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Background Acute brain injured (ABI) patients are at high risk of developing ventilator-associated pneumonia (VAP). However, incidence, risk factors and effects on outcome of VAP are not completely elucidated in this population. The primary aim of this study was to determine the incidence of VAP in a cohort of ABI patients. The secondary objectives included the identification of risk factors for development of VAP, and the impact of VAP on clinical outcomes. Clinical outcomes were defined as intensive care unit length of stay (ICU-LOS), duration of invasive mechanical ventilation (IMV), and ICU mortality. Methods Pre-planned sub-analysis of the Extubation strategies in Neuro-Intensive care unit (ICU) patients and associations with Outcomes (ENIO) international multi-center prospective observational study. Patients with available data on VAP, who received at least 48 h of IMV and ICU-LOS ≥ 72 h were included. Results Out of 1512 patients included in the ENIO study, 1285 were eligible for this analysis. The prevalence of VAP was 39.5% (33.7 cases /1000 ventilator-days), with a high heterogeneity across countries and according to the type of brain injury. VAP was significantly more frequent in male patients, in those with smoke habits and when intraparenchymal probe (IP), external ventricular drain (EVD) or hypothermia (p Conclusions VAP is common in ABI patients. Male gender, IP and EVD insertion, tracheobronchitis, and the use of therapeutic hypothermia were significantly associated with VAP occurrence. VAP did not affect mortality but increased ICU-LOS.

To support the global transition towards a climate-neutral economy by 2050, countries all over the world are implementing low carbon and energy effi ciency policies. This is leading to a rapid increase in the installations of distributed generation technologies. A hybrid system consisting of two or more energy sources could provide a more reliable supply of energy and mitigate storage require ments. In this context, this paper develops an environmental early analysis by the employment of a simplified Life Cycle Assessment approach. The above mentioned approach is used to investigate the environmental performances of the hybrid energy system of the smart polygeneration micro-grid of the University Campus of Savona, which integrates photovoltaics and combined heat and power generators with an electricity storage system. Moreover, two further compari son scenarios are analyzed: electricity demand met by importing energy from the electricity grid (Scenario 1) and management of cogeneration plants to primarily satisfy thermal demand (Scenario 2). An early environmental assessment analysis shows that the cogeneration systems have a predominant weight on the majority of the environmental impact categories analyzed. On the other hand, the PV sys tems are the most responsible for the impacts on human toxicity cancer effects (35%), freshwater ecotoxicity (60%), and resource depletion (72%); while the energy imported from the grid has a predominant weight on freshwater eutrophi cation (55%). Finally, the results show that the alternative scenarios investigated are responsible for higher environmental impacts than the case study. The only exceptions are represented by the resource depletion for Scenario 1 and the global warming potential for Scenario 2. In fact, for these indicators, the base case shows higher environmental impacts than those of the alternative scenarios.

The large penetration of renewable resources has resulted in rapidly changing net loads, resulting in the characteristic "duck curve". The resulting ramping requirements of bulk system resources is an operational challenge. To address this, we propose a distributed optimization framework within which distributed resources located in the distribution grid are coordinated to provide support to the bulk system. We model the power flow of the multi-phase unbalanced distribution grid using a Current Injection (CI) approach, which leverages McCormick Envelope based convex relaxation to render a linear model. We then solve this CI-OPF with an accelerated Proximal Atomic Coordination (PAC) which employs Nesterov type acceleration, termed NST-PAC. We evaluate our distributed approach against a local approach, on a case study of San Francisco, California, using a modified IEEE-34 node network and under a high penetration of solar PV, flexible loads, and battery units. Our distributed approach reduced the ramping requirements of bulk system generators by up to 23%., 5 pages, 4 figures, 1 table. This work has been accepted for presentation at the 2022 IEEE Power & Energy Society General Meeting, and will be a part of the conference proceedings.Copyright may be transferred without notice, after which this version may no longer be accessible

