ERCOT presents an interesting setting for analyzing AS market price behavior because formal markets for both energy and ASs have been established to foster competition, these markets are of significant size, and Texas leads the nation in electricity generation from wind farms. Increased generation from intermittent renewable energy sources, such as wind, has prompted concerns that the state’s need for ASs may increase in the future, perhaps raising the cost of maintaining adequate ASs. Consequently, an examination of how growing wind generation has affected market prices of ASs in Texas can inform other markets with large-scale renewable energy development.

ERCOT has undergone gradual restructuring since the mid-1990s (Zarnikau 2005). Legislation enacted in 1995 required the Public Utility Commission of Texas to establish rules to foster wholesale competition and create an independent system operator (ISO) to ensure nondiscriminatory access to the transmission network. Sweeping reforms were introduced through Senate Bill 7 in 1999, allowing customers of the investor-owned utilities within ERCOT to choose among competitive retail electric providers (REPs) for a retail supply of electricity beginning on January 1, 2002. Senate Bill 7 also enhanced the ISO’s centralized control over the wholesale market, replacing ten control centers formerly operated by various utilities. This led to the establishment of a formal DAM for ASs and an RTM for balancing energy.

An important structural change to the ERCOT market was completed on December 1, 2010: the introduction of a nodal market structure (Zarnikau et al 2014a). The DAM was expanded from merely a market for the procurement of operating reserves to also include energy. Similar to the DAM energy markets elsewhere (eg, PJM, New York, New England and California), ERCOT’s DAM matches day-ahead forward energy bids and offers (Zarnikau et al 2014b). Hourly market-clearing DAM energy prices used to settle DAM transactions result from a least-cost dispatch that co-optimizes energy generation with AS and congestion revenue rights.

Supply offers and bids begin at 06:00 and end at 10:00 on day $t-1$ for ERCOT’s day-ahead price determination. DAM is normally executed between 10:00 and 13:30, and prices for the following day are set around 14:00, though prices may be updated by 18:00 after any needed reliability unit commitment activities are completed.⁴⁴ 4 See ERCOT training module, Module 2: Day-Ahead Operations, available from the ERCOT website. As actual wind generation and total energy demand on day $t$ are not known on day $t-1$, the DAM energy prices for energy and ASs depend, in part, on forecasts of wind generation and load made on day $t-1$ in the 06:00–10:00 window.

There is presently no “capacity market” in ERCOT to maintain a target reserve margin. Consequently, ERCOT relies on market forces to achieve reliability and resource adequacy. To encourage investment in generation capacity, the offer cap on wholesale market prices was raised to USD3000/MWh at the start of the nodal market on December 1, 2010. The cap was further increased to USD4500/MWh, effective August 2012; USD5000/MWh on June 1, 2013; USD7000/MWh on June 1, 2014; and USD9000/MWh on June 1, 2015. ERCOT’s DAM energy prices, however, have seldom reached this cap since the nodal market’s establishment.

Texas is home to the most wind generation in the United States, thanks to its enormous resource potential; establishment of policy targets; a system of tradable renewable energy credits (RECs) and federal tax credits; and ERCOT’s favorable market rules (Zarnikau 2011). The completion of Competitive Renewable Energy Zone transmission in January 2014 resulted in 3600 circuit-miles of 345 kV transmission line to increase export capability in order to accommodate approximately 11 GW of additional power for a total of 18.5 GW of renewable capacity, further unlocking the wind potential in West Texas. The intermittent nature of wind generation resources has presented operational challenges to ERCOT, and the establishment of new market rules was necessary in order to accommodate this nondispatchable intermittent resource. In addition, ERCOT has been improving its ability to accurately forecast wind generation output.

As summarized in Table 1, ERCOT presently procures four types of system-wide operating reserves: REGUP, REGDN, RRS and NSRS, similar to the system operators elsewhere (eg, California, New York, New England and PJM).

