|Title||2025 California Demand Response Potential Study - Charting California's Demand Response Future: Final Report on Phase 2 Results|
|Year of Publication||2017|
|Authors||Peter Alstone, Jennifer Potter, Mary Ann Piette, Peter Schwartz, Michael A Berger, Laurel N Dunn, Sarah J Smith, Michael D Sohn, Arian Aghajanzadeh, Sofia Stensson, Julia Szinai, Travis Walter, Lucy McKenzie, Luke Lavin, Brendan Schneiderman, Ana Mileva, Eric Cutter, Arne Olson, Josh L Bode, Adriana Ciccone, Ankit Jain|
|Keywords||California Institute for Energy and Environment, California Public Utilities Commission, climate change, CPUC, demand response|
California's legislative and regulatory goals for renewable energy are changing the power grid's dynamics. Increased variable generation resource penetration connected to the bulk power system, as well as, distributed energy resources (DERs) connected to the distribution system affect the grid's reliable operation over many different time scales (e.g., days to hours to minutes to seconds). As the state continues this transition, it will require careful planning to ensure resources with the right characteristics are available to meet changing grid management needs.Demand response (DR) has the potential to provide important resources for keeping the electricity grid stable and efficient, to defer upgrades to generation, transmission and distribution systems, and to deliver customer economic benefits. This study estimates the potential size and cost of future DR resources for California's three investor-owned utilities (IOUs): Pacific Gas and Electric Company (PG&E), Southern California Edison Company (SCE), and San Diego Gas & Electric Company (SDG&E).Our goal is to provide data-driven insights as the California Public Utilities Commission (CPUC) evaluates how to enhance DR's role in meeting California's resource planning needs and operational requirements. We address two fundamental questions:
Demand response operates across a range of timescales from transient responses in seconds to long-run shifts in daily behavior, and the value created by DR depends on the timescale of the response. This dynamic necessitated a new framework for potential study analysis, and we developed a supply curve modeling framework to express the availability of system-level grid services from distributed resources, based on large samples of Smart Meter Load Shapes. To facilitate comparisons between the cost and value created from having a diverse set of flexible loads, we created a new DR services taxonomy and an analytic framework that groups these services into four core categories: Shape, Shift, Shed and Shimmy.
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