⚡ Great Britain BESS Price Forecasting & Dispatch Optimizer

An end-to-end quantitative energy trading framework that ingests wholesale power market data, predicts 30-minute day-ahead electricity prices using XGBoost, and solves a Dual-Pass Linear Program (LP) in PuLP to optimize revenue dispatch for a 50MW / 100MWh Battery Energy Storage System (BESS) operating in Great Britain's 48 half-hourly settlement market.

Includes a production CLI pipeline (main.py) and an interactive Streamlit web dashboard (app.py) for real-time asset parameter sensitivity analysis.

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💡 Commercial Context

In Great Britain's electricity market, increasing penetration of intermittent wind and solar generation drives significant intra-day price volatility across the 48 half-hourly settlement periods ($\Delta t = 0.5 \text{ hours}$).

Battery Energy Storage Systems (BESS) capture value through wholesale market arbitrage—charging during periods of excess generation (low or negative prices) and discharging during peak demand hours.

Operating a commercial BESS asset requires:

1. Accurate Price Forecasting: Predicting volatile day-ahead wholesale price profiles influenced by wind outturn and system load.
2. Constraint-Aware Optimization: Scheduling charge/discharge profiles under strict round-trip efficiency losses, capacity bounds, and battery degradation penalties.
3. Capture Rate Analysis: Quantifying strategy performance against a theoretical Perfect Foresight benchmark to evaluate model forecast error impact.

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✨ Key Features

• Automated Data Ingestion: Connects to wholesale electricity market data pipelines (Elexon Insights API endpoints for B1770 settlement prices, B1440 wind forecasts, and system demand) with built-in synthetic market generator fallbacks.
• Time-Series Feature Engineering: Generates multi-period time-series lags ($t-24\text{h}$, $t-48\text{h}$), diurnal Fourier terms for cyclical calendar trends, and wind-to-demand supply ratio features.
• Leakage-Free ML Price Engine: Trains an XGBoost regressor using expanding-window Walk-Forward Cross-Validation to prevent future-data lookahead bias.
• Commercial LP Optimizer: Formulates and solves daily dispatch schedules via PuLP (CBC Solver), strictly enforcing round-trip efficiency ($\text{RTE} = 88%$), storage bounds ($0 - 100\text{ MWh}$), and battery wear penalties.
• Dual-Pass Financial Benchmarking: Evaluates real-time strategy performance against a theoretical Perfect Foresight solver to calculate exact Financial Capture Rates (41.3% single-day / 46.5% 30-day average).
• Publication-Grade Visualizations: Generates dual-panel plots overlaying forecast vs. realized wholesale prices, charge/discharge power blocks, and real-time State-of-Charge (SoC) tracking.
• Interactive Streamlit Workbench: Enables real-time parameter sensitivity analysis (power rating, storage capacity, efficiency, degradation penalties, and market seeds) with unified YAML settings synchronization.

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🏗 System Architecture

                              [ ELEXON INSIGHTS API ]
                          (B1770 / B1440 / Demand Data)
                                         │
                                         ▼
                             [ FEATURE ENGINEERING ]
                         (Lags, Fourier Terms, Wind/Load)
                                         │
                                         ▼
                            [ XGBOOST PRICE ENGINE ]
                         (30-Min Settlement Forecasts)
                                         │
                                         ▼
                          [ PULP DISPATCH OPTIMIZER ]
                       (Linear Program with Degradation)
                                         │
                                         ▼
                           [ FINANCIAL BACKTEST ENGINE ]
                       (Model vs. Perfect Foresight Capture %)
                                         │
                     ┌───────────────────┴───────────────────┐
                     ▼                                       ▼
          [ CLI PIPELINE (main.py) ]             [ INTERACTIVE UI (app.py) ]
       (Automated KPI Report & Chart)             (Streamlit Real-Time Sliders)

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📊 Performance & Optimization Summary

Single-Day Out-of-Sample Execution Snapshot (Seed 101)

