Jiaqi Chen is interested in modeling and optimization of power systems with large integration of renewable and distributed energy resources. In her previous research, she implemented the data-driven linearization method for the analysis of three-phase active distribution networks. She proposed a data-driven linearization approach to analyze the three-phase unbalance in active distribution systems, a data-driven piecewise linearization for the distribution three-phase stochastic power flow, and a robust data-driven linearization for the distribution three-phase power flow. She also proposed an optimal dispatch scheme for DSO and prosumers by implementing three-phase distribution locational marginal prices. Her current focus is on applying the polynomial chaos expansion into the chance-constrained AC optimal power flow for distribution grids.
Publications
Jiaqi’s publications can be found on her Google Scholar page.
Presentations
Seventh Workshop on Autonomous Energy Systems, (Golden, CO, USA, Sept., 2024)
Real-time Assessment of Distribution Grid Security through Adaptive Smart Meter Measurements, (presentation)
2024 IEEE PES General Meeting, (Seattle, Wa, USA, Jul., 2024)
Real-time Assessment of Distribution Grid Security through Adaptive Smart Meter Measurements, (poster)
PSERC’s 2023 December IAB meeting, (Atlanta, GA, USA, Dec. 2023)
Data-driven Piecewise Linearization for Three-Phase Power Flow in Distribution Grids, (poster)
Sixth Workshop on Autonomous Energy Systems, (Golden, CO, USA, Sept., 2023)
Data-driven Piecewise Linearization for Three-Phase Power Flow in Distribution Grids, (presentation)
2023 IEEE PES General Meeting, (Orlando, FL, USA, Jul., 2023)
Data-driven Piecewise Linearization for Three-Phase Power Flow in Distribution Grids, (poster)
2023 Grid Science Winter School and Conference, (Santa Fe, USA, Jan., 2023)
Data-driven Piecewise Linearization for Three-Phase Power Flow in Distribution Grids, (poster)
Power Systems Computation Conference (PSCC), (Porto, Portugal, Jul., 2022)
A data-driven linearization approach to analyze the three-phase unbalance in active distribution systems, (presentation)
DTU Summer School 2022, (Copenhagen, Denmark, Jun., 2022)
Data-driven piecewise linearization for distribution three-phase stochastic power flow, (poster)
INFORMS Annual Meeting, (Anaheim, California, USA, Oct., 2021)
Data-driven piecewise linearization for distribution three-phase stochastic power flow, (presentation)
IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), (Wuhan, Hubei Province, China, Oct., 2020)
Robust data-driven linearization for distribution three-phase power flow, (poster)
IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), (Chengdu, Sichuan Province, China, May, 2019)
Asynchronous distributed dynamic economic dispatch inactive distribution networks, (presentation),
Distributed economic dispatch for active distribution networks with virtual power plants, (presentation),
Security evaluation under N-1 for active distribution networks coordinated with transmission grid, (poster)
IEEE 2th Conference on Energy Internet and Energy System Integration (EI2), (Beijing, China, Oct., 2018)
Risk assessment with high distributed generations penetration considering the interaction of transmission and distribution grids, (poster)
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Data-driven Piecewise Linearization for Three-Phase Power Flow in Distribution Grids
Link to the video series.
Abstract: As the penetration of distributed renewable energy increases, the stochastic power flow (SPF) method is becoming an increasingly essential tool to analyze the uncertainties in active distribution networks. This paper proposes a data-driven power flow (PF) linearization approach for three-phase SPF calculation. This three-phase piecewise linear power flow (LPF) model mitigates the errors of model based PF linearization approaches by approximating the nonlinear PF equations in a data-driven manner. Considering the challenges caused by the collinearity of the training data and the nonlinear nature of the PF model, an improved K-plane regression algorithm is proposed to achieve piecewise linear regression, which is implemented to obtain the piecewise LPF model offline. Based on the trained piecewise LPF model, we propose an online SPF calculation process that incorporates the Nataf transformation and the Monte Carlo method. The proposed SPF can handle complex operational conditions such as the correction of random variables and three-phase unbalance. Numerical tests demonstrate the proposed approach can tackle the issues of data collinearity and correlation, as well as achieve satisfactory calculation accuracy with high computational efficiency under different scenarios, which indicates its promising implementation value in SPF analysis in active distribution networks.
Jiaqi Chen
Jiaqi Chen (Graduate Student Member, IEEE) received the B.S. degree in electrical engineering and automation from Shandong University, Jinan, China, in 2017, and the M.S. degree in electrical engineering from Tsinghua University, Beijing, China, in 2020. She is currently pursuing the Ph.D. degree in electrical engineering with the Department of Electrical and Computer Engineering, University of Wisconsin–Madison, Madison, WI, USA. Her research interests include data- driven method and its application in power systems, optimization, and control in active distribution system.