Stochastic Synthetic Data Generation for Electric Net Load and Its Application

Date
2021-01-05
Authors
Liu, Mengwei
Reed, Patrick
Anderson, C. Lindsay
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
The increasing integration of renewable energy in electric power systems focuses attention on realistic representation of ”net load” because it aggregates the information from both demand and the renewable supply side; net load is the remaining demand that must be met by non-renewable resources. However, the net load data is not readily accessible because of cost, privacy, and security concerns. Furthermore, even if historical data is available, multiple stochastic scenarios are often needed for a wide range of power system applications. To address these issues, this paper proposes a stochastic synthetic net load profile generation approach. A seasonal detrending technique is combined with the modified Fractional Gaussian Noise method to deal with the complex multi-periodic seasonal trends in the net load profile. A thorough statistical validation and temporal correlation check are performed to show the quality of the synthetic data. The benefits of the synthetic data are demonstrated by a microgrid energy management problem.
Description
Keywords
Distributed, Renewable, and Mobile Resources, seasonal detrending technique, statistical validation, synthetic net load data generation, temporal correlation
Citation
Rights
Access Rights
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.