EMFC Addresses Head-on the Tectonic Industry Changes

With record attendance in one of the most iconic tourist destinations in the world, the 20th Annual Electric Market Forecasting Conference (EMFC) took place September 6-8 in Las Vegas, NV. This industry-leading conference assembled top-notch speakers and gave an exclusive networking experience to attendees from start to finish.

The pre-conference day featured in-depth sessions designed to maximize the value of the Aurora software for its users. Advanced sessions included discussions on resource modeling and improving model productivity, recent database enhancements including the disaggregation of U.S. resources, an update on the nodal capability and data, and other model enhancements.

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Michael Soni, Economist, Support | EPIS

Before the afternoon Users’ Group meeting started, EPIS announced that it was dropping “xmp” from the name of its flagship product to purely Aurora, and unveiled a fresh logo. Ben Thompson, CEO of EPIS said, “The new logo reflects our core principles of being solid and dependable, of continuously improving speed and performance, and of our commitment to helping our customers be successful well into this more complex future.”

That evening, attendees kicked-off the main conference with a night under the stars at Eldorado Canyon for drinks, a BBQ dinner and a tour of the Techatticup Mine; the oldest, richest and most famous gold mine in Southern Nevada.

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Eldorado Canyon, Techatticup Mine

On Thursday, thought leaders from across the industry presented various perspectives on the complex implications that recent industry changes will have on grid operations, future planning and investments. The forum session opened with Arne Olson, a partner with E3 Consulting in San Francisco, discussing California’s proposed legislation SB-100, which aimed to mandate that 100% of California’s energy must be met by renewable sources by 2045, along with the bill’s implications for Western power markets and systems. He pointed out that SB-32, last year’s expansion of earlier legislation, which mandates a 40% reduction in GHG emissions (below the 1990 levels by 2030), is actually more binding than SB-100. He explained the economics of negative prices, why solar output will be increasingly curtailed and posited that CAISO’s famous “duck curve” is becoming more an economic issue vs. the reliability issue it was originally intended to illustrate.

Other Thursday morning presentations included “The Rise of Utility-Scale Storage: past, present, and future” by Cody Hill, energy storage manager for IPP LS Power, who outlined the advances in utility-scale lithium ion batteries, and their expected contributions to reserves as well as energy; Masood Parvania, Ph.D., professor of electrical and computer engineering at the University of Utah, who described recent advances in continuous-time operation and pricing models that more accurately capture and compensate for the fast-ramping capability of demand response (DR) and energy storage device; and Mahesh Morjaria, Ph.D., vice president of PV systems for First Solar who discussed innovations in PV solar module technology, plant capabilities and integration with storage.

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Masood Parvania, Ph.D., Director – Utah Smart Energy Lab | The University of Utah

The afternoon proceeded with Mark Cook, general manager of Hoover Dam, who gave a fascinating glimpse into the operations and improvements of one of the most iconic sources of hydro power in the country; and concluded with Lee Alter, senior resource planning analyst and policy expert for Tucson Electric Power, who shared some of the challenges and lessons learned in integrating renewables at a mid-sized utility.

Networking continued Thursday afternoon with a few of the unique opportunities Las Vegas offers. In smaller groups attendees were able to better connect with each other while enjoying one of three options which included a delicious foodie tour, swinging clubs at TopGolf, or solving a mystery at the Mob Museum.

The final day of the conference was devoted to giving Aurora clients the opportunity to see how their peers are using the software to solve complex power market issues. It featured practical discussions on how to model battery storage, ancillary services, the integration of renewables and an analysis of the impact of clean energy policies all while using Aurora.

The conference adjourned and attendees headed out for a special tour of the Hoover Dam which included a comprehensive view of the massive dam and its operations, and highlighted many of the unique features around the site.

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Hoover Dam, Power Plant Tour

The EMFC is a once-a-year opportunity for industry professionals. The 20th Annual EMFC addressed head-on the tectonic industry changes (occurring and expected) from deep renewable penetration, advances in storage technologies, and greater uncertainty. Join EPIS next year for the 21st Annual EMFC!

For more information on the 2017 speakers, please visit http://epis.com/events/2017-emfc/speakers.html
To obtain a copy of any or all of the presentations from this year’s EMFC, Aurora clients can go to EPIS’s Knowledge Base website using their login credentials here. If you do not have login credentials, please email info@epis.com to request copies.

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How Good is the EIA at Predicting Henry Hub?

Natural gas power plants are a key component of bulk electrical systems in North America. In the U.S., natural gas power plants made up the largest portion of installed capacity, 42%, as of December 2016 and contributed more to generation than any other source. In Mexico, natural gas power plants supplied 54% of the required electricity in 2015 and are a key component of the capacity additions in development of the national electrical system. Natural gas is also likely to be the primary energy source in the U.S. due to increased regulation on coal units, uncertainty around the future of nuclear generation, and low natural gas prices.

Natural gas prices are a critical driver of electricity prices and a key input variable in electric power models. Due to the large amount of natural gas power plants in North America, and because fuel costs are the largest cost component of a thermal power plant, wholesale electricity prices are tightly coupled with natural gas prices. There is also an important feedback loop, in that natural gas demand, and price, is tightly coupled to the operation of natural gas power plants. Understanding the interplay between gas and power markets, and uncertainties in forecasts, is critical for forecasting either.

