2015 AURORAxmp Group Training
|Portland, OR||Houston, TX||Calgary, AB|
|March 5, 2015||March 10, 2015||March 12, 2015|
These one-day training and demo sessions are an ideal opportunity for customers and non-customers to see the latest developments in AURORAxmp, the ONE tool for energy industry modeling.
Attendees will interact with peers, senior EPIS developers and support staff to learn how AURORAxmp is addressing the challenges of today’s power markets.
This year’s agenda includes several thought-provoking sessions; designed to help you maximize the value of AURORAxmp to your organization. A registration fee of just $49 reserves your seat.
Included in the Advanced and General Sessions will be the following topics:
- The Clean Power Plan and Beyond: Constrained Dispatch Enhancements We will look at ways to model constrained resource scenarios like the Clean Power Plan and cover recent enhancements to the constrained dispatch logic; which provides tighter adherence to the input targets. We will also showcase the ability to model annual fuel limits with the constraint logic.
- The New Database Compare Tool A powerful new way to quickly compare input or output databases. This new tool will enable users to easily generate reports of changes to internal or EPIS-delivered input databases. It also allows for quick comparison of two output databases, which makes analysis of model results even easier.
- Modeling Complex Resources: Operating Rules and Resource Dependency An in-depth look at new functionality for modeling CCGTs and other complex resources. We will cover the recent enhancements to the Operating Rules table which allows more flexibility in constraining resource commitment and generation. We will also highlight recent Resource Dependency enhancements that make CCGT modeling even more flexible.
- Upcoming Development in AURORAxmp See what's coming soon to AURORAxmp and get a preview of major enhancements including; a new long-term optimization method, significant new change set handling capability, and other additions to improve model flexibility.