Optimizing the Economics of Solar PV A Case Study with Implications for Optimal Tariff Structure
Arne Kildegaard, Ph.D. University of Minnesota, Morris Jordan Wente, B.A. University of Minnesota, Morris
Highlights: • Case study of PV project economics for two Upper Midwest dairies • Presents a novel approach to optimizing the size of behind-the-meter PV • Demonstrates interaction of tax and financial parameters with load and insolation data • Identifies how behind-the-meter operation raises risk to project economics • Makes the policy case for value of solar tariffs
Big Picture: • Declining costs of installed PV • Rising costs of grid power from fossil fuels • Regulatory risk facing carbon-based fuels • @$39/ton (current U.S. SCC), raise average coal price 155%
Big Picture
MN Electricity Prices, 1990-2013 14
10
Residential
8
Commercial 6
Industrial
4
Total
2
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
0
1990
U.S. cents per kwh
12
Ownership/Development Models • Utility-owned • Independent Power Producer • Individual end-user owned • • • •
Household Farm Commercial Industrial
How will the investment be repaid? • Utility-owned: • cost-recovery through rate-base
• IPP • direct sales to wholesale market
• Individual end-user owned • • • • •
Negotiated PPA with utility Behind-the-meter* FIT (feed-in-tariff) NEM (net-energy metering) VOST (value of solar tariff)
Behind-the-meter • PURPA & EPA: grid transformed from read-only to read-write • Qualified facilities (QFs) sell back @ “avoided cost”
• Under BTM: • power used on-site offsets retail purchase (~$.10/kwh) • power sold back earns “avoided cost” (~$.03/kwh)
• Introduces a large premium on matching load to generation
Case Study: Two Dairies • Why dairies? • 7-day/week operations, with high power loads • Located in high-cost REC jurisdictions • Low opportunity cost of space
• Which dairies? • Malecha Dairy (Pope County, MN) • Global Dairy (Hamline County, SD)
Data • Short-interval, enterprise-specific operational loads • from utilities
• Short-interval, location-specific solar insolation data • from Solar Anywhere dataset
• Simulated short-interval generation • PV Watts
• Tax rates, discount rates, depreciation schedules, energy inflation rate, other financial parameters • Electricity tariffs (energy, demand, & fixed charges) • Runestone Electric Association (Malecha) • HD Electric Cooperative (Global)
Method • Determine generation profile of 1-kw system (8760 hours) • Subtract generation profile from load profile for each hour • Plug NET load profile into tariff to calculate cost-of-meeting-load • Cost = Capital Cost + Energy Cost + Demand Cost + Fixed Cost – Tax Savings • 25-year technology horizon • Net present cost
• OPTIMIZE: • Scale the size-of-system up/down to MINIMIZE net present cost of meeting load
Optimization % Consumed on-site (Global Dairy)
Diminishing returns to size
100.00% 90.00%
Capital cost
80.00% 70.00%
π*
60.00%
Value of Energy
$'s
50.00% 40.00% 30.00% 20.00% 10.00%
1 34 67 100 133 166 199 232 265 298 331 364 397 430 463 496 529 562 595 628 661 694 727 760 793 826 859 892 925 958 991
0.00%
Array Size (kw)
Q*
Array Size (kw)
Data & Assumptions Parameter Table
Solar PV Parameters Array Size (kW) Degradation Rate Installation Cost per Watt Annual Operating Cost ($/Watt) Nominal Capital Costs: Tax Parameters Tax credit (ITC) ITC awarded? (1=YES; 0=NO) REAP (After Taxes) REAP awarded? (1=YES; 0=NO)) Marginal tax bracket (federal/state combined) Depreciable Basis of Investment
Malecha Dairy
Global Dairy
100 0.992 $2.50 $0.014
100 0.992 $2.50 $0.014
$250,000
$250,000
0.3 1 17.00% 0 32.00% 85.00%
0.3 1 18.75% 0 25.00% 85.00%
Data & Assumptions (more) Utility and Energy Market Rate Parameters Retail rate (per kWh) Monthly fixed charges Sales tax rate Installation Exempt from Sales Tax? (1=YES; 0=NO) Electricity Bill Exempt from Sales Tax? (1=YES; 0=NO) Winter demand charge rate (per kW): Summer demand charge rate (per kW): Avoided cost rate (per kWh): Other: RECs (per kWh) RECs counted? (1=YES; 0=NO) Financial Parameters Real Energy Inflation Rate Real Cost of Capital (discount rate) Net Capital Cost
