Piece-Wise Linear (PWL) Probabilistic Analysis of Power Grid with High Penetration PV Integration

This paper aims at presenting a novel effective approach to probabilistic analysis of distribution power grid with high penetration of PV sources. The novel method adopts a Gaussian Mixture Model for reproducing the uncertainty of correlated PV sources along with a piece-wise-linear approximation of the voltage-power relationship established by load flow problem. The method allows the handling of scenarios with a large number of uncertain PV sources in an efficient yet accurate way. A distinctive feature of the proposed probabilistic analysis is that of directly providing, in closed-form, the joint probability distribution of the set of observable variables of interest. From such a comprehensive statistical representation, remarkable information about grid uncertainty can be deduced. This includes the probability of violating the safe operation conditions as a function of PV penetration

A Versatile Surrogate Model of the Power Distribution Grid Described by a Large Number of Parameters

This paper aims to present a general-purpose Surrogate Model for the probabilistic analysis of power distribution grids with a large number of input parameters. The distinctive feature of the novel technique is the employment of the partial derivatives of output variables versus input parameters to tame the “curse of dimensionality” problem exhibited by prior surrogate model calculation techniques. The second important feature of the proposed Surrogate Model method is that it does not require any a priori assumption about the nature or statistical distribution of the input parameters. In fact, it can be applied whenever design parameters are deterministic variables as well as when they are uncertain and represented by continuous and/or discrete random variables. Relevant applications presented in the paper refer to the probabilistic analysis of the distribution grid in the presence of a large number of photovoltaic sources and electric vehicle charging stations

Advanced probabilistic load flow methodology for voltage unbalance assessment in PV penetrated distribution grids

The balancing of three-phase node voltages in modern power distribution grids can be significantly deteriorated by the penetration of single-phase PV renewable sources. For a given grid topology and prescribed loads, voltage unbalance critically depends on the nodes where power is injected. Its amount can vary substantially at different observations Buses in the grid. In this paper, we present a methodology that can inform network operators about the critical Buses in the grid and critical injection scenarios. The method is based on a numerically efficient but accurate probabilistic load flow that can handle the case of many PV sources and provides detailed information on the probability distribution of voltage unbalance. The proposed methodology relies on the complex-domain modeling of voltage unbalance sensitivity and on accelerating Monte Carlo simulations via parameter space partitioning

Enhanced EV charging algorithm considering data-driven workplace chargers categorization with multiple vehicle types

The increasing penetration of Electric Vehicles (EVs) presents significant challenges in integrating EV chargers. To address this, precise smart EV charging strategies are imperative to prevent a surge in peak power demand and ensure seamless charger integration. In this article, a smart EV charging pool algorithm employing optimal control is proposed. The main objective is to minimize the charge point operator's cost while maximizing its EV chargers’ flexibility. The algorithm adeptly manages the charger pilot signal standard and accommodates the non-ideal behavior of EV batteries across various vehicle types. It ensures the fulfillment of vehicle owners’ preferences regarding the departure state of charge. Additionally, we develop a data-driven characterization of EV workplace chargers, considering power levels and estimated battery capacities. A novel methodology for computing the EV battery's arrival state of charge is also introduced. The efficacy of the EV charging algorithm is evaluated through multiple simulation campaigns, ranging from individual charger responses to comprehensive charging pool analyses. Simulation results are compared with those of a typical minimum-time strategy, revealing cost reductions and significant power savings based on the flexibility of EV chargers. This novel algorithm emerges as a valuable tool for accurately managing the power demanded by an EV charging station, offering flexible services to the electrical grid

Detection of possible factors favouring the evolution of migraine without aura into chronic migraine

In a minority of cases, the natural history of migraine without aura (MO) is characterised over time by its evolution into a form of chronic migraine (CM). In order to detect the possible factors predicting this negative evolution of MO, we searched in our Headache Centre files for all clinical records that met the following criteria: (a) first visit between 1976 and 1998; (b) diagnosis of MO or of common migraine at the first observation, with or without association with other primary headache types; (c) <15 days per month of migraine at the first observation; and (d) at least one follow-up visit at least 10 years after the first visit. The patients thus identified were then divided into two groups based on a favourable/steady evolution (Group A: n = 243, 195 women and 48 men) or an unfavourable evolution (Group B: n = 72, 62 women and 10 men) of their migraine over time. In the two groups, we compared various clinical parameters that were present at the first observation or emerged at the subsequent follow-up visits. The parameters that were statistically significantly more frequent in Group B--and can therefore be considered possible negative prognostic factors--were: (a) ≥ 10 days per month of migraine at the first observation; (b) presence of depression at the first visit in males; and (c) onset of depression or arterial hypertension after the first observation but before transformation to CM in females. Based on these findings, in MO patients the high frequency of migraine attacks, comorbidity with depression, and the tendency to develop arterial hypertension should require particular attention and careful management to prevent evolution into CM

