Using AURORAxmp to Meet Renewable Portfolio Standards (RPS)

According to the National Renewable Energy Laboratory (NREL), a renewable portfolio standard (RPS) is a “regulatory mandate to increase production of energy from renewable sources such as wind, solar, biomass and other alternatives to fossil and nuclear electric generation.” In 1983, Iowa became the first US state to adopt a renewable portfolio standard. In the last two decades, over half of the states in the country have adopted some form of RPS. Below is a chart displaying renewable capacity additions by state between 2000 and 2015:


Source: Lawrence Berkeley National Laboratory

Though RPS can be enforced in several ways, the mandates typically require utilities to provide some level of electric supply with renewable energy. The federal government, and sometimes state & local governments, provides financial incentives, often in the form of tax credits or rebates, to encourage investment in renewable energy. According to the Lawrence Berkeley National Laboratory, 60% of renewable electricity generation and 57% of renewable capacity builds since 2000 are tied to RPS mandates. The ultimate goal of these policies is to migrate away from fossil fuel generation in an attempt to reduce carbon and other various emissions.

AURORAxmp provides the flexibility to model various state RPS mandates and can be used to measure the impact of RPS standards on system cost, zonal prices, and emission reductions. The built-in constraint logic is used to easily specify the minimum amount of electric generation needed to meet any specified fleet of resources. Multiple parameters apply the constraint geographically as well; for example, RPS constraints can be applied on a local, state, and national level, and the model will solve all of them in the same run.

RPS constraints can also be defined in the form of renewable capacity rather than electric generation. For example, the Clean Power Plan (CPP), proposed by the EPA last year, contains several intricate details that specify how conventional fuel generators will be required to operate, both individually and as a group. Any capacity or generation constraints can be used in conjunction with a variety of other defined limits, such as emission rates, restrictions on fuel usage, and limitations on capacity factors. Additionally, these constraints are fluidly incorporated into various types of studies, such as long-term capacity expansion, risk, and scenario analysis. Below is a chart created using AURORAxmp to estimate the total system cost of different programs:


Generation attributed to RPS is expected to double by 2030 in the United States. As we look into the future, it is evident that the integration of renewable energy will continue to be a major point of interest in power markets. AURORAxmp offers an easy, reliable, and robust tool to analyze the impact of additional renewable generation on resources, the environment, and the electric grid.

Filed under: Renewable Portfolio StandardsTagged with: , , ,

19th Annual Electric Market Forecasting Conference to Focus on the Future of Energy Markets

The 2016 Electric Market Forecasting Conference (EMFC), a leading gathering of industry strategists and executives, will feature in-depth discussions on the driving forces of today’s energy markets. The 19th annual conference, organized by EPIS, LLC, will bring together a stellar lineup of speakers as well as senior executives in the industry.  The EMFC will be held at the Atlanta Evergreen Marriott Conference Resort in Atlanta, Georgia, September 14-16, 2016.


The EMFC features an optional one-day pre-conference training for both new and advanced power market modelers, as well as an AURORAxmp Users’ Group Meeting. Both clients and non-clients are welcome to attend. The two-day conference will include presentations and case studies from industry experts, as well as special events and networking opportunities. Speakers include: Larry Kellerman, managing partner of Twenty First Century Utilities, Morris Greenberg, managing director of gas and power modeling at PIRA Energy Group and Jeff Burleson, VP of system planning at Southern Company. A full list of speakers is available at

“Over the past 19 years, the Electric Market Forecasting Conference has become established as a valuable, strategic gathering for clients and non-clients alike,” said Ben Thompson, CEO of EPIS. “It is an event where executives and peers in the industry gather to share market intelligence and discuss the future of the industry.”

EMFC has developed a reputation for being an event that delivers real, actionable intelligence, not just abstract concepts. The organizers focus on an agenda filled with speakers who can share experience and takeaways that can be used to have a positive impact on attendees’ organizations. The conference’s intimate environment allows participants to create lasting relationships with peers and luminaries alike.

Now in its 19th year, EMFC is an essential conference for power industry professionals to come together to share best practices and market intelligence. The one-day pre-conference allows AURORAxmp users to learn techniques to master the AURORAxmp application and maximize ROI. More information can be found at:

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Integrated Gas-Power Modeling

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


  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.

