Battery Energy Storage Systems (BESS), Financial Model, Renewable Energy

Cash Flow Projections, Dashboard, Debt Schedule, Debt Service Coverage, Excel, Financial Feasibility, IRR (Internal Rate of Return), NPV (Net Present Value), Payback Period, Project Finance, Scenario Analysis

Most BESS models ask you to guess the price spread. This one calculates it.

The model includes the full year of 2025 hourly day-ahead prices for eight European bidding zones (Germany-Luxembourg, Netherlands, Belgium, France, Austria, Poland, Czech Republic and Northern Italy). Pick a zone and a battery size, and the model dispatches the battery day by day against real prices to calculate arbitrage revenue, then projects 25 years of cash flows.

What you get

Daily dispatch for 365 days: charge in the cheapest hours, discharge in the most expensive, net of round-trip losses and variable O&M
Low / Base / High scenarios (capture rate, spread growth, battery capex)
Battery degradation and augmentation
Ancillary services and capacity market revenue lines
Capex build-up, fixed and variable O&M, insurance, land lease, inflation
Annuity debt with DSCR, corporate tax with loss carry-forward, straight-line depreciation
Project and equity IRR, NPV, payback, minimum and average DSCR
Dashboard with KPIs and charts; monthly revenue profile
Market comparison of all eight zones (average spreads and revenue per MW for 1, 2 and 4-hour systems)

How to use it

All inputs sit on one Inputs sheet and are colour-coded. Select the bidding zone, enter the power (MW) and duration (hours), and adjust round-trip efficiency, availability and capture rate. Then set capex, operating costs, financing and tax assumptions. Every result updates automatically: the Daily sheet shows the daily charge and discharge decisions, the CashFlow sheet shows the 25-year projection, and the Dashboard summarises returns and debt metrics. Switch scenarios with a single selector to see how sensitive the project is to spreads and capex.

Why this model

Merchant battery revenue depends on real price volatility, which an assumed average spread cannot capture. Using actual hourly prices gives a defensible starting point for site screening, investment committee discussions and lender conversations. The model has about 19,000 formulas and no hard-coded results, and its revenue calculation has been checked against an independent Python implementation.

Who it is for: developers screening sites, investors and lenders checking merchant assumptions, consultants and students learning BESS economics.

Format: Excel (.xlsx), no macros, fully unlocked formulas, colour-coded inputs. Works in Microsoft Excel and LibreOffice.

Data source: Energy-Charts.info (Fraunhofer ISE), Bundesnetzagentur | SMARD.de, CC BY 4.0.

Limitations: one cycle per day on the day-ahead market; intraday and balancing markets are represented by a single ancillary revenue input. For planning purposes only, not investment advice.

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