Methodology & Technical Architecture

A detailed breakdown of the data sources, simulation engine, and financial models powering the Eighty760 Aggregation Platform.

1Core Simulation Engine

The platform runs a deterministic 8,760-hour (hourly) simulation of the entire year. Unlike simple monthly averages, this captures the volatility and correlation between renewable generation, market prices, and load demand.

For every single hour of the year (t=1...8760), the engine performs the following balance check:

Net_Load(t) = Load(t) - [ Σ Gen_Asset_i(t) + Battery_Discharge(t) - Battery_Charge(t) ]

Matching: If Generation exceeds Load, the surplus is stored in the battery (if available) or sold to the market/curtailed.
Deficit: If Load exceeds Generation, the deficit is met by discharging the battery or purchasing "brown power" from the grid.

2Data Sources & Accuracy

Market Pricing (ERCOT)

Pricing data is sourced from GridStatus.io, providing 15-minute Real-Time Market (RTM) Settlement Point Prices (SPPs).

Source: ERCOT RTM via GridStatus API.
Nodes & Hubs: We ingest data for key hubs: HB_NORTH, HB_SOUTH, HB_WEST, HB_HOUSTON, and HB_PAN.
Granularity: 15-minute intervals aggregated to hourly averages for the simulation (8,760 hours/year).
Completeness: Full historical datasets from 2020 through 2025.

Weather & Engineering Models

Weather data is sourced from Open-Meteo Historic Weather API (ERA5 Reanalysis).

Solar Model: Uses shortwave_radiation (GHI). Assumes Single-Axis Tracking with ~20% AC system efficiency.
Wind Model: Uses wind_speed_100m. Mapped to a standard IEC Class 2 onshore turbine power curve.
Battery Specs: Models a 4-hour Li-Ion BESS with 85% Round-Trip Efficiency (RTE) and standard VOM costs.

3Financial Modeling

The platform computes real-time net-cost (or net-profit) for every hour, accounting for:

PPA Costs: Fixed or variable pricing for each tech asset ($/MWh).
Market Revenue: Surplus power sold at local hub prices or purchased to cover deficit.
REC Revenue: Scarcity-based pricing model that captures higher REC value when clean energy is scarce.
Battery Arbitrage: Charging during low-price hours and discharging during peak-price hours.

Battery Financial Methodology

The battery storage asset is modeled as a Capacity, Variable, Tolling Agreement (CVTA). This structure separates the financial flows into payments made ("What We Pay") and revenues received ("What We Receive").

Costs (What We Pay)

Fixed Tolling Fee: A monthly capacity payment ($/MW-month) paid to the asset owner for the exclusive right to dispatch the battery.
Charging Cost: The cost to purchase electricity from the grid (or PPA) to charge the battery. This typically occurs during low-price hours to minimize expense.
VOM Charges: Variable Operations & Maintenance costs associated with cycling the battery.

Revenue (What We Receive)

Market Revenue: Revenue generated by discharging stored energy into the grid. The optimizer targets the highest-price hours of the day (Arbitrage) to maximize this value.
Ancillary Services: Additional revenue captured from grid support services (e.g., Regulation, Spinning Reserves), modeled as a percentage uplift on energy revenue or a fixed monthly stream.

All costs and revenues are tracked on an hourly granularity and then aggregated into annual summaries for easy interpretation.

4Key Assumptions & Limitations

No Transmission ConstraintsWe assume perfect transmission within ERCOT North. Real nodal congestion may impact prices.

Standard Turbine ModelsWind profiles use generic IEC Class 2 curves; actual farm performance may vary.

Battery DegradationLong-term degradation is not modeled in this version.

Historical WeatherSimulations use actual historical weather; future projections require climate modeling.