Technical Report

NREL/TP-550-46938

December 2009

Wind Plant Ramping Behavior

Erik Ela

National Renewable Energy Laboratory

Jason Kemper

Northern Arizona University
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393

303-275-3000 • www.nrel.gov

NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

Contract No. DE-AC36-08-GO28308

Technical Report

NREL/TP-550-46938

December 2009

Wind Plant Ramping Behavior

Erik Ela

National Renewable Energy Laboratory

Jason Kemper

Northern Arizona University

Prepared under Task No. WER8.5102
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iii

Wind Plant Ramping Behavior

Table of Contents

Table of Contents......................................................................................................................................... iii

Table of Figures............................................................................................................................................ iv

Table of Tables............................................................................................................................................. iv

1. Introduction .......................................................................................................................................... 1

2. Xcel Service Areas................................................................................................................................. 3

3. Wind Power Ramps............................................................................................................................... 5

A. PSCO.................................................................................................................................................. 6

B. SPS................................................................................................................................................... 13

C. PSCO and SPS combination............................................................................................................. 17

4. Net Load Ramps.................................................................................................................................. 22

A. PSCO................................................................................................................................................ 22

B. SPS................................................................................................................................................... 26

5. Characteristics and Correlation of Wind Plants.................................................................................. 28

A. PSCO................................................................................................................................................ 29

B. SPS................................................................................................................................................... 34

6. Conclusions......................................................................................................................................... 37

7. References .......................................................................................................................................... 38
iv

Table of Figures

Figure 1 − Power system time frames............................................................................................................................................... 2

Figure 2 − Contour plot showing PSCO wind ramps by time of day and month ............................................................................... 8

Figure 3 − PSCO wind up-ramp with many false ramps before it ..................................................................................................... 9

Figure 4 − Large PSCO wind up-ramp with no false ramps before it................................................................................................. 9

Figure 5 − Contributions from numerous PSCO wind plants during large down-ramp................................................................... 10

Figure 6 − Contributions from numerous PSCO wind plants during large up-ramp........................................................................ 11

Figure 7 − Up-ramp showing easterly Logan increasing before cedar creek .................................................................................. 12

Figure 8 − Contour plot showing SPS wind ramps by time of day and month................................................................................ 15

Figure 9 − Two SPS wind ramps...................................................................................................................................................... 15

Figure 10 − SPS wind ramp and wind plant contributions.............................................................................................................. 16

Figure 11 − SPS wind ramp and wind plant contributions.............................................................................................................. 16

Figure 12 − SPS wind down-ramp with preceding false ramps....................................................................................................... 17

Figure 13 − Wind up-ramp where SPS is constant and PSCO is ramping ........................................................................................ 19

Figure 14 − Combined up-ramp ...................................................................................................................................................... 20

Figure 15 − Frequency of ramp contributions to combined PSCO and SPS ramps ......................................................................... 22

Figure 17 − Contour plot showing PSCO load ramps by time of day and month ............................................................................ 25

Figure 18 − Contour plot showing PSCO net load ramps by time of day and month...................................................................... 25

Figure 19 − SPS net load ramp statistics by time of day and month............................................................................................... 27

Figure 20 − Correlations between PSCO wind power plants during ramp periods and all times.................................................... 29

Figure 21 − Step change correlations for PSCO during all times and ramp events......................................................................... 31

Figure 22 − Cross-correlations between Cedar Creek and PSCO wind plants as a function of a temporal shift............................. 32

Figure 23 − Cross-correlations between Logan and PSCO wind plants as a function of a temporal shift....................................... 32

Figure 24 − Cross-correlations between Logan and PSCO wind plants as a function of a temporal shift during ramp periods..... 33

Figure 25 − Cross-correlations between Logan and PSCO wind plants as a function of time shift during system-wide up- and 

down-ramps.................................................................................................................................................................................... 34

Figure 26 − Correlations between SPS wind power plants during ramp periods and all times....................................................... 35

Figure 27 − Step change correlations for PSCO during all times and ramp events......................................................................... 36

Figure 28 − Correlation coefficient for 10-min avgs by distance for SPS wind plants..................................................................... 37

Table of Tables

Table I − Distance between PSCO wind plants (miles)...................................................................................................................... 4

Table II − PSCO wind ramp statistics................................................................................................................................................. 6

Table III − PSCO wind ramp statistics by time of day ........................................................................................................................ 7

Table IV − PSCO ramp directions..................................................................................................................................................... 12

Table V − Individual wind plant ramps aligning or opposing system-wide wind ramps.................................................................. 13

Table VI − SPS wind ramp statistics................................................................................................................................................. 14

Table VII − SPS wind ramp statistics by time of day........................................................................................................................ 14

Table VIII − PSCO and SPS combination wind ramp statistics......................................................................................................... 18

Table IX − PSCO and SPS combination wind ramp statistics by time of day ................................................................................... 18

Table X − Frequency of contributions from PSCO and SPS toward combined ramps,

..................................................................... 21

Table XI − Frequency of contributions from PSCO and SPS toward combined ramps, normalized to region capacity................... 21

Table XII − Load and net load ramp statistics for PSCO................................................................................................................... 23

Table XIII − Load and net load ramp statistics for PSCO by time of day.......................................................................................... 24

Table XIV − Net load ramp statistics for SPS ................................................................................................................................... 26

Table XV − Net load ramp statistics for SPS by time of day ............................................................................................................ 27

Table XVI − Load and wind contributions to top 100 net load ramps in SPS, 

................................................................................. 28

Table XVII − Spatial diversity of PSCO wind power plants............................................................................................................... 30

Table XVIII − Distances between SPS wind plants........................................................................................................................... 36
1

1. Introduction

Wind power plants are becoming a much greater contributor to the national electrical energy mixture. 

In recent years, economies of scale have made large areas with hundreds or more megawatt-size 

turbines economic and practical. Xcel Energy, based in Minneapolis, Minnesota, has one of the largest 

amounts of wind capacity in the United States. According to [1], there were 2,906 MW of nameplate 

capacity distributed between Xcel’s three service territories at the end of 2008. Some reasons for the 

large amounts of wind being introduced in Xcel and other areas include its free fuel costs, zero 

emissions, and state RPS goals. With the increasing wind penetrations, utilities and Independent System 

Operators (ISO) are quickly trying to understand the impacts on system operations and planning.

One of the most important aspects of reliable power system operations is maintaining a balance 

between the demand and the generation used to meet the demand. Different time frames require 

different techniques of balancing the generation and load, as shown in Figure 1. In the unit commitment 

time frame, operations planners will turn on enough units to meet forecasted demand. This typically 

occurs between six hours and a few days ahead, and is performed to give long-start generating units

sufficient time to start up and synchronize to the grid. Throughout the day, operators and traders will 

typically give hourly schedules to units and will make hourly trades between balancing area neighbors 

based on economics and often, updated information (i.e., better forecasts of outcomes). As real-time 

approaches, operators tend to adjust generator schedules in their areas to meet the quickly changing 

demand. This is often referred to as load-following, and units with sufficient ramping capabilities and 

units that can start quickly and synchronize to the grid are used to meet demand on a five-minute to 

one-hour time frame. Lastly, because random fluctuations occur in demand and can impact both the 

frequency of the power system as well as interconnection tie-line schedules, units that have “automatic 

generation control” (AGC) will rapidly adjust output to meet actual demand levels on a time frame of 

about every few seconds to a few minutes. Frequency that is too high or too low from its nominal level 

(i.e., 60 Hz in the United States) can cause varying issues and in the most drastic instance lead to load 

shedding or instability. Frequency is common to an interconnection, and control areas generally try to 

balance the generation and load in their much smaller balancing area. Therefore, area control error 

(ACE) is the more common measure of determining how well a control area is balancing its system. ACE 

measures the imbalance of generation of load by subtracting tie-line flows from their scheduled values 

and considers the frequency response of generation and load when frequency is not at its nominal level.
2

Time (hour of day)

0 4 8 12 16 20 24

System Load (MW)

seconds to minutes

Regulation

tens of minutes to hours

Load 

Following

day

Scheduling

Days

Unit 

Commitment

Figure 1 − Power system time frames

The techniques described are used primarily because of the variability and uncertainty of regional 

demand patterns. It is difficult to predict what the load demand will be ahead of time, and it often 

changes throughout the day. Wind power adds to these impacts with its own characteristics of 

variability and uncertainty. An understanding of how wind impacts the system has been a challenge for 

operators and planners, but much work has been done to improve this understanding. Wind power 

integration studies have been performed in various areas in the United States and Europe to simulate 

the power system with modeled wind data, and have quantified the operating impacts that occur [2]. 