REGUP and REGDN capacities balance small fluctuations in supply and demand in real time to maintain system frequency close to 60 Hz. These services are primarily provided by generators with automatic generation control (AGC) or equivalent. Resources providing regulation services must respond every four seconds, either by increasing or decreasing output to address gaps between supply and demand, and must comply with ERCOT instructions within five minutes.

Similar to the spinning reserves maintained in many other electricity markets, an RRS restores the balance between generation and load after the sudden forced outage of a major generator or transmission line or some other major shock to the electrical system (eg, an unexpectedly large drop of wind speed in West Texas, where most of the state’s wind farms reside). The dispatcher must therefore have an RRS available to prevent an unacceptable drop in system frequency.

RRS can be provided by unloaded generation resources that are online but not generating at full capacity, and can therefore increase their output quickly to provide additional capacity to the system. As with generation resources providing REGUP and REGDN, generators providing RRSs must be responsive to automated ERCOT instructions, via AGC that translates instructions into the adjustment of the prime mover and electrical output. In addition, properly qualified load resources can provide RRSs. Nearly half of ERCOT’s RRS requirements are met by load resources equipped with under-frequency relays that instantaneously curtail load when frequency drops to 59.7 Hz. Resources providing this service must also be able to respond to verbal dispatch instructions. Requirements for RRSs are calculated in four-hour blocks on the basis of forecasted load and wind patterns. During the sample period, load resources were permitted to provide no more than 50% of RRS requirements, thus ensuring sufficient generation resources to maintain adequate inertia on the system; although the limit was subsequently raised to 60%, effective May 31, 2018.⁵⁵ 5 See ERCOT “Percent change in load-provided responsive reserve” on the ERCOT website.

NSRS ensures sufficient capacity to cover large forecast errors or replace a deployed RRS. While resources providing RRSs must increase output in compliance with ERCOT instructions within ten minutes, resources providing NSRSs must comply within thirty minutes. NSRSs may be provided by generation units that are offline as long as they are able to start up and increase their output to the target level within a predefined period of time, usually ten to thirty minutes. Small amounts of NSRS have historically been provided by load resources.

ERCOT establishes hourly requirements for each operating reserve depicted in Figure 1. Requirements changed greatly when the nodal market was introduced in December 1, 2010 and generation prices started to be calculated every five minutes (at most), rather than every fifteen minutes. In particular, the need for REGUP and REGDN was reduced accordingly, mirroring the shorter time between a security-constrained economic dispatch solution and the start of an operating period, which is presumed to decrease short-term forecasting errors. In light of the confounding structural changes that accompanied the introduction of the nodal market, the AS prices and procurements during the zonal market period will not be considered in this analysis.

For REGUP, requirements can change from hour to hour, depending on historical wind generation and other factors. At the start of the nodal market, regulation requirements were set based on an examination of changes in net load (demand minus wind generation) the same month of the previous year and the past thirty days prior to the time of a study; refinements were applied in February 2011.⁶⁶ 6 See 2010–2011 Ancillary Service Methodology, ERCOT Board of Directors, July 20, 2010. Considerable changes to the method used to set minimum regulation requirements were made on January 1, 2016, which resulted in lower requirements.⁷⁷ 7 See Item 9: 2016 Methodology for Determining Minimum Ancillary Service Requirements, ERCOT Board of Directors, December 8, 2015. A small change to the relationship between wind generation and regulation requirements was implemented at the beginning of 2014.⁸⁸ 8 See Item 8: Proposed Changes for the 2014 Methodology for Determining Minimum Ancillary Service Requirements, ERCOT Board of Directors, December 2013. The discussion for REGDN is entirely analogous and hence omitted for brevity.

RRS requirements change less often than other ASs. RRS requirements were historically nearly always 2300 MW, but on April 1, 2012 they were raised to 2800 MW,⁹⁹ 9 See Item 9: 2012 Ancillary Services Methodology Recommendation, Memo from John Dumas to ERCOT Board of Directors, February 21, 2012. and since June 1, 2015 they have been based on variable hourly needs.

