Production cost and air emissions impacts of coal cycling in power systems with large-scale wind penetration

Operation of an electric grid depends on dispatchable generating capacity, such as coal and natural gas power plants, which can be cycled in response to variability. Traditionally, variability was largely limited to the demand side. However, an ever-increasing penetration of variable renewable energy technologies, such as wind, promises to increase the amount of cycling performed by dispatchable generators.

APTECH (2009) defined cycling as 'The process of bringing plants on and offline or increasing and decreasing unit output to meet load demand'. Denny (2007) and Valentino et al (2012) used similar definitions. Here we adopted the definition suggested by APTECH and considered cycling operations to include both shutdown–startup cycles, where we differentiated between hot, warm, and cold cycles by the time spent offline; and load cycling, in which output is increased or decreased without shutting down. For reasons discussed later, we focused on shutdown–startup cycles.

As described by Katzenstein and Apt (2009) and Fertig et al (2012), the spectrum of variation introduced by wind generators is not flat across all frequencies. Wind power varies much more at multi-hour than it does at sub-hourly time-scales. Units with small ramp rate limits, such as coal, might therefore be expected to balance much of wind's variability. However, coal plants were by and large designed to provide baseload power Denny (2007), and increased cycling of these plants in response to high wind penetrations could have unforeseen consequences.

Previous work has suggested that using thermal plants to balance wind may result in emissions penalties. Katzenstein and Apt (2009) considered the implications of using a gas + wind + solar combination to produce baseload power. Their findings indicated that ramping a simple or combined cycle gas turbine over its full operating range to balance wind and solar variability resulted in significant emissions penalties for NO_X and SO₂. The emissions model used by the authors allowed emissions rates to vary with both power output and ramp rate.

Valentino et al (2012) examined the relative contribution of startups to fleet-wide emissions at a range of wind penetration levels. Focusing on the state of Indiana, their results suggested that while cycling increased the emissions rates of individual units, the effect was small compared to the overall emissions reductions that resulted from the displacement of fossil-fueled power by wind power.

Increased cycling of thermal power plants is also likely to result in increased operating costs. Intertek APTECH assessed the cost implications of cycling coal-fired thermal units (APTECH 2009, Shibli et al 2001), while Lefton (2004) and Shibli et al (2001) studied steam units generally. The large and time-varying thermal loads imposed by cycling were found to increase the risk of creep and fatigue damage, with implications for operations and maintenance (O&M) costs, capital maintenance costs, and several other cost categories. Using data from two coal-fired power plants, APTECH (2009) reported that these costs were highest for shutdown–startup cycles and could be on the order of $100 000/cycle. Bentek (2010) suggested that most modeling efforts do not account for the full costs of cycling coal power plants, an omission that might underestimate the costs of integrating wind.

We examined the distribution of startup costs bid into PJM and found that these are far lower than the costs calculated by APTECH, suggesting that actual markets may not fully account for the costs of cycling. The comparison is shown in figure 1. There are several possible reasons for this cycling cost gap. First, it is possible that operators are unable to attribute individual component failures and forced outages to cycling operations. Second, it is possible that while operators are aware of the costs incurred during cycling, market rules do not allow them to include these costs in their bids. While PJM's market rules appear to provide a mechanism to account for startup-related wear and tear in the form of a Start Maintenance Adder (PJM 2012b), it is unclear whether costs of the magnitude suggested by APTECH would gain approval by PJM. Finally, it is possible that operators have concluded that bidding actual startup costs would result in a reduction in their capacity factors and energy market revenues, and are bidding strategically in order to avoid this outcome.

