• Energy Research

Abstract Overall, there are numerous sustainable sources of renewable, low-temperature heat, principally solar energy, geothermal energy, and energy produced from industrial wastes. Extended utilization of these low-temperature alternatives has a certain capacity of decreasing fossil fuel use with its associated very hazardous greenhouse gas emissions. Researchers have commonly recognized the organic Rankine cycle (ORC) as a feasible and suitable system to produce electrical power from renewable sources based on its advantageous use of volatile organic fluids as working fluids (WFs). Researchers have similarly shown an affinity to the exploitation of zeotropic mixtures as ORC WFs due to their capability to enhance the thermodynamic performance of ORC systems, an achievement supported by improved fits of the temperature profiles of the WF and the heat source/sink. This paper determines both the technical feasibility and the benefits of using zeotropic mixtures as WFs by means of a simulation study of an ORC system. This study analyzes the thermodynamic performance of ORC systems using zeotropic WF mixtures to produce electricity driven by low-temperature solar heat sources for use in buildings. A thermodynamic model is created with an ORC system with and without a regenerator. Five zeotropic mixtures with diverse compositions between 0 and 1 in 0.2 increments of R245fa/propane, R245fa/hexane, R245fa/heptane, pentane/hexane, and isopentane/hexane are assessed and compared with identify the best blends of mixtures that are able to produce superior efficiency in their system cycles. Results disclosed that R245fa/propane (0.4/0.6) with regenerator produces the highest net power output of 7.9 kW and cycle efficiency of 9.4% at the operating condition with a hot source temperature of 85 °C. The study also investigates the effects of the volume flow ratio, and evaporation and condensation temperature glide on the ORC’s thermodynamic performance. Following a thorough analysis of each mixture, R245fa/propane is chosen for a parametric study to examine the effects of operating factors on the system’s efficiency and sustainability index. It was found that the highest cycle efficiency and highest second law cycle efficiency of around 10.5% and 84.0%, respectively, were attained with a mass composition of 0.6/0.4 at the hot source temperature of 95 °C and cold source temperature of 20 °C with a net power output of 9.6 kW. Moreover, results revealed that for zeotropic mixtures, there is an optimal composition range within which binary mixtures are tending to work more efficiently than the component pure fluids. In addition, a significant increase in cycle efficiency can be achieved with a regenerative ORC, with cycle efficiency in the range 3.1–9.8% versus 8.6–17.4% for ORC both without and with regeneration, respectively. In conclusion, utilizing zeotropic mixtures may well expand the restriction faced in choosing WFs for solar-powered ORC-based micro-combined heat and power (CHP) systems.

In recent years, a focused attention towards the conversion of conventional fleets in local public transport to fully electric fleets have gained momentum, instilled by awareness about the environment and significant trend of urbanization. Public transport operators in major cities all over the world have engaged themselves in fulfilling this change. An efficient electrification process is still a challenge for most operators. This paper aims to propose an energy demand model for an electric bus vehicle to monitor and verify the energy consumption. The real drive cycles of actual electric buses are used to simulate the energy demand characteristics and the battery state of charge of the vehicle. Real-time collected local data are used as input for the simulation model and discharge energy, regenerated energy and battery state of charge are computed. The simulated results are then validated with the real data available. The results are then used to consider an improvement for charging infrastructure.

Today, the concept of Smart Grid is very often strictly related to Electric Vehicles (EVs) infrastructures. EVs have not gas emissions, they have silent driving and much higher efficiency than internal combustion engine vehicles and, in particular, they are fundamental for the world's sustainable mobility. In fact, many industries and universities have decided to replace their corporate fleet with green vehicles. The aim of this work is to present the Smart Grid project at the Savona Campus of the Genoa University, with particular attention on the use of the EVs. The scope of this work is to evaluate the different habits of charging the vehicles and to study new scenarios to take advantage of the full potentiality of smart mobility.

