Reconstruction · Istanbul, Turkey
Istanbul PV Depot — Cutting a Charging Peak 40% Without Touching a Single Charger
Charge-schedule optimizer cut peak 40% at zero infrastructure cost
- IndustryMunicipal transit
- Fleet size100 buses
- RegionIstanbul, Turkey
Challenge: Model a solar-assisted depot's electrical peak and find whether smarter charge scheduling — not more infrastructure — could meaningfully shrink the depot's demand-charge exposure.
The Problem
Istanbul's second-largest transit depot faced the electrification question every large operator eventually faces at scale: with 100 buses on-site and a 4,000 kW electrical service, what does the fleet's charging pattern actually cost every month — and is there a cheaper way to run the same fleet without buying more equipment?
The naive answer is to just build more service capacity. The better question — the one this depot's planners actually needed answered — is whether the scheduling of when buses charge, not the size of the connection, is the real lever worth pulling first.
How the Depot Digital Twin Was Used
Step 1 — Model the full depot, including solar. The 100-bus fleet was built against 150 kW chargers and a 4,000 kW service, with 2,333 kW of on-site solar PV added as a generation asset — reflecting the depot's actual planned photovoltaic installation.
Step 2 — Establish the baseline peak. Running the worst-plausible day produced a clear starting point:
| Service capacity (nameplate) | 4,000 kW |
| Modeled charging peak (worst day) | 3,750 kW |
| Spare after charging | 250 kW |
Step 3 — Turn the peak into a dollar figure. The Demand-Charge Exposure module priced that 3,750 kW peak against an entered utility rate: a directional ~$450,000 per year — the number that turns an electrical engineering result into a business case a CFO can act on.
Step 4 — Run the charge-schedule optimizer. This is where the twin stopped being a diagnostic tool and became a design tool. Rather than simply reporting the peak, the optimizer searched for a per-vehicle charge schedule — spreading each bus's charging window across its full parked time instead of letting every bus draw power the instant it arrives — and re-simulated the result through the same physics engine to measure the real outcome, not a theoretical one.
Step 5 — Read the result, with the trade-off shown alongside it.
| Current modeled peak (on-demand) | 3,750 kW |
| Scheduled peak | 2,250 kW — 1,500 kW lower (40%) |
| Buses scheduled | 100 |
A 40% cut in coincident peak, achieved purely by timing — no new chargers, no larger service connection, no capital spend. At the same $10/kW-month rate, that peak reduction is worth roughly $180,000/year in avoided demand charges.
Step 6 — Adopt it with one click, or hold it for review. The proposed schedule can be written directly into the depot's vehicle charge windows in a single, reversible action — turning a modeled proposal into an operating plan the moment the fleet team signs off.
The Result
The Depot Digital Twin found that Istanbul's depot could cut its coincident charging peak by 40% — from 3,750 kW to 2,250 kW — purely through smarter charge timing, at zero infrastructure cost. That reduction is consistent with the direction and scale of managed-charging results reported in the published techno-economic study of this depot (Duman, Yılmaz & colleagues, 2025), which found PV-assisted charging management could cut the depot's 25-year net present cost by more than half when combined with charger-sharing strategies.
−40%
Coincident charging peak cut from 3,750 kW to 2,250 kW through charge timing alone
Benchmarked against Duman, A. C., et al. (2025), techno-economic analysis of PV-assisted depot charging, Istanbul
Why It Matters
"A 40% peak reduction that costs nothing to implement is the kind of result that makes an electrification business case close itself."
This is the scenario every fleet operator wants: proof, before spending a dollar on new infrastructure, that the existing connection can carry more fleet than a naive on-demand charging pattern would suggest — simply by scheduling smarter.
Features demonstrated
- Solar PV integration
- Spare-capacity ledger
- Demand-charge exposure with annualized dollar output
- Charge-schedule optimizer with one-click adopt
- Real-time charge timeline visualization (baseline vs. scheduled)
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