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This under-bidding may lead to problems as wind penetration increases. Under current market conditions coal operators may be able to generate sufficient revenues in the energy market to offset the cycling cost gap. However, as wind penetration increases and coal plants are required to cycle more often, profit margins may narrow to the point where cycling costs cannot be recovered. As pointed out by Cullen and Shcherbakov (2010), the operation of an energy market is often sensitive to unit startup costs. Here we determined the increase in coal cycling that could be expected in a high wind penetration scenario in the PJM West area under business as usual startup cost bidding, along with associated emissions and system cost penalties. We then considered low- and high wind scenarios where operators of coal plants bid startup costs at the elevated levels suggested by APTECH.

We constructed a unit commitment and economic dispatch (UCED) model with an hourly time step for the PJM West area, as it existed in 2006. This included the PJM sub-regions of AEP, AP, Dayton, Dominion, and Duquesne. Using simulated wind data generated by the National Renewable Energy Lab (EnerNex Corporation 2011), we considered a scenario with 20% wind energy penetration that approximated compliance with renewable portfolio standards in the region (PJM 2011). To account for the effects of cycling on emissions of CO₂, NO_X, and SO₂, we employed energy penalty curves derived from the EPA's Clean Air Markets data (EPA 2006) and input emissions rates from the National Electric Energy Data System (EPA 2010).

We found that a high level of wind penetration led to a large increase in the cycling of coal units and to an increase in cycling-induced cost and emissions penalties, but that these penalties were small compared to the system-wide savings associated with wind. This finding is consistent with the results of Valentino et al (2012). Furthermore, we found that coal cycling, along with its emissions penalties, was reduced if the full costs of cycling such units were taken into account in operator bids. The high wind with elevated coal start cost scenario was associated with higher normal operations costs, compared to the high wind scenario with low coal start costs, due to fuel switching from coal to gas. Total costs were still lower, however, than in the no-wind case. While incorporating elevated coal startup costs reduced coal unit capacity factors, the combination of reduced startup costs and increased energy prices led to increased aggregate coal unit profitability under the elevated coal start cost scenarios.

2.1. Unit commitment and economic dispatch model

2.2. Outages

2.3. Wind power

2.4. Emissions

Having obtained an operating schedule from our UCED¹, we used it and an emissions model to calculate emissions. Our emissions model consists of two parts. During normal operation of the unit, we used unit-level input emissions factors and heat rates from the National Electric Energy Data System and the EIA to calculate emissions of CO₂, NO_X, and SO₂ as a function of power output. Emissions factors are summarized in table 2. Note that with an hourly time step, observable ramp rates are too small to have an impact on emissions and are therefore not accounted for in the emissions model (see supplementary information, available at stacks.iop.org/ERL/8/024022/mmedia, for a detailed discussion on this issue).

Table 2.  Average steady-state emissions factors by unit type in PJM West from (CO₂ from EIA (2011), others from EPA (2010)). Uncontrolled NO_X rates were used during startup and shutdown.

	CO2 rate (ton/MMBTU)	SO2 permit rate (lb/MMBTU)	Uncontrolled NOX rate (lb/MMBTU)	Controlled NOX rate (lb/MMBTU)
Coal	0.10	2.2	0.50	0.19
NGCC	0.058	0.062	0.10	0.077
NG steam	0.058	1.3	0.097	0.097
NGCT	0.058	2.0	0.093	0.09
Oil steam	0.087	1.7	0.26	0.26
Oil CT	0.081	1.6	0.82	0.82
Other	0.069	0.68	1.7	1.6

To capture emissions during startup, we constructed heat rate penalty curves as a function of load for each unit type. As suggested by Klein (1998), the heat-input versus power-output curve of a unit can be modeled as a cubic function. Average heat rate curves can then be developed in the form of equation (1), where x represents power output in MW and HR represents the average heat rate in MMBTU MWh⁻¹. We extended this notion and calculated heat rate penalties P as the per cent difference between the heat rate and the steady-state heat rate, as in equation (2). We then generated heat rate penalty curves in the form of equation (3), where X represents the per cent load. Heat rate curves were averaged across all units of a particular type. The resulting heat rate penalty curves, shown in figure 2, were used to capture emissions penalties associated with startup and shutdown. The heat rate penalty curves were generated using a selection of units from CAMD (EPA 2006) in 2006 that were located in PJM and whose data was usable. Startup emissions calculated using heat rate penalties found in Valentino et al (2012) differed from those reported here by a maximum of 10%.

