skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: DEEP Solar: Data DrivEn Modeling and Analytics for Enhanced System Layer ImPlementation

Abstract

Realizing the SETO 2030 mission of reducing solar energy costs to 3-5 c/kWh will require innovative enabling research on effective, cost-efficient integration of local PV within distribution systems. However, the intermittent and variable nature of PVs compels operators to impose conservative hosting capacity constraints. Given the extremely high variability of (intermittent and unpredictable) solar energy generation, relaxing the capacity constraints (which are currently around 15%) and achieving 100% or greater integration of renewables will require a fundamental transformation of the power grid via the utilization of exponentially larger amounts of AMI enabled fine-grained data. To address the challenges in increasing the penetration of renewable energy based DERs, this project envisions an Enhanced System Layer (ESL) at the distribution network level that is reliable, cost-effective and scalable to millions of Distributed Energy Resources (DERs)/devices. This includes developing: 1) Transformative and highly scalable machine learning based predictive analytics tools that plug into distribution system planning and provide real-time situational awareness at the distribution level for short and long-term operational planning. The tools will be built using novel data-driven energy models of millions of active nodes with AMI, 2) Adaptive stochastic analysis and optimization algorithms for real-time grid operations, 3) Dynamic Scenario Analysismore » using parallel Cloudenabled implementations with < 1 minute computational cycle times.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. Univ. of Southern California, Los Angeles, CA (United States)
Publication Date:
Research Org.:
Univ. of Southern California, Los Angeles, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
OSTI Identifier:
1603790
Report Number(s):
DOE-USC-0008003
DOE Contract Number:  
EE0008003
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; data driven modeling; data driven optimization; reinforcement learning; machine learning; combinatorial optimization; smart grid; renewable integration

Citation Formats

Prasanna, Viktor, Kannan, Rajgopal, Kuppannagari, Sanmukh, Srivastava, Ajitesh, Cheung, Chung Ming, Rompokos, Athanasios, and Zhang, Chi. DEEP Solar: Data DrivEn Modeling and Analytics for Enhanced System Layer ImPlementation. United States: N. p., 2020. Web. doi:10.2172/1603790.
Prasanna, Viktor, Kannan, Rajgopal, Kuppannagari, Sanmukh, Srivastava, Ajitesh, Cheung, Chung Ming, Rompokos, Athanasios, & Zhang, Chi. DEEP Solar: Data DrivEn Modeling and Analytics for Enhanced System Layer ImPlementation. United States. https://doi.org/10.2172/1603790
Prasanna, Viktor, Kannan, Rajgopal, Kuppannagari, Sanmukh, Srivastava, Ajitesh, Cheung, Chung Ming, Rompokos, Athanasios, and Zhang, Chi. 2020. "DEEP Solar: Data DrivEn Modeling and Analytics for Enhanced System Layer ImPlementation". United States. https://doi.org/10.2172/1603790. https://www.osti.gov/servlets/purl/1603790.
@article{osti_1603790,
title = {DEEP Solar: Data DrivEn Modeling and Analytics for Enhanced System Layer ImPlementation},
author = {Prasanna, Viktor and Kannan, Rajgopal and Kuppannagari, Sanmukh and Srivastava, Ajitesh and Cheung, Chung Ming and Rompokos, Athanasios and Zhang, Chi},
abstractNote = {Realizing the SETO 2030 mission of reducing solar energy costs to 3-5 c/kWh will require innovative enabling research on effective, cost-efficient integration of local PV within distribution systems. However, the intermittent and variable nature of PVs compels operators to impose conservative hosting capacity constraints. Given the extremely high variability of (intermittent and unpredictable) solar energy generation, relaxing the capacity constraints (which are currently around 15%) and achieving 100% or greater integration of renewables will require a fundamental transformation of the power grid via the utilization of exponentially larger amounts of AMI enabled fine-grained data. To address the challenges in increasing the penetration of renewable energy based DERs, this project envisions an Enhanced System Layer (ESL) at the distribution network level that is reliable, cost-effective and scalable to millions of Distributed Energy Resources (DERs)/devices. This includes developing: 1) Transformative and highly scalable machine learning based predictive analytics tools that plug into distribution system planning and provide real-time situational awareness at the distribution level for short and long-term operational planning. The tools will be built using novel data-driven energy models of millions of active nodes with AMI, 2) Adaptive stochastic analysis and optimization algorithms for real-time grid operations, 3) Dynamic Scenario Analysis using parallel Cloudenabled implementations with < 1 minute computational cycle times.},
doi = {10.2172/1603790},
url = {https://www.osti.gov/biblio/1603790}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {3}
}