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Analyzing the Impacts of 

Variable Renewable Resources 

on California Net-Load 

Ramp Events

Preprint 

Bing Huang, Venkat Krishnan,

and Bri-Mathias Hodge

National Renewable Energy Laboratory

Presented at the IEEE Power and Energy Society General Meeting

Portland, Oregon

August 5–10, 2018 

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Conference Paper

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August 2018
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Analyzing the Impacts of Variable Renewable 

Resources on California Net-Load Ramp Events 

Bing Huang, IEEE St. Member, Venkat Krishnan, IEEE Member, and Bri-Mathias Hodge, IEEE Senior Member

Abstract—This paper characterizes the ramping features of

multiple renewable resources, load and net-load in California

using 2016 measured data. The goal of this analysis is to 

understand the interactions between variable renewable resource 

ramps and net-load ramps, and delineate the positive and negative 

impacts. The findings indicated that although the frequency and 

uncertainty in the system level net-load ramp occurrences 

increased due to variable renewable integration, there was also a 

decrease in the average ramp up and down magnitudes by about 

20% and 30% respectively, along with decreases in their 

respective standard deviations by 40% and 20%. At the zonal 

level, the analyses also indicated a 4-5% decrease in the net-load 

standard deviations due to wind integration alone at various 

market scheduling periods. The paper finally concludes with

future research needs to better integrate variable renewable ramp 

forecasts into system operations and planning for economic 

utilization of resources. 

Keywords—Ramping, Renewables, Net-load Ramps, Flexible 

Ramping Product, CAISO, Ramp Forecasts, Reserves

I. INTRODUCTION

ramp event can be defined as large or rapid changes in 

power and it can appear at both generation as well as for 

load in the power system. Such variations in either 

direction (i.e., up ramp when there is an increase and down 

ramp when there is a decrease) must be compensated in real 

time using load following resources to keep the generation-load 

balance and the consequent system frequency response in order. 

In situations when serious ramp events cause generation-load 

imbalances, especially of the generation scarcity types with 

insufficient ramp capability, power markets send out high realtime prices to quickly mitigate the scarcity situation by tapping 

into reserves or unplanned quick start generation [1]. With 

increases in renewable generation penetration such as wind and 

solar, that induce higher amount of variability and uncertainty 

on the system net-load (i.e., total load minus renewable 

generation), it becomes challenging for the system operators to 

maintain the generation-load balance efficiently. To address 

this issue, market-based flexible ramping products (FRPs) have 

been proposed in the industry, which embeds foresight about 

the anticipated net-load ramps into the Independent System 

Operator’s (ISO) market clearing procedures that co-optimize 

energy, reserves, and ramping. This in turn allows ISOs to 

utilize the existing ramping capabilities better by timely 

procurement of ramping resources in the current dispatch

Bing Huang (binghuang@utexas.edu) is a graduate student at the University of 

Texas, Austin, and was an intern at National Renewable Energy Laboratory 

(NREL) during 2017 summer. Venkat Krishnan, Senior Engineer

(Venkat.krishnan@nrel.gov) and Bri-Mathias Hodge, Manager, Power System 

intervals for future needs [1]. In 2016, California ISO (CAISO) 

implemented FRP in its fifteen-minute real-time market (FMM) 

[2] and the Midcontinent ISO (MISO) in both its day-ahead and

5-min real-time markets [3].

Under the current design, FRPs are provided by fast

responding peaking units and fast start units, typically gas-fired. 

Cui et al. [4] and Chen et al. [5] studied the feasibility and 

impacts of wind resources offering ramping capacity into the

market clearing processes. In order for this concept to mature, 

it is important to understand the ramp events in the renewable 

generation and their interactions with the system net-load ramps 

better. Sevlian and Rajagopal et. al. [6] proposed an optimal 

ramp detection technique and analyzed the distribution of 

different wind power ramp features using data from the 

Bonneville Power Authority (BPA) and NREL Western 

Integration Dataset. Kamath [7] showed the effectiveness of a 

simple statistical analysis on the characterizations of wind 

generation ramp events, including distributions of severity 

levels, start time within the day and occurrence by month from 

a BPA dataset. Based on systematic studies on wind power 

changes at different time steps, Wan [8] studied the statistics of 

wind power ramp events from ERCOT data and inspected their 

impacts on the system net-load ramps. However, all of these 

works only focused on detection and characterization of wind 

ramp events, and there is a dearth of works that can relate the 

impact of variable renewable resource ramps on the system netload ramps and understand the changes in their summary 

statistics. 

