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US Funding For Solar Systems Integration & AI Applications In Solar Energy

This second round of US DOE Solar Energy Technologies Office projects we’re looking at concern Systems Integration and AI Applications in Solar Energy with Emphasis on Machine Learning. Below are DOE summaries of how it intends to help on these topics as well as awardee details from the DOE.

Continuing a decades-long trend, the US Department of Energy (DOE) has put a few million dollars into further research and development of solar power technologies — $130 million, to be precise.

The goals of the funding are to “reduce the cost of solar, increase U.S. manufacturing competitiveness, and improve the reliability of the nation’s electric grid.”

$130 million may sound like a lot on the surface, but in the context of the US Department of Energy budget, it’s tiny. Also, considering that the $130 million gets spread across dozens of projects, the support for each effort is probably not as significant as it seems at first glance.

Though, $130 million is $130 million more than $0 — and a few million dollars here and a few million dollars there can lead to some exciting successes. So, we’re taking a look at the 67 research projects across 30 states that the DOE’s Office of Energy Efficiency and Renewable Energy’s Solar Energy Technologies Office is sending $130 million to.

This second round of projects that we’re looking at in this article concern Systems Integration and AI Applications in Solar Energy with Emphasis on Machine Learning. Below are DOE summaries of how it intends to help on these topics as well as awardee details from the DOE:


Systems Integration

“$34 million for 10 research projects that will develop resilient community microgrids to maintain power during and restore power after man-made or natural disasters, improve cybersecurity for PV inverters and power systems, and develop advanced hybrid plants that operate collaboratively with other resources for improved reliability and resilience.” Awardee details from the DOE:

Clean Energy States Alliance

Project Name: Effective Knowledge Dissemination for LMI Solar: The Roles of Community Organizations and State Governments
Location: Montpelier, VT
DOE Award Amount: $1,100,000
Awardee Cost Share: $270,000
Principal Investigator: Warren Leon
Project Summary: This project will identify ways that state energy agencies and community organizations can efficiently and effectively work together to reduce the cost of expanding access to solar by studying knowledge dissemination and collaboration practices in these partnerships. This project will also develop case studies of successful state/community collaborations and provide technical assistance to community organizations and state energy agencies trying to work together to increase solar access.

Electric Power Research Institute

Project Name: Customer Options Landscape: Opportunities for Co-Adoption and Technology Evolution of Distributed Energy Resources (CO-LOCATED)
Location: Palo Alto, CA
DOE Award Amount: $2,000,000
Awardee Cost Share: $500,000
Principal Investigator: Nadav Enbar
Project Summary: This project is examining how residential and commercial utility customers make decisions related to the co-adoption of solar power with other technologies, such as electric vehicles and energy storage. Using utility and survey data, the team will identify what drives customer decisions about co-adoption and assess the cost efficiencies and value of co-adoption to consumers. This data will be integrated into NREL’s open source agent-based adoption forecasting tool, dGen, which can be used by utilities, grid operators, and other stakeholders to better incorporate solar energy in integrated resources planning and grid reliability planning.

National Renewable Energy Laboratory

Project Name: Incorporating Solar Panels into the Ecosystem of Whole Home Re-roofing and New Construction Projects
Location: Golden, CO
DOE Award Amount: $2,000,000
Awardee Cost Share: $690,000
Principal Investigator: Ben Sigrin
Project Summary: Previous analysis indicates that installing rooftop solar during new construction or re-roofing could lower soft costs by as much as 73% compared to typical rooftop installation, but these have yet to become widespread. This project is identifying the barriers to these types of solar installation. The team will identify knowledge gaps of home builders, roofers, and consumers about solar in new construction and re-roofing and test strategies for disseminating information about the topic.

