EPIS Releases New Version of AURORA

Version 12.3 introduces significant enhancements

Las Vegas, Nevada – April 25, 2017 — EPIS (www.epis.com), the market leader in power market
simulation, forecasting and analysis, announced the release of version 12.3 of its AURORA
software at the Platts Global Power Markets™ Conference. The latest version boasts a number
of enhancements to storage logic, ancillary services, long-term logic, improved RPS modeling
and nodal capabilities.

AURORA 12.3 further solidifies its position as the most valuable power market forecasting
and analysis software on the market today. It is fast, easy to use, and transparent. Upgrades in
the new version include:

  • Enhanced Storage Logic—improved ability to model the intricacies of renewable and
    storage integration, electric vehicles, and other technologies.
  • Ancillary Services Enhancements—significant enhancements, including sub-hourly
    dispatch and use in nodal studies, and improved MW reporting for simultaneous
    contributions to multiple products
  • Improved RPS Modeling—offers new option to identify resources not eligible to set
    capacity prices– especially useful when modeling RPS policies where renewable
    resources must be built but cannot participate in capacity markets. Also, RPS constraints
    can now be input as a percentage of demand or MWh value, giving more flexibility to
    specifying RPS targets over time.
  • Long-Term (LT) Capacity Expansion Logic Enhancements—now have the option to
    change dispatch-hour sampling dynamically—accelerating studies, but still providing
    detail on final production run.
  • New LT Constraint Types—including capacity and energy max limits, which provide
    more flexibility for build decisions to targets in LT studies.
  • New LT Reporting Option—new build report output table making it helpful to quickly
    see which constraints were binding (min, max by technology/fuel/area).
  • Nodal SCUC—version 12.3 also includes an exciting new option to run a full security
    constrained unit commitment (SCUC). The mixed-integer program that performs the
    commitment decisions, now accounts for nodal constraints, including branch, corridor,
    and contingency constraints. The new SCUC ability is in addition to a new, proprietary
    solving method that significantly speeds nodal analysis.

AURORA v.12.3 is further enhanced by the proven and calibrated databases that either
come with the license or as an add-on, including: U.S.-Canada, Europe or Mexico. The calibrated
datasets simplify meaningful forecasting. All AURORA databases include a base-case 25-year
power price forecast and generator capacity expansion and retirement plan. The sources and
procedures used to update the data are thoroughly documented. Updates to the databases are
provided under the annual AURORA license.

For the past 20 years, AURORA has had a reputation for being best-in-class, with unmatched
support. Version 12.3 further establishes its position as the leader in power market forecasting
and analysis.

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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.

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