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Smoothing the Curve: DER Adoption & Performance Modeling

Posted to Clean Power Research® in the Digital Utility Group
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Hiro Hiraiwa's picture
Solution Strategist Clean Power Research

Hiro Hiraiwa is Solution Strategist at Clean Power Research focused on developing solutions to challenges facing the nation's electric utility decarbonization and renewable energy initiatives....

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  • Dec 7, 2022
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This item is part of the Data Analytics and Intelligence - November/December 2022 SPECIAL ISSUE, click here for more

As we delve into the Inflation Reduction Act’s (IRA) effect on the utility industry, one thing is certain: the IRA will further magnify the growth and penetration of distributed energy resources (DERs). The explosive acceleration of DER adoption may radically increase the challenges already faced by many utilities from the bulk-system level down to the distribution edge.

At the bulk-system level, the significant influx of solar and battery storage projects is complicating the interconnection study process, creating ever longer interconnection queues. Massive deployment of customer-owned DERs will increase the complexity of balancing supply and demand as they intensify the duck curve by introducing new, sizable loads and generation at the grid edge.  However, utilities can take specific actions to anticipate DER adoption and gain insights into DER performance and production, as shown in Figure 1.

Clean Power Research helps utilities extract insights from data they already have, including net load data from revenue meters and DER specs from interconnection records. In addition, these utilities are pairing their own data with third-party data, such as solar irradiance, geospatial and demographic data. By leveraging this data and insight, these utilities can anticipate how their customers may adopt and operate various DER technologies. This allows them to better understand DER impacts on different levels of the grid infrastructure and move from a reactive to a predictive posture on DER integration into the grid.

Using advanced analytics to scale DER adoption modeling

Today, many utilities are not well-equipped to understand their customers in the context of DER integration. Utilities typically cannot see behind-the-meter data to understand how their customers are operating their DER assets and how those assets are performing at any given time. Utilities could characterize these customers through sensor-based monitoring and in-person energy audits. However, such approaches are expensive and likely not cost-effective to scale.

Utilities that want a more proactive stance toward DER adoption and performance modeling can use advanced analytics to:

  1. Estimate DER adoption potential: Utilities can help future-proof their grid investment planning by understanding the potential magnitude of DER growth. Properly establishing the adoption potential for DERs, however, requires a high level of geospatial granularity appropriate for analyzing behind-the-meter, customer-owned assets. Using an analytics-based approach, pairing external data sources (e.g., solar irradiance and geospatial data) with customer data (e.g., behind-the-meter DER specifications) crucial in establishing credible “upper bounds” of the potential DER impact on utility systems. 
  2. Quantify the impact of DERs on the grid: With the acceleration of DER adoption across multiple technology types, utilities cannot rely on simplified assumptions to understand the impact of aggressive DER growth at the feeder and substation level. Many of our utility customers have a well-maintained database of existing DER interconnection data. This information is immensely valuable when analyzed against other data, such as historical load data, demographic data and mobility data. These utilities leverage this data in advanced predictive analytics to model DER adoption and forecast DER impact scenarios at different levels of their grid infrastructure.
  3. Infer DER attributes behind the meter: Net load data alone is not enough to understand how customers with DERs re consuming energy. Some of the most forward-thinking utilities are leveraging data analytics to estimate gross load in homes with on-site generation and detecting home shell efficiency to further refine load forecasting and program design. Furthermore, some utilities are looking to data analytics to supplement their existing but incomplete database of DER interconnection records by detecting the presence of behind the meter DERs and inferring their specifications (e.g., PV array capacity and orientation).

As DER adoption scales, many of our customers are leveraging advanced data analytics to anticipate DER adoption and conduct performance modeling to proactively plan grid impacts. Here is a specific example of how Eversource is using data and advanced modeling to predict future DER adoption and distribution grid impacts.  Leading utilities are taking necessary steps to proactively accommodate for, and manage the pervasive adoption of, DER technologies on their distribution grid. In doing so, they are contributing to grid stability and performance predictability. Utilities that have not yet taken steps to unlock the value of their DER data  can still do so, and we are here to help.

 

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Jim Stack's picture
Jim Stack on Dec 8, 2022

I don't feel the DER will add to the load on the Duck Curve. In FACT with Solar and Home Battery Storage it will take some of the load off the duck curve. Where are you getting for assumption of an increased load?

QUOTE=Massive deployment of customer-owned DERs will increase the complexity of balancing supply and demand as they intensify the duck curve by introducing new, sizable loads and generation at the grid edge.  

 

 

Hiro Hiraiwa's picture
Hiro Hiraiwa on Dec 9, 2022

Thanks Jim. I agree that solar + storage would likely contribute to mitigating the duck curve. However, my thought here is that the common ways we see DERs adopted today includes standalone rooftop PVs without storage and unmanaged EV chargers. If this approach continues for a high percentage of installs, it could result in higher peak relative to the duck back, and potentially steeper duck neck (if EVSE owners start charging their EVs in late afternoon/early evening hours). The PNNL study I referenced looks into implications of unmanaged EV charging in solar-rich regions, for example.

Hiro Hiraiwa's picture
Thank Hiro for the Post!
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