Backcast Optimizer | Sunney.io

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Analysis Period

Region ?

Start Date ?

End Date ?

⏱️ Times shown: NEM time (AEST, UTC+10)

Battery Configuration

Number of BESS Units ?

Power per Unit (MW) ?

Capacity per Unit (MWh) ?

Round-trip Efficiency (%) ?

Max Cycles per Day ?

Optimization Mode ?

Degradation Cost ($/MWh) ?

Site Mode ?

Network Tariff ?

Analyzing market data...

🧠 How Your Selected Optimizer Works

Key Parameters (Both Modes)

• Round-trip Efficiency: Energy lost in charge/discharge cycle (typically 85-90%)
• Max Cycles: Limits daily throughput to preserve battery life
• Degradation Cost: $/MWh penalty for battery wear (auto-calibrated or manual)
• Min Run Time: Enforces realistic 15-minute minimum operations

Disclaimer: Backcast results use historical NEM spot prices only and assume perfect foresight (DP mode). Actual BESS revenue depends on FCAS participation, marginal loss factors (MLF), network charges, degradation, bidding strategy, and market conditions. This tool is for indicative analysis only and should not be used as the sole basis for investment decisions. Consult professional advisors for project-specific feasibility studies.

Overview

Our forward pricing model integrates industry-leading forecasts from Aurora Energy Research with real-time AEMO market data to provide bankable revenue projections for battery energy storage systems (BESS) in the National Electricity Market (NEM).

Aurora Energy Research Forecasts

Aurora Energy Research is the globally recognized authority on power market analytics, trusted by over 400 organizations including major banks, infrastructure funds, and energy companies. Their Australian power market forecasts are considered the gold standard for project finance and investment decisions.

Key features of Aurora's methodology:

Fundamental market modeling: Bottom-up simulation of all generation assets, demand patterns, and network constraints
Policy integration: Incorporates federal and state renewable energy targets, capacity mechanisms, and regulatory changes
Technology evolution: Models the impact of increasing renewable penetration, battery deployment, and grid-forming inverters
Scenario analysis: Central case calibrated against futures markets with sensitivity scenarios for key uncertainties

Data Integration Hierarchy

Our platform seamlessly blends multiple data sources to provide optimal forecast accuracy across all time horizons:

0-2 hours: AEMO P5 Predispatch (5-minute granularity)
2-48 hours: AEMO PD30 Predispatch (30-minute granularity)
Beyond 48 hours: Aurora Energy Research seasonal forecasts

This hierarchical approach ensures maximum accuracy for near-term optimization while maintaining robust long-term projections aligned with market fundamentals.

Storage Spread Calculation

Battery arbitrage opportunities are quantified through rolling window spread calculations, consistent with industry-standard project finance models:

0.5-hour spread (S₀.₅): max(price) - min(price) within single interval
1-hour spread (S₁): max(avg₂) - min(avg₂) for 2-interval rolling windows
2-hour spread (S₂): max(avg₄) - min(avg₄) for 4-interval rolling windows
4-hour spread (S₄): max(avg₈) - min(avg₈) for 8-interval rolling windows

These spreads directly correlate with BESS revenue potential for different duration configurations, enabling accurate feasibility assessment and optimized system sizing.

Intraday Price Formation

Aurora's annual wholesale prices and storage spreads are translated into realistic 30-minute price patterns using NEM-specific characteristics:

Duck curve dynamics: Solar-induced midday price depression (10:00-15:00 AEST)
Evening peak premiums: Demand-driven price elevation (17:00-21:00 AEST)
Overnight baseload: Stable low prices during minimum demand periods
Volatility events: Statistically distributed price spikes and negative pricing periods based on historical frequency analysis

Bankability and Risk Assessment

The forward curves include P10/P90 confidence bands derived from:

Historical volatility patterns adjusted for changing market dynamics
Aurora's scenario analysis for policy and technology uncertainties
Weather-driven demand and renewable generation variability
Forced outage rates and network constraint probability distributions

This probabilistic approach enables comprehensive risk assessment for debt sizing, including:

Debt Service Coverage Ratio (DSCR) calculations under P90 downside scenarios
Revenue stability analysis for contracted vs merchant exposure
Sensitivity testing for key market drivers

Validation and Calibration

Forward prices are continuously validated against:

ASX Energy quarterly base futures for near-term periods
Recent AEMO spot price outcomes and predispatch accuracy
Observed storage cycling patterns from operational BESS assets
Power Purchase Agreement (PPA) pricing from recent transactions

Regulatory Compliance

All projections incorporate current and announced regulatory frameworks including:

Capacity Investment Scheme (CIS) tender parameters
Renewable Energy Zone (REZ) development timelines
Frequency Control Ancillary Services (FCAS) market co-optimization
Five-minute settlement and Global Settlement implementation

Note: This model is designed for preliminary feasibility assessment and strategic planning. Detailed investment decisions should incorporate site-specific factors, connection studies, and formal independent market reports as required by project finance lenders.