Xcel Energy has performed integration studies in its Northern States Power (NSP) area [3] as well as its 

Public Service of Colorado (PSCO) area [4][5]. Wind plant data are usually represented by meso-scale 

weather models of past atmospheric conditions, and time series of these data are used in power system 

operations simulations. Operating impacts that drive ancillary service requirements as well as 

integration costs are typically extracted from the study.

1

 1 Ancillary services include operating reserves and regulation reserves for these studies. Operating reserves are 

generally procured for use during system events like contingencies (e.g. generator failure and line outage) and 

regulation reserves are used for second-to-second variability of load and generation.

The studies provide tremendous value and give 

a better understanding of the characteristics and impacts of higher penetrations of wind power in a 

region. Real-time, high-resolution wind and power production data from systems such as those in Xcel 
3

Energy territories are becoming increasingly available, making it more feasible to evaluate the validity of 

these integration studies.

One universal conclusion from the studies and actual operations is that the proper use of wind power 

forecasting can significantly improve the integration of wind power by improving reliability and 

minimizing costs. Like load forecasting, wind forecasting can assist when making unit commitment 

decisions so that enough capacity is economically available to meet the next day’s demand. Wind 

forecasting can also be used in more real-time operations to help adjust dispatch decisions and relieve 

network congestion. Wind forecasting can also assist in the determination of ancillary service 

requirements, economic trading of power between balancing areas or utilities, and for operational 

awareness of strong incoming weather events. 

Strong weather events, though rare, can cause very large ramps in wind power that prove difficult for 

power system operators to manage. Forecasting large wind ramps is a new area of research, and many 

commercial providers of wind power forecasts are developing new methods to better predict these rare 

phenomena. Different regions of the world with different terrain and weather features can have very 

different ramping characteristics. In order to best predict these occurrences, the underlying 

meteorological conditions that drive wind power ramps must be well understood. Xcel Energy has 

teamed up with the National Center for Atmospheric Research (NCAR) and the National Renewable 

Energy Laboratory (NREL) to research ramping behavior and its impacts, and to develop a sophisticated 

wind prediction system for Xcel’s service territories.

This research focused on the specific ramping behavior of two of Xcel’s service territories, PSCO and 

Southwest Power System (SPS). Analysis using actual wind power data at high resolution was used to 

give a better understanding of typical behavior of the wind plants. The results were used to assist NCAR 

in using the power data of the ramps to investigate weather phenomena that were driving these events. 

The results also gave Xcel operators a better understanding of when and how different wind power 

ramps were occurring. A complete briefing on characteristics and behavior of wind power on various 

timescales and sizes can be found in [6]. 

The overall objective of this report is to give typical characteristics of large system-wide ramps and how 

correlations of wind plants and load can provide more information to a utility for predicting wind power 

ramps. This information will improve wind power ramp predictions and provide operators with better 

tools to integrate higher penetrations of wind power.

2. Xcel Service Areas

Xcel has three service areas in different parts of the country. Each area has a different market structure 

which leaves Xcel with different responsibilities for managing wind power in each area. Each area also 

has different patterns and concentrations of wind power on its system. This report will focus on the 

PSCO and SPS regions.

PSCO covers most of the state of Colorado. The area is not in an ISO market, so the responsibilities of 

unit commitment, balancing, ancillary services procurement and utilization, and tie-line scheduling fall 
4

within the utility’s role. This means that there are both reliability and economic incentives for Xcel to 

manage the wind power on the system as efficiently as possible. In one study, Xcel found that by 

reducing the day-ahead forecast error of total wind power in PSCO by one percent MAE2

, total savings 

would be $1.2M per year[7].3

The best wind resources in PSCO are found in the northeastern and southeastern plains, and most of the 

wind plants are concentrated in these regions. In fact, more than 700 MW of the approximately 1,060

MW of total wind capacity (about 2/3) is within 45 miles of each other in the northeast corner of 

Colorado. This concentration, shown in

Operators must constantly balance wind and load on the system and 

traders must know when it is most beneficial to trade energy with neighboring utilities. With large 

penetrations of wind, the understanding of how the wind will behave and when is a major driver of 

electricity prices and in some instances can affect the reliability of the system.

Table I, can cause difficulties because when large wind changes 

occur at one of these wind plants, the neighboring wind plants are often affected during similar time 

periods. This contributes to more severe wind ramps compared to areas with wind plants that are more 

spread out. Understanding the relationships between the wind plants in these areas is crucial to 

enhanced wind predictions of wind power ramping behavior.

Table I − Distance between PSCO wind plants (miles)

4

PSCO Wind Power Plant PEET PONN SGCA COLG CEDC LOGA

PEET 29.7MW 0 103 8 227 45 4

PONN 31.7MW 103 0 110 207 66 100

SGCA 60MW 8 110 0 228 53 12

COLG 237MW 227 207 228 0 233 228

CEDC 300.5MW 45 66 53 233 0 41

LOGA 400.5MW 4 100 12 228 41 0

SPS is located in the panhandle of Texas as well as parts of eastern New Mexico. The area is located 

within the Southwest Power Pool (SPP), an ISO that covers a large portion of the south central part of 

the United States [8]. Most ISOs have responsibilities to provide unit commitment for a day-ahead 

market, procure sufficient ancillary services, and send signals to resources within its borders to redispatch in times of network congestion or changing load. However, SPP only provides services related 

to re-dispatch. There is no day-ahead market or ancillary services market. The area of SPS is also in a 

very import-constrained part of SPP because there is little transmission capability to trade with other 

members of SPP. This means that even though there are responsibilities in the real-time for SPP to 

balance generation and load, there are still large responsibilities within SPS to commit units and provide 

ancillary service capabilities that make wind power ramps important for the area.

 2 MAE is the mean absolute error usually defined as a percentage of nameplate capacity of the wind plant or 

collection of wind plants being evaluated. It is a common metric in defining the accuracy of a wind power forecast. 3 With respect to the locations and total installed capacity of wind in 2008.

4 Note that throughout this paper, the four letter representation of wind plants refers to the connection point on 

the respective system, and may be different than common representations of these wind plants
5

The wind resources that are located in the SPS region include a larger number of smaller wind plants 

spread out through the region. As opposed to PSCO where large plants are located in close proximity, 

the smaller, more spread out plants in SPS tend to dampen the large wind ramp events. Significant 

weather events will take longer to reach multiple areas and may miss some areas all together. The 

results in the next section will quantify these findings further.

Northern States Power (NSP) is based in parts of Minnesota and Wisconsin. There is a large amount of 

installed wind capacity currently in this region and there are significant opportunities for Xcel to 

properly forecast wind output. However, this region lies fully in the Midwest Independent System 

Operator (MISO). MISO operates as a day 2 Regional Transmission Organization (RTO) and FERC-defined 

standard market design (SMD) [9][10]. This means that the normal responsibilities of day-ahead capacity 

planning and real-time dispatch over the MISO bulk power system are primarily under MISO control 

rather than the local utility. The importance of wind forecasting is still apparent to the utility, but the 

reliability aspects of wind power ramp forecasting are not as critical in this region as in the other two 

regions.

