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: , , , ,

Working With Data in Power Modeling

How Much Data Are We Talking About?

When planning the deployment of a power modeling and forecasting tool in a corporate environment, one of the most important considerations prior to implementation is the size of the data that will be used. IT personnel want to know how much data they are going to be storing, maintaining, backing up, and archiving so they can plan for the hardware and software resources to handle it. The answer varies widely depending on the types of analysis to be performed. Input databases may be relatively small (e.g. 100 megabytes), or they can be several gigabytes if many assumptions require information to be defined on the hourly or even sub-hourly level. Output databases can be anywhere from a few megabytes to several hundred gigabytes or even terabytes depending on what information needs to be reported and the required granularity of the reports. The data managed and stored by the IT department can quickly add up and become a challenge to maintain.

Here are a couple example scenarios:

A single planning analyst does a one-year hourly run (8760 hours) with modest reporting, which produces an output database of 40 MB. On average, the analyst runs about six studies per day over 50 weeks and the total space generated by this analyst is a modest 75GB. This is totally manageable for an IT department using inexpensive disk space.

Now, let’s say there are five analysts, they need more detailed reporting, they are looking at multiple years, and a regulatory agency states that they have to retain all of their data for 10 years. In this scenario, the total data size jumps to 500 MB for a single study. Given the same six studies per day those analysts would accumulate 3.75 TB of output data in a year, all needing to be backed up and archived for the auditors, which will take a considerable amount of hardware and IT resources.

What Are My Database Options?

There are dozens of database management systems available. Many power modeling tools support just one database system natively, so it’s important to know the data limitations of the different modeling tools when selecting one.

Some database systems are file-based. For example, one popular file-based database system is called SQLite. SQLite is fast, free, and flexible. This file-based database system is very efficient and is fairly easy to work with, but is best suited for individual users, as are many other file-based systems. These systems are great options for a single analyst working on a single machine.

As mentioned earlier, groups of analysts might decide to all share a common input database and write simultaneously to many output databases. Typically, this requires a dedicated server to handle all of the interaction between the forecasting systems and the source or destination databases. Microsoft SQL Server is one of the most popular database systems available in corporate environments, and the technical resources for it are usually available in most companies. Once you have your modeling database saved in SQL Server, assuming your modeling tool supports it, you can read from input databases and write to databases simultaneously and share the data with other departments with tools that they are already familiar with.

Here is a quick comparison of some of the more popular database systems used in power modeling:

Database System DB Size Limit (GB) Supported Hardware Client/Server Cost
MySQL Unlimited 64-bit or 32-bit Yes Free
Oracle Unlimited 64-bit or 32-bit Yes High
MS SQL Server 536,854,528 64-bit Only (as of 2016) Yes High
SQLite 131,072 64-bit or 32-bit No Free
XML / Text File OS File Size Limit 64-bit or 32-bit No Free
MS SQL Server Express 10 64-bit or 32-bit Yes Free
MS Access (JET)* 2 32-bit Only No Low

A Word About MS Access (JET)*

In the past, many Windows desktop applications requiring an inexpensive desktop database system used MS Access database (more formally known as the Microsoft JET Database Engine). As hardware and operating systems have transitioned to 64-bit architectures, the use of MS Access database has become less popular due to some of its limitations (2GB max database size, 32,768 objects, etc.), as well as to increasing alternatives. Microsoft has not produced a 64-bit version of JET and does not have plans to do so. There are several other free desktop database engines available that serve the same needs as JET but run natively on 64-bit systems, including Microsoft SQL Server Express, SQLite, or MySQL which offer many more features.

Which Databases Does AURORAxmp Support?

There are several input and output database options when using AURORAxmp for power modeling. Those options, coupled with some department workflow policies, will go a long way in making sure your data is manageable and organized.

EPIS delivers its native AURORAxmp databases in a SQLite format which we call xmpSQL. No external management tools are required to work with these database files – everything you need is built into AURORAxmp. You can read, write, view, change, query, etc., all within the application. Other users with AURORAxmp can also utilize these database files, but xmpSQL doesn’t really lend itself to a team of users all writing to it at the same time. Additionally, some of our customers have connected departments that would like to use the forecast data outside of the model, and that usually leads them to Microsoft SQL Server.

For groups of analysts collaborating on larger studies, AURORAxmp supports SQL Server database, although its use isn’t required. Rather than use SQL Server as the database standard for AURORAxmp (which might be expensive for some customers), the input databases are delivered in a low cost format (xmpSQL), but AURORAxmp offers the tools to easily change the format. Once the database is saved in SQL Server, you are using one of the most powerful, scalable, accessible database formats on the planet with AURORAxmp. Some of our customers also use the free version of SQL Server – called SQL Server Express Edition – which works the same way as the full version, but has a database size limit of 10GB.

