Curbside EV Charger Turnover Planner
Plan curbside charging around turnover, not just hardware
Curbside EV charging succeeds or fails on a simple operational question: when a driver arrives, is a connector likely to be available within a reasonable amount of time? That question is not answered by charger power alone. A block can have modern equipment and still perform poorly if vehicles stay plugged in too long, if the site is available for fewer hours than expected, or if utilization assumptions are unrealistic. This planner is designed to make those trade-offs visible before a city, utility, parking operator, or private charging provider commits to installation, pricing, or service promises.
The calculator estimates how many charging sessions a curbside site can support in a typical day and over an active year or season. It also translates that turnover estimate into energy delivered, gross charging revenue, idle-fee revenue, and a simple peak-load figure. Those outputs are useful in early feasibility studies, curb management planning, grant applications, utility coordination, and public communication. They are also useful when comparing policy choices. For example, a team can test whether adding connectors, shortening average dwell time, extending operating hours, or changing idle-fee assumptions has the biggest effect on access.
Unlike a detailed simulation, this tool uses average values. That makes it fast and transparent. You can discuss assumptions in a meeting, change one field, and immediately see how the scenario shifts. The trade-off is that the calculator is best for planning-level estimates rather than exact operational prediction. It will not tell you the queue length at 6:15 p.m. on a rainy Friday, but it will help you understand whether a site is likely to be lightly used, balanced, or strained.
What each input means in practice
Number of connectors is the total number of charging plugs available to drivers. In curbside planning, this is often more important than the number of pedestals because one pedestal may serve more than one vehicle if it has multiple connectors. The calculator assumes each connector can host one charging session at a time.
Operational hours per day is the number of hours the site is effectively available for the charging pattern you want to model. Some locations are available nearly all day, while others are constrained by street cleaning, loading zones, parking regulations, enforcement windows, or overnight restrictions. If a site is technically energized 24 hours a day but parking rules make it unusable for part of that time, use the realistic available hours rather than the theoretical maximum.
Average plug-in dwell time is the full time a vehicle remains connected, measured in minutes. This is broader than active charging time. If a driver charges for 90 minutes and then leaves the car connected for another 20 minutes, the operational effect is 110 minutes of occupied connector time. For turnover planning, that full occupied time matters because the next driver cannot use the connector until the first vehicle leaves.
Target utilization of available hours is the share of each connector's available time that you expect to be occupied on average. A value of 70% means the connector is in use for 70% of the modeled operating hours. This is a planning assumption, not a guarantee. Lower values may reflect a new site or a location with uncertain demand. Higher values may reflect a mature corridor, but they can also indicate a risk that drivers will frequently encounter a full curb.
Rated charger power is the nameplate power of the charger in kilowatts. Average energy per session can be entered directly if you have meter data or a strong estimate from a similar site. If you enter zero, the calculator estimates energy per session from charger power and dwell time. Energy price to driver is the tariff in dollars per kilowatt-hour. Idle fee after grace and average idle minutes billed per session estimate overstay revenue. Finally, active season days per year lets you annualize results for a full year, a pilot season, or a site that is periodically unavailable.
How to use: How the turnover model works
The model follows a time-budget approach. Each connector has a limited number of hours available per day. Only a portion of those hours is assumed to be occupied, based on the utilization target. Once occupied time is known, the calculator divides that time by the average dwell time to estimate how many sessions fit into the day. This is why dwell time is so influential. If average stays fall, more sessions fit into the same number of occupied hours. If average stays rise, turnover drops even when demand remains strong.
After estimating sessions, the calculator layers on energy and revenue. If you provide a direct energy-per-session value, the script uses that number. If you leave the field at zero, the script estimates energy from charger power multiplied by dwell hours. Revenue then combines energy sales and idle-fee income. Annual values are daily values multiplied by the number of active season days. Peak load is calculated as the number of connectors multiplied by rated charger power, which is a simple simultaneous-maximum assumption useful for screening discussions with utilities and facility planners.
