Microgrid Parasitic Loss Characterization Using Swing Machine Feedback
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Solution Overview
Problem
Microgrids often lack accurate characterization of parasitic losses due to resistive and reactive characteristics of powerlines, leading to inefficient power dispatch from generation assets to loads, which is time-consuming and laborious to measure.
Innovation Solution
A microgrid controller characterizes parasitic losses by commanding power output changes from generating assets and observing the swing machine's response, using the comparison to generate a parasitic loss model for each asset, enabling precise power dispatch considering transmission losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional measurement methods are used to characterize parasitic losses in microgrid powerlines, then measurement precision can be achieved, but the process becomes time-consuming and laborious
Solution Approach 1:
The microgrid system characterizes its own parasitic losses by having generating assets send power and measuring the swing machine's response, eliminating the need for external measurement equipment and personnel. The system uses its operational data to automatically determine resistive and reactive characteristics of powerlines.
Solution Approach 2:
The controller measures the swing machine's power output change in response to a generating asset's power change, uses this feedback to calculate parasitic losses, and then applies this information to optimize future power dispatch decisions, creating a continuous improvement loop.
2Productivity
If comprehensive microgrid network characterization is performed to account for parasitic losses, then power dispatch efficiency is improved, but system complexity increases
Solution Approach 1:
The swing machine acts as an intermediary that translates the electrical characteristics of the power network into measurable power output changes. By observing how the swing machine responds to power changes from different generating assets, the system can infer parasitic losses without directly measuring complex network parameters.
Solution Approach 2:
The swing machine serves multiple functions: it provides backup power to the microgrid, stabilizes the network, and simultaneously acts as a measurement instrument for characterizing parasitic losses. This multi-functionality reduces the need for separate characterization equipment.
Data Source
AI summary
An microgrid with a microgrid controller characterizes individual power generating assets of the microgrid by determining the level of expected parasitic losses in the delivery of power from the assets to loads that are being powered by the microgrid. The controller may command changes in the power delivery from a particular asset and then observe how a swing machine responds to those changes. By comparing the change in power delivery from the asset to the compensatory change in power delivered by the swing machine, the controller characterizes the expected parasitic losses in the delivery of power from the asset. The controller may generate and/or update a parasitic loss model for the asset and subsequently use that parasitic loss model to dispatch power from that asset in the future, such that the power dispatched takes into account the expected parasitic losses in the delivery of the power.