


































Energy and Earth Science 
Vol. 3, No. 2, 2020 

www.scholink.org/ojs/index.php/ees 

ISSN 2578-1359 (Print)   ISSN 2578-1367 (Online) 

113 
 

Original Paper 

Characterizing Opportunity Power in the California Independent 

System Operator (CAISO) in Years 2015-2017 

Andrew A. Chien
1
 

1
 Department of Computer Science, University of Chicago and Argonne National Laboratory, Chicago, 

USA  

 

Received: September 16, 2020   Accepted: October 2, 2020   Online Published: November 10, 2020 

doi:10.22158/ees.v3n2p113               URL: http://dx.doi.org/10.22158/ees.v3n2p113 

 

Abstract 

Studying the California Independent System Operator (CAISO) day-ahead and real-time markets for 

the period January 2015 to December 2017, we characterize the growth of curtailment and 

negative-priced power from renewable generators. Results show that renewable curtailment is growing 

rapidly, tripling to over 400 GWh from 2015-2018. Negative-priced renewable power is larger and 

also growing rapidly, reaching 1.5 TWh in 2017 for 40% CAGR. 

Resource-hours for negative pricing grew nearly 3-fold from 80,006 to 217,728 hours, with the highest 

single generator reaching 955 hours in 2017 or 33% of the daylight solar hours. Spatially, the quantity 

of negative-priced power is concentrated at a few dozen renewable generators, reaching peaks of 

170GWh at the largest generator. We also consider an averaged-price model (NetPrice) that smooths 

over fluctuations to estimate the usable quantities of low-priced power. Results for NetPrice show a 

much larger quantity of low-priced power available than with either negative-pricing or curtailment 

alone. Overall, these results suggest both that opportunity power is a substantial and growing resource 

and a number of opportunities to exploit it.  

Keywords 

Renewable generation, power markets, curtailment, negative prices, power grid 

 

1. Introduction 

Aggressive Renewable Portfolio Standards (RPS), and resulting rapid growth of variable renewable 

generation in power grids around the world aim to reduce the carbon-emissions and other negative 

environmental impacts of fossil-fuels. For example, California recently adopted even more aggressive 

goals of 60% renewable in 2030 and 100% carbon-free power by 2045 (Shoot, 2018). These ambitious 

goals are transforming the power generation and use landscape and pose serious power grid challenges 



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including ability to achieve “merit order”, efficiency, stability, and resiliency. And of course, social 

welfare is an important consideration. 

Evidence of challenges include growing curtailment, negative-priced generation, and RPS stagnation. 

Curtailment occurs where the power grid is unable to accept renewable generation due to congestion, or 

excess generation, causes power to be discarded at the generation site Europe and the United States 

(Lew, 2013; Bird, 2013) and China (GWEC, 2016). And, despite programs to increase transmission 

capacity and employ economic dispatch, curtailed power in Europe, United States, and China exceeds 

50 TWH per year (GWEC, 2016). Economic dispatch has successfully reduced curtailment, providing 

economic incentives (payments) and disincentives (negative payments) for generation. Renewables are 

increasing their participation in markets by bidding their opportunity costs, increasing the ability to 

manage and optimize supply and demand recognizing transmission constraints. However, one result is 

negative-priced power generation, power purchased by the power grid at a negative price (Gross, 2015; 

Reed, 2017). In this article we use the term Opportunity Power (OP) to describe both curtailed power 

and negative-priced generation as they together represent excess renewable power generation (Note 1). 

We use the term opportunity power to capture the notion that there is an opportunity if the negative 

pricing can be leveraged to enable productive use of the power. Recent studies examine not only 

increased curtailment, but also the lesser utility (capacity factor) of renewables as their fraction is 

increased (Wiser, 2017). Our prior studies characterized this opportunity power in the Midcontinent 

Independent System Operator (MISO) (Chien, 2018). 

This study analyzes opportunity power in the California Independent System Operator (CAISO) for the 

period January 2015 to December 2017 with the objective of characterizing the growth of curtailment 

and negative-priced generation to create insights that can inform strategies to both reduce their 

occurrence and to exploit the opportunity that this power provides for some productive use (Note 2). In 

the period of study, CAISO’s total renewable generation grew from 45 to 55 TWh (CAISO, 2018). Our 

prior studies characterized stranded power in the Midcontinent Independent System Operator (MISO) 

(Chien, 2018). 

We studied CAISO day-ahead and real-time market bids, market prices, dispatch prices, and production 

for over 375 renewable generation resources. Power is settled at 1 hour intervals in the day-ahead 

market, and dispatched at 15 and 5 minute intervals in the day-ahead and real-time markets respectively, 

so this data collection includes over 125 million records. We characterize negative pricing in the 

day-head and real-time markets and also include CAISO’s reported curtailment for renewable 

generation (initiated in 2016). We use this information to estimate negative-priced generation. Together 

these form an assessment of current opportunity power today and its trends of change. 

Findings include: 

 Negative-priced renewable power is significant and growing. Day-ahead Market (DAM) 

combined with estimated Real-Time Dispatch (RTD) show this at 1.5 TWh in 2017. Estimates 

based on DAM alone approach 1 TWh and RTD alone are 5 TWh in 2017. These quantities 



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and growing at nearly 40% CAGR, and are similar to MISO which is 2.5x larger. 

 While curtailment is a smaller quantity, it also continues to increase rapidly, tripling to over 400 

GWh from 2015 to 2018. And despite a pause in recent 2018 due to lower hydro generation, it 

is expected to resume its rapid growth in 2019 and beyond.  

 Resource-hours in CAISO for negative pricing are growing rapidly from 80,006 to over 217,728 

resource-hours, and already cover all of the renewable generators. All resources are 

experiencing steady increase in number of hours with averages growing, a peak of 955 

hours/year in 2017, a ~33% duty factor during solar hours, increasing to higher duty factors as 

RPS levels increase. Nearly all experienced >600 hours/year. 