University of Delaware

Project Name: Solar Rooftop PV and Residential Co-Adoption and Diffusion at the Intersection of Attitudes, Economics, Norms, and Policy Nudges
Location: Newark, DE
DOE Award Amount: $2,000,000
Awardee Cost Share: $500,000
Principal Investigator: Jeremy Firestone
Project Summary: This project is researching the decision-making of photovoltaic (PV) adopters, electric vehicle (EV) adopters and PV/EV co-adopters. This project will identify possible drivers of co-adoption, such as whether providing certain information about solar and electric vehicles impacts the decision to co-adopt. Then, the team will evaluate these potential drivers. This research will lead to a large, first-of-its-kind national dataset of PV-EV co-adopters and factors that facilitate or hinder co-adoption.

University of Michigan

Project Name: Mapping and Bridging Barriers in Knowledge Flows of How Solar Photovoltaics Affect Rural Community Economies
Location: Ann Arbor, MI
DOE Award Amount: $1,000,000
Awardee Cost Share: $280,000
Principal Investigator: Michael Craig
Project Summary: Rural counties do not necessarily include utility-scale solar in their zoning rules, which can cause delays and increase soft costs for solar developers. This project researches how rural communities in the Great Lakes region learn about and decide whether to zone for utility-scale solar. This project will also test various information dissemination strategies and study the impact of utility-scale solar on the economies of rural areas.

Yale University

Project Name: Patterns and Value of Co-Adoption of Solar and Related Energy Technologies
Location: New Haven, CT
DOE Award Amount: $1,600,000
Awardee Cost Share: $410,000
Principal Investigator: Kenneth Gillingham
Project Summary: This project will conduct two field trials to study the combined adoption of solar and other energy technologies, such as energy storage and electric vehicles. The field trial in Alaska will study the connection between energy efficiency audits and solar energy adoption. The field trial in Connecticut will study adoption patterns of residential solar and behind-the-meter energy storage and the value they add to the grid. The team will also examine the effect of this adoption on residential energy use and whether there is any change when electric vehicles are also involved.


AI Applications in Solar Energy with Emphasis on Machine Learning

“$7.3 million for 10 projects that use AI and machine learning to optimize operations and solar forecasting, improve situational awareness on the distribution system and behind the meter, and enable the integration of more solar generation.” Awardee details from the DOE:

Arizona State University (1)

Project Name: Photovoltaic Plant Predictive Maintenance Optimization under Uncertainties Using Probabilistic Information Fusion
Location: Tempe, AZ
DOE Award Amount: $750,000
Awardee Cost Share: $380,000
Principal Investigator: Hao Yan
Project Summary: This project uses artificial intelligence and machine learning methods to develop algorithms that will optimize operation and maintenance of photovoltaic (PV) power plants by detecting and classifying anomalies, predicting failures, and scheduling maintenance activities. Predictive maintenance is important to maintain the long-term financial performance of solar PV plants and reduce downtime. Real-time monitoring data such as power output, temperature, and weather information can be used to identify the common fault class patterns using a hierarchical generative model and probabilistic information fusion framework in the sensor level and system level. This project will use the power plant operated at Arizona State University and Arizona Public Service as the case study to demonstrate the proposed technology for predictive maintenance.

Arizona State University (2)

Project Name: Artificial Intelligence for Robust Integration of AMI and PMU Data to Significantly Boost Renewable Penetration
Location: Tempe, AZ
DOE Award Amount: $750,000
Awardee Cost Share: $190,000
Principal Investigator: Yang Weng
Project Summary: This project uses artificial intelligence and machine learning techniques to combine, synchronize, clean-up, and interpolate electric data from numerous sources in order to more accurately estimate the state of the electric grid. This will ultimately allow for the interconnection and/or operation of more photovoltaic (PV) systems and other distributed energy resources (DER) in power systems while simultaneously enhancing reliability, resiliency and power quality. The research team will innovative measurement synchronization, data mining for bad data detection and identification, robust algorithm design of machine learning for unobservable areas.

