Get documents about "Modeling Approach for Energy Deliverer
Document Sample
Proposed Stage 2 E3 GHG Calculator Modeling Approach 04/01/2008
Table of Contents
Description of Stage 2 GHG Modeling: Energy Deliverer ........................................ 1
Overview ..................................................................................................................... 1
1. Greenhouse gas market design: policy choices .................................................. 4
2. Electricity import assumptions ........................................................................... 8
3. Load serving entity resource choices .................................................................. 9
4. Rate and cost impact to LSEs, 2020 emissions levels ...................................... 10
Responses to Party Comments on Stage 2 Modeling Issues .................................... 11
1. Add marginal emission reduction cost for all measures in GHG calculator..... 11
2. Suggestions for modeling other sectors ............................................................ 11
3. Suggestions for expanded regional analysis ..................................................... 11
4. Suggestions for modeling point-of-regulation .................................................. 12
5. Suggestions for modeling flexible compliance mechanisms ............................ 12
6. Suggestions for the modeled timeframe ........................................................... 13
Description of Stage 2 GHG Modeling: Energy Deliverer
Overview
This document describes the ‘Stage 2’ proposed modifications to be made to the Energy
and Environmental Economics (E3) greenhouse (GHG) model of the electricity and
natural gas sectors. The revised model will be designed to reflect the California Energy
Commission and California Public Utilities Commission’s (CEC/CPUC)
recommendations to the California Air Resources Board (CARB) on greenhouse gas
regulatory strategies described in the “Interim Opinion on Greenhouse Gas Regulatory
Strategies” (CEC-100-2008-002-F, CPUC Decision 08-03-018). In addition, the
document describes how the Stage 2 issues of allocation/auction and flexible compliance
will be modeled, and addresses some of the stakeholder comments on the Stage 1 model,
submitted to the CPUC in January 2008, which relate to Stage 2 modeling issues.
In the Stage 1 GHG modeling effort, the E3 team modeled the electricity and natural gas
sectors assuming a 2020 load-based, electricity and natural gas sector cap on emissions.
Users of the GHG calculator selected among demand-side and renewable energy
resources for development, in order to bring GHG emissions in the electricity and natural
gas sectors down to a ‘target’ level.1 The principle output of the Stage 1 model included
the electricity and natural gas sector cost and rate impacts of reaching the GHG cap by
1
The Stage 1 modeling default assumption was that the ‘target’ emissions level for the electricity and
natural gas sectors was equal to the 1990 sectors’ emissions from the preliminary CARB GHG emissions
inventory, as of August 22, 2007. Subsequently, CARB revised the GHG inventory on November 19 th,
2007, which resulted in an adjusted 1990 emissions level for the electricity and natural gas sectors. This
change to the CARB GHG inventory occurred after the Stage 1 model was released and so was not
reflected in that version of the model.
1
Proposed Stage 2 E3 GHG Calculator Modeling Approach 04/01/2008
developing the selected resource mix. The model also calculated the marginal cost of
greenhouse gas emissions reductions resulting from the selected resource mix.
The intent in Stage 2 is to refine the model’s assumptions about LSE-specific resources
and to reflect the CEC/CPUC recommendations to CARB on greenhouse gas regulatory
strategies. The most obvious change in the Stage 2 model will be that analysts using the
GHG calculator will be able to select the level of a California-wide market-clearing price
for GHG emission allowances (in $/tonne of CO2). Analysts will also be able to choose
between a set of rules for the auction or allocation of emission allowances to the
electricity sector, and the regulations for the use of GHG offsets.
While the costs and/or revenues from emission allowances or offsets will initially be
incurred by ‘energy deliverers,’ these costs are passed on to LSEs through higher energy
prices in the model, which will affect the rate and cost impact calculation for LSEs. The
other resource selection functions of the Stage 1 model will remain essentially
unchanged. In the Stage 2 model, analysts will still select demand-side and renewable
energy resources to meet or exceed state policy targets and these costs will still flow
through to the calculation of cost and rate impacts on LSEs. Some of the key distinctions
between the Stage 1 and the Stage 2 model are shown in Table 1 below.