Some additional options for output databases within AURORAxmp are:

MySQL: Open source, free, server-based, simultaneous database platform that is only slightly less popular than SQL Server.
XML/Zipped XML: A simple file-based system that makes it easy to import and export data. Many customers like using this database type because the data is easily accessed and is human readable without additional expensive software.
MS Access (JET) : The 32-bit version of AURORAxmp will read from and write to MS Access databases. EPIS, however, does not recommend using it given the other database options available, and due to its 2 GB size limitation. MS Access was largely designed to be an inexpensive desktop database system and given its limitations as previously discussed, we recommend choosing another option such as xmpSQL, SQL Server Express or MySQL which offer far more features.

Where Do We Go From Here?

AURORAxmp is a fantastic tool for power system modeling and forecasting wholesale power market prices. It has been in the marketplace for over twenty years, and is relied upon by many customers to provide accurate and timely information about the markets they model. However, it really can’t do anything without an input database.

EPIS has a team of market analysts that are dedicated to researching, building, testing, and delivering databases for many national and international power markets. We provide these databases as part of the license for AURORAxmp. We have many customers that use our delivered databases and others who choose to model their own data. Either way, AURORAxmp has the power and the flexibility to utilize input data from many different database types.

If you are just finding AURORAxmp and want to see how all of this works, we have a team here that would love to show you the interface, speed and flexibility of our product. If you are already using our model but would like guidance on which database system is best for your situation, contact our EPIS Support Team and we’ll be glad to discuss it with you.

Filed under: Data Management, Power Market InsightsTagged with: , , ,

Large Scale Battery Storage

Some emerging technologies pave a bright path for the future of Large Scale Batteries

As we move towards more renewable energy sources and away from fossil fuels, we will need new technologies to capture energy production as well as provide new ways to store and deliver power. An ongoing issue with solar and wind production is the inability to predict exactly when you can produce and dispatch power. Additionally, we are seeing more interest for generating, storing, and time-shifting power in other ways to meet environmental goals. Large Scale Batteries are an exciting step toward meeting, and supporting, some of those goals.

While we don’t know what the future will bring, there are some forecasts that predict substantial drops in the cost of the various storage technologies as there is more adoption into the marketplace. Among these, in 2014 Citigroup analysts predicted a drop in battery storage costs to $230/kWh by 2020 and a further drop to $150/kWh in the years after that. Whether it is from the reduced cost or simply an increased need, Navigant Research forecasts worldwide battery storage to grow to almost 14 GW by 2023.

Wordwide forcast of battery storage capacity

Graph 1: Source

These potential reductions in costs could even lead to some ‘grid defection’ as the economics change and become less of a hindrance for adoption.

Lowest current and projected battery cell price by type

Graph 2: Source

Tesla founder Elon Musk has been working with lithium-ion technology both for vehicle battery and grid-level storage for over five years. Li-ion is a familiar battery type, typically a pair of solid electrodes and an electrolyte, and has been around for a long time in smaller applications… Tesla (and other companies) is currently testing larger scale battery installations, currently only for households and businesses. In the future they are looking at becoming scalable for utility systems. Some advantages and disadvantages to Li-ion are:


· High-energy density

· Low maintenance

· Low self-discharge


· High cost to manufacture

· Limited number of charging cycles (They age and will need to be replaced.)

· Heat generated during use graph

Figure 1: Source

Meanwhile others are pursuing what is known as “as “flow battery” technology. This is aptly named, in that it contains two liquids that flow next to each other, separated by a membrane, and as they move past each other create an electrical current. These batteries use two electrolytes in separate tanks, which are then pumped into a central stack. The central stack has an ion-conducting membrane that captures the electrons as the two liquids are pumped through the stack. Currently, most new flow batteries use a membrane containing vanadium. Some advantages and disadvantages to this technology include:


· Electrolyte solutions are safe, non-flammable, and non-corrosive

· The two electrolytes are compatible and easily rechargeable

· Expected to handle many more cycles than Li-ion batteries


· Maintenance cost of the tanks and pump system are high

· Overall cost is higher $/KWh than Li-ion

· Low energy density

· The volume of space that the tanks may take up Figure 2

Figure 2: Source

A promising hybrid of these two technologies is also being tested, using solid materials in two separate tanks with an electrolyte fluid that passes over them. The solid material can be lithium-based, while the flow of the liquid then conducts the electrons to the cell stack. Although still in testing, this looks to combine the scalability of flow batteries with the power density in Li-ion batteries.
Electrochemical Society Figure

Figure 3: Eletrochemical Society Source

The need for large scale energy storage will continue to grow as we move forward with renewable energy sources making up a larger portion of our energy generation. The inconsistency of renewables’ generation and the need for maintaining a stable grid will necessitate some form of storage. These are just a few of the most promising utility scale battery technologies that are currently available. Skeptics could argue that technologies like GTs or hydro which are currently used to firm up intermittent renewables will continue to do so in the future. That may be likely in some cases but those current technologies have their own issues. There are environmental and locational issues with hydro; there are pipeline access and saturation issues with small gas. Whatever technology moves us forward it seems apparent that battery storage will be an integral part of that future.

Filed under: Power Market Insights, Power StorageTagged with: , , ,