Long-term Power Research Articles (Page 10)

Machine learning-assisted wearable triboelectric-electromagnetic vibration sensor for monitoring human rehabilitation training

Federated learning (FL) represents a distributed machine learning approach that eliminates the necessity of transmitting privacy-sensitive local training samples. However, within wireless FL networks, resource heterogeneity introduces straggler clients, thereby decelerating the learning process. Additionally, the learning process is further slowed due to the non-independent and identically distributed (non-IID) nature of local training samples. Coupled with resource constraints during the learning process, there arises an imperative need for optimizing client selection and resource allocation strategies to mitigate these challenges. While numerous studies have made strides in this regard, few have considered the joint optimization of client selection and computational power (i.e., CPU frequency) for both clients and the edge server during each global iteration. In this paper, we initially define a cost function encompassing learning latency and non-IID characteristics. Subsequently, we pose a joint client selection and CPU frequency control problem that minimizes the time-averaged cost function subject to long-term power constraints. By utilizing Lyapunov optimization theory, the long-term optimization problem is transformed into a sequence of short-term problems. Finally, an algorithm is proposed to determine the optimal client selection decision and corresponding optimal CPU frequency for both the selected clients and the server. Theoretical analysis provides performance guarantees and our simulation results substantiate that our proposed algorithm outperforms comparative algorithms in terms of test accuracy while maintaining low power consumption.

In this paper, we investigate a long-term power minimization problem of cell-free massive multiple-input multiple-output (MIMO) systems. To address this issue and to ensure the system queue stability, we formulate a dynamic optimization problem aiming to minimize the average total power cost in a time-varying system under imperfect channel conditions. The problem is then converted into a real-time weighted sum rate maximization problem for each time slot using the Lyapunov optimization technique. We employ approximation techniques to design robust sparse beamforming, which enables energy savings of the network and mitigates channel uncertainty. By applying direct fractional programming (DFP) and alternating optimization, we can obtain a locally optimal solution. Our DFP-based algorithm minimizes the average total power consumption of the network while satisfying the quality of service requirements for each user. Simulation results demonstrate the rapid convergence of the proposed algorithm and illustrate the tradeoff between average network power consumption and queue latency.

Looking far or close: The explanatory role of myopic management in the relationship between CEO-TMT power disparity and corporate social responsibility

Long-term operation of isolated microgrids with renewables and hybrid seasonal-battery storage

Adama II wind farm long-term power generation forecasting based on machine learning models

This study proposes a novel approach to investigating the multifractality of time series using the multifractal cross-correlation detrended moving average analysis (MF-X-DMA). The study demonstrates the behavioral differences of MF-X-DMA in coherent and non-coherent time periods. Due to the lack of a mechanism to capture the dynamical cross-correlation in time series, correlated time series with multifractal structure present a barrier for analysis. The study shows that when the wavelet coherence method is applied to time series, co-movement between time series can be easily captured in certain time intervals, providing an efficient way to find time intervals to apply MF-X-DMA. The study applies the wavelet coherence method to the daily spot prices of gold and platinum from January 1987. It shows that the wavelet coherence method is an excellent engine to extract designated time series in certain frequency and time intervals, eliminating the need for windowing or shuffling methods. Additionally, the study observes a long-term power law cross-correlation using detrended cross-correlation analysis coefficients of inversed series for both low-correlated and high-correlated series. Finally, the findings indicate that MF-X-DMA leads to superior results compared to MF-DFA when provided with highly correlated data.

Continuous wave (CW) green lasers have a lot of important applications in many fields, including holography, interferometry, atom cooling and trapping, and quantum optics, and they are usually achieved by frequency-doubling 1 µm lasers based on the Nd3+ gain media. In this paper, we present an all-solid-state CW green laser with an output wavelength of 522 nm, which was directly attained by employing a Pr3+:YLF crystal pumped with a high-power fiber-coupled blue laser diode (LD) module as the gain medium. Due to the negative thermal lens effect of the Pr3+:YLF crystal, the designed laser resonator had to be lengthened with the increase in the incident pump power. As a result, when a 0.5% doped Pr3+:YLF crystal was employed as the gain medium and the incident pump power was 12 W, the length of the resonator was optimized to 311.3 mm and the maximum output power of 522 nm green laser was up to 886 mW. The obtained conversion efficiency and beam quality M2 were 11.25% and 1.15, respectively. The long-term power stability within 4.5 h was better than ±1.5% at an output power of 700 mW. The obtained watt-level green laser can also be used to generate high power CW deep UV laser for laser processing of silicon and organic materials, inspection, etc.

