Office of Energy Efficiency and Renewable Energy (EERE)

Department of Energy (DOE)

Office of Energy Efficiency and Renewable Energy (EERE)

Solar Forecasting II

Funding Opportunity Announcement (FOA) Number: DE-FOA-0001649

FOA Type: Initial

CFDA Number: 81.087

• Applicants must submit a Concept Paper by 5:00pm ET the due date listed above to be eligible to submit a Full Application.

• To apply to this FOA, applicants must register with and submit application materials through EERE Exchange at https://eere-Exchange.energy.gov, EERE’s online application portal.

• Applicants must designate primary and backup points-of-contact in EERE Exchange with whom EERE will communicate to conduct award negotiations. If an application is selected for award negotiations, it is not a commitment to issue an award. It is imperative that the applicant/selectee be responsive during award negotiations and meet negotiation deadlines. Failure to do so may result in cancelation of further award negotiations and rescission of the Selection.

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Table of Contents

I.Funding Opportunity Description

A.Description/Background

B.Topic Areas/Technical Areas of Interest

C.Applications Specifically Not of Interest

D.Authorizing Statutes

II.Award Information

A.Award Overview

i.Estimated Funding

ii.Period of Performance

iii.New Applications Only

B.EERE Funding Agreements

i.Cooperative Agreements

ii.Funding Agreements with FFRDCs

iii.Grants

iv.Technology Investment Agreements

III.Eligibility Information

A.Eligible Applicants

i.Individuals

ii.Domestic Entities

iii.Foreign Entities

iv.Incorporated Consortia

v.Unincorporated Consortia

B.Cost Sharing

i.Legal Responsibility

ii.Cost Share Allocation

iii.Cost Share Types and Allowability

iv.Cost Share Contributions by FFRDCs

v.Cost Share Verification

vi.Cost Share Payment

C.Compliance Criteria

i.Compliance Criteria

D.Responsiveness Criteria

E.Other Eligibility Requirements

i.Requirements for DOE/NNSA Federally Funded Research and Development Centers (FFRDC) Listed as the Applicant

ii.Requirements for DOE/NNSA and non-DOE/NNSA Federally Funded Research and Development Centers Included as a Subrecipient

F.Limitation on Number of Concept Papers and Full Applications Eligible for Review

G.Questions Regarding Eligibility

IV.Application and Submission Information

A.Application Process

i.Additional Information on EERE Exchange

B.Application Forms

C.Content and Form of the Concept Paper

i.Concept Paper Content Requirements

D.Content and Form of the Full Application

i.Full Application Content Requirements

ii.Technical Volume

iii.Statement of Project Objectives

iv.SF-424: Application for Federal Assistance

v.Budget Justification Workbook (EERE 335)

vi.Summary/Abstract for Public Release

vii.Summary Slide

viii.Subaward Budget Justification (EERE 335) (if applicable)

ix.Budget for DOE/NNSA FFRDC (if applicable)

x.Authorization for non-DOE/NNSA or DOE/NNSA FFRDCs (if applicable)

xi.SF-LLL: Disclosure of Lobbying Activities

xii.Waiver Requests: Foreign Entities and Performance of Work in the United States (if applicable)

xiii.U.S. Manufacturing Commitments

xiv.Data Management Plan

E.Content and Form of Replies to Reviewer Comments

F.Post-Award Information Requests

G.Dun and Bradstreet Universal Numbering System Number and System for Award Management

