1. 2012 LTPP Updates Nat Skinner, Patrick Young
Generation & Transmission Planning Section
Nat Skinner, Patrick Young
California Public Utilities Commission
12/10/2012

2. LTPP Overview Biennial planning process
Determines rules and guidelines for utility procurement and cost recovery
Establish long-term local area and system needs to ensure grid reliability and authorize any needed procurement
Involves coordination between CEC/CAISO/CPUC
Detailed analysis with 10 year planning horizon to make procurement decisions
Broad analysis with 20 year planning horizon to inform policy discussions
Example: we use CEC’s demand forecast in RA and LTPP, and studies examining potential for preferred resources in LTPP
We interact with CAISO’s TPP – historical MOU says: CPUC provides renewables portfolios to CAISO, CAISO uses CEC CED, CPUC “gives substantial weight” to project applications that are consistent with TPP
Coordination between all 3 agencies to compile NQC list, CPUC uses NQC list in RA and LTPP proceedings
We depend on CAISO for production simulation and powerflow modeling
What is NQC? QC is calculated for each resource. If a resource has some fraction of its QC not fully deliverable to aggregate ISO load, then it is adjusted down to a NET QC value. NQC is the deliverable capacity.
What counts as a resource? Anything with a Resource_ID on the ISO generating capability list – some DR programs may not have this Resource_ID but the DR program could potentially be useful and have an NQC.

3. 2012 LTPP Components Currently has three tracks
Track 1: Local Capacity needs for LA Basin & Big Creek / Ventura
Track 2: Planning assumptions, operating flexibility (i.e. renewable integration), system need
Track 3: Bundled IOU plans and procurement rules

4. Track 1 - Local Areas
Considers authorizing new resources for local reliability purposes over a 10 year horizon
Driven by state’s policy on phasing out once-through-cooled (OTC) technology
Informed by CAISO reliability studies given expected OTC retirements in local transmission-constrained areas
Proposed Decision authorizing procurement, if any is needed, expected December 2012
In CAISO studies, all scenarios assumed same demand forecast as 2009 IEPR ( CED)
CAISO uses power flow modeling to forecast LCR needs – it takes into consideration transmission constraints and limitations in specific local areas. Model is deterministic and looks at a defined set of contingencies to check if the local area has enough capacity to adapt to those contingencies. Capacity here means TX and generation sufficient to maintain operational integrity (stability) at peak demand conditions when a defined set of contingencies occur (e.g. loss of biggest TX or generator)

5. Track 2 - System Establishes assumptions for use in resource planning
Establishes scenarios to be modeled by CAISO to examine operating flexibility and system reliability needs
Proposed Decision establishing assumptions and scenarios was issued November 20, 2012
Modeling results (expected spring 2013) will inform system need determination
Decision authorizing procurement, if any is needed, expected end of 2013
Typically low, mid, high assumptions to capture range of uncertainty.
Then we assemble different combinations of assumptions to form a scenario, which we can think of as one possible future.

6. Track 3 - Bundled
Assesses and adopts (typically with modifications) the IOUs’ bundled procurement plans pursuant to AB 57
Looks at any new bundled procurement rules that may be needed
IOUs file Bundled Plans in March 2013 (tentative)

7. Distributed Generation in LTPP
Generation & Transmission Planning Section
Nat Skinner, Patrick Young
California Public Utilities Commission
12/10/2012

8. 2012 LTPP Track 1 CAISO power flow modeling in local areas
Uses 2009 California Energy Demand forecast (CED)
Only one of five modeling scenarios included any level of uncommitted DG projected by the CEC
Committed DG projections are embedded in the CED and include funded, established programs
Uncommitted DG projections are incremental to the CED and include unfunded or not yet established programs with a reasonable expectation of future implementation
Assumptions needed at the busbar level to sufficiently model grid within local areas
-ISO’s Environmentally Constrained scenario sensitivity includes reasonable max level of IUEE and DG
-Insufficient study of incremental DR impact (ISO did not model at all) so, need authorization will likely not be adjusted for incremental DR
-Need busbar level data to capture subarea effects in local area studies
-Current LTPP Track 2 defines method of Mike Jaske and Donald Brooks to estimate location of IUEE, DG, DR. We should improve on this in the future. These locational allocation methods were also used in Track 1