While initially set at a fixed hourly amount, NSRS requirements were based on the largest unit planned to be in operation for periods of projected higher risk through most of the zonal market period. This was changed to 1500 MW per hour beginning April 1, 2012.¹⁰¹⁰ 10 Ibid. Subsequently, the system requirement for NSRS was determined by first calculating the ninety-fifth percentile of net load uncertainty from both the previous thirty days and the same month of the previous year.¹¹¹¹ 11 Net load is defined as total load minus wind generation, and net load uncertainty is defined as the difference between the realized net load and forecast net load. Solar generation was also included in this definition beginning in January 2017. See Item 9: 2017 Methodology for Determining Minimum Ancillary Service Requirements, ERCOT Board of Directors, December 13, 2016. ERCOT would then subtract the REGUP requirement from this ninety-fifth percentile to obtain the NSRS requirement. In 2016, the formula was altered to remove consideration of net load uncertainty during the previous thirty days, and to instead consider net load uncertainty in the same month of the previous three years.¹²¹² 12 See Item 9: 2016 Methodology for Determining Minimum Ancillary Service Requirements, ERCOT Board of Directors, December 8, 2015. During the hours of 07:00 through 22:00 (central time), ERCOT also applies a minimum NSRS requirement equal to the largest single unit in the system. Generally, an improvement in ERCOT’s accuracy in predicting next-day load and wind generation levels reduces ERCOT’s requirements for this service.

Load-serving entities (LSEs) are assigned AS obligations based on their share of the load on the same hour of the previous day. If the LSE served 10% of the load at 14:00–15:00 yesterday, they have an obligation for 10% of each operating reserve AS at 14:00–15:00 today. With advanced notice to ERCOT before the DAM’s price determination, LSEs can self-arrange to meet part or all of their AS obligation. They can use their own generating capacity or interruptible load, or acquire this through a market outside of the DAM. Trades among market participants are common. Figures 2–4 indicate the percentage of AS obligations that are self-arranged for REGUP, RRS and NSRS.¹³¹³ 13 Per ERCOT Nodal Protocol Section 4.4.7.1.1, a scheduling entity (QSE) may submit a negative self-arranged AS quantity in the DAM, and ERCOT shall procure all negative self-arranged ASs submitted by a QSE.

Many of the larger LSEs that also have generation assets seek to fully hedge (ie, fully self-arrange) and thus avoid their risk exposure to the DAM’s AS price volatilities. The rural electric co-operatives contend that they may encounter federal tax problems if they profit from trading activities unrelated to satisfying the energy needs of their customers.¹⁴¹⁴ 14 See various statements and testimony filed in PUCT Docket No. 31540: Proceeding to Consider Protocols to Implement a Nodal Market in the Electric Reliability Council of Texas Pursuant to PUC Subst. R. 25.501. Consequently, some co-operatives may avoid using the DAM to sell any ASs. During the zonal market period, NSRS was typically fully self-arranged, so that there was no market for this operating reserve. However, following some market changes, this has become unusual.

Whatever portion of an LSE’s obligation is not self-arranged is procured on behalf of the LSE by ERCOT through the DAM.¹⁵¹⁵ 15 In certain ERCOT systems and databases, the amounts procured are referred to as the amount awarded. Certain ASs are not acquired through the DAM (eg, black start and voltage support) and shall be ignored here. These quantities are indicated in Figure 5. The DAM is an hourly market for both energy and ASs, whose prices are determined between 10:00 and 13:30 for the twenty-four hours of the following day. ERCOT bills the LSE for the cost of procuring ASs on the LSE’s behalf at a single market-clearing price of capacity (MCPC) for each AS for each hour.