In this article, a new mathematical formulation for the electric vehicle routing problem (EVRP) is proposed. This formulation extends the Green Vehicle Routing Problem (GVRP) considering time-of-use energy (TOU) prices, and including a detailed model for the EVs’ energy consumption. The main decisions for the considered EVRP are relevant to the choice among different types of charging modes at recharging stations, the speed of EVs, the loaded cargo and the battery charge. The model objective consists of minimizing the cost for the total travel distance and that for energy purchase, which depends on the selected recharging mode. A preprocessing algorithm used to reduce the problem dimension is presented. The experimental analysis performed on a large set of benchmark instances is reported.

The relevance and presence of Electric Vehicles (EVs) are increasing all over the world since they seem an effective way to fight pollution and greenhouse gas emissions, especially in urban areas. One of the main issues related to EVs is the necessity of modifying the existing infrastructure to allow the installation of new charging stations (CSs). In this scenario, one of the most important problems is the definition of smart policies for the sequencing and scheduling of the vehicle charging process. The presence of intermittent energy sources and variable execution times represent just a few of the specific features concerning vehicle charging systems. Even though optimization problems regarding energy systems are usually considered within a discrete time setting, in this paper a discrete event approach is proposed. The fundamental reason for this choice is the necessity of limiting the number of the decision variables, which grows beyond reasonable values when a short time discretization step is chosen. The considered optimization problem regards the charging of a series of vehicles by a CS connected with a renewable energy source, a storage element, and the main grid. The objective function to be minimized results from the weighted sum of the (net) cost for purchasing energy from the external grid, the weighted tardiness of the services provided to the customers, and a cost related to the occupancy of the socket during the charging. The approach is tested on a real case study. The limited computational burden allows also the implementation in real-case applications.

The relevance of electric vehicles (EVs) is increasing along with the relative issues. The definition of smart policies for scheduling the EVs charging process represents one of the most important problems. A discrete-event approach is proposed for the optimal scheduling of EVs in microgrids. This choice is due to the necessity of limiting the number of the decision variables, which rapidly grows when a small-time discretization step is chosen. The considered optimization problem regards the charging of a series of vehicles in a microgrid characterized by renewable energy source, a storage element, the connection to the main grid, and a charging station. The objective function to be minimized results from the weighted sum of the cost for purchasing energy from the external grid, the weighted tardiness of the services provided, and a cost related to the occupancy of the socket. The approach is tested on a real case study.

We present a unified framework for distributed convex optimization using an algorithm called proximal atomic coordination (PAC). PAC is based on the prox-linear approach and we prove that it achieves convergence in both objective values and distance to feasibility with rate o(1/τ), where τ is the number of algorithmic iterations. We further prove that linear convergence is achieved when the objective functions are strongly convex and strongly smooth with condition number k $_\text{f}$ , with the number of iterations on the order of square-root of k $_\text{f}$ . We demonstrate how various decomposition strategies and coordination graphs relate to the convergence rate of PAC. We then compare this convergence rate with that of a distributed algorithm based on the popular alternating direction method of multipliers (ADMM) method. We further compare the algorithmic complexities of PAC to ADMM and enumerate the ensuing advantages. Finally we demonstrate yet another advantage of PAC related to privacy. All theoretical results are validated using a power distribution grid model in the context of the optimal power flow problem.

As pressures to decarbonize the electricity grid increase, the grid edge is witnessing a rapid adoption of distributed and renewable generation. As a result, traditional methods for reactive power management and compensation may become ineffective. Current state of art for reactive power compensation, which rely primarily on capacity payments, exclude distributed generation (DG). We propose an alternative: a reactive power market at the distribution level designed to meet the needs of decentralized and decarbonized grids. The proposed market uses variable payments to compensate DGs equipped with smart inverters, at an increased spatial and temporal granularity, through a distribution-level Locational Marginal Price (d-LMP). We validate our proposed market with a case study of the US New England grid on a modified IEEE-123 bus, while varying DG penetration from 5% to 160%. Results show that our market can accommodate such a large penetration, with stable reactive power revenue streams. The market can leverage the considerable flexibility afforded by inverter-based resources to meet over 40% of reactive power load when operating in a power factor range of 0.6 to 1.0. DGs participating in the market can earn up to 11% of their total revenue from reactive power payments. Finally, the corresponding daily d-LMPs determined from the proposed market were observed to exhibit limited volatility., Comment: 26 pages, 9 figures, 3 tables

