Power Generation for Engineers
⚡ Power Drives Prosperity
Electric power production is the foundation of modern civilization, directly tied to prosperity and economic growth, with energy use strongly correlated to Gross Domestic Product. This importance is magnified by the explosive rise of data centers, particularly those powering artificial intelligence, with electricity demand that is doubling approximately every six months. Meeting this demand requires not only increasing generation capacity but also ensuring system reliability, efficiency, and sustainability. Engineers are at the forefront of this challenge: designing advanced power systems, integrating renewable and conventional energy sources, expanding transmission infrastructure, and building the physical plants that supply the world with electricity. This work is important for industrial and residential needs, but also for the digital infrastructure that now drives global innovation.
Power Generation and Distribution
Electric power generation converts primary energy sources (chemical, nuclear or renewable) into electricity. Most utility‑scale plants use fuel to create steam or hot gases that drive turbines coupled to synchronous generators. Mechanical rotation inside the generator induces alternating current (AC) according to Faraday’s law. To minimize line losses, electricity is stepped up to high voltages (hundreds of kilovolts) for transmission over long distances. High‑voltage AC is standard because transformers easily change voltage levels, but high‑voltage direct‑current (HVDC) lines are used for very long distances and to interconnect unsynchronised grids. HVDC lines transmit large amounts of power more efficiently because there is no reactive power and lower line charging current.
After transmission, the voltage is stepped down through substations and distribution transformers before reaching industrial loads or data centers. The U.S. produced about 4.18 trillion kWh of electricity in 2023. The electricity was generate with ~60% from fossil fuels, ~19% from nuclear and ~21% from renewables.
Calculating Power and Energy ⚙️
Power is measured in watts (W) and energy in watt‑hours (Wh). A gigawatt (GW) is one billion watts. The energy generated by a plant of power P over time t is:
$$E = P \times t$$
with E in watt‑hours when P is watts and t is hours. For example, a 2 GW plant operating continuously for 24 h produces:
$$E = 2~\text{GW} \times 24~\text{h} = 48~\text{GWh}$$
Over a 30‑day month, the same plant produces `2 \text{GW} \times 720 \text{h} = 1.44~\text{TWh}`.
Example: Two Data Centers Powered by a 2 GW Plant 🖥️
Assume a 2 GW power plant supplies two identical data centers, each receiving 1 GW. The energy consumed by each data center over a 30‑day month is:
$$E_\mathrm{dc} = 1\,\text{GW} \times 720\,\text{h} = 720\,\text{GWh} = 7.2\times 10^8\,\text{kWh}$$
The monthly peak demand is 1 GW (1 000 000 kW).
Billing Example 🔢
Utilities typically bill large customers using:
- Demand (power) charges: highest 15‑min demand during on‑peak periods.
- Energy charges: price per kWh consumed, often different for on‑peak and off‑peak hours.
- Fixed charges: customer service and facility charges.
Rocky Mountain Power (Utah)
The Utah Public Service Commission’s Schedule 8 (effective 25 April 2025) applies to customers with loads ≥ 1000 kW. Key charges are:
| Charge type | Typical rate (summer) | Notes |
|---|---|---|
| Customer service | $73 per month | per customer |
| Facilities charge | $4.95 per kW | applied to the measured kW demand |
| On-peak power charge | $16.18 per kW (Jun – Sep) $14.32 per kW (Oct – May) |
charged on maximum 15-min on-peak demand |
| Energy charge – on-peak | 5.9963 ¢/kWh (Jun – Sep) 5.3064 ¢/kWh (Oct – May) |
per kWh used during on-peak hours |
| Energy charge – off-peak | 3.0478 ¢/kWh (Jun – Sep) 2.6972 ¢/kWh (Oct – May) |
per kWh used during off-peak hours |
Example calculation: Suppose the data center peak demand is 1000 MW and on‑peak energy is 30% of total use. During summer months:
- Demand charges: 1,000,000 kW × $16.18/kW ≈ $16.18 M
- Facilities charges: 1,000,000 kW × $4.95/kW = $4.95 M
- Energy charges: on‑peak 30 % of 720 GWh × 5.9963¢/kWh + off‑peak 70 % × 3.0478¢/kWh ≈ $28.3 M
The total monthly bill is roughly $49 M. Actual bills include additional adjustments (power‑factor penalties, surcharges, etc.), but this illustrates how demand and energy charges dominate.