3. Wind Power Ramps

The main function of the joint project between Xcel, NCAR, and NREL is to develop a sophisticated wind 

prediction system that displays expected outputs of various wind plants to Xcel operators. At many 

times during the day, the wind is calm or steady, and its impacts on system operations are generally not 

significant. In terms of system reliability and costs, wind power forecast accuracy is most important 

during times when the wind power production is changing rapidly. During a wind down-ramp, an 

operator must compensate for the loss of generation by either ramping up other on-line generation, or 

by starting up a unit that is currently off-line. Conversely, a wind up-ramp must be compensated by 

ramping down units, shutting them off, or in some instances curtailing the high-producing wind. The 

different strategies are highly dependent on the speed (i.e., ramp rate), duration, magnitude, and timing 

of the ramp. It is also dependent on how far in advance the ramp is predicted. Different generating units

have different characteristics: start-up times; ramping rates; minimum on and off times; and in some 

cases, energy limitations. Units with longer start times are usually cheaper to run than units with shorter

start times. This is why predicting the event with confidence in advance can be very advantageous. Units 

with longer minimum on times are also generally cheaper to run than units with shorter minimum on 

times, assuming that the unit is needed for the entire minimum on time. This is why having a prediction 

of not only the duration of the ramp, but a prediction for how long the energy level of the wind is 

sustained following the ramp, can be very important. The magnitude and duration of the ramp can drive 

the capacity and energy requirements needed to compensate for the ramp. Finally, the ramp rate of a 

wind ramp is an important prediction as well; this clearly drives the ramping rate needs of the 

compensating generation during the ramp event.

Objectively, identifying and ranking ramps is a difficult task. Given hourly averaged time-series data for

power, examining the rates of change over one period (hour) may be sufficient for analysis. However, 

this level of resolution provides little useful information about what happened in real-time that a system 

operator had to handle. Thus, ten-minute (or less) averaged time-series data for power convey much 

more information. However, this resolution requires a method for defining a ramp that grows in 
6

complexity with increasing temporal resolution. Therefore, ramp defining tools were developed with 

two primary purposes in mind; the tools needed to be flexible to allow the user to define a ramp using 

multiple parameters and they needed to convey as much information regarding the ramp definition 

results as possible. Ramp defining tools were developed using Microsoft Excel and Matlab [11]. The tools 

allowed user-defined parameters, such as duration, ramp rate, and magnitude to determine when 

ramps had occurred. Once defined, all information for the observed ramps was displayed to the user 

and was used in this analysis.

A. PSCO

Definitions of the wind ramp events were developed for PSCO and the results for 2008 are presented 

below in Table II. 5

Table II − PSCO wind ramp statistics

Throughout this report, a ramp must exceed the magnitude in the defined duration 

period. Once this trigger is hit, the program determines the proper start and end time of the ramp 

event. For instance, if the ramp definition is 100 MW for 60 minutes, the power would have to change 

by more than 100 MW for any rolling 60-minute period. If this trigger was hit for consecutive intervals, 

the tool would only define one ramp rather than multiple. Start and end times were determined by a 

ten-minute period, either changing in direction or changing below some threshold (i.e., the ramp period 

had to be continuously changing above some level in order to be considered a continuous ramp. This 

level ranged from about 8 – 15 MW for ten minutes depending on the size of the system.). This 

threshold was used so that large ramps that tapered off with changes of only a few MW would not 

receive excessive durations and misrepresent ramp rate values. Sensitivities were pursued where the 

duration could be defined with one or two very small rates of change, or even opposite rates of change 

inside a long ramp. This would change the average durations and would slightly change the total number 

of ramps. However, it was decided to use the one interval threshold to decide on start and end times for 

all ramp statistics for this analysis.

PSCO Wind = 1060 MW Total Up Down Average duration Max duration

Ramps over 250 (25%) MW in 60 mins 353 203 150 77 min 220 min

Ramps over 350 (33%) MW in 60 mins 91 63 28 67 min 190 min

Ramps over 500 (50%) MW in 60 mins 15 13 2 49 min 90 min

Ramps over 250 MW in 30 mins 118 73 45 49 min 150 min

 5 Depending on the data available at the time of analysis, this could either be Calendar year 2008, or some 

combination of late 2007 and 2008, but always encompassing 366 days of 10-minute data. This is true for all 

analysis throughout this report.
7

One interesting observation from these results is that in all cases there are more occurrences of upramps compared to down-ramps. The more strict the ramp definition (i.e., the larger the threshold), the 

higher the percentage of up-ramps compared to down-ramps. For the 60-minute duration case, the 

percentage up-ramps versus down-ramps for 250 MW, 350 MW, and 500 MW is 58%, 69%, and 87%,

respectively. This shows that the more significant the ramp definition, the higher the ratio of up- to 

down-ramps.

The total amounts of ramp events on the system show interesting results as well. The results indicate 

that a ramp greater than 25% of total generation in PSCO is likely to occur almost once per day. A ramp 

event greater than 50% of the total capacity, however, is likely to occur about once per month. The 

results also show that a typical ramp takes about one hour to complete, with the longest ramps in some 

instances lasting up to four hours. 

Load demand has a pretty typical diurnal pattern depending on the season and whether it is a weekday 

or a weekend. Most load ramps occur in the morning when people are waking up, and in some 

instances, at night when people are turning on lights simultaneously. When wind ramps occur in the 

same direction as load ramps (i.e., they are both increasing or they are both decreasing), there is no

cause for concern as they are helping to keep the system balanced and canceling out the impacts of the 

other. However, when the ramps are opposite in direction, the situation is exacerbated. The following 

table and figure are results for different six-hour periods of the day. Table III shows numbers, maximum 

ramp rates, and false ramp (false ramp is defined below) statistics. Figure 2 shows a contour plot of time 

of day and month ramping statistics.

Table III − PSCO wind ramp statistics by time of day6

Ramps over 250 

MW in 60 mins

Ramps Max 60 min

up ramp

Max 60 min

down ramp

Average false ramps (50

MW in 10 min inside 

five-hour window)

Maximum false ramps

(50 MW in 10 min

inside five-hour 

window)

Hours 0 - 5 74 564 MW -422 0.66 4

Hours 6 - 11 70 536 MW -473 0.59 3

Hours 12 - 17 96 582 MW -535 0.89 6

Hours 18 - 23 119 740 MW -664 1.44 6

 6 For ramps that span two time frames, they are binned by the start time of the ramp
8

Figure 2 − Contour plot showing PSCO wind ramps by time of day and month

Notice that the time period of hours 18-23 stands out amongst the four periods. This period holds the 

largest ramps, the most ramps, and is the most volatile according to the number of false ramps. A false 

ramp is defined as a smaller duration ramp of similar ramp rate that can fool an operator into 

considering it as the predicted ramp. Since the exact timing of ramp events are so hard to predict, these 

false ramps become very important. A large wind power down-ramp may have been predicted with 

confidence that it may start within five hours of some given time. A quick 10-minute down-ramp with 

similar ramp rate may occur inside that five-hour window, but then stop abruptly. During this false 

ramp, an operator may turn on a fast-start combustion turbine, but then realizing that it was not needed 

since the false ramp was short-lived. This can have an impact on reliability and on operating costs. The 

1.44 average of false ramps during the evening/night period of hours 18-23 shows that whenever a 

ramp occurs, there was on average at least one significantly fast but smaller duration ramp in the same 

direction within five hours of the real ramp. An example of false ramps is shown below in Figure 3.

Lastly, Figure 2 shows that of these ramps occurring in the evening and night time periods, the majority 

of them occur during the summer months. This is a time of year where load is generally higher due to 

air-conditioning, so this may add an additional challenge to system operations. There also tends to be a 

load down-ramp in the later part of this time period (i.e., 22-23) due to declining demand, so when 

coupled with a wind up-ramp, additional operational challenges can occur.
9

Figure 3 − PSCO wind up-ramp with many false ramps before it

The up-ramp in Figure 3 that started around 21:45 had a series of false ramps preceding it. An operator 

anticipating the large ramp could easily have been misled into thinking any of the false ramps was the 

actual forecasted ramp. This is another aspect of a ramp event that is very valuable information to an 

operator. If the prediction of a ramp event was given with side information that there is anticipated

volatility around the ramp, an operator may choose different options than if the ramp was surrounded 

by less volatility on both sides. A smoother up-ramp is shown below in Figure 4.

