Hydrogen produced from renewable energy sources is a valuable energy carrier for linking growing renewable electricity generation with the hard-to-abate sectors, such as cement, steel, glass, chemical, and ceramics industries. In this context, this paper presents a new model of hydrogen production based on solar photovoltaics and wind energy with application to a real-world ceramics factory. For this task, a novel multipurpose profit-maximizing model is implemented using GAMS. The developed model explores hydrogen production with multiple value streams that enable technical and economical informed decisions under specific scenarios. Our results show that it is profitable to sell the hydrogen produced to the gas grid rather than using it for self-consumption for low-gas-price scenarios. On the other hand, when the price of gas is significantly high, it is more profitable to use as much hydrogen as possible for self-consumption to supply the factory and reduce the internal use of natural gas. The role of electricity self-consumption has proven to be key for the project’s profitability as, without this revenue stream, the project would not be profitable in any analysed scenario.

This paper aims to contribute to the urgent reflection as a society about environmental protection, in the ultimate challenge that is the sustainable use of energy resources. Since Portugal is at an early stage of market development internally, governmental and local stimulation policies play a central role and are a key element in the successful diffusion of Electric Mobility. The study will focus on the transition of a company car fleet, which currently consists of combustion vehicles, to electric vehicles. With this change it becomes necessary to understand how the electrical installation will be affected due to the installation of charging stations, allowing the company to have some autonomy from the public grid. The various changes resulting from the installation consumption profile will be studied and compared. The state of the art, the level of maturity and where the development of electric mobility in Portugal is heading will also be appreciated.

This paper presents a distributed model predictive control (DMPC) for indoor thermal comfort that simultaneously optimizes the consumption of a limited shared energy resource. The control objective of each subsystem is to minimize the heating/cooling energy cost while maintaining the indoor temperature and used power inside bounds. In a distributed coordinated environment, the control uses multiple dynamically decoupled agents (one for each subsystem/house) aiming to achieve satisfaction of coupling constraints. The limited energy resource is shared by a fixed order established from a previously done auction wherein the bids are made by each agent based on the energy price that they are willing to pay each day. The developed system is applied and simulated with three houses.

Solar photovoltaic simulators are valuable tools for the design and evaluation of several components of photovoltaic systems. They can also be used for several purposes, such as educational objectives regarding operation principles, control strategies, efficiency, maintenance, and other aspects. This paper presents an automated solar photovoltaic simulation system with the capability to generate automated tests considering different parameters of solar photovoltaic panels and different operation conditions. The proposed simulator is composed of three buck-boost DC–DC power converters controlled in such a way that will behave similarly to solar photovoltaic panels. It allows to introduce additional variable loads and maximum power point tracker algorithms similar to real systems. Some converters are controlled by a DSP microcontroller connected to a single programmable logic controller which generates the automated tests. Thus, using the presented solution, it is possible to implement the I-V and P-V characteristic curves of solar photovoltaic panels and evaluate different maximum power point tracker algorithms considering different meteorological conditions and load variations, being a useful tool to teach subjects related to renewable energy sources and related applications. Several simulation results using Matlab/Simulink and experimental results are presented to validate the operation of the proposed solution. Experimental results achieve a ripple between 2% and 5% of the desired average current in MPP conditions.

Renewable Energy Communities (RECs) play a vital role in driving the transition to sustainable energy systems by facilitating inclusive and cost-effective renewable energy production. They empower citizens to actively participate in the energy sector, promote local energy resource sharing, and improve local energy balancing efforts. This study presents a model for investment and operational decision-making within an REC framework, enabling multiple members to invest in renewable energy generation and battery energy storage systems. The model determines optimal capacities for each technology, facilitates energy sharing among members, and evaluates both individual and collective economic benefits through an internal electricity sharing price. By examining various scenarios within an established three-member REC, the research identifies key factors influencing the acceptance of a new member into the community. The findings indicate that the economic advantages of expanding the REC are significantly dependent on the characteristics of the prospective new member.