Because people don’t take the time to investigate claims, or have the knowledge to judge whether or not a claim is reasonable, politicians (and scientists) get away with making claims that are, in truth, pure fiction. This is also true when the subject is space solar power. An example of this is a recent headline on livescience.com: China plans to build enormous solar array in space — and it could collect more energy in a year than ‘all the oil on Earth’ at www.livescience.com/space/space-exploration/china-plans-to-build-enormous-solar-array-in-space-and-it-could-collect-more-energy-in-a-year-than-all-the-oil-on-earth (the omission of a live link is deliberate).
Having given presentations on the subject of space solar power, most recently to an amateur astronomy club, I was quite interested in this news story. What told me that this news was pure fiction was the quote given by lead scientist Long Lehao who said that “The energy collected in one year would be equivalent to the total amount of oil that can be extracted from the Earth.” That immediately struck me as being pure nonsense but it did raise an interesting question in my mind: how large would a space solar power satellite need to be in order to deliver an amount of energy equivalent to that of all the oil mined/produced in one year (ignoring Long’s multiyear claim).
The Energy of Oil
Using 2023 as a baseline, the sources I found stated that global crude oil production for 2023 totaled 4.5 billion metric tons. Other sources stated that a metric ton of oil contains approximately 42 gigajoules (GJ) or 11.63 megawatt-hours (MWh) of energy. For space solar power, the appropriate energy measurement is megawatt-hours (MWh) of energy. To determine the energy content of the 4.5 billion metric tons of oil produced in 2023, we just multiply that number by the 11.63 megawatt-hours (MWh) of energy.
4.5 billion metric tons * 11.63 megawatt-hours per metric ton = 52.3 billion megawatt-hours (MWh)
We now have the energy target of 52 billion megawatt-hours (MWh) of energy – if the sources I used are correct and if I have done my math correctly.
Total Solar Irradiance, aka Solar Constant
The next step is to determine how large an area a space solar power satellite would need to have in order to deliver 52 billion megawatt-hours (MWh) of energy. We start by looking at the value of total solar irradiance (TSI) at Earth’s distance from the Sun. This value is also known as the Solar Constant. Total solar irradiance (TSI) is the power per unit area, typically a square meter, received from the Sun at the top of Earth’s atmosphere. This quantity is 1,361 watts per square meter (W/m2). To convert this power value into an energy value, we simply multiply by the number of hours in a year.
1361 watts per square meter * 24 hours * 365.25 days = 11,930,526 watt hours per square meter (Wh/m2) / 1,000,000 =
11.93 megawatt-hours of energy per square meter (MWh/m2) .
The next step is to determine how much of that energy is actually captured by the photovoltaic solar array. For this exercise it is assumed that the solar arrays are perpendicular to the Sun. The conversion efficiency of a photovoltaic solar array depends on the technology used in building the solar cells that make up the array. The following table lists various types of photovoltaic solar cells for consideration.
Commercially Available Photovoltaic Solar Arrays
| Type of Solar Array Cell | Typical Efficiency |
| Monocrystalline silicon | 18–23% |
| Polycrystalline silicon | 15–18% |
| Thin-film (CdTe, a-Si) | 10–13% |
| High-efficiency commercial (e.g., SunPower) | 22–24% |
Space Grade Photovoltaic Solar Arrays
| Type of Solar Array Cell | Typical Efficiency |
| Triple-junction gallium arsenide (GaAs) | 28–34% |
| Advanced multijunction (e.g., 4-junction) | 35–38% |
| Lab-grade multijunction (record holders) | 40–47% (not typical) |
Given the high cost of Advanced 4-junction GaAs arrays and their marginal efficiency improvements, lets assume that Triple-junction gallium arsenide (GaAs) arrays are used and that the ones used have a conversion efficiency of 34 percent. From this we can determine how much of the Sun’s energy is actually captured for use:
11,930,526 watt hours per square meter (Wh/m²) * 0.34 efficiency = 4,056,378 watt hours per square meter (Wh/m²) / 1,000,000 = 4.05 megawatt-hours of energy per square meter (MWh/m2) .
