Distributed Generation Matching for Probabilistic Load Allocation
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Solution Overview
Problem
Distributed generation systems face challenges in providing consistent power supply due to intermittent energy production from sources like solar and wind, making it difficult to meet consumer demand with certainty, especially since existing systems cannot accurately predict power output or account for stochastic consumption patterns.
Innovation Solution
A forward-looking matching algorithm is applied to determine a matching matrix that allocates predicted energy supplies from multiple generators to loads, ensuring a high probability of meeting power demand criteria, and additional loads are admitted if a surplus is detected, using probabilistic stochastic optimization to model intermittency and variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distributed generators are connected to provide additional power supply capacity, then power supply diversity is improved, but power output predictability deteriorates due to intermittent generation from renewable sources
Solution Approach 1:
The system performs preliminary actions by predicting future power generation outputs from distributed generators and consumer demand patterns before the actual supply cycle begins. The forward-looking matching algorithm uses historical data and stochastic models to forecast generation and consumption, enabling proactive allocation decisions that account for the intermittent nature of renewable sources while maintaining predictability.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual power generation and consumption data, comparing it with predictions, and using this information to refine future predictions and matching decisions. This closed-loop approach improves the accuracy of stochastic models over time, enhancing both adaptability to renewable variability and reliability of power output predictions.
2Adaptability or versatility
If power allocation is based on stochastic output from distributed generators, then system flexibility is improved, but consumer power availability certainty deteriorates
Solution Approach 1:
The system performs preliminary matching of generators to consumers before the supply cycle begins, using predicted power outputs and demand patterns. The forward-looking matching algorithm allocates power in advance based on stochastic predictions, creating binding agreements that provide consumers with certainty about their power availability while allowing the system to accommodate the flexible, intermittent nature of distributed renewable generation.
Solution Approach 2:
The system introduces an intermediary layer of probabilistic prediction and matching algorithms between the stochastic distributed generators and consumers. This intermediary translates the uncertain outputs from renewable sources into reliable power allocation decisions, using statistical models to bridge the gap between variable generation and stable consumer supply requirements.
3Measurement precision
If forward-looking matching algorithm is applied to predict power output, then power allocation accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments the power system into discrete generators and consumers, creating a structured matching problem that can be solved using combinatorial optimization. By dividing the complex allocation problem into individual generator-consumer pairs and using systematic algorithms to evaluate combinations, the system achieves accurate power allocation predictions while managing computational complexity through structured decomposition.
Data Source
AI summary
Operation methods and systems for distributed generation from a plurality of generators, and computer readable media. One method comprises the step of applying a forward looking matching algorithm to determine a matching matrix with elements mi,j denoting the fraction of generator i's predicted supply assigned to respective load j of a plurality of loads such that a probability of meeting each load's associated power demand characteristic in a next supply cycle satisfies a threshold criterion.