H.Submission Dates and Times

I.Intergovernmental Review

J.Funding Restrictions

i.Allowable Costs

ii.Pre-Award Costs

iii.Performance of Work in the United States

iv.Construction

v.Foreign Travel

vi.Equipment and Supplies

vii.Lobbying

viii.Risk Assessment

ix.Invoice Review and Approval

V.Application Review Information

A.Technical Review Criteria

i.Concept Papers

ii.Full Applications

iii.Criteria for Replies to Reviewer Comments

B.Standards for Application Evaluation

C.Other Selection Factors

i.Program Policy Factors

D.Evaluation and Selection Process

i.Overview

ii.Pre-Selection Interviews

iii.Pre-Selection Clarification

iv.Recipient Integrity and Performance Matters

v.Selection

E.Anticipated Notice of Selection and Award Dates

VI.Award Administration Information

A.Award Notices

i.Ineligible Submissions

ii.Concept Paper Notifications

iii.Full Application Notifications

iv.Successful Applicants

v.Alternate Selection Determinations

vi.Unsuccessful Applicants

B.Administrative and National Policy Requirements

i.Registration Requirements

ii.Award Administrative Requirements

iii.Foreign National Access to DOE Sites

iv.Subaward and Executive Reporting

v.National Policy Requirements

vi.Environmental Review in Accordance with National Environmental Policy Act (NEPA)

vii.Applicant Representations and Certifications

viii.Statement of Federal Stewardship

ix.Statement of Substantial Involvement

x.Subject Invention Utilization Reporting

xi.Intellectual Property Provisions

xii.Reporting

xiii.Go/No-Go Review

xiv.Conference Spending

VII.Questions/Agency Contacts

VIII.Other Information

A.FOA Modifications

B.Informational Webinar

C.Government Right to Reject or Negotiate

D.Commitment of Public Funds

E.Treatment of Application Information

F.Evaluation and Administration by Non-Federal Personnel

G.Notice Regarding Eligible/Ineligible Activities

H.Notice of Right to Conduct a Review of Financial Capability

I.Notice of Potential Disclosure Under Freedom of Information Act (FOIA)

J.Requirement for Full and Complete Disclosure

K.Retention of Submissions

L.Title to Subject Inventions

M.Government Rights in Subject Inventions

i.Government Use License

ii.March-In Rights

N.Rights in Technical Data

O.Copyright

P.Personally Identifiable Information (PII)

Q.Annual Compliance Audits

Appendix A – Cost Share Information

Appendix B – Sample Cost Share Calculation for Blended Cost Share Percentage

Appendix C – Waiver Requests: Foreign Entity Participation as the Prime Recipient and Performance of Work in the United States

1.Waiver for Foreign Entity Participation as the Prime Recipient

2.Waiver for Performance of Work in the United States

Appendix D - Data Management Plan

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1

1. Funding Opportunity Description

A. Description/Background

Background

The DOE SunShot Initiative is a collaborative national effort launched in 2011 that aggressively drives innovation to make solar energy cost competitive, without subsidies, with traditional energy sources before the end of the decade. SunShot supports efforts by private companies, universities, non-profit organizations, state and local governments, and national laboratories to drive down the cost of solar electricity to $0.06 per kilowatt-hour, without incentives, by the year 2020, and to $0.03 per kilowatt-hour by 2030.

Within the SunShot Initiative, the Systems Integration (SI) subprogram seeks to enable the widespread deployment of high penetrations of safe, reliable, secure, and cost effective solar energy on the nation’s electricity grid by addressing the associated technical and regulatory challenges through targeted technology research, development, and demonstration (RD&D). Specifically, timely and cost-effective interconnections, optimal system planning, integration of solar forecast, real-time monitoring and control of distributed solar systems, and maintaining grid reliability are all challenges that require engineering innovations and technology breakthroughs. To proactively anticipate and address these challenges associated with a scenario in which hundreds (100s) of gigawatts of solar power are interconnected to the electricity grid, the SunShot Systems Integration subprogram has identified four broad, inter-related technical areas, as depicted in Figure 1, and described below.

Figure 1: DOE SunShot Systems Integration Vision

Grid Performance and Reliability: Maintain and enhance the efficiency and reliability of electricity transmission and distribution systems in a cost-effective, safe manner with hundreds of gigawatts of solar generation deployed onto the nation’s electricity grid.

Dispatchability: Ensure that solar power is available on-demand, when and where it is needed, in the desired quantities, and in a manner that is comparable to or better than conventional power plants.

Power Electronics: Develop inverters/converters and other intelligent devices that maximize the power output from solar power plants and interface with the electricity grid (or end use circuits), while ensuring overall system performance, safety, reliability, and controllability at minimum cost.

