Assessing Impacts in California’s Self-Generation Incentive Program (SGIP) and the California

Solar Initiative (CSI) Presentation to Demand Assessment Working Group George Simons, Director Itron March 28, 2013

2

Itron, Inc. Overview

Leading technology provider to global utility industry

110 million communication modules

8,000+ customers in 130 countries

8,000 employees $2.4 billion (2011 USD)

© 2009, Itron Inc. 3

Itron: Consulting and Analysis Group Who we are: Part of Itron’s Professional Services Group Staff of ~80 C&A Professionals

Economists, Engineers, Statisticians, Load and Market Researchers Offices in Oakland, CA; San Diego, CA; Davis CA, Vancouver, WA; and Madison,

WI

What we do: Energy Efficiency Demand Response Renewables and Distributed Generation Load Research Market Research Integration of Resources (IDSM and Smart Grid)

© 2009, Itron Inc. 4

Renewables and Distributed Generation Strongly focused on generation located on the distribution side of

the electricity system Includes solar (PV and thermal), wind, biomass and conventional and

renewable-fueled generation and combined heat and power

Services include: Market assessments for DG/renewables Program and project performance evaluations Cost-effectiveness and economic analyses Advanced DG technology cost and performance assessments Sub-metering (tied in with evaluation efforts) Assistance related to integrating DG technologies into the grid

© 2009, Itron Inc. 5

Itron’s Role in the SGIP Itron has been the SGIP prime evaluator since 2001 Services:

Impacts evaluation Process evaluation Performance metering Cost-effectiveness analysis Topical reports and products

Examples of products 10 annual impact evaluations 20 semi-annual renewable fuel reports DG cost-effectiveness framework (2005) SGIP cost-effectiveness evaluation (2007) DG Cost-effectiveness study and model (SGIPce) 2011 Optimizing Dispatch and Location of Distributed Generation (2010)

© 2009, Itron Inc. 6

Itron’s Role in the CSI Itron was prime evaluator

from CSI inception to 2011 Services: Impacts evaluations

2008-2010 Impact Evaluations

PV performance metering 500 meters measuring

combinations of PV generation and facility/home demand

© 2009, Itron Inc. 7

SGIP Impacts Evaluations Evaluations cover:

Status of program Critical trends Energy impacts

Annual Coincident peak demand

Transmission and distribution impacts Compliance with efficiency requirements

Useful thermal (waste heat recovery) Overall system efficiency

Reliability and performance Greenhouse gas impacts

© 2009, Itron Inc. 8

Overall Approach on Assessing Impacts SGIP population of technologies is varied

Legacy systems (IC engines, microturbines, gas turbines, fuel cells, wind) New systems (fuel cells, wind, storage)

Based on statistical sampling Targeting 90% confidence with 10% precision

Some legacy systems we can only achieve 70/30 Determine sample based on strata

Metered data needed: Fuel consumed by SGIP generator Net electricity produced by SGIP generator (interval data) Useful thermal energy recovered (for CHP systems)

Metered data sources: Host sites, project developers, utilities Third party providers

Performance data providers (PDPs) emerging in SGIP Itron installed metering (on behalf of PAs)

Net electricity (over 190) Useful thermal energy (over 120)

© 2009, Itron Inc. 9

Data Collection for Impacts Evaluation Not a one time process

Data collected on an on-going basis throughout the preceding year

Data collection and processing Converting multiple sources

of data in different formats to common formats

Time/date stamp alignment Data validation

When does zero mean zero generation vs no communication?

QA/QC Verifying that values “look”

correct

© 2009, Itron Inc. 10

SGIP: A Closer Look at Operational Status SGIP represents legacy projects installed over the past ten years and newer projects Important to distinguish “on-line” versus decommissioned projects

Decommissioned defined as equipment has been out of service and removed from the site On-line projects may be temporarily down at times Not always able to accurately identify decommissioned projects Loss of contacts and reporting from older projects leads to “unknown” designations

Technology

On-line Decommissioned Unknown

No. of Projects

Rebated Capacity

(MW)

Percent Total

Rebated Capacity

No. of Projects

Rebated Capacity

(MW)

Percent Total

Rebated Capacity

No. of Projects

Rebated Capacity

(MW)