Environmental issues have reached global attention from both political and social perspectives. Many countries and companies around the world are adopting measures to help change current trends. Awareness of decarbonization in the transportation sector has led to an increasing development of energy storage systems in recent years, especially for ground vehicles. Batteries, due to their high efficiency, are one of the most attractive energy storage systems for vehicle propulsion. As for road vehicles, the growing interest in Electric Vehicles (EVs) is motivated by the fact that they reduce local emissions compared to traditional Internal Combustion Engine (ICE) vehicles. The purpose of the paper is to present a study on how to plan and implement vehicle charging infrastructure on motorways. In particular, a specific road in Italy is analyzed: the motorway A1 from Milan to Naples with a length of about 800 km. This motorway can be considered representative because it passes through some of Italy’s most important cities and regions and may represent the backbone of Italy. A useful model for defining the optimal location of electric vehicle charging stations is presented within the paper. Starting with the data on the average daily traffic flows passing through the main nodes of the motorways section, the demand for the potential vehicles needed to define the number and dimension of charging stations and provide an adequate supply is estimated. The analysis was performed considering five-time horizons (year 2022 to year 2025) and four Scenarios involving the installation of 4, 8, 16, and 32 Charging Stations (CSs) in each service area, respectively.

Recent advances in computing technologies and the availability of large amounts of heterogeneous data in power grids are opening the way for the application of state-of-art machine learning techniques. Compared to traditional computational approaches, machine learning algorithms could gain an advantage from their intrinsic generalization capability, by also providing accurate short-term power flow forecasts from distributed measurement units, with greater computational efficiency and scalability. Several studies in the literature investigated the use of suitable machine learning models to address different issues in the field of power grid operation and management. Furthermore, the ongoing transition towards smart grids is generating new research opportunities for the real-time application of machine learning algorithms in power systems. In this paper, a literature survey on the application of different machine learning techniques in power systems is presented and critically reviewed, by evaluating the main advantages and criticalities of each technique for the selected applications.

The objective of this paper is to assess the probable effect that electric vehicles (EVs), already in wide circulation and likely to increase exponentially in the near future, will have on distribution networks. Analyses are conducted on the necessary interventions and evolutions that the distribution grid will have to undergo in order to manage this new and progressively increasing heavy load of energy. Thus, in order to understand the technical limitations of the current infrastructure and how transformers and lines will be able to withstand the increasing penetration of EVs, urban and rural grid models have been studied, to highlight the differences between the impacts on high- and low-density networks. In addition, an analysis of fast charging station impact has been carried out. MATLAB software was used to perform the simulations for the creation of scripts, which were then exploited within the DIgSILENT PowerFactory software. This allowed evaluation of the networks under examination and verification of the effectiveness of the proposed solutions. In concluding based on findings, some methods of managing the distribution network to optimise the network parameters analysed in the study and a solution involving electric vehicles are recommended.

This paper presents an optimisation methodology for simulating the integration of distributed generation and electric vehicles (EVs) in a residential district. A model of a smart residential district is proposed. Different charging scenarios (CS) for private cars are considered for simulating different power demand distributions during the day. Four different case studies are investigated, namely the Base Case, in which no EVs are present in the district and three study cases with different CSs. A global optimisation method based on a genetic algorithm approach was applied on the model to find the total power from PV panels installed and co-generative micro gas turbines while minimising the annual energy cost in the district for the four different scenarios. In conclusion, the results showed that the use of EVs in the district introduces considerable savings with respect to the Base Case. Moreover, the impact of the chosen CS is nearly insignificant under a purely economic perspective even if it is relevant for grid management. Additionally, the optimum amounts of installed power vary in a limited range if the distance travelled by EVs, users’ departure and arrival time change broadly.

Light is essential to life, as well as enable us to perform visual tasks. The importance of good lighting is undeniable, as it provides an essential service to people in different types of environment. The innovation is changing the way of energy consumption; in particular, it offers completely new lighting solutions for a lifestyle comfortable and healthy. The aim of this paper is that to analyze the strategies that allow to improve the energy efficiency of a new building in particular, the wellness center. In this case, the attention is on the aspects related to the lighting. Different solutions, in terms of luminaries technologies and light management strategies have been analyzed.