• Energy Research
• IT close
• Energies close

Islands are a constrained environment due to their geographical peculiarities and their land use accounting for, especially in the touristic locations, strong variability during the year. Consequently, the variation of energy demand to be met by variable renewable energy leads to a complex issue. This study aims at investigating the PRISMI Plus approach applied to the Island of Procida to drive the transition towards low-carbon and high-renewable energy system. The toolkit involves the analysis of local renewable energy potential, their potential matching of the energy demand, and the prioritization of the technological solutions to achieve the decarbonization targets set by the energy planning strategies. Three scenarios are designed for 2030 considering low, middle, and high penetration of renewable energy in the systems, results indicate that the amount of power production in low, middle, and high penetration of renewable energy scenarios are 0.18, 14.5, 34.57 GWh/year, respectively. The environmental and landscape constraints lead to a restricted set of available solutions. The decarbonization of the electricity supply is foreseen thanks to the available local solar resources plus the electrification of other sectors, i.e. heating by using Heat Pumps and transport by using Electric Vehicles.

Organic Rankine Cycle (ORC) power plants are characterized by high efficiency and flexibility, as a result of a high degree of maturity. These systems are particularly suited for recovering energy from low temperature heat sources, such as exhaust heat from other plants. Despite ORCs having been assumed to be appropriate for stationary power plants, since their layout, size and weight constraints are less stringent, they represent a possible solution for improving the efficiency of propulsion systems for road transportation. The present paper investigates an ORC system recovering heat from the exhaust gases of an internal combustion engine. A passenger car with a Diesel engine was tested over a Real Driving Emission (RDE) cycle. During the test exhaust gas mass flow rate and temperature have been measured, thus calculating the enthalpy stream content available as heat addition to ORC plant in actual driving conditions. Engine operating conditions during the test were discretized with a 10-point grid in the engine torque–speed plane. The ten discretized conditions were employed to evaluate the ORC power and the consequent engine efficiency increase in real driving conditions for the actual Rankine cycle. N-pentane (R601) was identified as the working fluid for ORC and R134a was employed as reference fluid for comparison purposes. The achievable power from the ORC system was calculated to be between 0.2 and 1.3 kW, with 13% system efficiency. The engine efficiency increment ranged from 2.0% to 7.5%, with an average efficiency increment of 4.6% over the RDE test.

Mismatching operating conditions affect the energetic performance of PhotoVoltaic (PV) systems because they decrease their efficiency and reliability. The two different approaches used to overcome this problem are Distributed Maximum Power Point Tracking (DMPPT) architecture and reconfigurable PV array architecture. These techniques can be considered not only as alternatives but can be combined to reach better performance. To this aim, the present paper presents a new algorithm, based on the joint action of the DMPPT and reconfiguration approaches, applied to a reconfigurable Series-Parallel-Series architecture, which is suitable for domestic PV application. The core of the algorithm is a deterministic cluster analysis based on the shape of the current vs. voltage characteristic of a single PV module combined with its DC/DC converter to perform the DMPPT function. Experimental results are provided to validate the effectiveness of the proposed algorithm and to demonstrate evidence of its major advantages: robustness, simplicity of implementation and time-saving.

The aim of this paper is to evaluate the overall life cycle environmental impact of an adiabatic compressed air energy storage (ACAES) system, which is designed to achieve the best match between the power production of a photovoltaic (PV) power plant and the power demand from the final user. The electrical energy demand of a small town, with a maximum power load of about 10 MW, is considered a case study. The ACAES system is designed with a compressor-rated power of about 10 MW and charging and discharging times of 10 and 24 h, respectively. Different sizes of the PV plant, ranging from 20 to 40 MWp, and two different solutions for the compressed air storage, an underground cavern, and a gas pipeline, are analyzed. The aim of this analysis is to compare the impacts on human health, ecosystem quality, climate change, and resource consumption of the PV power generation plant and the integrated PV-ACAES system with those of a reference scenario in which the end user demand is met entirely by the grid. The best results in terms of a reduction in environmental impact in comparison to the reference scenario are obtained for a small PV plant (20 MW) without the ACAES section, with reductions of about 85–95% depending on the category of impact. The integration of the ACAES system improves energy self-consumption but worsens the environmental impact, especially for air storage in gas pipelines. The best configuration in terms of environmental impact is based on a 30 MW PV plant integrated with an ACAES section using an underground cavern for air storage and allows for improvements in the energy self-consumption of between 38% and 61%, with a reduction in the environmental impact compared to the reference scenario of about 80–91% depending on the impact category.

