EMFC Delivers Practical and Strategic Insight

The conference will be held at the Atlanta Evergreen Marriott Conference Resort, September 14-16, 2016


(Photos courtesy of Sahabia Ahmed, Entergy; Cameron Porter, Robin Hood Studios; and EPIS employees.)


EPIS headed south to Georgia’s beautiful Stone Mountain as it hosted the premier Electric Market Forecasting Conference (EMFC) for the 19th consecutive year, which took place Sept. 14-16. The EMFC featured a stellar lineup of speakers and activities to facilitate Expanding Perspectives on the Future of Energy Markets and provide a unique networking opportunity for over 75 industry experts and professionals in attendance.

The conference kicked off with a fun and relaxing evening at Stone Mountain Park’s Memorial Hall with an impressive view of the mountain and a one-of-a-kind Mountainvision® laser show, inclusive of fireworks and musical scores.

Thursday’s speakers focused on industry-wide issues opening with Jeff Burleson, vice president, system planning of Southern Company, who said that utilities couldn’t ignore what happens on the customer side of the meter. Burleson went on to state that past planning has focused on wholesale generation and transmission, but going forward, utilities will need to consider how customers are shaping and changing their load with new technologies.

Other Thursday morning presentations included “Outlook on Opportunities in Renewable Development” from Mark Herrmann, vice president, structuring of NRG Energy; “Market Evolution for Renewable Integration” from Todd Levin, Ph.D., energy systems engineer of Argonne National Laboratory; and “Advances and Opportunities for Internal Combustion Engine Power Plants” from Joe Ferrari, market development analyst of Wärtsilä North America.

The afternoon proceeded with Lakshmi Alagappan, director, and Jack Moore, director, market analysis, of Energy and Environmental Economics (E3), who presented “California Clean Energy Policy: Implications for Western Markets”. In the session, Alagappan stated that as California’s aggressive RPS comes to fruition, the EIM Market may help alleviate some over-generation by reducing thermal dispatch across the West Interconnection to make room for cheap exports to flow out of California. Alagappan went on to say that over-generation is not an abstract concern. Already, roughly 10 percent of dispatch hours in CAISO this year have resulted in zero or negative prices.

Following Alagappan and Moore, Larry Kellerman, managing partner of Twenty First Century Utilities wrapped up Thursday’s session by proposing a new paradigm for utilities that would allow these organizations to take advantage of low cost of capital and play a role in developments on the customer side of the meter. Strategies included personalized rate structures and curated services and technologies. Kellerman said, “We talk about energy efficiency as a resource, but energy efficiency is only a resource when you can deploy the capital and make the investment.”

Networking continued outside the conference room during Thursday’s afternoon activities. Some attendees took in the scenic views of Stone Mountain on a championship golf course while others participated in a breezy cruise on beautiful Stone Mountain Lake in a 1940’s era Army DUKW followed by a guided tour, highlighting early Georgia life, through Stone Mountain’s Historic Square.

During Friday’s Electric Market Forum speakers, and expert users of AURORAxmp, showcased effective examples of how to enhance your modeling endeavors.  The morning began with Morris Greenberg, managing director, gas and power modeling of PIRA Energy Group. Greenberg, focusing on “Integrating Natural Gas and Power Modeling”, said that the electrical sector is one of the most price elastic categories of natural gas demand. Combining gas and electrical models can capture feedback loops between gas and power markets. Greenberg continued by saying that as the electrical market’s share of total gas consumption increases, the behavior of the electrical market will continue to have a larger and larger impact on gas prices.

Switching gears, Eina Ooka, senior structure and pricing analyst of The Energy Authority, gave a very well received presentation on “Discovering Insights from Outputs – Exploratory Visualization and Reporting Through R”. Ooka said, “Interfacing AURORAxmp with other tools, such as R, allows users to quickly and effectively perform detailed analysis by automating almost all stages of the process.” Ooka concluded with a detailed discussion and demonstration on the visualization of data to make complex information easily digestible.

Additional Friday presentations included “Investing in Mexico Gas and Power” from Brett Blankenship, research director Americas primary fuel fundamentals from Wood Mackenzie and “Challenges of Forecasting Reliability Prices – Capacity Price in PJM & ORDC in ERCOT” from Joo Hyun Jin, commercial analysis of E.ON Climate & Renewables North America.