The introduction of mechanized agricultural practices after the Second World War and the use of productive hybrids led to a gradual disappearance of local maize varieties. However, 13 landraces are still cultivated in North-Western Italy, in the Lombardy region, those that are cultivated in mountainous areas (roughly up to 1200 m in altitude) are often characterized by the pointed shape of their seeds (i.e., “Nero Spinoso”, “Rostrato Rosso di Rovetta”, “Spinato di Gandino” and “Scagliolo di Carenno”) and the presence of pigments (i.e., “Nero Spinoso”, “Rostrato Rosso di Rovetta”). The pointed shape of the seeds is an ancient characteristic of maize-ancestors, which negatively affects the yield by not allowing optimal “filling” of the ear. This study reports work on four different Italian varieties of pointed maize in order to assess the genetic bases of the “pointed character” and to try to explain the reasons for this adaptation to the mountain environment. The data obtained by genetic analysis, seed air-drying modeling and thermographic camera observations demonstrated that the “pointed trait” is controlled by the same genes across the different varieties studied and suggested that this peculiar shape has been selected in mountainous areas because it promotes faster drying of the seed, with the presence of pigments implementing this effect.

In this paper, a multi-objective Energy Management System (EMS) for polygeneration microgrids is presented. The proposed tool has been developed within the LIVING GRID project, funded by the Italian Ministry of Research (actions related to the Italian Technology Cluster on Energy), and it is characterized by a detailed representation of generation units and flexible loads, as well as electric/thermal networks and storage systems that can be present in microgrids and sustainable districts. An optimization model has been developed in which the objective function is related to the minimization of costs and emissions, and the maximization of the overall exergy efficiency of the system. The decision model is applied to a real case study represented by the Savona Campus of the University of Genoa in Italy.

In this paper, a multi-destination multi-source traffic network of a single kind of vehicles is considered. In particular, an analysis of the system model in steady-state conditions has been performed both as regards the input flows from the external and the splitting rates. It has been investigated under which conditions the system admits a unique asymptotically stable equilibrium point under Lyapunov's indirect method. The condition that will be described has a very clear physical significance and it represents an interesting insight about the way by which the unique asymptotically stable equilibrium point can be found analytically when the information about the external inputs, the values of the splitting rates, and the parameters characterizing each link in the network are made available.

This paper presents an energy management platform based on a receding-horizon scheme for the optimal control of active and reactive power flows in microgrids, including small-size photovoltaic, combined heat and power, wind generation, mini-hydro, and energy storage. The objective function to be minimized can be set both on the daily operational costs (economic indicator) and on the global CO2 emissions of the system (environmental indicator), whereas decision variables are the production schedules of the generators and the power flows across the grid. The tool includes the technical constraints characterizing low voltage/medium voltage (LV/MV) microgrids and gives the user the possibility to select different models for the electrical network (nonlinear power flow equations, linear approximation, and single bus-bar) and different optimization ranges. The energy management system has been validated through an experimental campaign on the smart polygeneration microgrid of the University of Genoa, which provides electricity and thermal energy to the Savona Campus, an “open-air” demo-site of an environmentally sustainable urban district with a population of about 2200 people. The results of the tests on the field highlight the robustness of the developed platform and the capability of the receding-horizon algorithm at the core of it of successfully treating data uncertainties.