Figure 4 − Large PSCO wind up-ramp with no false ramps before it

As shown in Figure 4, the wind does not fluctuate much before the ramp event. Knowing this ahead of 

time, an operator would have much more faith that the ramp event starting was indeed a forecasted 

ramp event. Many of the large up-ramps that occur in PSCO have durations of about 70 minutes. To 

start adjusting other generation within the first 10 minutes of the ramp is crucial for keeping ACE levels 

0

100

200

300

400

500

600

700

800

900

18:00 19:12 20:24 21:36 22:48 0:00 1:12 2:24 3:36

0

100

200

300

400

500

600

700

800

900

0:00 1:12 2:24 3:36 4:48 6:00 7:12 8:24 9:36
10

down in an economic manner. Because ramp events are currently very difficult to predict with perfect 

timing, it is often up to the operator to know when a predicted ramp is starting and act accordingly.

One important factor for understanding ramps on a particular system is how they are being caused and 

what parts of a fleet are contributing. During a system-wide ramp event, the behavior of different wind 

plants throughout the system can vary significantly. Interestingly, because of the geographical locations 

of the PSCO wind plants, the wind ramp events in this system generally are caused by the same wind 

plants each time. The northeast area of PSCO has the majority of the wind capacity and the wind plants 

are very close in proximity to each other. A large down-ramp and a large up-ramp are shown below in 

Figure 5 and Figure 6, respectively. The names of the wind plants are given by their meter-read injection 

points onto the PSCO system.

Figure 5 − Contributions from numerous PSCO wind plants during large down-ramp

-200

0

200

400

600

800

1000

1200

PSCO_PONN_NO1_GEN_PEAG

PSCO_CEDC_NO1_GEN_PESC

PSCO_LOGA_NO1_GEN_PESC

PSCO_PEET_NO1_GEN_PEAG

PSCO_SGCA_NO1_GEN_PEAG

PSCO_COLG_NO1_GEN_PEAG

PSCO_WIND_MW_

TOT
11

Figure 6 − Contributions from numerous PSCO wind plants during large up-ramp

It is important to know the locations of the plants. CEDC and LOGA are the largest plants on the PSCO 

system and are located in the northeast corner of Colorado along with PONN, PEET, and SGCA. COLG is 

located in the southeast corner of the state and over 200 miles away from each of the other connection 

points. In the northeast, the order of west to east is as follows: PONN, CEDC, PEET, LOGA, and SGCA. In 

both figures, the movement in power production starts with the western-most area first and moves 

east. For the up-ramp, this obviously shows an increase in wind coming from the west. For the downramp, Figure 5 shows that either the winds began to taper off west to east or the wind was increasing so 

much that cut-out levels were reached as it moved across the state towards the east. The latter looks 

possible given that most of the wind plants are operating at very close to their maximum capacity just 

prior to the beginning of the ramp. 

The behavior of ramp events in PSCO starting from west to east is very typical of the wind regime in this 

system, but as shown in Figure 7, it is not always the case. This plot shows the two larger plants that 

contribute the most to PSCO wind ramps. Unlike the last two plots and the more typical behavior of the 

system, this ramp starts with LOGA ramping up, and is followed shortly after by CEDC ramping up. This 

describes an increase in wind speed east to west and was seen as more common in certain months of 

the year.

-200

0

200

400

600

800

1000

1200

PSCO_PONN_NO1_GEN_PEAG

PSCO_CEDC_NO1_GEN_PESC

PSCO_LOGA_NO1_GEN_PESC

PSCO_PEET_NO1_GEN_PEAG

PSCO_SGCA_NO1_GEN_PEAG

PSCO_COLG_NO1_GEN_PEAG
12

Figure 7 − Up-ramp showing easterly Logan increasing before cedar creek

Table IV shows the number of times where large wind ramps are starting west to east and east to west.

7

Table IV − PSCO ramp directions

This analysis uses the two larger wind plants in the northeast as a proxy and determines the direction of 

the ramp by observing which plant contributed to the ramp first. A west to east up-ramp means that the 

western plant increased in production before the eastern plant and a west to east down-ramp means 

that the western plant started to decrease its production before the eastern plant. The results show 

minimal conclusions other than there is a slightly greater number of west to east ramps throughout the 

year.

West to East East to West

Total 47 41

Up-ramps 30 28

Down-ramps 17 13

Spring 14 13

Summer 11 12

Fall 13 8

Winter 9 8

The timing of individual wind plants’ ramping events was then further compared to the timing of 

system-wide ramp events for PSCO. First, system-wide wind ramps exceeding 20% of total capacity of 

the system were identified and then individual wind plant ramps exceeding 40% capacity of the wind 

plant were identified. The starting times of those ramp events were compared using a sub-program. The 

sub-program assumed that an individual plant was more likely to experience a ramp event of 

 7 Though useful, this analysis was very sensitive to how thresholds were set up. Both wind plants may be moving in 

a similar direction, but it is difficult to quantify a threshold on whether the movement is the start of the ramp or 

not.

0

200

400

600

800

1000

1200

LOGA

CEDC

TOTAL
13

corresponding sign (plus or minus) than a ramp event of opposing sign. The sub-program searched all of 

the PSCO up-ramp starting times and sought to find an individual plant up-ramp start within two hours 

before or after the system-wide ramp. If no corresponding up-ramp start existed, then the sub-program 

searched for a down-ramp start within the same four-hour period. Analysis in this manner may tend to 

over count aligned ramps and under count opposing ramps. Regardless, the methodology was applied 

equally to all individual plants. The results, summarized in Table V, show that Colorado Green was the 

least likely to have identified ramp starts that aligned with system-wide ramps with 27% of up-ramps 

and 24% of down-ramps aligning. Logan was the most likely to have aligned ramps with 61% of upramps and 54% of down-ramps aligning. 

Overall, all wind plants had higher ratios of aligned to opposed wind ramp starts during system-wide

down-ramps than up-ramps. This was due to the low numbers of individual power plant up-ramp starts 

coinciding with system-wide down-ramp starts (Table V, bottom half of column 2 versus the top half). 

Thus, at least for the ramp starting times, it appears that individual wind plant power profiles are more 

correlated during down-ramps than up-ramps. In other words, when the wind throughout the system is 

starting to ramp down, it is unlikely that an individual wind power plant is beginning to ramp up.

Table V − Individual wind plant ramps aligning or opposing system-wide wind ramps

PSCO 

ramping 

up vs.

Total aligned 

ramp event 

starts

Total opposed 

ramp event 

starts

% of PSCO up ramps 

that had an aligned 

(up) ramp at 

specified plant

% of PSCO up ramps 

that had an opposed 

(down) ramp at 

specified plant

Ratio of aligned to 

opposed ramp 

events

PEET 155 12 53% 4% 12.9

PONN 113 19 39% 6% 5.9

SGCA 155 16 53% 5% 9.7

COLG 78 15 27% 5% 5.2

CEDC 164 22 56% 8% 7.5

LOGA 179 16 61% 5% 11.2

PSCO 

ramping 

down vs.

Total aligned 

ramp event 

starts

Total opposed 

ramp event 

starts

% of PSCO down 

ramps that had an 

aligned (down) ramp 

at specified plant

% of PSCO down 

ramps that had an 

opposed (up) ramp 

at specified plant

Ratio of aligned to 

opposed ramp 

events

PEET 131 10 45% 3% 13.1

PONN 112 4 38% 1% 28.0

SGCA 129 4 44% 1% 32.3

COLG 69 8 24% 3% 8.6

CEDC 130 8 44% 3% 16.3

LOGA 159 7 54% 2% 22.7

B. SPS

As discussed earlier, SPS has a different geographical structure of its wind plants compared to PSCO. 

