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Renewable Energy Integration: Technological and Market Design
Challenges Shmuel Oren, UC Berkeley
with: Anthony Papavasiliou, Duncan Callaway,
Johanna Mathieu, UC Berkeley Timothy Mount, Robert Thomas, Max Zhang,
Cornell University Alejandro Dominguez-Garcia, George Gross
University of Illinois, Urbana/Champaign
Future Grid Initiative PSERC IAB Meeting December 5-7, 2012
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Uncertainty
2
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Negative Correlation with Load
0
50
100
150
200
250
win
d po
wer
out
put
(MW
)
24 48 72 96 120 144 168 3000
4000
5000
6000
7000
8000
load
(MW
)
hour
wind power
load
3
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All Rights Reserved to Shmuel Oren
4
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Conventional Solution
Source: CAISO
5
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I Need a Brain
Source: GE 6
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• Accounting explicitly for uncertainty in operation and planning
• Stochastic unit commitment (with endogenous reserves determination) to support renewable penetration and demand response
• Probabilistic planning and simulation models (accounting for renewables, storage and demand response
• Mobilizing demand response (DR) and a paradigm shift to “load following available supply” provides an economically viable and sustainable path to a renewable low carbon future.
• Price responsive load • Energy efficiency • Deferrable loads:
• EV/PHEV • HVAC • Water heaters • Electric space heaters • Refrigeration • Agricultural pumping
7
TCLs
Making The Grid Smarter
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Task 1: Stochastic unit commitment for high wind
penetration
Shmuel Oren Athony Papavasiliou
UC Berkeley 8
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Motivation
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Model Structure
10
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Scenario Selection
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Parallelization
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Computational Efficiency Study
13
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Unit Commitment Summer Weekday
14
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Results
15
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Task 2: Mitigating renewables intermittency through
nondisruptive load control
Duncan Callaway Johanna Mathieu
UC Berkeley 16
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Research Goals
• Development of new TCL population models. • Development of effective control strategies that
preserve end-use function while delivering systemic benefits.
• Analysis of the ability of TCLs to provide power systems services as a function of the information available for system identification, state estimation, and control.
• Analysis of the TCL resource potential, costs, and revenue potential.
17
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Modeling Aggregated TCLs: ‘State bin transition model’
A Markov Transition Matrix describes the movement of TCLs around the dead-band.
ON
OFF
normalized temperature
1 2 3 4
Nbin-1 Nbin-2 …
…
Nbin-3 Nbin Nbin 2 +4
Nbin 2 +3
Nbin 2 +2
Nbin 2
+1
Nbin 2
-3 Nbin 2
-2 Nbin 2
-1 Nbin 2
stat
e
18
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Controlling TCLs to track a 5-minute market signal
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How BIG is the Resource Potential?
Estimates for most of California (5 largest utilities) based on RECs and CEC data.
2012 Resource Duration Curve
2020 Resource Duration Curve, assuming
increased efficiency and 30% of water/space heaters converted to
electric
20
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21
Potential revenues for regulation and load following
(per TCL per year)
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Task 3: Planning and market design for using dispatchable loads to meet renewable portfolio standards and
emissions reduction targets
Max Zhang
Tim Mount
Bob Thomas
22
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“Intelligent” Charging of PEV I (20% penetration in NPCC region)
70/30 Level I/II 50/50 Level I/II 30/70 Level I/II
80% of PEV load is assigned to valley hours to take advantage of low steady-state cost The remaining 20% is assigned to shoulder and peak hours to reduce ramping cost.
Charging Flexibility Constraint (CFC) restricts PEV charging during morning commuting hours
Valentine, Temple and Zhang (2011) J. Power Sources 23
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“Intelligent” Charging of PEV II (Wholesale Energy Costs are Reduced)
Intelligent Charging
Valley-fill Charging
• Intelligent charging results in significant systems cost reductions compared to unregulated and valley-fill charging
• Higher system cost reductions with higher PEV penetrations
Valentine, Temple and Zhang (2011) J. Power Sources 24
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“Intelligent” Charging of PEV III (Less Wind Generation is Curtailed)
Higher PEV penetrations decrease the average percentage of wind curtailed for cases with both the high and low wind uncertainty.
A better wind forecast (low uncertainty) not only curtails less wind by itself (0% PEV penetration) but improves the PEV dispatch pattern to be more closely
aligned with real system conditions so that much less wind is curtailed with high PEV penetrations.
Valentine et al. Energy Policy, under review 25
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Price Responsive Ice Storage Systems I (Total System Costs for NYISO are Reduced)
We are evaluating the benefits of aggregating Ice Storage Systems in large commercial and industrial buildings in New York State to reduce the system costs of the NYISO system. Heuristic methods were used to reduce system costs for a two-settlement wholesale market that accounts for both the steady-state and ramping costs of generating units. The optimal management of storage significantly reduces both the peak load and total
system costs, and flattens out the daily load profile.