Percent Total

Rebated Capacity

IC Engines 176 115.4 62% 33 14.9 76% 46 25.9 57%

Fuel Cells 109 28.6 15% 6 1.3 6% 16 8.4 19%

Gas Turbines 7 24.5 13% 0 0 0 1 1.2 3%

Microturbines 92 18.3 10% 21 3.4 17% 27 3.1 7%

Wind 0 0 0 0 0 0 10 6.8 15%

Total 384 186.8 100% 60 19.6 100% 100 45.3 100%

© 2009, Itron Inc. 11

SGIP: Capacity and Utilization Capacity reflects program participation

Enables measurement of trends by technology, fuel, etc. Utilization reflects use of capacity

Critical in assessing impacts Also provides valuable information on aging trends

© 2009, Itron Inc. 12

Examples of Trending with Utilization and Capacity

Utilization trending can help identify how project age affects capacity factor Figure at left shows clear increase in off-line capacity with age and

associated decline of average annual capacity factor Capacity trending can show impacts due to capacity changes

Graph at right demonstrates how lower growth in IC engine capacity affected annual energy delivery from IC engines over time

© 2009, Itron Inc. 13

Treatment of Calendar vs Year in Operation (Age) Calendar year provides information that allows year to year comparisons

and trending Figure on left shows annual capacity factor trends by year

Year in operation (year) provides information on how performance of technologies vary with time in the field Figure on right shows changes in capacity factor as the technology ages

© 2009, Itron Inc. 14

SGIP: Annual Energy Impacts Annual energy

impacts estimated at different levels and timeframes: Program-wide

and at Program Administrator level

Broken out by technology and fuel type

By quarter and annual

Trended over time

Technology Q1-2011 Q2-2011 Q3-2011 Q4-2011 Total*

Fuel (GWh) (GWh) (GWh) (GWh) (GWh)

FC N 14 17 19 18 68 R 21 26 31 31 110 GT N 44 47 47 50 187 ICE N 57 61 78 62 257 R 11 16 18 18 62 MT N 14 19 18 20 71 R 1.2 0.9 1.4 1.4 5 TOTAL N 128 143 162 150 583 R 33 43 50 51 177 TOTAL 161 186 211 201 760

© 2009, Itron Inc. 15

SGIP: Coincident Peak We look at peak impacts at

various levels CAISO system demand

Summer peak Impacts at top 2000

hours Utility system peak

demand Intent is to determine

influence of SGIP technologies on resource adequacy Are SGIP DG technologies

available when needed? Assess using hourly

capacity factors during peak

© 2009, Itron Inc. 16

SGIP: Transmission and Distribution System Impacts With increasing amounts of

DG capacity projected for the future, peak impacts occurring below the CAISO and utility peak demand become more important Began examining DG

generation impacts on distribution feeder peaks

Significantly different investigation

Findings: DG can help unload

distribution feeder peaks Unloading impact tied to

DG capacity and may become more pronounced with increasing amounts of DG

DG impacts tied to feeder characteristics (e.g., customer mix)

Metered kW 3,636 11,914 360 2,024 203 0 0Total kW 26,516 55,515 744 5,975 688 467 67Metered # 78 55 3 28 4 1 1Total # 569 165 9 81 16 9 2

-

10,000

20,000

30,000

40,000

50,000

60,000

-- N R N R N R

PV ICE MT FC

kW

Metered kW Total kW

Distribution Coincident Peak Generation 89,952

PV ICE MT FC -- N R N R N R

PG&E Coast Afternoon 56%

85%

Evening 30%

SCE Coast Afternoon 46% 65% 44% Evening 6% 48% 52%

SDG&E Coast Afternoon 42%

33%

40%

Evening 1%

Inland Afternoon 63%

29%

Evening 26% Total by Technology/Fuel 35% 50% 12% 50% 23% 16% 0% Total by Technology 35% 48% 44% 9%

© 2009, Itron Inc. 17

SGIP: Optimizing DG Dispatch Feeder studies showed

DG can help unload distribution system Occurred haphazardly;

without design by project or utility Shown by example in

top figure Can DG resources be

operated to meet both needs of site and utility? Led to study on

optimizing dispatch and location of DG resources under the SGIP Bottom figure shows

how load following generator can help address feeder demand

Same demand curve

© 2009, Itron Inc. 18

SGIP: Optimizing DG Dispatch (cont’d) Affects of blending

multiple DG resources? Looked at same

representative feeder with intermittent PV and multiple load following DG Multiple DG not only

addresses feeder demand but firms intermittent PV

Created representative “look-up” tables

Full set of results in topical report: “Optimizing Dispatch

and Location of Distributed Generation”