2
Proposed Stage 2 E3 GHG Calculator Modeling Approach 04/01/2008
Table 1. Stage 1 and Stage 2 GHG Model Comparison
Key Inputs Stage 1 Stage 2
Electricity and natural gas sector GHG target level, “cap” in 2020 X
Amount of demand-side and renewable resources developed in 2020 X X
Key assumptions about resource costs, load growth, etc. X X
Market clearing price for California emission allowances in a multi-
X
sector “cap and trade” approach
Policy approach for allocation or auction of emission allowances and
X
use of offsets in the electricity sector
Key Outputs Stage 1 Stage 2
Absolute and relative cost and rate impacts to LSEs of achieving
X
electricity and natural gas sector GHG ‘target’
Cost of CO2 reductions in the combined electricity and natural gas
X
sectors
2020 emissions levels in California and in the Western Electricity
X X
Coordinating Council (WECC)
Cost of CO2 reductions by emission reduction measure X
Cost and rate impacts to LSEs of different market clearing prices for
X
GHG emission allowances
Cost and rate impacts to LSEs of different emission allocation or
X
auction policy approaches
Cost and rate impacts to LSEs of different GHG offset prices and offset
X
policy approaches
Absolute and relative cost and rate impacts to LSEs of a multi-sector
cap and trade program, and of meeting other CPUC/CEC suggested X
regulatory approaches to achieving GHG reductions
Other activities to improve the assumptions used in the Stage 1 model will also be
completed in Stage 2. Some of these changes were described in the “Proposed Changes to
Stage 1 Model” document circulated to stakeholders on February 29th, 2008, while other
changes and improvements are expected to come from further stakeholder and
CPUC/CEC/CARB agency staff feedback.
The Stage 2 modeling approach is described in more detail in the following four sections:
1. Greenhouse gas market design: policy choices
2. Electricity import assumptions
3. Load serving entity (LSE) resource choices
4. Rate and cost impacts by LSE; 2020 emissions levels
3
Proposed Stage 2 E3 GHG Calculator Modeling Approach 04/01/2008
1. Greenhouse gas market design: policy choices
Within the broad framework described in the energy deliverer decision there are many
potential policy details that will have an impact on California electricity consumers and
the amount of carbon reduction achieved by the sector. The proposed Stage 2 model
design will allow the analyst to change key policy assumptions and see the impact on key
metrics: average bill and rate impacts by LSE, the impacts of a variety of GHG regulatory
approaches on the electricity sector, as well as GHG emission levels both within
California and in the entire Western Electricity Coordinating Council (WECC).
The first assumption is that the carbon market will initially be California-only. This will
be the operating assumption in the Calculator, and the basis for most of the analysis.
Additional analysis will also be completed in PLEXOS and presented to stakeholders
separately from the GHG Calculator, which will show a dispatch of the WECC
generating system such that all generators in the WECC face a carbon price, simulating a
regional or federal GHG policy. However, federal and regional GHG policy scenarios
will not be built into the Calculator.
The Stage 2 model will also include policy inputs that define the market price for carbon
allowances and offsets, the method for allocation of allowances (auction, administrative
allocation), the treatment of offsets, and the potential for redistribution of auction
revenue.
The following ‘mock-up’ of the model input screen defines the minimum set of inputs
that define these policy variables: user inputs are shown in the yellow boxes. As the
model is developed these inputs may be refined, but we believe the general structure is
complete. Notes and assumptions explaining the inputs for each of the categories are
provided below.
