Power Distribution Network Control Under Probabilistic Grid Uncertainty
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Solution Overview
Problem
The increasing complexity and variability in electric power distribution networks due to distributed energy resources and load diversification make it difficult to determine and predict the network state, leading to challenges in maintaining stability and reducing the risk of network failures, especially with the reliance on rule-based local control schemes and limited computational feasibility of stochastic optimization.
Innovation Solution
A computer-implemented method using probabilistic modeling and a modular approach to optimize control parameters, incorporating probabilistic load and feed-in forecasts, reliability analysis, and control modules to minimize failure probability and ensure network stability, employing a linearized grid model for efficient computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distributed energy resources and load diversification are integrated into the power distribution network, then the network becomes more flexible and adaptable to different energy sources, but the complexity of determining and predicting network state increases significantly
Solution Approach 1:
The network is divided into multiple segments or zones with localized control. Each segment has its own control parameters that can be optimized independently, reducing the overall computational complexity while maintaining adaptability to distributed energy resources across the entire network.
Solution Approach 2:
The invention changes the approach from deterministic parameter optimization to probabilistic parameter optimization. By using stochastic optimization methods, the system can handle the increased complexity of network state determination caused by distributed energy resources, transforming the problem into one that accounts for uncertainty and variability in a computationally manageable way.
2Ease of manufacture
If traditional rule-based local control schemes are used, then the system is simple to implement, but the ability to handle uncertainty and predict future network states is insufficient
Solution Approach 1:
The invention implements a feedback mechanism where probabilistic forecasts of network states are continuously updated based on actual measurements and new information. This allows the system to maintain simplicity in implementation while significantly improving reliability by adapting to uncertainty through continuous feedback loops that refine predictions and control decisions.
Solution Approach 2:
The system performs preliminary probabilistic assessments and forecasts of future network states before actual operations occur. By pre-calculating probable network conditions and preparing control strategies in advance, the system enhances its ability to handle uncertainty while maintaining a relatively simple implementation structure.
3Reliability
If stochastic optimization methods are applied to optimize control parameters, then the reliability of network operation improves, but the computational feasibility decreases
Solution Approach 1:
The invention applies partial stochastic optimization by focusing computational efforts on the most critical control parameters and time periods rather than optimizing all parameters comprehensively. This selective approach maintains improved reliability for key network operations while keeping computational processing requirements at feasible levels.
Solution Approach 2:
The optimization problem is segmented into smaller sub-problems that can be solved independently and more quickly. By dividing the overall stochastic optimization into manageable segments, the system achieves improved reliability through rigorous optimization where needed while maintaining computational feasibility through problem decomposition.
Data Source
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AI summary
The present disclosure provides a computer-implemented method for optimizing an electric power distribution network, characterized in that the method comprises: applying, by a probability module, a probabilistic load and feed-in forecast to a grid model to generate a probabilistic state parameter forecast, and performing, by a reliability module, a reliability analysis on the probabilistic state parameter forecast to determine a failure probability, and optimizing, by a control module, values for control parameters of the electric power distribution network to achieve an operational objective. The disclosure also relates to a corresponding system and computer program product for optimizing an electric power distribution network.