Same demand curve

© 2009, Itron Inc. 19

SGIP: Combined Heat & Power Efficiencies CHP makes up an increasing amount of SGIP capacity Important to determine efficiencies

Useful thermal energy efficiency Overall system efficiency

𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝐻𝐻𝑈𝑈𝐻𝐻𝐻𝐻 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑈𝑈𝐶𝐶𝑈𝑈𝐶𝐶𝐶𝐶𝐶𝐶 𝐸𝐸𝑈𝑈𝑈𝑈𝐶𝐶𝐸𝐸𝐶𝐶𝑈𝑈𝐶𝐶𝐸𝐸𝐸𝐸 = 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝐻𝐻𝑈𝑈𝐻𝐻𝐻𝐻 𝑅𝑅𝑈𝑈𝐸𝐸𝐶𝐶𝐶𝐶𝑈𝑈𝐶𝐶𝑈𝑈𝑣𝑣

𝐹𝐹𝑈𝑈𝑈𝑈𝑈𝑈 𝐼𝐼𝐶𝐶𝐼𝐼𝑈𝑈𝐻𝐻 (𝐿𝐿𝐻𝐻𝐿𝐿) 𝐸𝐸𝐶𝐶𝐸𝐸 =

𝐸𝐸𝑈𝑈𝑈𝑈𝐸𝐸𝐻𝐻𝐶𝐶𝐶𝐶𝐸𝐸𝐻𝐻𝑈𝑈 𝑂𝑂𝑈𝑈𝐻𝐻𝐼𝐼𝑈𝑈𝐻𝐻𝐹𝐹𝑈𝑈𝑈𝑈𝑈𝑈 𝐼𝐼𝐶𝐶𝐼𝐼𝑈𝑈𝐻𝐻 (𝐿𝐿𝐻𝐻𝐿𝐿)

© 2009, Itron Inc. 20

SGIP: Useful Thermal Energy & Compliance Investigated applications for

thermal energy Over 50% of CHP capacity

and most projects recovered waste heat to offset boilers

However, a significant amount of CHP capacity used recovered energy for combined heating and cooling

CHP projects must achieve a minimum PUC 216.6(b) efficiency of 42.5 percent Gas turbines and

microturbines had compliance problems

But how typical is the problem?

End Use Application Completed Projects (n) Completed Capacity (kW)

Cooling Only 39 33,811 Heating & Cooling 83 62,960 Heating Only 309 104,654 To Be Determined 2 360

Total 433 201,785

© 2009, Itron Inc. 21

SGIP: A Deeper Dive into Useful Waste Heat Issues Average values can be

misleading Looked at distribution of

results for IC engines and microturbines Average microturbine PUC

216.6 (b) result was 32% However, at least 10% of

MT population came close to 42.5%

Similarly, ICE average PUC 216.6 (b) was 44%

But breakdown shows close to 20% of ICE population was approaching 32% efficiencies

In general, we found that many CHP systems have far lower useful waste heat recovery efficiencies than expected from manufacturer specifications

This has implications for overall system efficiencies and GHG emissions

© 2009, Itron Inc. 22

SGIP: Overall System Efficiencies SGIP requires non-renewable

CHP systems to achieve a minimum of 60%* overall system efficiency Represents sum of

electrical and thermal energy efficiency

None of the CHP projects met the requirement at the population level

Several observations: Fuel cells tend to get close

to 60% requirement primarily due to high electrical efficiency

IC engines tend to approach 60% requirement due to high thermal energy efficiency

Historically, IC engines have had higher NOx emissions than fuel cells or microturbines

* HHV basis

Technology Number of Projects

(n) Overall Project Efficiency

(%,HHV)

FC 72 48.9% GT 8 44.3% ICE 231 51.5% MT 122 37.0%

© 2009, Itron Inc. 23

SGIP: Trending of Electrical and Thermal Energy We examined delivery of energy by electricity and thermal from inception of

the SGIP going forward Interestingly, fuel cells have increasing capacity and associated electricity delivery

but provide little thermal energy delivery We’re seeing growth in fuel cell capacity. Most of emerging fuel cell capacity tied

to all electric fuel cells. Implications to GHG aspects?