9. 2012 LTPP Track 2
Aggregates different combinations of demand and supply assumptions to create several scenarios
The scenarios serve as basic inputs to advanced modeling that will study system reliability and operating flexibility needs
In general, each demand and supply assumption has a low, mid, and high forecast value
Assumptions needed at the zonal level
The demand assumption starts with the 2011 California Energy Demand forecast (CED)
Committed DG (self-generation) is embedded in the CED
Energy and peak demand impact are reported on CEC Forms 1.2 and 1.4
Installed capacity represented by this self-generation (by default, not currently reported) would be useful for informational purposes
Zonal means NP26 (PGE) and SP26 (SCE, SDGE), broadly to accommodate modeling methodology constraints
But another factor to consider is differences between NP26 and SP26 (even better would be climate zonal granularity) : example : EE programs focused on A/C would have different impacts in PGE area (load growth along coast, little A/C uptake) than in SCE area (load growth inland, big A/C uptake)
Current LTPP Track 2 defines method of Mike Jaske and Donald Brooks to estimate location of IUEE, DG, DR. We should improve on this in the future. These locational allocation methods were also used in Track 1

10. Managed demand forecast
The LTPP analysis creates a “managed” forecast assumption by adjusting the CED by a projection of programs or expectations not accounted for in the CED
Example: The CED was released before a Commission decision this year that effectively raised the Net Energy Metering cap. The projected impact is further small PV growth beyond that embedded in the CED.
Managed forecast calculation:
53,674 CED
– 4,506 incremental EE
– 1,803 incremental small PV
– incremental demand side CHP
= 46,833 MW
This was High DG DSM scenario in 2020

11. Demand side DG assumptions
Incremental small PV
Based on an extrapolation of the effects of Net Energy Metering cap definition described by D
Peak demand impact assumptions in 2020:
Low = 0 MW : assumes no effect from the NEM cap increase
Mid = 710 MW : assumes about 50% of the NEM cap increase is filled out by additional small PV installations
High = 1803 MW : assumes the NEM cap is reached
CSI effects are embedded in CED, adjustment arises from further expansion of behind meter programs, as separate from systems connected to distribution or transmission directly MW in 2020 total installed capacity came from data request to 3 IOUs to estimate level at which NEM cap would be reached MW mid case is a rough guess by analysts involved in CSI program at slightly more than 50% achievement. We subtract 2200 MW as an estimate of CSI effects already embedded in the CED. This is roughly consistent with self-generation PV forecast 2021 on Form 1.2, Small PV = 5 MW AC installed capacity

12. Demand side DG assumptions
Incremental demand side CHP
Based on CEC sponsored consultant report on CHP potential
Peak demand impact assumptions in 2020:
Low = 0 MW : assumes any new CHP replaces retiring CHP
Mid = 452 MW : reflects the “base” case of the report on CHP potential, which assumes SGIP expiration in 2016
High = 531 MW : reflects the “mid” case of the report on CHP potential, which assumes SGIP extension beyond 2016
Re: CHP, LTPP base case assumes no net change in CHP, which ED staff CHP analysts believe realistic given current uncertainties around additional new versus just retirement replacement CHP.
Ask Cem about this.
The CED Final Forecast Vol 1 Appendix B, B-6 says “onsite use from historical non-PV techs is held constant over the forecast horizon and is set to the level observed or estimated in the last historical year. Non-PV techs make up about 3,200 MW (statewide) of the total installed capacity in all three scenarios.” This is how we know the consultant report is ‘incremental’.

