Stefano Bracco

A system of systems model for the control of the university of Genoa Smart Polygeneration Microgrid

Experimental tests and demonstration projects are very useful to derive new methods and tools for the optimal control of smart grids. In this work, the University of Genoa Smart Polygeneration Microgrid (SPM) is firstly presented, in connection with the different sub-systems that compose the overall system. Then, a simplified mathematical dynamic model, that can be used for optimal control purposes, is described. Finally, a dynamic optimization problem is formalized and solved.

Planning and management of sustainable microgrids: The test-bed facilities at the University of Genoa

The aim of this paper is to describe the system composed by the SPM (Smart Polygeneration Microgrid) feeding the SEB (Sustainable Energy Building) at the University of Genoa (Savona Campus), and to assess the Campus operating costs, CO2 emissions, and primary energy annual savings determined by the combined SPM-SEB system. This work highlights the main difference between two scenarios (AS-IS and TO-BE) with specific reference to operation and management of various power units. As demonstrated by the work, besides research and testing of new devices, the SPM-SEB can be used also for demonstration and teaching activities, and contributes to increase the overall energy efficiency of the Campus, lowering its primary energy consumption.

An optimization algorithm for the operation planning of the University of Genoa smart polygeneration microgrid

The aim of this paper is to describe the Smart Polygeneration Microgrid (SPM), which is being constructed at the Savona Campus of the Genoa University, also thanks to funding from the Italian Ministry of Education, University and Research (amount 2.4 Million Euros). Specifically, a detailed model of the grid is presented and an optimization problem is defined, in order to achieve suitable goals, like the minimization of the production costs or the maximization of power quality or environmental indices.

An Energy Management System for the Savona Campus Smart Polygeneration Microgrid

The paper proposes an Energy Management System (EMS) for the optimal operation of the Savona Campus Smart Polygeneration Microgrid (SPM) to minimize the overall production costs while satisfying all the thermal and electric network constraints. To do this, first an adequate model of all the components installed in the SPM is presented, then a detailed and computationally efficient electric network representation is derived. The experimental validation performed on the SPM shows a good agreement with the energy management already present in the control room of the SPM and highlights the advantages of the proposed approach both in terms of CPU effort and of completeness of the model.

Sustainable electric mobility analysis in the Savona Campus of the University of Genoa

Today, the concept of Smart Grid is very often strictly related to Electric Vehicles (EVs) infrastructures. EVs have not gas emissions, they have silent driving and much higher efficiency than internal combustion engine vehicles and, in particular, they are fundamental for the worlds sustainable mobility. In fact, many industries and universities have decided to replace their corporate fleet with green vehicles. The aim of this work is to present the Smart Grid project at the Savona Campus of the Genoa University, with particular attention on the use of the EVs. The scope of this work is to evaluate the different habits of charging the vehicles and to study new scenarios to take advantage of the full potentiality of smart mobility.

Optimal planning of the energy production mix in smart districts including renewable and cogeneration power plants

The choice of the location and size of plants for energy production (both from renewables and fossil fuels) is fundamental to cope with sustainability and emission reduction in smart cities. When the electrical grid is considered, it is necessary to guarantee an electrical demand in each time interval of a single day while the decisions related to installation have to be considered for the whole life time of generation units. In this work, a decision model is proposed for the planning of the energy production in a smart grid feeding a smart district. Specifically, the considered system is characterized by wind turbines, photovoltaic plants, cogeneration micro-turbines, boilers and a connection to the electrical grid. The input parameters of the available renewable resources, the electrical and thermal demands have been estimated on the basis of real data. The proposed model has been applied to a neighborhood in Savona, Italy. The proposed tool is aimed at supporting a central decision maker in planning investments in different urban areas, in the context of the transition from a traditional city to a “smart” one.

The role of high efficiency trigeneration plants within sustainable smart microgrids: Performance analysis and experimental tests

The widespread of smart microgrids has been also determined by the development of more and more efficient cogeneration and trigeneration power plants fed by fossil fuels or renewable sources. In the residential and the tertiary sectors, many projects are focused on the installation of low size microturbines, engines, chillers and heat pumps to sustainably provide electrical and thermal energy to final users. In the present paper, the case of the Savona University Campus Smart Polygeneration Microgrid is highlighted, focusing on the performance of the trigeneration plant installed in the microgrid. The operation of the aforementioned trigeneration system, considered innovative for its installation within a microgrid, is described by analyzing real data gathered through an experimental campaign conducted at the Savona University Campus during a summer month. Furthermore, some performance indicators are proposed and the reported results are compared with literature data.

A receding-horizon approach for active and reactive power flows optimization in microgrids

An approach based on a receding-horizon control scheme is here proposed for the optimal control of active and reactive power flows in microgrids (an aggregation of distributed energy resources (DER) of small size (such as photovoltaics, wind generation, cogeneration units - CHP, concentrated solar power - CSP, mini-hydro, energy storage)). Microgrids represent today one of the most promising technology for DERs integration, as they can alleviate management and monitoring burden for the Distribution System Operator (DSO) by clustering several DERs in a single entity which interacts with the grid as a single source. The formalized decision model is applied to an innovative test-bed facility (the University of Genova Smart Polygeneration Microgrid).

The smart microgrid pilot project of the University of Genoa: Power and communication architectures

The aim of this paper is to describe the Smart Polygeneration Microgrid (SPM) test-bed facility at the Savona Campus of the Genoa University. The SPM constitutes a pilot plant for research, development and testing of management strategies, devices and components for smart grid applications. The main features of this infrastructure will be outlined, focusing on its power and communication architecture, based on the use of the IEC 61850 protocol and on a close integration between electric and heating networks. Furthermore, the activities which will be carried out thanks to the SPM and the challenges in its operation and optimal management will be reported, in the context of the current research on microgrid and smart grid technologies.

A model predictive control approach for the optimization of polygeneration microgrids and demand response strategies

In this paper, the optimization of a microgrid operating in a “urban district-like” environment is considered, combining both the optimal scheduling of the microgrid sources and demand response strategies, implemented in the district buildings. In particular, the overall system is optimized in order to schedule generation plants, storage systems, electrical vehicles, deferrable and variable loads with the minimum daily operating costs, taking also into account network constraints. Binary and auxiliary variables have been used to reduce the nonlinearity of the model. Moreover, a technique based on Model Predictive Control (MPC) is proposed to minimize uncertainties coming from renewable resources and to reduce the complexity of the overall decision problem solution. The proposed approach has been developed exploiting, as a reference and practical test-case, the Savona Campus of the Genoa University, where the research infrastructure Smart Polygeneration Microgrid (SPM) is in operation; day-ahead optimization results have been compared with those of the proposed MPC approach.