© 2009, Itron Inc. 24

SGIP: Greenhouse Gas Emissions A primary goal of

SGIP is to achieve net GHG emissions reductions (relative to baseline use) Reductions tied

to: Electrical

load Heating load Cooling load Also affected

by use of renewable fuels

Estimates based on 8760 hour per year treatment

© 2009, Itron Inc. 25

SGIP: GHG Emissions from Non-Renewable CHP Electricity:

Baseline: CA mix of resources and GHG from E3 calculator

SGIP: generated electricity on 8760 basis

Heating: Baseline: boiler fuel used

on-site SGIP: useful waste heat is

assumed to offset boiler fuel

Cooling: Baseline: on-site cooling

from electric chillers SGIP: useful waste heat

directed to absorption chillers

Observations: Only all-electric fuel cells

showed clear net GHG emission reductions for non-renewable CHP

What is happening and why?

Type

SGIP Emissions

(Metric Tons of CO2 per

Year) A

Baseline Emissions (Metric Tons of CO2 per Year) GHG Emissions

Impact (Metric Tons of CO2 per

Year) F=A-E

Electric Power Plant

B

Heating Services

C

Cooling Services

D Total Baseline

E=B+C+D

FC - CHP 23,522 20,126 2,487 32 22,645 877 FC - Elec. 7,561 7,811 0 0 7,811 -250 FC - PEM 656 529 67 0 596 61 GT 111,071 78,780 12,218 2,002 93,000 18,071 ICE 157,237 109,878 33,038 2,861 145,778 11,459 MT 58,447 29,949 9,430 529 39,908 18,539

Total 358,495 247,073 57,240 5,425 309,738 48,756

© 2009, Itron Inc. 26

SGIP: CHP Electrical Efficiency & GHG Emissions SGIP net GHG emissions driven

by several factors: CA electricity mix

Historically driven by mostly natural gas fueled central station systems – Most of the year, the grid

supplies electricity from efficient (45% plus) combined cycle systems

– During peak (< 500 hrs per year) is generated from older, less efficient (30-35%) combustion turbines

SGIP CHP resources With exception of fuel cells,

SGIP CHP have low electrical efficiency – Can’t “beat” combined

cycle for most of the year on an efficiency basis

– Results in grid having lower GHG emissions than SGIP generator

Technology Number of Metered

Projects (n) Mean Electrical Conversion

Efficiency (%,LHV)

FC 94 45.9% GT 6 31.9% ICE 102 30.9% MT 50 23.0%

CAISO Load Duration

© 2009, Itron Inc. 27

SGIP: Thermal Efficiency & GHG Emissions Except for all electric fuel

cells, non-renewable CHP can’t rely on electrical conversion efficiency to obtain net GHG reductions Instead, must rely on

thermal efficiencies to obtain net GHG reductions

Examined SGIP CHP historical useful heat efficiencies

Compared to theoretical useful heat recovered needed to obtain net GHG reductions In general, non-renewable

CHP must consistently have higher than observed useful waste heat recovery to achieve net GHG emission reductions

© 2009, Itron Inc. 28

CALIFORNIA SOLAR INITIATIVE (CSI)

© 2009, Itron Inc. 29

CSI Impacts Evaluations Evaluations cover:

Status of program Critical trends Energy impacts

Annual Coincident peak demand Net export

Transmission and distribution system impacts

Reliability and performance Greenhouse gas impacts 2009 also covered

integration of EE with PV

© 2009, Itron Inc. 30

CSI: Programs Analyzed and Data Methodology 2010 impacts analyzed for the following PV programs:

Emerging Renewables Program (ERP) Self-Generation Incentive Program (SGIP) California Solar Initiative (CSI):

General Market (GM) Single-family Affordable Solar Homes (SASH) Multi-family Affordable Solar Housing (MASH)

New Solar Homes Partnership (NSHP) Metered generation data was collected from a sample of

systems, primarily in SGIP and CSI – GM programs Generation was estimated for non-metered systems using

data from metered systems with similar characteristics

© 2009, Itron Inc. 31

CSI: Programs Analyzed and Data Methodology (cont.) System Characteristics Used for Estimation: 1. Program Administrator (CCSE, PG&E, SCE) 2. Configuration (Near Flat, Tilted, Tracking) 3. Locale (Inland, Coastal) 4. Program and Incentive Type (CSI GM Residential and Small

Commercial (<10kW) EPBB, CSI GM Residential and Small Commercial (<10kW) PBI, CSI GM Large Commercial (≥ 10 kW) EPBB, CSI GM Large Commercial ( ≥ 10 kW) PBI, SGIP > 300 kW, SGIP ≤ 300 kW)

5. Installation Year Group (2002-2004, 2005-2007, 2008-2010) 6. Installation Year

© 2009, Itron Inc. 32

CSI: Data Sources CSI GM

EPBB Performance Monitoring Reporting Services (PMRS), which provide data to host

customers on EPBB systems requiring monitoring (EPBB PMRS), EPBB-Exempt System Service Providers, which provide data on systems that have

non-required PV monitoring systems (EPBB exempt), and Itron Meters, which were installed at randomly selected sites in the latter part of

2010 to provide a statistically significant random sample of CSI systems beyond that available from EPBB system data sources.

