| Item | Input | Notes |
|---|---|---|
| Residential | 220 units × 4.2 kW, DF 0.55 | High diversity at peak periods. |
| Commercial | 18,000 m² × 55 W/m², DF 0.70 | Office/retail mixed occupancy. |
| Industrial | 350 kW, DF 0.85 | Near-continuous process load. |
| Street lighting | 420 poles × 120 W, SF 0.90 | Dimming schedules reduce peak. |
| EV charging | 48 chargers × 22 kW, SF 0.35 | Managed charging lowers coincidence. |
| Public facilities | 180 kW, DF 0.75 | Schools/clinics with time-based peaks. |
| IoT & telecom | 25 kW, DF 0.90 | Always-on infrastructure. |
| Custom load | 6 × 7.5 kW pumps, DF 0.60 | Example irrigation or drainage pumps. |
- Connected load (kW): sum of all nameplate loads converted to kW.
- Diversified peak (kW): for each category: Diversified = Connected × Factor, then summed across categories.
- Design load (kW): Design kW = Diversified kW × (1+Growth%) × (1+Redundancy%).
- Apparent power (kVA): kVA = kW ÷ PF.
- Current (A):
- Three-phase: I = (kVA×1000) ÷ (√3×V)
- Single-phase: I = (kVA×1000) ÷ V
- Energy estimate (kWh/day): Average kW = Peak kW × Profile factor, then kWh/day = Average kW × Hours/day.
- Enter connected loads for each city component (residential, commercial, lighting, EV, and more).
- Set demand/simultaneity factors to reflect non-coincident operation at peak.
- Add special consumers using custom rows for pumps, stations, data rooms, or kiosks.
- Apply growth and redundancy percentages to cover future expansion and reserve capacity.
- Choose power factor, phase, and voltage to estimate transformer kVA and current.
- Use hours/day and profile factor to estimate daily energy for utility forecasting.
- Click calculate, then download CSV or PDF for records.
Smart city power planning starts with a clear boundary: define what “served by this feeder or transformer” really includes (district lighting, traffic systems, public Wi‑Fi, CCTV, smart meters, pumping, and EV charging). The goal is not to add nameplate ratings; it is to estimate the coincident peak that can realistically occur at the same time. That is why demand or simultaneity factors matter more than perfect load inventories.
A reliable workflow is: list connected loads by subsystem, assign a defensible demand factor, then apply growth and redundancy. Growth covers new connections and higher utilization over time. Redundancy (or reserve margin) protects service continuity for critical assets such as water booster stations, command centers, and telecom shelters. After you compute design kW, convert to kVA using power factor, then estimate current from voltage and phase. These outputs help you shortlist transformer size, feeder ampacity, and protective devices before detailed design.
Energy is the other half of the story. Peak demand drives infrastructure sizing, but daily kWh influences utility coordination, operating cost, and sustainability targets. If you do not have interval data, a practical approximation is to scale peak kW using a load profile factor and multiply by operating hours. For public lighting, the profile factor may be high during evening hours and near zero in daylight; for data infrastructure, it can stay consistently high.
For mixed-use corridors, consider separating “always-on” digital infrastructure from highly variable community loads. This can improve resilience and power quality, especially where LED drivers, VFDs, and fast chargers introduce harmonics. Early segregation also simplifies metering, demand response, and staged expansion without repeated outages.
Suppose a district has 1,200 kW connected load across buildings, 50 kW for smart lighting, and 300 kW for EV chargers. Using demand factors of 0.60, 0.90, and 0.35 gives a demand kW of: 1,200×0.60 + 50×0.90 + 300×0.35 = 870 kW. Add 20% growth and 10% redundancy to get 1,131 kW design kW.
With PF 0.90, required capacity is about 1,131 / 0.90 ≈ 1,257 kVA. At 0.70 profile factor and 12 hours/day, energy is 1,131×0.70×12 ≈ 9,502 kWh/day. Use the calculator’s example table for a fuller subsystem breakdown.
Always validate assumptions with local codes, utility requirements, and operational schedules. When comparing options, keep the same factors across scenarios so differences reflect design changes, not inconsistent inputs.
1) Why use demand factors instead of nameplate totals?
Because most loads do not peak together. Demand factors estimate coincident peak realistically, preventing oversized transformers and feeders while maintaining acceptable reserve capacity for critical services.
2) What is a good growth margin for new districts?
Typical planning ranges are 10–30%, depending on development certainty and connection timelines. Use higher values when occupancy is expected to rise quickly or future phases are not yet quantified.
3) How do I choose redundancy or reserve percentage?
Tie it to service criticality. Essential systems (water pumping, control rooms, telecom) may justify 10–25% reserve. Noncritical amenities can use lower reserves if outage tolerance is acceptable.
4) What power factor should I enter if unknown?
Use 0.90 as a common planning value for mixed loads. If the site has many motors or poorly corrected lighting, PF may be lower; if correction capacitors are specified, PF may be higher.
5) How should EV charging be treated in planning?
Avoid assuming all chargers run at full power. Apply a managed-charging demand factor based on control strategy, arrival patterns, and charger type. Utility diversity guidance or measured data improves accuracy.
6) What does the profile factor represent?
It scales peak demand down to an average operating level for energy estimates. A flatter load curve uses a higher factor; strongly time‑dependent loads (events, lighting) use a lower factor.
7) Are the results suitable for final electrical design?
They are best for feasibility and preliminary sizing. Final design must check cable voltage drop, fault levels, protection coordination, harmonics, and local code requirements using detailed load schedules and utility data.