Sizing Solar + BESS: why the answer lives in sub-hourly data

The conventional way to size a solar plant is volumetric: take annual consumption, divide by expected generation per MW, apply a target offset percentage, and quote a capacity. The arithmetic is easy and the answer is incomplete in a way that flatters the project. Annual totals say nothing about when energy arrives versus when it is needed — and in a market that settles in 15-minute blocks, when is most of the answer.

A year contains 35,040 fifteen-minute blocks. A solar plant delivers its energy into perhaps a third of them, concentrated in a midday window. An industrial load spreads across all of them — shaped by shifts, processes, seasons, and holidays. The economics of a Solar + BESS system are decided block by block, in the gap between those two shapes.

The Shapes Never Match

Plot a typical industrial load curve against a solar generation curve and the mismatch is immediate. Solar rises steeply mid-morning, peaks around noon, and is gone by early evening. The load may be flat around the clock (continuous process plants), day-heavy with an evening tail (discrete manufacturing), or spiky and shift-driven. Even a good match leaves structural gaps: the early-morning ramp before sunrise, the evening shoulder after sundown, and monsoon weeks when generation halves.

The Mismatch Is Where the Money Is

Every block of mismatch has a price. Surplus solar in a midday block is banked at a haircut, compensated at a low rate, or lost — depending on the state's rules. Deficit in an evening block is bought from the grid at the peak time-of-day tariff, or exposes the consumer to deviation charges if scheduled poorly. Summed across 35,040 blocks, the mismatch cost can quietly claim a large share of the project's headline savings. Sizing that ignores the mismatch does not remove the cost; it discovers it after commissioning.

A battery earns its keep by moving energy between blocks. That single physical capability serves four economically distinct roles:

• Peak shaving: capping the demand peaks that set contract demand and billing demand charges — a few hundred well-chosen blocks a year can carry a disproportionate share of the fixed-charge bill
• Time-of-day arbitrage: charging from surplus solar (or cheap off-peak grid power) and discharging into peak-tariff evening blocks — converting the day's lowest-value energy into its highest-value energy
• Settlement compliance: absorbing schedule deviations and shaping injection under 15-minute DSM regimes, so that what the plant delivers matches what was declared
• Backup: riding through grid interruptions that would otherwise trip sensitive processes — a role that competes for the same stored energy as the economic roles above

The roles overlap in time and compete for the same megawatt-hours. A battery cannot simultaneously hold reserve for backup and empty itself into the evening peak. Which role wins in which block — and therefore how much power and how much energy the battery needs — is precisely what sizing has to decide.

The industry's shortcuts — "size solar to 80% of annual consumption", "add a two-hour battery at a quarter of the solar capacity" — fail because each of the four roles implies a different power-to-energy ratio, and the right blend depends on the specific load shape, the state's tariff structure, and the settlement rules. Peak shaving wants power; arbitrage wants energy; DSM compliance wants both, at the times the other two want them. A heuristic tuned to one consumer's shape transfers poorly to another's — and a heuristic tuned to yesterday's banking rules ages badly.

There is a second, subtler failure: solar and battery sizes are not independent. A larger plant creates more midday surplus, which raises the value of storage; more storage raises the tolerable plant size. Sizing them sequentially — plant first, battery after — leaves that interaction on the table. They have to be sized together.

The defensible way to size a Solar + BESS system is to pose it as a single mathematical optimisation: choose the solar capacity, wind capacity (where relevant), battery power, and battery energy — and simultaneously the battery's charge/discharge decision in every one of the 35,040 blocks — that minimise total energy cost or maximise project returns, subject to the real constraints: the tariff structure, time-of-day slabs, banking rules, deviation settlement, contract demand, and the physics of the battery itself.

This is a Mixed-Integer Linear Programming (MILP) problem, and it is how EarthSync's planning engine approaches it: consumption, generation, and storage modelled at 15-minute resolution, co-optimised across the full year against the market's actual rules — upwards of 50 million data points in a single simulation. The optimiser has no attachment to round numbers. It routinely returns configurations a rule of thumb would never propose, because it can see the block-level interactions a heuristic cannot.

A single optimal answer is the beginning of the analysis, not the end. The decision-grade question is how the optimum moves when assumptions do:

• Capex: battery costs in particular are on a steep trajectory; the configuration that is optimal at today's prices may be under-stored at next year's. Testing the optimum across a capex band reveals whether to build storage now or leave space to retrofit
• Tariff escalation: most of a project's savings arrive in later years, so the assumed grid-tariff escalation rate does heavy lifting in any IRR. Run the model across escalation scenarios rather than betting on one
• Degradation: solar output declines with age, and battery capacity fades with cycling and calendar life — an augmentation plan changes both the capex profile and the effective energy over life. A 15-minute model can carry degradation year by year instead of applying a flat derate
• Regulatory drift: banking windows, ToD slabs, and DSM provisions are all moving. The most valuable output of a sensitivity run is knowing which rule change would actually hurt — and which configuration is robust to it

Annual arithmetic sizes a project for the market as it used to be. Sub-hourly optimisation sizes it for the market as it settles — 15 minutes at a time. The gap between those two answers is real money, and it compounds for twenty-five years.