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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.

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 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.

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.

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.

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.

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).

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 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).