Optimisation of distributed energy resources




Distributed energy resources (DERs) are changing the way how electricity is generated and managed. Traditionally, electricity has been generated by big power plants. Today, it is also starting to come from DER located in millions of homes and businesses. Common examples of DERs include photovoltaic systems, battery energy storage systems, electric vehicles, and home energy management technologies.

The demand for DERs in Australia is expected to grow in the next years. Energy Networks Australia estimates that by 2050, DERs may contribute up to 45% of Australia’s electricity generation capacity. A high number of DERs may significantly impact the operation of the Australian power system. For instance, the operation of DERs may generate voltage and congestion problems in the distribution networks, if not well managed.


The goal of this project is to develop an optimisation model to plan, upkeep, or operate a power system characterized by a high integration of DERs. The optimisation model may address technical, economic, or environmental aspects, depending on the interest and motivation of the student. Examples of optimisation models are:

  • Bidding optimisation models for DER aggregators;
  • Portfolio optimisation models for DER aggregators;
  • Distributed optimisation approaches for energy system applications;
  • Planning optimisation models for distribution networks.


  • Knowledge of optimisation.
  • Programming skills, such as python.

Background Literature

Bidding optimisation models for aggregators:

J. Iria, F. Soares, M. Matos, Optimal bidding strategy for an aggregator of prosumers in energy and secondary reserve markets, Applied Energy. 238 (2019) 1361–1372. https://doi.org/10.1016/j.apenergy.2019.01.191

J.P. Iria, F.J. Soares, M.A. Matos, Trading Small Prosumers Flexibility in the Energy and Tertiary Reserve Markets, IEEE Transactions on Smart Grid. 10 (2019) 2371–2382. https://doi.org/10.1109/TSG.2018.2797001

J. Iria, F. Soares, M. Matos, Optimal supply and demand bidding strategy for an aggregator of small prosumers, Applied Energy. 213 (2018) 658–669. https://doi.org/10.1016/j.apenergy.2017.09.002

Distributed optimisation approaches for energy system applications:

A. Coelho, J. Iria, F. Soares, Network-secure bidding optimization of aggregators of multi-energy systems in electricity, gas, and carbon markets, Applied Energy. 301 (2021) 117460. https://doi.org/10.1016/j.apenergy.2021.117460

Planning optimisation models for distribution networks:

J. Iria, M. Heleno, G. Cardoso, Optimal sizing and placement of energy storage systems and on-load tap changer transformers in distribution networks, Applied Energy. 250 (2019) 1147–1157. https://doi.org/10.1016/j.apenergy.2019.04.120

Access to the background literature: https://www.researchgate.net/profile/Jose-Iria-2


Student learning gains:

  • Power system concepts;
  • Optimisation modelling and techniques;
  • Modelling of optimisation problems in Python;
  • Applying optimisation to real-world energy problems;
  • A conference/journal publication in case of a good project.

ANU students can contact me via email for more details.

Updated:  10 August 2021/Responsible Officer:  Dean, CECS/Page Contact:  CECS Marketing