Table VI shows wind ramp statistics for various definitions of wind ramps (contrast with Table II).
14

Table VI − SPS wind ramp statistics

SPS Wind ~ 570-600 MW Total Up Down Average duration Max duration

Ramps over 135 MW in 60 mins (25%) 208 117 91 83 min 230 min

Ramps over 200 MW in 60 mins (33%) 19 12 7 79 min 160 min

Ramps over 240 MW in 60 mins (40%)8 3 3 0 67min 100 min

Ramps over 135 MW in 30 mins 43 24 19 50 min 120 min

For 2008, these definitions of ramps closely resemble those of PSCO in terms of percentage of total wind 

plant capacity. It is observed that when normalizing the ramp definitions with total installed capacity,

that SPS has far less ramping issues related to wind power. For the middle 60-minute definition, 

corresponding to about 33% of installed capacity change in 60 minutes, there are about 5 times as many 

ramps in PSCO as there are in SPS. From an operator’s perspective, it is more comforting to know that 

an up-ramp of 40% or more may only occur 3 times in one year and that a down-ramp of the same size 

is extremely rare. Another observation is that the average duration of the ramps in SPS is a bit longer 

than that of PSCO. This is most likely due to the geographical spread in SPS. Lastly, you can see that 

there are significantly more up-ramps compared to down-ramps, similar to PSCO.

Table VII shows specific time-of-day statistics of wind ramps in SPS. In this table, false ramps are defined 

as a 10-minute ramp of 25 MW or more inside a 5-hour window. Figure 8 shows ramp statistics by time 

of day and by month.

Table VII − SPS wind ramp statistics by time of day

Ramps over 135 

MW in 60 mins 

Ramps Max 60 m 

up ramp

Max 60 m 

down ramp

Average false ramps (25

MW in 10 min inside 5-

hour window)

Maximum false ramps 

(25 MW in 10 min

inside 5-hour window)

Hours 0 - 5 41 229 MW -228 MW 1.54 8

Hours 6 - 11 43 205 MW -230 MW 1 7

Hours 12 - 17 54 241 MW -207 MW 0.93 6

Hours 18 - 23 70 292 MW -206 MW 1.36 6

 8 50% of SPS wind ramp definition showed no ramps at all and therefore, 40% was displayed.
15

Figure 8 − Contour plot showing SPS wind ramps by time of day and month

These results slightly resemble the PSCO results in that there are a greater number of ramps in the 

period of hours 18-23 compared to the other time periods. However, the occurrences of false ramps 

seem to be more consistent between all time periods. Figure 8 shows when up-ramps are occurring 

compared to down-ramps. It appears that the majority of the ramps occurring during the evening and 

night period are up-ramps and that most down-ramps occur earlier in the day. This type of correlation 

between wind ramp direction and time of day was not seen in PSCO.

Figure 9 shows two of the three ramps identified as a 40% or greater ramp in 60 minutes.

Figure 9 − Two SPS wind ramps

0

100

200

300

400

500

600

16:48 19:12 21:36

0

100

200

300

400

500

600

16:48 19:12 21:36
16

Figure 9 shows a 50-minute ramp on the left and a 100-minute ramp on the right. The ramp on the left is

also followed by a slower down-ramp. Figure 10 and Figure 11 show the contributions from different 

wind plants for these two wind ramps.

Figure 10 − SPS wind ramp and wind plant contributions

Figure 11 − SPS wind ramp and wind plant contributions

In Figure 10 most wind plants have a rapid increase in power production which makes the ramp more 

difficult to manage. In Figure 11, many of the plants have a longer duration increase giving the operators 

more flexibility in what units to adjust or what techniques to use to compensate for the changing wind. 

In Figure 11, because a down-ramp is immediately following the up-ramp, the operator must be careful 

about what is done in this situation. Many generators have minimum down-times (times the unit must 

stay off-line when shut down before being started up again); therefore, another option would have most 

likely been more practical than turning a unit off. It is possible that the most economic and reliable 

option in this case may be to curtail the wind during its up-ramp, had the event been perfectly 

predicted.

-100

0

100

200

300

400

15:36 18:00 20:24

SPS TOTAL WIND

Wildorado Wind

San Juan Mesa 

Wind

Norton (Caprock) 

Wind

-100

0

100

200

300

400

500

600

16:48 18:00 19:12 20:24 21:36

SPS TOTAL WIND

Wildorado Wind

San Juan Mesa 

Wind

Norton (Caprock) 

Wind
17

Figure 12 − SPS wind down-ramp with preceding false ramps

Figure 12 shows a highly variable time period where two significant false ramps occurred before the 

major down-ramp. These are some of the hardest events to react to in that it is never fully known when 

to ramp units all the way up or turn units on to compensate for the largest predicted ramp. When 

normalized to each other, there was no significant difference between the average number of false 

ramps inside the same forecast window between PSCO and SPS.

C. PSCO and SPS combination

The team also pursued an analysis of impacts of wind ramps assuming SPS and PSCO were considered as 

one balancing area. These areas are not geographically connected and are in fact on different sides of 

the US electrical interconnections.9

Table VIII

Therefore, this assessment is an unrealistic scenario but the results 

of showing what further geographic diversity could bring made it worthwhile. shows the ramp 

statistics for different definitions, still keeping consistent the percentage of installed wind capacity with 

the last two ramp statistics tables.

 9 The eastern interconnection ends just west of the SPS region and PSCO is part of the western interconnection. 

The two interconnections are only connected via limited DC transmission links so balancing between the two 

regions would not be a normal scenario.

0

50

100

150

200

250

300

350

400

450

20:24 21:36 22:48 0:00 1:12 2:24
18

Table VIII − PSCO and SPS combination wind ramp statistics

Total Wind ~1650 MW Total Up Down Average duration Max duration

Ramps over 385 MW (250+135) in 60 

mins (25%)

81 57 24 86 min 220 min

Ramps over 550 MW (350 +200) in 60 

mins (33%)

11 10 1 86 min 140 min

Ramps over 740 MW (500 + 240) in 60 

mins (45%)

1 1 0 70 min 70 min

Ramps over 385 MW in 30 mins 17 13 4 52 min 100 min

As would be expected, the number of ramps is substantially lowered assuming similar definitions based 

on percent of nameplate capacity. Combining the two areas result in ramps of 25% of capacity in 60 

minutes about once every 4 or 5 days, whereas PSCO had ramps of almost once per day and SPS more 

than one every two days for the same percentage. Ramps of over 33% occurred 11 times and ramps 

over 50% occurred once in 2008. This clearly shows that the spreading of wind plant locations 

throughout very long distances (i.e., increasing geographic diversity) greatly reduces the per-unit wind 

ramp events on a combined system. Table IX shows time-of-day statistics for the combined PSCO and 

SPS wind production. Here, a false ramp is defined as a 65 MW change in 10 minutes within a 5-hour 

window of a defined ramp. The results look similar to the others, most likely because they are 

dominated by PSCO events. The maximum ramps are almost identical to the PSCO maximum ramps.

Table IX − PSCO and SPS combination wind ramp statistics by time of day

Ramps over 385

MW in 60 mins 

Ramps Max 60 min

up ramp

Max 60 min

down ramp

Average false ramps 

(65 MW in 10 min 

inside 5-hour window)

Maximum false ramps 

(65 MW in 10 min 

inside 5-hour window)

Hours 0 - 5 13 574 MW -456 MW 0 0

Hours 6 - 11 16 552 MW -504 MW 0.38 2

Hours 12 - 17 22 613 MW -546 MW 0.64 3

Hours 18 - 23 30 746 MW -663 MW 1.03 4
19

One large ramp event stood out in the analysis and is shown in Figure 13. It was also a large ramp event 

for PSCO. The plot is shown below with both SPS and PSCO wind production contributions.