The use of electrical buses (EBs) is increasing all over the world to reduce pollution in cities. However, EBs’ charging represents a high load and, to minimize costs, it is necessary to optimally manage the recharge according to the intrinsically periodic arrivals and departures of vehicles. Moreover, they can participate in demand response programs to help the distribution system operator to manage the electrical grid in emergencies. In this paper, we propose a new model and a periodic discrete event approach for the optimal charging of EBs. The considered optimization problem minimizes costs, the maximum power taken from the external grid, and the dissatisfaction related to demand response requests. The considered system is characterized by different vehicles that should be charged by the same charging station in a depot in which storage systems are present too. The developed model can be used either for the management of charging in a depot or to participate to demand response programs. The resulting optimization problem has been applied to a real case study in the Genoa Municipality in which a fleet of EBs is present.

In this paper, a survey is presented on the use of optimization models for the integration of electric vehicles (EVs) and charging stations (CSs) in the energy system, paying particular attention both to planning problems (i.e., those problems related to long term decisions such as the siting and sizing of CSs), and operational management problems (i.e., the optimal scheduling of EVs in smart grids, microgrids and buildings taking into account vehicle-to-grid (V2G) capabilities). Moreover, specific attention was dedicated to decision problems that couple transportation and electrical networks, such as the energy demand assessment for a vehicle over a path and routing and charging decision problems for goods and people transportation. Finally, an effort was dedicated to highlighting the integration and the use of EVs in very recent regulation frameworks, with specific reference to the participation in the balancing market through the figure of an aggregator and the inclusion in the management of Energy Communities (ECs) and sustainable districts.

The present paper describes an Energy Management System developed to operate a polygeneration microgrid where demand response strategies are applied. The proposed tool is characterized by a detailed representation of generation units and flexible loads, as well as electric/thermal networks and storage systems. Furthermore, the interaction between the microgrid and a smart building is modeled, taking into account the inner comfort level in the objective function of the optimization problem. The tool is applied to a real case study represented by the Savona Campus of the University of Genoa in Italy, where a polygeneration microgrid and a smart building are present.

In the last few years, one of the most important challenges of power technologies has been the integration of traditional energy production systems and distributed energy resources. Large-scale photovoltaic systems and wind farms may decrease the quality of the electrical grid service, mainly due to voltage and frequency peaks and fluctuations. Besides, new functionalities, such as the operation in islanded mode of some portions of the medium-voltage grid, are more and more required. In this respect, a model predictive control for voltage and frequency regulation in interconnected local distribution systems is presented. In the proposed model, each local system represents a collection of intelligent buildings and microgrids with a large capacity in active and reactive power regulation. The related model formalization includes a linear approximation of the power flow equations, based on stochastic variables related to the electrical load and to the production from renewable sources. A model predictive control problem is formalized, and a closed-loop linear control law has been obtained. In the results section, the proposed approach has been tested on the Institute of Electrical and Electronics Engineers(IEEE) 5 bus system, considering multiple loads and renewable sources variations on each local system.

The electrical grid has been changing in the last decade due to the presence of renewables, distributed generation, storage systems, microgrids, and electric vehicles. The introduction of new legislation and actors in the smart grid’s system opens new challenges for the activities of companies, and for the development of new energy management systems, models, and methods. A new optimization-based bi-level architecture is proposed for an aggregator of consumers in the balancing market, in which incentives for local users (i.e., microgrids, buildings) are considered, as well as flexibility and a fair assignment in reducing the overall load. At the lower level, consumers try to follow the aggregator’s reference values and perform demand response programs to contain their costs and satisfy demands. The approach is applied to a real case study.

A multi-objective optimization problem is here formalized for a distribution grid in which are present both traditional generators and aggregators of microgrids, buildings, or general local prosumers. At the lower level, generators and aggregators want to maximize their profit, taking into account the limits of the flexibility they can sell. At the higher level, the distribution system operator (DSO) receives power/price bids from market participants and, based on technical and economic considerations, decides which bid to accept. For the lower-level decision problem, the Karush Khun Tucker (KKT) conditions are developed and included as constraints within the higher-level optimization problem. The resulting optimization problem is applied to a modified IEEE 13-bus system.

