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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.

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.

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.

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.

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.

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.

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.