Quantifying the Impacts of the EPA’s Clean Power Plan

Notwithstanding the recent legal stay from the U.S. Supreme Court, it is still important to understand the U.S. EPA’s Clean Power Plan (CPP) and its impact in the larger context of natural gas markets and its role in electric power generation. Because these two markets are becoming even more highly interrelated, integrated gas-power modeling is the most realistic approach for such analyses. EPIS has tested interfacing AURORAxmp® with GPCM®, a calibrated NG model developed by RBAC, Inc. The following is a brief discussion of our experimental setup as well as some of our findings.

Integration Approach

Monthly prices for 39 major natural gas hubs for the next 20 years are represented in AURORAxmp (as an input). They were developed utilizing GPCM’s market model (as an output) in pipeline capacity expansion mode. AURORAxmp then simulates a long-term capacity expansion that utilizes the GPCM-generated gas prices, and produces many results: power prices, transmission flows, generation by each resource/resource type including gas-consumption data. This gas-consumption (output from AURORAxmp) is fed back into GPCM as gas demand by the electricity sector (input to GPCM) for a subsequent market balancing and pipeline capacity expansion simulation which generates a new set of monthly gas hub prices. The iterative process begins at some arbitrary, but plausible, starting point and continues until the solution has converged. Convergence is measured in terms of changes in the gas-burn figures and monthly gas-hub prices between subsequent iterations.

This two-model feedback loop can be utilized as a tool to evaluate energy policies and regulations. To quantify the impact of an energy policy, we need two sets of integrated gas-power runs which are identical in all respects except the specific policy being evaluated. For example, to understand the likely impacts of emission regulation such as CPP, we need two integrated gas-power models with the identical setup, except the implementation of CPP.

Before presenting our findings on the impact of “CPP vs No CPP”, we first provide some further details on the setup of the GPCM and AURORAxmp models.

GPCM Setup Details

• Footprint: All of North America (Alaska, Canada, contiguous USA, and Mexico), including liquefied natural gas terminals for imports, and exports to rest-of-world.
• Time Period: 2016-2036 (monthly)
• CPP Program: All the effects of CPP on the gas market derived from changes to gas demand in the power generation sector.
• Economics: Competitive market produces economically efficient levels of gas production, transmission, storage and consumption, as well as pipeline capacity expansion where needed.

AURORAxmp Setup Details

• Footprint: All three major interconnections in North America (WECC, ERCOT, and the East Interconnect; which includes the contiguous U.S., most Canadian provinces and Baja California).
• Time Period: 2016 – 2036 (CPP regulatory period + 6 years to account for economic evaluation)
• CPP Program: mass-based with new source complement for all U.S. states
  • Mass limits for the CPP were applied using the Constraint table
  • Mass limits were set to arbitrarily high values in the Constraint table for the “No CPP” case.
• RPS targets were not explicitly enforced in this particular experiment. Future studies will account for these.
• LT Logic: MIP Maximize Value objective function

Notations

1. “CPP” – Convergent result from integrated gas-power model with CPP mass limits.
2. “No CPP” – Convergent result from integrated gas-power model with arbitrarily high mass limits.
3. “Starting Point” – Gas prices used in the first iteration of integrated gas-power modeling.
   • This is the same for both “CPP” and “No CPP” case.

Quantifying the CPP vs. No CPP

Impact on Gas and Electricity Prices

1. Both No CPP and CPP cases have generally lower prices than the Starting Point case in our experiment. However, post-2030, CPP prices are higher than the Starting Point.
   • This happens due to capacity expansion in both markets.
   • We stress that the final convergent solutions are independent of the Starting Point case. The lower prices in CPP and No CPP cases compared to the Starting Point case are a feature of our particular setup. If we had selected any other starting price trajectories, the integrated NG-power feedback model would have converged on the same CPP and No CPP price trajectories.
2. CPP prices are always higher than the No CPP case.
   • This is likely driven by increased NG consumption in CPP over No CPP case.

This behavior was observed in all major gas hubs. Figure 1 shows the average monthly Henry Hub price (in $/mmBTU) for the three cases.

Figure 1: Monthly gas prices at Henry Hub for all three cases.

Figure 2 presents the monthly average power prices in a representative AURORAxmp zone.

Figure 2: Average monthly price in AURORAxmp zone PJM_Dominion_VP with and without CPP.

Figure 3 shows the impact of CPP as a ratio of average monthly prices in AURORAxmp’s zones for the CPP case over No CPP case. As expected, power prices with the additional CPP constraints are at the same level or higher than those in the No CPP case. However, it is interesting to note that the increase in power prices happens largely in the second half of CPP regulatory period (2026 onwards). It appears that while gas prices go up as soon as the CPP regulation is effective, there is latency in the increase in power prices.

Figure 3: Impact of CPP on electricity prices expressed as a ratio of CPP prices over No CPP prices.

Figure 4 presents a comparison of total annual production cost (in $billions) for each of the three regions.

Figure 4: Total annual production costs by region for CPP and No CPP case.

Figure 5 presents the same comparison as a percentage increase in production cost for the CPP case. The results show that while the CPP drives up the cost of production in all regions, the most dramatic increase is likely to occur in the Eastern Interconnect.

Figure 5: Percent increase in production cost for CPP case.

Electricity Capacity Expansions

Comparing the power capacity expansions in Figure 6 and Figure 7, we see that AURORAxmp projected building more SCCTs in the CPP case vs. the No CPP case in the Eastern Interconnect. We believe this is primarily driven by the higher gas prices in the CPP case over No CPP case. SCCTs typically have slightly higher fuel prices compared to CCCTs, which get their fuel directly from the gas hub for the most part. In this long-term analysis, AURORAxmp is seeking to create the mix of new resources that are most profitable while adhering to all of the constraints. The higher gas prices in the CPP case are just high enough to make the SCCTs return on investment whole.

Figure 6: Capacity expansion for Eastern Interconnect – No CPP Case.

Figure 7: Capacity expansion for Eastern Interconnect – CPP Case.

Table 1: Capacity expansion by fuel type in total MW.

Table 1 shows the details of power capacity expansion in the three regions with and without CPP emission constraints. In addition to increasing the expansion of SCCTs, we can see that CPP implementation incentivizes growth of wind generation, as well as accelerates retirements. Coal and Peaking Fuel Oil units form the majority of economic retirements in the CPP case.

Fuel Share Displacement

Figure 8 shows the percent share of the three dominant fuels used for power generation: coal, gas, and nuclear. Figure 9 shows the same data as the change in the fuel percentage share between the CPP and No CPP case. Looking at North American as a whole, we see that coal-fired generation is essentially being replaced by gas-fired generation. Our regional data shows that this is most prominent in the Eastern Interconnect and ERCOT regions.

Figure 8: Percentage share of dominant fuel type.

Figure 9: Change in fuel share for power generation (CPP – No CPP).

Natural Gas Pipeline Expansions
The following chart presents a measure of needed additional capacity for the two cases. The needed capacity is highly seasonal, so the real expansion need would follow the upper boundary for both cases.

 

Figure 10: Pipeline capacity needed for the CPP and No CPP cases.

Our analysis shows that the CPP will drive an increase in natural gas consumption for electricity generation. The following chart quantifies the additional capacity required to meet CPP demand for NG.

While the analysis presented here assumes a very specific CPP scenario, we stress that the integrated gas-power modeling is an apt tool for obtaining key insights into the potential impacts of CPP on both electricity and gas markets. We are continuously refining the AURORAxmp®-GPCM® integration process as well as performing impact studies for different CPP scenarios. We plan to publish additional findings as they become available.