Optimise Battery Dispatch Behaviour

This project aims to develop algorithms using linear programming to optimize the dispatch behavior of a battery located in Victoria. The goal is to maximize revenues by charging the battery when electricity prices are low and discharging it when prices are high.

Project Contributors

• Haonan Zhong (867492)
• Zhi Hern Tom (1068268)
• Kaixin Yu (1118795)
• Lissy Xun (1074284)
• Jiabao Zhang (1118553)

Project Stages

• Stage One: Maximize revenue while assuming perfect foresight of future spot prices.
• Stage Two: Maximize revenue without the benefit of perfect foresight of future spot prices.

Dependencies

• Language: Python 3.8.8
• Python Packages/Libraries: pandas, numpy, matplotlib, statsmodels, pyomo, pyutilib, glpk, logging
• To install all the required packages and libraries, please locate the text file requirements.txt

Directory

• algorithms: contains algorithms and notebook for this project
• data: contains data for this project
• modelling: contains model for spot price forecasting
• result: contains simulated dispatch results of the proposed battery
• visualization: contains basic visualization of the spot price

Reference

• Sandia National Laboratories. (2018). About. Pyomo.
• Sandia National Laboratories. (2017). Pyomo Documentation 6.1.2. Pyomo Documentation.
• Sandia National Laboratories. (2016). Pyomo Tutorial. Pyomo Tutorial.
• Brent Austgen - UT Austin INFORMS. (2021). Pyomo Tutorial. YouTube.
• Free Software Foundation, Inc. (2012). GLPK - GNU Project - Free Software Foundation (FSF). GNU.
• AEMO. (2021). National Electricity Market (NEM).
• Brakels, R. (2021). How Does 5-Minute Settlement Help Batteries? Solar Quotes Blog.