Data-driven based coordinated smart inverter control for distributed energy resources

Conference · Thu Dec 01 00:00:00 EST 2022

DOI:https://doi.org/10.1049/icp.2023.0088· OSTI ID:1984349

Qiu, Wei ^[1]; Yin, He ^[2]; Wu, Yuru ^[2]; Bu, Siqi ^[3]; Chung, Chi Yung ^[3]; Park, Byungkwon  ^[4]; Kuruganti, Teja  ^[4]; Yadav, Ajay ^[4]; Xue, Yaosuo  ^[4]; Yao, Wenxuan ^[2]; Liu, Yilu ^[4]

---

1. University of Tennessee (UT)
2. University of Tennessee, Knoxville (UTK)
3. Hong Kong Polytechnic University
4. ORNL

Smart inverters (SI) for distributed energy resources (DER) are becoming popular since they have the ability to stabilize as well as restore the voltage and frequency of power systems. Aiming at establishing the mathematical models combined with SI control methods, multiple optimization methods are developed. However, the computational complexity of solving such a mathematical model with various uncertainties limits the real-time application of the SI control. To conquer this challenge, a data-driven-based SI control approach is developed to achieve coordinated control in the high penetration DER system. First, an optimization problem for maximizing the active power generation and minimizing the power loss is designed using the Volt/VAR control. To reduce the time consumption, the recurrent neural network (RNN) is proposed to model the relationship between the uncertainties and control actions during the offline site. The RNN with different sub-structures such as the long short-term memory cell and gated recurrent unit cell are included to enrich the diversity of features. In the last stage, different experiment comparisons, including multiple uncertainties maps and stateof- art machine learning methods, are conducted to verify the effectiveness of the proposed method based on the IEEE 123 bus power system. The results demonstrate that the proposed method can effectively achieve a rapid and coordinated control with a lower error rate.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1984349
• Resource Relation: Conference: 12th IET International Conference on Advances in Power System Control, Operation and Management (APSCOM) - Hong Kong, , Hong Kong - 11/7/2022 5:00:00 AM-11/9/2022 5:00:00 AM
• Country of Publication: United States
• Language: English