4
Proposed Stage 2 E3 GHG Calculator Modeling Approach 04/01/2008
Figure 1. ‘Mock-up’ of Carbon Market Policy Assumptions (illustrative values only)
1. Auction of emission allowances 2012 2020
California Auction Price ($/tonne CO2e) $ 20.00 $ 40.00
2. Adminstrative Allocation 2012 2020
Quantity (Million metric tonnes of CO2e) 100 50
Basis of allocation
% allocation based on historic energy sales of fossil fuel plant 50% 100%
% allocation based on historic emissions of fossil fuel plant 50% 0%
3. Offsets
Offsets Price ($/tonne CO2e) 2012 2020
California offsets $ 10.00 $ 10.00
Regional offsets $ 20.00 $ 20.00
International offsets $ 30.00 $ 30.00
Max % of first deliverer requirement that can be purchased from the market
California offsets 8% 8%
Regional offsets 2% 2%
International offsets 10% 10%
California auction 100% 100%
4. Auction Revenue Redistribution to LSEs 2012 2020
Percent of Revenue returned 100% 100%
% revenue returned proportional to LSE sales 0% 50%
% of revenue returned proportional to LSE emissions 100% 50%
Required LSE use of auction revenue
% used for purchase of additional EE and renewables 0% 0%
% applied to rate reduction 100% 100%
Note: all values in the Figure 1 are illustrative ONLY
1. Model Input: Market Clearing Price for GHG Permits in a Multi-Sector “Cap
and Trade” Auction. Users of the GHG Calculator will be able to select whether or
not to model an auction for GHG allowances in a multi-sector “cap and trade”
system. If selected, the auction is modeled simply by allowing the analyst to input a
market clearing price for GHG permits. We do not attempt to endogenously model
the market clearing price for GHG permits in a multi-sector cap and trade program
because the price will be the result of a number of policy and economic variables
which fall outside the scope of this utility sector model, including: the overall multi-
sector ‘cap’ on emissions, which sectors are included in the cap, the availability and
price of qualifying offsets, the auction design, and other factors.2 Instead, we will
2
The California Air Resources Board is modeling different scenarios of multi-sector GHG regulatory
regimes and how these scenarios impact the state using the Energy 2020 model. In contrast, the E3
Calculator focuses exclusively on the impacts of GHG polices to the electricity and natural gas sectors.
5
Proposed Stage 2 E3 GHG Calculator Modeling Approach 04/01/2008
evaluate the impacts on the electricity sector of GHG permit price scenarios, using the
market clearing price as input.
Analysts will also be able to see how the Stage 1 results have changed in the Stage 2
model, by eliminating the market price for emission allowances and GHG offsets.
2. Model Input: Level of “Administrative Allocation” of GHG emission allowances.
Analysts will be able to select whether, and how much, administrative allocation of
emission allowances occur in the electricity sector. There are two steps to defining
administrative allocation: 1) the quantity to allocate administratively, and 2) the basis
for the distribution of emission allowances.
The quantity of administratively allocated allowances is defined for the first year of
the market (2012) and the target year (2020), with a linear interpolation between these
years.
We plan to model distributing these allowances in one or a combination of sales-
based and/or emissions-based allocation schemes. The distribution will initially be
based on the year the allowances are allocated. For example, administratively
allocated allowances based on emissions for 2012 would be based on emissions levels
for generators in 2012. In reality, this would not be a feasible policy choice for
allocating emissions because current year emissions could not be known in advance.
In the case that the user inputs a percentage of both sales-based and emissions-based
allocation, the model will compute the administrative allowance by determining the
quantity in each, multiplying by the user specified percentages and then scaling to the
total amount of emissions to be allocated.
3. Model Input: Price and Quantity of Different Types of Offsets. Similar to the
market for GHG emission allowances, offset prices will be specified by the analyst.
However, the model will allow an additional control, limiting the percent of a
generator’s GHG compliance obligation that may be met with different types of
offsets. The maximum amount of offsets that can be purchased by the generator is
specified as a percentage of their total requirement. The offset prices and quantity
limits are set independently depending on whether the offsets originate within: 1) a
non-capped sector in California, 2) the region or the U.S., or 3) internationally.
Once the analyst has defined the GHG emission allocation and offsets policy, the
calculator can evaluate the allowance/offset purchase decisions made by the energy
deliverers. We categorize two types of energy deliverers: 1) California LSEs, and 2)
a broader group that includes merchant generators, power marketers delivering into
California, and out-of-state LSEs delivering into the California market.
We assume that both LSE and non-LSE energy deliverers make allowance and offset
purchase decisions to minimize costs. Energy deliverers purchase the lowest cost
combination of allowances and offsets to meet market requirements. California LSEs
6
Proposed Stage 2 E3 GHG Calculator Modeling Approach 04/01/2008
make decisions to minimize costs to customers, and other energy deliverers make
decisions to maximize their profits.