ERCOT / Texas (Oncor)
Texas operates a deregulated market. Retail energy providers sell electricity at a flat energy rate, and the Transmission and Distribution Utility (TDU) charges for delivery. For the Oncor area, distribution charges in August 2025 are $0.038447 per kWh. Many retail plans advertise energy rates around 9 ¢/kWh for businesses. These delivery charges are added to the energy price.
Example calculation: For the 1 GW data center consuming 720 GWh in a month:
- Energy cost: 720 million kWh × $0.09/kWh = $64.8 M.
- Delivery cost: 720 million kWh × $0.038447/kWh = $27.7 M.
The total monthly bill is about $92.5 M. ERCOT typically does not levy separate demand charges in retail contracts, so the bill is dominated by energy and delivery rates. The example highlights how regulated utilities (like Rocky Mountain Power) combine large demand charges with lower energy rates, while deregulated markets (ERCOT) rely more on per‑kWh pricing plus delivery charges.
Power Generation Sources in the U.S. 🌍
- Natural gas plants burn methane to produce high‑pressure steam or drive turbines directly. They offer fast ramp‑up and currently dominate U.S. generation (~43 % in 2023).
- Coal plants combust coal to make steam; their share has fallen from ~45 % in 2010 to ~16 % by 2023 as cleaner sources and regulations have reduced coal usage.
- Nuclear power uses fission in pressurized reactors to generate steam; it provided about 18.6 % of U.S. electricity in 2023 and offers high capacity factors but faces high capital costs and waste‑management challenges.
- Hydroelectric power captures energy from flowing water and contributed about 5.7 % in 2023 with output that varies with rainfall and snowpack.
- Wind turbines convert kinetic energy from wind into electricity. Wind generation grew 2.4‑fold over the last decade and provided 10.2 % of U.S. electricity in 2023.
- Solar photovoltaic and thermal plants convert sunlight into electrical energy; solar generation grew 7.8‑fold from 2015 to 2024. Together, solar and wind supplied 17 % of U.S. electricity in 2024.
Each source has unique advantages and limitations (availability of fuel, emissions, cost, and intermittency). A diversified generation portfolio improves grid reliability and reduces environmental impacts.
Python‑Generated Trend Figure 📈
The figure below shows an approximate trend of U.S. electricity generation share by major sources (coal, natural gas, nuclear, wind + solar, hydro) from 2010 to 2024. Coal declines steadily, natural gas rises then levels off, nuclear remains roughly constant, and wind/solar increase rapidly.
import matplotlib.pyplot as plt
# normalized shares for US electricity generation (sum to 100%)
years = np.array([2010, 2012, 2014, 2016, 2018, 2020, 2022, 2024])
coal = np.array([46.2, 41.63, 39.1, 30.99, 28.07, 19.53, 20.37, 17.54])
natural_gas = np.array([24.64, 29.73, 31.08, 35.12, 36.38, 41.11, 39.71, 40.25])
nuclear = np.array([20.53, 18.83, 19.05, 20.66, 19.75, 20.55, 19.35, 18.58])
wind = np.array([2.36, 3.47, 4.51, 5.68, 6.86, 8.63, 9.37, 10.42])
solar = np.array([0.1, 0.1, 0.25, 0.83, 1.66, 2.67, 4.79, 7.53])
hydro = np.array([6.16, 6.24, 6.02, 6.71, 7.28, 7.5, 6.42, 5.68])
fig, ax = plt.subplots(figsize=(8,5))
# reverse order for stacking and labels
sources = [hydro, solar, wind, nuclear, natural_gas, coal]
labels = ["Hydropower","Solar","Wind","Nuclear","Natural gas","Coal"]
ax.stackplot(years, sources, labels=labels)
ax.set_title("US Electricity Generation by Source (2010-2024)")
ax.set_xlabel("Year")
ax.set_ylabel("Total generation (%)")
ax.set_ylim(0,100)
ax.set_xlim(2010,2024)
# reverse legend order so Coal shows at top
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[::-1], labels[::-1], loc="upper right")
ax.grid(True, linestyle="--", alpha=0.5)
plt.tight_layout()
plt.savefig('power_generation_trend.png', dpi=300)
plt.show()
The code uses Matplotlib to build a stacked area chart. Update the years and share arrays with current data from EIA to regenerate the figure.
Summary
Electrical power systems convert fuel or renewable energy into electricity, transmit it at high voltage, and distribute it to end users. Engineers must understand how demand and energy charges affect the cost of operating large industrial loads such as data centres. In regulated markets like Utah, demand charges can exceed energy costs, while deregulated markets like ERCOT rely more on per‑kWh pricing. Diversifying generation sources, especially increasing solar and wind, is changing the U.S. electricity mix and reducing reliance on coal.