$$\begin{eqnarray} \mathrm{HR}&=&a{x}^{2}+b x+c+\frac{d}{x} \end{eqnarray} \tag{ 1 }$$

$$\begin{eqnarray} P&=&\frac{\mathrm{HR}-{\mathrm{HR}}_{0}}{{\mathrm{HR}}_{0}} \end{eqnarray} \tag{ 2 }$$

$$\begin{eqnarray} P&=&A{X}^{2}+B X+C+\frac{D}{X}. \end{eqnarray} \tag{ 3 }$$

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NO_X emissions result from oxidation of atmospheric nitrogen and emissions rates are a complex function of control technology and the thermodynamic state of the boiler (Mulkey 2003). It is unlikely that NO_X emissions control technologies would operate properly during startup or shut down. To calculate startup emissions, we therefore used uncontrolled emissions factors from NEEDS, as listed in table 2.

The low coal start cost/20% wind scenario was associated with increased startups of all types of units, increased startup-related costs, and increased startup-related emissions compared to the no-wind case. Figures 3(A) and (B) show annual startups across the three scenarios for coal units and flexible units (simple- and combined cycle gas units), respectively. Coal startups of all types increased by 14%–640% in the high wind scenarios compared to the no-wind scenarios at each startup cost. The percentage increase was greater with elevated coal startup costs, though the absolute values were greater with low start costs. Flexible-unit startups increased by 40%–90% in the high wind scenarios. Despite the increase in startups, system-wide costs and emissions were lower in the high wind scenarios due to displacement of fossil fuels by wind.

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The scenarios in which elevated coal startup costs were incorporated showed dramatic reductions in coal startups over the low coal start cost scenarios. As expected, flexible units appear to have compensated and startups for these units increased dramatically over the low coal startup cost scenarios. As shown in figure 3(C), coal capacity factors were reduced under the high wind and elevated coal startup cost scenarios. Capacity factors for natural gas simple and combined cycle units increased slightly with wind penetration and more substantially with elevated coal startup costs. Figure 3(D) shows that incorporating elevated coal startup costs had the effect of increasing curtailment of wind up to nearly 2% of available wind energy, most of it occurring in the early morning hours.

Table 3 shows emissions from normal operations, as well as startup-related emissions of CO₂, SO₂, and NO_X, which increased by approximately a factor of 2 under the 20% wind scenario. However, startups represented a small portion of overall emissions: a maximum of 2% across all scenarios. Incorporating elevated coal startup costs had the effect of substantially reducing startup-induced emissions, but, more significantly, also reduced emissions during normal operations. This is due to increased utilization of gas units as opposed to coal units.

Resource use varied substantially across the three scenarios considered and is shown in figure 4. Comparing the first and third columns shows that the addition of 20% wind largely had the effect of displacing energy production by coal units, leaving nuclear and hydro production virtually unchanged. The scenario incorporating elevated startup costs for coal units had the effect of further displacing coal and replacing it by combined cycle gas.

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Table 4 shows a range of economic indicators across the four scenarios, for suppliers as a whole, and for coal units in particular. Producer surpluses, and changes in consumer surplus over the 0% wind/low coal start cost scenario, were calculated. Producer surplus here includes the difference between annual revenues and total production costs, and consumer surplus is the area between the demand curve and the market-clearing price in each period, summed over the year. Note that since we have modeled demand as inelastic, we set a price cap and report only differences in consumer surplus. Revenue and surplus figures were calculated using prices generated endogenously in the UCED model. These prices do not account for congestion or transmission costs and we include them simply to illustrate that startup costs can make an important contribution to energy prices.