Camus Energy

Project Name: Improving Grid Awareness by Empowering Utilities with Machine Learning and Artificial Intelligence
Location: San Francisco, CA
DOE Award Amount: $750,000
Awardee Cost Share: $750,000
Principal Investigator: Cody Smith
Project Summary: This project uses artificial intelligence and machine learning methods to provide grid operators and engineers with real-time analysis and visualization capabilities of the electric power system. Cloud computing approaches to system monitoring and real-time analytics provide a model for leveraging multiple data sources to correlate, verify, and interpret system telemetry in environments with high scale and low data fidelity. Experience from systems design in related fields shows that in sufficiently complex systems, no single data source can be entirely accurate or trustworthy, but an approach that leverages multiple sources and applies intelligent data interpretation can provide an extremely reliable, high-fidelity systems view. This project leverage the team’s past experience with cloud systems monitoring approaches and abundant data for artificial intelligence model training, along with capabilities in integrated power system simulation and monitoring data analytics with machine learning and deep learning to provide advanced, integrated situational awareness for the distribution grid and contributions to area-wide flexibility.

Case Western Reserve University

Project Name: Robust PV Performance Loss Rate Determination and Power Forecasting: Using Spatiotemporal Graph Neural Network Models in a Reliable System-Topology-Aware Learning Framework
Location: Cleveland, OH
DOE Award Amount: $750,000
Awardee Cost Share: $200,000
Principal Investigator: Roger French
Project Summary: This project uses artificial intelligence and machine learning techniques to analyze data from a large number of neighboring photovoltaic (PV) systems in order to extract information about their short- and long-term performance. Machine learning methods will be used to overcome data quality issues affecting individual plants. The development of spatiotemporal graph neural network models will address critical questions of long- and short-term performance for fleets of PV plants. The proposed learning techniques advance both analytical techniques for long-term performance of PV power plants and deep learning techniques, and can mitigate the negative impact of PV plant or sensor failure or unreliable input data.

National Renewable Energy Laboratory

Project Name: Deep-Learning-Powered Probabilistic Net-Load Forecasting with Enhanced Behind-the-Meter PV Visibility
Location: Golden, CO
DOE Award Amount: $750,000
Awardee Cost Share: $230,000
Principal Investigator: Rui Yang
Project Summary: This project uses artificial intelligence and machine learning techniques to predict the electric load one day in advance in areas that have large amounts of behind-the-meter solar. That information will allow operators to manage the electric grid more efficiently and cost-effectively. The deep-learning-powered probabilistic forecasting framework for day-ahead net-load at substations will separate behind-the-meter photovoltaic (PV) generation from net-load measurements and quantify its impact on net-load patterns. A novel transfer learning method will be developed to transfer the knowledge learned from geographic locations with rich sensor data to diverse locations where only the substation measurements are available. The framework will be validated using measurement data from Hawaiian Electric Company and on the Solar Forecast Arbiter platform.

North Carolina State University

Project Name: Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage
Location: Raleigh, NC
DOE Award Amount: $750,000
Awardee Cost Share: $190,000
Principal Investigator: Wenyuan Tang
Project Summary: This project leverages artificial intelligence and machine learning techniques to predict the electric load in areas with large amounts of solar energy and enable more efficient grid operation. The technology will also be able to forecast the capacity available to the grid from electric loads that can be turned on or off depending on the balance between electric demand and generation. Recent advances in artificial intelligence can enhance the accuracy of net-load forecasting, the observability of net-load variability, and the understanding of the coupling between net-load and demand response potentials. The two models under development for addressing hybrid probabilistic forecasting can provide better spatiotemporal information.

Northeastern University

Project Name: Graph-Learning-Assisted State and Event Tracking for Solar-Penetrated Power Grids with Heterogeneous Data Sources
Location: Boston, MA
DOE Award Amount: $750,000
Awardee Cost Share: $420,000
Principal Investigator: Ali Abur
Project Summary: This project uses artificial intelligence and machine learning techniques to integrate electric data and use it to calculate the state of the electric network. The resulting tool will be able to detect connectivity changes and faults in the grid and update grid models accordingly, which will improve the situational awareness of power grids with large amounts of solar energy by exploiting a large volume of data and measurements available from a highly diverse set of sources. The project will also provide tools to detect and identify network topology changes due to unexpected disturbances or switching events by exploiting the recently developed sparse estimation methods in the data analytics area.