Palacio et al. In preparation 26
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Price Responsive Ice Storage Systems II (Dispatch Patterns for a hot summer day in NPCC I)
Case 1: Base Case 2: Base + 32GW Wind
Results from simulations using the SuperOPF Case 1:Ramping for the daily load profile is provided by oil and natural gas generation Case 2:Wind displaces mainly oil and natural gas capacity and the remaining capacity
also provides additional ramping services to mitigate wind variability
Mount et al. HICSS 2013
27
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Price Responsive Ice Storage Systems III (Dispatch Patterns for a hot summer day in NPCC II)
Case 3: Base + 32GW Wind + 136GWh Deferrable Demand
Case 4: Base + 32GW Wind + 136GWh Collocated Storage
Case 3 v Case 2: More wind is dispatched, the daily pattern of Conventional Generation (CG) is flatter and the peak power delivered to customers is lower Case 4 v Case 2: Even more wind is dispatched and the daily pattern of CG is flatter than Case 3, but the peak power delivered to customers is still the same as Case 2
Mount et al. HICSS 2013
28
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Price Responsive Ice Storage Systems IV (Total System Costs for a hot summer day in NPCC)
Case 3 has DD, Deferrable Demand (Thermal Storage), at six load centers Case 4 has ESS, Energy Storage Systems, collocated at 16 wind sites Annual Capital Cost for a Peaking Unit is $88k/MW/year, allocated to 100 peak hours. Assuming there are 2 peak hours on this hot day, the capital cost is $1,760/MW
Adding wind capacity in Case 2 reduces the Total System Costs by 6.6% Adding deferrable demand in Case 3 reduces the Total System Cost by 16.7% The smaller decrease of 4.9% in Case 4 results from charging for storage capacity (using the same capacity price as a peaking unit) because customers will eventually have to pay for all system costs incurred by suppliers
Mount et al. HICSS 2013
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Task 4: Probabilistic Simulation Methodology for Evaluating the
Impact of Renewables Intermittency on Operation and
Planning
Alejandro Dominguez-Garcia, George Gross
University of Illinois, Urbana/Champaign
30
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THRUST OF THE SIMULATION APPROACH
• We develop a comprehensive, computationally efficient Monte Carlo simulation approach to emulate the behavior of the power system with integrated storage and renewable energy resources
• We model the system load and the resources by discrete-time stochastic processes
• We develop the storage scheduler to exploit arbitrage opportunities in the storage unit operations
• We emulate the transmission-constrained hourly day-ahead
markets (DAMs) to determine the power system operations in a competitive environment
31
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PROPOSED SIMULATION APPROACH: CONCEPTUAL STRUCTURE
“dri
ver”
stoc
hast
ic p
roce
sses
renewable power outputs
conventional generator available
capacities
loads
storage
schedule
market
clearing
procedure
(DCOPF)
congestion rents
CO 2
emissions
storage
operations
. .
.
“out
com
e” st
ocha
stic
pro
cess
es
LMPs
32
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KEY ELEMENTS OF THE APPROACH
• We construct appropriate c.d.f. approximations to evaluate the expected system variable effects
• Metrics we evaluate include:
• nodal electricity prices (LMPs)
• wholesale purchase payments
• generation by resource and revenues
• congestion rents
• CO 2 emissions
• LOLP and EUE system reliability indices 33
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CASE STUDY: IMPACTS OF DEEPENING WIND PENETRATION
• We perform a wind penetration sensitivity analysis and quantify the impacts of wind integration on power system economics, reliability and CO 2 emissions over a one-year period
• We use a modified IEEE 118-bus system with 4 wind farms in Midwest integrated with total nameplate installed capacity in multiples of 680 MW
annual peak load: 8,090.3 MW
conventional generation resource mix: 9,714 MW
unit commitment uses a 15 % reserves margin provided by conventional units
wind power is assumed to be offered at 0 $/MWh 34
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ANNUAL INDICES VS. TOTAL WIND NAMEPLATE CAPACITY
thou
sand
$
110
120
130
140
150
160
170
180
- 9
.1 %
- 1
7.9
%
- 2
5.2
%
- 3
1.1
%
- 5
.6 %
- 1
0.5
%
- 1
4.5
%
- 1
7.5
%
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
thou
sand
met
ric
tons
power system variable effects
relia
bilit
y
CO2 emissions
total wholesale purchase payments
2
4
6
8
10
12
x 10-4
- 3
7.5
%
- 5
0.0
%
- 6
5.6
%
- 8
0.1
%
14 LOLP
35
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• The deeper penetration of wind resources consis-tently reduces the total wholesale purchase payments and the CO2 emissions, while improving system reliability
• However, the diminishing returns in the benefits of integrating deeper penetrations of wind resources is a key limitation
• Future research to include the incorporation of storage resources into the simulation approach and the assessment of storage resource impacts, in combination with the wind resources, on the power system variable effects
Results
36