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Electrolyte imbalance caused by the undesired vanadium-ions cross-over and water transport through the membrane is one of the main critical issues of vanadium redox flow batteries, leading to battery capacity loss and electrolytes volume variation. In this work, the evolution of discharged capacity and electrolyte volume variation were firstly investigated adopting commercial electrolyte for hundreds of charge-discharge cycles in vanadium redox flow batteries employing different membranes, varying thickness and equivalent weight. Subsequently, with the support of a 1D physics-based model, the origin of the main phenomena regulating capacity decay and volume variation has been identified and different modifications in the preparation of electrolytes have been proposed. Electrolytes characterized by an equal proton concentration between the two tanks at the beginning of cycling operation turned out to limit capacity decay, while increasing electrolyte proton concentration was effective also in the mitigation of volume variation. The most promising electrolyte preparation combined the effect of high proton concentration and null osmotic pressure gradient between the two tanks: compared to commercial electrolyte this preparation reduced the capacity decay from 47.7% to 20.9%, increased the coulombic efficiency from 96.2% to 98.9% and the energy one from 79.9% to 83.4%, and also implied a negligible volume variation during cycles. The effectiveness of this electrolyte preparation has been verified with different membranes, increasing the range of validity of the results, that could be thus applied in a real system regardless of the adopted membrane.

Abstract Accurate rooftop solar energy potential characterization is critically important for promoting the wide penetration of renewable energy in high-density cities. However, it has been a long-standing challenge due to the complex building shading effects and diversified rooftop availabilities. To overcome the challenge, this study proposed a novel 3D-geographic information system (GIS) and deep learning integrated approach, in which a 3D-GIS-based solar irradiance analyzer was developed to predict dynamic rooftop solar irradiance by taking shading effects of surrounding buildings into account. A deep learning framework was developed to identify the rooftop availabilities. Experimental validations have shown their high accuracies. As a case study, a real urban region of Hong Kong was used. The results showed that the annual solar energy potential of the entire building group was reduced by 35.7% due to the shading effect and the reduced rooftop availability. The reductions of individual buildings varied from 13.4% to 74.5%. In spite of the substantial reductions of the annual solar energy, the shading effect could only slightly reduce the peak solar power. In fact, the annual solar energy reduction could be five times higher than the peak solar power reduction. Further analysis showed that simple addition of the respective solar energy potential reductions, caused by the shading effect and the rooftop availability, tends to highly overestimate the total reduction by up to 26%. For this reason, their impacts cannot be considered separately but as joint effects. The integrated approach provides a viable means to accurately characterize rooftop solar energy potential in urban regions, which can help facilitate solar energy applications in high-density cities.

Energy production from clean sources is mandatory to reduce pollutant emissions. Among different options for hidden hydropower potential exploitation, Pump-as-Turbine (PaT) represents a viable solution in pico- and micro-hydropower applications for its flexibility and low-cost. Pumps are widely available in the global market in terms of both sizes and spare parts. To date, there are several PaTs’ performance prediction models in the literature, but very few of them use optimization algorithms and only for specific and limited prediction goals. The present work proposes evolutionary Artificial Neural Networks (ANNs) based on JADE, which is a typology of differential evolution algorithm, to forecast Best Efficiency Point (BEP) and performance curves of a PaT starting from the pump operational data. In this model, JADE is employed as optimizer of basic ANNs to upgrade parameter values of the learning rate, weights, and biases. The accuracy of the proposed model is evaluated through experimental data available from the literature and compared to a basic ANN and two versions of the differential evolution algorithm. Results are also validated with experiments on a PaT showing that the proposed method can achieve an average R2-value of 0.97, which is 5% higher than the one obtained with a basic ANN.