Thus, the goal of this paper is to analyze the ramp events 

in the California system’s load and net-load data, and quantify 

the impact of variable renewable ramps on net-load ramps from 

both negative and positive perspectives. For this purpose, the 

optimized swinging door algorithm (OpSDA) developed in [9] 

was used in this study to detect and assess the ramp events and 

their features such as start times, magnitudes, duration and 

rates. Based on the ramp characterizations shown in Sections II 

(load and net-load) and III (renewable resources), the 

possibilities of future power market innovations for variable 

renewable ramp forecast integration are discussed in Section 

IV. Finally, Section V presents conclusions.

Design and Studies Group (bri.mathias.hodge@nrel.gov), are with the Power 

Systems Engineering Center at NREL. 

A
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II. LOAD AND NET-LOAD RAMPS CHARACTERIZATION

The 2016 California system measured data, at 1 min

resolution [10], was used to analyze the ramp up and ramp 

down events for load and net-load. The net-load is defined as 

system load minus renewable generation, including distributed 

generation, solar PVs, solar thermal, and wind power in 

California. Table I shows the number of ramp events detected 

in the load and net-load data (using OpSDA [9], with a 

qualifying ramp event defined as 5% of the total load), where 

we observe an increase in the net-load ramp events influenced 

by the addition of renewable generation. Fig. 1 shows the 

distributions of ramp event start times across the 24-hour 

period, where we observe that the ramp event occurrences are 

relatively less predictable in the net-load data (i.e., occurring 

throughout the day) compared to the load data which had ramps

mostly happening during morning and evening hours. 

TABLE I NUMBER OF RAMP EVENTS IN 2016 CAISO LOAD & NETLOAD DATA 

Load Net Load

Ramp Up 313 731

Ramp Down 245 622

Fig. 1 Daily Distributions of CAISO Ramp Up and Down Event Start Times 

Fig. 2 Distributions of CAISO Ramp Up and Down Event Magnitudes 

However, it is also interesting to note from Fig. 1 that there 

are less ramp events in the net-load data during early morning 

(5-7 am) and evening peak hours (16-18 pm). Upon analyzing 

ramp magnitudes, Fig. 2 shows that the probability of certain 

magnitudes (around 8-14 GW, 18-20 GW) decreases in the netload ramp data compared to the load data. The summary 

statistics in Table II for ramp magnitudes and rates further 

corroborate this finding, where we can observe that the ramps 

in the net-load have lower maximum and average magnitudes 

and rates, along with a smaller standard deviations compared to 

the load ramps. This indicates that the ramp magnitude and rates 

in net-load is distributed in lower values with a smaller range. 

Therefore, though the integration of renewables has increased 

the number of ramp events and their uncertainties, there are 

times when they seem to alleviate the net-load ramps in either 

direction, thereby implicitly providing ramping capability to the 

grid for reliable operation.

TABLE II 2016 CAISO LOAD AND NET-LOAD RAMP STATISTICS

Load (w/o renewables) Net load (% change)

Up Down Up Down

Ramp magnitude (MW)

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

Average 5,406 8,075 4,317(-

20%)

5,694(-

30%)

Std. Dev. 5,077 4,227 2,894(-

43%)

3,349(-

20%)

Ramp rate (MW/min)

Max 3,540 3,692 3,537 3,134

Average 104 118 80(-28%) 69(-41%)

Std. Dev. 370 439 252(-32%) 264(-40%)

III. RENEWABLE GENERATION RAMPS CHARACTERIZATION

Figure 3 shows the ramp up (left) and down (right) events

start time distributions for different renewable generation, that 

include distributed generation (DG), PV Solar (PV), Thermal 

Solar (TS) and Wind. One can draw the following inferences 

about the aggregated impact of solar-dependent resources on 

net-load ramps from Figs 1 and 3: 

• Negative impacts: The worsening of ramp up

requirements in the net-load data during the evening 15-

19 hours in Fig. 1 may be primarily due to solardependent resources as they tend to ramp down during

those hours as seen in Fig. 3 (right).