Now that we know that we can get 4.05 megawatt-hours (MWh) of energy per square meter from our photovoltaic array, we can determine how many square meters of solar array will be needed to collect 52.335 billion megawatt-hours (MWh) of energy.
52.335 billion MWh / 4.05 MWh/m² = 12,901,901,440 square meters of solar array
Given that the article states that the array will be 1 kilometer wide, our width becomes 1,000 meters. To calculate the length, we simply divide the area by the width.
12,901,901,440 square meters of solar array / 1,000 meters wide = 12,901,901 meters long
Converting meters to kilometers, we find that the area of the solar array will need to be 1 kilometer wide by 12,901 kilometers long!
Outlandish claims, such as the one being made by the Chinese scientist Long Lehao as reported by livescience.com do nothing to help the credibility of those who are advocates for space solar power as a possible clean, green energy solution for planet Earth.
Update: The statement by Long Lehao was apparently made at a conference at the Chinese Academy of Sciences as reported by the South China Morning Post.
A Python Program To Calculate Solar Array Size
I performed the original solar array sizing calculations using Libreoffice Calc. As a way to confirm that the formulas and numbers were correct, I wrote a Python program that performed the same calculations (different tool – different thought process). The Python program makes it easy to play with the PV efficiency and total desired energy values and I added this program to a library of other space solar power programs I’ve written. The source code for the Python program follows and should be sufficiently documented so as to make its operation clear. I encourage you to make a copy of the program and use it as a starting point in your own investigations of the space solar power concept.
"""
Program Name: CalcArraySize.py
Created: May 1 2025
@author: Jim Plaxco
Description: Calculate the area of a space-based solar array in square kilometers (km²)
that is needed to deliver a specified amount of energy, given in megawatt-hours
per year (MWh/year).
The fixed parameters are the solar constant and the number of hours in a year.
The variable parameters are:
* the photovoltaic efficiency
* the amount of energy desired measured in megawatt-hours per year (MWh/year).
The output is the area in kilometers of solar array needed to deliver the energy
required as defined by the Target_Energy_Output variable.
"""
# Constant Parameters
Solar_Constant = 1361 # W/m² (TSI (total solar irradiance) at Earth's orbit)
Hours_Per_Year = 365.25 * 24 # Includes leap year correction
# Variable Parameters
PV_efficiency = 0.34 # % (space-grade triple-junction cells)
# Target_Energy_Output is the desired energy in megawatt-hours per year (MWh/year)
Target_Energy_Output = 52335000000
# Calculate the total energy received per square meter in 1 year in megawatt hours
Energy_received_megawatt_hours_per_year = (Solar_Constant * Hours_Per_Year) / 1_000_000
print(f"Energy received in 1 square meter during 1 year: {Energy_received_megawatt_hours_per_year:.4f} MWh/year")
Energy_megawatt_hours_per_year = Energy_received_megawatt_hours_per_year * PV_efficiency
print(f"Energy captured by PV array in 1 square meter during 1 year: {Energy_megawatt_hours_per_year:.4f} MWh/year")
# Total required area in square kilometers
PV_area_required_km2 = (Target_Energy_Output / Energy_megawatt_hours_per_year) / 1000000
print(f"Required solar array size: {PV_area_required_km2:.2f} km²")
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UPDATE 05/16/2025: A member of the Facebook Commercial Spaceflight Group to which a link for this article was posted used ChatGPT 4o Deep-Research to perform an analysis of the correctness of this article. ChatGPT’s conclusion was that
the article “Space Solar Power: Marketing Versus Math” provides a mathematically rigorous reality-check on a bold claim. Our analysis confirms that the math in the article is fundamentally correct: there are no computational errors in the conversion of oil production to energy, the use of the solar constant, the application of PV efficiency, or the area calculation. The author’s quantitative reasoning is internally consistent and uses valid data.
You can read the full ChatGPT analysis here: Analysis of Space Solar Power: Marketing Versus Math on ChicagoSpace.org