During recent years the electricity supply chain (electricity production, consumption, distribution, storage, and load management) has changed. In this context a new term appears: the prosumer, that is both a consumer and producer of energy in the same entity. In the paper, an energy management approach of several prosumers aggregated in a coalition, the energy district (ED), and coordinated through a central control entity, the coalition coordinator, is proposed. The aim of the coalition coordinator is to maximize the coalition utility also reducing in this way the reverse energy flows at the point of delivery by an appropriate demand response program. The application on an Italian residential housing area is illustrated. The numerical results demonstrate the effectiveness and attractiveness of the proposed energy management scheme.

The urgent need for sustainable energy solutions in light of escalating global energy demands and environmental concerns has brought hydrogen to the forefront as a promising renewable resource. This study provides a comprehensive analysis of the technologies essential for the production and operation of hydrogen fuel cell vehicles, which are emerging as a viable alternative to traditional combustion engine vehicles. It examines various fuel cell types, hydrogen storage methods, refueling logistics, and the role of batteries in fuel cell vehicles. The paper also explores the potential impact of advancements in artificial intelligence and quantum computing on the development of fuel cell vehicles. A global assessment reveals that South Korea (19,270) and the United States (12,283) are leading in the adoption of fuel-cell vehicles, particularly in the passenger car segment (82%), followed by buses (9.2%) and trucks (8.7%). The study highlights the challenges hindering fuel cell vehicle implementation, such as the need for consistent investment and collaboration among industry stakeholders to promote sustainable transportation systems. The analysis underscores the practicality of fuel cell vehicles, exemplified by models like the Toyota Mirai and Hyundai Nexo, which offer significant driving ranges and demonstrate the integration of advanced technologies. The paper discusses the environmental benefits of fuel cell vehicles, including their ability to operate with zero emissions when paired with renewable energy sources. Graphical Abstract

The 2018/2001/EU renewable energy directive (RED II) underlined the strategic role of energy communities in the EU transition process towards sustainable and renewable energy. In line with the path traced by RED II, this paper proposes a solution that may help local energy communities in increasing self-consumption. The proposed solution is based on the combination of smart metering and smart charging. A set of smart meters returns the profile of each member of the community with a time resolution of 5 s; the aggregator calculates the community profile and regulates the charging of electric vehicles accordingly. An experimental test is performed on a local community composed of four users, where the first is a consumer with a Nissan Leaf, whereas the remaining three users are prosumers with a photovoltaic generator mounted on the roof of their home. The results of the experimental test show the feasibility of the proposed solution and demonstrate its effectiveness in increasing self-consumption. The paper also calculates the subsidy that the community under investigation would receive if the current Italian incentive policies for renewables were extended to local energy communities; this subsidy is discussed in comparison with the subsidies that the three prosumers individually receive thanks to the net metering mechanism. This paper ends with an economic analysis and calculation of savings on bills when the four users create the local energy community and adopt the proposed combination of smart metering and smart charging.

In this paper the authors propose a novel method for tracking the maximum power point of a PV array. This method is derived by the well known incremental conductance method and the novelty consists in varying the duty cycle of the DC-DC switching converter used as MPPT tracker using a variable step size instead of a fixed step size. A practical way to determine the minimum and the maximum values of the variable step size is explained in detail. The laboratory results demonstrate the effectiveness of the proposed method both in terms of speed in MPP tracking and accuracy in the MPP individuation.

Residential buildings are a significant source of energy consumption and greenhouse gas emissions, making it crucial to accurately predict their energy demand for reducing their environmental impact. In this study, machine-learning techniques such as linear regression, decision tree classification, logistic regression, and neural networks were applied to forecast the heating and cooling loads of 12 different building types using their area and height attributes. The correlation coefficient was utilized to assign weights to the predictors in linear regression, and the models’ performance was evaluated using metrics such as equations of R2, MAE, and RMSE. The decision tree technique demonstrated the highest accuracy of 98.96% and 93.24% for predicting heating and cooling loads, respectively, among the classification methods. Notably, the cooling load prediction was more accurate than the heating load prediction. The height and area of the roof and floor, along with the relative compactness of the building, were identified as the most influential factors in the heating and cooling loads. These findings have significant implications for optimizing energy efficiency in residential buildings and mitigating their impact on climate change.