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TRANSCRIPT
The future of the provision of electricity services
• Examine how distributed energy resources (DERs) and information and communication technologies (ICTs) are creating new options for the provision of electricity services…
• …with a focus on the USA & Europe over the next decade & beyond
• To make policy and regulatory recommendations…
• … to facilitate an affordable, reliable, low-carbon electricity system that encourages efficient utilization of all resources, whether centralized or decentralized
Principal Investigators Ignacio Perez-Arriaga, Professor, Electrical Engineering, Institute for Research in Technology Comillas Pontifical University; Visiting Professor, MIT Energy Initiative
Christopher Knittel, George P. Shultz Professor of Applied Economics, Sloan School of Management, MIT; Director, Center for Energy and Environmental Policy Research, MIT
Project Directors Raanan Miller, Executive Director, Utility of the Future Study, MIT Energy Initiative
Richard Tabors, Visiting Scholar, MIT Energy Initiative
Research Team Ashwini Bharatkumar, PhD Student, Institute for Data, Systems, and Society, MIT
Michael Birk, SM, Technology and Policy Program (’16), MIT
Scott Burger, PhD Student, Institute for Data, Systems, and Society, MIT
Jose Pablo Chaves, Research Scientist, Institute for Research in Technology, Comillas Pontifical University
Research Team (continued)Pablo Duenas-Martinez, Postdoctoral Associate, MIT Energy Initiative
Ignacio Herrero, Research Assistant, Institute for Research in Technology Comillas Pontifical University
Sam Huntington, SM, Technology and Policy Program (’16), MIT
Jesse Jenkins, PhD Candidate, Institute for Data, Systems and Society, MIT
Max Luke, SM, Technology and Policy Program (’16), MIT
Raanan Miller, Executive Director, Utility of the Future Study MIT Energy Initiative
Pablo Rodilla, Research Scientist, Institute for Research in Technology Comillas Pontifical University
Richard Tabors, Visiting Scholar, MIT Energy Initiative
Karen Tapia-Ahumada, Research Scientist, MIT Energy Initiative
Claudio Vergara, Postdoctoral Associate, MIT Energy Initiative
Nora Xu, SM, Technology and Policy Program (’16), MIT
The MIT Utility of the Future Study Team
Robert Armstrong, Director, MIT Energy Initiative
Carlos Batlle, Research Scholar, MIT Energy Initiative; Professor, Institute for Research in Technology, ComillasPontifical University
Michael Caramanis, Professor of Mechanical Engineering and Systems Engineering, College of Engineering, Boston University
John Deutch, Institute Professor, Department of Chemistry, MIT
Tomás Gómez, Professor, Director of the Institute for Research in Technology, Comillas Pontifical University
William Hogan, Raymond Plank Professor of Global Energy Policy, John F. Kennedy School of Government, Harvard University
Steven Leeb, Professor, Electrical Engineering & Computer Science and Mechanical Engineering, MITRichard Lester, Associate Provost and Japan Steel Industry Professor of Nuclear Science and Engineering, Office of the Provost, MITLeslie Norford, Professor, Department of Architecture, MIT
John Parsons, Senior Lecturer, Sloan School of Management, MIT
Richard Schmalensee, Howard W. Johnson Professor of Economics and Management, EmeritusDean, Emeritus, Sloan School of Management, MIT
Lou Carranza, Associate Director, MIT Energy Initiative
Stephen Connors, Director, Analysis Group for Regional Energy Alternatives, MIT Energy Initiative
Cyril Draffin, Project Advisor, MIT Energy Initiative
Paul McManus, Master Lecturer, Questrom School of Business, Boston University
Álvaro Sánchez Miralles, Senior Associate Professor, Institute for Research in Technology, Comillas Pontifical University
Francis O’Sullivan, Research Director, MIT Energy Initiative
Robert Stoner, Deputy Director for Science and Technology, MIT Energy Initiative
Faculty Committee
Research and Project Advisors
Chair: Phil Sharp, President, Resources for the Future
Vice Chair: Richard O’Neill, Chief Economic Advisor, FERC
Janet Gail Besser, Executive Vice President NortheastClean Energy Council
Alain Burtin, Director, Energy Management, EDF R&D
Paul Centolella, President, Paul Centolella & Associates LC, Senior Consultant, Tabors Caramanis Rudkevich
Martin Crouch, Head of Profession for Economists and Senior Partner, Improving Regulation, Ofgem
Elizabeth Endler, Research Program Manager, Shell International Exploration & Production (US) Inc.
Phil Giuidice, CEO, President, and Board Member, Ambri Inc.