Key formulas used in the planner
The page preserves the calculator formulas in MathML so they remain machine-readable and accessible. Let C be connectors, H operating hours per day, U utilization as a fraction, D dwell time in minutes, P charger power in kilowatts, Es energy per session in kilowatt-hours, pe the energy price, pi the idle fee per minute, and I billed idle minutes per session.
First, total sessions per day are estimated from occupied time divided by dwell time:
If energy per session is left at zero, the calculator estimates it from charger power and dwell time:
In planning discussions, f can represent a load factor that accounts for real-world charging behavior. In the current script on this page, the automatic estimate effectively behaves like a full-power approximation, so users should treat it as a convenient planning shortcut rather than a precise metered forecast.
Daily energy delivered is sessions multiplied by energy per session:
Daily energy revenue is daily energy multiplied by the charging tariff:
Daily idle-fee revenue is sessions multiplied by billed idle minutes and the idle-fee rate:
Total daily revenue combines energy sales and idle fees:
Peak load is estimated as all connectors charging at rated power at the same time:
Worked example
Imagine a city evaluating four curbside Level 2 connectors on a mixed-use street. The chargers are expected to be available for 18 hours per day. Average plug-in dwell time is 120 minutes, and the planning team targets 70% utilization. Under those assumptions, each connector is occupied for 12.6 hours per day on average. Dividing 12.6 occupied hours by a 2-hour dwell time gives 6.3 sessions per connector per day. Across four connectors, that becomes 25.2 daily sessions.
If the chargers are rated at 11 kW and the energy-per-session field is left at zero, the calculator estimates about 22 kWh per session from 11 kW multiplied by 2 hours. Multiplying 25.2 sessions by 22 kWh gives roughly 554.4 kWh delivered per day. At an energy price of $0.32 per kWh, energy revenue is about $177.41 per day. If drivers also incur an average of 10 billed idle minutes at $0.15 per minute, idle-fee revenue adds another $37.80 per day. Combined daily revenue is therefore about $215.21. If the site operates for 330 active days per year, annual gross revenue would be a little over $71,000 under this simplified scenario.
This example highlights why turnover planning matters. If average dwell time falls from 120 minutes to 90 minutes, more sessions fit into the same occupied hours and access improves. If utilization falls from 70% to 50%, sessions and revenue decline even though the hardware remains unchanged. If the idle fee rises, revenue may increase, but the more important effect may be behavioral: drivers may move sooner, reducing overstays and improving availability for the next user.
How to interpret the results
Daily sessions is the clearest measure of how many charging visits the site can support. For curbside planning, sessions per connector are often especially useful because they let you compare sites of different sizes on equal footing. A site with high sessions per connector is turning over quickly. A site with low sessions per connector may be serving longer stays, lower demand, or both.
Energy delivered helps you understand the service value of the site. A high-turnover location may serve many vehicles but deliver modest energy per visit. An overnight residential curb may serve fewer vehicles but deliver more energy per session. Neither pattern is automatically better. The right outcome depends on whether your goal is broad access, neighborhood charging coverage, convenience for short stops, or total kilowatt-hours sold.
Revenue should be read as gross revenue, not profit. The calculator does not subtract network fees, maintenance, payment processing, parking enforcement costs, demand charges, utility upgrades, or capital recovery. Even so, the revenue estimate is useful for comparing pricing strategies and understanding whether a site is likely to rely mostly on energy sales or partly on idle-fee income.
Peak load is a screening metric for utility coordination. It assumes all connectors draw rated power at once. Real sites may use power sharing, managed charging, or experience lower coincidence, but this simple figure provides a conservative starting point for service-capacity discussions.