With less than a decade remaining until 2030, global investment in clean energy access falls short of the anticipated levels required to achieve the sustainable development goals. Notably, nations with the greatest gaps in electricity access, particularly those in Sub-Saharan Africa, have been largely excluded from energy access funding. Interestingly, the energy sector policy documents of these countries have neglected to incorporate financing strategies or plans for photovoltaic (PV) power generation. This discrepancy in the literature underscores the need to assess the economic impact of finance and investment policies that align with long-term PV power generation targets. To address this gap, our study employs a dynamic Computable General Equilibrium model to evaluate the macroeconomic consequences of achieving Cameroon’s Nationally Determined Contributions for PV power generation through optimized PV investment and finance. The model examines three policy scenarios: the Business-as-Usual, SC1 scenario involving a stable 100% increase in PV investment, and SC2 scenario featuring a stepwise 5%–100% increase in PV investment. By simulating these scenarios, we aim to shed light on their effects. The results reveal that SC1 and SC2 exhibit a 50% higher final demand for PV investment compared to the BAU scenario. Optimizing PV finance and investment in both scenarios leads to a slowdown in Cameroon’s economic growth, with SC1 showing a more pronounced impact. Additionally, SC2 encourages rapid decarbonization in energy-intensive sectors such as crude oil production and electricity generation industries. However, the SC1 policy scenario results in a rapid reduction in total investment expenditure for PV power generation. By 2035, PV power generation is projected to be three times higher in both SC1 and SC2 compared to the BAU scenario. The SC2 policy scenario also predicts relatively high levels of consumption among rural affluent and urban impoverished households. In conclusion, our study highlights the pressing need for enhanced investment and finance strategies to propel PV power generation, particularly in underserved regions. By leveraging the findings of this research, policymakers can make informed decisions and implement policies that promote sustainable and inclusive energy access, driving progress towards the fulfillment of SDGs.

Abstract Reasonable determination of the installation inclination and array spacing of PV power plant modules is essential to improve the power generation efficiency of PV power plants. This paper firstly derives the formula for calculating the north-south spacing of PV arrays with arbitrary slope inclination and visualizes the north-south spacing of complex mountain PV arrays using ArcGIS. Secondly, a mountain PV array system is proposed to ensure that the system can still operate at the maximum power point in real-time when the solar radiation intensity changes drastically due to unpredictable environmental variables. Finally, to verify the feasibility of the active PV array system in real-life production, an experimental platform is built, and an operational test experiment of the active PV array system under partial shading conditions is conducted, as well as a long-term power boost comparison test. The experimental results show that the mountain PV array system has a 95.7% matching degree in the operation test experiment, which can be perfectly adapted to most PV plants; in the power boost comparison test, the power generation of the traditional PV system is 254Wh, and the power generation of the mountain PV array system is 483Wh, which is about 1.9 times higher than the performance of the traditional PV system. The mountain PV array system has good adaptability to various harsh and unexpected conditions and solves the problem of improving the power output of PV systems in the shadow-shaded environment of mountainous areas, which improves the general usability of PV.

The interrelationship between coastal, Great Lakes, Inland, and deep-sea freight rates: A longitudinal approach

The optimal scheduling of cascade reservoirs is an important water resource management and regulation method. In the actual operation process, its nonlinear, high-dimensional, and coupled characteristics become increasingly apparent under the influence of multiple constraints. In this study, an integrated multistrategy particle swarm optimization (IMPSO) algorithm is proposed to realize the optimal operation of mid- and long-term power generation in cascade reservoirs according to the solution problem in the scheduling process of cascade reservoirs. In IMPSO, a variety of effective improvement strategies are used, which are combined with the standard PSO algorithm in different steps, among which beta distribution initialization improves population diversity, parameter adaptive adjustment accelerates convergence speed, and the Lévy flight mechanism and adaptive variable spiral search strategy balance the global and local search capabilities of the algorithm. To handle complex constraints effectively, an explicit–implicit coupled constraint handling technique based on constraint normalization is designed to guide the update process into the feasible domain of the search space. The feasibility of the proposed method is verified in the mid- and long-term power generation optimization scheduling of the lower reaches of the Jinsha River–Three Gorges cascade hydropower reservoirs. The results show that the proposed method outperforms the other methods in terms of search accuracy and has the potential to improve hydropower resource utilization and power generation efficiency significantly.