4. Model Input: Amount of Auction Revenue Distributed to LSEs. If users decide to
model auction revenue distribution to LSEs, there are three steps to defining this
policy in the model: 1) determining the amount of revenue that gets redistributed, 2)
selecting the basis for the redistribution (sales-based or emissions-based), and 3)
defining any required uses for the redistributed revenues. The model will only
consider redistribution of auction revenue to LSEs, although in reality other
alternatives are possible.
In step 1, the amount of auction revenue redistributed to LSEs is selected by the
analyst as a percentage of the auction revenue generated. If the analyst selects 100%
of auction revenue to be redistributed, all of the auction revenue generated from the
electricity sector will be provided back to LSEs. The use of offsets will reduce the
emission allowance auction revenue. We assume that funds spent on the purchase of
offsets are not re-distributed to the electricity sector.
In step 2, the analyst selects the inputs that define the basis for revenue distribution.
The analyst selects what percentage of the auction revenue is distributed to LSEs, and
selects whether the redistribution is based on the LSEs’ sales or level of emissions in
the first year of the market (2012) and the target year (2020). The model will assume
a linear interpolation between these two points.
In step 3, the analyst may select whether there are any stipulations about how the
redistributed auction revenue is used by the LSEs: namely, the percentage of the
redistributed revenue that must be used to purchase additional energy efficiency or
renewable energy beyond the LSEs’ policy requirements. In the absence of
stipulations on the use of auction revenue, the model will assume that LSEs must use
the revenue distribution towards rate reductions, offsetting higher market costs for
electricity.
We have considered the fact that some policy options for auction revenue
redistribution could provide less incentive to LSEs to purchase low-cost offsets. For
example, if LSEs get 100% of their expenditures in the auction refunded, the LSE
may purchase emission allowances instead of offsets, even if the offsets are lower
cost than emission allowances. However, we do not plan to model this type of
strategic choice by LSEs; we assume that the cost-minimization incentive prevails.
Carbon price effect on the production simulation dispatch. In the California-only
GHG policy scenario, which will be the focus of the Stage 2 modeling, we intend to
model the WECC system dispatch in PLEXOS without a carbon price. A California-only
policy scenario will leave out-of-state generators the option of not selling power to
California, and using their generation to serve other load. These out-of-state generators
will dispatch if it is economic to sell to a non-California-based LSE or power market.
7
Proposed Stage 2 E3 GHG Calculator Modeling Approach 04/01/2008
California-based generators, on the other hand, will face the cost of purchasing necessary
emission allowances and offsets, and the allowances and offsets will therefore be an
additional variable cost of operation. However, given that the mix of generation in
California includes low variable cost units (hydro, nuclear) and natural gas, including a
carbon price in the dispatch does not change the economic dispatch order in the State.
With California’s gas-based system, the plants with the highest GHG emissions are
already dispatched last, in economic order.
To evaluate generation operational changes in a regional or federal GHG policy scenario,
PLEXOS will run several scenarios in which the dispatch includes a carbon price in the
operating costs for all of the generators in the WECC that emit CO2. The scenarios we
are currently considering include emissions allowance market price assumptions from
$0/ton to $100/ton of CO2, in $10/ton increments, plus scenarios with prices of $120/ton
and $150/ton. This analysis will provide an estimate of the CO2 reductions due to
operational or dispatch changes of the 2020 WECC generator fleet, from a region-wide
market for carbon allowances. This analysis will not be built into the GHG Calculator,
however stakeholders will have an opportunity to comment on the results in the CPUC
R.04.06.009 proceeding.
2. Electricity import assumptions
The analyst must also select from among the options for treatment of electricity imports
and contracts with out-of-state generators. For the default option, we assume that LSE’s
long-term contracts with generators out-of-state will continue until their current
expiration dates and that the contracts will not be renewed upon expiration. After the
contract expiration, LSEs will procure power from the least cost resource. We also
assume that the cost of the emissions allowances and/or offsets will be paid by the energy
deliverer, but passed on to the LSE.
