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

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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)

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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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Hourly Load: Global Dairy Estelline, SD

Pearson’s Correlation Coefficient (generation & load): .054

Raw Demand

(200.00)

(100.00)

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Net Demand: [Optimized] Global Dairy

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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

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NPV of Savings: Sensitivity to Discount Rate

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Optimal Array: Sensitivity to $/W Installed Cost

$/W=1.50 $/W=2.00 $/W=2.50

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NPV of Savings: Sensitivity to $/W Installed Cost

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Optimal Array: Sensitivity to Initial Retail Energy

Price

$/kwh=$.049 $/kwh=$.07

$/kwh=.09

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NPV of Savings: Sensitivity to Initial Retail Energy

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$/kwh=$.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