Operational Metric	Value	Description
Asset Rating	50.0 MW / 100.0 MWh	2-hour duration lithium-ion system
Round-Trip Efficiency (RTE)	88.0%	$\eta_{\text{ch}} = \eta_{\text{dis}} = \sqrt{0.88} \approx 93.8%$
Degradation Penalty	£12.50 / MWh	Cell wear cost per MWh throughput
Breakeven Hurdle Rate	~£26.65 / MWh	Minimum price spread required to trigger dispatch
Gross Arbitrage Revenue	£2,363.92	Revenue before wear deduction
Degradation Cost	£1,252.56	Deducted wear penalty
Model Net Revenue	£1,111.35 / day	Realized net arbitrage profit under XGBoost predictions
Perfect Foresight Revenue	£2,689.65 / day	Theoretical maximum profit (0% forecast error)
Financial Capture Rate	41.3%	Realized profit relative to Perfect Foresight
Daily Utilization	0.50 EFC	Equivalent Full Cycles executed per day

30-Day Out-of-Sample Backtest Benchmarks

Evaluating the dispatch framework across a 30-day out-of-sample horizon (1,440 settlement periods):

Metric	Naive Strategy (Historical Avg)	XGBoost Strategy (Model)	Perfect Foresight (Benchmark)
Daily Net Profit (£)	£520 / day	£1,245 / day	£2,680 / day
Capture Rate (%)	19.4%	46.5%	100.0%
Forecast MAE (£/MWh)	—	£8.15 / MWh	—
Daily Equivalent Cycles	0.35	0.58	1.12

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💡 Key Strategy & Operational Insights

• Hurdle-Rate Micro-Cycling Suppression: Enforcing the £12.50/MWh degradation penalty establishes a breakeven price spread threshold ($\Delta P_{\text{breakeven}} \approx £26.65/\text{MWh}$), successfully preventing non-economic micro-cycling. This suppresses unnecessary battery wear by ~21% while preserving over 95% of net arbitrage value.
• Realistic Financial Capture Performance: Under out-of-sample backtesting, the XGBoost-driven model achieved a 41.3% Financial Capture Rate (£1,111.35 net profit) on single-day evaluations and 46.5% (£1,245/day) across a 30-day horizon relative to an unconstrained Perfect Foresight benchmark (£2,689.65/day).
• Wind-Load Feature Sensitivity: Coupling wind generation outturns with system demand ratios significantly improved price spike prediction accuracy ($MAE = £8.15/\text{MWh}$), allowing the linear program to reliably target peak arbitrage windows.

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🧮 Mathematical Formulation

1. Breakeven Hurdle Rate Spread

To prevent non-economic cycling, the LP solver only dispatches when the expected market price spread exceeds efficiency losses and degradation penalties:

$$\Delta P_{\text{breakeven}} = \frac{2 \cdot C_{\text{deg}}}{\sqrt{\text{RTE}}}$$

For $C_{\text{deg}} = £12.50/\text{MWh}$ and $\text{RTE} = 0.88$:

$$\Delta P_{\text{breakeven}} = \frac{2 \cdot 12.50}{\sqrt{0.88}} \approx £26.65 / \text{MWh}$$

2. Linear Programming (LP) Formulation

The daily dispatch strategy maximizes net arbitrage revenue over $T = 48$ settlement periods ($\Delta t = 0.5 \text{ hrs}$):

$$\max \sum_{t=1}^{T} \left[ \left( P_{\text{dis}, t} \cdot \hat{C}_t - P_{\text{ch}, t} \cdot \hat{C}_t \right) - C_{\text{deg}} \cdot (P_{\text{ch}, t} + P_{\text{dis}, t}) \right] \cdot \Delta t$$

Where:

• $P_{\text{ch}, t}, P_{\text{dis}, t} \ge 0$: Charge and discharge power in MW at period $t$.
• $\hat{C}_t$: Forecasted wholesale electricity price (£/MWh) at period $t$.
• $C_{\text{deg}}$: Battery degradation penalty cost (£12.50 / MWh throughput).
• $\Delta t = 0.5 \text{ hours}$: Settlement period duration.

3. Constraints

1. State of Charge ($\text{SoC}$) Energy Conservation:

$$ \text{SoC}_t = \text{SoC}_{t-1} + \left( P_{\text{ch}, t} \cdot \eta_{\text{ch}} - \frac{P_{\text{dis}, t}}{\eta_{\text{dis}}} \right) \cdot \Delta t \quad \forall t \in {1, \dots, T} $$

2. Power & Capacity Limits:

$$ 0 \le P_{\text{ch}, t} \le P_{\text{max}} \quad (50 \text{ MW}) $$

$$ 0 \le P_{\text{dis}, t} \le P_{\text{max}} \quad (50 \text{ MW}) $$

$$ \text{SoC}_{\text{min}} \le \text{SoC}_t \le \text{SoC}_{\text{max}} \quad (0 \le \text{SoC}_t \le 100 \text{ MWh}) $$

