Curbside EV Charger Turnover Planner
Configure your curbside charging posts
This planner estimates how many EVs a set of curbside chargers can serve per day, how much energy they deliver, and what that means for revenue and grid load. It is designed for city transportation planners, curbside charging operators, utilities, and consultants who need quick turnover and utilization scenarios for a specific blockface or corridor.
Unlike a generic EV charging ROI calculator, this tool emphasizes turnover (sessions per connector), dwell time (how long vehicles stay plugged in), and target utilization (how intensively you intend the posts to be used). You can use it to compare different curbside designs, pricing policies, and operating hours before committing to civil works or grid upgrades.
How the curbside turnover model works
The core of the model is a time-budget for each connector. You specify how many hours per day the curbside posts operate and what percentage of that time you aim to keep them occupied. From this, the calculator derives the total charging time available per connector and divides it by the average plug-in dwell time to estimate daily sessions.
At a high level:
- Available connector-hours per day = operational hours × target utilization.
- Sessions per connector per day = available connector-hours ÷ average dwell time (in hours).
- Total daily sessions = sessions per connector × number of connectors.
Energy delivered per session is either entered directly or, if you set it to zero, auto-calculated from charger power and dwell time with a load factor assumption. Daily and annual energy throughput then combine with your energy and idle-fee prices to estimate revenue.
Key formulas used in the planner
The relationships used in the calculator can be written as simple equations. Let:
- C = number of connectors
- H = operational hours per day
- U = target utilization (as a fraction, e.g. 0.7 for 70%)
- D = average plug-in dwell time (minutes)
- P = charger power (kW)
- Es = energy per session (kWh)
- pe = energy price to drivers ($/kWh)
- pi = idle fee ($/minute)
- I = billed idle minutes per session
First, the calculator converts dwell time into hours and computes sessions:
where S is total sessions per day. If you leave energy per session at zero, the model approximates:
where f is an internal load factor to reflect that most AC chargers do not run at full nameplate power for the entire session.
Total daily energy and revenue are then:
- Daily energy (kWh) = S × Es
- Daily energy revenue ($) = S × Es × pe
- Daily idle-fee revenue ($) = S × I × pi
Annual values multiply the daily results by your active-season days per year.
Interpreting daily and annual results
The results panel reports both daily and annual metrics so you can look at operational capacity and long-term impact:
- Sessions per day and per connector indicate how many vehicles you can realistically serve. For curbside AC posts, 4–10 sessions per connector per day is a common planning range.
- Daily and annual kWh help you understand grid impact and energy sales volume. Higher power or longer dwell times raise throughput but can reduce turnover.
- Energy vs idle-fee revenue show whether your pricing structure relies primarily on kWh or on managing overstays. High idle-fee revenue can signal that dwell limits or user communication should be improved.
- Implied blockface turnover—how many unique vehicles use the curb each day—comes directly from total sessions if each session represents a distinct vehicle.
When comparing scenarios, keep an eye on both sessions and kWh. A configuration with many short sessions may maximize turnover but yield lower average energy per visit, which can matter for driver satisfaction and grid planning.
Example curbside scenarios
The table below compares a few stylized setups to illustrate typical trade-offs. These are general planning examples, not exact outputs from the calculator.
| Scenario | Connectors | Hours/day | Dwell time | Target utilization | Turnover insight |
|---|---|---|---|---|---|
| Short-stay retail curb | 2 | 12 | 60 min | 60% | High sessions per connector, good for quick-turnover shopping districts. |
| Mixed-use neighborhood | 4 | 18 | 120 min | 70% | Balanced turnover and energy per session; suitable for overnight plus daytime visitors. |
| Overnight-focused curb | 4 | 10 | 360 min | 50% | Few sessions but high kWh per session; lower turnover but higher per-vehicle charge. |
Use the calculator to plug in your own blockface assumptions and see how sessions and energy change as you adjust dwell time, utilization, and operating hours.
Model assumptions and limitations
This planner is a simplified, planning-level tool. It makes several assumptions that you should keep in mind when interpreting results:
- Average behavior: It uses a single average dwell time and idle duration instead of a full distribution. Actual usage will have short and long stays that this model smooths out.
- No queuing model: Target utilization is an input, not an outcome of demand and waiting lines. The calculator does not simulate queues, reservations, or drivers leaving after seeing a full curb.
- Constant charger power: Power is treated as roughly constant during the charging portion of each session. Real EVs taper as they approach full state of charge and may be limited by onboard chargers.
- Single tariff: The model assumes a single energy price and a single idle-fee rate. It does not differentiate by time-of-day pricing or user segment.
- Homogeneous vehicles: All sessions are treated as if they have the same energy demand and charging behavior, regardless of battery size or arrival state-of-charge.
- Seasonality via days only: Active-season days account for seasonal pilots, snow removal, or construction, but intra-day variations (weekdays vs weekends) are not modeled separately.
Because of these simplifications, you should treat outputs as indicative scenarios rather than precise forecasts. For high-stakes investment or policy decisions, pair this tool with local usage data, demand studies, or more detailed simulation models.