In the present paper we introduce a new distributed optimization approach to the solution of general Optimal Power Flow problem for unbalanced and mesh networks. A new convex formulation, based on McCormick Envelopes, is proposed with a decomposition profile and a distributed approach based on proximal coordination. The resulting algorithm is shown to converge with a rate of o(1/τ), where τ is the number of iterations. The approach is validated on a modified IEEE 13 bus network, with added distributed energy resources including distributed generation and demand response.

The introduction of renewables, distributed generation, microgrids, electric vehicles, and new market actors, such as aggregators, have led to a remarkable change in the power network. To address the issues that such a profound modification implies on a modern energy system, here a new hierarchical architecture is presented. Specifically, the proposed approach considers the case of an aggregator of consumers in the balancing market, in which incentives for local users (i.e., microgrids, buildings) are considered as well as flexibility assessment for demand response, and CO2 emissions. The main innovation is related to the overall architecture and to the formalization of the upper level decision problem that aims at coordinating local users in a democratic way, while, at the lower level, consumers want to track the aggregator’s reference values performing demand response programs. The approach is applied to a real case study.

A new bi-level approach is proposed for the location and sizing of charging stations, considering both the transportation and energy demands. The lower level considers the User Equilibrium traffic assignment conditions for Electric Vehicles (EVs) which are derived and inserted as constraints in the overall optimization problem. The higher level presents the formalization of an optimization problem for the optimal planning of locations, sizes and unit prices of a set of new charging stations in a territory characterized by the presence of an already existing set of charging stations. A case study in the Genoa Municipality is considered for the application of the proposed model.

The optimal planning and sizing of charging stations (CSs) over a territory is an interdisciplinary issue that should consider the transportation and electrical networks, and the characteristics of CSs. A new bi-level approach is here proposed that takes into account both transportation and energy demands. At the higher level, the optimal locations, sizes, and unit prices must be found for new CSs in a territory with existing CSs. The decision model includes, as constraints, the behavior of EVs (Electric Vehicles) at the lower level, i.e. the User Equilibrium traffic assignment conditions derived for the case of EVs. The developed model has been applied to a case study in the Genoa Municipality.

Islanded microgrids are assuming a key role for the support in the distribution grid’s management, which is more and more difficult due to the massive increase of distributed generation and energy production plants from renewable resources. In this paper, a new architecture for the optimal control of tertiary and secondary levels in small-size islanded microgrids is proposed. Specifically, at each optimization step, by using a receding horizon approach, the tertiary control provides the optimal power schedule for the microgrid on the basis of economic and environmental criteria. Then, the secondary control, to provide set points for primary control, uses another objective function that minimizes the quadratic deviation from the reference values provided by the tertiary level and the desired frequency for good performances. The approach is applied to a real case study (a portion of the Savona Campus Smart Polygeneration Microgrid) characterized by a diesel engine connected to a synchronous generator, a photovoltaic plant, and an electrical storage system.

An optimization model is formalized for the energy planning of urban districts equipped with renewable energy power plants (photovoltaic fields and wind micro-turbines), combined heat and power units, and traditional boilers; districts are connected to the public distribution grid. The main decision variables of the model are the number and typology of wind turbines and cogeneration plants, the size of photovoltaic fields and the power exchange with the public grid. The optimal values of the variables come from the minimization of installation, maintenance and operational costs, and satisfy load and technical constraints. The model is applied to three districts in the Savona Municipality that represent pilot sites of an extended project concerning the planning and management of smart urban areas. The analysis of different scenarios allows evaluating the role of uncertainties of renewable sources over the optimal solution of the planning problem.

