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How much will electrification and distributed energy resource adoption impact the grid in your neighborhood?

Portland General Electric (PGE)
neighborhood in Portland, OR

PGE Distributed Energy Resource and Flexible Load Potential Study

Portland General Electric (PGE) sought an open, transparent tool that would quantify the impacts of electrification and distributed energy resource adoption across its service territory. This tool would address two specific needs for PGE: distributed energy resource (DER) supply curves for their integrated resource plan and a locational load forecast for their distribution system planning activities.

The Cadeo team (project partners: Brattle Group, Lighthouse Consulting, and Resilient Edge) developed a tool that allowed PGE to meet these objectives. We estimated the amount of technical, economic, and achievable potential for more than 50 DER technologies and programmatic measures in PGE’s service territory. These measures included behind-the-meter solar photovoltaics, behind-the-meter energy storage, demand response, direct load control, transportation electrification, building electrification, and more.

Phase 1: DER and Flexible Load Potential

First, we conducted a comprehensive measure characterization task that defined eligibility criteria, adoption curves, costs, lifetimes, and hourly load impacts for each measure. In order to collect this information, we employed a wide variety of tactics, including:

  • Analyzing PGE customer data
  • Reviewing evaluation reports and program filings from PGE and other utilities
  • Running building simulation models
  • Acquiring data from public APIs
  • Employing web-scraping techniques

To estimate the technical, economic, and achievable potential over the study’s 30-year horizon (2021 through 2050), we developed and implemented our AdopDER model. AdopDER simulates the adoption and load impacts of each measure at each site (agent) to estimate DER potential for the service territory. We calibrated AdopDER’s results against system-level data. This ensured that the potentials were internally consistent with PGE load forecasts and program planning estimates. We also conducted a scenario analysis that estimated adoption and load impacts under various scenarios (reference, high, and low cases).

Phase 2: Locational Load Forecasting

Building on Phase 1, we added locational load forecasting capabilities to AdopDER in Phase 2. We used machine learning techniques to estimate site-level adoption probabilities for specific measures (electric vehicles and solar photovoltaics). We then used CalTRACK methods to create load profiles for each feeder and large customer. Finally, we used location-specific assumptions in AdopDER to generate a feeder-level load forecast, with and without projected DER adoption.

Results

Cadeo’s feeder-level forecast has become a critical component of PGE’s distribution system planning processes. It characterizes load growth at specific areas on PGE’s grid, informs non-wires solution investments, and provides hosting capacity analysis. Thus, AdopDER gives PGE a powerful tool for exploring varying scenarios of DER adoption and how they propagate over time at each level of the distribution system.

PGE featured this work in its 2021 Distribution System Plan and its 2022 Distribution System Plan.

Strategy and Planning

Strategy & Planning

As the demand side of the energy equation becomes ever more dynamic, Cadeo helps its clients plan for an uncertain future with cutting edge forecasting tools that integrate EE, DR, and DERs into load forecasts down to the feeder level. We work with clients to plan and position their organizations to meet goals related to cost-effective resource acquisition, non-wires solutions, market transformation, and diversity, equity, and inclusion.