The EMFC is a once-a-year opportunity for industry professionals. Attendees of the 19th Annual EMFC gained new connections and an enriched market perspective.  As one attendee put it, “I really enjoyed the [presentations]… it was great to have exposure to such a wide range of topics from such qualified speakers. Congrats for doing such a great job with conference planning and execution.” Join EPIS next year for the 20th anniversary in Las Vegas!

For more information on this year’s speakers, please visit http://epis.com/events/2016-emfc/speakers.html

To obtain a copy of any or all of the presentations from this year’s EMFC, please go to EPIS’s Knowledge Base using your login credentials here. If you do not have login credentials, please email info@epis.com to request copies.

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Living in the Past?

Living in the past is not healthy. Is your database up-to-date? EPIS just launched the latest update to the North American Database, version 2016_v4, marking the fourth North American data update this year! Recent changes in the power industry present challenges to database management which will be discussed in this post.

In general, the transformation in power generation sources in the U.S. coupled with evolving electricity demand and grid management represents a paradigm shift in the power sector. In order to accurately model power prices in the midst of such change, one must have a model built on fundamentals and a database that is up-to-date, has reasonable assumptions, is transparent and is flexible. A recent post described the technical side of working with databases in power modeling. This entry outlines important changes in the East Interconnect, the impacts those changes have on data assumptions and configuration and the steps we are taking to provide excellent databases to our clients.

Recent shifts in power generation sources challenge database assumptions and management. New plant construction and generation in the U.S. are heavily weighted towards renewables, mostly wind and solar and as a result, record generation from renewables has been reported across the East Interconnect. Specifically, on April 6, 2016, the Southwest Power Pool (SPP) set the record for wind penetration:

Record Wind Penetration Levels 2015

Figure 1. Record wind penetration levels in Eastern ISOs compared with average penetration in 2015. SPP holds the record which was reported on April 6, 2016. Record sources: NYISO, SPP, MISO, ISO-NE, PJM. 2015 Averages compiled from ISO reports, for example: NYISO, SPP, MISO, ISO-NE, PJM. *Average 2015 generation used to calculate penetration.

Similarly, the New York City area reached a milestone of over 100 MW in installed solar distributed resources. Accompanying the increase in renewables are increases in natural gas generation and reductions in coal generation. In ISO-NE, natural gas production has increased 34 percent and coal has decreased 14 percent since 2000, as highlighted in their 2016 Regional Electricity Outlook. These rapid changes in power generation sources require frequent and rigorous database updates.

Continued electric grid management changes in the East Interconnect also requires flexibility in databases. One recent change in grid management was the Integrated System joining the Southwest Power Pool, resulting in Western Area Power Administration’s Heartland Consumers Power District, Basin Electric Power Cooperative and Upper Great Plains Region joining the RTO. The full operational control changed on October 1, 2015, thus expanding SPPs footprint to 14 states, increasing load by approximately 10 percent and tripling hydro capacity. Grid management change is not new, with the integration of MISO South in 2013 as an example. Changes such as these require flexibility in data configuration that allow for easy restructuring of areas, systems and transmission connections.

Variability in parameters, such as fuel prices and demand, introduce further difficulty in modeling power markets. The so called “Polar Vortex” weather phenomena shocked North Eastern power markets in the winter of 2013/2014 with cold temperatures and high natural gas prices resulting in average January 2014 energy prices exceeding $180/MWh in ISO-NE. It seemed like the polar opposite situation occurred this last winter. December 2015 was the mildest since 1960, and together with low natural gas prices, the average wholesale power price hit a 13-year low at $21/MW. The trend continued into Q1 of 2016:

Monthly average power price in ISO-NE Q1 2014 and 2016

Figure 2. Monthly average power price in ISO-NE in Q1 2014 and 2016. Variability between years is a result of high natural gas prices and cold weather in 2014 versus low natural gas prices and mild weather in 2016.

Whether extreme events, evolving demand or volatile markets, capturing uncertainty in power modeling databases is challenging. In AURORAxmp, users can go one step further by performing risk simulations; specifying parameters such as fuel prices and demand to vary across a range of simulations. This is a very powerful approach to understanding the implications of uncertainty within the input data.

The aforementioned changes in generation, grid management and demand, offer exciting new challenges to test power market models and data assumptions. To test our platform, EPIS performs a historical analysis as a part of each database release. Inputs of historical demand and fuel prices are used to ensure basic drivers are captured and model output is evaluated not only in terms of capacity, but monthly generation, fuel usage and power prices. The result of this process is a default database that is accurate, current, contains reasonable assumptions, is transparent and is flexible to ensure you have the proper starting point for analysis and a springboard for success.