Scenario guidance for common curbside contexts
Different streets produce different charging behavior. A retail curb with short errands may justify lower dwell times and stronger turnover policies. A residential curb may tolerate longer stays because drivers are charging while parked overnight. A mixed-use corridor often sits between those extremes, with daytime visitors and evening residents sharing the same infrastructure. The calculator is most useful when you test several realistic scenarios rather than relying on a single forecast.
| Scenario | Connectors | Hours/day | Dwell time | Target utilization | Planning takeaway |
|---|---|---|---|---|---|
| Short-stay retail curb | 2 | 12 | 60 min | 60% | Higher turnover, useful where many drivers need brief top-ups during shopping or errands. |
| Mixed-use neighborhood | 4 | 18 | 120 min | 70% | Balanced access and energy delivery, often a practical starting point for urban pilots. |
| Overnight-focused curb | 4 | 10 | 360 min | 50% | Lower turnover but more energy per visit, suitable where residents lack off-street parking. |
These examples are not fixed recommendations. They simply show how the same hardware can behave very differently depending on local parking patterns, enforcement, and user expectations. In practice, many cities start with moderate assumptions, collect a few months of charging data, and then refine pricing, signage, and curb rules to improve turnover.
Assumptions and limitations
This is a planning-level calculator, not a full simulation. It uses average values rather than a distribution of arrival times, charging needs, and overstays. That means it is best for early sizing and policy comparison, not for predicting exact queue lengths or minute-by-minute occupancy. If a site is likely to operate near saturation, a queueing model or observed utilization data will provide a more realistic picture of wait times and unmet demand.
The tool also assumes a single average dwell time and a single average idle duration. Real curbside behavior is more varied. Some drivers unplug quickly, while others remain parked long after charging ends. Likewise, the revenue model assumes one energy price and one idle-fee rate. It does not account for time-of-use pricing, resident discounts, escalating penalties, parking fees, or permit systems that may interact with charging behavior.
On the electrical side, peak load is treated as the sum of all connector ratings. That is a useful screening assumption, but it may overstate actual simultaneous demand if the site uses load management or if vehicles rarely charge at full power together. The automatic energy-per-session estimate can also overstate delivered energy when vehicles taper or when onboard chargers limit AC intake below the station rating. If you have real metered data from a similar site, entering average energy per session directly will usually produce a better planning estimate.
Even with those simplifications, the calculator remains valuable because it makes the main trade-offs visible. It shows how turnover, energy, revenue, and load move together. That is often enough to improve early planning decisions, identify where more detailed analysis is worth the effort, and communicate assumptions clearly to stakeholders who may not work with charging data every day.
Frequently asked questions
How many EVs can a curbside charger serve per day?
It depends mainly on dwell time, utilization, and operating hours. A connector with long stays may only serve a few vehicles per day, while a short-stay site can serve many more. The calculator expresses this directly through sessions per connector and total daily sessions, which makes it easier to compare candidate sites or policy options.
How do idle fees affect turnover?
Idle fees matter less because of the revenue they generate and more because of the behavior they encourage. If drivers move sooner after charging ends, average occupied time falls and more sessions fit into the day. In other words, a well-designed idle-fee policy can improve access without adding hardware, although the exact effect depends on enforcement, signage, and driver awareness.
Introduction: What is a reasonable target utilization for curbside posts?
Many early-stage curbside programs test scenarios in the 30% to 70% range. Lower values may reflect a new or lightly used site. Higher values may indicate a mature, high-demand location, but they can also signal a risk that drivers will often find the curb full. It is usually wise to test several utilization levels rather than relying on a single point estimate.
Should I enter energy per session or leave it at zero?
If you have reliable meter data or a strong estimate from a similar site, entering energy per session directly usually gives a better planning result. Leaving it at zero is helpful when you only know charger power and dwell time, but remember that the automatic estimate is a simplified approximation. It is best used for early screening rather than detailed financial forecasting.
Calculator inputs
Arcade Mini-Game: Curbside EV Charger Turnover Planner Calibration Run
Use this quick arcade run to practice separating useful scenario inputs from common planning mistakes before you rely on the calculator output.
Start the game, then use your pointer or arrow keys to catch useful inputs and avoid bad assumptions.