Search

The exponential proliferation of renewable energy has resulted in a significant mismatch between power supply and demand, especially during extreme events. This incongruity presents challenges in efficiently harnessing renewable energy and enhancing the resilience of the power grid. To address this issue, this paper proposes shared energy storage (SES) planning based on the adaptive alternating direction method of multipliers (AADMM). The objective is to fully leverage SES, enhance the local consumption level of renewable energy, ensure power grid resilience, and reduce operational costs. First, to ensure the effective utilization of SES while minimizing initial investment and construction costs, a planning model for SES is formulated. Secondly, to maximize the benefits for multiple prosumers within the renewable energy and SES station, a profit maximization model for multiple prosumers is established. Lastly, to guarantee the privacy security of SES and multi-prosumers while optimizing computational efficiency, a distributed computing model for SES based on AADMM is developed. The results of the example show that the proposed model can not only reduce the cost of 47.96 CNY, but also increase the power self-sufficiency rate by 21.86%. In addition, compared with the traditional distributed optimization, the number of iterations of AADMM is increased by 47.05%, and the computational efficiency is increased by 54.67%. In addition, market prices have a great impact on energy trading, and the impact of market pricing on the operation of the park is not considered in our current research. In this case, our future research aims to consider how to price reasonably between prosumers and between prosumers and SES, so as to realize the stable participation of each subject in the energy market.

In recent years, in order to achieve the goal of “carbon peaking and carbon neutralization”, many countries have focused on the development of clean energy, and the prediction of photovoltaic power generation has become a hot research topic. However, many traditional methods only use meteorological factors such as temperature and irradiance as the features of photovoltaic power generation, and they rarely consider the multi-features fusion methods for power prediction. This paper first preprocesses abnormal data points and missing values in the data from 18 power stations in Northwest China, and then carries out correlation analysis to screen out 8 meteorological features as the most relevant to power generation. Next, the historical generating power and 8 meteorological features are fused in different ways to construct three types of experimental datasets. Finally, traditional time series prediction methods, such as Recurrent Neural Network (RNN), Convolution Neural Network (CNN) combined with eXtreme Gradient Boosting (XGBoost), are applied to study the impact of different feature fusion methods on power prediction. The results show that the prediction accuracy of Long Short-Term Memory (LSTM), stacked Long Short-Term Memory (stacked LSTM), Bi-directional LSTM (Bi-LSTM), Temporal Convolutional Network (TCN), and XGBoost algorithms can be greatly improved by the method of integrating historical generation power and meteorological features. Therefore, the feature fusion based photovoltaic power prediction method proposed in this paper is of great significance to the development of the photovoltaic power generation industry.

Accurate photovoltaic (PV) power forecasting is of great significance for safe and stable operation for PV power plant and reasonable dispatching of power grids, and it is also a fundamental technology to ensure the high ratio of PV power generation access to the power grid, but its forecasting error cannot meet the actual requirement. Based on data analysis from a 10 kW distributed PV power plant and a 60 MW large-scale power plant, this study shows the PV power short-term forecasting error mainly comes from the numerical weather prediction (NWP) and forecasting process. To improve the forecasting performance, a short-term PV power forecasting method based on irradiance correction and error forecasting is proposed. First, the measured irradiance is used to correct the irradiance in the NWP. Then, the particle swarm optimization (PSO) algorithm is used to optimize the forecasting model. Finally, an error forecasting model is introduced to correct the forecasting result further. It is proved that the proposed method can effectively improve the short-term forecasting accuracy through verification with an actual power plant. Taking the 6 steps correction as an example, the NWP correction can reduce RMSE(1) by 3.0%, the model optimization can reduce RMSE(2) by 0.4%, and the error correction can reduce RMSE by 0.4%.

In the past decade, China’s new energy has experienced a prosperous development and has become an important main power supply in China. With the promotion of China’s power market–oriented reform, the market-oriented transaction mechanism will play an essential role for China to achieve a large-scale and efficient national allocation of new energy. This study systematically analyzes and designs the principle of China’s new energy cross-provincial marketization mechanism, including those for a compound system such as an interprovincial medium and long-term transaction and spot transaction. The proposed mechanism is validated by its implemental results on a prototype power system with new energy.