The U.S. Energy Information Administration (EIA) provides natural gas price short-term forecasts through the Short-Term Energy Outlook (STEO) and long-term forecasts through the Annual Energy Outlook (AEO). For the purposes of this article, we will focus on the STEO. The STEO is a monthly report with, among other items, a natural gas consumption and price forecast for 13 to 24 months in the future depending on the month published. The model predicts consumption and prices for three sectors (commercial, industrial, and residential) in the nine U.S. census districts. To do this, the model calculates natural gas consumption and supply levels to build an inventory. Prices are derived from a regression equation using the inventory and heating and cooling degree days, and analysts then make adjustments for final prices. Detailed information on each equation and method is provided by EIA Natural Gas Consumption and Prices document.

How good is the EIA at forecasting natural gas prices from a month to a year out?

To evaluate the STEO forecasts of natural gas prices, we downloaded each monthly STEO report from January 2012 to December 2016 to allow for at least a full year of analysis with historical prices. This period was selected because it is representative of the current trend of low natural gas prices (relative to historical). The mean absolute error (MAE) and mean absolute percent error (MAPE) were calculated for each forecasted value. Prices were then evaluated for the first forecast in each year and a subset of forecasts from consecutive months during a price spike. The mean absolute percent error was also evaluated for each report year and across all reports.

For the period analyzed (2012 to 2016, shown in orange below), the wholesale Henry Hub gas price averaged $3.30/mmbtu with a high price of $6.19/mmbtu in early 2014 due to the extreme Northeast weather (i.e., the polar vortex) and a low price of $1.78/mmbtu due to warm weather conditions and large amount of storage late in 2016. This period is representative of relatively low natural gas prices as compared to the previous five-year period with high prices exceeding $10/mmbtu driven by high oil prices and an average of $5.63/mmbtu despite the sharp decline due to the financial crisis in 2008-2009.

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Figure 1. Historical Henry Hub natural gas prices. The yellow period denotes the study period used for this analysis. Source: EIA.

We started by looking at the longest-term forecasts (24 months) that are delivered in January of each year, and saw an inability to capture rapid fluctuations in prices in the study period:

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Figure 2. Historical Henry Hub gas prices with 24 month forecasts from the January STEO of each year starting in 2012 and ending in 2015 using the base case data. Source: EIA STEO.

The January 2012 forecast missed the sharp reduction in prices from the winter to summer that were driven by high storage volumes. Less volatility occurred over the first part of the January 2013 forecast, however this forecast missed the large increase in prices to over $6/mmbtu which were driven by extreme weather conditions. The January 2014 forecast also missed the weather-driven high price for this period and then was high-biased in the later months of the forecast. The January 2015 forecast was high-biased the entire forecast period and missed the lower prices which were driven by a combination of mild weather and high storage volumes.
The STEO forecast is very sensitive to the initial conditions or starting month’s price. For example, plotting each month’s forecast during the increase from $3.74/mmbtu in November 2013 to $6.19/mmbtu in February of 2014 shows the impact of the rapid change in initial condition (last known price) on the first month forecasted value:

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Figure 3. Historical Henry Hub gas prices with forecasted values from the months leading up to the rapid price spike in February 2014.

Presumably the long-term fundamental drivers of the STEO do not change as much as the initial conditions, and thus the longer-term forecast is much less sensitive to initial conditions.
Despite missing the fluctuation events, on average across the years analyzed the STEO is within 8% of the price in the first month of the forecast, 25% of the price out to eight months and 33% of the price out to 13 months:

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Figure 4. Mean absolute percent error calculated for each forecasted month of STEO reports. Data are averaged over a report year, as well as over all of the report years. Maximum and minimum percent error is calculated over all STEO reports.

On average, the trend has increasing error with forecast length, however, this does not occur in the 13-month 2012 or 2013 STEOs. The expected error growth with time does appear in the 2014 and 2015 STEOs, reaching nearly 60% in the 2014 STEO. The maximum percent error in any given forecast grows rapidly from 26% in the first forecasted month to 75% in the fourth forecasted month, and reaches a high of over 100% 12 and 13 months out.
In absolute terms, the error ranges on average from $0.25/mmbtu in the first forecasted month to $0.88/mmbtu 13 months out. Maximum and minimum errors range from less than a penny up to $2.45/mmbtu.

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Figure 5. Mean absolute error calculated for each forecasted month of STEO reports. Data are averaged over a report year, as well as over all of the report years. Maximum and minimum absolute error is calculated over all STEO reports.

Is the STEO forecast good enough? Unfortunately, as with many answers, it depends. More importantly, however, is understanding the limitations and uncertainties in their gas forecasts. If relying on EIA forecasts, you must realize the sensitivity to initial conditions and the typical error growth in the first months to year of the forecast. With this information, sensitivity studies can be formulated to capture possible fluctuations in gas prices. Taken together with other uncertainties such as demand, transmission outages, and plant outages, you can begin to form an ensemble of forecasts.

Filed under: Natural Gas, UncategorizedTagged 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.

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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 http://epis.com/events/2016-emfc/speakers.html.

“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: http://epis.com/events/2016-emfc/index.html.

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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.

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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: , , , , ,