Coordinated Ramping Product 

and Regulation Reserve 

Procurements Using Probabilistic 

Solar Power Forecasts

ESIG Spring Technical Workshop

Working Group—System Operations and Market Design

Albuquerque, NM, March 19, 2019

Venkat Krishnan, Ph.D.

Senior Engineer, Sensing and Predictive Analytics Group

Power Systems Engineering Center

National Renewable Energy Laboratory, Golden, CO, USA
Team

Johns Hopkins University: Benjamin Hobbs (PI), 

Qingyu Xu

National Renewable Energy Laboratory (NREL): 

Venkat Krishnan (Co-PI), Elina Spyrou, Paul Edwards, 

Haiku Sky

IBM: Hendrik Hamann (Co-PI), Rui Zhang

University of Texas at Dallas: Jie Zhang (Co-PI), 

Binghui Li

Industry partners: Amber Motley, Clyde Loutan, 

Rebecca Web (California Independent System Operator 

[CAISO]), Blagoy Borissov, Steven Rose (Midcontinent 

Independent System Operator [MISO])

2 Funding provided by DOE Solar Energy Technology Office (SETO) under Solar Forecasting II FOA
Project Motivation: Efficient System Operations

 Increasing variable renewable penetration

 Ramping and regulation issues 

2040 PV Energy Fraction

Rooftop

Source: NREL’s 2016 Standard Scenarios from ReEDS model

Procurement needs up-to-date information on energy forecasts and their 

uncertainties to result in appropriate reserves that reflect the risks of imbalances. 

Utility

3

 Current reserve procurement practice: based on 

historical data and offline analysis

 Requirements that are too conservative for most 

conditions but inadequate or risky for other 

conditions.

# Load Net Load

Up 313 731

Down 245 622

Ramp Events Analysis1

1 B. Huang, V. Krishnan, and B.-M. Hodge, “Analyzing the Impacts of Variable Renewable Resources on California Net-Load Ramp Events,” 

presented at the IEEE PES General Meeting, Portland, Oregon, August 6–9, 2018.
Project Summary

Objective: Integrate probabilistic short-term (0–6 hours ahead) and midterm (dayahead) solar power forecasts into operations of two ISOs: 

• CAISO

• MISO.

2. Integrate probabilistic 

forecasts into ISO 

operations for ramp product 

and regulation requirements.

3. Provide situational 

awareness via 

visualizations of 

probabilistic ramp forecasts 

and alerts.

4

Approach:

1. Advanced big data-driven “probabilistic” solar power forecasting technology

• IBM Watt-Sun and PAIRS: Big data information processing

• Machine learning approaches to blend outputs from multiple models.
• Introduced to ensure load-following flexibility in market clearing

– Ensure sufficient ramping capability in the current binding interval to meet the 

forecasted net load ramps (and their upward and downward uncertainties) in 

the future advisory intervals.

– Insure against insufficient ramp capability in real-time markets that might 

result in extreme prices. 

• Not spinning reserves, deployed after a contingency event.

• Not regulation reserves, used to meet fine-grained fluctuations. 

Flexible Ramping Product?

5 2 Figure source: CAISO, “Flexible Ramping Product: Revised Draft Final Proposal,” Dec. 17, 2015.
Flexible Ramping Product 

for Market Efficiency

Gain in market efficiency and the consequent savings (reduced price spikes, 

unplanned quick starts, uplift payments) 

much greater than

slight increase in energy market locational marginal price + ramp product 

payment. 

E

($20/

MWh)

Reg

SR

E

($10/

MWh)

Reg

SR

E

($20/

MWh)

Reg

SR

E

($10/

MWh)

Reg

SR

Gen 1 Gen 2

Gen 1 Gen 2

Scenario 1

Scenario 2

Ramp 

limited

✖

✔

✔

Generation scarcity (ramp-limited) event  real-time price spikes 

unplanned quick starts (+ regulation services to offset area control error)

Regulation and contingency reserves: “MW reserved” unavailable to 

the market when needed and results in real-time price spikes. (Make 

whole or uplift payments for unplanned starts.)

Market efficiency improves if future flexibility/ramping 

needs explicitly modeled.

6

Scenario 1

Scenario 2

Note: The cited examples in this slide assumes single-period dispatches without (Scenario 1) and with (Scenario 2) 

flexible ramping product (FRP). Multiperiod dispatches will help mitigate the ramping issues to a certain extent (as in 

NYISO and CAISO), but uncertainties in forecasted net-load ramps are still an issue that requires procuring FRP.
MISO: May 2016 in day-ahead 

market (24-h, foresight) and realtime economic dispatch (5-min)

CAISO3: Nov. 2016 in real-time 

market (unit commitment, 15-

min, foresight) and economic 

dispatch (5-min)

(Latest developments, to be included in the 

day-ahead market at 15-min. intervals.)

ISO-New England4 and Southwest Power 

Pool are also looking to implement ramp 

products.

