Important Dates
Paper submission: Now closed
Paper decisions: 8 Aug 2016
Final submission: 5 Sep 2016
12 Sep 2016
Registration: Now open
Early-bird closes: 12 Sep 2016
Tutorials: 28 Nov 2016
Conference: 29 Nov -
1 Dec 2016
Tutorial: Modelling and assessment of smart districts, community based energy systems, and microgrids
Presenter: Prof. Pierluigi Mancarella
The University of Melbourne, Australia, and The University of Manchester, UK
Date and time: Monday November 28, 9:00 – 12:30
Location: Brown Theatre, Electrical Engineering Building, Melbourne University

Tutorial topics:

The decrease in cost of distributed renewable energy sources (particularly PV) and energy storage and the ubiquitous penetration of ICT technologies are rapidly changing the power system landscape. In this context, the rise of the smart grid control-based operational paradigm to replace the classical asset-based investment-heavy paradigm to develop a cost effective low carbon energy system is creating new opportunities for end-users to participate in and profit from energy system and market operation. Smart buildings can thus exploit multi-energy vectors (e.g., electricity, heat, cooling, etc.) to flexibly meet their energy needs and sell services (e.g., reserves, local capacity support, etc.) to various actors (e.g., aggregators, retailers, network operators etc.). Further, smart buildings can be coordinated at the district level for optimal energy management of clusters of buildings or district energy systems. Special cases of such smart districts are represented by community-based energy systems, where resources can be optimally shared within the members of the community, and by microgrids that could operate in both islanded and grid-connected mode, with further reliability and economic benefits.

Modelling and optimization of single/multiple buildings require comprehensive understanding of: (i) the energy services required by the building occupants; (ii) technical characteristics and constraints of the buildings, local and district-level distributed energy resources (including different types of storage), and different energy networks (e.g., electricity, heat, cooling, gas) that interconnect the buildings; (iii) the economic implications of multiple services that could be provided throughout the value chain to different actors; and (iv) what financial arrangements and risk-hedge strategies might be put in place to facilitate the development of new business cases for local energy systems considering relevant uncertainties.
This tutorial provides a holistic overview of technical and economic modelling and optimization principles of smart buildings and districts, community-based energy systems, and microgrids, based on recent research conducted at the University of Manchester in various UK, European and international projects.

Topics that will be covered include:

  • High-resolution stochastic energy service modelling in residential buildings equipped with solar PV and solar thermal technologies, heat pumps, air conditioning, electric vehicles, water heaters and thermal storage, electrical batteries, etc.;
  • Virtual (thermal and electrical) storage modelling of building-level resources, and assessment of the flexibility that they can provide against end-users’ loss of comfort;
  • Multi-criteria optimization and control algorithms for home energy management systems which take into account uncertainty in local generation and consumption as well as energy prices and users’ comfort;
  • Technical and commercial electrical, thermal, and virtual aggregation modelling of buildings;
  • Integrated load flow and optimal power flow algorithms to deal with multiple energy vectors (e.g., electricity, heat, cooling, and gas) at the district level;
  • Flexibility modelling of district energy systems;
  • Multi-criteria optimization and control algorithms for district energy management systems and provision of real time demand response;
  • Optimal operation and design of community based energy systems, including PV and battery resources;
  • Algorithms for planning under uncertainty of district energy systems, including methodologies based on stochastic optimization, robust optimization, real options, and decision theory;
  • Cost benefit analysis and assessment of different business case options for smart districts, community based energy systems, and microgrids.

Real world applications and case studies from existing projects will be used to demonstrate the above concepts. An extensive list of references and modelling tools will also be discussed and provided.


pierluigimancarellaProf. Pierluigi Mancarella is Chair of Electrical Power Systems at the University of Melbourne, Australia, and Professor of Smart Energy Systems at The University of Manchester, UK.

He received his MSc and PhD degrees from the Politecnico di Torino, Italy, and has been a Research Associate at Politecnico di Torino and at Imperial College London, UK. Pierluigi holds/has held visiting positions at NTNU, Trondheim, Norway, at Ecole Centrale de Lille, France, at Universidad de Chile, Santiago, Chile, at NREL, Golden, Colorado, and at the University of Melbourne, Australia.

Pierluigi’s areas of expertise and interests include techno-economic and environmental modelling of multi-energy systems; integration of low carbon technologies into power systems; new business models for smart technologies, community energy systems and microgrids; planning under uncertainty of integrated energy infrastructure; and risk and resilience assessment of future networks.

Pierluigi is/has been responsible for over 15 UK, European, and international projects and work packages in the area of low carbon and resilient smart energy systems. He is the author of four books, several book chapters, and about 250 research papers and reports.

He is an Editor of the IEEE Transactions on Smart Grid, an Associate Editor of the International Journal of Electrical Power&Energy Systems, an Associate Editor of the IEEE Systems Journal, and the Chair of the Working Group on Energy of the IEEE European Public Policy Initiative.