Our Research

Our research spans power and energy system modelling (transmission and distribution), techno-economic modelling, stochastic optimisation, integration of low-carbon technologies, and Smart Grids. Key research work streams include:

Techno-economic modelling and optimisation of integrated energy systems

We develop state-of-the-art models and tools that address optimal operation and planning of future energy systems from technical and economic perspectives. These include:

  • operational modelling of multi-energy systems: electricity, gas, hydrogen, heat, cooling, water and transport
  • planning under the uncertainty of large-scale integrated energy systems
  • multi-commodity co-optimisation, business case assessment and market analysis for energy technology portfolios such as community energy systems, smart districts, grid-connected and distributed energy storage, demand response, microgrids, and virtual power plants
  • large-scale multi-market clearing, for example, of energy, frequency control ancillary services, and fast frequency response provided by distributed energy resources

Distribution network integration of distributed energy resources (DER)

The distribution network supplies electricity directly from the transmission system to end-users and has been historically designed for unidirectional power flows. The infrastructure that delivers electricity for our everyday use will need to undergo significant developments to cope with the increased levels of small-to-medium scale distributed energy resources (DER), such as solar PV systems, wind farms, storage, and electric vehicles.

The challenges of adapting the operation and planning of the distribution network to new and variable levels of demand and generation require forward thinking and technical innovation. We are at the forefront of research and development in this sector and the identification of cost-effective integration solutions for network operators and end users.

Smart Distribution Networks and Distribution System Operators

The traditional Distribution Network Operator (or Distribution Network Service Provider as known in Australia) needs to evolve into an engaged, flexible Distribution System Operator (DSO) in which network elements and participants (consumers, generators, and those that are both) are managed to achieve technical, economic, and environmental objectives. This requires advanced Distribution Network Management Systems and adequate operational architectures to ensure coordination across the power system.

We are leading in this area by investigating the large-scale applicability of centralised and hierarchical real-time control techniques, including the use of multi-voltage level unbalanced Optimal Power Flow. Our research is also looking at the diverse interactions and unexpected consequences (eg: network issues) resulting from the future provision of bottom-up services, ie: end users with flexible elements (such as storage) providing services (eg: energy, active/reactive power, voltage regulation) to the national system operator or even the DSO. Understanding these interactions will be key to determining the most adequate architecture for the implementation of future DSOs.

Reliability and resilience of future energy infrastructure

Low-carbon power systems require new technologies and approaches to provide reliable services that were traditionally supplied by conventional power plants. In addition, there is an increasing need to incorporate high-impact low-probability events in operation and planning of future energy infrastructure, to cope with extreme events, such as weather events from climate change. Our research aims to systematically model the risk profile of future systems—comprising renewables and smart grid technologies—to assess their reliability and resilience and how new concepts, such as microgrids and virtual power plants, can support renewables-based power systems.

Optimal operation and control of low-carbon power systems

Most renewable energy sources are highly variable and partly unpredictable, and low-carbon technologies (including storage and electric vehicles) are typically asynchronously connected to the system. This introduces active and reactive supply-demand challenges to ensure secure power system operation over different time scales, from sub-second to minutes and hours. New operational models and tools are needed to run low-carbon, low-inertia power systems and markets. We work at the interface between control, power systems, and energy markets, developing models such as:

  • resource scheduling with pseudo-dynamic security constraints in low-inertia networks
  • scenario-based optimal power flows with model predictive control and chance constraints
  • provision of dynamic services from aggregation of new technologies, including FACTS and HVDC
  • distributed optimisation-based frequency control with centralised and highly decentralised technologies, such as thermostatic loads and energy storage devices

Socio-technical modelling and low-carbon energy transition in developing countries

Rising energy demand and the 2016 Paris climate commitment to reduce carbon dioxide and other greenhouse gas emissions is driving a worldwide energy transition towards low-carbon solutions. Rapid economic growth and the need for more electricity have also led to massive capacity additions in developing countries, for which electricity generation is at the centre of many of the transformations needed.

Our research analyses advanced technology trends in the low-carbon energy transition taking place in developing countries with specific applications to South Asia, as well as the opportunities to simultaneously leapfrog technology and social equity gaps. The breakthrough socio-technical modelling research that we are developing includes assessment of DER and microgrids to meet rural electrification needs in villages and in small towns with weak grid connections, incorporating advanced technology transfer and know-how into low-income communities to foster local economic development while also integrating measurable social inclusion criteria. These models are verified through test cases in the field, monitored over a multi-year period.