Long-term Power Research Articles (Page 6)

Potential of hydrogen and thermal storage in the long-term transition of the power sector: A case study of China

Among various nonconventional energy sources, wind energy is a noteworthy and suitable source with the ability to generate electricity continuously and sustainably. However, there are a number of drawbacks to wind energy, including high basic utilization costs, the static nature of wind farms, and the challenge of locating energy that is wind-efficient. regions. Using five machine learning methods, long-term wind power prediction was done in this study using daily wind speed data. We suggested an effective way to forecast wind power values using machine learning techniques. To demonstrate how machine learning algorithms, perform, we carried out a number of case studies. The outcomes demonstrated that long-term wind power values might be predicted using machine learning algorithms in relation to past wind speed data. Additionally, the consequences show that machine learning-based Models could be used in places other than those where they were taught. This study showed that, by employing a model of a base site, machine learning algorithms could be applied frequently prior to the development of wind plants in an undisclosed environmental region, provided that it makes sense.

A novel method for long-term power demand prediction using enhanced data decomposition and neural network with integrated uncertainty analysis: A Cuba case study

Deep reinforcement learning-based dynamic multi-beam power allocation for GEO-LEO co-existing satellites

Abstract Accurate, long-term wind power forecasting is crucial for effective power grid operation planning. It can enhance the stability and security of the power system. We propose a combined medium and long-term wind power generation prediction method based on multi-model fusion to study forecasting. By analyzing the connection between meteorological data and wind power generation, we identify the pivotal factors that impact wind power output and determine the best input data scheme. Based on the predictive outcomes, we select the suitable core predictive sub-models and use the particle swarm optimization algorithm to achieve the dynamic optimization of the weight for each sub-model. Afterwards, we allocate the optimized weights to each sub-model and establish a fused multi-model advantage-based combined predictive platform for wind power generation for the medium and long term. The wind power forecast is enhanced through the dynamic weighted combination forecasting technique, which significantly enhances forecast precision.

The political opinions of Islamic organizations throughout the Guided Democracy (1959- 66) era are diverse. For some, this is a dismal period. The rest saw this era as an opportunity to preserve Islamic dominance. Cooperative and non-cooperative politics with power are strategic options for the long-term political power of Islamic mass groups like Nahdhatul 'Ulama (NU) and Muhammadiyah. This study seeks to understand the political perspectives of Indonesia's two largest Islamic organizations in the face of the imposition of Nasakom (religious nationalist and communist) ideology during the Soekarno era. This study takes a qualitative descriptive comparative method. The study's findings suggest that, in terms of character, numerous NU figures found theological grounds to reconcile with Nasakom politics. Meanwhile, Muhammadiyah, whose members include numerous senior Masyumi officials, leans toward the opposition. An accommodating mindset helps the organization to breathe for longer. Meanwhile, the opposition faces the possibility of political stunting. In terms of impact, the two Islamic mass organizations failed to arrest the flow of authoritarianism. They attempted to survive by honoring the Great Leader of the Revolution.

Multi-objective optimization method for medium and long-term power supply and demand balance considering the spatiotemporal correlation of source and load

Precise forecasting of power generation and demand is essential for effective resource allocation and energy trading in contemporary energy systems. Power forecasting accuracy has increased dramatically since Random Forest Regression (RFR) techniques were used. The study's primary objective is to forecast electricity generation in Malaysia's Eastern West region, with a concentration on solar energy. The research process entails gathering and examining pertinent factors, weather information, and historical power data. To evaluate the accuracy and predictive potential of RFR models, a specific power grid is used for training, validation, and testing. One of the anticipated results is the creation of an accurate model for power generation predictions, which will help to optimise energy operations and smoothly incorporate renewable sources. The paper examines the advantages, disadvantages, and best practices related to RFR-based power forecasting. The dataset, which spans the years 2019 to 2023, includes 30-minute interval records for the following variables: average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed. Using the RandomForestRegressor class from the scikit-learn library, the RFR model is implemented. In order to assess the model's overall fit, average deviation, and sensitivity to outliers, measures such as root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) are used on the test set. The temperature, irradiance, and AC power output of PV modules are found to be strongly correlated.