The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

Deep learning graphical tool inspired by correlation matrix for reporting long-term power quality data at multiple locations of an MV/LV distribution grid

Stand-alone microgrids have become reliable and efficient solutions for remote areas and critical infrastructures. However, the converters within these microgrids experience long-term complex power fluctuations caused by random variations in micro sources and loads. These power fluctuations induce thermal cycling in semiconductor chips, leading to thermal fatigue failure and compromising the safety and reliability of both the converter and microgrid operation. To address this issue, this paper proposes a reactive power injection algorithm aimed at reducing the output power fluctuation of the converter. The algorithm implements reactive power injection at the converter control level, thereby restructuring the output power profile and resulting in reduced junction temperature fluctuations in IGBTs. This approach effectively mitigates thermal stress within the material layers of the module, extending the lifetime of power devices and improving the operational reliability of the microgrid. The algorithm implementation is based on the PQ control strategy, integrating the power triangle with the envelope detection technique. Furthermore, the lifetime prediction process utilizes the electro-thermal coupling model, the rainflow counting algorithm, and the Lesit model. Simulation results demonstrate that, for an active power fluctuation range of 10 kW to 80 kW and an equivalent RC time constant of 22.5 s, the algorithm achieves a significant reduction of 62.64% in the amplitude of output power fluctuation and extends the lifetime of power devices by 74.13%. The obtained data showcase the effectiveness of the algorithm in enhancing the lifetime of power devices and further improving the microgrid operational reliability under specific parameter conditions.

Cavity growth behavior and fracture mechanism of 9.5Cr-1.5MoCoVNbNB heat-resistant steel during long-term tensile rupture at 620 °C

Improving Solar PV Prediction Performance with RF-CatBoost Ensemble: A Robust and Complementary Approach

Community consumption power in the use of electricity resources is growing very rapidly. This is because the power source is the main need for the community. Without electricity, daily needs cannot run smoothly. In various regions, especially Kediri, where the needs are changing, it is necessary to predict and provide electricity to meet the consumption needs of the people. There are many things that can be done to predict the need for electric power, one of which is forecasting. Forecasting methods that can be used include trend and Monte Carlo Simulation. The results of this study indicate that Monte Carlo simulations are better at predicting long-term electrical power requirements at ULP3 Kediri. Long-term electricity demand can be predicted with the equation: Yt =138691 + 6709 x t + 231 x t2. The results of the research can help PLN UP3 Kediri to provide electricity for consumers.

The use of acoustic and magnetic methods of non-destructive testing to detect zones of stable localization of deformation in order to assess and predict the performance of long-term equipment is of scientific and practical interest at present. A structural–mechanical criterion was developed that reflects the relationships between the structural and substructural states, internal stress fields and stable localization of deformations with the characteristics of non-destructive tests in the metal of long-term equipment made of structural 0.2 C steel and heat-resistant 0.12C-1Cr-1Mo-1V steel. The values of the structural–mechanical criteria Ks.-m for structural 0.2 C steel and for heat-resistant 0.12C-1Cr-1Mo-1V steel, corresponding to the moment of stable localization of deformation, are established. At the same time, it is recommended to replace the checked equipment nodes due to the exhaustion of the resource. The proposed and justified approach to assessing and predicting the performance and residual life of long-term power equipment, based on the identified relationships between the structural and substructural states, internal stress fields and stable localization of deformations with the characteristics of non-destructive tests and the calculation of the structural–mechanical criterion, was applied at a number of power plants in the Kemerovo region—Kuzbass. A methodology was developed for evaluating the residual life, based on the identification and use of relationships between structural and substructural states, internal stress fields and stable localization of deformations with the characteristics of non-destructive tests and the calculation of a structural–mechanical criterion.

Abstract Following a global trend, intermittent sources, especially wind, have been experiencing accelerated growth in Brazil—in the last decade, wind power grew 13 times and became the second largest source in the electricity mix (12%), just behind hydropower (60%). Currently, although following regulatory guidelines, the representation of wind power in the long-term operation planning model is done in a simplified way, based on the monthly average of the last five years of aggregated generation, thus demanding improvements. The objective of this work is to describe an approach to be used by the Brazilian power industry to represent the uncertainties of monthly wind power production in the SDDP algorithm applied in the long-term operation planning model, keeping the large-scale stochastic problem still computationally viable, when applied to large interconnected systems, especially with hydroelectric predominance. The proposed methodology comprises statistical clustering of wind regimes and definition of equivalent wind farms; evaluation of monthly transfer functions between wind speed and power production; integrated generation of monthly multivariate synthetic scenarios of inflows and winds, considering associated cross-correlations; and representing monthly wind power in the SDDP algorithm. The application to real configurations of the Brazilian interconnected system, including case studies related to the monthly operation program and the calculation of the maximum amount of energy that can be traded in long-term power purchase agreements, points to its effectiveness and the relevance of modeling the wind uncertainties in the long-term operation planning of large hydro-dominated systems.