Energy arbitrage economic analysis performed as part of MIT course on smart and resilient power grids

1. Valuation of Storage in Peak
Shifting Applications
December 12, 2016
Laura Gilson, Linda Jing, Kevin Schell,
Max Tuttman

2. Contents
2
• Introduction & methodology
• Optimization model results
• Next steps

3. Power is the only basic commodity which cannot be readily stored in
bulk; market and technology trends may be changing this
3
Market development Technology development
• “Duck pond” of net demand creates
large ramping requirements and
increases price fly-ups
• Declining inertia on the grid; more
demand for ancillary services
• Increased social barriers and financial
incentive to defer investment in new
transmission
• Large learning curve cost reductions
driven by non-grid applications of
batteries
• Basic research into aging processes
holds potential to significantly
extend battery lifetimes
• Improvements in cost and safety of
anode and cathode materials

4. Our calculations only focus on part of the value stream for batteries
4SOURCE: U.S. Solar Market Insight
Energy Cost
Reduction
T&D Cost Reduction
Ancillary Services and Wholesale
Market Cost Reductions
Reduced Peaking Capacity Requirements ~47%
~17%
~13%
~12%
~10%
Enhanced Distributed
Renewable Integration
% of Value Creation
Energy storage uses lower cost
energy at off-peak to replace
higher cost peak generation
• Reduced peak prices
• Reduced overall average
energy price

5. An optimization model was developed to analyze the value of storage
within a peak shifting application in the CAISO region
5
Optimization vs.
heuristic
Model
simplification
Constraint
formulation
• Simplified optimization was selected as the
initial methodology
• Optimization can be used to inform
heuristic which can handle larger data sets
• Value generation confined to wholesale,
peak shifting applications
• Days classified into sets based on price
variability
• Constraints were linearized to facilitate
optimization algorithm
• Idiosyncratic constraints required (e.g.
storage level same at hour 0 and 24)

6. The team applied the model to evaluate the viability six “traditional”
and battery storage technologies
6
Pumped Hydro Storage Compressed Air Energy Storage (CAES)
Lead acid batteries Lithium Ion Batteries (Li-ion)
Sodium-Sulfur Batteries (NaS) Redox Flow Batteries (VRFB)
SOURCE: Fraunhofer institute, EPRI
• Proven, economically viable
legacy technology
• ~127 GW installed globally
• Geographically constrained
• More loss than hydro due to
heating of pressurized air
• ~440 MW installed globally
• Geographically constrained
• Reliable and able to withstand
variable charging conditions
• O&M intensive
• Variable cycle lifetime
• High power & energy density
• Substantial manufacturing
base due to electronics
• Variable lifetime
• Most prolific battery in grid
applications
• ~316 MW installed globally
• High temp, can be unstable
• Energy can be scaled at low
cost relative to overall system
• Long cycle lifetimes
• Small install base

7. Contents
2
• Introduction & methodology
• Optimization model results
• Next steps

8. The optimization model yielded several key results
8
• NPV Negative: Peak-shifting alone does not provide
sufficient value to justify storage investments
• Fly-ups drive value capture: more than 50% of value
is captured from only ~30 days per year
• CapEx sensitivity: rebate incentives may be most
effective channel to encourage development
$
$
$

9. Peak-shifting alone recovers a minority of capital investment for
battery technologies
9
0.21
0.130.100.09
0.54
0.65
Li-ionLead-Acid NaS VRFB
1
Pumped
Hydro
CAES
20-year Normalized Present Value in Peak Shifting Application
USD PV per USD Invested
SOURCE: Peak-shifting optimization model
Traditional grid storage Battery storage
Breakeven present value
• No storage technology yielded
positive NPV from strict peak-
shifting application
– Constrained lifetime
– Aggregation obfuscates value
– Matched charge constraint
likely impacted hydro
• “Traditional” storage
significantly outperforms
battery storage technologies
• Although Li-ion generated the
most revenue, high capital cost
depressed project NPV
$

10. 0%
40%
20%10%
60%
30%
80% 100%
90%
50%
80%
20%
70%
0%
90%
100%
70%
10%
60%50%40%30%
Cumulative value generated vs. proportion of days
% of days | % of value
Majority of value was captured during the high, peak, and super-
peak which accounted for less than 20% of operational days
10SOURCE: Peak-shifting optimization model
• Across all storage types,
more than 50% of revenue
was generated on ~10-15%
of days; fly-ups drive value
capture
• Shape of curve is sensitive
to discount rate; more
linear at higher rates
• High power, short lifetime
technologies captured a
greater proportion of value
during fly-ups
• Viability of peak shifting
storage is very sensitive to
marginal dispatch on peak
demand days
Hydro CAES Lead Acid Li-ion NaS VRFB

