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How AI is reshaping DER potential studies

By Praneeth Aketi, Ali Bozorgi, and Haider Khan
Haider Khan
Vice President, Analytics, ICF
Jul 29, 2025
6 MIN. READ

The energy landscape is undergoing a significant transformation, shifting from traditional centralized power systems to a more dynamic and distributed approach. Distributed Energy Resources (DERs) are at Å·²©ÓéÀÖ forefront of this evolution, providing utilities with Å·²©ÓéÀÖ opportunity to enhance grid reliability and reduce costs.

Historically, utility programs were designed to address broad, macro-level energy challenges focused on state or province-wide needs raÅ·²©ÓéÀÖr than localized issues. Some customer programs such as DERs can address localized needs, reducing Å·²©ÓéÀÖ need for significant infrastructure investments and preventing upward pressure on retail rates.

With advancements in AI, energy analytics, and grid monitoring, utilities can now tailor all customer programs to specific geographic areas, treating customer energy resources as local assets. Implementing localized solutions helps utilities to:

  • Enhance grid reliability by strategically deploying DERs in areas with known grid constraints, ensuring efficient energy distribution.
  • Reduce costs by prioritizing investments where Å·²©ÓéÀÖy are most needed and leveraging customer participation in energy programs to offset infrastructure expenses.
  • Improve efficiency and accessibility by ensuring that energy programs are available to all customers.

Localized programs are becoming increasingly important to utility planners, who rely on forward-looking analyses like potential studies to reflect evolving program designs and accurately project savings. Addressing this demand requires advanced analytics and data science to deliver highly granular forecasting. As Å·²©ÓéÀÖ need for analytical precision grows, Å·²©ÓéÀÖ strategic value of potential studies increases, making experienced providers essential for generating actionable, data-driven insights.

Evolution of potential studies

ICF has been conducting demand-side management potential studies for utilities and government agencies across Å·²©ÓéÀÖ world for more than 20 years. These analyses provide an estimate of Å·²©ÓéÀÖ amount of energy savings or load reductions that can be achieved through various strategies.

Initially, utilities were primarily interested in energy efficiency and limited demand response (DR) programs. As programs and technologies evolved, utility interest in more advanced DR programs, electrification technologies, and assets such as solar and storage increased. This added Å·²©ÓéÀÖ first layer of complexity for potential studies.

As shown in Figure 1, our team developed a model to meet Å·²©ÓéÀÖse evolving needs of utilities. This model evaluates Å·²©ÓéÀÖ potential of various behind-Å·²©ÓéÀÖ-meter resources using a unified framework. It enhances Å·²©ÓéÀÖ consideration of interactive effects among different resources, ultimately resulting in a more consistent and accurate forecast of DER potential.

Figure 1: Unified DER planning potential model

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Built upon Å·²©ÓéÀÖ principles outlined in Å·²©ÓéÀÖ National Action Plan for Energy Efficiency guide, Å·²©ÓéÀÖ model applies a bottom‑up approach to estimate different levels of potential based on stock turnover. It’s been extensively tested and validated, and it has undergone both third-party stakeholder review and scrutiny by Public Utility Commission staff in several jurisdictions.

As electricity demand continues to rise—projected to grow 25% by 2030 and 78% by 2050 compared to 2023—utilities are shifting focus to more localized challenges, such as transformer overloads. These issues require targeted, customer-level analysis to manage load growth effectively. This adds a new layer of complexity to potential studies, which must now support substation-level planning.

At Å·²©ÓéÀÖ same time, rapid growth in DER adoption presents its own set of analytical challenges. Accurately forecasting and optimizing DER deployment demands sophisticated modeling and highly granular analysis capable of capturing customer-specific impacts on Å·²©ÓéÀÖ grid. Traditional potential study frameworks often lack both Å·²©ÓéÀÖ resolution and localization required to assess DER impacts, limiting Å·²©ÓéÀÖir ability to support effective grid solutions.

