Rebecca Johnson, Ph.D. PUC Smart Grid Policy Specialist E-mail: rebecca.johnson@dora.state.co.us

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Nov 21, 2013 (3 years and 4 months ago)

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Rebecca Johnson, Ph.D.

PUC Smart Grid Policy Specialist

E
-
mail: rebecca.johnson@dora.state.co.us






Results from national studies on the energy
and CO2 impacts of smart grid



Colorado smart grid case study


Evaluation of Colorado
-
specific changes in CO2
and levelized cost under a variety of smart grid
scenarios



Key policy implications


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Source: Electric Power Research Institute. “The Green Grid: Energy Savings and Carbon
Emissions Reductions Enabled by a Smart Grid”. 2008

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Source: Pacific Northwest National Laboratory. “The Smart Grid: An Estimation of the
Energy and CO2 Benefits”. 2010

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Source: The Brattle Group. “How Green is the Smart Grid?”. 2009

Source: EIA 2006 Electricity Profiles







National
-
to
-
state and state
-
to
-
state electricity fuel mixes
vary dramatically.



Changes in CO2 due to
changes in the electricity
infrastructure are fuel mix
dependent and are therefore
state specific.



Electricity policy is largely
developed at the state level.





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Quantified Colorado
-
specific changes in CO2 and
levelized cost under a variety of smart grid
scenarios.



Modeled all generating units in the state plus
Laramie River Station in Wyoming (coal unit owned
by Tri
-
State)



Evaluated smart grid enabled:


demand response


large scale wind integration


energy efficiency


plug
-
in hybrid electric vehicle (PHEV) integration




Degrees of Grid Intelligence



Demand Response (Demand Flattening)



Wind Generation



Energy Efficiency (Demand Destruction)



Plug
-
in Hybrid Electric Vehicles (PHEVs)


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Conventional Grid


Business
-
as
-
usual operation.



Intermediate Grid (non
-
dynamic load shaping)


Time
-
of
-
use pricing, enhanced consumer information, and
programmable appliances shift demand from peak to off
-
peak.


Demand curve is flattened in a predictable way, but system does not
have the ability to dynamically shape demand to match supply.



Advanced Grid (dynamic load shaping)


Dynamic demand shaping via real
-
time pricing, enhanced consumer
information, price
-
responsive programmable appliances, and direct
load control.


System dynamically matches supply and demand using all generating
options, storage, and demand response.


Managed PHEV load follows renewable generation.

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Intermediate Grid

(non
-
dynamic load shaping)


Time
-
of
-
use pricing, enhanced consumer
information, and programmable appliances
shift demand from peak to off
-
peak.



Demand curve is flattened in a predictable
way, but system does not have the ability to
dynamically shape demand to match supply.





Advanced Grid


(dynamic load shaping)


Dynamic demand shaping via real
-
time pricing,
enhanced consumer information, price
-
responsive programmable appliances, and direct
load control.


System dynamically matches supply and demand
using all generating options, storage, and
demand response.


Managed PHEV load follows renewable
generation.


Without wind, perfect ability to flatten load increases
CO2 by 1% and decreases
levelized

costs by 0.2%.


More relevant to municipalities and rural electric
associations than to
PSCo
.



With 20% wind, demand response reduces wind
integration costs by up to $18 million per year. Smart
grid contributes <1% of total CO2 reductions.



With 50% wind, demand response reduces wind
integration costs by up to $226 million per year. Smart
grid contributes up to 9% of total CO2 reductions.

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Smart grid supports
wind integration by
aligning demand with
renewable generation.


Smart grid reduces wind integration costs by reducing
curtailment.



Curtailment expense is calculated as levelized cost
plus foregone production tax credit ($86.50 per MWh).


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Source: Ventyx Consulting

Modeled 5% and 15% energy efficiency improvements



Consumer demand reductions


highly uncertain


Feedback


4% to 12% (
Neenan

& Robinson, 2009; PNNL, 2010)


Time
-
based pricing


4% (King &
Delurey
, 2005)



Reductions in Transmission and Distribution Losses


relatively certain


2.4% (Xcel Energy, 2008)




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Sources: Ventyx Consulting, General Motors


A ‘typical’ PHEV in Colorado would emit 48%
less CO2 than an internal combustion vehicle.



Very high penetrations of PHEVs would rarely
overwhelm system generating capabilities.



However, highly problematic from the
distribution level perspective (7/1 CIM).



Managed charging is critical.


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Problem:


The traditional utility business model is a disincentive
to efficiency.



Potential State
-
Level Policy Solutions:


Alternate Business Models


Shared Savings


Bonus Return on Equity


Virtual Power Plant


Performance
-
Based

Renewable Energy and Energy
Efficiency Targets



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Smart grid’s wind integration benefits require
consumer adoption.



If consumers don’t adjust their behavior in response to
smart grid, the technology will become an expensive
mechanism to marginally improve electric utility
operational efficiency.



Consumer
-
centric mechanisms to promote adoption.


Outreach and education


Time
-
based pricing


Incentives and rebates


Privacy and data security assurance


Data ownership clarity



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June 7
th
, 9:00 am to 11:00 am


Topic: Electricity use feedback and customer behavior


Speakers:


Dr. Ahmad Faruqui
, The Brattle Group


Dr. Karen
Ehrhardt
-
Martinez, formally with NRRI, now consulting with her own firm, Human
Dimensions Research Associates


Nancy Brockway, former NH PUC Commissioner and current consultant on consumer and low
income issues



July 1
st
, 9:00 am to 11:00 am


Topic: Smart grid’s role in emerging markets


Speakers:


Peter Fox
-
Penner
, the Brattle Group


Emerging markets overview


Paul
Denholm
, the National Renewable Energy Laboratory


Plug
-
in hybrid electric vehicles impact on the electric grid



August , date and time
tbd


Technical aspects of smart grid


Communications platforms


IT infrastructure


Interoperability standards




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E
-
mail:
rebecca.johnson@dora.state.co.us



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Research supported by CU’s Renewable and
Sustainable Energy Institute (RASEI)



Data provided by Ventyx Consulting



Research guidance from the National
Renewable Energy Laboratory (NREL), Ventyx
Consulting, and Xcel Energy




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