Planning of smart buildings

People

Supervisor

Description

Smart building technologies will change the way how people experience comfort and consume energy. They will provide occupants security, comfort, convenience, and energy efficiency. Smart building technologies include sensors, devices, and appliances networked together to enable automation and control of the domestic environment. Flexible appliances and devices comprise air conditioning systems, electric vehicles (EVs), battery energy storage systems, and photovoltaic systems (PVs), among others.

The diversity of smart building technologies and electricity tariffs will produce multiple possible configurations of smart buildings, some of them not cost-effective neither environmentally friendly. So, the challenge here is to develop a planning optimisation model to determine the optimal configuration of a smart building that satisfies the needs of the occupants in a cost-effective and environmentally friendly way.

Goals

The goal of this project is to develop an optimisation model to plan the buildings of the future characterized by smart, green, and flexible technologies. The optimisation model will support building owners to make decisions, such as selecting the electricity tariff, sizing a PV-battery system, selecting the right air conditioning system, and buying or not an EV. The optimisation model may address economic, environmental, and reliability aspects, depending on the interest and motivation of the student.

Requirements

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

Background Literature

J. Iria, Q. Huang, Optimal Sizing of PV-Battery Systems in Buildings Considering Carbon Pricing, in: 2021 31st Australasian Universities Power Engineering Conference (AUPEC), IEEE, 2021: pp. 1–6. https://doi.org/10.1109/AUPEC52110.2021.9597757

J. Iria, F. Soares, Optimal Planning of Smart Home Technologies, in: 2020 International Conference on Smart Grids and Energy Systems (SGES), IEEE, 2020: pp. 215–220.  https://doi.org/10.1109/SGES51519.2020.00045

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

Gain

Student learning gains:

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

Research papers published with students under the scope of this project:

  • J. Iria, Q. Huang, Optimal Sizing of PV-Battery Systems in Buildings Considering Carbon Pricing, in: 2021 31st Australasian Universities Power Engineering Conference (AUPEC), IEEE, 2021: pp. 1–6. https://doi.org/10.1109/AUPEC52110.2021.9597757

ANU students can contact me via email for more details.

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