• Energy Research

Abstract The recent evolution of distribution systems has shed light on innovative solutions aimed at enhancing the performance of such systems. In this paper, the authors focus their attention on two modular, non-isolated multiport DC/DC converters and their application to low voltage DC distribution systems. The first topology is based on a capacitive internal link, while the second one is based on a less usual inductive link. Unconventional control strategies based on a lower-level pseudo-sliding mode control algorithm and a higher-level control based on inverse dynamics are proposed for both topologies by means of a general, fully modular control design. A six-port application is discussed in detail and its operation is analyzed in steady state, under abrupt load/generation changes and in case of pole-to-pole faults. Finally, a systematic comparison between the two topologies is performed.

G3-powerline communication (G3-PLC) is a robust communication protocol originally developed for smart metering in low-voltage power distribution networks. Modeling G3-PLC modems is an essential task to investigate electromagnetic compatibility (EMC) issues related to the coexistence of the PLC signal with the high-frequency noise affecting low-voltage networks, mainly due to the presence of power converters and non-linear loads. Since detailed information on the modem internal architecture is usually not available to the end-user, this work investigates the possibility of developing behavioral (black-box) models of G3-PLC modems, whose parameters can be estimated starting from measurements carried out at the modem output ports. To this end, suitable test benches are set up and used for model-parameter extraction as well as for validation purposes. Experiments have proven that an equivalent representation involving non-ideal voltage sources (i.e., in terms of extended Thevenin/Norton equivalent circuits) is no longer feasible for the transmitting modem, since the presence of a closed-loop control system invalidates the linearity assumption. Hence, while the receiving modem is still modeled through an impedance matrix (since it behaves as a linear device), an alternative representation is proposed for the transmitting modem, which resorts to the use of two ideal voltage sources in accordance with the substitution theorem. Experimental results prove that the proposed modeling strategy leads to satisfactory predictions of the currents propagating on the PLC system in the frequency interval of interest. Hence, it could be used in combination with high-frequency models of the other components in the network to investigate EMC and the coexistence of the PLC signal with the high-frequency noise generated by power converters.

Electricity access in developing countries, where the availability of public distribution grids is still poor, is considered a key factor for improvement of people life conditions. In these situations, the lack of a reliable grid can be mitigated by the introduction of stand-alone DC microgrids, including small Photovoltaic (PV) generators and storage devices. This paper focuses on optimal energy management and power supply reliability of such a microgrid. In particular, a Model-Predictive-Control (MPC) - based control system is introduced to optimally manage storage devices and coordinate load shedding actions. Additionally, an Artificial-Neural-Network (ANN) - based predictor is introduced to manage unpredictable solar irradiance and temperature variations. The availability of reliable adaptive forecasts provided by the ANN-based predictor increases the efficiency of the optimization performed by the MPC-based control over the prediction horizon, avoiding the well-known issues related to optimization performed on unreliable forecast. In this paper, the proposed control approach is detailed for a specific case study and its advantages with respect to traditional controller algorithms are highlighted by comprehensive numerical simulations. The presented results highlight that the proposed MPC controller provides a substantial increment in power supply reliability with respect to standard controls, especially for priority loads. This is obtained at the expense of an increased battery stress, which is acceptable for electricity access applications where power supply reliability is usually the foremost need.

This paper addresses and compares the modeling of typical EMI filters used in three-phase power equipment obtained through two different approaches, namely, physical and behavioral. Firstly, an innovative physical EMI filter modeling procedure is presented, which relies on an analytical formulation in terms of chain-parameters matrices and allows for an easy evaluation of its attenuation characteristics. The considered procedure builds an overall filter model by combining simple components models, requiring the determination of only a limited number of parasitic elements. Furthermore, the latter can be easily estimated without the need to build any prototype, avoiding the costly trial-and-error design procedures currently applied in the industrial context. Additionally, the considered physical model is tailored for high-power filters, built with thick wires, while most of the physical models available in the literature are aimed at small-sized filters built on printed circuit boards. The procedure is validated step by step, discussing the accuracy of each component model and its impact on overall accuracy compared to the actual measurements of the final assembled filter. Secondly, a behavioral modeling procedure is presented, which is based on external measurements performed on the filter prototype and provides an equivalent circuit model. Specifically, it extends to three-phase filters a circuit model previously developed for the single-phase case. Lastly, a critical comparison between the proposed physical and behavioral models is presented, highlighting the strengths and limitations of both and suggesting optimal uses for each.

This paper presents the second phase of the Naval Smart Grid (NaSG) research project. In particular, it regards the study of an Integrated Power System (IPS) for an Italian Navy electric propelled ship, endowed with improved control and reliability characteristics. Aim of the project is to develop guidelines useful for designing a new ship, capable of exploiting innovative features: modular power system, subsystem’s flexible integration, improved efficiency, enable new weapon systems’ installation, improved survivability. In this regard, this paper presents the aims and the most significant technology research results of the NaSG project second phase.

Future residential applications could benefit from nanogrids that integrate photovoltaics (PV) and battery energy storage systems (BESS), especially after the establishment of recent European Community directives on renewable energy communities (RECs) and jointly acting renewable self-consumers (JARSCs). These entities consist of aggregations of users who share locally produced energy with the aim of gaining economic, environmental, and social benefits by enhancing their independence from the electricity grid. In this regard, the sizing of the PV and BESS systems is an important aspect that results in a trade-off from technical, economic, and environmental perspectives. To this end, this paper presents an investigation on the optimal PV-BESS system sizing of a condominium acting as a JARSC community, which includes a common PV plant and EMS, operated by rule-based criteria. PV-BESS sizing results are investigated from economic and environmental perspectives, considering a case study located in Milan, Italy. In these regards, in addition to the common techno-economic criteria, carbon dioxide emissions are considered with particular attention, as their reduction is the driving ethos behind recent EU directives.

Recent European Community directives introduce Renewable Energy Communities (REC) and Jointly Acting Renewable Self-Consumers (JARSC). Both entities are constituted by communities of residential and/or non-residential prosumers, located in proximity of renewable generators and Electrical Storage Systems (ESS) owned and managed by the REC/JARSCs. These aggregations of prosumers are aimed at providing environ-mental and economic benefits by maximizing their global self-consumption. In this frame, it is relevant to introduce a control strategy which considers the whole system represented by the REC/JARSCs and performs optimal management of energy production, storage and consumption. The present paper proposes a Model Predictive Control (MPC) based control design, targeted at the minimization of electricity cost and equivalent CO2 emissions, considering the whole ensemble of loads included in the REC/JARSCs over a 24-h prediction horizon. To exploit the MPC ability of including forecasts in the optimization problem, predictors including Artificial Neural Networks (ANN) are developed for solar irradiance, air temperature, electricity price and carbon intensity. The proposed control performance is evaluated considering a case study located in Milan, Italy, and its advantages with respect to traditional control algorithms are highlighted by comprehensive numerical simula-tions. Lastly, an economic evaluation of the considered system is presented.