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The coupling of a reversible Solid Oxide Cell (rSOC) with an offshore wind turbine is investigated to evaluate the mutual benefits in terms of local energy management. This integrated system has been simulated with a dynamic model under a control algorithm which manages the rSOC operation in relation to the wind resource, implementing a local hydrogen storage with a double function: (i) assure power supply to the wind turbine auxiliary systems during power shortages, (ii) valorize the heat produced to cover the desalinization system needs. With an export-based strategy, which maximize the rSOC capacity factor, up to 15 tons of hydrogen could be produced for other purposes. The results show the compatibility between the auxiliary systems supply of a 2.3 MW wind turbine and a 120/21 kWe rSOC system which can cover the auxiliaries demand during wind shortages or maintenance. The total volume required by such a system occupy less than the 2%, if compared with the turbine tower volume. Additionally, thermal availability exceeds the desalination needs, representing a promising solution for small-scale onsite desalination in offshore environments.

Bioeconomy, as a key strategic innovation pillar, supports energy transition and involves biomass resource assessment and its application. Biomass has an enormous potential to fulfil the domestic energy demand in Brazil. Hence, this study aims to quantify the theoretical, technical and techno-economic potential of agricultural and forestry residues to generate electricity via thermochemical processes in Brazil for the 2018–2050 timeframe. The analysis was carried out via an integrated approach, according to statistical reports and literature data. Conversion technologies for the estimation of technical potential consist of organic rankine cycle, combustion with conventional rankine cycle and gasification with an internal combustion engine. The results showed that Brazil’s theoretical potential is 7185.39 PJ and 16,708.12 PJ in 2018 and 2050. The technical potential in 2018 is 1795.16 PJ, generating 71,236.47 MWe, while the potential in 2050 is projected as 5771.49.4 PJ providing 229,027.31 MWe. The main restrictions are dispersed distribution of biomass over a large area, supply costs, and low efficiencies for energy conversion. Therefore, the techno-economic potential is around 19,704.01 MWe and 63,348.95 MWe for 2018 and 2050. The north region has the lowest electricity generation potential, while the southeast region takes a prominent position to generate electricity mainly from sugarcane residues. Levelized costs of electricity values varied from 10.532 to 15.507 US$/MWh for 2018 and from 7.320 to 13.002 US$/MWh for 2050, which highly influenced by the biomass price. The results of this study can support policy-making towards future planning on Brazilian bioenergy.

In the digitalization era, the increasing number of connected appliances and the rise of artificial intelligence (AI) enabled a new realm of possibilities in the residential energy sector, including the chance for a consumer to play an active role in flexibility programs. We talk about demand-side flexibility (DSF) when a consumer adapts his/her energy consumption behavior in response to variable energy prices or market incentives. The procedure depends on a two-way communication between an energy supplier and a customer, and his/her willingness to act on the electricity consumption. The success of the different DSF approaches is strongly related to the estimation of appliance usage patterns and AI techniques represent a viable solution.

Further technological improvements are needed in the high-temperature fuel cell field in order to definitely boost market penetration of this technology in the energy scenario. More robust techniques concerning monitoring, management and control of the power unit are considered to be indispensable for safeguarding its health. This paper presents a preliminary analysis for monitoring the performance of a solid oxide fuel cell short stack, when operating in transient conditions, using a linear autoregressive exogenous (ARX) model. This model has demonstrated to predict the outputs from the stack remarkably well in a fast and cost-effective way.

Batteries are highly flexible energy storages and they can be easily integrated in energy systems. However, the modeling of batteries must be coherent and robust to be effectively included in the energy systems; in particular, the aging phenomena are known to significantly impact the storage capacity, charging/discharging behaviours, and the state of charge. While data-driven lithium-ion battery models, which consider both cyclic and calendar aging, are widely reported in the scientific literature, their robustness in dealing with different data compared to trained ones has not been deeply investigated so far. In this study, a sensitivity analysis is performed using three aging models available in the scientific literature (e.g., Wang’s, Baghdadi’s, and Omar’s ones) to investigate their performance under different operating conditions of both temperature and current. Results of both univariate and multivariate sensitivity analyses refer to a 160 Ah battery, showing that all the three models are able to accurately predict the battery aging under different operating conditions, but some of these models are more sensitive to specific factors than others. Specifically, the multivariate analysis shows that Wang’s model is the most robust one in the face of simultaneous changes in both temperature and current.

