학술논문

Power Loss Prediction for Distributed Energy Resources: Rapid Loss Estimation Equation
Document Type
Periodical
Source
IEEE Transactions on Industrial Electronics IEEE Trans. Ind. Electron. Industrial Electronics, IEEE Transactions on. 68(3):2289-2299 Mar, 2021
Subject
Power, Energy and Industry Applications
Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Mathematical model
Computational modeling
Estimation
Microgrids
Analytical models
Topology
Predictive models
Battery storage
computationally simple
converter efficiency
dc microgrid
distributed energy resources
electric vehicle charging stations (EVCS)
loss prediction
power losses
photovoltaic (PV) generation
Language
ISSN
0278-0046
1557-9948
Abstract
The rapid expansion of distributed energy resources has led to increasingly complex systems with numerous power converters. Accurate converter loss prediction in large grids and microgrids is essential for financial and reliability evaluation. Existing system-level analysis focuses on distribution losses and oversimplifies converter losses by assuming fixed efficiency. However, converter losses are highly variable under different operating conditions. Moreover, commercially-available multidomain simulation tools are too slow to be applied to system-level analysis. To provide computationally simple loss prediction under all operating conditions, the rapid loss estimation (RLE) equation is proposed. First, the real operating conditions of the converter are determined for the intended application. Then, accurate loss information is extracted from detailed converter behavior in multidomain simulations. Finally, the RLE equation is obtained: a parametric equation, which is fast enough for system-level simulation while capturing the converter's complexity at different operating conditions. A dc microgrid with three different converters, one each for solar generation, electric vehicle charging stations and battery storage, is considered to highlight the benefits of the proposed loss estimation tool.