ZIP load modeling for single and aggregate loads and CVR factor estimation
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Yiqi Zhang
,
Evan Jones
Abstract
ZIP load modeling has been used in various power system applications. The aggregate load modeling is common practice in utility companies. However, little research has been done on the theoretical formulation of the aggregate load and on various factors that affect accurate estimation of the parameters. This paper proposes new methods to systematically formulate the aggregate ZIP load model using the single ZIP load model and comprehensively examines the factors that may affect aggregate ZIP load estimation. Moreover, novel analysis of reactive power ZIP parameter calculation considering different compensating device modeling is presented. ZIP parameter estimation methods including least squares method, optimization method, and neural network method have been used in this paper to estimate ZIP parameters. The proposed new method was illustrated using the IEEE 13-bus and 34-bus systems built in OpenDSS. In addition, the ZIP parameter estimation is also performed using field data, and the conservation through voltage reduction (CVR) factor is further computed based on the estimated ZIP load model. The results provide guidance on calculation and interpretation of CVR estimates. Note that this paper was first presented at PAC world conference 2021 which provides no published conference proceedings.
Funding source: LG&E and KU Energy LLC
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: This work was funded by LG&E and KU Energy LLC.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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Artikel in diesem Heft
- Frontmatter
- Research Articles
- Fault diagnosis of ship power equipment based on adaptive neural network
- Application of sustainable power and laser washing device in garment design
- Automatic monitoring system of power equipment based on Internet of Things technology
- Intelligent algorithm of electrical fire monitoring system based on data mining technology
- Vector correlation learning and pairwise optimization feature selection for false data injection attack detection in smart grid
- ZIP load modeling for single and aggregate loads and CVR factor estimation
- Upper limb movement simulation and biomechanical characteristics during human movement
- Intelligent home control system based on BP neural network speech recognition
- User-side precision marketing model of integrated energy service system
- Building carbon neutrality goals break down strategies for sustainable energy development
- Energy-saving intelligent manufacturing optimization scheme for new energy vehicles
