Finding a renewables project site in ERCOT by considering “the shift factor” – pv magazine USA

September 23, 2024 Sultan Annab

Today’s renewable energy developers have access to the data they need to streamline site analysis – saving time and cutting costs during the critical first phase of project development. It is worth exploring how “shift factor analysis” uncovers overlooked sites and how locational marginal price (LMP) and curtailment data can help maximize developer return on investment (ROI).

Increasing grid congestion and a lengthening queue of backlogged projects can mean that viable renewable energy sites are becoming harder to find. Now, when developers look at grid capacity maps of their target areas, they often see a lot of red, representing transmission lines that do not have capacity for the power generated by new projects.

One tool that developers can use to uncover hidden grid capacity is shift factor – also known as “distribution factor” – analysis. A full shift factor analysis is complex but a streamlined version can quickly screen for viable clean energy sites, saving developers time and money.

Consider shift factors as a measure of how transmission lines are impacted by new power injection at interconnection sites. They represent how likely a line is to become a sort of “traffic jam” for new renewables generation projects. Grid operators rely on shift factors to determine generator curtailment during periods of congestion and oversupply, which is something that directly affects the ROI of developers.

Standard modeling typically assumes a low shift factor of 3% but that can inaccurately capture transmission line sensitivity and can “hide” various locations that would actually be viable for interconnection. New tools that employ realistic empirical data can offer more accurate shift factor estimation, thereby revealing such locations. These improved analyzes are key to truly understanding the effect of renewables generation on power systems and ensuring that hidden capacity is fully realised.

Lone Star State

A proposed 100 MW solar farm in Texas connected to the ERCOT grid during a summer peak event in 2027 can serve as a case study. The shift factor assumption in main image above is set at 3%, and to the right at 5%. When the developer runs the model with a higher shift factor assumption, they see that the number of locations with capacity – shown in green – increases from 3,296 locations to 4,012.

For another example, consider southeastern Texas, where a new 345 kV line is being built. In theory, that should mean more capacity for new renewable energy projects, but when developers search for sites, they typically see a capacity map that looks very red or “undevelopable.” That’s because the shift factor assumption is set at ERCOT’s default of 3%. By contrast, when the shift factor is bumped from 3% to 17%, more locations with capacity are shown.

Maximizing ROI

When attempting to determine whether a site would be profitable, renewable energy developers consider multiple factors. The main questions they ask are “where is the power most valuable” and “where can I generate the most power for the grid?”

To assess where power is worth the most, developers look at LMP data forecasts, which show high- and low-value areas. When assessing where a project can generate the most power, developers look at projected curtailment during times of grid constraint, which varies depending on location and time. Developers now have the ability to do a full “8,760 analysis” to forecast every hour of a year. These insights enable developers to more precisely predict curtailment and define previously unknown variables during the early phases of project development.

Using new software solutions, developers can open a map that shows red areas representing more curtailment and green areas representing low curtailment – ​​or more opportunity. Software solutions can also provide combined price and curtailment data analysis maps. This helps developers assess the overall revenue potential for a proposed solar farm. Green areas on the map would represent the highest potential value.

Risk assessment

Due to challenges such as grid congestion and intense competition for viable land, developers require comprehensive data to select sites for renewable energy projects. Advanced site selection tools enhance site analysis by providing developers with extensive data and insights integrated into the latest maps.

The grid may offer more capacity than is initially apparent and curtailment may not be as significant an issue as once thought. With tools precise and data, developers can identify optimal interconnection points, even in seemingly unfeasible grid areas.

These tools offer developers a competitive advantage during the critical first step of site selection, enabling better risk assessment and ROI. This foundation allows for effective collaboration with power engineers throughout the project lifecycle, transforming a developer’s solar, wind, or battery projects into reality.

About the author: Sultan Annab is senior vice president of software at EPE. With 30-plus years of experience, it leads EPE’s Software Business Unit, which includes business and technology teams. EPE works with developers on every phase of the project lifecycle and offers software solutions and consulting support to engineer a successful energy transition.