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Discrete Choice Modeling of a Firm’s Decision to Adopt Photovoltaic Technology Chrystie Burr May 2, 2011 TexPoint fonts used in EMF. Read the TexPoint.

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Presentation on theme: "Discrete Choice Modeling of a Firm’s Decision to Adopt Photovoltaic Technology Chrystie Burr May 2, 2011 TexPoint fonts used in EMF. Read the TexPoint."— Presentation transcript:

1 Discrete Choice Modeling of a Firm’s Decision to Adopt Photovoltaic Technology Chrystie Burr May 2, 2011 TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA

2 2 of 20 Firm’s Decision in Adopting PV Technology Research Aims Develop an understanding of how firms respond differently to upfront subsidies and production subsidies. Develop a policy optimization framework for solar technology (policy target).

3 3 of 20 Firm’s Decision in Adopting PV Technology Introduction: Photovoltaic(PV) System diagram

4 4 of 20 Firm’s Decision in Adopting PV Technology Introduction: What is grid-connected PV? Grid-connected solar power system

5 5 of 20 Firm’s Decision in Adopting PV Technology Background - U.S. PV Market Cumulative Installation ( )

6 6 of 20 Firm’s Decision in Adopting PV Technology Background Global Market Share Solar PV Existing Capacity, 2009 (source: REN21)

7 7 of 20 Firm’s Decision in Adopting PV Technology Trends in Photovoltaic Application Fastest growing energy technology in the last 5 years.

8 8 of 20 Firm’s Decision in Adopting PV Technology Driver for the PV boom Lower cost Government Incentive Programs

9 9 of 20 Firm’s Decision in Adopting PV Technology Background- PV Price Trends Price of crystalline modules declined by 50-60% from $3.5/W to $2/W in 2008/2009.

10 10 of 20 Firm’s Decision in Adopting PV Technology Incentive Programs in the U.S.

11 11 of 20 Firm’s Decision in Adopting PV Technology Data Annual installed capacity ( ) by states: Larry Sherwood (IREC) Subsidy: Dollar amount recovered from DSIRE database Electricity price: EIA Solar Irradiation: NREL # businesses: US small business admin.

12 12 of 20 Firm’s Decision in Adopting PV Technology Summary Statistics VariableMeanStd. Dev.MinMax share0.18% revenue28,2142, ,179 upfront % sub upfront (size) sub.33,43866,998041,500 elec. price

13 13 of 20 Firm’s Decision in Adopting PV Technology Assumptions Potential market: 30% Annual discount rate: 8% System lifespan: 20 years Average PV size: 20kW Elec. escalation rate: 10 year average Maintenance cost: $0.01/kWh Inverter cost: $0.75/W Annual degradation factor: 1% Solar electricity conversion factor: 76% Net metering: null Company located in the largest metropolitan area in a state

14 14 of 20 Firm’s Decision in Adopting PV Technology Discrete Choice Model At each time period, a non-residential unit (commercial firm) can choose to install an average sized PV panel or not adopt PV technology Decision is based on the annual revenue generated by the system and the upfront cost, both affected by the incentive programs. The purchasers leave the market.

15 15 of 20 Firm’s Decision in Adopting PV Technology Model Firm’s profit function if not installed if installed R: NPV of the future benefit and costs Avoided utility cost Production incentive FC: Upfront installed cost τ uf : Upfront subsidy (% based) ξ mt : Fixed effect f(ε) = e ε /(1+ e ε )

16 16 of 20 Firm’s Decision in Adopting PV Technology Model if not installed if installed C AC : Avoided electricity cost for next 20 years Local solar Irradiation Electricity price τ p : Production subsidy X: Increased revenue from improved brand image P AV : Ave. cost of 20kW system W: State wage deviation from national mean L : Learning effect. f(cum. install) Code: Building codes depend on seismic activity and hurricane

17 17 of 20 Firm’s Decision in Adopting PV Technology Estimation Hierarchical Bayesian approach Let A =, B i = [ ] T ~ lognormal(b, D), Prior: b ~ N(0, s) s ∞, D ~ IW(3, V 0 ) Likelihood: Posterior: K(B i, b, D| Y) Conditional posterior:

18 18 of 20 Firm’s Decision in Adopting PV Technology Estimation Bayesian Procedure on BLP model Yang, S., Y. Chen, and G. Allenby (2003), ‘Bayesian analysis of simultaneous demand and supply’, Quantitative Marketing and Economics 1. Jiang, R., P. Manchanda, and P. Rossi (2009), ‘Bayesian analysis of random coefficient logit models using aggregate data’, Journal of Econometrics 149(2).

19 19 of 20 Firm’s Decision in Adopting PV Technology U.S. Solar Potential Map


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