Multi-Level Optimal Power Flow Solver in Large Distribution Networks

Zhou, Xinyang; Chen, Yue; Liu, Zhiyuan; Zhao, Changhong; Chen, Lijun

doi:10.1109/smartgridcomm47815.2020.9303000

Home/Archive/DOE OSTI

DOE OSTI · 1766868

Multi-Level Optimal Power Flow Solver in Large Distribution Networks

Zhou, Xinyang·Chen, Yue·Liu, Zhiyuan·Zhao, Changhong·Chen, Lijun

View original source ↗Export metadata ↓

Abstract

Solving optimal power flow (OPF) problems for large distribution networks incurs high computational complexity. We consider a large multi-phase distribution network of tree topology with a deep penetration of active devices. We divide the network into collaborating areas featuring subtree topology and subareas featuring subsubtree topology. We design a multilevel implementation of the primal-dual gradient algorithm to solve the voltage regulation OPF problems while preserving nodal voltage information and topological information within areas and subareas. Numerical results on a 4,521-node system verify that the proposed algorithm can significantly improve the computational speed without compromising any optimality.

Explore related subjects

41 EE - Solar Energy Technologies Office (EE-4S)distributed algorithms gradient methods large-scale systems privacy preservation voltage control

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Zhou, Xinyang, Chen, Yue, Liu, Zhiyuan, Zhao, Changhong, Chen, Lijun. 2020-12-30. Multi-Level Optimal Power Flow Solver in Large Distribution Networks. https://doi.org/10.1109/smartgridcomm47815.2020.9303000

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2021

A Machine Learning Framework for Bridging the Gap Between the Steady-State Scheduling and Dynamic Security Operation for Future Power Grids

This presentation was part of an invited talk for a panel session presented at the 2021 IEEE Power & Energy Society General Meeting, held 26-29 July 2021.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

Assessment of Cloud Mask Forecasts from the WRF-Solar Ensemble Prediction System: Preprint

Numerical weather prediction (NWP) models are important tools used by government agencies and the renewable energy enterprise to forecast solar radiation. Cloud prediction, a key process of NWP models, has the highest impact on the accuracy of solar forecasting. This study uses satellite observations from the National Solar Radiation Data Base (NSRDB) to evaluate the cloud mask forecast by the WRF-Solar Ensemble Prediction System (WRF-Solar EPS). Preliminary analysis of the data in 2018 demonstrates the need of further improvement in predicting thin and low-level clouds. The information obtained from this work will be used to enhance the WRF-Solar EPS in reproducing the cloud field over the contiguous U.S. and reducing solar forecasting errors.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

Evaluating Cell Temperature Models and the Effect of Wind Speed in PV System Capacity Testing: Preprint

Capacity testing is a routine procedure for assessing a photovoltaic system's performance relative to expectations. The most common test method involves fitting a regression model that predicts system output power using operating weather conditions including wind speed. Structural modifications to the regression model to incorporate wind in different ways improved the model's ability to fit measured system performance, but the observed improvements were small and unlikely to change the result of a capacity test. However, the results showed that the choice of reporting wind speed and inclusion or exclusion of wind speed in the performance model used as the test benchmark can significantly change the test result.

41 EE - Solar Energy Technologies Office (EE-4S)↗