Canonical Forms of Multi-Port Dynamic Thermal Networks

In this paper it is shown that multi-port dynamic thermal networks admit four canonical representations which generalize the four canonical representations of passive lumped RC networks : Foster I and II canonical forms, Cauer I and Cauer II canonical forms. In particular the generalized Foster I canonical form is equivalent to the time-constant representation and the generalized Cauer I canonical form is a passive multi-conductor RC transmission line

Nonlinear Projection-Based Approach for Generating Compact Models of Nonlinear Thermal Networks

A nonlinear projection-base approach for generating compact models of nonlinear thermal networks is proposed. This approach is an extension of Galerkin's method, based on the theory of kernels. High accuracy for large temperature variations and high compactness of the generated models can be obtained

Lumped electro-thermal model of on-chip interconnects

The paper proposes a compact but accurate electro-thermal model of a long on-chip interconnect embedded in a ULSI circuit. The model is well suited to be interfaced with the commercially available tools employed in ICs design for interconnect parasitic extraction

Uncertainty-aware computational tools for power distribution networks including electrical vehicle charging and load profiles

As new services and business models are being associated with the power distribution network, it becomes of great importance to include load uncertainty in predictive computational tools. In this paper, an efficient uncertainty-aware load flow analysis is described which relies on generalized polynomial chaos and stochastic testing methods. It is described how the method can be implemented in order to account for real data-based load profiles due to two different usage models: residential loads and electrical vehicle charging profiles. Hence, it is shown how some relevant information affecting the quality of service can be deduced by means of non-elementary post-processing computations. The proposed technique is tested by using a benchmark scenario for typical European low voltage networks, considering the variation of both residential loads and EV charging profiles. The results are compared with the same simulation done by means of the Monte Carlo methodology. The consideration done during the analysis will be useful to clarify the application of the methodology but also to understand the effect of load variations on the grid characteristic quantities

Assessment of DXA derived bone quality indexes and bone geometry parameters in early breast cancer patients: A single center cross-sectional study

Background: Bone mineral density (BMD) lacks sensitivity in individual fracture risk assessment in early breast cancer (EBC) patients treated with aromatase inhibitors (AIs). New dual-energy X-ray absorptiometry (DXA) based risk factors are needed. Methods: Trabecular bone score (TBS), bone strain index (BSI) and DXA parameters of bone geometry were evaluated in postmenopausal women diagnosed with EBC. The aim was to explore their association with morphometric vertebral fractures (VFs). Subjects were categorized in 3 groups in order to evaluate the impact of AIs and denosumab on bone geometry: AI-naive, AI-treated minus (AIDen-) or plus (AIDen+) denosumab. Results: A total of 610 EBC patients entered the study: 305 were AI-naive, 187 AIDen-, and 118 AIDen+. In the AI-naive group, the presence of VFs was associated with lower total hip BMD and T-score and higher femoral BSI. As regards as bone geometry parameters, AI-naive fractured patients reported a significant increase in femoral narrow neck (NN) endocortical width, femoral NN subperiosteal width, intertrochanteric buckling ratio (BR), intertrochanteric endocortical width, femoral shaft (FS) BR and endocortical width, as compared to non-fractured patients. Intertrochanteric BR and intertrochanteric cortical thickness significantly increased in the presence of VFs in AIDen- patients, not in AIDen+ ones. An increase in cross-sectional area and cross-sectional moment of inertia, both intertrochanteric and at FS, significantly correlated with VFs only in AIDen+. No association with VFs was found for either lumbar BSI or TBS in all groups. Conclusions: Bone geometry parameters are variably associated with VFs in EBC patients, either AI-naive or AI treated in combination with denosumab. These data suggest a tailored choice of fracture risk parameters in the 3 subgroups of EBC patients