• Positive impacts: However, reduction in the ramp up and

down magnitudes in the net-load data compared to load

data (Fig. 1) during morning and evening hours

respectively may be primarily attributed to the solarbased variable renewable resources, which ramp up in

the morning and ramp down in the afternoon (Fig. 3).

Wind resources, however, are different from the rest of the 

renewable resources, in that the majority of ramp up start times 

is in the evening, and ramp down start times is in the early 

morning. The following inferences can be drawn about their 

aggregated impact on net-load ramping events: 

• Negative impacts: Comparing Figs. 1 and 3, during

morning hours when load is expected to ramp up;

aggregate wind resources ramping down may have an

exacerbating effect. Additionally, the spread out nature of 

wind ramping events in Fig. 3 throughout the day may
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contribute to the similar observation in the net-load ramp 

events and increase uncertainties

• Positive impacts: Comparing Figs. 1 and 3, load and wind 

ramp up events coincide to some extent during evening

hours, when the system could possibly face ramping 

capability limitations.

Fig. 3 Distributions of Renewable Ramp Start Times: Ramp up (left) and Ramp down (right)

To further confirm the above observations, investigations 

at the zonal level were performed. In this analysis, load ramp 

features were compared with wind affected net-load ramp 

features (i.e., Net-loadw or Netload (wind) defined as load 

minus wind) at three different reserve zones in California, 

pertaining to the Pacific Gas & Electric (PG&E) bay area (with 

~3% wind by capacity), the Sacramento Municipal Utility 

District (SMUD) (with no wind) and the San Diego Gas & 

Electric (SDGE) (with ~9% wind by capacity) utility regions. 

Based on the work in [11], yearly load and renewable resource 

data at 1 min-resolution for the three CAISO zones were used 

to estimate the load and wind resource distribution factors 

among the three zones, which was applied to the 2016 

California system time series data [10]. To study the impact of 

wind generation on net-load ramps, three different maximum 

instantaneous wind energy penetration levels (estimated as the 

largest wind to load ratio in the time series data) were chosen, 

i.e., 15% (2016 CAISO system data), 30%, and 50%.

Fig. 4 California Zone 3 Ramp Start Time Distribution of Load and Netload(wind) at Different Wind Penetration Levels 

Figure 4 shows the ramp start time distribution of load and 

net-loadw at different wind penetration levels in zone 3. When 

the penetration level increases from 0% indicated by blue lines 

(load data) to 50% indicated by green lines, we observe that 

although wind resources do spread the ramp events across the 

day (8-15 hours), they do contribute to the decrease in the 

frequencies of ramping events, especially during early morning 

(4-7) and peak evening hours (16-18) for ramp up events, and 

late evening/night hours (20-23) for ramp down events. Table 

III shows the statistics of ramp up and ramp down magnitudes 

for load and net-loadw at different wind penetrations in zone 3. 

Though the number of net load ramps does increase with the 

increase in wind penetration level (doubles at 30% wind and 

almost triples at 50% wind), the values of ramp magnitude 

statistics including the average and standard deviations 

decrease (decreases by 40% and 50% for ramp up, and 28% and 

33% for ramp down under 30% and 50% wind penetrations 

respectively). These are further evidences that wind energy in 

zone 3 is indeed helping the system by reducing ramps, 

including during peak hours.