 Negative-priced power is several times larger than curtailed power for renewable generation; 4x 

based on conservative estimation (DAM-RTD-scaled), more than 10x by our aggressive 

measure (RTD). 

 Spatially, the quantity negative-priced power is concentrated at a few dozen renewable generators. 

And, using RTD-LMP measures, reach significant quantity—170 GWh at a single generator, 

comparable to 19MW continuous or 47MW in sunlight hours. 

 The duty factor negative-pricing is more dispersed across resources, with a median close to the 

average grown to 576 hours/year in 2017. The peak of 955 hours represents a duty factor of 

11% (24 hours) or 33% (for 10 hours). 

 We also consider average power price models (called NetPrice) that have been shown to smooth 

over market fluctuations, increasing realistic estimates of duration and quantity of 

negative-priced power (Chien, 2018). These price models have similar impact in CAISO, and 

show greater quantity of negative-priced power available. 

In summary, opportunity power, both negative-priced and curtailment is growing rapidly. Quantities 

are increasing at more than 40% per year, and duty factors rising across all renewable generators. These 

trends represent significant challenges for the power grid and perhaps opportunities for alternate usage. 

Further study in this area is a worthwhile endeavor. 

The remainder of the paper is organized as follows. We define opportunity power and methods for 

computing it in Section 2. Curtailment in CAISO is discussed in Section 3. In Section 4, we estimate 

negative-priced power, using three different models. In Section 5, we combine negative-priced power 

and curtailment to provide an overall opportunity power assessment. In Sections 6 and 7, we refine this 

view, using the real-time market data to examine spatial and temporal distribution of negative-priced 

power. In Section 8, we consider usage-oriented models that look at average power pricing, exploring 

how they would impact the usability of the resource. In Section 9, we discuss our results and compare 

to related work. Section 10 summarizes the results of the study and suggests directions for future work. 

Finally, in the Appendix (Section 11), we present several scatter plots illustrating the statistical 

temporal properties of opportunity power. 

 



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2. Definition of Opportunity Power 

We define opportunity power as two components: 1) curtailment, the power a generator would like to 

generate and transmit into the power grid, but ultimately does not, and 2) negative-priced power, that 

which is generated and transmitted into the grid, but at a market price that is negative. That is, in effect, 

the generator is paying the load to take the power. 

2.1 Curtailed Renewable Generation 

The methodology of (CAISO, 2018) computes renewable generation as the difference between forecast 

and actual generation for each dispatch interval. This data is publicly reported at (CAISO, 2018), and is 

available only at the level of the entire CAISO region. We plot and analyze curtailed renewable 

generation from several perspectives in Section 3. 

2.2 Negative-Priced Power 

CAISO employs an intricate set of markets and dispatch mechanisms to ensure reliable power to a 

diverse set of loads, dispatching generation and scheduling transmission. We use several methods to 

estimate negative-priced power. 

1) DAM-LMP: Using the Day-Ahead Market (DAM) hourly settlements, we characterize the 

quantity of negative-priced power. Because the DAM accounts for only a portion of the power 

transacted by CAISO, and DAM negative pricing is less frequent than RTD, this is a 

conservative estimate. 

2) RTD-LMP, RTD-NP: Using the real-time market (RTM or RTD) 5-minute settlements, at the 

resource level, for RTD-LMP, we sum the total power produced when there is a negative price 

(RTD price) for the interval. For RTD-NP, again at the resource level, we sum the total power 

produced as long as the average price for that interval remains negative. We consider NP0 and 

NP5 for average price thresholds of $0/MWh and $5/MWh respectively. This is an aggressive 

model and likely an overestimate (Note 3), but matches the methodology in (Chien, 2018), 

allowing direct comparison to MISO. 

3) DAM-RTD-Scaled: Combine the DAM-LMP with an estimate of the additional 

negative-priced power dispatched in the real-time market, using the fraction of the power 

covered by RTD but not DAM. This model uses the assumption that the fraction of 

negative-priced power in the RTD is the same as that in the DAM, perhaps a conservative 

projection.  

In the above three models, we use two underlying definitions of negative-priced. The first is 

instantaneous (single interval) negative pricing. 

 

We call this model LMPx or locational marginal pricing with threshold of $x. Analyzing detailed 

market and dispatch data, we can compute both CAISO aggregate negative-priced power at the 



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resolution of dispatch intervals, and also per anonymized market participant. The second is an average 

price model for negative-priced power. 

 

We call this model NetPriced, because it computes intervals for which the average price of power is 

less than d. 

We studied three years of CAISO renewable bids, dispatch prices, and production for over 375 

renewable generation resources for a three-year period. We consider negative pricing in both the 

day-ahead and real-time markets, calculating the transacted power for each generator. As discussed, the 

definition of negative-priced power varies with our LMP and NP models. In CAISO, power is 

dispatched at 1-hour intervals in the day-ahead and 5 minute intervals in the real-time market, to this 

data collection includes over 125 million records. 

 

3. Curtailed Renewable Power 

3.1 System-wide Seasonal Characterization 

Renewable generation bid into the market is sometimes not dispatched; it is curtailed. This is another 

element opportunity power beyond the negative-priced power. As shown in Table 1, the quantity of 

renewable power curtailed has grown significantly from 2015 to 2017. This power represents a small 

portion of renewable generation, and an even smaller portion of the overall CAISO generation. 

However, at 402 Gigawatt-hours it represents over $12M of power (at $30/MWh), so its loss is a 

significant cost. We report aggregate renewable numbers, but it is worth noting that renewable 

curtailment in CAISO is dominated by solar, at a 4.5:1 ratio to wind generation. 