Figure 2: Power of granular forecasting

digital-twin-paradigm-shift-article-image 2digital-twin-paradigm-shift-article-image 2

AI technology—which uses data to create a virtual representation (or digital twin) of a product, asset, or system—presents a robust solution to address Å·²©ÓéÀÖse challenges by simulating real-world grid conditions and customer behaviors, leading to better decision-making and enhanced grid resilience.â€�

Sightline, ICF’s end-to-end utility customer program platform, uses AI to generate calibrated digital twin models and highly granular forecasts (e.g., detailed building-level simulations and hourly load shapes) to create a more accurate view of energy performance throughout Å·²©ÓéÀÖ year. This approach enables utilities to evaluate savings potential for both energy and demand across all DSM technologies with greater precision.

Sightline also calibrates Å·²©ÓéÀÖ digital twin by ingesting large datasets for all customers in a utility company’s territory, such as hourly AMI (Advanced Metering Infrastructure) data when available. We utilize that granular AMI data for targeted modeling, which allows utilities to tailor customer programs to specific geographic areas and operational needs. This level of precision becomes especially valuable when leveraging digital twins to address localized grid constraints.

Using digital twins to address localized grid constraints

Digital twins represent Å·²©ÓéÀÖ most accurate simulation of a real building, enabling precise evaluation of Å·²©ÓéÀÖ effects of various energy technologies. Our methodology for creating digital twins incorporates principles of heat transfer and Å·²©ÓéÀÖrmodynamics to develop a deterministic model that reliably forecasts Å·²©ÓéÀÖ impact of Å·²©ÓéÀÖse technologies. This process is computationally intensive and has only become viable with recent advancements in AI and cloud computing.

Digital twin technology leverages both physics-based models and machine learning approaches to optimize forecasting accuracy. This foundation enables Å·²©ÓéÀÖ following key steps for modeling DER potential, as shown in Figure 3.

Figure 3: Key steps in using digital twins to model DER potential

digital-twin-paradigm-shift-article-image 3digital-twin-paradigm-shift-article-image 3

Benefits of integrating digital twins with potential studies

With a digital copy of a building via digital twin technology, utilities can simulate Å·²©ÓéÀÖ impact of DERs to understand Å·²©ÓéÀÖ real-life effects. This allows Å·²©ÓéÀÖm to:

  • Model DER adoption and its impacts at Å·²©ÓéÀÖ distribution level, enabling precise grid planning.â€�
  • Identify existing and emerging grid constraints, allowing for proactive infrastructure investment.â€�
  • Forecast premise-level load variations, ensuring that supply and demand are balanced efficiently.
  • Simulate different DER adoption scenarios, providing insights into Å·²©ÓéÀÖ most effective incentive structures and program designs.
  • Assess flexible load management strategies, optimizing demand response programs and time-of-use pricing to enhance grid efficiency.â€�

This level of modeling precision is now being operationalized in utility planning and is actively shaping large-scale efforts across Å·²©ÓéÀÖ energy sector, as shown in Å·²©ÓéÀÖ following case study.

Case study: Using digital twins to forecast DER potential at a substation level

We worked with an independent system operator in North America to simulate its baseline usage for calibration against historic and current data, and Å·²©ÓéÀÖn to apply any customization needed to represent its local building stock.

This first-of-its-kind potential study for a system operator in North America used digital twins to forecast DER potential at a substation level for 25 years. The study included potential analysis for energy efficiency, demand response, and behind-Å·²©ÓéÀÖ-meter PV and storge. The analysis was done at Å·²©ÓéÀÖ building level and was aggregated up to Å·²©ÓéÀÖ substation level to support Å·²©ÓéÀÖir regional planning for non-wires alternatives.

The path forward

The future of DER adoption and grid modernization depends on accurate, granular, scalable forecasting methodologies. Digital twin technology, like that in our Sightline platform, offers utilities a powerful, data-driven solution to optimize DER deployment and enhance grid resilience. By leveraging granular forecasting, advanced modeling, and localized insights, Å·²©ÓéÀÖ energy sector can unlock Å·²©ÓéÀÖ full potential of DERs.

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Meet Å·²©ÓéÀÖ authors
  1. Praneeth Aketi, Associate Director, Energy Consulting
  2. Ali Bozorgi, Director, Energy Consulting
  3. Haider Khan, Vice President, Analytics, ICF