TechArena 2025 Phase 1 - EMS Algorithm Solution

Overview

This solution implements an Energy Management System (EMS) algorithm for optimizing Battery Energy Storage System (BESS) operations across multiple European electricity markets. The algorithm maximizes revenue through strategic participation in day-ahead energy markets and ancillary services (FCR and aFRR).

Approach

1. Operation Optimization

The algorithm optimizes BESS charge/discharge strategies using:

• Energy Arbitrage: Identifies optimal times to charge (low prices) and discharge (high prices)
• Ancillary Services: Allocates capacity to FCR and aFRR markets when profitable
• Constraint Management: Respects battery physical limits (C-rate, SOC bounds, daily cycles)

Key features:

• 15-minute resolution optimization matching German/Austrian market structure
• Greedy algorithm with daily optimization windows
• Dynamic capacity allocation between energy and ancillary services

2. Investment Optimization

Evaluates ROI across 5 countries (DE, AT, CH, HU, CZ) considering:

• Country-specific WACC and inflation rates
• 10-year project horizon with NPV calculations
• Levelized ROI accounting for time value of money

3. Configuration Optimization

Tests multiple battery configurations:

• C-rates: 0.25, 0.33, 0.50 (determines maximum charge/discharge power)
• Daily cycles: 1.0, 1.5, 2.0 (limits daily energy throughput)
• Identifies optimal configuration for each market

Technical Implementation

Algorithm Components

1. Data Processing: Loads and aligns multi-resolution market data (15-min day-ahead, 4-hour ancillary)
2. Optimization Engine: Daily optimization with greedy price-based strategy
3. Revenue Calculation: Aggregates revenue from all market participation
4. ROI Analysis: Multi-year financial analysis with discounting

Key Assumptions

• Battery efficiency: 95% round-trip
• SOC limits: 10% - 90%
• Investment cost: 200 EUR/kWh
• 1 MWh nominal capacity for normalized calculations

Results Structure

Output Files

1. TechArena_Phase1_Configuration.csv: Comparative analysis of all C-rate and cycle combinations
2. TechArena_Phase1_Investment.csv: Detailed ROI analysis with year-by-year projections
3. TechArena_Phase1_Operation.csv: Time-series operational data (charge/discharge/SOC/market participation)

Installation & Execution

Requirements

• Python 3.8+
• Dependencies listed in requirements.txt

Running the Solution

# Install dependencies
pip install -r requirements.txt

# Run optimization
python main.py

Performance Optimization

The algorithm balances computational efficiency with solution quality through:

• Daily optimization windows to reduce problem size
• Greedy heuristics for rapid decision-making
• Vectorized operations using NumPy/Pandas

Future Enhancements

Potential improvements for subsequent phases:

• Mixed-integer linear programming for global optimization
• Stochastic optimization for price uncertainty
• Machine learning for price forecasting
• Battery degradation modeling

Authors

TechArena 2025 Competition Submission - Phase 1