The Grand Challenge


Our research focuses on residential smart micro-grids, which represent the modern evolution of the low-voltage distribution grid, and may include a variety of distributed energy resources (PV, wind, batteries, fuel cells, micro-turbines). In this scenario, every energy source is linked to the distribution grid by an electronic power processors (referred to as Local Energy Manager, LEM), and the micro-grid operation can greatly be improved by a synergic control of such distributed power processors. Our research aims at developing an ICT architecture for the control of distributed LEMs. To this end, we follow a plug & play control approach, by which every power processor identifies the surrounding network and communicates with neighbor units to establish a distributed and close-to-optimal control rule. Our main goals are:

  • to fully exploit every existing energy source;
  • to minimize the local distribution loss;
  • to increment the hosting capability of the micro-grid, and
  • to stabilize the voltages at the grid nodes.

In addition, the plug & play approach has the flexibility and scalability needed to integrate an increasing number of distributed resources. The project development will make use of modern theories on distributed control, broadband power line communication, modeling and experimentation of energy sources, including those new technologies (e.g. high-temperature PEM fuel cells, molten salt batteries) which seem particularly suited for micro-grid applications. The distributed plug & play control will be firstly approached from a theoretical point of view, then analyzed by simulations extended to the entire micro-grid, and finally tested in the Smart Micro-Grid Facility of the Department of Information Engineering.

The realization of this complex system entails the cooperation of different disciplines. Below, we break this grand view of the problem into specific technological challenges, which are being tackled by our team.

Research on Distributed Control

Our research deals with the design of distributed control, identification, and prediction algorithms which enable the cooperation between micro power generators inside a smart microgrid. Two main issues have to be solved by these algorithms: the intermittent character of renewable power generation, and the need for optimal cooperation of LMEs. The objective of our control algorithms is that of achieving cooperation among a large number of LMEs in order to pursue the optimal behavior of the system as a whole. We believe that this approach is extremely promising in the smart grid scenario. In fact:

  • each microgrid can potentially host a huge number of microgenerators, and it would be impractical to command all al them from a central location;
  • micro generators can connect and disconnect, because of their small size and relatively low reliability; their operation as a whole must be robust to agent insertion and failure;
  • data communication inside a micro grid will probably be constrained to low data rates and limited reliability;
  • the cooperation between microgenerators in a microgrid is expected at different levels, including: optimal control (dispatchment) of both active and reactive power at every microgenerator, in order to minimize losses, support voltages, reduce power line congestion, and guarantee stability of operation;
  • forecasting of the availability of power generated from renewable power sources, in order to allow intelligent scheduling of the loads and to enable the microgrid participation in the energy market.

A promising solution to deal with different levels of interaction between microgenerators consists of a “layered architecture”, where lower level algorithms run transparently and provide services to higher level ones. In the specific case of dispersed power generation, this would correspond to enabling micro generators to participate in a fair energy market, while other mechanisms and algorithms would ensure that physical constraints and load demands are met and that the grid is operated in a safe and energy efficient way.

Measurements and Smart Grid Components

Our Instrumentation & Measurements group is working on the definition of LMEs as the advanced interfacing units of smart micro-grids. Our current activity includes:

  • measurement - the comprehensive monitoring and control of distributed generation capacity and loads call for a variety of functions: 
    • measurement of current, voltage and power values and distributions;
    • accurate measurement of phase angles, unsymmetrical conditions, harmonics etc.;
    • synchronization to (at least) a microgrid-wide common time reference;
    • estimation of local electrical grid parameters;
    • power quality analysis.
  • modeling and testing - in a smart microgrid, aggregate power demand exhibits a markedly bursty behavior that can impact on local control and management algorithms. The investigation of power profiles at such scale is still an open research issue. Validation of the micro-grid concept requires the analysis of demand aggregation properties and the development of models to test the impact of microgrid dynamics on control and protection.


We are currently working on the communication infrastructure that will allow the communication of distributed LMEs (including time synchronization, as required by the control algorithms). Our main investigation efforts consists of the study of suitable communication technologies and architectures:

  • Communication technologies:
    • A candidate ICT solution is powerline communications (PLC), which offers a natural communication channel for micro-grids. Besides, it provides a communication topology that perfectly matches with the grid topology, it is more secure than wireless communications and does not require deployment of new cables. Various standardization efforts have been initiated recently to support smart grids with powerline. However, a clear solutions has not yet been identified. A first step of our work consists of the identification of suitable PLC solutions so as to support the required data rates, as demanded by the control strategies of above.
    • We additionally envision a scenario including wireless communications: this may be the case of remote generators whose control may be more easily implemented using cellular systems, or the case of a very high number of loads and sources (e.g., many homes closed to each other) where again cellular solutions or even existing WLAN/WiMax solutions may be used to control the electrical neighborhood. For indoor environments we are also planning to develop IEEE 802.15.4-based controllers, adopting solutions driven by the popular ZigBee communication standard. Beyond investigating the critical issues of these alternative solutions, we will also investigate how suitable protocols may be developed for the interoperability of the various communication platforms.
  • Communication architectures: scalable and efficient communication architectures must be identified to facilitate the interaction of distributed LMEs, their time synchronization and, ultimately, the operation of distributed control algorithms.