Explore the vast range of titles available.

Stability of a standalone hybrid power system (HPS) in a smart grid is always a challenging task. Further, the operational stability of the power system depends on the associated communication infrastructure. Therefore, it is always crucial to pick up a controller that can assure system's stability along with performance, despite disturbances like (load and input wind variations) with communication delays. Present study focuses on reactive power management and voltage stability issues of an isolated HPS. The stability aspects of HPS are improved through reactive power compensation, by custom power devices like static var compensator. The control aspects of SVC as well as the whole hybrid system are taken care by H ∞ linear matrix inequalities approach. Further, H‐infinity control, Lyapunov stability along with linear matrix inequalities techniques estimate the delay boundary of controllers. The iterative performance of the proportional–integral–derivative controllers, and robust H ∞ damping controller of the HPS, are designed through LMI approach. Later experimental study of the HPS is done, with a prototype model in dSPACE real‐time control environment. In this case, dSPACE 1104 is added for data acquisition and control. Adaptability and robustness of the proposed controllers are verified under fluctuating loads and uncertain wind power input.

Climate and weather-propelled wind power is characterized by significant spatial and temporal variability. It has been substantiated that the variability of wind power, in addition to contributing hugely to the instability of power grids, can also send the balancing costs of electricity markets soaring. Existing studies on the same establish that curtailment of such variability can be achieved through the geographic aggregation of various widespread production sites; however, there exists a dearth of comprehensive evaluation concerning different levels/scales of such aggregation, especially from a global perspective. This paper primarily offers a fundamental understanding of the relationship between the wind power variations and aggregations from a systematic viewpoint based on extensive wind power data, thereby enabling the benefits of these aggregations to be quantified from a state scale ranging up to a global scale. Firstly, a meticulous analysis of the wind power variations is undertaken at 6 different levels by converting the 7-year hourly meteorological re-analysis data with a high spatial resolution of 0.25° χ 0.25° (approximate 28 km χ 28 km) into a wind power series globally. Subsequently, the proposed assessment framework employs a coefficient of variation of wind power as well as a standard deviation of wind power ramping rate to quantify the variations of wind power and wind power ramping rate to exhibit the characteristics and benefits yielded by the wind power aggregation at 6 different levels. A system planning example is adopted to illustrate the correlation between the coefficient of variation reduction of wind power and investment reduction, thereby emphasizing the benefits pertaining to significant investment reduction via aggregation. Furthermore, a wind power duration curve is used to exemplify the availability of wind power aggregated at different levels. Finally, the results provide insights into devising a universal approach towards the depl...

The dynamic development of energy storage technologies makes it possible to solve many problems related to the negative impact of renewable sources and fluctuating loads on the power and voltage quality parameters at their point of connection to the distribution grid. By absorbing temporary energy surpluses and covering temporary energy deficits, these technologies enable the smoothing of output power profiles of wind turbines, as well as the reduction in peak power values, for example, in traction substations or fast-charging hubs for electric vehicles. This article discusses the specifics of both applications with particular emphasis on methods for sizing energy storage parameters, methods for their control, and the special effects they allow us to achieve. The methods proposed by the authors allow for the more optimal selection of energy storage parameters in existing energy facilities based on their measured power profiles. The proposed control methods, in turn, allow for not only a reduction in relative changes in power and voltage but also enable an increase in the installed power of wind farms without investing in the modernization of the distribution network, as well as reducing the contracted power of traction substations. The analyses presented in this article are based on power profile measurements of real objects, and the proposed solutions are already being implemented in power infrastructure.

The present pace of integration of renewable sources into the electrical grid is insufficient, failing to fulfill the expectations of producers or coincide with sustainable national objectives. Furthermore, sustainable national policies are not being executed. Despite the growth of the solar and wind energy industry and the installation of decentralized energy production systems, this scenario has emerged. Several factors contribute to this scenario, including advancements in administration, forecasting, and oversight, along with enhancements in infrastructure. These issues may arise notwithstanding the decentralized nature of renewable energy sources. The integration rate of renewable energy sources into networks, along with the efficiency of these networks, is clearly hindered as a result of this. Furthermore, we will examine the problems associated with the implementation of this network. We will focus on the low injection rate and the balance between supply and demand. Subsequently, we will examine the impact they have on the operation of the interconnected system. We will provide management solutions tailored to each detected issue, along with the suggested cures for any recognized concerns. The aim is to discover the structures, procedures, and tools that will enhance the network's reliability and energy efficiency while simultaneously reducing installation costs and fortifying the network. The findings indicate that the interruptions in voltage, frequency, and power have been mitigated due to the dynamic simulations using the proposed method. The calculations were predicated on an integration of solar and wind energy, with twenty percent of the energy derived from wind.

