Dispatch Model

Introduction

Our core strategy uses mixed-integer linear programming (MILP). This allows the model to maximise battery revenues by finding the optimal times to charge and discharge, considering both the wholesale energy market and ancillary service opportunities.

The model is designed as a forward-looking “look-ahead” optimisation that runs for each site scenario (e.g., a 2-hour battery, performing one cycle per day, with a specific degradation level).

Our modelling approach

The global dispatch model maximises total revenue across all streams, while adhering to constraints that reflect the physical limitations of the battery and the specific rules of each region. Key features include:

• Multi-market Optimisation: Simultaneously optimises participation across energy markets (ie. day-ahead, intraday, balancing) and ancillary service markets
• Co-located Renewables: Supports solar co-location with batteries, including clipping and curtailment modelling
• Physical Constraints: Respects battery physical limitations including state-of-charge, power limits, efficiency losses, and cycling constraints
• Market Rules: Incorporates market-specific rules, stacking limitations, and participation requirements
• Multi-step Optimisation: Enables sequential optimisation across different market timeframes (ie. day-ahead followed by intraday)
• Grid Connection Limits: Accounts for import/export limitations at the grid connection point

Regional Adaptability

The model is designed with a flexible architecture that allows adaptation to different electricity markets worldwide, with region-specific implementations for markets such as:

• Great Britain (GB)
• European markets
• ERCOT
• NEM
• And others

Each regional implementation incorporates the specific market structures, rules, and constraints relevant to that electricity market.

Model Components

The dispatch model consists of several key components:

1. Assets

• Battery: Defined by capacity (MWh), power rating (MW), efficiency, cycling limits, and operational parameters
• Solar: Characterised by capacity, AC/DC coupling type, tracking type, and generation profiles

2. Markets

Each region implements its own specific market definitions, which can include:

• Energy Markets: Day-ahead, intraday, and balancing mechanism markets with associated prices
• Ancillary Services: Frequency response, reserve, and other grid services with associated prices, acceptance rates, and throughputs
• Fixed Contracts: Capacity market, Transmission network use of system (TNUoS), and other fixed revenue streams

3. Constraints

• Physical Constraints: State-of-charge evolution, ramping limits, efficiency losses
• Market Constraints: Participation rules, stacking limitations, delivery requirements
• Grid Constraints: Import/export limits, connection capacity

4. Optimisation Steps

Each region has its own unique multi-step optimisation framework, typically including:

• Day-ahead: Initial optimisation for day-ahead market participation
• Intraday: Re-optimisation considering day-ahead commitments and updated forecasts
• Real-time: Final adjustments based on actual conditions and balancing opportunities

Steps are solved sequentially. Each step can optionally solve with a rolling time horizon and limited foresight of prices (ie. perfect foresight over the 2-hour rolling window, then imperfect foresight beyond that).

Optimisation Process

1. Data Preparation: Market prices, renewable generation forecasts, and grid constraints are processed
2. Model Construction: A mathematical optimisation model is built with all relevant constraints
3. Solver Execution: A commercial solver solves the MILP problem
4. Results Processing: Optimal dispatch schedules and revenue projections are generated
5. Multi-step Execution: Results from earlier steps constrain later optimisation steps

Applications

The dispatch model can be used for:

• Revenue Forecasting: Projecting future revenues for existing or planned assets
• Investment Analysis: Evaluating the economic viability of new storage or renewable projects
• Operational Strategy: Determining optimal bidding and dispatch strategies
• Market Analysis: Understanding value streams and revenue potential across different markets

Explore Dispatch Model Components