Generation and load resources supplying ASs and energy are co-optimized in the DAM. Thus, a resource offered to the DAM will end up, if selected or awarded, providing AS or energy, depending on where it has the most value. However, not all resources are qualified to provide all types of AS. A resource that responds slowly will not be selected to provide a service that requires a fast response, such as REGUP.

There is often an overabundance of load resources to provide RRSs at USD0/MW in hopes of ensuring their offer is accepted. These resources have negligible deployment costs and count on generators to set the market-clearing price. Formerly, load resources were allowed to offer capacity at negative prices, leading to occasional negative market-clearing prices. Negative-price bidding by load is no longer allowed.¹⁶¹⁶ 16 See the background materials for NPRR 150: Responsive Reserve Service Offer Floor, 2008; and Nodal Protocol Section 4.4.7.2.1 (2). These are available from http://www.ercot.com.

Contrary to the expectations of many, it has been asserted that the quantity of ASs required by the ERCOT system operator has declined very slightly in recent years, despite a large increase in wind generation (Potomac Economics 2018, p. 37).¹⁷¹⁷ 17 As noted by Potomac Economics, total AS requirements were 5300 MW in 2015, 4900 MW in 2016 and 4800 MW in 2018. This may, in large part, be due to improvements in ERCOT’s near-term forecasts of load and wind generation and a multitude of changes in the ERCOT Protocols (Andrade et al 2017). As suggested by Figure 6, there was a slight decrease in AS requirements between January 1, 2010 and December 31, 2017, as suggested by the negative slope coefficient in the trend line regression equation presented in this figure.

The amounts of ASs procured or awarded through the DAM may have declined or increased, depending on the period selected (Tsai 2018). Figure 7 suggests a slight increase in total ASs procured or awarded through the DAM in the period since the beginning of 2011. Changes in the procurement amounts may be explained by changes in requirements and changes in the self-arrangement of ASs, as noted in the following section.

The market-clearing prices of ASs have generally declined. But how have the changes in AS market prices been affected by the changes in the quantities procured through the DAM, fluctuations in natural gas prices and other factors? This question is explored in the following sections.

Obtained from the ERCOT website and through public data requests to ERCOT, our sample contains hourly data from January 1, 2011 to December 30, 2017 for the following variables, which are used in our price regressions described below.

• •

  Generation (MWh) by fuel type for baseload nuclear power plants and wind farms for the ERCOT system.

• •

  Hourly quantities required by ERCOT, offered to the DAM, and procured through the DAM of each AS (RRS, NSRS, REGUP and REGDN) in each hour (GW in each hour).¹⁸¹⁸ 18 Some of our calculations may omit the small amounts that ERCOT procures through “supplemental auctions” for ASs. ERCOT has implemented supplemental ancillary services markets (SASMs, or supplemental auctions) 391 times since the start of the nodal market (see ERCOT 2018). ERCOT reports quantities in MW. The values in MW were converted to values in GW so that the coefficient estimates could be better illuminated.

• •

  Hourly market-clearing price (USD per MW) of each AS as well as the price of energy (USD/MWh) procured (ie, awarded) through the DAM.

• •

  For RRSs, the percentage or share of the required quantity provided by interruptible load resources.

• •

  The system-wide forecast of load for each hour of the following day made available by ERCOT at the market at the start of each day’s DAM process.

• •

  The system-wide forecast of wind generation for each hour of the following day made available by ERCOT to the market at the start of each day’s DAM procurement process.

In addition, the price of natural gas at the Henry Hub was obtained from the US Department of Energy (DOE) Energy Information Agency. Daily values were repeated for each hour of each day, and prices for days in which no trading took place (ie, weekdays and holidays) were based on prices for the prior trading day.

Our sample period closely matches the initiation of nodal pricing and the DAM on December 1, 2010. It begins on January 1, 2011 because coherent price data reflecting the new markets and zones was unavailable for December 2010. It ends on December 31, 2017, reflecting the data supplied to us by ERCOT at time of writing.