 

Table 1. CAISO System-wide Renewable Curtailment, 2015-2017 

Year 
Curtailed Renewable 

Power (Wind + Solar) 
Increase YoY 

2015 187,770 MWh  

2016 308,423 MWh 64% YoY 

2017 401,972 MWh 30% YoY 

2018 (thru July) 317,213 MWh N.A. 

 

 

 



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Looking at seasonal variation in curtailment (see Table 2), the largest quantity occurs in the winter 

(January to March) and the spring (April to June), and that quantity is has grown at an annual rate 

exceeding an average of 50% CAGR from 2015 to 2018 (tripling). Reasons for this might include 

greater mandatory hydropower in the winter and spring. Curtailment in summer is much smaller, and 

while larger in the fall, it is fluctuating. In 2018, this growth has slowed due to decreased hydro 

generation, but it expected to resume its rapid growth in 2019 and beyond. 

 

Table 2. CAISO System-wide Renewable Curtailment, 2015-2017 

Year 
Winter 

(MWh) 

Increase 

YoY 

Spring 

(MWh) 

Increase 

YoY 

Summer 

(MWh) 

Increase 

YoY 

Fall 

(MWh) 

Increase 

YoY 

2015 47,024 n.a. 84,453 n.a. 21,496 n.a. 34,797  

2016 74,885 59% YoY 92,597 2% YoY 38,899 81% YoY 102,043 194% YoY 

2017 185,116 147% YoY 142,855 54% YoY 31,329 -20% YoY 42,672 -59% YoY 

2018 145,191 -22% YoY 163,309 14% YoY n.a.  n.a.  

 

3.2 CAISO Daily and Monthly Reporting 

CAISO provides a nice graphical summary of monthly renewable curtailment on is “Managing 

Oversupply” web page. We reproduce the June 2018 version of this graph in Figure 1. Of course, its 

captures similar trends to those we have summarized above. Daily reporting of curtailed renewables is 

also available on this site (CAISO, 2018). 

 

 

Figure 1. CAISO Renewable Curtailment by Month, January 2016 thru July 2018 

 



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4. Negative-Priced Power 

We first consider the aggregate occurrence of negative-priced power in the day-ahead and real-time 

markets for renewable generation, system-wide. Subsequently, using the real-time market data, we 

characterize seasonal variations. 

4.1 System-wide Negative-Priced Power 

As shown in Figure 2, the quantity of negative-priced power in both the day-ahead and real-time 

markets has grown rapidly in the period. A significant quantity of power is transacted at negative prices 

in the both the day-ahead and the real-time markets, indicating a growing quantity of excess renewable 

power. The DAM negative priced power is nearly 1TWh for 2017. The DAM-RTD-Scaled estimates of 

negative-priced power are significantly larger, as they reflect both the growing DAM negative priced 

power, and a scaled portion of the RTD negative priced power. Finally, the RTD-LMP negative-priced 

power is the largest quantity and growing rapidly. This quantity of 2.5 to over 5 terawatt-hours 

overestimates the power transacted at negative prices (the DAM prices the majority of the power), but 

does reflect the net balance at real-time dispatch, and can be directly compared to studies in other 

ISO’s. 

 

 

Figure 2. Estimates for Negative-priced Power from Day-ahead, Real-time, and a Scaled 

Estimate, CAISO, 2015-017 

 

Looking at Table 3, we see that all three estimates of negative-priced power have large 

CAGR’s: >300% for DAM-LMP, 49% and 102% for DAM-RTD-scaled, and 35% and 54% for 

RTD-LMP. All of these measures are growing fast, and at terawatt-hours, these are large quantities of 

power. 

The RTD-LMP negative-priced power represents a significant fraction of total renewable generation in 

the CAISO region and is now a magnitude that is comparable to the negative-priced power documented 

in the MISO region (6 TWh in 2013 increasing to over 7 TWh in 2016 (Chien, 2018)). The growth 



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observed from 2015-17, if extrapolated, suggests a doubling period of 2 years that if realized would 

produce in excess of 10 TWh of negative-priced power in the real-time market. This quantity would be 

far larger than that occurring in MISO, and larger as a fraction of total power delivered by more than 

2.5x.  

 

Table 3. CAISO System-wide Negative-priced Renewable Generation, 2015-2017 

Year 

DAM-L

MP 

(MWh) 

DAM-RT

D-Scaled 

(MWh) 

YoY 
RTD-LMP 

(MWh) 
YoY 

Total 

Renewables 

DAM (MWh) 

Total 

Renewables 

RTD (MWh) 

Fraction 

Total 

Renewables 

2015 48,655 539,271 n.a. 2,512,571 n.a. 20,377,783 25,322,336 9.9% 

2016 213,952 801,911 49% 3,879,929 54% 26,770,145 31,551,400 12.3% 

2017 945,791 1,626,848 102% 5,263,853 35% 32,686,789 37,544,424 14.0% 

 

4.2 Real-time Dispatch (RTD-LMP) Seasonal Characterization 

Study of negative-priced generation using RTD-LMP across seasons is shown Table 4 and Figure 3. 

The largest quantities occur in the winter (January thru March) and spring (April thru June) seasons, 

with those seasons accounting for 44% and 37% of the annual quantities respectively. Increase in these 

seasons has been rapid and continued, but fluctuating, perhaps due to changes in mandatory generation 

due to runoff requirements in hydroelectric generation. However, while those fluctuations may mask 

the trend, they present no mechanistic argument for changing it. The winter 2017 negative-priced 

generation corresponds to 25.5 GWh/day, a 24-hour rate of 1.06 GW, but if over the solar generation 

hours, closer to 2.6 GW. In Summer and Fall, negative-priced generation is much smaller, due to 

higher loads, but still significant, 5.5 GWh/day, with corresponding 24-hour rates of 0.23 GW, and 

solar generation hours of 0.55 GW. 