TABLE III ZONE 3 RAMP MAGNITUDE STATISTICS AT DIFFERENT WIND 

PENETRATIONS (% CHANGE FROM LOAD ALSO SHOWN)

Load

(MW)

NetloadW

15% (MW)

NetloadW 30% 

(MW)

NetloadW

50% (MW)

Ramp Up

Average 1,514.2 1,283.7(-15%) 1,204.7(-20%) 1,194.6(-

21%)

Std. Dev. 1,062.3 770.8 (-27%) 613.6 (-42%) 520.6 (-51%)

Number 731 1,018 1,423 1,966

Ramp Down

Average 1,271.4 1,209.5 (-5%) 1,172.8 (-8%) 1,179.9 (-7%)

Std. Dev. 743.7 656.9 (-12%) 531.7 (-28%) 495.8 (-33%)

Number 777 1,001 1,433 1,963

IV. DISCUSSION ON FUTURE NEEDS: BETTER INTEGRATION

OF VARIABLE RENEWABLE RAMP FORECASTS

The analyses in Sections II and III indicate a need for better 

integration of variable renewable ramp forecast information 

into the market operations and planning tools. Currently, 

average forecasts of variable renewable generation at different 

time intervals suitable for different market clearing processes 

are accounted, i.e., day-ahead forecasts for day-ahead market, a 

few hours-ahead forecasts for look-ahead reliability unit 

commitments, and intra-hour ahead (15-min and 5-min) 
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forecasts for real-time markets. Though average forecasts of 

renewable generation will provide an estimation of the expected 

ramps, nevertheless, markets have to explicitly take into 

account the accurate ramp forecasts of renewables in order to

1) better cope with their anticipated negative influences on the 

net-load, and 2) be in a position to take advantage of their 

positive impacts on net-load ramps that could alleviate the 

reserve needs. In this context, we envision at least two kinds of 

integrations of renewable ramp forecasts.

A. Better FRP procurements using net-load ramp forecasts

ISOs that have implemented FRPs estimate the ramping 

capability procurements for their day-ahead and real-time 

markets based on day-ahead and real-time net-load forecasts,

respectively. In addition to the net-load ramp forecast, the 

ramping requirements are also informed by ramp uncertainties 

(typically a function of standard deviations in historical netload ramp data). Therefore, any improvement in the renewable 

generation ramp forecasts will directly impact the net-load 

ramp forecasts and consequently the FRP procurement in the 

markets. This can be observed from the standard deviations of 

the 5-min, 15-min, and 1-hour net-load ramps shown in Table 

IV. The standard deviations at various time-intervals decrease 

in the net-loadw data under 50% wind penetration compared to 

15% wind penetration data (~1% reduction in zone 1, ~4-5% 

reduction in zone 3 and ~2% reduction in system (zone 2 has 

no wind)), which will reduce the ramp procurement needs in the 

respective markets and consequently reduce the reserve costs 

from conventional generation and the system production costs

[4]. Therefore, precise information of renewable ramp forecasts 

and their uncertainties through advanced probabilistic forecasts 

[12], and the integration of such information to extract accurate 

net-load ramp forecasts will likely benefit system economics. 

TABLE IV NET-LOADW RAMP STANDARD DEVIATION: 15% VS. 50% WIND

Region 5-min 15-min 1-hour

CAIS

O

141.70 vs.

138.99

213.81 vs. 209.96 392.30 vs.

386.84

Zone 1 102.19 vs.

101.22

126.86 vs. 125.64 200.16 vs.

198.49

Zone 2 66.63 vs. 66.63 75.73 vs. 75.73 107.31 vs.

107.31

Zone 3 77.49 vs. 74.39 92.70 vs. 87.99 135.98 vs.

129.07

B. Plant-level ramp forecasts for situational awareness and 

ramp products from variable renewable generation

The previous discussion was from the system perspective, 

where aggregated wind and net-load ramp forecasts are to be 

used. Under increasing levels of variable renewable 

penetrations, plant-level ramp forecasts will also be important. 

Already, the Electric Reliability Council of Texas (ERCOT) has 

implemented programs to gain visibility into renewable 

resources in their energy management system (EMS) that will 

inform the operators about impending wind ramping events

along with their probabilities [13]. This feature provides

necessary situational awareness of variable renewables for the 

operators to take timely corrective actions including calling on 

unplanned quick start units to offset any ramp up deficits. 