With the release of North_American_DB_2016_v4, EPIS continues to provide clients with superb data for rigorous power modelling. The 2016_v4 update focuses on the East Interconnect and includes updates to demand, fuels, resources, DSM and other miscellaneous items. Clients can login to our support site now to download the database and full release notes. Other interested parties can contact us for more information.

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

The Fundamentals of Energy Efficiency and Demand Response

What are Energy Efficiency & Demand Response Programs?

Though the Energy Information Administration states, “there does not seem to be a single commonly-accepted definition of energy efficiency,” efficient energy use, sometimes simply called energy efficiency, refers to the reduction in the amount of energy required to provide the equivalent quality of products and services. Examples include improvements to home insulation, installation of fluorescent lighting & efficient appliances, or improving building design to minimize energy waste.

Demand response, according to the Department of Energy, is defined as, “a tariff or program established to motivate changes in electric use by end-use customers in response to changes in the price of electricity over time, or to give incentive payments designed to induce lower electricity use at times of high market prices or when grid reliability is jeopardized.” Utilities can signal demand reduction to consumers, either through price-based incentives or through explicit requests. Unlike energy efficiency, which reduces energy consumption at all times, demand response programs aim to shift load away from peak hours towards hours where demand is lower.

What are the Benefits of Energy Efficiency & Demand Response Programs?

The decreasing and ‘flattening’ of the demand curve can directly contribute to improved system and grid reliability. This ultimately translates to lower energy costs, resulting in a financial cost saving to consumers, assuming the energy savings are greater than the cost of implementing these programs and policies. In 2010, Dan Delurey, then president of the Demand Response and Smart Grid Coalition, pointed out that the top 100 hours (or just over 1% of the hours in a year) account for 10-20% of total electricity costs in the United States. Slashing energy consumption during these high peak hours, or at least shifting demand to off-peak hours, relieves stress on the grid and should make electricity cheaper.

Additionally, decreasing energy consumption directly contributes to the reduction of greenhouse gas emissions. According to the International Energy Agency, improved energy efficiency in buildings, industrial processes and transportation prevented the emission of 10.2 gigatonnes of CO2, helping to minimize global emissions of greenhouse gases.

Lastly, reductions in energy consumption can provide domestic benefits in the forms of avoided energy capital expenditure and increased energy security. The chart below displays the value of avoided imports by country in 2014 due to the investments in energy efficiency since 1990:

Added Volume and Value of Imports Figure 1: Avoided volume and value of imports in 2014 from efficiency investments in IEA countries since 1990. Source

Based on these estimated savings, energy efficiency not only benefits a country’s trade balance, but also reduces their reliance on foreign countries to meet energy needs.

Modeling the Impacts of Energy Efficiency and Demand Response

Using AURORAxmp, we are able to quantify the impact of energy efficiency and demand response programs. In this simple exercise, we compare the difference between California with 2 GW of energy efficiency and 2 GW of demand response versus a case without energy efficiency or demand response from 2016 to 2030. The charts below show the average wholesale electricity prices & system production costs:

average electricity price $-MWhAverage System Cost (000's)

 Figure 2: Note these are 2014 real dollars.

Holding all else equal, adding demand response and energy efficiency programs into the system decreased average wholesale electricity prices by about $2.88 (5.4%) and the average system production cost fell by $496,000,000 (5.1%). This is a simple example in one part of the country, but one can easily include additional assumptions about the grid, resources characteristics, and load shape as they desire.

Both demand response and energy efficiency programs are intended to be more cost effective and efficient mechanisms of meeting power needs than adding generation. Emphasis on the demand side can lead to lower system production costs, increased grid reliability, and cheaper electric bills; all of which lie in the best interest of governments, utilities, and consumers.

Filed under: Energy Efficency, Power Market InsightsTagged 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: , , ,

Power Market Insights

Welcome to EPIS Power Market Insights

This blog is designed to provide a convenient medium and central location to publish and share time-sensitive content for those interested in the future of the power industry and items relevant to the modeling, forecasting, and analysis of it.

It will include news, commentary and citations of relevant studies, information on recent or upcoming events, and model and market insights. It is expected to include original content authored by EPIS as well as citations of and links to works published by others.

We welcome your comments and suggestions anytime.

We still plan to continuously update our AURORAxmp Help system as well as our website collection of tools, examples, and other knowledge base content for more detailed and specific information regarding the AURORAxmp application. To see if you qualify to obtain login credentials to access this content, please contact us.


Filed under: Power Market InsightsTagged with: , , ,