Effects of the size and cost reduction on a discounted payback period and levelized cost of energy of a zero-export photovoltaic system with green hydrogen storage.

Zero-export photovoltaic systems are an option to transition to Smart Grids. They decarbonize the sector without affecting third parties. This paper proposes the analysis of a zero-export PVS with a green hydrogen generation and storage system. This configuration is feasible to apply by any self-generation entity; it allows the user to increase their resilience and independence from the electrical network. The technical issue is simplified because the grid supplies no power. The main challenge is finding an economic balance between the savings in electricity billing, proportional to the local electricity rate, and the complete system's investment, operation, and maintenance expenses. This manuscript presents the effects of the power sizing on the efficacy of economic savings in billing (ηSaving) and the effects of the cost reduction on the levelized cost of energy (LCOE) and a discounted payback period (DPP) based on net present value. In addition, this study established an analytical relationship between LCOE and DPP. The designed methodology proposes to size and selects systems to use and store green hydrogen from the zero-export photovoltaic system. The input data in the case study are obtained experimentally from the Autonomous University of the State of Quintana Roo, located on Mexico's southern border. The maximum power of the load is LPmax = 500 kW, and the average power is LPmean = 250 kW; the tariff of the electricity network operator has hourly conditions for a medium voltage demand. A suggested semi-empirical equation allows for determining the efficiency of the fuel cell and electrolyzer as a function of the local operating conditions and the nominal power of the components. The analytical strategy, the energy balance equations, and the identity functions that delimit the operating conditions are detailed to be generalized to other case studies. The results are obtained by a computer code programmed in C++ language. According to our boundary conditions, results show no significant savings generated by the installation of the hydrogen system when the zero-export photovoltaic system Power ≤ LPmax and DPP ≤ 20 years is possible only with LCOE ≤ 0.1 $/kWh. Specifically for the Mexico University case study, zero-export photovoltaic system cost must be less than 310 $/kW, fuel cell cost less than 395 $/kW, and electrolyzer cost less than 460 $/kW.

Effect of the Agglomerate Geometry on the Effective Electrical Conductivity of a Porous Electrode.

The study of the microstructure of random heterogeneous materials, related to an electrochemical device, is relevant because their effective macroscopic properties, e.g., electrical or proton conductivity, are a function of their effective transport coefficients (ETC). The magnitude of ETC depends on the distribution and properties of the material phase. In this work, an algorithm is developed to generate stochastic two-phase (binary) image configurations with multiple geometries and polydispersed particle sizes. The recognizable geometry in the images is represented by the white phase dispersed and characterized by statistical descriptors (two-point and line-path correlation functions). Percolation is obtained for the geometries by identifying an infinite cluster to guarantee the connection between the edges of the microstructures. Finally, the finite volume method is used to determine the ETC. Agglomerate phase results show that the geometry with the highest local current distribution is the triangular geometry. In the matrix phase, the most significant results are obtained by circular geometry, while the lowest is obtained by the 3-sided polygon. The proposed methodology allows to establish criteria based on percolation and surface fraction to assure effective electrical conduction according to their geometric distribution; results provide an insight for the microstructure development with high projection to be used to improve the electrode of a Membrane Electrode Assembly (MEA).

Effect of An Image Resolution Change on the Effective Transport Coefficient of Heterogeneous Materials.

Electrochemical electrodes comprise multiple phenomena at different scales. Several works have tried to model such phenomena using statistical techniques. This paper proposes a novel process to work with reduced size images to reconstruct microstructures with the Simulated Annealing method. Later, using the Finite Volume Method, it is verified the effect of the image resolution on the effective transport coefficient (ETC). The method can be applied to synthetic images or images from the Scanning Electron Microscope. The first stage consists of obtaining the image of minimum size, which contains at least 98% of the statistical information of the original image, allowing an equivalent statistical study. The image size reduction was made by applying an iterative decimation over the image using the normalized coarseness to compare the amount of information contained at each step. Representative improvements, especially in processing time, are achieved by reducing the size of the reconstructed microstructures without affecting their statistical behavior. The process ends computing the conduction efficiency from the microstructures. The simulation results, obtained from two kinds of images from different materials, demonstrate the effectivity of the proposed approach. It is important to remark that the controlled decimation allows a reduction of the processor and memory use during the reconstruction and ETC computation of electrodes.

Effect of the image resolution on the statistical descriptors of heterogeneous media.

The characterization and reconstruction of heterogeneous materials, such as porous media and electrode materials, involve the application of image processing methods to data acquired by scanning electron microscopy or other microscopy techniques. Among them, binarization and decimation are critical in order to compute the correlation functions that characterize the microstructure of the above-mentioned materials. In this study, we present a theoretical analysis of the effects of the image-size reduction, due to the progressive and sequential decimation of the original image. Three different decimation procedures (random, bilinear, and bicubic) were implemented and their consequences on the discrete correlation functions (two-point, line-path, and pore-size distribution) and the coarseness (derived from the local volume fraction) are reported and analyzed. The chosen statistical descriptors (correlation functions and coarseness) are typically employed to characterize and reconstruct heterogeneous materials. A normalization for each of the correlation functions has been performed. When the loss of statistical information has not been significant for a decimated image, its normalized correlation function is forecast by the trend of the original image (reference function). In contrast, when the decimated image does not hold statistical evidence of the original one, the normalized correlation function diverts from the reference function. Moreover, the equally weighted sum of the average of the squared difference, between the discrete correlation functions of the decimated images and the reference functions, leads to a definition of an overall error. During the first stages of the gradual decimation, the error remains relatively small and independent of the decimation procedure. Above a threshold defined by the correlation length of the reference function, the error becomes a function of the number of decimation steps. At this stage, some statistical information is lost and the error becomes dependent on the decimation procedure. These results may help us to restrict the amount of information that one can afford to lose during a decimation process, in order to reduce the computational and memory cost, when one aims to diminish the time consumed by a characterization or reconstruction technique, yet maintaining the statistical quality of the digitized sample.