Key input definitions and tips
- Target utilization of available hours (%): The share of each connector's operating hours you aim to have it actively occupied. It is a planning target per connector, not a guaranteed outcome.
- Average plug-in dwell time: The full time a vehicle remains connected, including both active charging and any billed idle time. Shorter dwell times increase turnover; longer dwell times increase kWh per session.
- Average energy per session (kWh): If you know typical energy per visit from meter data, enter it directly. If you enter 0, the tool estimates energy per session from charger power and dwell time using an internal utilization factor.
- Active season days per year: Use fewer than 365 days if posts are removed or blocked in winter, if the program is a limited pilot, or if construction regularly interrupts service.
Frequently asked questions
How many EVs can a curbside charger serve per day?
It depends mainly on dwell time, utilization, and operating hours. With 4 connectors, 18 operating hours, 70% target utilization, and 2-hour dwell times, planners often see on the order of 8–12 sessions per connector per day. Use this calculator to adjust those parameters and see a realistic range for your location.
How do idle fees affect turnover?
Idle fees primarily influence how long vehicles stay connected after charging completes. Higher idle fees can encourage drivers to move sooner, effectively reducing average dwell time and allowing more sessions per day. In the model, you directly control average idle minutes and the idle-fee rate, which together shape idle-fee revenue and implied turnover.
What is a reasonable target utilization for curbside posts?
For curbside AC charging, many programs plan for 30–70% utilization in early years, with higher values in mature, high-demand corridors. Very high targets (above ~80%) may signal that drivers will often find the curb full, leading to queuing or spillover to other parking uses. Treat utilization here as a scenario variable you can test across a range.
How does dwell time affect energy throughput and revenue?
Longer dwell times increase the energy delivered per session but reduce the number of sessions you can serve per connector each day. Shorter dwell times improve turnover and access but may reduce kWh and revenue per visit. The optimal balance depends on your policy goals: maximizing access and turnover, maximizing energy sales, or managing curb competition with other uses.
Why a Curbside Turnover Planner Matters
Cities across the globe are rushing to install curbside electric vehicle chargers, but the way drivers use those plugs differs dramatically from garage or workplace settings. The dwell time combines parking, charging, and occasional quick errands; occupancy swings with nightlife, commute waves, and tourists; and idle fees are a politically sensitive enforcement tool. Many municipalities rely on spreadsheets copied from parking studies, which rarely translate kilowatts, idle fees, and utilization into a unified picture. The curbside EV charger turnover planner addresses that gap by simulating how a row of posts will actually cycle through sessions, estimating both grid throughput and dollars earned. Instead of guessing whether four connectors on a blockface will keep up with demand, planners can model utilization, experiment with idle fee policies, and feed the outputs into broader capital plans. When paired with the EV charger load management planner and the EV charger idle fee cost calculator, street teams gain a clear roadmap for both electrical design and driver experience.
Unlike parking-only calculators, this tool treats energy and time as intertwined resources. Because curbside chargers tap the distribution grid already burdened with lighting, signage, and crosswalk signals, engineers must ensure the block’s load stays within transformer limits. Simultaneously, transportation departments aim to maximize turnover so each connector serves multiple vehicles per day, reducing the perception of scarcity. By entering operational hours, typical dwell length, and target utilization, users reveal how many sessions a connector can host. From there the planner layers on energy price, idle fee policy, and seasonality to estimate revenue streams that can fund maintenance, meter readers, or future expansion. Even community-led curbside pilots can use the tool to validate grant applications or public-private partnership pitches by showing transparent assumptions about throughput and cash flow.
How the Calculations Work
The planner begins by translating the dwell time into potential sessions. Every connector can theoretically serve sessions per day, where is the operational hours, is the dwell time in hours, is utilization as a decimal, and is the connector count. If users enter zero for energy per session, the tool defaults to charger power multiplied by dwell hours, acknowledging that some parking programs have little data on kWh draw. Energy revenue multiplies delivered kilowatt-hours by the posted tariff. Idle fee revenue estimates the impact of overstays by multiplying billed idle minutes by the per-minute fee and by daily sessions. The grid load indicator reports the simultaneous peak, computed as connectors multiplied by rated power, to help utilities confirm that service laterals and transformers can handle the demand.
Defensive programming ensures unrealistic entries do not skew the estimates. The script checks for missing or negative inputs, caps utilization between 0 and 100, and prevents division by zero when dwell minutes are extremely small. If net sessions fall below one per day, the output warns that connectors may sit idle and suggests re-evaluating deployment. Conversely, if utilization pushes turnover beyond practical limits, the planner flags the risk of queues that might require an enforcement blitz or additional connectors. These safeguards align with best practices in operational modeling and reflect lessons learned from tools like the EV fast charger queue time calculator, which also underscores how sensitive public perception is to wait times.
