The specific types of loads such as cranes, in particular, ship to shore (STS), rubber tyred gantry (RTG), rail mounted gantry (RMG) and cold ironing system in the ports present a distinctive load profile, due to their sudden peak load demand. To deal with the problem and avoid extra costs, it is possible to apply a delay time management to cranes operation and/or to implement energy storage systems (ESSs) to take benefit of regenerative energy. At this aim, the load profile characteristics require both energy storage with high power and energy densities and fast response time. Peak shaving can optimize the load demand and facilitate the participation of small power generation units based on renewable energy resources. In this regard, many approaches are introduced such as energy management strategies, modern technologies, and installing high-tech devices such as battery energy storage (BES), ultracapacitors (UCs), and flywheel energy storage (FES) acting as ESS. Therefore, the goal of this paper is to deal with an investigation for an integrated vision and a combination of ESSs application in the ports loads. Since the ports cannot persist to have independent and uncontrolled power systems, hence, the paper proposes the organization of their global design in a microgrid approach and the coordinated management for all the services such as cranes, reefers, col ironing, trucks, offices to increase the operation and energy performance. The statistical results show that the integration of ESSs can provide peak shaving, energy saving, and cost reduction in ports.

The new concept of renewable energy communities introduced by the Revised European Directive on the promotion of renewable sources (2018/2001) has opened new possibilities for microgrids. In fact, it permits to enhance the value of the energy produced by renewable sources sharing it inside an energy community and to increase the social welfare. In the present paper, the authors investigated about the actual legislation framework on energy communities at European and Italian level, highlighting regulatory problems and barriers that are delaying their constitutions. The authors propose a power sharing model (PSM) which is useful for energy communities based on the sharing of renewables and other energy services. The PSM is suitable for both building level and larger communities. PSM has been analyzed through a case study that regards the preliminary study of a smart microgrid that should be realized in Campobasso, Italy, to connect buildings of the public administration. This scenario was investigated through a simulation conducted in Simulink environment were the control strategy was implemented, and the results were compared to a traditional configuration for renewable sources integration. The results highlight the adequacy of PSM for the energy community for the building's microgrids application.

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs.

Currently, despite the use of renewable energy sources (RESs), distribution networks are facing problems, such as complexity and low productivity. Emerging microgrids (MGs) with RESs based on supervisory control and data acquisition (SCADA) are an effective solution to control, manage, and finally deal with these challenges. The development and success of MGs is highly dependent on the use of power electronic interfaces. The use of these interfaces is directly related to the progress of SCADA systems and communication infrastructures. The use of SCADA systems for the control and operation of MGs and active distribution networks promotes productivity and efficiency. This paper presents a real MG case study called the LAMBDA MG testbed laboratory, which has been implemented in the electrical department of the Sapienza University of Rome with a centralized energy management system (CEMS). The real-time results of the SCADA system show that a CEMS can create proper energy balance in a LAMBDA MG testbed and, consequently, minimize the exchange power of the LAMBDA MG and main grid.

In this paper, a cost-based mathematical optimization is used to evaluate the optimal amount of imported power from the public main grid to a private microgrid, that is the LAMBDA lab Microgrid testbed placed at Sapienza University of Rome. In this regard, this study considers five tests based on using different sources, including a photovoltaic array, an emergency generator set, a fuel cell and the main grid, for load satisfaction. The LAMBDA lab can be considered as a multi-source multi-output energy hub with three optional sources and both electrical and heat demands in output. This paper considers photovoltaic production and load demand as indeterministic parameters and evaluates the problem under uncertainties. As a result, a stochastic programming model is defined, and a powerful optimization function is used to reach the optimal power received from the main grid. In addition, information gap decision theory (IGDT) is used to model the robustness of the problem against uncertainties associated with renewable generation unit (Photovoltaic system) and electricity loads applied on a real case for the first time. In the result section, the contribution of each source in electrical and heat load demands is presented in addition to the cost of each test by evaluating the effect of DR of 15%. Finally, a comparison between the stochastic programming method and IGDT has been accomplished.