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: , , , , ,