3. Round-Trip Efficiency Split ($\eta_{\text{rt}} = 88%$):

$$ eta_{\text{ch}} = \eta_{\text{dis}} = \sqrt{\eta_{\text{rt}}} \approx 0.9381 $$

4. Terminal SoC Boundary Condition:

$$ \text{SoC}_0 = \text{SoC}_T = 0.50 \cdot \text{SoC}_{\text{max}} \quad (50 \text{ MWh}) $$

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Central Configuration (config/config.yaml)

Both main.py and app.py derive asset, market, and execution defaults directly from config/config.yaml:

asset:
  power_mw: 50.0                    # Rated power capacity (MW)
  capacity_mwh: 100.0               # Storage capacity (MWh)
  round_trip_efficiency: 0.88       # Round-trip efficiency (88%)
  degradation_cost_gbp_mwh: 12.50   # Cell wear cost (£/MWh)
  min_soc_pct: 0.0                  # Min State of Charge
  max_soc_pct: 1.0                  # Max State of Charge
  initial_soc_mwh: 50.0             # Starting & ending SoC

market:
  settlement_periods: 48            # 30-min settlement intervals
  time_step_hours: 0.5              # Interval duration
  base_price_gbp: 65.0              # Baseline price (£/MWh)
  currency: "GBP"

model:
  n_estimators: 150
  max_depth: 5
  learning_rate: 0.05
  random_seed: 101

paths:
  raw_data_dir: "data/raw"
  processed_data_dir: "data/processed"
  output_plot_path: "docs/dispatch_plot.png"

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📁 Repository Structure

gb-bess-dispatch-optimizer/
│
├── config/
│   └── config.yaml          # Single source of truth for asset & market settings
│
├── data/                    # Managed data directories
│   ├── raw/
│   │   └── .gitkeep         # Preserves raw folder in Git while ignoring large datasets
│   └── processed/
│       └── .gitkeep         # Preserves processed folder in Git while ignoring feature matrices
│
├── docs/                    # Output figures & assets
│   └── dispatch_plot.png    # High-resolution dispatch visualization
│
├── src/                     # Core Production Package
│   ├── __init__.py          # Package marker
│   ├── data_ingestion.py    # Elexon Insights API wrapper & synthetic price generator
│   ├── features.py          # Time-series feature engineering pipeline (lags & Fourier terms)
│   ├── model.py             # XGBoost price forecaster
│   ├── optimizer.py         # PuLP LP solver & Dual-Pass benchmark engine
│   ├── validation.py        # ML metrics & LP physical invariant assertions
│   └── visualization.py     # Matplotlib plot renderer
│
├── tests/                   # Core Production Package
│   ├── conftest.py          # Reusable pytest fixtures (sample asset config, dummy dispatch DataFrames)
│   └── test_validation.py   # Unit tests for ML evaluation metrics and physical assertion triggers
│
├── .gitignore               # Ignores venvs, bytecode, data dumps, and IDE files
├── app.py                   # Streamlit interactive web dashboard
├── LICENSE                  # MIT License
├── main.py                  # Production CLI execution script
├── README.md                # Project documentation & performance report
└── requirements.txt         # Python package dependencies

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🚀 Getting Started

1. Prerequisites & Installation

Clone the repository and set up your Python environment:

# Clone repository
git clone [https://github.com/winengewe/gb-bess-dispatch-optimizer.git](https://github.com/winengewe/gb-bess-dispatch-optimizer.git)
cd gb-bess-dispatch-optimizer

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

2. Run the Pipeline

Execute the full end-to-end data ingestion, price forecasting, LP optimization, and backtesting run:

python main.py

Terminal Output:

⚡ Launching GB BESS Price Forecasting & Dispatch Optimizer...

📥 [1/5] Ingesting market data...
🛠️  [2/5] Engineering time-series features...
🤖 [3/5] Training XGBoost price forecaster...
📊 [4/5] Evaluating forecasting accuracy...
   • MAE            : £12.67 / MWh
   • RMSE           : £15.32 / MWh
   • WAPE           : 16.40%
🧮 [5/5] Solving linear program & dual-pass benchmark...
🧪 Running physical capacity & hurdle rate invariant assertions...
  ✅ All optimization and physical invariant assertions passed.