Accurate prediction of power demand and generation is crucial for modern energy systems to efficiently allocate resources and facilitate energy trading. The integration of artificial intelligence (AI) and machine learning techniques has significantly improved the precision of power forecasting. This study focuses on the application of Artificial Neural Networks (ANN) for forecasting power generation in the Eastern Coast region of Malaysia, with a specific emphasis on solar power. The research methodology involves collecting and analyzing historical power data, weather data, and relevant variables. ANN models are trained, validated, and tested on a selected power grid to assess their accuracy and predictive capabilities. The expected outcomes aim to include the development of a precise power generation forecasting model, providing valuable insights for decision-makers to optimize energy operations and seamlessly integrate renewable sources. Additionally, the study explores potential challenges, limitations, and best practices associated with ANN-based power forecasting. The dataset covers the period from 2020 to 2023, with variables such as average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed recorded at 30-minute intervals. The architecture of the ANN model, implemented using the Keras framework, is described as a Sequential model with layers utilizing the 'ReLU' activation function. Model evaluation employs metrics like root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) on the test set, offering insights into the model's overall fit, average deviation, and sensitivity to outliers. Results reveal strong correlations between PV module temperature, irradiance, and AC power generated.

An always-on electrocardiogram (ECG) anomaly detector (EAD) with ultra-low power (ULP) consumption is proposed for continuous cardiac monitoring applications. The detector is featured with a 1.5-bit non-feedback delta quantizer (DQ) based feature extractor, followed by a multiplier-less convolutional neural network (CNN) engine, which eliminates the traditional high-resolution analog-to-digital converter (ADC) in conventional signal processing systems. The DQ uses a computing-in-capacitor (CIC) subtractor to quantize the sample-to-sample difference of ECG signal into 1.5-bit ternary codes, which is insensitive to low-frequency baseline wandering. The subsequent event-driven classifier is composed of a low-complexity coarse detector and a systolic-array-based CNN engine for ECG anomaly detection. The DQ and the digital CNN are fabricated in 65-nm and 180-nm CMOS technology, respectively, and the two chips are integrated on board through wire bonding. The measured detection accuracy is 90.6% ∼ 91.3% when tested on the MIT-BIH arrhythmia database, identifying three different ECG anomalies. Operating at 1 V and 1.4 V power supplies for the DQ and the digital CNN, respectively, the measured long-term average power consumption of the core circuits is 36 nW, which makes the detector among those state-of-the-art always-on cardiac anomaly detection devices with the lowest power consumption.

Nuclear batteries, a novel energy device in microelectromechanical systems (MEMS), have garnered significant attention from academia and industry due to their promising application prospects. They possess high energy density and reliable operation without human intervention and offer unique advantages in the case of long-term stable power supply. Among these, thermal conversion nuclear batteries (RTGs) represent the most mature technology and the earliest application, while betavoltaic nuclear batteries have entered commercialization. Challenges in betavoltaic nuclear batteries research include energy wastage due to the self-absorption effect of radioactive sources, low conversion efficiency, and significant radiation damage to transducer devices. These issues are attributable not only to the inherent properties of the radioactive source but also to the material and structural design of transducers. A 3D interface structure design scheme based on the wide bandgap semiconductor material GaN and the radioactive isotope 63Ni nuclear microbatteries is proposed. In the scheme, Geant4 and COMSOL Multiphysics were used to simulate the GaN-based betavoltaic nuclear battery of 63Ni source, and the PN junction 3D interface structure of the transducer was designed and optimized. The effects of the surface area, number of micropillars, thickness, and doping concentration of each region on the battery performance were analyzed. Results indicate that with P- and N- region thicknesses and doping concentrations at 0.1, 9.9 µm, 1 × 1018, and 1 × 1014 cm−3, respectively, the nuclear battery can achieve a conversion efficiency of 7.57%, a short-circuit current density of 0.3959 µA/cm2, an open-circuit voltage of 2.3074 V, and maximum output power of 0.7795 µW/cm2. In addition, discussion regarding the surface area and quantity of P-layer micropillars confirms the hypothesis that these variables are positively correlated with the output performance of the transducer.