Figure 13 − Wind up-ramp where SPS is constant and PSCO is ramping

This was one of the largest PSCO ramps and was the largest combined ramp as well. The SPS plants 

stayed relatively constant while the PSCO fleet was increasing greatly. Therefore, the absolute 

magnitude of the ramp was not affected with the addition of SPS. This behavior was generally the case 

for most of the large PSCO ramps because SPS production had little to no (to even a beneficial) impact 

on the combined ramp. Figure 14 shows an up-ramp where both PSCO and SPS were ramping up in 

production. This type of a combined ramp with both contributions was very rare in the data. The 

correlation of the two areas during this time frame appear strong and the ramp start and end times

appear to be identical between the two distant regions.

0

200

400

600

800

1000

1200

1400

1600

14:24 16:48 19:12

Total Combined

SPS

PSCO
20

Figure 14 − Combined up-ramp

Further analysis was performed as to how each region was contributing to the top 81 ramps defined in 

Table VIII. Table X shows the results. PSCO was the major contributor to the majority of wind ramps. A 

total of 54% of the ramps had PSCO contributing to over 90% of the total magnitude of the ramp. SPS 

contributes less than 30% of the total ramp magnitude in most cases. Table XI shows the contributions 

normalized to the regions’ size. There are a few more ramps of larger contributions from SPS when they 

are normalized but it still shows the majority are due to PSCO. However, the 81 ramps were defined 

from the original data with PSCO dominating the contributions so this is expected. Figure 15 shows 

histograms of the frequency of percent contribution for the absolute and normalized cases.

0

200

400

600

800

1000

1200

1400

14:24 16:48 19:12 21:36 0:00

SPS

PSCO

Combined
21

Table X − Frequency of contributions from PSCO and SPS toward combined ramps10,11

PSCO

Bins (%) Occurrences Cumulative

SPS

Bins (%) Occurrences Cumulative

< 50% 1 1 < 0% 11 11

55% 1 2 5% 17 28

60% 2 4 10% 16 44

65% 1 5 15% 9 53

70% 6 11 20% 9 62

75% 2 13 25% 6 68

80% 6 19 30% 2 70

85% 9 28 35% 6 76

90% 9 37 40% 1 77

95% 16 53 45% 2 79

100% 17 70 50% 1 80

> 100% 11 81 > 50% 1 81

Table XI − Frequency of contributions from PSCO and SPS toward combined ramps, normalized to 

region capacity

PSCO

Bins (%) Occurrences Cumulative

SPS

Bins (%) Occurrences Cumulative

< 50% 4 4 < 0% 11 11

55% 6 10 5% 13 24

60% 3 13 10% 7 31

65% 0 13 15% 12 43

70% 8 21 20% 6 49

75% 6 27 25% 5 54

80% 5 32 30% 6 60

85% 6 38 35% 8 68

90% 12 50 40% 0 68

95% 7 57 45% 3 71

100% 13 70 50% 6 77

> 100% 6 81 > 50% 4 81

 10 A contribution of greater than 100% means that the regions ramp was larger than the combination ramp, and 

the other region must have been opposing the combination ramp. The other region would have a negative 

contribution.

11 55% means between 50 and 55%. Each percentage represents less than or equal to that percentage and greater 

than the prior percentage.
22

Figure 15 − Frequency of ramp contributions to combined PSCO and SPS ramps

4. Net Load Ramps

In reality, operators are responsible for balancing the generation and load together, not independently. 

Wind up-ramps that occur at the same time as load up-ramps are not of concern to an operator. Wind 

down-ramps that happen at the same time as load up-ramps, however, can exacerbate the ramping 

issue of one or the other by itself. The following analysis is of net load ramps. Net load ramps are the 

total load minus total wind production that is ramping in either direction. Net load ramps are what an 

operator would need for its conventional generation fleet or other sources of flexibility12

A. PSCO

to follow.

Table XII shows load and net load ramp statistics for PSCO.

 12 Other sources of flexibility can include demand response, energy storage, or in some cases, curtailment of wind 

generation.

0

10

20

30

40

50

60

70

80

90

0.5 0.6 0.7 0.8 0.9 1

PSCO frequency of ramp contributions

Frequency

Cumulative 

Frequency

ratio contribution

frequency

0

10

20

30

40

50

60

70

80

90

0 0.1 0.2 0.3 0.4 0.5

SPS frequency of ramp contributions

Frequency

Cumulative 

Frequency

ratio contribution

frequency

0

10

20

30

40

50

60

70

80

0.5 0.6 0.7 0.8 0.9 1

PSCO frequency of ramp contributions 

- normalized

Frequency

Cumulative 

Frequency

ratio contribution

frequency

0

10

20

30

40

50

60

70

80

90

0 0.1 0.2 0.3 0.4 0.5

SPS frequency of ramp contributions -

normalized

Frequency

Cumulative 

Frequency

ratio contribution

frequency
23

Table XII − Load and net load ramp statistics for PSCO

Load Ramps

Peak demand = 6800 MW Total Up Down Average 

duration

Max duration

Load ramps over 500 MW in 60 mins 396 214 182 85 min 310 min

Load ramps over 600 MW in 60 mins 104 52 52 107 min 310 min

Load ramps over 750 MW in 60 mins 8 6 2 93 min 140 min

Net Load Ramps

Peak demand = 6800 MW

Wind capacity = 1060 MW

Total Up Down Average 

duration

Max duration

Net load ramps over 500 MW in 60 mins 503 254 249 86 min 340 min

Net load ramps over 600 MW in 60 mins 205 94 111 97 min 310 min

Net load ramps over 750 MW in 60 mins 35 10 25 110 min 190 min

This table shows the impacts that 1 GW of concentrated wind plant additions have on the ramping 

characteristics of the PSCO system. Ramp definitions used are 500, 600, and 750 MW in a 1-hour period. 

Each of these different definitions is impacted differently with wind added on to the system. Larger 

ramp thresholds have increasingly higher numbers when wind is added to the system. The number of 

ramps increases by 25%, 100%, and well over 300% for the 500, 600, and 750 MW cases, respectively. 

Each of these different categories may have different mitigation strategies (if not inclusive as they are 

displayed here), and therefore, the impact on how often each is occurring is important. Given that the 

absolute number of ramp occurrences increases are about the same for the 500 MW and 600 MW cases

shows that there are very few increases in ramps between 500 MW and 600 MW in 60 minutes when 

wind is added to the system. These types of ramps are probably characteristic of a morning ramp-up or 

an evening ramp-down, and operators are used to dealing with these ramps. Having double the number

of ramps over 600 MW and over four times as many ramps over 650 MW is a substantial impact, 
24

however. For ramps of these magnitudes, new operational practices may need to be developed to keep 

the same reliability levels and costs down. The change in distribution of up- and down-ramps is also 

interesting. For the major ramps that are greater than 750 MW in magnitude, the majority of the 

increases are in down-ramps, signifying that the wind up-ramps are mostly contributing to this increase. 

Another impact, as shown in Table XIII, is the change and possibly lessened predictability of the timing of 

these ramps.

Table XIII − Load and net load ramp statistics for PSCO by time of day

Load Ramps

Load ramps over 600 

MW in 60 mins 

Ramps Max 60 min

up ramp

Max 60 min

down ramp

Average false 

ramps

Maximum false 

ramps

Hours 0 - 5 11 527 MW -653 MW 1.27 4

Hours 6 - 11 9 692 MW -536 MW 3 4

Hours 12 - 17 32 786 MW -495 MW 0.53 3

Hours 18 - 23 52 611 MW -801 MW 3.12 8

Net Load Ramps

Net load ramps over 

600 MW in 60 mins 

Ramps Max 60 min

up ramp

Max 60 min

down ramp

Average false 

Ramps

Maximum false 

ramps

Hours 0 - 5 29 683 MW -1066 MW 1.28 4

Hours 6 - 11 31 921 MW -571 MW 3.32 7

Hours 12 - 17 38 837 MW -671 MW 1.13 5

Hours 18 - 23 107 740 MW -1048 MW 3.33 9
25

Figure 16 − Contour plot showing PSCO load ramps by time of day and month

Figure 17 − Contour plot showing PSCO net load ramps by time of day and month
26

Figure 16 and Figure 17 show the differences in the timing of significant ramps with added wind in PSCO. 