Impact of CPP on Henry Hub PricesFigure 1: Monthly gas prices at Henry Hub for all three cases.

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

Comparison of Power Prices in PJM Dominion VPFigure 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.

Impact of CPP on Zone Price (CPP/No CPP)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.

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.

Percentage increase in production cost total for CPP over No CPP CaseFigure 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.

Eastern Interconnect Build Out - No CPPFigure 6: Capacity expansion for Eastern Interconnect – No CPP Case.

Eastern Interconnect Build Out - CPPFigure 7: Capacity expansion for Eastern Interconnect – CPP Case.

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



East Int.












































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.

Percentage Share of Dominate Fuel TypeFigure 8: Percentage share of dominant fuel type.

Change in fuel share for power generation (cpp - no cpp)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.


Additional NG Pipeline Capacity RequiredFigure 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.
Additional NG Capacity Required CPP vs No-CPP (bcf/day)

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.

Filed under: Clean Power Plan, Natural Gas, Power Market Insights, UncategorizedTagged with: , , , ,

Simple-Cycle Combustion Turbines in the CPP

The Environmental Protection Agency’s (EPA) Clean Power Plan (CPP) is full of interesting caveats and exceptions on many issues. One notable quirk is the exclusion of simple-cycle combustion turbines (SCCT) from the list of affected electricity generating units. States must detail how they intend to limit carbon emissions from combined-cycle combustion turbines (CCCT) and coal-powered steam generators, but carbon from SCCTs is not regulated under the CPP.

The EPA’s rationale is that SCCTs cannot meaningfully contribute to emission reductions because they run so rarely. In the full report, the EPA states that it does not expect this to change:

“In addition, while approximately one-fifth of overall fossil fuel-fired capacity (GW) consists of simple cycle turbines, these units historically have operated at capacity factors of less than 5 percent and only provide about 1 percent of the fossil fuel-fired generation (GWh)…the EPA expects existing simple cycle turbines to continue to operate as they historically have operated, as peaking units.”

Is this a realistic assumption? Simple-cycle units currently have low capacity factors, but that is mostly because they are relatively expensive to operate. Natural gas has historically been more expensive than coal. Among units burning natural gas, combined-cycle units are more efficient than simple-cycle units. As such, simple-cycle units are generally kept offline due to their higher operating costs. However, this is not a rule, it is a relationship. If you add costs to one set of generators and not another, the relationship may change.

To illustrate this point, let’s consider a few hypothetical units, operating in 2025, and see how they may respond to carbon pricing. One is a relatively modern and efficient simple-cycle gas plant, another is a typical combined-cycle gas plant, and the last is an older coal plant. Unit characteristics vary significantly within each of these technologies, but we will take a highly competitive simple-cycle and compare it to some of the least competitive coal generation to see where simple-cycle units may start to become cheaper than coal.

Operating characteristics for hypothetical units (2025)

Technology Heat Rate
CO2 Emission Rate
Fuel Cost
Operating Cost
Efficient SCCT 10,000 8.00 18.50 80.00
Typical CCCT 7,500 118 7.00 6.50 52.50
Older Coal ST 12,000 210 3.50 8.50 42.00

We exclude an emission rate for our simple-cycle unit, because they are not regulated under the CPP and will not experience an increase in operating costs due to carbon restrictions or pricing. If we add a carbon price ($/ton) to each of these units, their operating costs will shift accordingly.

Hypothetical Operating Costs by Source and Carbon Price

As the price of carbon reaches $10/ton, the coal unit starts to become more expensive to operate (per MWh of generation) than the combined-cycle unit (Point A). This is expected and intended by the CPP. One of the fundamental building blocks of emission reductions is a shift of generation from coal to combined-cycle units. However, by the time we reach a carbon price of around $30/ton, coal units also become more expensive to operate than simple-cycle generators! Because the SCCT unit is not subject to carbon regulations under the CPP, its costs remain constant, while the operating cost of the coal plant rise quickly as carbon pricing increases.

A carbon price of $30/ton would be unprecedented in the U.S., but not inconceivable. Depending on which discount rate you prefer, the official social cost of carbon can exceed $30/ton. At EPIS, our modeling of mass-based compliance approaches to the CPP have shown that allowance prices greater than $30/ton may be needed for some states to meet their emission goals through a carbon market.

