Smart Grid Path Optimization for Renewable Integration
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Solution Overview
Problem
Traditional electric utility grids face challenges in balancing supply and demand, particularly with the integration of intermittent renewable energy sources like wind and solar, which can lead to network overloading, instability, and potential blackouts due to their weather-dependent nature.
Innovation Solution
A method is implemented to dynamically predict and adjust energy transmission paths in real-time by computing probable power outputs and loads for generating and consuming units, using stochastic forecasting and weather predictions to ensure that energy is transmitted within the grid's capacity, and adjusting generation plans to avoid overloading and maintain grid stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If renewable energy sources (wind and solar) are integrated into the grid, then the diversity and sustainability of energy supply is improved, but the stability and reliability of the grid deteriorates due to weather-dependent intermittency
Solution Approach 1:
The system performs preliminary stochastic forecasting of renewable energy generation and consumer demand to predict future grid conditions before they occur. This advance prediction allows the grid operator to proactively adjust generation plans and transmission paths, preventing instability before it arises from intermittent renewable sources
Solution Approach 2:
The invention dynamically re-structures the grid by continuously computing optimal transmission paths and adjusting generation plans based on real-time and forecasted conditions. The system adapts the grid configuration dynamically rather than using static hierarchical structures, allowing it to respond to the intermittency of renewable sources while maintaining stability
2Ease of operation
If the grid operates with a hierarchical centralized structure, then the simplicity and ease of operation is improved, but the adaptability to distributed prosumers and renewable sources deteriorates
Solution Approach 1:
The system segments the grid into multiple possible transmission paths between generating units and consuming units. Instead of a single hierarchical path, the invention creates a network of alternative routes that can be selectively activated based on conditions, enabling distributed prosumers to connect while maintaining operational simplicity through automated path selection
Solution Approach 2:
The stochastic forecasting system serves multiple functions: predicting renewable generation, estimating consumer demand, identifying transmission paths, and optimizing generation plans. This multi-functional approach allows the same system to handle both traditional centralized operation and distributed prosumer integration without requiring separate complex control mechanisms
3Productivity
If maximum generation capacity is utilized, then the productivity and energy supply is improved, but the risk of network overloading and blackouts increases
Solution Approach 1:
The system performs preliminary stochastic forecasting to predict future generation output and consumer demand before actual transmission occurs. This advance prediction allows the grid to operate near maximum capacity safely by identifying potential overload conditions beforehand and adjusting transmission paths or generation plans preventively
Solution Approach 2:
The invention implements a feedback mechanism where stochastic forecasts of generation and demand continuously inform the optimization of transmission paths and generation plans. This closed-loop system monitors predicted conditions and adjusts operations to maintain productivity while preventing overloading through real-time feedback-driven decision-making
Data Source
AI summary
A method for structuring an electric utility grid having traditional and renewable sources of electric power uses a weather forecasting system to determine, for a time interval, whether there are paths between all generating units and all consuming units satisfying certain constraints. The method includes computing a probable output for each of the generating units, both traditional and renewable, and computing a probable load for each of said consuming units for the time interval using both historical and weather forecast data. The method also includes determining the maximum load capacity of each segment in the utility grid for the time interval, the segments being the power lines making up the grid, and assigning the maximum load capacity of each segment to be a constraint therefor for the time interval and computing whether there are paths from each of the generating units to each of the consuming units for which the maximum load capacities will not be exceeded. If such paths exist, the method further concludes with setting the utility grid to the paths; and transmitting electric power over the paths for the time interval. If such paths do not exist from each generating unit to each consuming unit, the method instead continues by reducing the maximum capacities of the generating units; and recomputing whether there are paths from each of the generating units to each of the consuming units as many times as is necessary to find paths satisfying the constraints, each time further reducing the maximum capacities of the generating units.