 

Table 4. CAISO System-wide Negative-priced Renewable Generation (RTD-LMP), by Season, 

2015-2017 

Year 
Winter 

(MWh) 

Increase 

YoY 

Spring 

(MWh) 

Increase 

YoY 

Summer 

(MWh) 

Increase 

YoY 

Fall 

(MWh) 

Increase 

YoY 

2015 656,484 n.a. 1,294,340 n.a. 224,489 n.a. 336,677  

2016 962,276 47% YoY 1,273,677 (-2%) YoY 468,794 109% YoY 975,180 190% YoY 

2017 2,316,267 140% YoY 1,964,827 54% YoY 535,722 14% YoY 447,037 -55% YoY 

 

 



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Figure 3. Seasonal Growth of Negative-priced Power (RTD-LMP), CAISO 2015-2017 

 

4.3 Daily Variation of CAISO System-wide Negative-priced Renewable Generation 

Aggregate statistics cannot capture the variation in generation and load that create negative-priced 

power in power markets. Such variation is a principal challenge in efforts to reduce negative-priced 

generation through better planning and management, as well as to efforts that seek to exploit it as a 

resource (Kim, 2017),(Yang, 2016), (Yang, 2017). We present temporal graphs of the daily quantities 

of negative-priced generation in the MISO region and basic statistics that highlight its variability. 

Figures 4, 5, and 6 plot magnitudes of Negative-priced power occurrences, on an irregular x-axis scale 

of 5-minute intervals (bears no fixed relationship to time as the many 0-value data points have been 

omitted). And while the overall quantity of negative-priced power has grown YoY, there is a striking 

drop in the magnitude of events from 2015 to 2016. Presumably, this reflects some improvements in 

the markets (forecasting, dispatch, etc.). All three of the figures show that negative-priced power varies 

widely in quantity and seasonally. In all three years, the January-June period accounts for significantly 

more than half of the x-axis, and the smaller magnitude of events in the summer is striking—though a 

significant number of events are still occurring. Likewise the fall seems to have smaller magnitude and 

fewer occurrences of negative-priced generation. 

 



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Figure 4. Daily Negative-priced Power (MWh) in the CAISO Real-time Market, 2015 

 

 

Figure 5. Daily Negative-priced Power (MWh) in the CAISO Real-time Market, 2016 

 

 

Figure 6. Daily Negative-priced Power (MWh) in the CAISO Real-time Market, 2017 

 



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5. Opportunity Power (Curtailed and Negative-Priced) 

Opportunity power comprises two components—curtailed and negative-priced generation. We have 

examined both, and here we combine them to gain perspective for relative impact. As shown in Figures 

7 and 8 as well as Table 5, both curtailment and negative priced power are growing rapidly within 

CAISO. In Figure 7 shows that RTD-LMP is far larger than curtailment (13x), but is perhaps an 

overestimate of negative priced power. However, DAM-RTD-Scaled is a more conservative estimate of 

negative priced power, and is still 3-4x larger than curtailment. This is also consistent with studies of 

the MISO real-time markets (Chien, 2018). 

 

 

Figure 7. Opportunity Power=Curtailment+Negative-priced for RTD-LMP, CAISO, 2015-2017 

 

 

Figure 8. Opportunity Power=Curtailment+Negative-priced for the DAM-RTD-Scaled Model, 

CAISO, 2015-2017 

 

 

 



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In Table 5, we summarize several estimates of opportunity power, and compare them to curtailment 

data. Note that all are growing rapidly, and the ratios for DAM-RTD-Scaled/curtailed have recently 

increased significantly. The RTD-LMP/Curtailed ratio is remarkably stable. With the rapid growth all 

are large quantities of power, with 2017 DAM-RTD-Scaled reaching more than 1.6 Terawatt-hours. 

 

Table 5. Opportunity Power by Type, CAISO, 2015-2017 

Year 

DAM-RTD

-Scaled 

(MWh) 

Increase 

YoY 

DAM-RTD-

Scaled/ 

Curtailed 

RTD-LM

P (MWh) 

Increase 

YoY 

Curtailment 

(MWh) 

Increase 

YoY 

RTD-LMP

/Curtailed 

2015 539,271 n.a. 2.9x 2,512,571 n.a. 187,770  13.4x 

2016 801,911 49% 2.6x 3,879,929 54% YoY 308,423 64% 12.6x 

2017 1,626,848 102% 4.0x 5,263,853 35% YoY 402,972 30% 13.1x 

 

6. Opportunity Power Spatial Dispersion (across Resources) 

6.1 Per Resource Negative-priced Generation 

We further analyze the RTD-LMP data to characterize the experience of individual generators, 

computing basic summary statistics as shown in Table 6. They shows that the rapid growth in CAISO 

system negative-priced generation has already spread across all of the renewable generators, and 

doubling in quantity from 2015 to 2017. Further, the variation across resources is large—a large 

multiple of the average. This is reflected in quantity of negative-priced generation at the highest power 

generator increasing steadily from 130 GWh to 168 GWh. Note that this amount of power corresponds 

to 19MW (24-hour basis) or 46MW (10-hour basis) of generation. 

 

Table 6. Per-resource Annual Negative-priced Renewable Generation (RTD-LMP), 2015-2017 

Year 
RTD-LMP 

(GWh) 
Resources 

Average 

Negative-priced 

(GWh) 

Standard 

Deviation (GWh) 

Maximum 

Negative-priced @ 

1 Resource (GWh) 

2015 2,513 GWh 367 7 17.7 131 

2016 3,880 GWh 313 12 24.5 170 

2017 5,264 GWh 378 14 26.1 168 

 

6.2 Distribution across Resources of Negative-priced generation 

We examine the detailed distribution of negative-priced generation across resources to explore the 

magnitude of the greatest quantities of opportunity power available at a single physical location, but 

also to explore differences in the allocation of negative prices by the market. 