Flexible Ramping Product: Implementation

Source: MISO presentation at FERC technical conference, June ’17

7

3 http://www.caiso.com/informed/Pages/StakeholderProcesses/CompletedClosedStakeholderInitiatives/FlexibleRampingProduct.aspx

4 https://www.iso-ne.com/static-assets/documents/2017/09/20170920-procurement-pricing-of-ramping-capability.pdf

5 https://www.spp.org/documents/29342/ramp%20product%20design.pdf

Note: Though the overarching objective of ramping product is the 

same, implementing entities have differences in the way ramping 

products are procured. Refer to the respective resources.
8

Probability of X MW forecast error * Penalty (price spike) = 

$/MWh price for procuring X MW flexibility (risk index)

Current Practice: Flexible Ramping Product 

Procurement Process

Step 1

Step 2

Step 3

(demand curve for uncertainty)

• For each hour, assess last 

30–40 days of historical 

data for “advisory interval” 

forecast errors.

• Introduce bias for weather 

events based on forecasts.

Note: Refer to Footnote 3 in previous slide for details and 

nuances of each step in the ISO process. This slide shows 

a high-level summary only.

7 Figure source: CAISO, “Market Performance and 

Planning Forum,” presentation slides, Feb. 20, 

2018. 

6 Figure source: CAISO, “Flexible Ramping Product: 

Revised Draft Final Proposal,” Dec. 17, 2015.
For a selected 

hour (4–5 

p.m.), we have 

four 15-min. 

upward 

forecast errors.

Current 

practice will 

aim to cover 

97.5 percentile 

of forecast 

errors.

Use of Probabilistic Forecasts

Probabilistic 

forecasts

Improvement over current practice: Robust, dynamic FRP procurements using latest 

probabilistic forecasts  avoid costly over-procurement and risky under-procurement.

9

15-min. solar 

forecast 

errors 

(historical 

data analysis 

of upward 

errors)
Big Data-Driven Probabilistic 

Forecasting System

Situation-dependent blending of

forecast models: 

• Apply multi-expert deep machine 

learning with historical forecasts and 

weather data. 

• Learn which model is better, when and 

where.

10

Leverage “big” data platform and 

advanced machine learning-based 

analytics to complement NWP 

models  high-fidelity probabilistic 

forecasts and adaptive and more 

accurate forecasts
PAIRS Big Data Platform

Physical Analytics Integrated Data Repository and Services (PAIRS)

Lagrangian Weather Forecast Models Climate

Models Sky Cam GOES-R RAP HRRR SREF NAM GFS ECMWF CFS

Spatial

Res. & 

Coverage

Local

10 m

Global

0.5~

1 km

U.S.

13 km

U.S.

3 km

U.S.

16 km/40 km

U.S.

5 km

Global

0.5 deg

Global

0.1 deg

Global

0.5 deg

Temporal

Resolution 1 min 15 min

15 min 

2D,

1 h 3D

15 min 

2D,

1h 3D

1 h (40 km); 

3 h (16 km) 1 h 3 h 1 h 6 h

Forecasting

Horizon 10 min 4 h 18 h 15 h 0–87 h 0–60 h

6–

192 h 0–60 h 6 mo

Ensemble

Forecast No No No No CTL, P1, P2, 

P3, N1, N2, N3 No No N.A. 4

members

Harmonized data curation: aligned to a global spatio-temporal 

reference and indexing system 

• GOES-R/16 satellite can generate up to 16 GB/d. 

• GOES-R will be used with deep convolutional 

neural network for next-generation solar forecast 

models. 
IBM Watt-Sun Output via IBM 

PAIRS Geoscope 

12 Contact: hendrikh@us.ibm.com or Rui.Zhang@ibm.com
Market Impact Assessment

• Ramping procurement passed on to ISO scheduling procedures 

• Align process with well-documented ISO procedures. 

• ~July 2019: Test 1 on modified IEEE 118-bus test system with 

high solar penetration

• Resource mix, load, and solar profiles that mimic ISOs.

• ~April/July 2020: Test 2 on Western Electricity Coordinating 

Council system, with CAISO as focus area. 

• Metrics: 

• Economic: Production costs, prices (average and real-time spikes), makewhole payments, procurement vs. actual deployment

• Reliability: Quick starts, area control error, and Control Performance 

Standard 2.

13
Visualization of Probabilistic Ramp Forecasts 

for Situational Awareness

2-hr

Expected ±yσ

up

ramp -ramp down-ramp

RTD RUC

1-hr look 

ahead

n-hr

RTD

Ramp 

alert

Will provide: 

1) Forecasted net-load ramps 

and their uncertainties

2) “Ramp alerts” at shorter 

timescales, when ramp 

forecast > available 

capability.

5

5

Up

Down

10 MW ± 3 MW

20$

5 10 20 30 40 50 60

Site-specific solar ramp visualization

Forecast look-ahead time (minutes) 

Regional solar power ramp 

forecasts: may identify 

economic output control 

options in event of ramping 

alerts.