Communications, Sensing, and Data Analytics: Develop technologies and infrastructure that allow for more effective monitoring and control of solar energy generation, transmission, distribution and consumption under wide spatial and temporal scales.

These interrelated technical areas also directly support the Energy Department’s broader Grid Modernization Initiative, a crosscutting effort that aligns grid modernization efforts across the Office of Energy Efficiency and Renewable Energy (EERE), the Office of Electricity Delivery and Energy Reliability (OE), and the Office of Energy Policy and Systems Analysis (EPSA). The Grid Modernization Initiative focuses on the development of the tools and technologies that measure, analyze, predict, protect, and control the grid of the future. This funding opportunity will help address several key challenges identified in the Grid Modernization Multi-Year Program Plan under the technology areas of Sensing and Measurement, and System Operations, Power Flow, and Control. Progress on improving accuracy of forecasts at various temporal and spatial scales, and functional integration with Energy Management Systems (EMS) is considered crucial for the safe, reliable, and cost-effective integration of renewable energy.

The forecasting of power generated by variable energy resources such as wind and solar has been a subject of academic and industrial research and development for as long as significant amounts of these renewable energy resources have been connected to the electric grid. The progress of forecasting capabilities has largely followed the penetration of the respective resources, with wind forecasting having achieved a more mature state-of-the-art compared to its solar equivalent[1].

Still, in the last five years, there has been substantial and material progress in the state-of-the-art of solar forecasting[2]. Numerical Weather Prediction (NWP) models became more sophisticated in assessing cloud interactions with aerosols; infrared satellite imagery allowed discovery of pre-sunrise cloud formations; advanced data processing methods such as deep machine learning became increasingly accessible; probabilistic forecasts began replacing deterministic ones; and, in balancing areas with high PV penetration, solar forecasts are now used operationally.

As solar electricity penetration in the distribution grid is increasing, the power generated by those Distributed Energy Resources (DERs) needs to be taken into account in the operations and planning of Independent Power Producers (IPPs), Independent System Operators (ISOs), and Balancing Authorities (BAs)[3]. Since the reliable performance of the bulk grid depends on the balancing of a continuously varying load with equal amounts of generation, knowledge of the load ahead of time (forecasting) is necessary for the economically optimal – and technically feasible – dispatch of generation sources.

Solar electricity generation presents two challenges to the load-generation balancing process. First, a very large fraction of it comes from distributed PV systems (DPV) that are connected behind-the-meter (BTM) and are thus only visible to the system operator as (reduced) load. This gives rise to the “net load” curve which represents passive load net of (i.e. “masked” by) solar PV electricity generation. Second, PV plants, even utility-scale that are connected directly to the distribution or transmission systems as generation assets, are not normally dispatchable due to the variability of their fuel (solar resource).

The first challenge manifests as load uncertainty that cannot be adequately described by the traditional load forecasting techniques, mainly because of the uncertainty associated with forecasting solar irradiance. The second one requires that any short-term imbalances due to over/under-generation by PV plants be compensated by ramping other dispatchable resources (reserves), utilizing demand/response, and/or by curtailing solar generation where possible, resulting in increased operational costs. Those costs comprise fuel costs from expensive generators, start & shutdown costs for fast-responding generators, transaction and other costs for demand/response and they scale with increased solar penetration[4]. These challenges can be mitigated by an improved-accuracy forecast of the solar power generation.

Because solar power generation depends mostly on incident irradiance, the cost-efficient integration of significant amounts of solar electricity in the grid ultimately depends on the ability to forecast accurately Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) at various time horizons.

However, knowledge of the future level of irradiance is not by itself adequate for the calculation of solar power output. Knowledge of the attributes of the interconnected systems (such as DC and AC nameplate capacities, orientation, PV module and inverter properties, etc.) is also necessary, and that information can be largely elusive, inaccurate, or outdated for BTM systems. Therefore, an advanced capability of modeling the output from large numbers of PV plants is also essential for the network operator[5].