Under business as usual coal startup costs, the wind-induced increase in startups was associated with an 80% increase in total startup costs. The majority of this increase was borne by coal units. Incorporating elevated coal startup costs reduced coal cycling costs dramatically and though it is clear from figure 3(C) that coal capacity factors were reduced in this scenario, the impact on coal unit surpluses was less clear. Table 4 shows that, on the aggregate, producer surpluses were −$300M for coal plants in the high wind/low start cost scenario. However, when elevated coal startup costs were incorporated, aggregate coal unit producer surpluses increased to $610M due partially to a reduction in cycling and partially to a 12% increase in average energy prices over the high wind/low start cost scenario. We again emphasize the limited ability of our modeling framework to forecast prices.

A study was conducted for the PJM West area to determine the effect of wind on the cycling of coal-fired power plants, emissions of CO₂, NO_X, and SO₂, and production costs. High wind penetration scenarios were examined both under business as usual coal startup cost bidding, and under elevated coal startup cost bidding. When only the business as usual coal startup costs were used in the optimization, results indicated that wind penetration increased cycling of slow-ramping coal units and that this cycling led to emissions and cost penalties for these plants. On a system level, the magnitude of the penalties was found to be small compared to the overall savings associated with wind. By incorporating the full costs of coal cycling in the dispatch model, emissions and cost penalties associated with cycling were reduced. This scenario is representative of the decision of coal operators to change their bidding strategies to reflect full startup costs. Including full startup costs reduced coal capacity factors due to switching from coal to gas. However, the increase in energy prices associated with this switching led to increased surplus for coal plants compared to the high wind/business as usual startup cost scenario.

Our result that coal producer surpluses increased when elevated coal startup costs were incorporated deserves further discussion. Predictions of energy prices are inherently uncertain. This means that the result should be viewed with some caution. Also, while we have argued that bidding elevated startup costs would increase producer surpluses for coal operators in the high wind scenario, it is not certain that such bidding would occur. Since operators are trying to maximize overall profits, it may be more optimal to under-bid startup costs so as to earn higher revenues in the energy market. Under the increased startup requirements of a high wind scenario, we consider it likely, but not certain, that such a strategy would be less viable. Our high wind, high coal startup cost scenario should therefore be considered as a bounding case, and interpreted with the uncertainty in bidding strategies in mind.

Our modeling framework made a number of assumptions and approximations. While we do not expect the lack of transmission constraints to affect the implications of the aggregate results, it could affect the individual generators called on to perform cycling. Though we have focused on shutdown–startup cycles because of their larger per unit costs, it remains unclear whether changes in load cycling, in which output is increased or decreased without shutting down, could be a major cost driver. Also, while we have focused here on the current fleet of plants, several policy and economic factors may lead to a change in the composition of the fleet in the coming years. Finally, our analysis was based on the PJM West region. While we expect many of our qualitative results to hold for systems with different fuel mixes, care must be taken in generalizing our results to these other systems. In future work, we expect to examine some of these issues in more detail.

Despite the aforementioned limitations, we expect our qualitative conclusions regarding coal cycling are robust. Incorporating full cycling costs in bids transfers the burden of balancing wind to units that can perform this service at lower cost, has environmental benefits and could improve the profitability of coal plants in a high wind scenario. Incorporating full startup costs into unit commitment may be a good strategy for reducing the burden of coal cycling in a high wind future.

This work was conducted with the support of CMU's RenewElec Project and as part of the National Energy Technology Laboratory's Regional University Alliance (NETL-RUA), a collaborative initiative of the NETL, under the RES contract DE-FE0004000. The results and conclusion of this paper are the sole responsibility of the authors and do not the represent the views of the funding sources. We would like to thank Bri Matthias Hodge, Jay Apt, Roger Lueken, Allison Weis, and Todd Ryan for helpful comments and discussions.