$0.080 $60.00 0.06875 1 1 $6.50 $9.50 $0.030 $0.002 0
$0.049 $194.00 0.04 0 0 $12.40 $12.40 $0.029 $0.002 0
3.50% 3.00%
3.50% 3.00%
$175,000
$185,000
Raw Demand Hourly Load: Malecha Dairy Villard, MN
250.00
Hourly Load: Global Dairy Estelline, SD
400.00 350.00
200.00
300.00 250.00
150.00
kw
kw
200.00 150.00 100.00 100.00
50.00
50.00
0.00 0.00 0
1500
3000
4500
6000
7500
Hour Pearson’s Correlation Coefficient (generation & load): .248
9000
(50.00) 0
1500
3000
4500
6000
7500
Hour Pearson’s Correlation Coefficient (generation & load): .054
9000
Net Demand Net Demand: [Optimized] Global Dairy
Net Demand: [Optimized] Malecha Dairy
200.00
300.00
100.00
200.00
kw
400.00
kw
300.00
0.00
100.00
(100.00)
0.00
(200.00)
(100.00)
(300.00)
(200.00)
0
1500
3000
4500 Hour
6000
7500
9000
0
1500
3000
4500 Hour
6000
7500
9000
Results 1st Simulation (Parameters from Table 1)
Global Dairy
Malecha Dairy
Optimal Array Size (kW) Nominal Capital Costs: Net Capital Cost
221.85 $554,613.61 $410,414.07
335.14 $837,843.98 $586,490.78
($55,704.86) ($2,537,544.72) $80,364.85 ($410,414.07) ($1,176,718.99) ($64,188.70) ($4,164,206.48)
($84,152.24) ($1,480,635.05) $146,999.23 ($586,490.78) ($406,229.52) ($20,400.97) ($2,430,909.34)
99.35% $388,918.42 $4,521.47 $7,685.82
73.51% $739,938.59 $13,575.07 $229,869.86
Present Value of Operating Cost Present Value of Energy Cost Present Value of Tax Saving Present Value of Capital Cost Present Value of DD Charge Present Value of Fixed Costs Net Present Cost of Service Percent Consumed On-Site Present Value of Savings on Energy Costs Present Value of Savings on DD charge (relative to zero PV) Present Value of Total Savings (relative to zero PV)
Basecase Simulation 400.00 350.00 300.00
kw, $
250.00 200.00 150.00 100.00 50.00 0.00 Global Dairy
Optimal Array Size (kW)
Malecha Dairy
NPV of Savings ($1,000s)
Sensitivity Tests Optimal Array: Sensitivity to Discount Rate
Optimal Array: Sensitivity to $/W Installed Cost
500
kw
300 200 100 0
Global r=1%
r=2%
Malecha r=3%
600
r=4%
$/W=1.50
400
200 0
Global Global $/W=2.00
Malecha
$/kwh=$.049
$/W=2.50
Malecha $/kwh=$.07
$/kwh=.09
NPV of Savings: Sensitivity to $/W Installed Cost
500
600
NPV of Savings: Sensitivity to Initial Retail Energy Price
400
500
800
400
600
$1,000s
NPV of Savings: Sensitivity to Discount Rate
$1,000s
800
300 200
300 200
100
100
0
0
Global r=1%
r=2%
Malecha r=3%
r=4%
$1,000s
kw
400
1600 1400 1200 1000 800 600 400 200 0
kw
600
Optimal Array: Sensitivity to Initial Retail Energy Price
200 0
Global Global
$/W=1.50
400
$/W=2.00
Malecha $/W=2.50
$/kwh=$.049 $/kwh=.09
Malecha $/kwh=$.07
Conclusions & Policy Implications • General lack of generality • Very different results for similarly sized dairies in same region • Results very sensitive to complex interactions in the model
• Uncertainty is the enemy of investment • Some uncertainty is irreducible • Some uncertainty due to BTM • The party best positioned to understand the problem has mixed incentives
Uncertainty Due to BTM Contract • Structure creates strong premium on concurrence between load and generation • At the system level, this correlation matters, but at the individual consumer/farm/business level, it does not • An efficient price system aligns individual reward with system value
Value of Solar Tariffs (VOSTs) • Calculate the value (avoided costs) to the electricity system of solar power production • • • • • • • •
Energy Capacity Operating Costs Ancillary Services Delayed Transmission and Generation Investment Costs Reduced Line Losses Avoided Environmental Damages ...
VOSTs (cont’d.) • Methodology for calculating VOSTs exists • Austin, TX (Tariff published) • State of MN (Methodology published)
• Without a VOST: • Unnecessary degrees of uncertainty • A drag on the rate of investment
Thanks, • Tom Karas • Todd Malecha (Malecha Dairy) • Mike Crinion (Global Dairy) • Runestone Electrical Cooperative • HD Electric
