Quantifying the cost savings of global solar photovoltaic supply chains

## Quantifying the cost savings of global solar photovoltaic supply chains

**.white[John Paul Helveston]**, George Washington University<br>
Gang He, Stonybrook University<br>
Michael Davidson, UC San Diego

November 18, 2022

]]

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## Between 2010 - 2020, global levelized cost of energy (LCOE) of utility-scale solar PV fell by 85%

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## China's "gift to the world"

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## China's "gift to the world"

]

China comprises ~70% of global PV manufacturing

- **Tariffs** on imported Chinese PV panels (US and EU)
- **Defense Production Act** invoked to accelerate US PV manufacturing (June 2022)
- **Inflation Reduction Act** passes w/incentives for domestic PV manufacturing (July 2022)

]]

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# .center[Same tensions in every low-carbon technology]

## .center[China is manufacturing leader in almost every sector]

Technology | Scale
----|------
Solar Panels | From 1% to 70% (2001 - 2019)
Wind Turbines | 1/3 of global supply (2020)
Electric Vehicles | 51% of global sales (2021)
Lithium-ion Batteries | 70% of global production (76% by 2025)
Nuclear Reactors | From 45 to 88 plants by 2030

]

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# What's the cost of<br>.red[national] versus .blue[global]<br>supply chains?

???

Restricting the free flow of capital, talent, and innovation

Localizing benefits in terms of growth, employment, and trade surpluses

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background-color: #FFFFFF

## .center[Learning curve model]

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background-color: #FFFFFF

## .center[Learning curve model]

]

In context of solar PV:

- X: Cumulative installed cap.
- Y: = Price per kW

<br>

Log transformation:

`$$\ln Y = \ln a + b \ln X$$`

]

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## Two-factor learning curve model:

<br>

## `$$\ln p_{it} = \ln \alpha_i + \beta_i \ln q_{t} + \gamma_i \ln s_{t} + \varepsilon_{it}$$`

<br>

## price ($ / kW) = intercept + installed capacity + silicon price

## for country _i_ and year _t_

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background-color: #fff

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## Two-factor learning curve model:

<br>

## `$$\ln p_{it} = \ln \alpha_i + \beta_i \ln q_{t} + \gamma_i \ln s_{t} + \varepsilon_{it}$$`

<br>

## Learning rate:

## `$$L_i = 1 - 2^{\beta_i}$$`

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## .center[Data Sources]
 
Country | Data | Source
--------|-----|----------------
Global | Installed PV capacity and prices | International Renewable Energy Agency (IRENA)
U.S. | Installed capacity | Solar Energy Industries Association (SEIA)
U.S. | Module prices  | Lawrence Berkeley National Laboratory (LBNL) & National Renewable Energy Laboratory (NREL)
China | Installed capacity & module prices | Energy Research Institute (ERI) & China Photovoltaic Industry Association
Germany | Installed capacity | IRENA
Germany | Module prices | Fraunhofer ISE50

All prices are in $2020 USD<br>(inflation adjustments from IMF, exchange rates from Federal Reserve Bank)

---

## Model results

|                                     | United States        | China                | Germany              |
| ----------------------------------- | -------------------- | -------------------- | -------------------- |
|                                     | Est. (Std. Err.)     | Est. (Std. Err.)     | Est. (Std. Err.)     |
| (Intercept)                         | 15 (1.04)\*\*\*      | 18 (1.58)\*\*\*      | 12 (0.96)\*\*\*      |
| log(cum\_capacity\_kw)              | \-0.44 (0.045)\*\*\* | \-0.57 (0.070)\*\*\* | \-0.33 (0.042)\*\*\* |
| log(price\_si)                      | 0.15 (0.058)\*       | 0.23 (0.079)         | 0.21 (0.054)         |

\*p<0.05; \*\*p<0.01; \*\*\*p<0.001

## Learning rates: `$L_i = 1 - 2^{\beta_i}$`

- U.S.: 26%
- China: 33%
- Germany: 20%

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## U.S.: 26%; China: 33%; Germany: 20%
 
<center>
<img src="images/cost_historical_global_plot.png" width=100%>
</center>

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## .center["National Markets" Counterfactual Scenario]

**Assumption**: learning-related price decreases in country _i_ in year _t_ are derived from incrementally more nationally-installed PV capacity

## `$$q_t - q_{t-1} = (q_{it} - q_{it-1}) + (1 - \lambda_t) (q_{jt} - q_{jt - 1})$$`

## `$(q_{it} - q_{it-1})$`: Amount installed in country _i_
## `$(q_{jt} - q_{jt-1})$`: Amount installed in all other countries

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## `$$q_t - q_{t-1} = (q_{it} - q_{it-1}) + (1 - \lambda_t) (q_{jt} - q_{jt - 1})$$`

<br>

## **Global markets**

`$\lambda_t = 0$`

Capacity from all countries

`$$(q_{it} - q_{it-1}) + (q_{jt} - q_{jt - 1})$$`

]

## **National markets**

`$\lambda_t = 1$`

Capacity only from country _i_

`$$(q_{it} - q_{it-1})$$`

<br>

`$\lambda_t$` -> 1 over 10-year period

]

---

**Higher prices in 2020**:

- 54% higher in China ($387 versus $250 per kW)
- 83% in higher Germany ($652 versus $357 per kW)
- 107% higher in the U.S. ($877 versus $424 per kW)

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## **Total Savings: $67 billion ($50 - $84 billion)**

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# Future projections

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## Two future projection scenarios out to 2030

#### National Trends (NT)

| Country | 2030 Target (GW) | Implied CAGR |
| ------- | ---------------- | ------------ |
| U.S.    | 295              | 12%          |
| China   | 750              | 12%          |
| Germany | 103              | 7%           |
| World   | 2,115            | 11%          |

]

#### Sustainable Development (SD)

| Country | 2030 Target (GW) | Implied CAGR |
| ------- | ---------------- | ------------ |
| U.S.    | 628              | 21%          |
| China   | 1,106            | 17%          |
| Germany | 147              | 11%          |
| World   | 3,125            | 16%          |

(Sustainable Development Scenario in the 2020 IEA World Energy Outlook)

]

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background-color: #FFFFFF

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## .center[Higher prices in 2030]

#### .center[National Trends (NT)]

- China: $162 versus $135 per kW
- Germany: $298 versus $251 per kW
- U.S.: $320 versus $262 per kW

]

#### .center[Sustainable Development (SD)]

- China: $136 versus $108 per kW
- Germany: $276 versus $221 per kW
- U.S.: $276 versus $221 per kW

]

<br>

For comparison, NREL's 2021 Annual Technology Baseline report predicts $170, $190, and $320 / kW by 2030 in advanced, moderate, and conservative improvement scenarios.

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# Sensitivity analysis app

## https://jhelvy.shinyapps.io/solar-learning-2021/

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class: inverse 
background-image: url("images/climate-bars.png")
background-size: cover

# Thanks!

### Slides:

### https://slides.jhelvy.com/2022-appam-solar-learning/

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# Extra slides

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