In the default case, we will assume that unspecified imports continue to receive the
deemed unspecified emissions rate of 1100 lbs CO2/MWh through 2020. Since this
relatively high unspecified emissions rate creates an incentive to specify power purchases
from lower-emissions generators, the analyst can vary the assumption on import
emissions intensity. This sensitivity models the impact on LSEs’ cost and rates of
specifying more of the clean generation for import into California while operating the
same fleet of generation WECC-wide. We note that increasing the level of clean,
specified imports into the state would reduce the state’s total emissions level, but would
not result in any actual greenhouse gas reductions WECC-wide.
As an additional sensitivity, we plan to add to the model the ability to change the contract
expiration date for coal contracts, to evaluate the impact on emissions levels of early
contract termination, or repeated short-term contract renewals. We do not anticipate that
changes to contracts will result in changes to the operating pattern of the coal plants.
Therefore, changing coal contract expiration dates will affect the California emissions
level, but will result in no change to overall WECC-wide emissions levels. The operating
8
Proposed Stage 2 E3 GHG Calculator Modeling Approach 04/01/2008
patterns of these plants reflect the constrained least-cost dispatch results from the
PLEXOS production simulation model.
Users will also be able to select an option of modeling LSE costs and rate impacts
whereby the decision to purchase energy from coal contracts are evaluated on an
economic basis – LSEs procure from coal plants as long as the carbon price is low
enough to make the coal power economic. The default and alternative modeling options
surrounding coal contracts are shown in the diagram below:
Choices for Modeling
Out-of-State Coal Contracts
Before contract expires: Default option: LSEs hold coal contracts until the
expiration date, regardless of the carbon price.
Alternative scenario: LSEs break coal contracts
if the carbon price becomes too expensive.
After contract expires: Default option: After coal contract expires, LSEs
are prevented from contracting with coal plants,
even if it is economic
Alternative scenario: After contract expires,
LSEs can buy coal power with short-term
contracts, if it is economic
3. Load serving entity resource choices
In the third step, after the expected carbon price and the carbon market policy choices
have been defined, and after electricity import assumptions are specified, analysts will
select LSE resource choices in 2020. This portion of the model will be very similar to
that developed in the Stage 1 model whereby users choose how much LSEs will develop
energy efficiency, demand response and renewable resources by 2020. We plan to
improve the user interface, and to implement the changes discussed in the “Proposed
Changes to Stage 1 Model” document, emailed to parties on the R.04.06.009 service list
on February 29th 2008. However, the same general approach seen previously by parties
will be maintained.
9
Proposed Stage 2 E3 GHG Calculator Modeling Approach 04/01/2008
One important difference is that in the Stage 2 model, under a multi-sector cap and trade
approach, there is not ‘target’ level of emissions reductions specifically required of the
electricity sector. Instead, in the LSE decision-making process we assume that LSEs
comply with all of the policy requirements (including energy efficiency (EE), Renewable
Portfolio Standard (RPS), and requirements for auction revenue reallocation
expenditures), and minimize costs to customers. The ultimate level of emissions in the
electricity sector will be the result of the selected ‘least cost’ plan. As in the Stage 1
model, we do not intend to ‘optimize’ the resource plan and automatically choose
resources based on cost – this choice will be left to the analyst using the GHG calculator.
However, we will add information about the levelized and marginal cost of different
choices in order to guide the analyst in resource selection.
As in the Stage 1 model, the order of the resource selection follows the loading order
defined in the Energy Action Plan II. There is an additional step in EE and RPS
compliance to verify that the distribution of allowance auction revenues, if selected by
the analyst using the GHG calculator, is spent. The analyst will:
1. Select the level of energy efficiency achievements for each load serving entity
2. Select new renewable resources statewide, either to meet or exceed a 20% or 33%
renewable portfolio standard target.
3. If the analyst has chosen to ‘earmark’ emission allowance auction revenue for
renewable energy and/or energy efficiency investment by LSE, the analyst must
decide how to allocate the auction revenue to energy efficiency and/or renewable
energy investments, above and beyond EE and RPS policy requirements.
