An Energy Management Script in Python for the Control of Battery Storage in a Grid-Connected Microgrid Using Mixed Integer Linear Programming

⚡️ Energy Cost Optimizer for Solar + Battery Systems (Python-Based EMS)

Looking to minimize grid costs while making the most of solar PV and battery storage? This Energy Management System (EMS) does exactly that — backed by real mathematical optimization.

✅ What It Does

This Python script intelligently schedules:

• Solar PV usage
• Battery charging/discharging
• Grid power purchases

…to minimize daily electricity costs, subject to battery limits and cycle constraints.

💡 Perfect For:

• Engineers designing PV + battery setups
• Researchers simulating battery behavior
• Energy consultants optimizing microgrids
• Dev teams building energy dashboards
• Anyone who wants a plug-and-play EMS model with real optimization logic

📦 Features

• 🔋 Accurate battery SoC tracking & charge/discharge limits
• ☀️ Hourly PV consumption forecasting
• ⚡ Grid power cost minimized (with max power constraint)
• 🔄 Smart handling of battery cycles (with customizable cycle limit)
• 📈 Automatic plotting of daily & monthly energy dispatch
• 🧠 Based on MILP optimization using pulp
• ✅ Clean outputs in CSV + PNG (for reporting or ML training)

🛠️ Tech Stack

• Python 3
• pulp for MILP optimization
• PostgreSQL as the database source
• matplotlib & pandas for analytics
• Modular and easy to adapt to any custom dataset or battery setup

🔐 Real-World Ready

• Integrates with real energy usage and PV forecast data
• Includes state-of-the-art constraints from academic EMS literature (e.g. cycle tracking, SoC bounds, grid limits)
• Handles imperfect or incomplete hourly data

📊 Outputs You Get

• CSV: Hourly dispatch and cost breakdown (yearly_hourly_dispatch.csv)
• Graphs: Daily & monthly energy dispatch and SoC profiles
• Bar chart: Daily operating costs
• Ready to visualize, report, or train machine learning models