PBI Performance Data Providers (PDP), which provide data to the PAs for PBI payment

calculation (PBI PDP),

SGIP Third Party Providers Itron Meters, which were installed at randomly selected sites to provide a statistically

significant random sample

© 2009, Itron Inc. 33

CSI GM EPBB – Available and Validated Data

Majority of the data from four providers Introduces potential for bias Limited how much we used from each provider on a per strata

basis

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CSI GM – EPBB Data Availability

Lots of data but not randomly sampled Additional meters installed in random sample from mid 2010 –

early 2011 will allow random sampling in the future

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CSI GM – PBI Available Data

Near census, no sampling

© 2009, Itron Inc. 36

The ERP, SASH, and NSHP programs were 100% estimated, while MASH was 92% estimated.

CSI: Amount of Energy Metered vs. Estimated

© 2009, Itron Inc. 37

CSI: Cumulative Installations and Installed Capacity

From 2007- 2010, total installed PV capacity increased by a factor of four and the number of installations increased three and half times

© 2009, Itron Inc. 38

CSI: Annual Installations and Installed Capacity

By 2007, annual capacity pushed past 100 MW per year while annual installs topped 10,000 systems per year. By 2010, this reached 180 MW and 18,000 systems per year.

© 2009, Itron Inc. 39

CSI: Trend in Annual Installed Capacity by Program

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Trend in Annual Installed Capacity, Number of Installations, and Costs CSI GM, SGIP, MASH, and ERP

© 2009, Itron Inc. 41

Parallel Metering of Sites with Available Data

Third Party CTs (green)

Itron CTs (black)

•Done on a random sample split amongst

•Data providers, •PA’s, •Incentive types and, •Customer sectors

•Not intended for statistical significance

© 2009, Itron Inc. 42

CSI: Installed Systems by Program

CSI – GM is the largest program by both number of systems and total capacity.

SGIP has the largest average system size.

© 2009, Itron Inc. 43

CSI: Annual Generation by Program (all PAs)

PV systems generated nearly 1,000 GWh in 2010. CSI – GM, SGIP, and ERP are the largest contributors. CSI – GM generation has increased by about 250 GWh during

each of the last 2 years.

© 2009, Itron Inc. 44

CSI: Annual Capacity Factor by PA (CSI-GM, SGIP)

Capacity factors vary slightly between PA’s. Southern California has more annual solar resource

SGIP’s lower values attributed to the systems’ age and a higher percentage of flat commercial systems.

© 2009, Itron Inc. 45

CSI: Capacity Factor by Month (SGIP and CSI – GM)

Notable seasonal variation, peaking around 0.27 during the summer and dropping just below 0.10 in the winter.

© 2009, Itron Inc. 46

CSI: Resource Adequacy or How Much Solar Can we Really Count on?

Intermittent resources pose a challenge for CAISO and CPUC in determining how much generation California needs to meet growing demand.

‘Resource Adequacy Methodology’ is an approach used to determine ‘Net Qualifying Capacity’ for utility scale wind and solar. It is based on 70% probability to exceed performance during key hours.

Distributed Generation (DG) resources are not counted toward Net Qualifying Capacity.

However, we leverage this methodology to expand analysis beyond a single peak demand hour.

© 2009, Itron Inc. 47

CSI: Generation During Top CAISO Demand Hours

Fixed-tilted systems generate at about one-third of their rated capacity during most top demand hours.

West facing and tracking systems perform better during top demand hours.

© 2009, Itron Inc. 48

CSI: Emissions Analysis Methodology PV generation is assumed to offset energy that would have

been generated from a central power plant Emissions reductions are credited based on the amount of

emissions that would have resulted if central power plants produced equivalent amount of energy

Power plant emissions were assigned to each hour of the year based on the mix of generators for each utility. This hourly assignment provides emissions variations due to usage of base-load and peaking plants.