Figure 8 displays the pattern of AS prices over time. A simple trend analysis suggests a small decline in the prices of each AS over the period under consideration. Yet, simple trends are rather meaningless due to the large spikes indicated in this figure.

Our regression setup reflects that the DAMs for energy and ASs are interdependent because (a) many generation resources can offer energy or ASs, or both, to DAMs, and (b) resources are assigned to provide energy or ASs through ERCOT’s co-optimization routine. It also recognizes that DAM energy prices are mainly driven by such fundamental drivers as natural gas price, nuclear generation and day-ahead forecasts of system load and wind generation (Woo et al 2011; Zarnikau et al 2014a, 2016).

Let $P_{jht}$ be the price for market $j$ (equal to 1 for DAM energy, 2 for RRSs, 3 for NSRSs, 4 for REGUP and 5 for REGDN) in hour $h$ ($h=1,\mathrm{\dots },24$) on day $t$ ($t=01/01/2011,\mathrm{\dots },12/30/2017$). For the DAM energy price, the regression with random error ${\epsilon }_{1ht}$ is¹⁹¹⁹ 19 We do not use the double-log specification because of the presence of zero and negative prices, whose natural logs are missing values that cause data gaps in our regression analysis. The values of all quantity variables representing generation and load were converted from MWh values to GWh values, so the coefficients associated with some of the variables could be better illuminated and all of these variables could be represented in the same units.

In (2.1), coefficient ${\theta }_{ht}$ is a time-varying intercept that captures the fixed effects of hour-of-day and month-of-year. Coefficient ${\theta }_G>0$ is the market-based heat rate that measures the DAM energy price increase due to a USD1/MMBtu increase in the natural gas price $G_t$ (Woo et al 2016b). Coefficient is the marginal price effect of baseload nuclear generation $N_{ht}$. Coefficient ${\theta }_D>0$ aims to capture the marginal price effect of ERCOT’s day-ahead load forecast $D_{ht}$. Coefficient measures the price-reduction (or merit-order) effect of the day-ahead forecast of wind generation $W_{ht}$. We do not consider other renewable generation types such as solar because of its small presence in Texas until recently and the absence of reliable data throughout the time frame of this analysis.

Equation (2.1) uses $\{Y_{jht}\}$ to capture the effects of ERCOT’s reliability-driven AS requirements on the DAM energy price, thus recognizing the ERCOT DAM’s energy–ASs interdependence ignored by prior price regression analyses for Texas (Woo et al 2011; Zarnikau et al 2014a, 2016). We expect $\{{\theta }_j\}$ to be positive because prices tend to increase as more ASs are required.

Let $Q_{jht}$ denote ERCOT’s AS procurement quantity of type $j$ ($j=2,3,4,5$) in hour $h$ on day $t$; let $S_{ht}$ denote the portion of RRSs of $Q_{2ht}$ provided by load resources; and let $Z_{jth}$ denote the total AS offer $j$ in hour $h$ on day $t$. To allay concerns of estimation bias due to endogenous regressors, we reason that $\{Q_{jht}\}$ are predetermined variables that measure the AS procurement amounts based on ERCOT’s reliability requirements less LSEs’ total self-arranged amounts, known to ERCOT before its calculation of the DAM’s AS prices. We similarly reason that $\{Z_{jht}\}$ are predetermined variables, as they are made prior to ERCOT’s AS price determination.²⁰²⁰ 20 This model does not use self-procured AS quantities as additional regressors because these quantities are endogenously determined by an LSE with self-arrangement capability based on its least-cost offer decisions. In particular, it will buy from the AS market when the AS price is below the per MW cost of self-arrangement. Thus, even if self-procured AS quantities have appeared in the model, DAM energy and AS prices move with the fundamental drivers and AS procurements according to the price regressions postulated here.