However, as discussed before, there are scenarios when certain 

renewable resource plants also provide positive impacts on 

system net-load ramps. Therefore, future EMS applications will 

benefit from having precise plant-level ramp forecast 

information, along with associated uncertainties in the form of 

probabilistic forecasts [14], which will enable system operators 

to gain accurate foresight into such situations. Such abilities

could also enable operators in the real-time EMS environment 

to use variable renewable ramps better, including strategically 

curtailing certain plants in order to offset down-ramp deficits

(instead of cycling a conventional plant), and then compensate 

the respective renewable plant with lost opportunity cost or FRP 

clearing prices.

Additionally, integrating plant-level ramp forecast 

information in the planning and market dispatch processes

could enable variable renewables to offer their services for the 

flexible ramping product explicitly, unlike the conventional 

idea of implicitly accounting for their impacts on net-load 

ramping requirements (as discussed in Section IV.A). Figure 5

shows probability distributions of wind ramp start times, 

magnitudes and duration based on 2013 simulated wind power 

data at 5-min resolution from NREL’s WIND toolkit [15] over 

24 hrs. The selected wind site is from a city named Felicity, on 

the border between California and Mexico, with a site ID 14960

in the WIND toolkit, a capacity of 16 MW, a wind speed of 

6.36m/s, and a capacity factor of 0.33. We can observe that the 

peaks of the distribution curve of ramp up events are in the early 

morning (around 4 am to 8 am) while the peaks of the 

distribution curve of ramp down events are in the afternoon (13-

15 pm), except for in the Summer. The ramp up events from 

this plant in the morning are beneficial to the system since the 

system load also ramps up at the same time, as seen in Fig. 1. 

While looking at the aggregated wind ramp up start times in 

Fig. 3, wind resources contributions to ramping up events in the 

morning hours seemed trivial, but in looking at this plant-level 

information, one can see opportunities for this plant to offer its

ramping capacity explicitly into the FRP. Fig. 5 shows that this 

wind plant can provide a ramping capability of 4-6 MW (~32% 

of 16 MW plant) with high probability, and that some of these 

ramps could also last for hours, thereby creating opportunity for 

this and any other wind plants with correlated outputs to offer 

their ramping capability explicitly for FRP services, thereby 

having a chance to gain additional revenues from FRP marginal 

clearing prices. Apart from aiding system ramps when a 

particular wind plant’s ramp is forecasted to be in sync with the 

load ramps, wind generation can also consider controlling their

outputs or ramp rates under situations when a wind resource’s 

ramp is forecasted to exacerbate the system ramping needs (i.e., 

wind ramps in opposite direction to net-load). One such 

example is when wind resources can consider strategically 
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curtailing the wind power and thereby provide down-ramp 

under situations when system down-ramp capability is limited, 

under situation when it is economically more attractive than 

providing power to the energy market.

Therefore, having accurate forecasts of plant-level ramps 

and their uncertainties will open the door for better 

understanding of the synergies between variable renewable

generation and load ramps, and even utilizing the renewable 

ramps for grid services. Consequently, ramping provisions by 

individual renewable plants will reduce the FRP needs from 

conventional units and reduce total system production costs.

Fig. 5 Distributions of Wind Site Ramp: 1) Start Time, 2) Magnitude, and 3) Duration

V. CONCLUSIONS

This paper presented an analysis of the impacts of 

variable renewable generation ramps on the system net-load 

ramps, taking California 2016 measured data as an example

and extracting ramp features using the optimized swinging 

door algorithm. The analysis showed that integration of 

variable renewables definitely increased the overall annual 

frequencies of ramp events and uncertainties related to their 

occurrences throughout the day. Nevertheless, during certain 

ramping challenged situations like early morning or evening 

hours, renewable generation ramps seemed to implicitly 

provide support to net-load ramps and decrease the 

magnitudes and ramp rates of net load. The summary 

statistics showed the average of system aggregate net-load 

ramps to decrease by 20% in magnitude for ramp ups and 

30% for ramp downs, with their standard deviations 

decreasing by 40% and 20% respectively. 