© 2009, Itron Inc. 49

CSI: CO2 Emissions Reductions (by PA)

Nearly 400,000 tons of CO2 was avoided by CSI – GM and SGIP, with an additional 90,000 tons avoided by other programs.

Reductions correlate to installed capacity and generation.

© 2009, Itron Inc. 50

CSI: PV Performance Over Time Objective:

To investigate and quantify system performance over time Scope:

Types of factors analyzed and statistics implemented Results

Graphical analysis of performance over time Statistical analysis of performance over time

Key Findings

© 2009, Itron Inc. 51

CSI: The Approach to Performance Over Time Analysis

Average System Performance by Year of Operation

Minimizes the effects of year-to-year insolation and seasonal bias

Uses weighted average annual capacity factor Incorporates groups based on similar characteristics Includes systems with at least 90% of the days in the operation

year to minimize seasonality

© 2009, Itron Inc. 52

CSI: Performance by Year of Installation Group

System installations grouped by 3-year intervals. 2002-2004 mainly SGIP, 2005-2007 mostly SGIP, and 2008-2010 mainly CSI GM systems

Recently installed systems perform better, partly due to more tilted systems but also likely due to technological progress and installation advancements

© 2009, Itron Inc. 53

CSI: Performance by Location

Inland systems consisting of climate zones 8-16 initially perform better than coastal systems but this difference narrows over time. Additionally, CSI GM inland systems perform 7% better in the first year than their respective coastal

systems. The separation drops to 1.5% by the third year SGIP inland systems perform 3.8% better than SGIP coastal systems. The difference

decreases to 1.1% by the fifth year

© 2009, Itron Inc. 54

CSI: Performance by Incentive Type

PBI systems perform higher than EPBB systems in the third year; driven in part by more tracking systems

The differences in performance between EPBB and PBI systems for PG&E and CCSE is not significant

© 2009, Itron Inc. 55

CSI: Performance by Customer Type

CSI Large Commercial systems perform somewhat better, again driven by trackers. ANOVA analysis also shows PBI large commercial outperforms both EPBB large commercial and EPBB residential PBI residential performs better than EPBB large commercial and EPBB residential

© 2009, Itron Inc. 56

CSI: Performance by Module Type

Degradation over the years, all things equal, considers other system components and environmental factors.

Multi-c-Si indicates better performance than HIT-Si, Mono-c-Si, or Thin Film modules.

© 2009, Itron Inc. 57

CSI: Performance by Module Type (all shown)

Modules, consolidated from the previous slide, reflect differences in performance.

© 2009, Itron Inc. 58

CSI: Net Export of PV Generated Electricity Monthly Billing Data Exports

Data available for most of CSI population Net export of generation over entire billing periods Frequency, timing, and magnitude of exported PV generation Statistical modeling

Interval Data Exports Small sample of sites for SCE and CCSE with interval data Export of PV generation by hour Combined with PV generation data for smaller subset of sites

© 2009, Itron Inc. 59

CSI: Example of Billing Period Net Export A net excess of PV generation over a billing period appears as

a negative bill, as seen in the May and July following installation

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CSI: Maximum Number of Exports Over 12 Months How many times over any 12-bill (month) period did a site

have net exports of PV generation?

© 2009, Itron Inc. 61

CSI: Annual Net Export of PV Generation Sites with net exports over a 12-bill period were classified as

“annual” net exporters

© 2009, Itron Inc. 62

CSI: PG&E Residential Pre- and Post-Install Monthly Bills

The annual net exporters show very little increase in summer loads in the post-install period

© 2009, Itron Inc. 63

CSI: SCE Residential Pre- and Post-Install Monthly Bills

Increased summer load is more apparent in both groups

© 2009, Itron Inc. 64

CSI: CCSE Residential Pre- and Post-Install Monthly Bills

Annual net exporters have negative bills for nearly half of the year

© 2009, Itron Inc. 65

CSI: Example Load Profile for a Net Exporter Monthly billing records only show when there has been net

excess generation over a month

© 2009, Itron Inc. 66

CSI: Example Load Profile for a Non-Net Exporter Even though PV generation did not exceed the total

consumption, a large share of the generated electricity was exported

© 2009, Itron Inc. 67

CSI: Typical Residential Usage Profiles

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CSI: Typical Non-Net Vs Annual Net Profiles

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CSI:Typical Non-Residential Usage Profiles

© 2009, Itron Inc. 70

CSI: Residential Percent of PV Exported