Worked Example
Consider a downtown corridor planning to install four Level 2 curbside chargers powered at 11 kilowatts apiece. The city expects enforcement officers to cover 18 hours per day, leaving overnight free for residents. Surveys show the average driver remains plugged in for two hours, while the parking team hopes to achieve 70% utilization by adjusting signage and partnering with nearby retailers to advertise the chargers. Assuming drivers consume roughly 22 kilowatt-hours per session and an idle fee of $0.15 per minute kicks in for about ten minutes on average, the planner reports 25.2 daily sessions, or 6.3 sessions per connector. That yields 554 kilowatt-hours delivered daily and $177 in combined energy and idle revenue. Annualized across a 330-day active season, the system could deliver 182,820 kilowatt-hours, generating $58,410 in gross revenue. The block’s peak load lands at 44 kilowatts, a crucial data point for coordinating with the utility on transformer upgrades.
The results table also reveals planning nuances. Daily idle fee revenue of $37.80 may seem modest, but it signals to policy staff that even small overstays add up; shifting idle minutes from ten to five would cut the enforcement-funded budget in half. Meanwhile, the forecasted 6.3 sessions per connector helps equity advocates judge whether chargers will be accessible to multiple neighborhoods or dominated by repeat users. By experimenting with the inputs—for example, trimming dwell time to 90 minutes or boosting utilization to 85%—planners can test whether changes in signage, marketing, or dynamic pricing would materially increase turnover without overwhelming the grid.
Scenario Comparison
The table below contrasts three policies a city might debate before issuing a curbside charging request for proposals. Each row assumes four connectors but adjusts dwell time, utilization, and idle fees to represent a laissez-faire approach, a balanced enforcement regime, and a high-turnover push. Seeing the differences in sessions, revenue, and energy helps stakeholders align on a target state before procurement begins.
| Policy | Sessions/Day | Energy/Day (kWh) | Revenue/Day ($) |
|---|---|---|---|
| Minimal enforcement | 16.8 | 370 | 92 |
| Balanced turnover | 25.2 | 554 | 177 |
| Aggressive rotation | 33.6 | 739 | 248 |
Limitations and Assumptions
The planner assumes every connector is independently metered and that simultaneous peak load equals the sum of rated power. In reality, power sharing schemes can clamp load to protect transformers, which would reduce instantaneous draw and stretch dwell times. The energy-per-session estimate also simplifies the messy relationship between state of charge, ambient temperature, and driver behavior; real-world data may show wide variance that should be modeled separately in tools like the EV fleet charging load balance planner. Idle fee revenue is treated as linear, even though some cities escalate fines after repeat offenses or cap total charges per session. Additionally, the calculator ignores capital expenses, demand charges, and network subscription fees that affect net operating income.
Seasonal adjustments reflect the number of days the site operates at modeled assumptions. Snow routes, street festivals, or maintenance may temporarily disable chargers, but those nuances are best handled by tweaking the season input rather than embedding complex calendars. The tool does not simulate queueing beyond highlighting high utilization, nor does it incorporate behavioral responses such as drivers avoiding streets with aggressive enforcement. Finally, while the planner surfaces peak load, it does not size conductors, breakers, or service panels; engineers should still validate electrical infrastructure using the EV charger installation calculator or consult local codes.
Putting the Outputs to Work
Planners can export the results to summarize grant applications, compare business models, or design pilot evaluations. For example, if a program aims to support low-income drivers with discounted charging, the turnover output reveals how many subsidized sessions could be offered per day before affecting availability. Business improvement districts can use the revenue estimates to negotiate cost-sharing for streetscape upgrades or to justify investments in wayfinding signage that advertises charger availability. Utilities reviewing interconnection requests can cross-reference the peak load figure with feeder maps to determine whether to require on-site energy storage or advanced load management.
The planner also aids community engagement. Residents worried about parking scarcity can see exactly how many vehicles the chargers will serve, while small businesses can evaluate whether turnover aligns with customer visit lengths. Advocacy groups promoting equitable EV adoption can combine the tool’s throughput forecasts with demographic maps to ensure chargers are sited where renters and curb-dependent drivers live. Because the calculator is transparent and runs entirely in the browser, cities can embed it in online surveys or public workshops, inviting participants to explore trade-offs interactively rather than relying on static posters.
Pathways for Further Analysis
Once planners are satisfied with blockface turnover, they can dive deeper into network resilience. Pairing the outputs with the microgrid islanding failure risk calculator helps evaluate whether onsite storage could maintain service during outages. Meanwhile, energy managers comparing curbside and garage deployments can contrast turnover predictions with the EV charger load management planner to highlight differences in load profiles. Each of these tools builds on transparent math and open inputs, empowering teams to iterate without expensive consultants or proprietary software.
Ultimately, the curbside EV charger turnover planner is designed to shrink the gap between visionary policy and street-level execution. By translating policy levers into operational metrics—sessions, energy, revenue, and peak load—it equips decision-makers to phase investments intelligently, justify public spending, and deliver a curbside experience that accelerates electric mobility adoption. As cities experiment with dynamic pricing, smart load balancing, or shared mobility hubs, this foundational analysis ensures every connector earns its keep while keeping residents moving.