Databricks/Python Developer – Energy Modeling

1. Project Overview

This Statement of Work (SOW) defines the design, development and delivery of a software solution to model the operational and financial performance of Battery Energy Storage Systems (BESS) over a specified contract duration.

The solution is intended to support business development activities by providing robust analytical outputs to demonstrate project performance, viability and value to both external clients and internal stakeholders.

2. Scope of Work

The consultant will be responsible for the end-to-end design, development and delivery of the solution, including:

• Development of a Python-based modeling engine for operational simulation and financial analysis
• Implementation of a Databricks App interface for data input, scenario configuration, and output visualization
• Design and implementation of data ingestion, transformation, validation, and processing pipelines

3. Functional Requirements

3.1 Data Ingestion & Processing

The software shall ingest and process:

1. Technical system parameters: battery capacity (kWh), power rating (kW), round-trip efficiency, etc.
2. Client electricity consumption and billing data: interval meter data (15-min or hourly), demand profiles, historical peak demand, billing structure
3. Market energy price projections: day-ahead LMP, real-time LMP, ancillary service clearing prices, capacity market revenues, demand response program parameters
4. Financial assumptions: discount rate, escalation rates, O&M costs, degradation replacement thresholds, contract term, incentive/rebate schedules
5. Grid and regulatory parameters: interconnection limits, export constraints, participation eligibility by market/program

3.2 Load Forecasting

The software shall include a load forecasting capability to project client electricity consumption over the relevant time horizons, serving as a primary input to dispatch optimization and financial modeling.

3.3 Operational Modes

The modeling engine shall simulate the following BESS operating strategies:

1. Peak Shaving

Objective: Reduce site peak demand (kW) to lower demand charges.

Logic: Identify forecast or historical peak periods; discharge battery to cap facility demand at a defined threshold.

Key parameters: Target peak (kW), demand charge rate ($/kW)

Output metrics: Peak reduction (kW), demand charge savings ($/month), number of discharge cycles consumed.

1. Demand Response (DR)

Objective: Curtail or shift load during utility/ISO-called DR events for capacity or event-based payments.

Logic: Model event dispatch based on program rules — notification lead time, event duration, max number of calls per season, performance measurement methodology (baseline vs. actual).

Key parameters: Program type (capacity-based, energy-based), event window, minimum sustained discharge duration, penalty for underperformance.

Output metrics: Committed capacity (kW), event energy delivered (kWh), DR revenue ($), performance score.

1. Energy Arbitrage

Objective: Exploit time-of-use or wholesale price differentials — charge at low-cost periods, discharge at high-cost periods.

Logic: Optimize charge/discharge schedule against a price signal (TOU tariff or LMP forecast) subject to battery constraints.

Key parameters: Price curve (hourly), charge/discharge efficiency losses, min price spread threshold to trigger cycle.

Output metrics: Energy shifted (kWh), gross arbitrage revenue ($), net revenue after efficiency losses, marginal cycle cost vs. spread.

1. Frequency Regulation

Objective: Provide fast-response power injection/absorption to support grid frequency stability (e.g., PJM RegD, ERCOT Fast Frequency Response).

Logic: Model continuous symmetric or asymmetric response around a setpoint; account for performance score (mileage-based compensation), state-of-charge management, and signal-following accuracy.

Key parameters: Regulation capacity (MW), clearing price ($/MWh), signal type (RegA/RegD or equivalent).

Output metrics: Regulation capacity offered (MW), regulation revenue ($), SOC deviation, impact on degradation.

1. Voltage Regulation (Reactive Power Support)

Objective: Provide reactive power (VAR) support to maintain local voltage within acceptable bands.

Logic: Model inverter reactive power capability (four-quadrant operation) independent of or concurrent with active power dispatch; typically non-revenue in behind-the-meter applications but may be contractually required or compensated in front-of-meter contexts.

Key parameters: Inverter apparent power rating (kVA), power factor range, reactive power priority vs. active power, voltage setpoint/droop curve.

Output metrics: Reactive energy provided (kVARh), voltage compliance (%), active power curtailment due to reactive priority (if any).

3.4 Dispatch Optimization & Revenue Stacking

The engine shall implement a dispatch optimization layer capable of:

• Co-optimizing across multiple value streams simultaneously (e.g., peak shaving + arbitrage + frequency regulation)
• Respecting physical constraints: SOC bounds, power limits, ramp rates, minimum rest periods, etc.
• Prioritization logic: Configurable hierarchy or economic optimization to resolve conflicts between competing.

Methodology options (to be confirmed in Phase 1):

• Rule-based heuristic dispatch (faster, more transparent)
• Linear/mixed-integer programming (optimal but higher complexity)
• Hybrid approach (heuristic with LP refinement)

3.5 Degradation Modeling

The software shall model battery capacity and efficiency degradation over the project lifetime:

• Calendar aging: Time-dependent capacity fade as a function of temperature and average SOC
• Cycle aging: Throughput-dependent degradation as a function of depth of discharge, C-rate, and temperature
• Cumulative effect: Track equivalent full cycles, remaining capacity (SOH%), and trigger augmentation/replacement when capacity falls below contractual threshold
• Feedback loop: Degradation impacts available energy in future periods, dynamically adjusting dispatch feasibility

3.6 Financial Performance Outputs

• Net Present Value (NPV): Discounted net cash flows over contract term
• Internal Rate of Return (IRR): Project and equity IRR
• Simple Payback Period: Years to recover initial investment
• Annual Revenue by Stream: Breakdown across peak shaving, DR, arbitrage, regulation
• Demand Charge Savings: Monthly/annual reduction in billed demand
• Degradation Cost: Estimated cost of capacity fade / augmentation

3.7 Output & Reporting

• Interactive dashboards (Databricks App): time-series dispatch profiles, SOC heatmaps, revenue waterfall charts, scenario comparison
• Exportable summary reports (PDF/Excel — format to be confirmed)

4. Deliverables

The consultant shall provide:

• A fully functional modeling software solution (Python backend and Databricks App frontend)

Comprehensive documentation, including:

• Technical documentation
• Modeling methodology
• Training sessions
• Supporting materials