11. NPV vs. Baseline (NaS battery)
USD ‘000s
• CapEx is the most
critical driver for battery
storage technology
viability in a peak
shifting application
followed by discount
rate
• Sacrificing performance
to achieve cost savings
may make sense in real-
world applications
• Incentives should be
focused on reducing up-
front capital cost (e.g.
SGIP rebates) and
financing cost for smaller
developers (e.g. “Green
Banks”)
Within the boundaries examined, battery storage viability is most
strongly affected by project CapEx
11SOURCE: Peak shifting optimization model
2.3
2.6
10.6
4.5
46.2
-2.6
-3.5
-8.9
Max
Cycle Efficiency
Min
-38.5Discount Rate
Rated Power
Lifetime
-12.4
Capital Cost
Geography
-96.6 103.4
Baseline NPV: (367) K
Parameter baseline
Parameter range
3.5%
10.0% to 3.0%
$463 per kWh
$560 to $360 per kWh
131 kW
110 kW to 150 kW
82.5%
75% to 90%
3,500 cycles
3,000 to 4,000 cycles
$
$
NP15
ZP26 and SP15

12. Contents
2
• Introduction & methodology
• Optimization model results
• Next steps

13. The optimization model could be extended along several dimensions
to further understand energy storage viability
13
Heuristic
development
Ancillary market
value capture
Commercial
application
Renewable
integration
• Analyze optimization behavior; develop a set
of logical rules to approximate optimization
• More realistic; longer data tranches
• Approximate value from participation in
ancillary markets in addition to wholesale
peak shifting
• Apply commercial tariff structures to charge
cost; understand commercial financials
• Incentive to pair with generation, load
• Integrate commercial model with solar
• Economic synergies from avoiding T&D cost
to charge
$

14. 14
APPENDIX

15. Hourly wholesale power cost vs. demand (CAISO NP15, PG&E)
MW | $ per MWh
An analysis of hourly price vs. demand suggests that regional marginal
cost of dispatch may not be primary driver of price fly ups
15SOURCE: CAISO real-time price and demand data
• Significant price fly-ups
do not appear to be
strongly correlated to
demand, even when
controlled for solar
influence
• If marginal dispatch was
primary driver of price
fly-ups, would expect a
hockey-stick shaped
chart
• Suggests that congestion
or localized imbalances
may be driving large
price deviations-100
0
100
200
300
400
500
600
700
800
900
1,000
12,000 16,00014,000 18,000 20,00010,000

16. Different regions have different market behavior and yielded different
technology valuations; NP15 was the “midpoint” region
16
0.31
0.19
0.21
SP15ZP26NP15
20-year Normalized PV by Geography
USD PV per USD Invested
• Northern California,
NP15, was selected as
the “baseline” region
• SP15, Southern
California, was the
highest value region;
almost 50% more
revenue generated
• SP15 likely experiences
a larger swing in prices
due to higher solar
penetration
SOURCE: Peak shifting optimization model

17. Overall model behavior, NaS batteries
17
Flat
Low
Mid
High
Peak
Super-Peak
-200
0
200
400
600
800
1,000
$150
$200
$0
$100
$250
$300
$50
20106 1484 16 22181220
-200
0
200
400
600
800
1,000
$400
$500
$600
$700
$0
$100
$200
$300
22201820 8 12 164 14106
-200
0
200
400
600
800
1,000 $1,000
$800
$200
$600
$0
$(200)
$400
106 8420 222018161412
-200
0
200
400
600
800
1,000 $1,000
$200
$400
$600
$800
$0
18 222016148 126 10420
0
200
400
600
800
1,000 $50
$40
$30
$20
$10
$0
0 2 2210864 181614 2012
-200
0
200
400
600
800
1,000
$60
$80
$120
$40
$20
$0
$100
201814121082 4 60 16 22
SOURCE: Peak shifting optimization model
Charge (Discharge) Rate (kW) Charge Level (kWh) Power price ($/kWh)

18. Model Formulation
18
Decision Variables
Bp,c Energy Bought
Sp,c Energy Sold
L0 Initial Energy Storage Level
Ymax Years of Operation
(p:15-minute period index, c: Class of day index)
Constraints
Ey  Emax
𝑝=1
96
𝐵𝑝,𝑐 =
𝑝=1
96
𝑆 𝑝,𝑐 𝑐
Bp,c  Bmax Bmax: Charge Power Rating
Sp,c  Smax Smax: Discharge Power Rating
Life  By  ymax Life: Storage Life in kWh
Lp,c  0
Lp,c  Lmax
L1,c = L0c
ymax  20
Objective
𝑚𝑎𝑥
𝑦=1
𝑦 𝑚𝑎𝑥
𝑃𝑉𝑦
PV: present value
E: Earnings
DF: Discount Factor
y: yearPVy = Ey  D F
Where
𝐸𝑚𝑎𝑥 =
𝑐=1
6
𝐷𝑐
𝑝=1
96
𝐸 𝑝,𝑐
Dc: Number of days in class
R: Electricity Rate
r: Discount Rate
Ep,c = Rp,c (Sp,c – Bp,c)
𝐵𝑦
=
𝑐=1
6
𝐷𝑐
𝑝=1
96
𝐵𝑝,𝑐
Lp,c = Lp-1,c + Bp-1,c – Sp-1,c
𝐷𝐹 =
1
(1 + 𝑟) 𝑦