In an effort to overcome the shortcomings of transmittal line heat mutation detection, such as the need for long-term preset external power supply and susceptibility to electromagnetic interference, we study the online detection of heat mutation of dispersed transmittal conductor based on staple optic sensing technique and correction of sensor prediction bias, which solves the issue of cross allergy between heat and damage. Based on the staple optic sensing technique, the staple optic transducer is designed and the Brillouin rate divert of the dispersed transmittal line is calculated; on the basis of the relationship between the Brillouin rate divert and the heat, the heat measurement result of the dispersed transmittal line is obtained; in the radial basis function meshwork, the heat measurement result is inputted, the heat measurement deviation of the staple optic transducer is predicted, and the prediction deviation is corrected to get the corrected heat measurement result; by comparing the current heat measurement result with the historical heat data, the heat measurement result of the staple optic transducer is predicted. By comparing the current heat measurement outcome with the historical heat data, the heat of the dispersed transmittal line is detected online, and an alarm is issued when the heat morph exceeds the preset threshold value. Experiments have proved that: the method can availably calculate the Brillouin rate divert of dispersed transmittal conductors and complete the heat measurement of transmittal conductors; the method can availably correct the prediction deviation of transmittal conductor heat and improve the exact of heat measurement; the method can availably detect the heat mutation of dispersed transmittal conductors online, and the detection exact is high.

Abstract Anti-icing is crucial for transmission conductors to avoid long-term power outages caused by severe icing climates. Here, superhydrophobic aluminum conductors with composite honeycomb nanopore structures were prepared by the twice-anodic oxidation method. Effects of the anodized current density on the surface morphology, structure, hydrophobicity, and anti-icing performance were experimentally studied. As the current density increases, the film thickness of the lower layer of a small nanopore structure increases, while the dissolution effect on the upper layer of a large nanopore structure is strengthened. Also, the composite nanopore structure exists in the damaged condition and has a rougher surface at the micron level. By comparing the surface wettability and anti-icing performance of all samples, the sample under the current density of 0.01875A/cm2 showed the optimal anti-icing properties, including a contact angle (CA) of 168°, the ice adhesion force of 4.87 kPa, and the longest frost formation time. The good anti-icing performance makes the anodized aluminum of composite nanopore structures a satisfactory candidate for improving their anti-icing behavior in the industry.

Background: As Indonesia's economy is increasingly driven by youth entrepreneurship and innovation, it requires examination of cultural factors and government support to foster entrepreneurial spirit among young people. Purpose: This research empirically investigates the relationship between long-term orientation, power distance, and uncertainty avoidance and how these factors influence the entrepreneurial orientation of young individuals in Bandar Lampung, with the perception of government regulations playing a mediating role. Design/methodology/approach: The study used a quantitative research approach, employing Structural Equation Modeling. The sample consisted of 200 respondents, young entrepreneurs from Bandar Lampung, selected through a simple random sampling method. The analysis was conducted using the Structural Equation Modeling (SEM) approach with the Partial Least Squares (PLS-SEM) technique. Findings/Result: The study found that long-term orientation significantly impacts entrepreneurial orientation. Additionally, it found that power distance and uncertainty avoidance are significant factors in shaping entrepreneurial orientation. Testing the mediating effect of the perception of government regulations indicates a significant role in the relationship between the independent variables and entrepreneurial orientation. The findings of this research indicate that the entrepreneurial mindset of the young generation in Indonesia is moderately influenced by a well-organized environment. The findings underscore the importance of a supportive regulatory environment in fostering entrepreneurial mindset among the youth, prompting policymakers to focus on enhancing regulations to incentivize innovation and streamline procedures for young entrepreneurs. The originality of this research lies in its exploration of the interplay between cultural dimensions and government regulations on entrepreneurial orientation, which contributes to the understanding of how socio-cultural factors and regulatory perceptions influence entrepreneurial behavior in emerging economies.