Note that Figure 17 uses a different definition to maintain the top 100 criteria. In general, these figures,

as well as Table XIII, show that adding the wind to the load will present the morning ramps as more 

significant, especially during summer months. These are all net load up-ramps and are being amplified 

with down-ramps of wind. Table XIII shows that the impacts of evening and night time ramps are 

probably the most severe with the added wind production. The maximum down-ramp has increased by 

over 250 MW and the number of ramps during this period more than doubled.

B. SPS

Table XIV shows the total net load ramp statistics for SPS. As a fraction of peak demand, the 375 MW 

ramp definition is most closely proportional to the 500 MW definition of PSCO in Table XII. The 250 MW

ramp definition is lower but was shown to provide additional information.

Table XIV − Net load ramp statistics for SPS

Peak demand = 5500 MW

Wind capacity = ~570 MW

Total Up Down Average 

duration

Max duration

Ramps over 250 MW in 60 mins 207 97 110 175 min 550 min

Ramps over 375 MW in 60 mins 9 3 6 152 min 280 min

Any stricter definitions on this table would have resulted in very few or zero ramps, and were taken out 

of the analysis. One observation from this table is that the ramps of SPS net load were of much longer 

duration than that of PSCO. Even with less stringent ramp definitions, average durations of ramps were 

approaching three hours. Longer duration ramps are more manageable operationally than shorter 

duration ramps of the same magnitude. Table XV shows different ramp statistics by time of day for SPS 

net load.
27

Table XV − Net load ramp statistics for SPS by time of day

Ramps over 250 MW in 

60 mins 

Ramps Max 60 min

up ramp

Max 60 min

down ramp

Average false 

ramps (40

MW, 10 min)

Maximum false 

Ramps (40 MW,

10 min)

Hours 0 - 5 55 387 MW -323 MW 0.56 9

Hours 6 - 11 31 448 MW -245 MW 0.77 5

Hours 12 - 17 25 390 MW -308 MW 0.68 4

Hours 18 - 23 96 274 MW -434 MW 2.35 12

Figure 18 − SPS net load ramp statistics by time of day and month 
28

The most significant period appears to be the evening period of hours 18 through 23. Of the 96 ramp 

periods identified in this period, 95 of them were down-ramps, the typical expectation of load behavior

during this period. In fact, as seen in Figure 18, the ramps are more prevalent during the most typical 

load ramp periods; up-ramps in the summer morning and early afternoon, and down-ramps in the late 

night and early morning in the summer. Comparing Figures 17 and 18, the temporal characteristics of 

net load down-ramps for SPS are very similar to PSCO. However, the temporal characteristics of net load 

up-ramps differ with more evening up-ramps in PSCO during the winter months indicative of heating 

demands and more summer daytime up-ramps in SPS indicative of cooling demands. These contour 

plots look similar to the contour plots of the load without wind. This indicates that at current wind 

penetration levels, addition of wind does not significantly alter the timing of ramping events even 

though the addition of wind increases variability.

A correlation between the load and the wind in SPS for 10-minute changes was very weak. Further 

analysis was done to observe contributions during the top ramp periods. Table XVI shows the number of 

times both wind and load contributed to net load ramps in terms of percentages. In 87 out of 100 

ramps, wind contributed less than 50% to the total net load ramp.

Table XVI − Load and wind contributions to top 100 net load ramps in SPS13, 14

Load Occurrences Cumulative Wind Occurrences Cumulative

<0% 0 0 <0% 4 4

10% 0 0 10% 16 20

20% 0 0 20% 23 43

30% 1 1 30% 27 70

40% 7 8 40% 10 80

50% 5 13 50% 7 87

60% 7 20 60% 5 92

70% 10 30 70% 7 99

80% 27 57 80% 1 100

90% 23 80 90% 0 100

100% 16 96 100% 0 100

>100% 4 100 >100% 0 100

5. Characteristics and Correlation of Wind Plants

Since wind power is being driven by wind speed that can travel vast distances from wind plant to wind 

plant, it is important to quantify the correlation between wind plants and times when producing wind 

 13 A contribution of greater than 100% means that the wind or load ramp was larger than the net load ramp, and 

the other contributor (wind or load) must have been opposing the net load ramp. The opposing contributor would 

have a negative contribution.

14 10% means between 0 and 10%. Each percentage represents less than or equal to that percentage and greater 

than the prior percentage.
29

power production forecasts. Generally, the closer in distance two wind plants are to one another, the 

more correlated their output will be. Terrain features can also impact the correlation between wind 

plants. A wind plant may also have a high correlation with the output of a distant wind plant at several 

time periods ahead or behind. Additionally, correlations may have certain seasonal or hourly 

characteristics or unique characteristics during ramp periods or non-ramp periods. Knowing correlation 

of one wind plant from another can assist in the forecasting of ramps when they start to occur.

A. PSCO

The correlations between individual wind power plants within PSCO were tallied and compared during 

all times and during times that were identified as up-ramps, down-ramps, and all ramp events 

collectively, for the top 100 ramps of 2008. These results are presented in Figure 19. For ramp periods 

and all times, Colorado Green was the least correlated with other wind power plants. However, COLG

appeared to be better correlated during down-ramp events than during all times. Other trends also 

appeared to be sensitive to the direction of the ramp event. For example, CEDC is less correlated with 

SGCA and PEET during down-ramps than during up-ramps or for all times.

Figure 19 − Correlations between PSCO wind power plants during ramp periods and all times

COLG and PONN, based on both the assessment of ramp starting times (see Table V above) and 

correlations with other wind power plants during ramp events, contribute the least to system-wide

ramp events. Wind power plants contribute differently to the total power output of wind in a particular 
30

area for a variety of reasons: local topography, size and age of machines, the number of machines, the 

distance between turbines within a wind power plant and the distance between wind power plants. The 

distance between turbines within a power plant and the distance between wind power plants determine 

the level of spatial or geographic diversity within a wind power plant or region, respectively. Thus, wind 

power plants located farther apart will experience more “smoothing” of the variability of the output of 

their resources due to high levels of geographic diversity. This smoothing effect will generally reduce the 

impact of system-wide ramp events.

The PSCO wind plants are shown in descending isolation in Table XVII. COLG and PONN, the weakest 

contributors to PSCO ramp events, are shown to be the most isolated from the other wind power plants 

which are confined to northeastern Colorado. 

Table XVII − Spatial diversity of PSCO wind power plants

Most isolated PSCO wind 

plants (descending) Name

1 COLG

2 PONN

3 SGCA

4 CEDC

5 PEET

6 LOGA

The impact of geographic diversity was more profound when considering the correlations of rates of 

change between wind plants. The rates of change are the ten-minute step changes from one period to 

another. Interestingly, these step changes are better correlated during ramp events than during all 

times. The correlations increase further during more extreme ramp events. During the top 40 ramp 

events, a negative correlation between Colorado Green and Logan develops. Although fairly weak (r = -

0.05), with this much data even very weak correlations are statistically significant. 
31

Figure 20 − Step change correlations for PSCO during all times and ramp events

In [6], it was shown that wind power correlations decay exponentially with increasing distances and that 

the rate of decay increases with decreasing levels of averaging in the time series data. Thus, correlations 

between ten-minute averaged power outputs, as above, will decay more quickly than for hourly 

averages, but much more slowly than for one-minute data. It is therefore important to consider the 

resolution of data when presenting correlation and whether wind power forecasts for production or 

specifically for predicting ramps are considering the same resolutions. 