14
NREL | 15

Ramp Visualization for Situational 

Awareness (RaViS): User Interface

Features:

• Forecast data from 

IBM integrated

• Probabilistic and 

deterministic values

• RaViS refresh rate of 

60 s

• User interface is 

implemented as a 

single-page Web 

application

• Open-source libraries

• Shows site-specific

metadata via hover

• Highly flexible and 

easily configurable

• Adaptable to other 

kinds of events: 

outage/trip, cyber 

threats.

Event Outlook

Geolocation All-regions 

Geolocation 

Region Detail

Forecast Detail

Contact: Venkat.Krishnan@nrel.gov or Paul.Edwards@nrel.gov, or Haiku.Sky@nrel.gov
Summary: Impact and Significance

• Probabilistic forecasting approach will significantly transform the forecasting 

software (for solar power, load, and wind).

• Enhance existing ISO optimizations by supplying probabilistic forecasts in the 

form of reserve procurement targets and visualization aids. 

 Has the ability to create a better way for markets to optimize the 

uncertainty embedded with renewable resources into market products.

• Future work: 

 Data-driven dynamic regulation requirements, a) finer resolution forecasts 

and error characterization, b) using deployed regulation and area control 

error historical data.

16
www.nrel.gov

NREL/PR-5D00-73568

Thank You

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, 

LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. 

Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office. The 

views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. 

Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. 

Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published 

form of this work, or allow others to do so, for U.S. Government purposes.
EXTRA SLIDES

18
CAISO Baseline Market Simulation

Day-ahead 

market

Bids submitted 

(incl. renewables)

Real-time 

unit 

commitment

TD-1 10:00 TH-75

Real-time 

dispatch

TD-2 18:00

Regulation 

requirements 

posted 

• Bids submitted

• Demand frequency 

update: 30 min.

Resolution

Horizon

Hourly 15-min. 5-min.

24 hours 1–3 h 65-min.

<TH-7.5

Market power mitigation and reliability runs not included.

(No virtual bids, single forecast across market participants, no reliability must-run units.)

Commit Fast- to long-start units Fast- and short-cycle time if 

<3 h 

+ medium start

N/A

Fixed: 3 h 

• Renewable forecast 

frequency update: 

5 min. (private)

Flexible Energy Scheduling Tool for Integrating Variable Generation 

(FESTIV) 

19
NREL | 20

Ramp Visualization for Situational 

Awareness (RaViS): Design

The user interface is implemented as a single page Web 

application in which all user interaction takes place within a 

customizable dashboard. This is achieved via a modular 

architecture (using open-source libraries) in which each 

functional aspect is contained in a set of files constituting a 

single component. 
Can Solar Resources Provide 

Flexible Ramping Product?

California load and net load ramp event characterization

Load (MW) Net Load (MW)

Up Down Up Down

Max 21,704 21,058 18,400 16,594

Min 2,566 2,561 2,270 2,271

Mean 5,406 8,075 4,317 5,694

Std 5,077 4,227 2,894 3,349

Ramp Magnitude: Load and Net Load

“Implicit ramp product” from 

renewable energy resources? 

Solar explicit ramp product?

21

... but also decrease large ramp 

magnitudes in some hours.

Solar (other renewable energy) resources contribute 

to increase in the frequency of ramp events ...

# Load Net Load

Up 313 731

Down 245 622

1 B. Huang, V. Krishnan, and B.-M. Hodge, “Analyzing the Impacts of Variable Renewable Resources on California Net-Load Ramp Events,” 

presented at the IEEE PES General Meeting, Portland, Oregon, August 6–9, 2018.
Solar Ramping Product Taxonomy

Quadrant 1: Natural ramp up

Net load

Storage

Solar

Net load Solar

Net load Solar

Net load Solar

Quadrant 3: Control/curtail Quadrant 2: Natural ramp down • Curtail (control ramp rate or store) and 

provide D-FRP

• When D-FRP $ > $ from energy and PTC?

Quadrant 4: Control/storage • Least likely?

• Controls or complementary tech will 

enable? For example, ramp rate control, 

storage integration?

Controllable Natural sync

U

-FRP

D

-FRP

Implicit FRP

22

 Ways solar resources can provide FRP and regulation, possible coordination 

with storage technologies 

 Impact of solar FRP on total FRP procurements (i.e., net load estimations), 

use of site-specific solar forecasts.
Example of Evolving CAISO 

Regulation Requirements

23

Source: Amber Motley, CAISO Market Performance and Planning Forum, presented on Jan. 18, 2017, Slide 1 7, 

www.caiso.com/Pages/DocumentsByGroup.aspx?GroupID=04839625-a0b1-4a88-825c-d7fe028481e5. 

• Use of historical data 

to set regulation 

requirements based 

on probabilistic 

criterion (automatic 

generation control 

needs)

• Opportunity to 

condition forecast of 

distribution on most 

recent weather.