Distributed Generation Allocation Using Forward-Looking Load Matching
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Solution Overview
Problem
Distributed generation systems face challenges in providing consistent power supply due to intermittent nature of generators, uncertainty in user power consumption, and stochastic output, making it difficult to predict power output and meet consumer demands effectively.
Innovation Solution
A forward-looking matching algorithm is applied to determine a matching matrix that assigns fractions of predicted generator supply to loads, ensuring a probability of meeting power demand characteristics, with additional loads admitted if a surplus is determined, thereby optimizing energy distribution and supply.
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 and stochastic nature
Solution Approach 1:
The system performs preliminary actions by predicting future power generation outputs and consumer demands before the actual supply cycle begins. The forward-looking matching algorithm uses historical data and statistical models to forecast intermittent generator outputs and stochastic consumer demands, establishing probability distributions in advance. This allows the system to proactively allocate power resources and manage uncertainty before it materializes, resolving the contradiction between accommodating intermittent sources and 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 allocations. The audit module verifies whether power demand characteristics were met and feeds this information back into the prediction and matching processes, improving the system's ability to handle intermittent sources while maintaining reliability over time.
2Ease of operation
If power allocation is optimized to meet specific consumer demands, then consumer satisfaction is improved, but system complexity increases due to multiple uncertain variables
Solution Approach 1:
The system segments the complex power allocation problem into distinct functional modules: prediction modules for generators and consumers, a matching algorithm for allocation, and an audit module for verification. Each module handles specific aspects of the uncertainty independently - generator predictions handle supply-side variability, consumer predictions handle demand-side variability, and the matching algorithm coordinates them. This segmentation reduces overall system complexity while maintaining the ability to fulfill consumer demands.
Solution Approach 2:
The forward-looking matching algorithm acts as an intermediary between uncertain generator outputs and uncertain consumer demands. Rather than directly managing the complexity of matching intermittent sources with variable demands, the algorithm uses probability distributions and threshold criteria as intermediaries to facilitate allocation. This intermediary layer simplifies the coordination task while still achieving consumer satisfaction.
3Reliability
If probability threshold criterion is applied to ensure reliable power delivery, then power supply reliability is improved, but energy utilization efficiency deteriorates due to conservative allocation
Solution Approach 1:
The system applies partial action by using threshold criteria that allow for probabilistic rather than absolute guarantees. Instead of requiring 100% certainty for all allocations (which would be excessively conservative), the system sets appropriate threshold levels that provide sufficient reliability for critical loads while allowing more flexible allocation for non-critical loads. This partial application of strict criteria maintains energy utilization efficiency while still improving overall power delivery reliability.
Solution Approach 2:
The system changes parameters by using probability distributions and threshold criteria instead of fixed deterministic values. The matching algorithm adjusts allocation decisions based on varying probability thresholds, generator output distributions, and consumer demand distributions. This parameter transformation from deterministic to probabilistic allows the system to balance reliability and efficiency dynamically, rather than being constrained by fixed conservative allocations.
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.