Post on 11-Jul-2020
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 • @$39/ton (current U.S. SCC), raise average coal price 155%
Big Picture
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MN Electricity Prices, 1990-2013
Residential
Commercial
Industrial
Total
Ownership/Development Models
• Utility-owned
• Independent Power Producer
• Individual end-user owned • Household
• Farm
• Commercial
• Industrial
How will the investment be repaid?
• Utility-owned: • cost-recovery through rate-base
• IPP • direct sales to wholesale market
• Individual end-user owned • Negotiated PPA with utility
• Behind-the-meter*
• FIT (feed-in-tariff)
• NEM (net-energy metering)
• VOST (value of solar tariff)
Behind-the-meter
• PURPA & EPA: grid transformed from read-only to read-write • Qualified facilities (QFs) sell back @ “avoided cost”
• Under BTM: • power used on-site offsets retail purchase (~$.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
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1Array Size (kw)
% Consumed on-site (Global Dairy)
$'s
Array Size (kw)
Diminishing returns to size
Capital cost
Value ofEnergy
π*
Q*
Parameter Table Malecha Dairy Global Dairy
Solar PV Parameters
Array Size (kW) 100 100
Degradation Rate 0.992 0.992
Installation Cost per Watt $2.50 $2.50
Annual Operating Cost ($/Watt) $0.014 $0.014
Nominal Capital Costs: $250,000 $250,000
Tax Parameters
Tax credit (ITC) 0.3 0.3
ITC awarded? (1=YES; 0=NO) 1 1
REAP (After Taxes) 17.00% 18.75%
REAP awarded? (1=YES; 0=NO)) 0 0
Marginal tax bracket (federal/state combined) 32.00% 25.00%
Depreciable Basis of Investment 85.00% 85.00%
Data & Assumptions
Utility and Energy Market Rate Parameters
Retail rate (per kWh) $0.080 $0.049
Monthly fixed charges $60.00 $194.00
Sales tax rate 0.06875 0.04
Installation Exempt from Sales Tax? (1=YES; 0=NO) 1 0
Electricity Bill Exempt from Sales Tax? (1=YES; 0=NO) 1 0
Winter demand charge rate (per kW): $6.50 $12.40
Summer demand charge rate (per kW): $9.50 $12.40
Avoided cost rate (per kWh): $0.030 $0.029
Other: RECs (per kWh) $0.002 $0.002
RECs counted? (1=YES; 0=NO) 0 0
Financial Parameters
Real Energy Inflation Rate 3.50% 3.50%
Real Cost of Capital (discount rate) 3.00% 3.00%
Net Capital Cost $175,000 $185,000
Data & Assumptions (more)
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Hourly Load: Malecha Dairy Villard, MN
Pearson’s Correlation Coefficient (generation & load): .248
(50.00)
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Hour
Hourly Load: Global Dairy Estelline, SD
Pearson’s Correlation Coefficient (generation & load): .054
Raw Demand
(200.00)
(100.00)
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Hour
Net Demand: [Optimized] Global Dairy
(300.00)
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Net Demand: [Optimized] Malecha Dairy
Net Demand
1st Simulation (Parameters from Table 1) Global Dairy Malecha Dairy
Optimal Array Size (kW) 221.85 335.14
Nominal Capital Costs: $554,613.61 $837,843.98
Net Capital Cost $410,414.07 $586,490.78
Present Value of Operating Cost ($55,704.86) ($84,152.24)
Present Value of Energy Cost ($2,537,544.72) ($1,480,635.05)
Present Value of Tax Saving $80,364.85 $146,999.23
Present Value of Capital Cost ($410,414.07) ($586,490.78)
Present Value of DD Charge ($1,176,718.99) ($406,229.52)
Present Value of Fixed Costs ($64,188.70) ($20,400.97)
Net Present Cost of Service ($4,164,206.48) ($2,430,909.34)
Percent Consumed On-Site 99.35% 73.51%
Present Value of Savings on Energy Costs $388,918.42 $739,938.59
Present Value of Savings on DD charge (relative to zero PV) $4,521.47 $13,575.07
Present Value of Total Savings (relative to zero PV) $7,685.82 $229,869.86
Results
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Global Dairy Malecha Dairy
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Basecase Simulation
Optimal Array Size (kW) NPV of Savings ($1,000s)
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Optimal Array: Sensitivity to Discount Rate
r=1% r=2% r=3% r=4%
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NPV of Savings: Sensitivity to Discount Rate
r=1% r=2% r=3% r=4%
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Global Malecha
kw
Optimal Array: Sensitivity to $/W Installed Cost
$/W=1.50 $/W=2.00 $/W=2.50
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Global Malecha
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NPV of Savings: Sensitivity to $/W Installed Cost
$/W=1.50 $/W=2.00 $/W=2.50
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Global Malecha
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Optimal Array: Sensitivity to Initial Retail Energy
Price
$/kwh=$.049 $/kwh=$.07
$/kwh=.09
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Global Malecha
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NPV of Savings: Sensitivity to Initial Retail Energy
Price
$/kwh=$.049 $/kwh=$.07
$/kwh=.09
Sensitivity Tests
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