Since weather systems moving across the PSCO region will bring similar meteorological conditions to 

different wind power plants at different times, it is useful to compare the correlations between wind 

power plants several periods earlier or later. With this in mind, the team considered a geographictemporal analysis at various time lags and leads. Results from two plants were examined with this 

approach. Figure 21 shows correlations between Cedar Creek (CEDC) and other plants at various time 

shifts. Similarly, Figure 22 shows temporally shifted correlations between Logan (LOGA) and other 

selected plants. In Figure 21, the strongest correlation between Cedar Creek and PONN is found with 

PONN leading Cedar Creek by 60 minutes. In Figure 22, the strongest correlation between Logan and 

PONN is found with PONN leading Logan by 90 minutes. Considering that the prevailing winds in the 

region are from west to east and recalling the order of wind plants from above (west to east- PONN, 

CEDC, PEET, LOGA, and SGCA), one can see strong agreement with expected cross-correlations in the 

figures at temporal shifts. COLG was excluded from this analysis due to the weak correlations with all 

other plants.
32

Figure 21 − Cross-correlations between Cedar Creek and PSCO wind plants as a function of a temporal shift

Figure 22 − Cross-correlations between Logan and PSCO wind plants as a function of a temporal shift

The temporally dependent cross-correlations between wind plants during ramp events are more 

important than during all times. As these extreme events move through a region, they bring rapid, 

turbulent changes for short durations. Lower correlations of power outputs, though difficult to see at 

the resolution shown in Figure 19 above, are quite evident in comparing Figure 22 and Figure 23 at a 
33

time shift of zero. However, the sensitivity of the cross-correlations to the temporal shift is more 

pronounced during ramp events compared to all times, as shown in Figure 23 and Figure 24. This 

suggests that there is some benefit in using outlying wind power plants’ power output during short time 

scales and at different lead times for different plant combinations to warn of expected wind ramping 

behavior system-wide. Figure 24 shows that in some cases, as with LOGA and CEDC, the highest 

correlation can change from leading to lagging. Lastly, this suggests that there is added benefit for 

utilizing modified output statistics in forecasting and the usefulness of having a centralized forecasting 

operation. The benefits of centralizing forecasting operations to take advantage of correlations between 

upstream and downstream wind power plants are key conclusions from other studies [12].

Figure 23 − Cross-correlations between Logan and PSCO wind plants as a function of a temporal shift during ramp periods
34

Figure 24 − Cross-correlations between Logan and PSCO wind plants as a function of time shift during system-wide up- and 

down-ramps

B. SPS

Similar correlation analyses were performed for SPS. In Figure 25, cross-correlations between selected 

SPS wind power plants and the system-wide total output of these wind power plants were compared. In 

comparison to Figure 19 for PSCO, Figure 25 shows more distinct deterioration of cross-correlations 

during ramping events as compared to all times. This may be due to better data quality for SPS. The 

behavior of the correlations during all ramp events very closely follows the results during up-ramp 

events. This reflects the findings in total numbers of up-ramps compared to down-ramps.
35

Figure 25 − Correlations between SPS wind power plants during ramp periods and all times

The cross-correlations for select SPS wind power plant step-changes mirrored the findings from PSCO as 

shown in Figure 26. Step change correlations are greater during periods of ramp events, but decay with 

distance much faster than correlations of power output. This figure is slightly skewed by showing 

system-wide results; however, the correlations between any wind power plant and the system-wide 

results are more telling than cross-correlations between other individual wind power plants. Table XVIII

shows the distances between the SPS wind plants and Figure 27 shows the relationship between 

correlation and distance for all times. You can see that increasing distances correspond to lower 

correlation; however, this plot is also showing more noise around this trend. It is important to 

remember that the level of decay is dependent on the resolution of the data, and that higher resolution 

(i.e., shorter intervals) will generally show a greater level of decay with distance.
36

Figure 26 − Step change correlations for PSCO during all times and ramp events

Table XVIII − Distances between SPS wind plants

Distance 

between SPS 

wind plants 

(miles) NCAP JD1_2 JD3 JD4 JD5_6 JD7_8 JD9_10 JD10_11 SJWD LLANO WILD HPWD SUNR

Norton.Caprock,

80 MW

0 149 152 148 132 109 111 96 76 124 63 106 106

JDWind1_2, 20

MW

149 0 8 9 21 40 41 55 220 72 96 80 45

JDWind3, 10 MW

152 8 0 4 28 42 42 57 222 68 96 76 47

JDWind4, 79.8

MW

148 9 4 0 26 39 38 53 218 65 93 73 42

JDWind5_6, 20

MW

132 21 28 26 0 30 34 45 206 76 85 79 36

JDWind7_8, 20

MW

109 40 42 39 30 0 6 16 180 52 56 50 6

JDWind9, 10 MW

111 41 42 38 34 6 0 15 180 47 55 45 5

JDWind10_11,

20 MW

96 55 57 53 45 16 15 0 165 49 40 41 11

SJuanWind, 120

MW

76 220 222 218 206 180 180 165 0 181 126 162 175

Llano, 80 MW

124 72 68 65 76 52 47 49 181 0 62 19 48

Wildorado, 161

MW

63 96 96 93 85 56 55 40 126 62 0 43 50

HighPlainsWinds,

10 MW

106 80 76 73 79 50 45 41 162 19 43 0 45

Sunray1, 9 MW

106 45 47 42 36 6 5 11 175 48 50 45 0
37

Figure 27 − Correlation coefficient for 10-min avgs by distance for SPS wind plants

6. Conclusions

Wind power is a growing resource in the United States and elsewhere. As utilities and system operators 

introduce greater penetrations, the operational impacts become more apparent. Most utilities and ISOs

are now using wind power forecasting as a means to integrate more wind power on the system reliably 

and efficiently. As more of industry begins to use wind power forecasting in day-to-day operations, they 

also understand the importance of wind power forecasting during ramp periods. To better forecast 

these events, it is important to know the characteristics of these events on the particular system. The 

ramp events have certain days or periods in which they occur more frequently. They also have different 

contributions at different times depending on the geographical dispersion of wind plants, or wind plant 

correlation with load. These types of characteristics will vary from region to region depending on local 

weather patterns, geographic diversity, topography, site layout, and other factors.

The analysis in this report looks at past wind and load data to show characteristics of ramp events. 

Analysis showed statistics on number of ramps, when they most occur, and volatility. It also showed 

how plants correlated to each other in time and space, and how different factors contributed to wind 

and net load ramps. By comparing different ramp statistics, it was shown that geographic diversity 

lessened the per-unit impact on ramps. All of this information becomes valuable for areas preparing for 

higher wind penetrations and for improvements in ramp forecasting.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

SPS Wind Plants Correlation by 

Distance
38

7. References

1. R Wiser, M Bolinger, 2008 Wind Technologies Market Report 2008, Washington, D.C., U.S. 

Department of Energy, 2008. 

2. J. Charles Smith, Michael Milligan, Edgar Demeo, and Brian Parsons, “Utility Wind Integration and 

Operating Impact State of the Art,” IEEE Trans. Power Systems, vol. 22, pp. 900-908, Aug. 2007.

3. Enernex Corporation and Wind Logics, Inc., “Xcel Energy and the Minnesota Department of 

Commerce Wind Integration Study – Final Report,” Sep. 2004. 

4. Enernex Corporation, “Final Report: Wind Integration Study for Public Service Company of 

Colorado,” May 2006.

5. Enernex Corporation, “Wind Integration Study for Public Service of Colorado Addendum Detailed 

Analysis of 20% Wind Penetration,” Dec. 2008.

6. Y. Wan, Wind Power Plant Behaviors: Analysis of Long-Term Wind Power Data, National Renewable 

Energy Lab., Golden, CO, NREL/TP-500-36651, 2004.

7. K. Parks, “Value to Real-Time Operations,” Utility Wind Integration Group Forecasting Workshop,

February 2009.

8. Southwest Power Pool. www.spp.org

9. Midwest ISO. www.midwestiso.org

10. FERC Order, Standard Market Design (SMD), Notice of Proposed Rulemaking (NOPR), July 31, 2002

11. Matlab. www.mathworks.com

12. GE Energy, The Effects of Integrating Wind Power on Transmission System Planning, Reliability, and 

Operations: Report on Phase 2, prepared for the New York State Energy Research and Development 

Authority, Mar. 2005.
F1147-E(10/2008)

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Wind Plant Ramping Behavior 

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With the increasing wind penetrations, utilities and Independent System Operators (ISO) are quickly trying to 

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