 



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Figure 9. Annual Quantity of Negative-priced Power per Resource, CAISO 2015 (Sorted, Largest 

First) 

 

We consider the year 2015 data, presenting the per-site total negative-priced power at renewable 

resources in Figure 9. Note that the maximum negative-priced power at one site was 131 GWh ($3.9M 

power @ $30/MWh), and all of the resources experienced negative pricing (except one!). If we 

compare to the average negative-priced power for 2015, 6.9 GWh/resource, we see that 68 resources 

exceed this average out of a total of 367. Even with a large standard deviation of 17.7GWh, we see that 

the maximum is 7 standard deviations above the mean. And a collection of resources are well above the 

mean, with the Top 10 accounting for 930 GWh ($27.9M). The Top 20 account for 1,427 GWh 

($42.8M). The Top 30 account for 1,704 GWh ($51.1M). The Top 50 account for 2,064 GWh 

($61.9M), or over 80% of the opportunity power (2,512 GWh overall). 

 

 

Figure 10. Annual Quantity of Negative-priced Power per Resource, CAISO 2016 (Sorted, 

Largest First) 



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We consider the year 2016 data, presenting the per-site total negative-priced power at renewable 

resources in Figure 10. Note that the maximum negative-priced power at one site was 171 GWh 

($5.1M power @ $30/MWh) and all of the resources experienced negative pricing. If we compare to 

the average negative-priced power for 2016, 12 GWh/resource, we see that approximately 68 resources 

exceed this average out of a total of 313. Even with a large standard deviation of 24.4 GWh, we see 

that the maximum is 6.5 standard deviations above the mean. And a collection of resources are well 

above the mean, with the Top 10 accounting for 1,170 GWh ($35.1M). The Top 20 account for 1,892 

GWh ($56.7M). The Top 30 account for 2,294 GWh ($68.8M). The Top 50 account for 2,840 GWh 

($85.2M), or over 73% of the opportunity power (3,880 GWh overall). 

 

 

Figure 11. Annual Quantity of Negative-priced Power per Resource, CAISO 2017 (Sorted, 

Largest First) 

 

We consider the year 2017 data, presenting the per-site total negative-priced power at renewable 

resources in Figure 11. The picture is similar to 2015 and 2016, but slightly different in degree. Note 

that the maximum negative-priced power at one site was 168 GWh ($5.1M power @ $30/MWh), about 

the same as 2016. And, that again, all of the resources experienced negative pricing. If we compare to 

the average negative-priced power for 2017, 14 GWh/resource, we see that approximately 85 resources 

exceed this average out of a total of 378. Even with a large standard deviation of 26 GWh, we see that 

the maximum is 5.9 standard deviations above the mean. And a collection of resources are well above 

the mean, with the Top 10 accounting for 1,307 GWh ($39.2M). The Top 20 accounts for 2,181 GWh 

($65.4M). The Top 30 accounts for 2,723 GWh ($81.7M). The Top 50 accounts for 3,389 GWh 

($101.7M), or over 64% of the opportunity power (5,264 GWh overall). 

 



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6.3 Year to Year Changes (per-resource) 

Looking at trends from 2015 to 2017, we see a dramatic increase not only in the total quantity of 

negative-priced power but also in its breadth of impact (resources participating). Further we see that as 

the overall quantify of negative-priced power increases from year to year, the disparities become large 

for a growing number of resources (see Table 7). If we look at the top-ranked resources by quantity of 

negative-priced power, we see significant growth in all of the top groups. We compute the average 

daily negative-priced generation per resource by simple dividing the total negative-priced power in the 

group by 365 days and by the number of resources in the group. All of these show steady growth and 

significant quantities of power. Note that if these renewables are solar, their generation will be 

concentrated in the daylight hours, so the effective daylight negative-priced generation will be 2 or 

even 3 times larger (e.g., >150MW rate per site for Top 10 in 2017). 

 

Table 7. Negative-priced Power at the Top 10, 30, and 50 Resources in CAISO 

Year 
Top 10 Total 

(MWh) 

Daily Average 

@ Resource 

(MWh) 

Top 30 

Total 

(MWh) 

Top 30 Daily 

Average 

(MWh) 

Top 50 

Total 

(MWh) 

Top 50 Daily 

Average  

(MWh) 

2015 930,000 255 1,704,000 155 2,064,000 113 

2016 1,170,000 321 2,294,000 209 2,840,000 156 

2017 5,264,000 1,442 2,723,000 249 3,389,000 186 

 

7. Opportunity Power Temporal Dispersion 

The quantity of negative-priced power is important, but its temporal distribution (all in one hour vs. 

spread evenly) is critical for its utility for loads. We further analyze the RTD-LMP data to characterize 

the experience of individual generators, “duty factor” of negative-priced power at individual generators. 

That is, the number of hours that each site is experiencing negative-priced dispatch, a useful measure to 

understand if a physically localized load could exploit the negative-priced power. We then normalize to 

a 24-hour window (to compare against reliable power), and also to a 10-hour window (to compare 

against solar generation). 