4. The analyst may also select from among other conventional generation or non-
renewable, low-carbon generation, such as nuclear power or coal generation with
carbon capture and sequestration.
5. The GHG calculator will automatically adjust the energy and capacity balance with
new natural gas combined cycle units (CCGTs) and combustion turbines (CTs), based
on the user-selected resource mix.
4. Rate and cost impact to LSEs, 2020 emissions levels
The primary results of the model will be rate and cost impacts by LSE between a
reference case (with no carbon price) and an AB32 ‘compliant’ case. In addition, the
outputs will show the quantity of allowances and offsets purchased by energy deliverers
in the sector, and the change in California and overall WECC emissions between the two
cases.
Detail of Energy Deliverer Modeling: In the model, since our goal is to identify the
impact on California consumers, we do not intend to model each potential ‘energy
deliverer’ explicitly. Instead, our intent is to model the impact of emission allowance
options on individual generators, and then model the costs to LSEs as energy deliverers
pass these carbon costs on to LSEs through contract and market prices.
10
Proposed Stage 2 E3 GHG Calculator Modeling Approach 04/01/2008
Responses to Party Comments on Stage 2 Modeling Issues
Some of the comments made by parties in January on the Stage 1 results addressed the
Stage 2 model. We have grouped these comments by topic and provide a response
below.
1. Add marginal emission reduction cost for all measures in GHG
calculator
Proposed Change: A new output in the GHG calculator will show a measure-by-
measure net cost of reductions in a given scenario (in $/tonne of CO2). The data reflected
in the chart will capture both the direct costs as well as the avoided costs of each
emissions reduction measure, reflecting time-of-use differences among resources. This
feature will show the relative cost of sector emissions reductions and provide a basis for
comparison to proposed flexibility mechanisms in $ per tonne CO2e. This output may be
used to create a supply curve for the emission reduction costs of all measures in the GHG
calculator.
Comments: This change is being made in response to opening comments from
NRDC/UCS (p. 18, 19) and PG&E, (p. 36) comments. In their reply comments, DRA (p.
3) supported the supply curve output request. Energy Division staff also requested this
change.
2. Suggestions for modeling other sectors
Comment: A number of stakeholders suggested that the electrification of the
transportation sector should be a key piece of the Stage 2 analysis (SCE, p. 8, PacifiCorp,
p. 24-25, Solar Alliance, p. 6, CEERT, p. 12, IEPA, p. 5).
Response: In the Stage 2 analysis, a scenario reflecting greater electricification of the
transportation sector could be modeled by simply increasing the load forecast. We do not
intend to account for the emissions savings in the transportation sector resulting from
increased use of electric vehicles.
3. Suggestions for expanded regional analysis
Comment: A number of stakeholders requested that the regional scope of the modeling
effort be expanded to consider scenarios beyond implementation of California’s AB 32
legislation. PacifiCorp, (p. 19) suggested a scenario with other Western states limiting
GHG emissions. WPTF, (p. 3) requested that the model analyze the effect of the WCI, or
other GHG policies, being implemented in other WECC jurisdictions. While SMUD, (p.
9) and WPTF, (p. 3) requested the modeling of a carbon price in PLEXOS to simulate a
federal cap and trade regulated at the generator level.
Response: We plan to run PLEXOS with a carbon price added to all generators in
WECC, to simulate a WECC-wide GHG cap-and-trade policy, regulated at the generator-
level. This carbon price will change the least-cost dispatch of plants in the West. The
11
Proposed Stage 2 E3 GHG Calculator Modeling Approach 04/01/2008
results of this WECC-wide carbon-price dispatch will be discussed in the final project
report but will not be built directly into the GHG calculator. The GHG calculator will
continue to focus on the impacts of California-only GHG policies.
4. Suggestions for modeling point-of-regulation
Comment: Many stakeholders expressed opinions about which point-of-regulation to
model in Stage 2 of the GHG calculator analysis. WPTF, (p. 9) asked that the first-seller
approach be modeled, while DRA, (p. 3) and NCPA, (p. 10) both suggested flexibility to
model a variety of point-of-regulation policies. DRA (p. 6) also suggested modeling the
impacts of both cap-and-trade, as well as a cap with no trade.