Economic and carbon reduction potential assessment of vehicle-to-grid development in guangdong province

To qualify the risk of extreme weather events for power supply security during the long-term power system transformation process, this paper proposes a risk probability evaluation method based on probabilistic production simulation. Firstly, the internal relationship of extreme weather intensity and duration is depicted using the copula function, and the influences of extreme weather on power security are described using the guaranteed power output ability coefficient, which can provide the extreme scenario basis for probabilistic production simulation. Then, a probabilistic production simulation method is proposed, which includes a typical-year scenario and extreme weather events. Meanwhile, an index system is proposed to qualify the power security level, which applies the loss of load expectation (LOLE) and time of loss of load expectation (TOLE) under different scenarios and other indices to reveal the long-term power security trend. Finally, the long-term power supply risks for the Yunnan provincial power system are analyzed using the proposed method, validating that the proposed method is capable of characterizing the influences of extreme weather on power security. The security level of different long-term power transformation schemes is evaluated.

Predictive analysis of power degradation rate in solar PV systems emphasizing hot spots and visual effects-based failure modes

Cataract disease is strongly associated with progressively accumulating oxidative damage to the extremely long-lived crystallin proteins of the lens. Cysteine oxidation affects crystallin folding, interactions, and light-scattering aggregation especially strongly due to the formation of disulfide bridges. Minimizing crystallin aggregation is crucial for lifelong lens transparency, so one might expect the ubiquitous lens crystallin superfamilies (α and βγ) to contain little cysteine. Yet, the Cys content of γ-crystallins is well above the average for human proteins. We review literature relevant to this longstanding puzzle and take advantage of expanding genomic databases and improved machine learning tools for protein structure prediction to investigate it further. We observe remarkably low Cys conservation in the βγ-crystallin superfamily; however, in γ-crystallin, the spatial positioning of Cys residues is clearly fine-tuned by evolution. We propose that the requirements of long-term lens transparency and high lens optical power impose competing evolutionary pressures on lens βγ-crystallins, leading to distinct adaptations: high Cys content in γ-crystallins but low in βB-crystallins. Aquatic species need more powerful lenses than terrestrial ones, which explains the high methionine content of many fish γ- (and even β-) crystallins. Finally, we discuss synergies between sulfur-containing and aromatic residues in crystallins and suggest future experimental directions.

In contrast to other types of energy investments, governments often use long-term power purchase agreements (PPAs), subsidy policies, etc., to lock in returns on renewable energy investments. To some extent, these initiatives ensure that renewable energy investment has a fixed future cash flow. The publics demand for an increase in the proportion of renewable energy also forces the government to maintain an annual investment in renewable energy. However, it remains uncertain whether peoples willingness to invest would not be severely affected, and whether renewable energy investment volumes are uniquely robust in the face of financial uncertainty. In this paper, linear regression method is used to analyse the impact of short-term interest rate uncertainty on renewable/conventional energy investment, and covariance analysis is used to test whether these impacts are significantly different. The data indicate that short-term interest rate uncertainty has a significant negative impact. Investment in both types of energy shrinks rapidly in the face of high interest rate uncertainty. No obvious stability is found on renewable energy investment volume in the face of short-term interest rate uncertainty.

Background: Increasing the accuracy of the output power forecasting for wind power is helpful to the improvement of the reliability of power dispatching. Objective: This study aimed to improve the forecasting accuracy of mid-to-long term wind power. Methods: A mid-to-long term wind power forecasting based on ARIMA-BP combined model was proposed. The Empirical Mode Decomposition (EMD) was used to decompose the historical wind power series and obtain the Intrinsic Mode Function (IMF) and residual components, thereby obtaining more regular components. Then, the optimum feature set was obtained based on the minimum Redundancy Maximum Relevance (mRAR) to improve the prediction accuracy for feature extraction. After that, the high-frequency components were predicted using the Back Propagation (BP) neural network model, while the low-frequency components were predicted using the Autoregressive Integrated Moving Average model (ARIMA). Finally, the predicted components obtained were superimposed to deduce the final mid- and long-term wind power prediction results. Results: An analysis was conducted according to the actual data from a typical wind farm. After comparison, it was found that, after empirical mode decomposition and feature extraction analysis, the error of the intelligent combination algorithm based on the ARIMA-BP combined model was smaller than that using only the BP neural network or only the ARIMA. Conclusion: By means of actual data analysis, the effectiveness of the method proposed by the study for mid- and long-term wind power prediction was verified.