 

Table 8. Temporal Distribution of Negative-priced Power Occurrence at Generators 

Year 

Total Negative 

Priced 

Generator-Hours 

Resources 

Average 

Negative-priced 

Hours / Resource 

Std Deviation of 

Negative-priced 

Hours / Resource 

Maximum 

Negative-priced 

Hours / resource 

2015 80,006 hours 367 218 hours 211 hours 604 hours 

2016 148,675 hours 313 475 hours 183 hours 883 hours 

2017 217,728 hours 378 576 hours 157 hours 955 hours 



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The data shows that the number of negative priced hours is growing rapidly, over 2.5-fold from 2015 to 

2017. There is a large variation across resources, as shown by the large standard deviation, and the 

maximum hours is only 2.5x standard deviations above the mean. This is a much lower variation than 

we saw with quantities of negative-priced power; this difference is presumably due to different peak 

generation capacities. For the sites with maximum hours, the numbers are large enough to correspond 

to a significant duty factor, particularly for a variable renewable generator. For example, the 2015 

number, 604 hours, corresponds to duty factor of 7% (vs. 24 hours), and 21% (vs solar hours). The 

maximum for 2016, 883 hours, corresponds to duty factor of 10% (vs. 24 hours), and 30% (vs solar 

hours). And finally, the maximum for 2017, 955 hours, corresponds to a duty factor of 11% (vs. 24 

hours) and 33% (vs. solar hours). Obviously, a generator that is receiving negative prices for 33% of its 

operating hours, even if it only applies to a fraction of its power, is not likely to be happy about it. On 

the positive side, if an opportunistic load had the goal of exploitation of the negative-priced power, 

33% x 10 hours yields an availability of about 3.3 hours per day—conceivably the duty factor of an 

unreliable, emerging region grid. 

In Figures 12, 13, and 14, we examine the distributions of negative-priced power hours across 

renewable generation resources. We first consider 2015 data, presenting the per-site total 

negative-priced power hours at renewable resources in Figure 12. Note that the maximum 

negative-priced hours at one site was 604 hours. About two-thirds of the renewable resources 

experienced negative pricing. If we compare to the average negative-priced power hours for 2015, 218 

hours, we see that approximately 155 resources exceed this average out of a total of 367 resources; not 

far from one-half the resources, so the negative-priced hours are reasonably spread out. 

 

 

Figure 12. Negative-priced Power Hours by Resource, CAISO 2015 

 

 

 



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We next consider the year 2016 data, presenting the per-site total negative-priced power hours at 

renewable resources in Figure 13. Note that the maximum negative-priced hours at one site increased to 

883 hours. 6 resources experienced >800 hours of negative pricing. The number of resources 

experiencing negative pricing increased, with nearly all of the renewable generators included. If we 

compare to the average negative-priced power hours for 2016, 475 hours, we see that approximately 

198 resources exceed this average out of a total of 313 resources; a large fraction (63%). So compared 

to 2015, the negative-priced hours are spread over a much larger group of resources, and falling much 

harder (more hours). However, there is a notable spike in the maximum number of negative-priced 

hours with six resource suffering nearly 50% more than peers. It would be interesting to understand the 

market dynamic that leads to this disparity. 

 

 

Figure 13. Negative-priced Power Hours by Resource, CAISO 2016 

 

We next consider 2017 data, presenting the per-site total negative-priced power hours at renewable 

resources in Figure 14. Note that the maximum negative-priced hours at one site increased to 955 hours. 

At the high-end, now 6 resources experienced >900 hours of negative pricing, and the number 

experiencing >800 hours has grown from 6 in 2016, to 17 in 2017. Amongst this group, there is some 

consistency, with many of the same sites ranking high from year to year. The number of resources 

experiencing negative pricing increased, with nearly all of the renewable generators included. If we 

compare to the average negative-priced power hours for 2016, 576 hours, we see that around 250 

resources exceed this average out of a total of 378 resources; a large fraction (66%). So, the 

negative-priced hours are being spread over a much larger group of resources, and much more hours. 

However, there is a notable spike in the maximum number of negative-priced hours with six resource 

suffering nearly 50% more than peers. It would be interesting to understand the market dynamic that 

leads to this disparity. Compared to 2016, the notable spike at the highest levels of negative-priced 



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hours is less pronounced, but a small group of ~15 generators are suffering significantly higher 

numbers of negative-priced hours. 

 

 

Figure 14. Negative-priced Power Hours by Resource, CAISO 2017 

 

Looking across the period from 2015 to 2017, we see a steady increase in the number of 

negative-priced hours, as the phenomenon grows. However, in contrast to the quantity of 

negative-priced power, the hours of negative priced power are spread more evenly across resources. 

This suggests some degree of shared fate—that perhaps the increases are defined by greater durations 

where there is widespread negative-pricing, rather than spatially localized negative pricing. Confirming 

this with further study should be possible. 

 

8. Usage-oriented Characterization (Opportunity Power Models)  

8.1 Quantity Versus Opportunity Power Model 

Market pricing mechanisms can be prone to short-term price fluctuations as documented in (Chien, 

2018) with little connection to the longer term economic performance of a generator resource or 

long-term price of power to a load. To explore the magnitude of this effect, we apply a net price model 

of power cost to the RTD data, smoothing local price fluctuations by computing weighted average price 

of power as defined in Section 2. Here we compare RTD-NP0 and RTD-NP5 “average price” studies to 

our RTD-LMP studies based on instantaneous negative prices (LMP0 model) used in prior sections. 

 

 

 

 

 

 



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Table 9. Model Sensitivity Study, Negative-priced Power Model, 2015-2017 (RTD with Various) 

Year 

Opportunity 

power 

Model 

Total 

Opportunity 

power 

(MWh) 

Intervals 

Longest 

Interval 

(hours) 

Average 

Length 

(hours) 

St Dev 

(hours) 

Total 

Hours 

Interval 

Average 

Power 

(MWh) 

Interval 

Stdev 

Power 

(MWh) 

2015 LMP0 2,512,571        

2015 NP0 3,816,788 395,917 311 0.31 2.57 124,180 9.64 107 

2015 NP5 4,663,958 426,667 622 0.37 3.57 157,921 10.93 142 

2016 LMP0 3,879,929        

2016 NP0 6,018,084 362,168 165 0.64 2.4 231,384 16.6 115 

2016 NP5 8,178,759 473,059 169 0.68 3.2 321,486 17.3 129 

2017 LMP0 5,263,853        

2017 NP0 7,796,664 501,693 557 0.64 2.8 320,600 15.54 99.06 

2017 NP5 7,941,630 706,170 557 0.64 2.6 319,698 15.83 106.49 

 