Timothy Healy, CEO, Chairman and Co-founder, EnerNOC
Mariana Heinrich, Manager, Climate & Energy, World Business Council for Sustainable Development
Paul Joskow, President and CEO, Alfred P. Sloan Foundation, MIT Professor Emeritus
Melanie Kenderdine, Director of the Office of Energy Policy and Systems Analysis and Energy Counselor to the Secretary, U.S. Department of Energy
Christina La Marca, Head of Innovation, Global Thermal Generation, Enel
Alex Laskey, President & Founder, Opower
Andrew Levitt, Sr. Market Strategist, PJM Interconnection
Luca Lo Schiavo Deputy Director, Infrastructure Regulation, Italian Regulatory Authority for Electricity, Gas and Water (AEEGSI)
Gary Rahl, Executive Vice President, Booz Allen Hamilton
Mark Ruth, Principal Project Lead, Strategic Energy Analysis Center, National Renewable Energy Laboratory
Miguel Sánchez-Fornie, Director, Global Smart Grids, Iberdrola
Manuel Sánchez-Jiménez, Team Leader, Smart Grids, European Commission
Laurent Yana, Director Advisor of Global Bus, Group Strategy Division, Engie
Audrey Zibelman, Former Chair, New York State Public Service Commission
Advisory Committee
“L'avenir, tu n'as point à le prévoir mais à le permettre”
Citadelle, Antoine de Saint-Exupéry, 1948
“The future, you do not have to foresee it,
but to enable it”
1. A framework for an efficient and evolving power system
2. Insights into the value of Distributed Energy Resources
Presentation contents
The MIT Utility of the Future Study
A framework for an efficient and evolving power system
1. An Efficient System of Prices and Regulated Charges for Electricity Services
2. Improved Incentives for Regulated Distribution Utilities
3. Restructuring Revisited: Electricity Industry Structure in a More Distributed Context
4. Updating Short- and Long-Term Electricity Market Design
The MIT Utility of the Future Study
Towards a comprehensive and efficient system of prices and charges…
The best way to put all resources (centralized & distributed) on a level playing
field and achieve efficient operation and planning (and thus more affordable
electricity) is to dramatically improve prices and regulated charges for electricity
services.
1 3 5 7 9 11 13 15 17 19 21 23
Metered Withdrawals and Injections
?
1. Ensure that all prices and charges are non-discriminatory, technology neutral, and symmetrical
2. Progressively improve the granularity of price signals with respect to both time and location
Sp
atia
l gra
nu
lari
ty
Temporal granularity
Distribution nodal LMPs(DLMPs, real & reactive)
Intermediate DLMPs(substation/zonal/other)
Wholesale LMPs +distribution losses
Wholesale nodal LMPs
Wholesale zonal LMPs
Real-timespot price
Day-aheadhourly price
Critical peakpricing
Time-of-usepricing
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
$/
kW
h
Hour
Energy charge Network capacity charge
3. Forward-looking peak-coincident network capacity charges
3. …and scarcity-coincident generation capacity charges
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
$/
kW
h
Hour
Energy charge Generation capacity charge Network capacity charge
5. Carefully consider which costs are included in fixed electricity tariffs to avoid inefficient grid defection
Grid defection costs
Grid defection savings
Elec
tric
ity
Tari
ff
Co
mp
on
ents
Questions etc.6. Address distributional concerns without sacrificing efficient incentives that reduce costs for everyone
1. Cross-subsidies: Lump-sum bill credits or surcharges can restore desired cross-subsidies between high and low cost of service areas
2. Bill stability: Pre-payments and hedging arrangements can address bill variability
3. Low income support: need-based assistance programs can replace implicit (and imprecise) subsidies in volumetric rates
The MIT Utility of the Future Study
1. Reward utilities for cost-savings
Time
$
1st regulatory period 2nd regulatory period 3rd regulatory period
?
Realized cost savings (shared between utility
& ratepayers)
Multi-year revenue trajectory
Realized utility expenditures
The MIT Utility of the Future Study
2. State of the art regulatory tools to reduce information asymmetry & manage uncertainty
Incentive• -compatible menu of contracts to induce accurate utility forecasts and minimize
strategy behavior
Engineering• -based reference network models to equip regulators for forward-looking benchmarks
and analyze uncertainty scenarios
Automatic adjustment mechanisms to account •
for forecast errorsSee Jenkins & Pérez-Arriaga (2017), “Improved Regulatory Approaches for the Remuneration of Electricity
Distribution Utilities with High Penetrations of Distributed Energy Resources,” The Energy Journal 38(3): 63-91
The MIT Utility of the Future Study
Reduction in average outage duration in Italy following implementation of performance incentives for continuity of supply
3. Outcome-based performance incentives
Total annual service interruptions per customer
falls from more than 180 min to roughly 60 min
Average cost per customer: less than €2.5 per year
The MIT Utility of the Future Study
4. Incentives for long-term innovation
• Core remuneration incentives reward behavior that reduces costs or improves performance within the regulatory period (e.g. a few years)
• For utilities to engage in longer-term innovation, regulators must establish appropriate incentives
• Accelerate investment in applied R&D, demonstration projects, and learning about
capabilities of novel technologies and practices
• The goal: ensure utilities are cost-efficient not just in the short-term but in the long-term as well
The MIT Utility of the Future Study
1. Three critical functions (and a fourth?)
System Operator
Market Platform
Network Provider
Data hub?