Pacific Northwest National Laboratory

Project Name: VRN3P: Variational Recurrent Neural Network Based Net-Load Prediction under High Solar Penetration
Location: Richland, WA
DOE Award Amount: $750,000
Awardee Cost Share: $220,000
Principal Investigator: Soumya Kundu
Project Summary: This project is using artificial intelligence and machine learning techniques to create an open-source tool that can predict the day-ahead electric load in areas with large amounts of behind-the-meter solar and deliver savings in the operation of the electric network. The project team will develop and validate a variational recurrent model-based algorithm for time-series forecasting of net-load under high solar penetration scenarios. Considering the uncertainty of cloud covering, solar irradiance, geographical information, and end-use load, theoretically guaranteed tight bounds on the net-load prediction will be delivered. Comprehensive validation of the proposed variational recurrent model-based net-load prediction algorithm will be performed using real-world industry and utility data.

Stanford University

Project Name: Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity
Location: Redwood City, CA
DOE Award Amount: $500,000
Awardee Cost Share: $250,000
Principal Investigator: Ram Rajagopal
Project Summary: The widespread adoption of rooftop photovoltaic systems and grid-scale solar systems requires a significant change in how system operators, utilities and solar system providers map system adoption, track it is impact, and plan new deployments. Currently available information often lacks crucial details about time and location. The availability of such information would change how the system is planned and managed. This proposal plans to use artificial intelligence (AI) and machine learning (ML) methods to map the deployment of photovoltaic (PV) systems and the distribution network across the country with high accuracy and detail. The final product will be an up-to-date country-wide database that can be made available to researchers or utilities, while protecting private data. The team will utilize advances in AI to combine information available at a large scale — such as satellite imagery, Google street view images, and high-resolution irradiance data from weather stations — to generate the historical location and size of all solar deployments in any given country and reconstruct the transmission or distribution grids as necessary. The project will develop novel machine learning approaches to use this data and address a variety of applications such as identifying bottlenecks, estimating the hosting capacity of distribution systems, locating electric storage, improving wholesale price predictions, and creating more accurate models of consumer adoption.

University of Wisconsin–Madison

Project Name: Learned Productivity Under Variable Solar Conditions
Location: Madison, WI
DOE Award Amount: $750,000
Awardee Cost Share: $190,000
Principal Investigator: Michael Wagner
Project Summary: This project leverages artificial intelligence and machine learning techniques to model a number of concentrating solar thermal power (CSP) plant operations in order to assist human operators in their decisions, especially during variable cloudiness conditions. The machine learning techniques will be applied to extensive, high-resolution, inferred DNI data, cloud profile and vector data, and related solar field thermal collection data in order to develop prescriptive models to optimize solar field collection under variable conditions while minimizing long-term receiver damage and other negative effects. The project will validate the method at an operating CSP facility and publish methodological details for broader use.


The first article in this series: US Government Puts $130 Million More Into Solar Tech.

Followup articles in this series:


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Written By

Zach is tryin' to help society help itself one word at a time. He spends most of his time here on CleanTechnica as its director, chief editor, and CEO. Zach is recognized globally as an electric vehicle, solar energy, and energy storage expert. He has presented about cleantech at conferences in India, the UAE, Ukraine, Poland, Germany, the Netherlands, the USA, Canada, and Curaçao. Zach has long-term investments in Tesla [TSLA], NIO [NIO], Xpeng [XPEV], Ford [F], Amazon [AMZN], Piedmont Lithium [PLL], Lithium Americas [LAC], and Starbucks [SBUX]. But he does not offer (explicitly or implicitly) investment advice of any sort.

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