This paper presents a hydrogen powered hybrid solid oxide fuel cell-steam turbine (SOFC-ST) system and studies its optimal operating conditions. This type of installation can be very appropriate to complement the intermittent generation of renewable energies, such as wind generation. A dynamic model of an alternative hybrid SOFC-ST configuration that is especially suited to work with hydrogen is developed. The proposed system recuperates the waste heat of the high temperature fuel cell, to feed a bottoming cycle (BC) based on a steam turbine (ST). In order to optimize the behavior and performance of the system, a two-level control structure is proposed. Two controllers have been implemented for the stack temperature and fuel utilization factor. An upper supervisor generates optimal set-points in order to reach a maximal hydrogen efficiency. The simulation results obtained show that the proposed system allows one to reach high efficiencies at rated power levels.

The existing trend towards increased penetration of renewable energies in the traditional grid, and the intermittent nature of the weather conditions on which these energy sources depend, make the development of tools for the forecasting of renewable energy production more necessary than ever. Likewise, the prediction of the energy generated in these renewable production plants is key to the implementation of efficient Energy Management Systems (EMS) in buildings. These will aim both to increase the energy efficiency of the building itself, as well as to encourage self-consumption or, where appropriate, collective self-consumption (CSC). This paper presents a comparison between four different models, the former one being an analytical model and the remaining three machine learning (ML) based models. All of them will forecast the photovoltaic (PV) production curve for the next day. In order to validate these models, a case study of a PV system installed on the roof of a university building located in Bidart (France) is proposed. The model that most accurately forecasts the PV production during the period of July 2021 is the support vector regression (SVR), which has a mean R² of 0.934 for July, being 0.97 on sunny days and 0.85 on cloudy ones. This is an improvement of 5.14%, 4.07%, and 4.18% over the nonlinear autoregressive with exogenous inputs (NARX), feedforward neural network (FFNN), and analytical model, respectively.

The building sector has an important role in the decarbonization of energy. Buildings energy management systems can act on flexible loads to contribute to the integration of renewable energies. This article presents the design, implementation and evaluation of a rule-based energy management systems (RB-EMS) in the frame of a collective self-consumption (CSC) system based on photovoltaic (PV) energy in the Technology Park of Izarbel, in Bidart, France. The RB-EMS acts on the heat ventilation and air conditioning (HVAC) system of one of the buildings involved in the CSC in order to maximize the PV energy self-consumption rate (SCR). Internet of things (IoT) has been developed and implemented in order to retrieve consumption, production and temperature data, and to be able to act on the HVAC. A new 5-step methodology has been developed to design and adjust the RB-EMS. This methodology is mainly based on tests carried out to find the relationship between ON/OFF states of the internal units of the HVAC and the heat pumps’ consumption. Finally, the RB-EMS is tested in heating operating mode. The results show that the RB-EMS allows obtaining a SCR of 89%, and that the users comfort limits are respected.

This research study analyses different types of photovoltaic (PV) energy sharing in a collective self-consumption (CSC) real-case in the Izarbel technological park in France. The analysis is carried out above all from the point of view of the self-consumption rate (SCR) and the savings.