Various types of measurement techniques, such as Light Detection and Ranging (LiDAR) devices, anemometers, and wind vanes, are extensively utilized in wind energy to characterize the inflow. However, these methods typically gather data at limited points within local wind fields, capturing only a fraction of the wind field's characteristics at wind turbine sites, thus hindering detailed wind field analysis. This study introduces a framework using Physics-informed Neural Networks to assimilate diverse sensor data types. This includes line-of-sight wind speed, velocity magnitude and direction, velocity components, and pressure. Moreover, the parameterized Navier-Stokes equations are integrated as physical constraints, ensuring that the neural networks accurately represent atmospheric flow dynamics. The framework accounts for the turbulent nature of atmospheric boundary layer flow by including artificial eddy viscosity in the network outputs, enhancing the model's ability to learn and accurately depict large-scale flow structures. The reconstructed flow field and the effective wind speed are in good agreement with the actual data. Furthermore, a transfer learning strategy is employed for the online deployment of pre-trained PINN, which requires less time than that of the actual physical flow. This capability allows the framework to reconstruct wind flow fields in real time based on live data. In the demo cases, the maximum error between the effective wind speed reconstructed online and the actual value at the wind turbine site is only 3.7%. The proposed data assimilation framework provides a universal tool for reconstructing spatiotemporal wind flow fields using various measurement data. Additionally, it presents a viable approach for the online assimilation of real-time measurements. To facilitate the utilization of wind energy, our framework's source code is openly accessible.

Abstract Variable renewable energy sources are continuously increasing their share in the world energy mix. However, their uncertainty has a deep impact in the electric grid safe operation. One of the most promising solutions to tackle such challenge at a distribution grid level consists of quasi-real-time, peer-to-peer electricity markets. These can use the blockchain to be successfully implemented in a secure and privacy preserving way, making effective use of the increasing intelligence of Internet of Things appliances. Nevertheless, providing blockchain-based solutions compatible with the energy sector is not an easy task. In particular, in order to be compliant with typical electricity costs of the order of some €cents/kWh, blockchain transactions are required to be very cheap. Unfortunately, none of the existing research works took the issue of transaction costs into account. As a result, real-world feasibility of existing solutions is rather to be investigated. In this work, we propose an ad-hoc Ethereum-based consortium blockchain explicitly addressing this issue. Leveraging on this platform, we design a blockchain-based electricity market. The market consists of two-steps. The first (day-ahead) step settles the amounts and prices of the energy traded among peers. The second step is instead quasi-real-time, thus enabling the unexpected excess or lack of electricity to be directly negotiated on a peer-to-peer basis. Negotiations occurs without the need of any intermediary, except for the check of grid physical limits, performed by the distribution system operator. To demonstrate the functionality of the solution, a proof-of-concept has been implemented at the University of Pisa (Italy).

Abstract This study aims to assess the deployment potential of hybrid concentrated solar biomass (HCSB) plants for dispatchable renewable electricity generation in New South Wales (NSW), Australia. We present an approach for identifying the most suitable locations for siting new plants. HCSB plants generate steam using a biomass boiler and a concentrated solar power (CSP) system and utilise a shared steam turbine for power generation. The total power generation opportunity was estimated based on available resources. This was achieved by mapping solid biomass (bagasse, stubble and forestry residues) and solar resources (direct normal irradiation) in proximity to zone substations with new grid connection capacity. The total installed capacity of HCSB plants at suitable grid connection locations was calculated to be 874 MWe at a cost of about AU$ 6.3 billion. We also estimated the CO2-e emission abatement potential to be about 6 billion kg of CO2-e per year. The Riverina region was identified to be the most prospective region for HCSB plants in NSW owing to excellent biomass and solar resources and 25 suitable grid connection points. These findings underline NSW’s excellent deployment potential for HCSB plants, a technology that can utilize the vast and currently under-exploited biomass residues and solar resources for dispatchable renewable electricity generation.

This work investigates the design optimization of aggregated energy systems (multi-energy systems, microgrids, energy districts, etc.) with (N-1)-reliability requirements. The problem is formulated as a two-stage stochastic Mixed Integer Linear Program which optimizes design (first stage variables) and operation variables (second stage variables) simultaneously considering a set of typical and extreme days. The analysis proposes and compares different approaches to include the (N-1) reliability requirement in the optimization problem. Moreover, the paper proposes two effective decomposition algorithms to solve the large-scale Mixed Integer Linear Program suitable for design problems with and without (N-1) reliability requirements. Depending on the instance, such decomposition algorithms allow reducing the computational time by one or more orders of magnitude (from days to a few hours, in the worst cases tested in this work). The proposed methodology is tested to design the aggregated energy system for a real case study considering both a grid-connected and off-grid installation. Results indicate that the actual reliability of the design solutions depends by the profiles of energy demand and renewable production considered in the failure scenarios included in the design problem. Including N-1 reliability requirements causes an increase in the total annual cost in the range 15–20%, due to the increase in capital costs.