Reflecting the energy–AS markets’ interdependence and the predetermined nature of $\{Q_{jht}\}$, $S_{ht}$ and $\{Z_{jht}\}$, our postulated AS price regressions with random errors $e_{jht}$ are

Coefficient $\alpha$ in (2.2) is the intercept. Coefficient ${\alpha }_1>0$ measures the DAM energy price’s effect on the RRS price.²¹²¹ 21 Under the efficient market hypothesis (EMH), we expect ${\alpha }_1$ to equal 1 based on the arbitrage behavior of suppliers. ERCOT’s reliability and operation constraints, however, imply that the EMH is unlikely to hold for all AS products. Hence, the change in a fundamental driver that alters the DAM energy price also impacts the RRS price. For example, ${\theta }_G{\alpha }_1$ is the RRS price effect of an increase in the natural gas price. Coefficient ${\alpha }_2$ captures the demand-side price effect of $Q_{2ht}$. Finally, (2.2) uses coefficient to measure the supply-side effect of the hourly total RSS offer $Z_{2ht}$ on the RRS price. As the interpretation of the remaining AS price regression is entirely analogous, it is omitted for brevity.

We tested a number of alternative models including dummy variables to represent relevant changes in market rules and modifications to the formulas used by ERCOT to calculate AS requirements; however, we concluded that the effects of these changes were already reflected in $\{Q_{jht}\}$, leading to these dummy variables’ statistical insignificance.

Thanks to market interdependence, the random errors in (2.1)–(2.5) are contemporaneously correlated. As the hourly price data is likely serially correlated, we assume that the random errors follow an autoregressive ($\mathrm{AR}(n)$) process, whose order $n$ is to be determined empirically. The endogenous DAM energy price appears as a regressor in (2.2)–(2.4). Thus, (2.1)–(2.5) form a system of simultaneous regressions, which can be readily estimated using the iterated three-stage-least square (IT3SLS) technique in the PROC MODEL of SAS software.

Table 2 reports the descriptive statistics and price correlations of the variables used in our price regressions. All data series are found to be stationary at the 1% significance level based on the Phillip–Perron unit-root test (Phillips and Perron 1988), obviating concerns of spurious regressions due to nonstationary data that follows a random walk (Granger and Newbold 1974).

It is noteworthy that RRS prices tend to be greater than the prices of REGUP, since REGUP has stricter performance requirements. Some of the reasons explaining differences in the relative average prices of the two products include the following.²²²² 22 These explanations were provided through correspondence with Beth Garza of Potomac Economics.

• •

  ERCOT requires much more RRS than REGUP (approximately 2800 MW for RRSs versus approximately 300 MW of REGUP).

• •

  Generating units are limited in how much RRS they can provide. Historically, this limit was no more than 20% of a unit’s maximum capacity.

• •

  While generation capacity providing RRSs is required to be frequency responsive, REGUP capacity is not required to have that capability.

The data series in Table 2 are volatile, with large standard deviations and wide ranges defined by the series’ minimum and maximum values. The DAM energy price is highly correlated with the RRS, NSRS and REGUP prices ($r>0.71$), though not with the REGDN price ($r=0.24$). The positive correlation coefficients ($r>0.79$) for the market prices of RRS, NSRS and REGUP suggest that these three market prices tend to move in tandem, thus affirming their interdependence. However, the positive correlations of the REGDN price with the DAM energy and other AS prices are weak (), presaging an empirical challenge in modeling REGDN price movements. Happily, the regressions results reported in Table 3 demonstrate that this challenge is an unwarranted concern.

DAM energy prices are positively correlated with natural gas prices and system loads but negatively correlated with wind generation output levels. Further, RRS, REGUP and REGDN prices are negatively correlated with AS offers. Still further, RRS prices are negatively correlated with the shares of RRS met by load resources.

Many of the remaining correlation coefficients in Table 2 fail to attain their expected signs, thus motivating our estimation of the price regressions given by (2.1)–(2.5), which controls for the effects of confounding variables and yields the generally reasonable empirics reported in the section below.