-wide cooperation while preserving each utility's operational independence and economic autonomy.
2Productivity
If sensitive data is shared between transmission and distribution utilities for collaborative planning, then grid-wide optimization improves, but confidentiality and security risks increase
Solution Approach 1:
Instead of sharing actual sensitive data between utilities, the system creates standardized copies through the common data model. This abstraction layer reproduces only the necessary planning information in a standardized format, enabling grid-wide optimization while protecting the confidentiality of underlying sensitive data.
Solution Approach 2:
The common data model serves as an intermediary that transforms and standardizes data before exchange. This mediator filters and structures information to include only what is necessary for collaborative planning, reducing confidentiality risks while maintaining optimization capabilities.
3Measurement precision
If all possible grid configurations are analyzed to ensure economically sound planning, then planning accuracy improves, but computational burden increases exponentially
Solution Approach 1:
The analysis process is segmented into hierarchical levels: transmission-level configurations are analyzed separately from distribution-level configurations. This segmentation reduces the combinatorial explosion by breaking down the overall analysis into manageable segments that can be processed independently and then integrated.
Solution Approach 2:
The system performs preliminary analysis and filtering of grid configurations at the transmission level before passing selected configurations to distribution-level analysis. This preliminary action reduces the number of configurations that require full detailed analysis, thereby reducing computational burden while maintaining planning accuracy for the most promising options.
Data Source
AI summary
Systems and methods facilitate cooperation between transmission and distribution grids to optimize power flow and grid planning. Grid equipment data along with demand and renewable energy information are analyzed over a designated planning period to generate grid measure data, which is then used to create various grid configurations. These configurations are processed to determine power flows and boundary condition data for both transmission grids and distribution grids. The resulting data is utilized to calculate optimal grid configurations and power flows, which are then evaluated for investment and operational costs. Costs are compared to determine differential costs for each configuration, aiding in the development of an economically efficient grid plan. Advantageously, the systems and methods optimize grid configurations based on time-series data, while satisfying reliability standards, enhancing grid stability and performance, and maintaining cost-effectiveness.