The reduction of greenhouse gas emissions is a priority for European Union. National Authorities are encouraged to promote the use of electric and hybrid vehicles. In this paper, the problem of planning a freight transportation service provided by electrical vehicles (EVs) is considered, and a new decision model is proposed. An extension of the Green Vehicle Routing Problem (GVRP) is formalized, adding time variant prices for energy purchase, a detailed model for EVs consumes and different types of charging modes. The main decisions for the considered problem also refer to the velocity of EVs, the loaded cargo and the battery charge at recharging nodes, with the objective of minimizing the cost for the total travel distance and that for energy purchase, which depends on the selected recharging mode. A case study is provided and the related results are discussed.

A bi-level Energy Management System (EMS) is here proposed for the day ahead schedule and real-time optimal control of microgrids. The upper-level optimization problem (UDP) takes into account active and reactive power flows and a detailed formalization of the system model, with the aim of minimizing costs and emissions. For the lower level decision problem (LDP), the optimal results of the UDP are taken as reference values for the plant’s schedule, which can be slightly modified in real time due to the actual values of renewables and energy demands. An analytical solution for the LDP has been found, which allows a fast and simple implementation of the microgrid’s schedule.

In this paper, a discrete event approach is proposed for the optimal charging of electrical vehicles in microgrids. In particular, the considered system is characterized by renewable energy sources (RES), non-renewable energy sources, electrical storage, a connection to the external grid and a charging station for electric vehicles (EVs). The decision variables are relevant to the schedule of production plants, storage systems and EVs' charging. The objective function to be minimized is related to the cost of purchasing energy from the external grid, the use of nonrenewable energy sources and tardiness of customer's service. The proposed approach is applied to a real case study and it is shown that it allows to considerably reduce the dimension of the problem (and thus the computational time required) as compared to a discrete-time approach.

Battery systems are becoming more and more widespread for stationary applications both at power grid level and user level. In this second context, small battery-based storage systems are frequently proposed for the installation in combination with local renewable sources, to increase the self-consumption of the locally generated renewable energy and, in some cases, even to enable the user to disconnect from the main network. Increasing use of storage devices for stationary applications implies a more detailed characterization of the “behavior at the terminals” of these systems. In the same time, the development of new Energy Management Systems is required in order to take advantage of both local information and data from the service provider, such as radiation forecasts from weather forecast services. In this paper, a new EMS is proposed, characterized by a two-level architecture: the higher level, based on a receding horizon control scheme, optimally schedules the operation of the storage by using information on radiation from a forecast service provider; the lower level implements a heuristic procedure (if–then) on a low-cost local controller, in order to perform corrective actions. The proposed architecture has been tested on a real case study.

The development of policies for sustainable development has led to an increase in electric vehicles (EVs). This has given rise to new problems, such as the location and sizing of charging stations over the considered traffic network. The solution of such problems requires the application of analysis techniques in order to predict the joint allocation of transportation and energy demands. In this paper, an approach is proposed that extends the User Equilibrium (UE) principle in order to determine, besides to the flow over the network links, the service requests from the drivers to the various service stations. The application of this method is preliminary for the statement of any problem related to the optimization of location and sizing of service stations. The proposed approach is applied to a case study in Genoa Municipality (Liguria Region).

The exploitation of biomass in power production systems is particularly attractive to reduce CO2 emissions. However, the installation of biomass-fed power plants is not always technically and economically competitive with respect to traditional fossil fuels. This paper aims to provide a decision support system that can help the decision maker in assessing the feasibility of the plant, from a technical and economic point of view, and to provide its optimal design. The model is adapted for a plant that can produce pellets, electricity, and heat, through the gasification process. The developed model is applied to a real case study in the Alessandria Province (Italy).