8.2 Interval Statistics vs Opportunity power Model 

The quantity and duration of opportunity power in CAISO exhibits sensitivity to the pricing/acquisition 

model used. For example, the increase in opportunity power from LMP0 to NP0 varies from 50% to 

over 100% (see Table 8). However, the increase for NP0 to NP5 is varied, producing a smaller increase 

from ~0% to 35%. The maximum interval durations for opportunity power are surprisingly long! The 

maximum duration for a continuous interval exceeds the daylight hours for a single day, and 

presumably arises from wind generation sites. Changing the pricing model causes the average length of 

intervals to increase, but are still short as there are many short intervals. However, the growing 

standard deviation indicates that there are also an increasing number of longer intervals. At the far right 

of Table 8, we present average power per interval and the standard deviation of the power. These show 

significant quantity and increasing standard deviation, but not much increase in average power. This 

suggests that only a small number of the intervals are being substantially augmented by the more 

aggressive pricing models. The conclusions drawn here for opportunity power in CAISO in 2017 are 



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qualitatively similar to those in MISO, where NP0 yielded 30% more opportunity power over LMP0, 

and NP5 yielded 15-20% more power than NP0 over a multi-year period (Chien, 2018).   

 

9. Discussion and Related Work 

The period of analysis, 2015-2017 encompasses extraordinary changes in the CAISO balancing area. In 

the three-year period from 2015 to 2017, total renewable generation grew from 45 to 55 TWh (CAISO, 

2018). While that is a significant increase of 22% over two years, it is clear that quantity of curtailed 

and negative-priced power, by all of our measures conservative to aggressive (DAM-LMP, RTD-LMP, 

and DAM-RTD-Scaled) is growing much faster—in excess of 50% per year. Amongst these, the 

moderate estimate, DAM-RTD-scaled is over 1.6 TWh in 2017, and projected to exceed 2 TWh in 

2018. The RTD-LMP measure is already proportionately larger than that seen in MISO, and exceeds 5 

TWh in 2017. These number represent 10’s of millions to 100’s of millions of dollars of power at 

average CAISO prices. 

As the quantity increases, greater duty factors are observed with a number of sites seeing large numbers 

of “full day” intervals of 15.5 hours of negative pricing. Section 6 shows that the distribution of the 

quantity of opportunity power is extremely uneven across the opportunity power 

generators—concentrated on a small number of sites. This information should be analyzed with respect 

to generator capacities to see how much of this extreme imbalance can be accounted for by this, or 

should be attributed to other factors such as transmission congestion or bidding behavior of renewable 

generators. Regardless, the concentration of opportunity power increases its potential for productive 

used if a consuming load can be geographically localized to where the opportunity power is occurring. 

Alternatively, the concentration may suggest that an increase in transmission resources in the area is 

appropriate. 

Section 7 shows a steady shift of the entire distribution of renewable generators to higher and higher 

duty factors of negative pricing. In 2017, the majority of renewable generators saw >600 hours of 

negative pricing—the equivalent of nearly two full months of 12-hour negative pricing days. This 

suggests that opportunity power could be useful resource for intermittent, but power-intensive activities 

such as fertilizer manufacture, water desalination, or perhaps bitcoin mining. A more challenging, but 

promising area that we are pursuing is to exploit opportunity power for networks of hyperscale cloud 

data centers—a rapidly growing user of electric power (Kim, 2017; Yang, 2016; Yang, 2017; Chien, 

2018). 

Opportunity power has been documented as a large, growing phenomenon in studies of curtailment in 

the Mid-continent Independent System Operator (MISO) where 2.2 terawatt-hours (TWh), and 5.5 

TWh negative priced power for a total of 7.7 TWh of opportunity power from wind resources (Bird, 

2013; Lew, 2013; Yang, 2016). And in China, curtailed wind power has grown to 34 TWh in 2015 

(GWEC, 2016). Around the world, as renewable generation fraction increases due to rising RPS 

standards, opportunity power is projected to increase significantly in both wind-heavy (Kim, 2017, 



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MISO, 2018) and solar-heavy (Denholm, 2016a, 2016b, E3) renewable power grids. Notably, a recent 

Department of Energy summary reports documents this phenomena as a reduced capacity factor for 

renewables as RPS above 15% to 25% (Wiser, 2017). All of these studies suggest that opportunity 

power is of increasing quantity, making its study of increasing importance.  

Studies that characterize opportunity power (curtailed and negative priced power) availability at a 

fine-grained temporal level are rare. Chien and colleagues studied two and a half years of opportunity 

power in the Midcontinent Independent System Operator (Chien, 2018), documenting quantities, 

spatial and temporal distributions, and the potential impact and limitations of adding storage to increase 

utility of opportunity power. The MISO grid has significant renewables that are predominantly wind 

turbines, and much of it dispersed widely in unpopulated areas, and thus often transmission constrained. 

Further, MISO generally operates at a renewable portfolio fraction below 15%, and this fraction has 

changed only slowly over the past few years. This situation differs significantly from CAISO which has 

predominately solar renewables, an RPS exceeding 33%, and is less transmission limited.  

While both the MISO study and this report document that opportunity power is available in >5TWh 

quantities on an annual basis, the relative magnitude of opportunity power in CAISO is much 

greater—given its smaller size. Both ISO’s exhibit stable geographic patterns of concentration of 

opportunity power, both conferring a disproportionate economic impact on certain generators, but also 

creating a geographically localized opportunity for exploitation. The occurrence of opportunity power 

in the two ISO’s differs in its temporal structure as well. Because of the large quantities of solar power 

in CAISO (generation largely synchronized by sunlight hours), the occurrence of opportunity power is 

heavily concentrated in those daylight hours, as well as in certain seasons. In contrast, MISO’s wind 

generation is seasonal, but the occurrence of opportunity power tends to occur at night, when load is 

lower. 

