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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.

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.

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.

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.

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.

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.

Modelling and optimization techniques for water resources allocation are proposed to identify the economic value of the unsatisfied municipal water demand against demands emerging from other sectors. While this is always an important step in integrated water resource management perspective, it became crucial for water scarce Countries. In fact, since the competition for the resource is high, they are in crucial need to trade values which will help them in satisfying their policies and needs. In this framework, hydro-economic, social equity and environmental constraints need to be satisfied. In the present study, a hydro-economic decision model based on optimization schemes has been developed for water resources allocation, that enable the evaluation of the economic cost of a deficiency in fulfilling the municipal demand. Moreover, the model enables efficient water resources management, satisfying the demand and proposing additional water resources options. The formulated model is designed to maximize the demand satisfaction and minimize water production cost subject to system priorities, preferences and constraints. The demand priorities are defined based on the effect of demand dissatisfaction, while hydrogeological and physical characteristics of the resources are embedded as constraints in the optimization problem. The application to the City of Amman is presented. Amman is the Capital City of the Hashemite Kingdom of Jordan, a Country located in the south-eastern area of the Mediterranean, on the East Bank of the Jordan River. The main challenge for Jordan, that threat the development and prosperity of all sectors, is the extreme water scarcity. In fact, Jordan is classified as semi-arid to arid region with limited financial resources and unprecedented population growth. While the easy solution directly goes to the simple but expensive approach to cover the demand, case study results show that the proposed model plays a major role in providing directions to decision makers to orient their policies and strategies in order to achieve sustainability of scarce water resources, satisfaction of the minimum required demand as well as financial sustainability. In addition, results map out national needs and priorities that are crucial in understanding and controlling the complexity of Jordan's water sector, mainly for the city of Amman.

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.

Smart grid planning and control is becoming a theme of high interest in the last years. This is due to the presence of distributed generation, power from renewable resources and storage systems, to the different actors present over the territory, and to the difficulty of defining appropriate models for decision support. A bilevel optimal control scheme is proposed for grids characterized by renewable and traditional power production, bidirectional power flows, dynamic storage systems, and stochastic modeling issues. In this scheme, the upper level decision maker (UDM) views the lower level decision makers (LDMs) or microgrids as single nodes. In the statement of the UDM problem, the LDM control strategies are structurally and parametrically constrained inside a nonlinear optimization problem that includes load flow equations. Then, the LDMs can follow references from the UDM and use available information at the local level to solve a stochastic optimization problem. The proposed control architecture has been applied to a specific case study (Savona, Italy).

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.

This work refers to the development of an approach for planning wet waste (food waste and other) collection at a metropolitan scale. Some specific modeling features distinguish this specific waste collection problem from the other ones. For instance, there may be significant differences as regards the values of the parameters (such as weight and volume) characterizing the various collection points. As it happens for classical waste collection planning, even in the case of wet waste, one has to deal with difficult combinatorial problems, where the determination of an optimal solution may require a very large computational effort, in the case of problem instances having a noticeable dimensionality. For this reason, in this work, a heuristic procedure for the optimal planning of wet waste is developed and applied to problem instances drawn from a real case study. The performances that can be obtained by applying such a procedure are evaluated by a comparison with those obtainable via a general-purpose mathematical programming software package, as well as those obtained by applying very simple decision rules commonly used in practice. The considered case study consists in an area corresponding to the historical center of the Municipality of Genoa.

The use of forest biomass for energy production requires a careful attention to the sustainable silvicultural practices. This is a complex task because of the different environmental and economic issues to be taken into account. To this aim, suitable tools must be used as regards the representation of the dynamics of forest biomass and the economic assessment. In this paper, a user-friendly optimization-based decision support system (DSS) that can help decision makers in the optimal management of forest biomass use for energy production is presented. Attention is focused on the forest system in order to take into account sustainable silvicultural practices and on the minimization of costs for the collection plans over years. Specifically, a non linear optimization model (that includes forest growth models) is formalized, aiming at determining, over a certain period, the optimal exploitation policy of forest biomass through a single plant whose location and size are assumed known, in order to minimize costs and to respect silvicultural constraints. The decision model is solved through a receding horizon approach and is applied to the case study of Val Bormida (Savona Province, Italy). Different tests and sensitivity analysis have been performed to validate the model and the approach. From an application point of view, observing the obtained results, it is evident that results are strongly influenced by the old average age of the vegetation in the specific case study. However, depending on the species, different trends for the results of annual mean increment and harvesting plans are observed.

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.

Saltwater intrusion and upconing phenomena affect coastal aquifers worldwide. These phenomena can be partially mitigated by an adequate management of the aquifer. In this work, the optimal pumping schedule for one coastal well has been defined by a decision model that minimizes desalination and pumping costs, while taking into account the aquifer salinity levels near the well. The dynamics of the aquifer is described in terms of two state equations related to salinity concentration in the pumped water and cumulative pumped water up to a specific instant. A case study is presented with application to a well in Hawaii islands.