13. Demand side resources comparison
Tracking DG goals
Left chart: to compare types of DSM and magnitude of contribution to reducing load
Right chart: to compare types of “gov’s goal” DG capacity within the CAISO area (both supply and demand side here)
In 2022:
Base net peak demand MW, 1 in 2 weather year
Replic. TPP net peak demand MW, 1 in 5 weather year
High DG net peak demand MW, 1 in 2 weather year

14. Demand side DG modeling
For advanced modeling, demand side DG will be modeled as supply resource to quantify its effects/benefits to grid reliability
Modeling types:
Production cost simulation, operational flexibility studies
System reliability, planning reserve margins
Powerflow modeling used in Transmission Planning Process (TPP), and stability and local reliability studies
Current self generation data is disaggregated by service area which is adequate for operational flexibility and system studies
Current LTPP modeling underway in January 2013
TPP modeling expected to start mid 2013
Disconnect between TPP and LTPP assumptions in terms of load assumptions
CAISO uses CPUC’s renewable portfolio scenarios but disagrees on several other key assumptions, e.g.:
-Incremental, uncommitted, energy efficiency
-Incremental, demand-side combined heat and power (“CHP”)
-Demand Response
Thus, analysis of need for new generation at CPUC may be out of sync with CAISO’s analysis of new transmission. The two are intertwined.

15. DG data requirements Have limited data: Need:
Installed capacity (aggregate MW)
Peak demand impact (aggregate MW)
Energy production (aggregate GWh)
Technology type (rooftop PV, CHP, others)
Need:
More granularity (disaggregation) of the current data, e.g. :
Locational busbar)
Technology characteristics (includes smart inverter?)
Locational and tech char is additional data besides tech type, installed capacity information already provided
“One of the biggest challenges with photovoltaic power is the existing requirement in the IEEE 1547 and UL1741 standards for
inverters to disconnect from the grid at the first sign of instability, which limits the inverter’s ability to help stabilize the grid. As the penetration of PV power production increases, such behavior threatens to undermine grid stability and the real potential of this important renewable source of energy. With smarter inverters capable of contributing to grid stability, utilities stand to gain the monitoring and control they need to successfully integrate PV power on a large-scale, distributed basis.”
“Currently, most inverters cannot differentiate between a true utility outage (when an anti-islanding disconnect is required) and a grid disturbance or brownout situation during which the PV system could actually assist in supporting the electrical grid. Smart
Islanding Detection is an enhancement intended to better distinguish between a true island condition and a voltage or frequency disturbance that could benefit from additional power generation by the inverter.”
“On the distribution side, smart PV inverters are used to correct the power factor by providing VARs close to where they are being used, rather than importing them from far away. Traditionally, power factor correction is done by connecting large, paralleled capacitor banks to many of the voltage levels of the distribution system. These shunt switched capacitors are strategically placed to adjust voltage along the feeder, as the tap-changing voltage regulators only control voltage at the beginning of the branch. Not only can both power factor correction and AC voltage regulation be performed much more economically by distributed three-phase smart PV inverters along the feeder, but they will also do it in a continuous and smooth fashion, without any step changes or noticeable switching events.”

16. Questions CEC and CAISO to specify exact data needs?
(Currently, they do a lot of the advanced modeling requiring granular data)
How do we forecast where DG will be sited?
Is sufficient technical data on DG installations being collected and by whom?
What data can CPUC’s Customer Generation group provide, now and in the future?
To what extent can DG data be included in the IEPR?
(IEPR may not need the granular data, but LTPP and TPP do)
What about cost information?
What about impacts on distribution networks and costs to upgrade if needed?
Are there qualitative or better yet, quantitative probabilities we can assign to groups of DG, or what fraction of a group of DG is more/less certain?
Why should DG be part of DAWG?
-public/multi-stakeholder process
-demand side resource, similar to incremental EE it may be useful to integrate with demand side forecast
-CAISO tariff says it shall use CEC demand forecast (CAISO will then have more comprehensive account of DG)
-an analysis by the CEC may mean no re-litigation in LTPP
-CEC is equipped to quantify precisely what DG is embedded in CEC demand forecast (and what is incremental)
-But remember that IEPR may not have granular data requirements, but consumers of the data (LTPP, TPP) do have granular needs

17. For Additional Information:
Thank you!
For Additional Information: