Optimizing the Economics of Solar PV

Transcription

1 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

2 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

3 Big Picture: Declining costs of installed PV Rising costs of grid power from fossil fuels Regulatory risk facing carbon-based (current U.S. SCC), raise average coal price 155%

4 U.S. cents per kwh Big Picture MN Electricity Prices, Residential Commercial Industrial Total 2 0

5 Ownership/Development Models Utility-owned Independent Power Producer Individual end-user owned Household Farm Commercial Industrial

6 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)

7 Behind-the-meter PURPA & EPA: grid transformed from read-only to read-write Qualified facilities (QFs) sell 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

8 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)

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10 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)

11 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

12 Optimization % % Consumed on-site (Global Dairy) Diminishing returns to size 90.00% 80.00% 70.00% 60.00% 50.00% $'s π* Capital cost Value of Energy 40.00% 30.00% 20.00% 10.00% 0.00% Array Size (kw) Q* Array Size (kw)

13 Tax Parameters Tax credit (ITC) 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 Parameter Table Malecha Dairy Global Dairy Solar PV Parameters Array Size (kw) Degradation Rate Installation Cost per Watt $2.50 $2.50 Annual Operating Cost ($/Watt) $0.014 $0.014 Nominal Capital Costs: $250,000 $250,000

14 Data & Assumptions (more) Utility and Energy Market Rate Parameters Retail rate (per kwh) $0.080 $0.049 Monthly fixed charges $60.00 $ Sales tax rate 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

15 kw kw Raw Demand Hourly Load: Malecha Dairy Villard, MN Hourly Load: Global Dairy Estelline, SD (50.00) Hour Pearson s Correlation Coefficient (generation & load):.248 Hour Pearson s Correlation Coefficient (generation & load):.054

16 kw kw Net Demand Net Demand: [Optimized] Malecha Dairy Net Demand: [Optimized] Global Dairy (100.00) 0.00 (200.00) (100.00) (300.00) (200.00) Hour Hour

17 Percent Consumed On-Site 99.35% 73.51% Present Value of Savings on Energy Costs $388, $739, Present Value of Savings on DD charge (relative to zero PV) $4, $13, Present Value of Total Savings (relative to zero PV) $7, $229, Results 1st Simulation (Parameters from Table 1) Global Dairy Malecha Dairy Optimal Array Size (kw) Nominal Capital Costs: $554, $837, Net Capital Cost $410, $586, 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, $146, 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)

18 kw, $ Basecase Simulation Global Dairy Optimal Array Size (kw) Malecha Dairy NPV of Savings ($1,000s)

19 $1,000s $1,000s $1,000s kw kw kw Sensitivity Tests Optimal Array: Sensitivity to Discount Rate 0 Global Malecha r=1% r=2% r=3% r=4% Optimal Array: Sensitivity to $/W Installed Cost Global Malecha $/W=1.50 $/W=2.00 $/W=2.50 Optimal Array: Sensitivity to Initial Retail Energy Price Global $/kwh=$.049 $/kwh=.09 Malecha $/kwh=$ NPV of Savings: Sensitivity to Discount Rate 0 Global Malecha r=1% r=2% r=3% r=4% NPV of Savings: Sensitivity to $/W Installed Cost Global Malecha $/W=1.50 $/W=2.00 $/W=2.50 NPV of Savings: Sensitivity to Initial Retail Energy Price Global $/kwh=$.049 $/kwh=.09 Malecha $/kwh=$.07

20 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

21 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

22 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...

23 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

24 Thanks, Tom Karas Todd Malecha (Malecha Dairy) Mike Crinion (Global Dairy) Runestone Electrical Cooperative HD Electric