The MIT Utility of the Future Study
2. Carefully assign responsibility to minimize potential conflicts of interest
Retailing / Aggregation
DER Provision / Ownership
Generation
System Operator
Market Platform
Network Provider
Data hub?
The MIT Utility of the Future Study
1. Enable wholesale market transactions to be made closer to real time
• This rewards flexibility and incentivizes improves forecasting and balancing
The MIT Utility of the Future Study
2. Enable new resources to play in existing and emerging markets
• Update wholesale market rules (such as bidding formats) to reflect the operational constraints of new resources
The MIT Utility of the Future Study
3. Align reserves & energy markets & establish the flexibility requirements for participation
• Energy prices should reflect the increased probability of scarcity events as reserves are dispatched
0
20
40
60
80
Baseload
Midload
Peaker
1 2 3
Opti
mal
cap
acit
y m
ix [
GW
]
0 2,000 4,000 6,000 8,000 10,000 12,000
Operating reserve level [MW]
0
2,000
4,000
6,000
8,000
10,000
Oper
atin
g r
eser
ve
val
ue
[$/M
W/h
]
1 2 3
The MIT Utility of the Future Study
4. Minimize the interference of support mechanisms for clean technologies in electricity markets
Production• -based subsidies distort marginal incentives and can cause undesired outcomes
Frequency of 5-minute negative price spikes in CAISO
The MIT Utility of the Future Study
Distributed resources are not unicorns, but they do provide locational services
1. The value of a distributed energy resource is highly dependent on the specific location of that resource in the power system.
2. “Locational value” can vary dramatically within a given power system. Efforts to define a single “value of solar” etc. will prove futile.
3. The marginal value of DERs can decline rapidly as locational value is exhausted.
4. Economies of scale still matter. Smaller isn’t always better.
The MIT Utility of the Future Study
1. Distributed resources create value by providing locational services
Locational Non-locational
Power system values
• Energy
• Network capacity margin
• Network constraint mitigation
• Power quality
• Reliability and resiliency
• Black-start
• Firm generation capacity
• Operating reserves
• Price hedging
Other values
• Land use
• Employment
• Premium values*
• Emissions mitigation
• Energy security
* Private values; do not need to be reflected in prices and charges
The MIT Utility of the Future Study
2. Locational value can be meaningful, but it varies dramatically within a power system
0.1% 0.1%
8.4%
50.4%
26.2%
7.3%
2.3% 0.9% 0.4% 0.4% 0.5%2.9%
<1 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 >100
USD per MWh
Distribution of 2015 Average Nodal LMPs in PJM
More than three quarters of nodes between $21-40/MWh
Approximately 3 percent of nodes with very high locational value, 3-10 times the average
The MIT Utility of the Future Study
3. Marginal locational value can be rapidly diminishing
0%
5%
10%
15%
20%
1% 6% 11% 16% 21% 26% 31% 36% 41% 46%
Pro
du
ctio
n-w
eig
hte
d a
vera
ge
mar
gin
al lo
sse
s av
oid
ed
by
PV
Percent of annual energy from distributed solar PV
Marginal Distribution Network Losses Avoided by Distributed Solar PV: Texas ERCOT Example
4% Avg Distribution Losses 8% Avg Distribution Losses
The MIT Utility of the Future Study
4. Economies of scale still matter
$48$98
$229
0
50
100
150
200
250
300
350
30 MW Utility-scale 1.5 MW Community 1 MW C&I Rooftop 5 kW Residential Rooftop
Leve
lize
dC
ost
of
Ele
ctri
city
(2
01
5 U
SD/M
Wh
)
Incremental cost relative to utility scale, NY insolation
The MIT Utility of the Future Study
5. High locational value can outweigh distributed incremental costs…
-$10
$10
$30
$50
$70
$90
$110
$130
$150
$170
$190
$210
$230
$250
LMPLocationalPremium
Distr. lossreduction
CVR Networkinvest.
deferral
Generationdeferral
Reliability Totallocational
value
1.5 MW 1.0 MW 5.0 kW
Ave
rage
val
ue
pe
r M
Wh
pro
du
ced
Locational Value and Distributed Opportunity Cost of Distributed Solar PV: Long Island, New York Example,
High Value Case
84.7
48.0
98.0
229.0
Distributed Opportunity CostsLocational Value
The MIT Utility of the Future Study
6. But not always. Smaller is not always better
-$10
$10
$30
$50
$70
$90
$110
$130
$150
$170
$190
$210
$230
$250
LMPLocationalPremium
Distr. lossreduction
CVR Networkinvest.
deferral
Generationdeferral
Reliability Totallocational
value
1.5 MW 1.0 MW 5.0 kW
Ave
rage
val
ue
pe
r M
Wh
pro
du
ced
Locational Value and Distributed Opportunity Cost of Distributed Solar PV: Mohawk Valley, New York Example,
Average Value Case
7.9
48.0
98.0
229.0
Distributed Opportunity CostsLocational Value