Response: Given the CEC/CPUC Interim Decision to the ARB on GHG regulatory
strategies (CEC-100-2008-002-F, D.08-03-018), we plan to model the impacts on the
electricity sector of a multi-sector cap and trade approach with the energy deliverer
proposal as the model’s default assumption.
5. Suggestions for modeling flexible compliance mechanisms
Comment: Many stakeholders requested that offsets be modeled in Stage 2. PacifiCorp,
(p. 24-25), DRA, (p. 6), and SMUD, (p. 10) suggested the need to model the possibility
for the electricity sector to purchase offsets from other sectors.
Response: As described in this document, we plan to include an option for the use of
offsets in the Stage 2 model.
Comment: Many stakeholders requested that multi-year compliance periods, banking
and borrowing be modeled in Stage 2 (SDG&E, p. 10, DRA, p. 6, WPTF, p. 9,
NRDC/UCS, p. 18).
Response: We do not plan to model multi-year compliance periods or banking and
borrowing of emissions allowances in the Stage 2 model. These mechanisms are
primarily intended to account for the excess (or lack) of allowances in low and high
hydro years, and to generally ease year-to-year emission allowance market volatility. The
GHG Calculator, as a spreadsheet tool using preprocessed production simulation runs, is
not well-suited to evaluate the variations in hydro levels or other factors which would
cause intertemporal market volatility. In addition, we are not modeling the strategic
behavior of market participants other than the fixed assumptions described in this
document.
Comment: The GHG model may be able to test the impact of a low hydro-year or high
load growth on GHG emissions (SDG&E, p. 10).
Response: I would be possible to model variation in GHG emissions of the system in low
hydro-year or high hydro-year cases. However, given that the ARB emission inventory
already shows the impacts of hydro variation on the state’s emissions level, we believe
that investigations of the impact of changes in hydro-levels are better examined with this
historical data. Therefore, we do not plan separate Plexos runs to evaluate variation in
emissions due to hydro variation. The model can, however, evaluate the impact of
changes in load growth on GHG emissions.
Comment: Model a REC trading system with GHG cap and trade. (WPTF, p. 5)
12
Proposed Stage 2 E3 GHG Calculator Modeling Approach 04/01/2008
Response: In effect, the model already assumes that a REC trading system is in place in
California in 2020 along with cap and trade. This is because renewable resources
developed for the state to meet its GHG targets, and to meet its RPS targets, only require
that transmission be built to the nearest California load center. In this way, any California
LSE can ‘get credit’ for renewable resources developed anywhere in the state. Also, with
the energy deliverer decision, we are modeling the impacts of GHG cap and trade on the
electricity sector.
6. Suggestions for the modeled timeframe
Comment: NRDC/UCS, (p. 18) suggested modeling the trajectory of emissions from
2012 to 2020, and providing as an output the market price of allowances every year from
2012 to 2020. NCPA, (p. 2, 6) argues that the model is limited by its inability to measure
AB32 impacts between 2008 and 2020 and beyond 2020.
Response: In Stage 2 of the analysis, we do plan to disaggregate emissions by year based
on known changes to coal contracts, the CEC’s yearly load forecast, as well as a linear
extrapolation of other variables between 2008 and 2020. Since information on 2020 is the
primary factor required for the GHG Docket by the CEC and CPUC for the decisions
these agencies must make this year, we intend to keep 2020 as the farthest date modeled.
Comment: CEERT, (p. 12) suggested that in the business-as-usual reference case, the
state should meet a 20% RPS by 2013 and that the RPS should remain constant to 2020.
The aggressive reference case scenario should have 20% RPS by 2013 and 33% RPS by
2020.
Response: Currently, in the yearly emissions estimate analysis, we envision a simple
model reflecting a linear development of renewable resources between 2008 and 2020.
Since our primary point of focus is the year 2020, the year-to-year timing in which new
renewable resources are developed prior to 2020 is not a key driver in the overall results
of our analysis.
13