The yeast Yarrowia lipolytica is an industrially relevant microorganism, which is able to convert low-value wastes into different high-value, bio-based products, such as enzymes, lipids, and other important metabolites. Waste cooking oil (WCO) represents one of the main streams generated in the food supply chain, especially from the domestic sector. The need to avoid its incorrect disposal makes this waste a resource for developing bioprocesses in the perspective of a circular bioeconomy. To this end, the strain Y. lipolytica W29 was used as a platform for the simultaneous production of intracellular lipids and extracellular lipases. Three different minimal media conditions with different pH controls were utilized in a small-scale (50 mL final volume) screening strategy, and the best condition was tested for an up-scaling procedure in higher volumes (800 mL) by selecting the best-performing possibility. The tested media were constituted by YNB media with high nitrogen restriction (1 g L−1 (NH4)2SO4) and different carbon sources (3% w v−1 glucose and 10% v v−1 WCO) with different levels of pH controls. Lipase production and SCO content were analyzed. A direct correlation was found between decreasing FFA availability in the media and increasing SCO levels and lipase activity. The simultaneous production of extracellular lipase (1.164 ± 0.025 U mL−1) and intracellular single-cell oil accumulation by Y. lipolytica W29 growing on WCO demonstrates the potential and the industrial relevance of this biorefinery model.

This paper presents a novel control system for the participation of plug-in electric vehicles (PEVs) in the provisioning of ancillary services for frequency regulation, in a way that is transparent to the driver and harmonized with the smart charging service requirements. Given a power-frequency droop curve, which specifies how the set of PEVs collectively participate to the provisioning of the frequency regulation service (we call this curve a “global” droop curve), we propose an algorithm to compute “local” droop curves (one for each PEV), which are optimized according to the current status of the PEV and the current progress of the smart recharging session. Once aggregated, the local droop curves match the global one (so that the PEVs contribute as expected to the provisioning of the ancillary service). One innovative aspect of the proposed algorithm is that it is specifically designed to be interoperable with the algorithms that control the PEV recharging process; hence, it is transparent to the PEV drivers. Simulation results are presented to validate the proposed solution.

The growing concern about climate change and the contemporary increase in mobility requirements call for faster, cheaper, safer, and cleaner means of transportation. The retrofitting of fossil-fueled piston engine ultralight aerial vehicles to hydrogen power systems is an option recently proposed in this direction. The goal of this investigation is a comparative analysis of the environmental impact of conventional and hydrogen-based propulsive systems. As a case study, a hybrid electric configuration consisting of a fuel cell with a nominal power of about 30 kW, a 6 kWh LFP battery, and a pressurized hydrogen vessel is proposed to replace a piston prop configuration for an ultralight aerial vehicle. Both power systems are modeled with a backward approach that allows the efficiency of the main components to be evaluated based on the load and altitude at every moment of the flight with a time step of 1 s. A typical 90 min flight mission is considered for the comparative analysis, which is performed in terms of direct and indirect emissions of carbon dioxide, water, and pollutant substances. For the hydrogen-based configuration, two possible strategies are adopted for the use of the battery: charge sustaining and charge depleting. Moreover, the effect of the altitude on the parasitic power of the fuel cell compressor and, consequently, on the net efficiency of the fuel cell system is taken into account. The results showed that even if the use of hydrogen confines the direct environmental impact to the emission of water (in a similar quantity to the fossil fuel case), the indirect emissions associated with the production, transportation, and delivery of hydrogen and electricity compromise the desired achievement of pollutant-free propulsion in terms of equivalent emissions of CO2 and VOCs if hydrogen is obtained from natural gas reforming. However, in the case of green hydrogen from electrolysis with wind energy, the total (direct and indirect) emissions of CO2 can be reduced up to 1/5 of the fossil fuel case. The proposed configuration has the additional advantage of eliminating the problem of lead, which is used as an additive in the AVGAS 100LL.

The management of the distribution network is becoming increasingly important as the penetration of distributed energy resources is increasing. Reliable knowledge of the real-time status of the network is essential if algorithms are to be used to help distribution system operators define network configurations. State Estimation (SE) algorithms are capable of producing such an accurate snapshot of the network state but, in turn, require a wide range of information, e.g., network topology, real-time measurement and power profiles from customers/productions. Those profiles which may, in principle, be provided by smart meters are not always available due to technical limitations of existing Advanced Metering Infrastructure (AMI) in terms of communication, storage and computing power. That means that power profiles are only available for a subset of customers. The paper proposes an approach that can overcome these limitations: the remaining profiles, required by SE algorithms, are generated on the basis of customer-related information, identifying clusters of customers with similar features, such as the same contract and pattern of energy consumption. For each cluster, a power profile estimator is generated using long-term power profiles of a limited sub-set of customers, randomly selected from the cluster itself. The synthesized full power profile, representing each customer of the distribution network, is then obtained by scaling the power profile estimator of the cluster to which the customer belongs, by the monthly energy exchanged by that customer, data that are easily available. The feasibility of the proposed approach was validated considering the distribution grid of Unareti SpA, an Italian Distribution System Operator (DSO), operating in northern Italy and serving approximately one million customers. The application of the proposed approach to the actual infrastructure shows some limitations in terms of the accuracy of the estimation of the power profile of the customer. In particular, the proposed methodology is not fully able to properly represent clusters composed of customers with a large variability in terms of power exchange with the distribution network. In any case, the root mean square error of the synthesized full power profile with the respect to validation power profiles belonging to the same cluster is, in the worst case, on the order of 6.3%, while in the rest of cases is well below 5%. Thus, the proposed approach represents a good compromise between accuracy in representing the behavior of customers on the network and resources (in terms of computational power, data storage and communication resources) to achieve that results.