Upon analyzing the solar and wind resources separately, 

it was observed that solar ramps had positive influences on 

overall ne-load ramps during morning and early evening 

hours when their trend coincided with diurnal load, except

during late evening hours when net-load ramp up was 

exacerbated due to solar ramp down. On the other hand, wind 

[1] N. Navid and G. Rosenwald, "Market Solutions for Managing Ramp 

Flexibility With High Penetration of Renewable Resource," in IEEE 

Transactions on Sustainable Energy, vol. 3, no. 4, pp. 784-790, Oct. 2012.

[2] California ISO, “Flexible Ramping Product- Revised Draft Final 

Proposal,” Dec 2015 

[3] N. Navid and G. Rosenwald, “Ramp Capability Product Design for MISO 

Markets,” Market Development and Analysis, July 2013

[4] M. Cui, J. Zhang and B.-M. Hodge, “Wind-Friendly Flexible Ramping 

Product Design in Multi-Timescale Power System Operations,” IEEE Trans. 

Sustain. Energy, vol. 8, no. 3, pp. 1064–1075, Jan. 2017.

resources seemed to contribute to the decrease in the 

frequencies of ramping events, especially during early 

morning and late evening hours for ramp up events, and late 

evening/night hours for ramp down events, thereby providing 

implicit support to the grid when solar ramped down. A 

further zonal level investigation of wind ramp impacts on 

California net-load showed that in zone 3 (depicting SDGE 

with higher wind capacity) wind ramps could decrease ramp 

standard deviations by up to 4-5% at 5-min, 15-min, and 1-

hour intervals, which would mean the required ramping 

reserve procurements will be reduced.

All of these analyses indicated a need for better 

integration of variable renewable ramp forecasts information, 

including their uncertainties via probabilistic forecasts, into 

the market operations and planning tools, both at the 

individual plant level, as well as aggregated regional or 

system level. This will enable better utilization of existing 

ramping capabilities in the grid, including from the variable 

renewable resources, reducing unplanned quick starts, 

reducing system production costs, and finally achieving more 

efficient markets.

REFERENCES

[5] R. Chen et. al., “Wind Power Providing Flexible Ramp Product,” IEEE 

Trans. Power Syst., vol. 32, no. 3, pp. 2049-2061, May 2013.

[6] R. Sevlian and R. Rajagopal, “Detection and statistics of wind power 

ramps,” IEEE Trans. Power Syst., vol. 28, no. 4, pp. 3610–3620, 2013.

[7] C. Kamath, “Understanding wind ramp events through analysis of 

historical data,” in Proc., Transmission Distribution Conf. Expo., New 

Orleans, LA, USA, pp. 1–6, 2010

[8] Y. Wan, “Analysis of Wind Power Ramping Behavior in ERCOT,” 

NREL, Golden, CO. Mar 2011.
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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

[9] M. Cui et. al., “An optimized swinging door algorithm for identifying 

wind ramping events,” IEEE Trans. Sustain. Energy, vol. 7, no. 1, pp. 150–

162, Jan. 2016.

[10] California ISO OASIS [Online]. Available FTP: 

http://oasis.caiso.com/mrioasis/logon.do

[11] I. Pena; C. Brancucci; B. M. Hodge, "An Extended IEEE 118-bus Test 

System with High Renewable Penetration," IEEE Transactions on Power 

Systems , vol.PP, no.99, pp.1-1, April 2017

[12] M. Cui et. al., "Wind Power Ramp Event Forecasting Using a Stochastic

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[13] ERCOT operating procedure manual, Power Operations Bulletin # 794, 

http://www.ercot.com/mktrules/guides/procedures/index.html (also seen in 

the news release: http://www.ercot.com/news/releases/show/326)

[14] M. Cui et. al., “A Data-Driven Methodology for Probabilistic Wind 

Power Ramp Forecasting,” to appear in IEEE Trans. Smart Grid, Oct 2017

[15] Draxl, C et. al., "The Wind Integration National Dataset (WIND) 

Toolkit." Applied Energy 151: 355366, 2015