The fractal or Hausdorff dimension is a measure of roughness (or smoothness) for time series and spatial data. The graph of a smooth, differentiable surface indexed in ℝ d has topological and fractal dimension d. If the surface is nondifferentiable and rough, the fractal dimension takes values between the topological dimension, d, and d + 1. We review and assess estimators of fractal dimension by their large sample behavior under infill asymptotics, in extensive finite sample simulation studies, and in a data example on arctic sea-ice profiles. For time series or line transect data, boxcount, Hall-Wood, semi-periodogram, discrete cosine transform and wavelet estimators are studied along with variation estimators with power indices 2 (variogram) and 1 (madogram), all implemented in the R package fractaldim. Considering both efficiency and robustness, we recommend the use of the madogram estimator, which can be interpreted as a statistically more efficient version of the Hall–Wood estimator. For two-dimensional lattice data, we propose robust transect estimators that use the median of variation estimates along rows and columns. Generally, the link between power variations of index p > 0 for stochastic processes, and the Hausdorff dimension of their sample paths, appears to be particularly robust and inclusive when p = 1.

Wind power, as a clean and renewable energy source, plays an increasingly important role in the global transition to low-carbon energy systems. However, its inherent volatility and unpredictability pose challenges for accurate short-term prediction. This study proposes an ultra-short-term wind power prediction framework that integrates multiple technical indicators with the extreme gradient boosting (XGBoost) algorithm. Inspired by financial time series analysis, the model incorporates K-line representations, power fluctuation features, and classical technical indicators, including the moving average convergence divergence (MACD), Bollinger bands (BOLL), and average true range (ATR), to enhance sensitivity to short-term variations. The proposed method is validated on two real-world wind power datasets from Inner Mongolia, China, and Germany, sourced from the European network of transmission system operators for electricity (ENTSO-E). The experimental results show that the model achieves strong performance on both datasets, demonstrating good generalization ability. For instance, on the Inner Mongolia dataset, the proposed model reduces the mean squared error (MSE) by approximately 11.4% compared to the long short-term memory (LSTM) model, significantly improving prediction accuracy.

Cloud datacenters are compute facilities formed by hundreds or even thousands of servers. With the increasing demand of cloud services, energy efficiency of servers in data center has become a significant issue. The knowledge of the energy consumption corresponding to hardware and software configuration is important for operators to optimize energy efficiency of a data center. We are currently working on a predictive model for energy consumption of a server, with inputs as service provided, hardware material equipped (type and quantity of processor, memory and hard drive) and technical environment (energy conversion and cooling). In this article, we characterize some potential factors on the power variation of the servers, such as: original fabrication, position in the rack, voltage variation and temperature of components on motherboard. The results show that certain factors, such as original fabrication, ambient temperature and CPU temperature, have noticeable effects on the power consumption of servers. The experimental results emphasize the importance of adding these external factors into the metric, so as to build an energy predictive model adaptable in real situations.

In this paper, we present some new limit theorems for power variation of kth order increments of stationary increments Lévy driven moving averages. In the infill asymptotic setting, where the sampling frequency converges to zero while the time span remains fixed, the asymptotic theory gives novel results, which (partially) have no counterpart in the theory of discrete moving averages. More specifically, we show that the first-order limit theory and the mode of convergence strongly depend on the interplay between the given order of the increments k ≥ 1, the considered power p > 0, the Blumenthal–Getoor index β ∈ [0, 2) of the driving pure jump Lévy process L and the behaviour of the kernel function g at 0 determined by the power α. First-order asymptotic theory essentially comprises three cases: stable convergence towards a certain infinitely divisible distribution, an ergodic type limit theorem and convergence in probability towards an integrated random process. We also prove a second-order limit theorem connected to the ergodic type result. When the driving Lévy process L is a symmetric β-stable process, we obtain two different limits: a central limit theorem and convergence in distribution towards a (k – α)β-stable totally right skewed random variable.

The present work deals with the parameter estimation problem for an n th-order mixed fractional Brownian motion (fBm) of the form X ( t ) = θ P ( t ) + α W ( t ) + σ B H n ( t ) , where W ( t ) is a Wiener process and B H n ( t ) is the n th-order fBm ( n ≥ 2 ) with Hurst index H ∈ ( n - 1 , n ) . By using power-variations method we estimate α , then we build maximum likelihood estimators of the parameters θ and σ . Both weak and almost sure behaviour of the proposed estimators are established.