Progress in energy storage is promising, but the low price of power makes its large-scale deployment 

for time-shifting challenging and some studies suggest on energy return-on-investment (EROI) criteria, 

it may never make sense to store large quantities of wind power, though solar’s higher energy quotient 

makes it more attractive. Grid-scale storage for time-shifting, rather than small quantities for peak 

shaving and regulation, faces significant economic challenges. CAISO is a leader in this area, with a 

plan to deploy 1.3 GW of storage by 2024, but even this is a small fraction of the storage capacity 

needed to time-shift power. In fact, recent studies show that much larger quantities of storage had little 

net impact on opportunity power duty (Chien, 2018).  

 

10. Summary and Future Work 

Detailed analysis of CAISO’s day-ahead and real-time markets focused on renewables shows the 

rapidly changing dynamics as the fraction of renewables grows to 30% and beyond. Highlights include 

terawatt-hours of opportunity power, dominated by negative-priced power, not curtailment. The 

number of hours that negative pricing occurs—at all resources—is growing rapidly; however, the 



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quantity of opportunity power is concentrated at a few dozen generators. In 2017, nearly all of the 

renewable generators are experiencing close to 600 hours or more of negative pricing per year. Finally, 

flexible power acquisition models such as “NetPrice” significantly increase the duty factor of 

opportunity power, but not as much in CAISO’s solar-dominated renewables grid, when compared to 

MISO’s wind-dominated renewables grid. 

The rise of negative-priced power in both markets, and the additional growth of renewable curtailment 

in every dimension—quantity, duty factor, fraction of generators impacted, etc. reflect significant 

challenges for renewable integration at high RPS levels. The result is a rising tide of opportunity power. 

This opportunity power is a new and growing opportunity. It creates opportunities for new uses of 

power that can act as “compliant loads”, adaptable in time and consumption level to availability of this 

low cost and environmentally friendly power resource. Integrating such loads into current power 

markets requires new innovation. 

While we have studied three years of CAISO data, spanning a recent period of rapid change, this 

change is expected to continue. So study of future data, 2018 and beyond, to explore new trends, and to 

assess if the trends we have identified persist. To date, there are detailed published studies on 

opportunity power in MISO and CAISO (this one), and we would hope to see such studies for further 

ISO’s, particularly those with large fractions of renewables. One area we were not able to explore is the 

impact of new market products for storage and distributed energy resources. Those products are being 

designed to affect metrics used in this study, and others. Study of these impacts as renewable 

generation continues to grow would doubtless yield valuable insights. 

The growing quantity of Opportunity Power (OP) poses the natural question—how can it be exploited? 

We have been exploring the possibility of using it to power cloud data centers, the fastest growing 

source of electric power consumption in the developed world (Yang, 2016, 2017). By adapting the 

magnitude of the computational load and its geographic distribution, the power needs of a network of 

cloud data centers can be used directly to consume negative priced power. If matched well, the result 

would include both financial savings for the cloud computing provider, but also better integration of 

even higher levels of renewable generation that overall reduce the carbon impact of both cloud 

computing and broadly the power absorbed by the power grid.  

 

 

 

 

 

 

 

 

 



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Acknowledgements 

Thanks to the team at CAISO, notably Mark Rothleder and Shucheng Liu for hosting my visit, and to 

Zhu Liang for invaluable help with the data. Also thanks to Angela Glover, Hong Zhou, and other 

members of the Market Quality and Renewable Integration team for putting up with endless questions 

about data and how CAISO markets work! 

Thanks also to the University of Chicago whose support of Andrew A. Chien with a sabbatical leave 

for time at CAISO, essential to learning about myriad data systems and products, and the current and 

emerging operations of the CAISO markets and Energy Imbalance Markets (EIM). This work was 

supported by in part by the National Science Foundation under Award CMMI-1832230 

 

References 

Bird, L., Milligan, M., & Lew, D. (2013). “Integrating Variable Renewable Energy: Challenges and 

Solutions. In NREL, Technical Report. https://doi.org/10.2172/1097911 

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Lew, D., Bird, L., Milligan, M., Speer, B., Wang, X., Carlini, E.M., … Yoh, Y. (2013). Wind and Solar 

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MISO. (2018). The Mid-continent Independent System Operator (MISO). Retrieved from 

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Notes 

Note 1. In prior research, we have referred to this Opportunity Power (OP) as Stranded Power (SP) 

(Chien, 2018; Yang, 2017; Yang, 2016). 

Note 2. We are currently exploring how to couple rapidly growing networks of hyperscale cloud data 

centers (Shehabi, 2016) to the power grid to both enhance renewable integration and reduce cloud 

computing’s growing carbon footprint, as well as increase the resilience of both critical infrastructures 

(Kim, 2017; Yang, 2017). 

Note 3. This simple methodology overestimates negative-priced power by including power that may 

have been settled at a positive price in the Day-Ahead Market (DAM). 

 

https://doi.org/10.2172/1372902
https://doi.org/10.2172/1411668
https://doi.org/10.1109/IPDPS.2016.96
https://doi.org/10.1109/TPDS.2017.2782677


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Appendixes 

Sample scatter plots for resources with high quantities and incidences of negative-priced power.  As 

shown in Figure 15, res_170 has maximum power of just below 10 MW, and the scatter plot shows 

maximum duration of opportunity power interval of nearly 14 hours.  It was the highest duty factor 

resource in our study for 2017. 

 

 

Figure 15. Power/Duration Scatter Plot for res_170 

 

As shown in Figure 16, res_102 has maximum power of just below 130 MW, and the scatter plot shows 

maximum duration of opportunity power interval of nearly 13 hours.  It was the 2
nd

 highest duty factor 

resource in our study for 2017. 

 



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Figure 16. Power/Duration Scatter Plot for res_102 

 

 

 

 