Of course, unit operation cannot be summed up by a single operating cost. Many factors can influence a generator’s decision to run, such as start costs, other environmental regulations, and participation in reserve or ancillary service markets. There may be reasons beyond per-MWh costs why an SCCT unit would continue to provide only peaking services in a high carbon price environment. However, some power providers may find that the strict emission limits placed on coal and combined-cycle plants opens up a unique opportunity for the relatively unregulated SCCT units. Anyone concerned with modeling the CPP would do well to carefully consider the potentially changing role of SCCTs in an uneven regulatory environment, which gives them a free pass while hindering coal and combined-cycle plants.

Will simple-cycle units increase their utilization if the CPP is implemented, becoming more than just peak power providers? Only time will tell. Let us know what you think in the comments.

Filed under: Clean Power Plan, Power Market InsightsTagged with: , , , , , , , ,

US Supreme Court Issues Stay on CPP

On Tuesday, the U.S. Supreme Court issued a stay on the Environmental Protection Agency’s (EPA) Carbon Pollution Emission Guidelines for Existing Stationary Sources, more commonly known as the Clean Power Plan (CPP). This means that states will not be obligated to comply with any part of the CPP until a decision is reached on Chamber of Commerce, et al. v. EPA

The D.C. Circuit Court will begin hearing oral arguments on the merits of the CPP on June 2 of this year. The lower court’s ruling is expected to be appealed to the U.S. Supreme Court, regardless of the outcome. If past regulations of a similar scale are any indication, the U.S. Supreme Court will hear the case.

The final impact of this stay will depend largely on the outcome of Chamber of Commerce, et al. v. EPA. Even if the courts uphold the CPP, it is likely that the initial state submittal deadline of September 6, 2016 will be affected. However, if the case is concluded swiftly in favor of the EPA, they may be able to hold onto their final submittal deadline in 2018, despite the stay.

If the courts rule against the EPA, the CPP may be revised, or it may need to be scrapped all together. However, unless the court ruling overturns Massachusetts v. EPA (2007), the EPA will still be obligated to eventually regulate carbon as a hazardous air pollutant.

In December of 2011, the D.C. Circuit Court issued a similar stay on the Cross-State Air Pollution Rule (CSAPR). That rule went through a series of revisions and court battles, but the stay was eventually lifted in October of 2014.

The future of the CPP remains uncertain, but most industry experts would agree that participants still need to prepare and plan for the eventual impact of some kind of federal limit on carbon emissions.

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Navigating Clean Power Plan Building Blocks

The EPA’s recently published Clean Power Plan (CPP) has left many companies wondering how to correctly and thoroughly assess the impact of these rules on the market, as well as their future planning.  Energy market participants of all types are struggling to determine what analyses can best evaluate all the available options.  Will they need to comply with a rate-based or mass-based approach?  How will they implement all the pieces of the new legislation?  How will the market be impacted?

In addition to flexible modeling of both mass-based and rate-based constraints, AURORAxmp has all the tools required to model each of the CPP’s building blocks individually or in combination.


Navigating the Clean Power Plan with AURORAxmp

(percentages can be found on page 3 of document for CPP Final Rule)

Regardless of your position or what type of market participant you are, AURORAxmp can provide powerful flexibility that will allow you to generate plant-by-plant or fleet results at the state or regional level. AURORAxmp delivers unparalleled scenario management, exceptionally fast runtimes, collaborative project management, as well as automation in a single multi-functional platform.  Experts across North America have already been using AURORAxmp in CPP studies.

“EVA’s Power Market Advisory team has been using AURORAxmp to analyze multiple flavors of the Clean Power Plan for 18 months now and have had great success. The accuracy and flexibility of the built-in emission constraint logic combined with AURORAxmp’s speed and granularity help provide robust and comprehensive results that meet and exceed our clients’ expectations. Whether we’re assessing a state-level rate-based compliance strategy or a mass-based approach with interstate trading, AURORAxmp’s user interface allows us to perform a wide range of analyses with ease. Further, EPIS’s top-notch customer support team is always available to help us solve issues and discuss modeling techniques. They take our feedback seriously and work consistently to make model updates that help us better simulate the power sector.” – Rob Jennings, Analyst, Energy Ventures Analysis

For additional information, please contact us

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