A photovoltaic (PV) plant model is presented. It is based on a detailed electrothermal description of the panels forming strings that, in turn, form the power plant. It accounts for environmental working conditions, such as temperature and wind speed, and specific plant configuration, such as plant topology and power losses due to interconnections. The input variables of the model are the ambient temperature, irradiance, and wind speed. The model derives the working temperature of the panel taking into account also the power conversion performed by the panel; the electrical operating point is determined by simulating the actions done by the maximum power point tracker that operates at plant level. This model has been tested using a large database of experimental data from industrial PV plants characterized by power levels ranging from 250 kW to 1 MW. As shown, the model is capable to predict power production when “fed” by forecast irradiance, ambient temperature, and wind speed data

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This paper considers the formulation of the variational model (VM) of autonomous circuits (oscillators) working in periodic steady-state conditions. The shooting method, which is largely used to compute the solution in the time domain when the VM is forced by a small-signal perturbation, is studied. The proposed analytical approach can be exploited to improve accuracy in the simulation of the effects of noise sources. In particular, we justify from an analytical standpoint the adoption of a suitable periodicity constraint in the shooting method. We exploit the properties of block circulant matrices that naturally arise in the description of the problem. We prove that the frequency of the small-signal perturbation must be different from that of the unperturbed oscillator to avoid inaccuracy of the shooting method due to the existence of singularities in the VM formulation, and derive a method that allows us to get closer to the singularity

Demand Response (DR) is a program designed to match supply and demand by modifying consumption profile. Some of these programs are based on economic incentives, in which, a user is paid to reduce his energy requirements according to an estimated baseline. Literature review and practice have shown that the counter-factual models of employing baselines are vulnerable for gaming. Classical solutions of mechanism design require that agents communicate their full types which result in greater difficulties for its practical implementation. In this paper, a novel contract is developed to induce individual rationality (voluntary participation) and asymptotic incentive-compatibility (truthfulness) through probability of call, where an agent does not require to report the marginal utility. In this approach, a consumer only announces the baseline and reduction capacity, given a payment scheme that includes cost of electricity, incentive price, and penalty caused by any deviation between self-reported and actual energy consumption. The aggregator decides randomly what users are called to perform the energy reduction. As result, asymptotic truth-telling behavior in incentive-based DR is managed by the aggregator through the probability of call for each agent. Mathematical proofs and numerical studies are provided to demonstrate the properties and advantages of this contract in limiting gaming opportunities and in terms of its implementation.

Due to the statistical uncertainty of loads and power sources found in smart grids, effective computational tools for probabilistic load flow analysis and planning are now becoming indispensable. In this research, we describe a unified simulation framework that allows quantifying the probability distributions of a set of observation variables as well as evaluating their sensitivity to potential variations in the power demands. The proposed probabilistic technique relies on the generalized Polynomial Chaos algorithm and on a region-wise aggregation/description of the time-varying load profiles. It is shown how detailed statistical distributions of some important figures of merit, which includes voltage unbalance factor in distribution networks, can be calculated with a two-orders of magnitude acceleration compared to standard Monte Carlo analysis. In addition, it is highlighted how the associated sensitivity analysis is of guidance for the optimal allocation and planning of new loads.

SUMMARYIn this paper it is shown that a numerical method largely adopted for the simulation of noise in autonomous circuits is affected by singularities that manifest when the frequency at which the noise analysis is carried out approaches a harmonic of the autonomous circuit. The resulting noise power spectral density (PSD) is thus characterized by spurious spikes. The presence of these singularities is for the first time justified from an analytical standpoint and their effects are shown by simulating some oscillators, employed as benchmarks. Furthermore, the presented approach justifies the 1/( f s − f ) 2 shape of the PSD of noise at the output when the f s frequency approaches the f fundamental of a stable oscillator and the 1/| f s − f | 3 shape when the effects of flicker noise are manifest.

Lithium-ion (Li-Ion) batteries are rechargeable batteries which can maximize battery lifespan thanks to their chemical abilities, at the same time increasing power energy density. For these reasons, Li-Ion batteries have earned considerable popularity, and they are widely used both in mobile computing devices (e.g. smartphones and smartwatches) and automotive systems (e.g. hybrid and electric vehicles). A fundamental parameter for battery health monitoring is the State of Health (SoH), which is computed from the maximum releasable capacity, and which represents battery functionality in energy storage and delivery. Among the most used data-driven approaches are Machine Learning (ML) algorithms, such as Support Vector Machines (SVMs), Random Forest (RF) regressions, and Artificial Neural Networks (ANNs). This article presents a comparison of different ML algorithms for estimating maximum releasable capacity of Li-Ion batteries, with a special focus on the implementation of both Forward and Recurrent ANNs (FNNs and RNNs, respectively), using prognostic Li-Ion battery data sets provided by the National Aeronautics and Space Administration (NASA). After an evaluation of models performances in terms of RMSE and MAE, STM32Cube.AI tool was used to convert pre-trained ANNs to optimized ANSI C code for STM32 microcontrollers (MCUs), and to profile their complexity automatically. Finally, in order to decrease models size with minimal accuracy loss, the implemented ANNs were quantized via STM32Cube.AI, converting weights and activations from 32-bit floating-point to 8-bit integer precision. TensorFlow Lite for Microcontrollers (TFLM) was used as benchmark in the analysis and validation of both non-quantized and quantized models, and the performances obtained via STM32Cube.AI and TFLM were compared.

This paper presents and compares two different approaches to the analysis and design of nonferrous shields for extremely low-frequency magnetic fields. The first method is based on a circuital approach where the shield is modeled by a set of conductors coupled with each other by a matrix of self and mutual inductances. The second technique is based on an algebraic formulation of electromagnetic fields and adapted by means of integral equations to the analysis of thin conductive sheets. The paper shows the accuracy of the two methods for calculating induced current density inside the shield and for evaluating magnetic flux density in the shielded region.Index Terms-Conductive shielding, extremely low frequency, finite formulation of electromagnetic fields, thin conductor.