At the same time, efficient operation of the grid requires the accurately projected contribution of solar generation to be presented in a format that allows optimally-timed decision making by the operators and/or the automated systems they use during Unit Commitment and Economic Dispatch operations.

In summary, from a load-balancing perspective, the reliable and economically optimal operation of an electric grid with high penetration of solar (especially distributed solar) generation depends on:

1. Accurate forecasting of the solar irradiance over the area of interest, with 1-km spatial resolution and temporal resolutions that range from 5 minutes to hourly for time horizons between 0 and 72 hours, with 0-6 hour and day-ahead horizons being of particular importance;

2. Accurate forecasting of solar power output (and its evolution in time, including variability) over the area of interest, including an estimate of the forecast’s uncertainty; and

3. Effective integration of the projected solar power output information with the systems used to manage and operate the network and other generation sources.

The accuracy of the irradiance forecast at a given location over any time horizon depends primarily on the accuracy of predicting the opacity of any clouds that might be present in the path between the solar disk and the solar collector array. Despite the considerable recent progress in solar forecasting with a variety of methodologies that are optimized for different forecast horizons (Figure 2) the forecast skill is affected by specific local conditions, such as the marine layer in the coastal region of California[6]. As for particular cloud types and sizes, even if they are detected it can be more challenging to predict their evolution. Additionally, the spatial resolution of these techniques may be limited by computational capacity. Therefore, improvements in cloud detection, cloud creation and dissipation, and modeling of atmospheric physics are still active areas of research.

Figure 2: Forecast model classification based on temporal and spatial resolution[7]. Colored outlines show resolutions for which a particular type of forecast is most accurate or applicable.
AR(I)MA: Auto-Regressive (Integrated) Moving Average; CARDS: Coupled Auto-Regressive and Dynamical System; ANN: Artificial Neural Network; WRF: Weather Research & Forecasting model; MMS: Meteorological Measurement System; GFS: Global Forecast System; ECMWF: European Center for Medium-range Weather Forecasts; NWP: Numerical Weather Prediction models

Previously funded projects

The DOE has funded a number of forecasting-related projects during this decade. Under the first Solar Forecasting FOA[8] published in 2012, the National Center for Atmospheric Research (NCAR) and IBM Corp. led two consortia with partners from industry, academia, and national laboratories targeting a range of objectives, from quantifying the value of solar forecasting and improving the accuracy of short-term and day-ahead forecasts, to calculating the economic benefits of improved solar forecasting. To meet those objectives, the awardees and their collaborators engaged in a long list of research activities that, among others, included definition of metrics for forecast accuracy, advection algorithms, radiative transfer, power conversion, and analysis of economic effects. The awardees were also mandated to demonstrate their products integrated with balancing authority operations and to disseminate results to the broader community via publications, workshops and technical reports.

Improvements in the accuracy of irradiance forecast have a direct impact on the cost of energy that must generated to meet demand. The impact depends heavily on conditions specific to each region, including penetration level, energy mix, conventional fuel cost, and the technology of available generators. Production cost models set up by the NCAR and IBM-led teams have shown that an improvement of 50% in terms of forecast accuracy (50% reduction in Mean Absolute Error) can lead to annual savings of $1.1M for the PSCo territory[9] with an assumed penetration of ~8% (by energy) and to annual savings of $13.2.M for the ISO-NE territory[10] with an assumed penetration of 13.5%.

The SUNRISE FOA[11], issued in 2013, resulted in funding 3 forecasting-related projects, each led by Clean Power Research; University of California, San Diego; and the Hawaiian Electric Company. The awardees and their collaborators worked on behind-the-meter PV fleet forecasting, forecast optimization, and visualization of forecasts. Again, all projects culminated in the demonstration of the research and development products in the operational flow of balancing authorities.

Technology Office Objectives

The DOE solicited input from a significant number of stakeholders regarding areas in solar forecasting that are in need of further development via a workshop[12] and a tailored questionnaire in order to shape the objectives of this FOA.