State, Local and University Decision-Makers Dive Deep with STAT Solar Tools Workshop
Nov. 8, 2017 by Lars Lisell
What are the cash flow and performance predictions associated with a proposed solar project? What combination of energy technologies and storage options will help my community meet its energy goals? How many people might adopt distributed energy technologies in my jurisdiction under different policy scenarios? These are just some of the questions that state and local decision-makers are grappling with in evaluating solar energy development strategies. Providing decision support on these and other questions is the basis for several NREL tools and models designed to help decision-makers answer these questions. In late August, NREL's Solar Technical Assistance Team (STAT) hosted a group of state and local government and university representatives at our South Table Mountain campus in Golden, Colorado for a deep-dive training on NREL’s solar evaluation tools. The training focused on the System Advisor Model (SAM) and offered glimpses into the Renewable Energy Integration and Optimization (REopt) tool and the Distributed Generation Market Demand (dGen) model. Attendees spent the majority of the day working with SAM project examples, learning about the different types of analyses that can be performed, and running their own analyses with assistance from NREL “power users.”
|Figure 1: Attendees work through their projects and receive real-time technical assistance from NREL solar model and tool experts during the STAT Solar Decision-Making Tools Workshop.|
Several new features of SAM, a renewable energy system performance and financial model, were showcased during the training, including an energy storage module. This new built-in functionality makes it possible to quantify the on-grid benefits (i.e., peak shaving, energy arbitrage) of coupling energy storage with solar photovoltaics (PV).
Workshop attendees also experimented with parametric analysis, which can efficiently show how changing an input to a model effects the key outputs. Figure 2 displays the outputs of one of the example problems, showing how decreasing module costs increases the net present value (NPV) of a project.
|Figure 2: A parametric analysis showing how the NPV of a project decreasing with increasing module cost.|
The attendees also had an opportunity to bring their own projects and receive real-time technical assistance from NREL solar model and tool experts. Project topics ranged from solar policy implications to verifying power purchase agreement (PPA) prices offered by solar developers. One attendee was interested in finding out how electric vehicle charging stations were going to impact the peak demand of city buildings, and whether demand could be mitigated with stationary energy storage. Another attendee wanted to compare the PPA price for a solar project with retail electricity rates in the area, with the idea that associated energy cost savings could be directed toward low- and moderate-income residents to help alleviate their energy burden. The STAT team worked with attendees to demonstrate how NREL's solar decision-making tools and models could be employed in evaluating those questions and ultimately making decisions.
Attendees not only gained useful SAM skills and direct NREL technical assistance, they also benefited from peer learning and exchange opportunities. For more information on which NREL tools to use to address different solar project or policy questions, check out a new STAT fact sheet, An Introduction to Solar Decision-Making Tools. For assistance in using a solar analysis tool, or for more information about solar technical assistance in general, visit www.nrel.gov/state-local-tribal/states.html or email email@example.com.