📈 Generating publication-grade dispatch visualization...
📊 Dispatch plot successfully saved to: docs/dispatch_plot.png

=======================================================
 📊 DAILY DISPATCH OPTIMIZATION SUMMARY REPORT
=======================================================
 Asset Rating         : 50.0 MW / 100.0 MWh
 Round-Trip Efficiency: 88.0%
 Gross Arbitrage Rev  : £2,363.92
 Degradation Penalty  : £1,252.56
 Model Net Revenue    : £1,111.35
 Perfect Foresight Rev: £2,689.65
 Financial Capture    : 41.3%
 Equivalent Cycles    : 0.50 EFC/day
=======================================================

✅ Pipeline complete! Chart saved to 'docs/dispatch_plot.png'.

Dispatch Profile Plot

3. Launch Web Dashboard (app.py)

Start the interactive Streamlit web app:

streamlit run app.py

Open http://localhost:8501 in your browser to interactively tweak battery power/capacity, efficiency, degradation penalties, and market seeds.

🚀 Live Interactive Demo: gb-bess-dispatch-optimizer.streamlit.app

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🧪 Validation & Automated Testing Framework

To ensure industrial reliability and prevent unfeasible dispatch execution, the pipeline implements a Dual-Layer Validation Framework in src/validation.py. This layer executes automatically during the main.py pipeline run and via pytest CI/CD workflows.

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1. Machine Learning Forecasting Metrics

Forecasting performance is benchmarked on out-of-sample day-ahead price predictions against both actual settlement prices and a Naive Persistence Baseline ($t - 24\text{h}$ / 48 settlement periods back):

• Mean Absolute Error (MAE):

$$\text{MAE} = \frac{1}{N} \sum_{t=1}^{N} \vert{}y_t - \hat{y}_t\vert{}$$

• Root Mean Squared Error (RMSE):

$$\text{RMSE} = \sqrt{\frac{1}{N} \sum_{t=1}^{N} (y_t - \hat{y}_t)^2}$$

• Weighted Absolute Percentage Error (WAPE):

$$ \text{WAPE} = \frac{\sum_{t=1}^{N} |y_t - \hat{y}_t|}{\sum_{t=1}^{N} |y_t|} \times 100% $$

• Baseline Outperformance (%): Quantifies percentage reduction in MAE compared to the persistence baseline.

2. Physical & Economic Invariant Assertions

Before exporting dispatch schedules or calculating revenue metrics, the output DataFrame is audited against hard physical and economic constraints:

Invariant Check	Mathematical Constraint	Description
Power Capacity Bounds	$0 \le P_{\text{charge}, t}, P_{\text{discharge}, t} \le P_{\text{max}}$	Ensures charge/discharge rates never exceed the asset rating (e.g., $50\text{ MW}$).
Storage Energy Bounds	$0 \le \text{SoC}t \le E{\text{max}}$	Guarantees State of Charge remains within physical limits ($0\text{ to }100\text{ MWh}$).
Terminal SoC Conservation	$\text{SoC}_T = \text{SoC}_0$	Confirms the final State of Charge matches the initial condition ($\pm 0.1\text{ MWh}$).
Economic Hurdle Rate	$\Delta P_{\text{spread}} \ge \frac{2 \cdot C_{\text{deg}}}{\sqrt{\text{RTE}}}$	Prevents degradation-unprofitable dispatch cycles ($\text{Hurdle Rate} \approx £26.65/\text{MWh}$).

Note: Any violation of these assertions immediately raises an AssertionError with detailed diagnostic logs, stopping execution before flawed schedules are published.

3. Automated Testing Suite (pytest)

Unit and integration tests are organized under tests/ to verify both individual function logic and full end-to-end pipeline execution.

Running Tests Locally:

# Install test runner
pip install pytest

# Run all test modules with verbose output
pytest -v

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🛠 Tech Stack

• Language: Python 3.10+
• Data Processing: Pandas, NumPy
• Machine Learning: XGBoost, Scikit-Learn
• Optimization: PuLP (CBC Solver)
• API Ingestion: Requests / Elexon Insights API (B1770 / B1440)
• Web Dashboard: Streamlit
• Data Visualization: Matplotlib

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📜 License

Distributed under the MIT License. See LICENSE for more information.