Factorization Machine Learning for Disaggregation of Transmission Load Profiles with High Penetration of Behind-the-Meter Solar

Conference · Fri Dec 29 04:00:00 EST 2023

DOI:https://doi.org/10.1109/ECCE53617.2023.10362108· OSTI ID:2376150

Zhao, Zhenyu ^[1]; Moscovitz, Daniel ^[2]; Du, Liang ^[1]; Fan, Xiaoyuan ^[3]

---

1. Temple University
2. PJM Interconnection
3. BATTELLE (PACIFIC NW LAB)

The ever-growing high penetration of ubiquitously distributed energy resources, especially behind-the-meter solar (BTM) generations, has significant impacts on nodal load (i.e., net injection) profiles and consequently caused imperative operational challenges to system operators such as regional transmission organizations (RTOs). Illustrated by real-world nodal data and examples at PJM Interconnection, this paper first discusses the application and necessity of effectively extracting daily nodal load profiles in a non-intrusive manner. More importantly, a novel bi-level architecture, including Factorization Machines (FM) learning procedure has been proposed to effectively disaggregate not only one node but every node in an RTO service territory. Specifically, FM leaning is adopted to capture the interconnections between related features to better utilize the correlation between buses in the same region and between a single bus and the zonal load. The proposed bi-level technique is numerically validated using real-world, minute-level, normalized, and anonymized nodal data at PJM service territory.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 2376150
• Report Number(s): PNNL-SA-192618
• Country of Publication: United States
• Language: English

Related Subjects

behind the meter solar
load disaggregation
load modeling