ENERGY STORAGE FINANCIAL ANALYSIS & MODELS

Energy Storage Financial Analysis

Fractal specializes in robust energy storage financial analysis and models. Fractal has spent years developing and optimizing powerful models that simulate performance, degradation, costs and revenues to evaluate total cost of ownership and to optimize the economics of your project. We have applied these tools for electric utilities and top renewable energy developers to evaluate energy storage and hybrid projects. Our models enable successful analysis of multiple technology configurations, chemistries and ownership scenarios.

We Don’t Use Black Box Software

Fractal develops and applies custom technical and financial models to evaluate energy storage and hybrid project economics. We don’t use black box software. Our models consider the specific technology and design parameters of your project. Fractal goes beyond manufacturer’s datasheets and marketing collateral. Instead, Fractal applies real world parameters drawing from primary research of behavior and maintenance cycles from hands-on operation. Fractal takes the following into account and creates a 20-year financial model:

  • Project Life
  • Battery Swap
  • Salvage Value
  • O&M Costs
  • O&M Inflation
  • Incentives / Rebates
  • Debt
  • Price Escalaction
  • Discount/Hurdle Rate
  • Taxes
  • Roundtrip Efficiency Losses
  • Cycle / Calendar Life Degradation
  • Aux. Load Losses
  • Energy Prices
  • Ownership

Technical Design and Robust Financial Analysis

Fractal performs a more robust financial analysis on potential business models using granular costs and revenue. The output of the financial analysis includes:

  • Internal Rate of Return (IRR)
  • Net Present Value (NPV)
  • Payback Period
  • Seasonal Dispatch Models
  • Capital Outlay (CAPEX / OPEX)
  • Degradation Management Strategy

Project Optimization

Fractal can assess the economic viability and internal rate of return for a variety of project scenarios.

Fractal can optimize your project from a technical, financial and ownership basis to include:

  • Optimize system sizing and sub-chemistry selection to maximize IRR
  • Optimize control algorithm to increase revenue and/or reduced sizing
  • Perform sensitivity analysis of key parameters and their effect on ROI
  • Perform ownership evaluation and ITC utilitization
  • Complete financial, technical and environmental risk assessment
  • Account for recent or pending policy that may influence the system sizing
Inputs Outputs
Base LCOE Model
  • Energy production
  • Hard costs – by component
  • Soft costs – by component
  • Incentives
  • Base LCOE model:
  • LCOE
  • Energy yield: kWh/kW
Single Party Owned
  • Financing details and cost of capital
  • Price and schedule of energy sale
  • NPV
  • IRR
  • Payback period
Flip With or Without Debt
  • Capital structure for all participants
  • Cost of capital
  • Target return
  • Year for target return
  • Year of flip
  • Details of flip relationship – how revenue flows before and after flip
  • NPV, IRR and payback period for all investors
  • Actual year of reaching the IRR target – for all parties
Sale Leaseback
  • A tax sponsor buys a project from a developer and leases it back to the developer after getting the tax incentives. Customer pays the developer
  • Terms of sale lease back
  • Price of electricity
  • Returns for all parties involved – tax sponsor, developer, and customer
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