Energy consumption in buildings can be efficiently reduced through energy management systems that take into account several issues like comfort, technical requirements and economic aspects. This implies a detailed schedule of plants and devices (washing machines, electrical vehicles, etc.) according to forecasting of loads and renewables. The behavior of “active buildings” strongly affects the electrical grid and its consequent management in terms of power quality and costs. In this work, a system composed by buildings electrically interconnected is considered. Each one has a storage system, renewables, and needs to satisfy electrical and thermal demands. An architecture based on Model Predictive Control is proposed, in which, first, an upper decision maker solves an optimization problem to minimize its own costs and power losses, and provides references for power exchanges with buildings that respect power flow constraints. Then, consumers, on the basis of more detailed local information, manage storage systems and devices in order to follow the reference values, to contain costs, and to achieve comfort requirements. The architecture is applied to a case study in Genoa Municipality

Energy Management Systems (EMSs) are recognized as essential tools for the optimal management of smart grids. However, few of them consider, in their whole complexity, the integration of electrical vehicles (EVs) in smart grids, taking into account the requirements and the time specifications characterizing the service requests. In this paper, attention is focused on the formalization of a model for the optimal scheduling of charging of EVs in a smart grid, also considering the vehicle to grid process (i.e., the possibility for the EV to inject power during the charging process). In the formalization of an optimization problem for a smart grid, a deferrable demand is considered, which is represented by the charging demand of the set of EVs. The cost to be optimized for the considered problem includes the economic cost of energy production/acquisition (from the main grid) and the cost relevant to the delay in the satisfaction of the customers’ demand (is represented as a tardiness cost). Also, the income coming from the service provided to vehicles is taken into account. The developed model is tested and applied in connection with a real case study characterized by a photovoltaic plant, two batteries, power production plants that use natural gas as primary energy, and a charging station.

In this paper, an optimization model for polygeneration microgrids is presented. In particular, the system is characterized by trigeneration plants, renewables, district heating, thermal and electrical storage systems and flexible loads. The control variables are represented by the schedule of the power plants and of the deferrable demand, while the state variables are the energy stored in batteries and water tanks. The objective function minimizes operational costs in the day-ahead. The model is applied to the University of Genoa research infrastructures at Savona Campus, characterized by a Smart Polygeneration Microgrid and a Smart Energy Building.

The aim of this paper is to propose a new voltage controller for Distribution System Operators (DSOs), based on a multi-objective optimization, which considers the presence of microgrids in the distribution network. This paper is focused on DSO's optimization problem, which has to minimize grid losses, and to take into account some ancillary services provided by the microgrids, such as demand response policies and reactive power injections, in order to obtain an efficient voltage regulation in all grid's nodes. The developed model has been applied to a case study with real data that includes two microgrids, a medium voltage load, and a primary substation with a transformer equipped with on-load tap changer.

During the last years, the number of distributed generators has grown significantly and it is expected to get higher in the future. Several new technologies are being developed for this type of generation (including microturbines, photovoltaic plants, wind turbines and electrical storage systems) and have to be integrated in the electrical grid. In this framework, active loads (i.e., shift-able demands like electrical vehicles, intelligent buildings, etc.) and storage systems are crucial to make the distribution system more flexible and smart. The aim of this paper is to develop a model for the optimal integration of microgrids with renewable sources, smart buildings, and electrical vehicles (EVs) taking into account two different technologies: smart charging and vehicle to grid (V2G).

Microgrids that are able to work both in grid-connected and islanded modes need a hierarchical structure based on tertiary, secondary and primary controllers, which are fundamental for the maintenance of frequency and stability. In this paper, two types of controllers are formalized and solved as optimal control problems: tertiary and secondary controls. For secondary control two different scaled single-phase models are used for the microgrid: one merely electric and the other one based on the synchronous machine. The approach is applied to a real test-bed facility: the University of Genova Smart Polygeneration Microgrid (SPM). Specifically, a portion of the microgrid is selected to study the behavior in islanded mode. The considered grid is characterized by: a diesel engine connected to an electrically excited synchronous generator, a photovoltaic plant and an electric storage (these last two are respectively connected to a couple of inverters set as current source generator (CSI)).