Baseline Battery Storage in the IRP Modeling Datasets 2019
CPUC staff developed a baseline amount of battery storage to include in the RESOLVE and SERVM models used in the 2019-2020 IRP process. The baseline includes units that are already online, projects under development that are LSE-owned or contracted, assumed achievement of AB 2514 mandated amounts of storage procurement in each interconnection domain (transmission, distribution, and customer-connected), and projections of behind-the-meter (BTM) storage based on the CEC’s 2018 IEPR demand forecast and what LSEs reported in response to a data request from CPUC staff.
Already online battery storage units in SERVM are itemized in the Baseline Generator Unit List for SERVM and RESOLVE with the data field definitions defined in the file Data Field Definitions All Units. The total amount located in the CAISO footprint is 138 MW (filtering rows for servm_type: Battery Storage and summing MAXGEN for units online and located in the CAISO region).
Not yet online baseline generic battery storage has its nameplate MW or “capmax” itemized in a separate file Baseline Generic Battery Storage Annual Parameters, since the projection of baseline generic battery storage increases annually. This file includes:
- Pivot tables to illustrate how to sum up total “capmax” MW amounts of storage by category and year
- A worksheet identifying the amounts of storage assumed in-front-of-the-meter (IFOM), BTM and the amounts included with the 2018 IEPR demand forecast
- Data on assumed durations and other parameters for both the generic baseline batteries and existing batteries
Data fields are defined in the file Data Field Definitions Battery Storage. The table below summarizes battery MW assumptions by category for selected years.
The portion of baseline battery storage that is already online will be modeled as individual units in SERVM. The portion not yet online will be modeled as 10 generic battery storage units per region (PGE_Bay, PGE_Valley, SCE, SDGE). The choice of 10 generic units is a modeling simplifying assumption to limit the number of unique units to dispatch in the model and aggregate information collected from LSEs about planned storage procurement.
Users of this baseline battery storage dataset should take care not to double count BTM storage that was included in the 2018 IEPR demand forecast. The IRP models (RESOLVE and SERVM) treat all BTM storage as an explicitly modeled “supply” resource type, rather than a fixed adjustment to hourly electric demand. Thus, BTM storage that was included in the 2018 IEPR demand forecast must be removed from the demand forecast inputs to the IRP models. In this way, the IRP models count all BTM storage in only one place, on the “supply” side.
Other analysis informing IRP or other planning processes may wish to use counting conventions consistent with the CPUC’s Resource Adequacy (RA) program. This convention uses the IEPR demand forecast as-is and does not move any BTM resources in the demand forecast to the “supply” side. If using this counting convention, take care not to double count BTM storage by excluding the 2018 IEPR demand forecast amounts of BTM storage from the baseline battery storage datasets on this page. Furthermore, resources can only count towards RA if they can sustain maximum output for 4 hours or longer. Thus, 2 hour duration batteries would only have half of its nameplate capacity count toward RA. In the table below for example, the 2 hour 200 MW BTM batteries would only offset 100 MW of RA requirements.
Note that all annual projected amounts are for the end of the forecast year. In some earlier years, e.g. 2021, data reported by LSEs indicated that projected amounts of storage by the end of 2021 are substantially higher than projected amounts of storage by the middle of 2021. This detail may be important to consider if evaluating capacity needs in the summer of 2021.
The Modeling Advisory Group (MAG) presentation on June 17, 2019 (slides 27-29) further explains the development of baseline battery storage amounts to be used in IRP modeling for both utility-scale storage and BTM storage.