This study examines the use of distributed power plants coupled to desalination systems to achieve combined production of electrical energy and of water for drinking and similar purposes in Marches, an Adriatic region in central Italy. To reduce Marches’ large electricity deficit, the regional government has drawn up an environmental and energy plan promoting distributed generation through power plants built right where the electricity is consumed. At the same time, the region’s abundant water resources are increasingly threatened by aquifer pollution, due particularly to fertilizers and to the phenomenon of saltwater intrusion near the coast. Distributed electrical energy generation combined with water production can help meet the goal of reducing the electrical deficit while at the same time improving ground-water quality and reducing the hydrogeological risk. The solution is also efficient, since the more the useful heat recovered by the energy system, the higher its global efficiency. Furthermore, coupling power plants to desalination units is also an excellent application of cogeneration systems, allowing useful recovery of thermal energy in the summer. The study examines different sized plants for the distributed generation of water and power in Marches, from small units to thermal desalination systems that can be coupled to existing power plants. It shows that although endowing existing large power plants with MED (multiple effect distillation) units makes it possible to meet 58% of the freshwater demand in Marches, it further increases the electricity deficit, whereas coupling small or medium-sized distributed power plants to reverse osmosis systems allows meeting 75% of the demand for freshwater and reduces the electrical energy demand by up to 50% in the more energy-intensive districts.

This study examines the use of distributed power plants coupled to desalination systems to achieve combined production of electrical energy and of water for drinking and similar purposes in Marches, an Adriatic region in central Italy. To reduce Marches’ large electricity deficit, the regional government has drawn up an environmental and energy plan promoting distributed generation through power plants built right where the electricity is consumed. At the same time, the region’s abundant water resources are increasingly threatened by aquifer pollution, due particularly to fertilizers and to the phenomenon of saltwater intrusion near the coast. Distributed electrical energy generation combined with water production can help meet the goal of reducing the electrical deficit while at the same time improving ground-water quality and reducing the hydrogeological risk. The solution is also efficient, since the more the useful heat recovered by the energy system, the higher its global efficiency. Furthermore, coupling power plants to desalination units is also an excellent application of cogeneration systems, allowing useful recovery of thermal energy in the summer. The study examines different sized plants for the distributed generation of water and power in Marches, from small units to thermal desalination systems that can be coupled to existing power plants. It shows that although endowing existing large power plants with MED (multiple effect distillation) units makes it possible to meet 58% of the freshwater demand in Marches, it further increases the electricity deficit, whereas coupling small or medium-sized distributed power plants to reverse osmosis systems allows meeting 75% of the demand for freshwater and reduces the electrical energy demand by up to 50% in the more energy-intensive districts.

•Energy efficiency in urea plants through Hydraulic Power Recovery Turbines (HPRTs).•Three HPRTs’ layouts, as well as four urea capacities, have been investigated.•A numerical model is built in ASPEN PLUS® to resemble the urea synthesis process.•The studied energy efficiency system allows recovering a maximum of 4,000 MWh/year.•The lowest obtainable PayBack Period (PBP) from this intervention is of 3 years.

•A methodology to assess potential energy recovery in Water Supply Systems is used.•The presented methodology was validated with measured data coming from flow meters.•Pumps-as-Turbines were used as energy recovery units in a Water Supply System.•The most profitable result led to a Net Present Value of 52,843 € after 20 years.•The possible overall energy recovery in the analysed WSS is equal to 90.76 MW h.

In this study the optimal sizing of a hybrid battery/hydrogen Energy Storage System “ESS” is assessed via a model-based parametric analysis in the context of a real hybrid renewable microgrid located in Huelva, Spain, supplying a realtime monitored residential load (3.5 kW; 5.6 MWh/year) in island mode. Four storage configurations (battery-only, H2-only, hybrid battery priority and hybrid H2priority) are assessed under different Energy Management Strategies, analysing system performance parameters such as Loss of Load “LL” (kWh; %), Over Production “OP” (kWh; %), round-trip storage efficiency ηESS(%) and total storage cost (€) depending on the ESS sizing characteristics. A parallel approach to the storage optimal sizing via both multi-dimensional sensitivity analysis and PSO is carried out, in order to address both sub-optimal and optimal regions, respectively. Results show that a hybridised ESS capacity is beneficial from an energy security and efficiency point of view but can represent a substantial additional total cost (between 100-300 k€) to the hybrid energy system, especially for the H2ESS which presents higher costs. Reaching 100% supply from renewables is challenging and introducing a LL threshold induces a substantial relaxation of the sizing and cost requirements. Increase in battery capacity is more beneficial for the LL abatement while increasing H2capacity is more useful to absorb large quantities of excess energy. The optimal design via PSO technique is complemented to the parametric study.

Further technological improvements are needed in the high-temperature fuel cell field in order to definitely boost market penetration of this technology in the energy scenario. More robust techniques concerning monitoring, management and control of the power unit are considered to be indispensable for safeguarding its health. This paper presents a preliminary analysis for monitoring the performance of a solid oxide fuel cell short stack, when operating in transient conditions, using a linear autoregressive exogenous (ARX) model. This model has demonstrated to predict the outputs from the stack remarkably well in a fast and cost-effective way.

The present work encompasses the immediate performance characterization of a first generation LSC-based SOFC single cell by means of polarization curves, electrochemical impedance spectroscopy (EIS), the distribution relaxation times (DRT) method and equivalent circuit modeling (ECM). Besides, an endurance performance characterization based on the FCTESQAtesting protocols along with EIS measurements, the aforementioned ECM and computational fluid dynamics (CFD) modelling theory has enabled to generate a time-dependent model capable of predicting performance degradation of the single cell when operating under predetermined conditions.