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern and early assessment would be vital. Here, the authors show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission.

Cancer is still one of the most life threatening disease and by far it is still difficult to prevent, prone to recurrence and metastasis and high in mortality. Lots of studies indicate that early cancer diagnosis can effectively increase the survival rate of patients. But early stage cancer is difficult to be detected because of its inconspicuous features. Hence, convenient and effective cancer detection methods are urgently needed. In this paper, we propose to utilize deep autoencoder to learn latent representation of high-dimensional mass spectrometry data. Meanwhile, as a contrast, traditional particle swarm optimization (PSO) optimization algorithm are also used to select optimized features from mass spectrometry data. The learned features are further evaluated on three cancer datasets. The experimental results demonstrate that the cancer detection accuracy by learned features is as high as 100%. As our main contribution, the deep autoencoder method used in this study is a feasible and powerful instrument for mass spectrometry feature learning and also cancer diagnosis.

Electricity load forecasting has seen increasing importance recently, especially with the effectiveness of deep learning methods growing. Improving the accuracy of electricity load forecasting is vital for public resources management departments. Traditional neural network methods such as long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) have been widely used in electricity load forecasting. However, LSTM and its variants are not sensitive to the dynamic change of inputs and miss the internal nonperiodic rules of series, due to their discrete observation interval. In this paper, a novel neural ordinary differential equation (NODE) method, which can be seen as a continuous version of residual network (ResNet), is applied to electricity load forecasting to learn dynamics of time series. We design three groups of models based on LSTM and BiLSTM and compare the accuracy between models using NODE and without NODE. The experimental results show that NODE can improve the prediction accuracy of LSTM and BiLSTM. It indicates that NODE is an effective approach to improving the accuracy of electricity load forecasting.

At present, battery energy storage systems (BESS) have become an important resource for improving the frequency control performance of power grids under the situation of high penetration rates of new energy. Aiming at the problem that the existing control strategy is not sufficient for allocating the frequency regulation power instructions, a hierarchical distributed coordinated control strategy for BESS to participate in the automatic generation control (AGC) of a regional power grid is proposed. At the upper layer, the state of charge (SOC) of BESS and the technical characteristics of different frequency regulation power sources are comprehensively considered to complete the coordinated distribution of frequency regulation commands between BESS and traditional generators; at the lower layer, for the purpose of optimizing the economic operation of the regional power grid, the distributed consistency algorithm is used to control the distributed BESS in order to realize the fine management of power output of BESS. The simulation results indicate that this control strategy can give full play to the technical characteristics of different frequency power sources and improve the frequency regulation of the power grid. The excessive power consumption of BESS is successfully avoided, and the continuous operation of BESS is realized.

Background Older adults have been reported to be a population with high-risk of death in the COVID-19 outbreak. Rapid detection of high-risk patients is crucial to reduce mortality in this population. The aim of this study was to evaluate the prognositc accuracy of the Modified Early Warning Score (MEWS) for in-hospital mortality in older adults with COVID-19. Methods A retrospective cohort study was conducted in Wuhan Hankou Hospital in China from 1 January 2020 to 29 February 2020. Receiver operating characteristic (ROC) analysis was used to evaluate the predictive value of MEWS, Acute Physiology and Chronic Health Evaluation II (APACHE II), Sequential Organ Function Assessment (SOFA), quick Sequential Organ Function Assessment (qSOFA), Pneumonia Severity Index (PSI), Combination of Confusion, Urea, Respiratory Rate, Blood Pressure, and Age ≥65 (CURB-65), and the Systemic Inflammatory Response Syndrome Criteria (SIRS) for in-hospital mortality. Logistic regression models were performed to detect the high-risk older adults with COVID-19. Results Among the 235 patients included in this study, 37 (15.74%) died and 131 (55.74%) were male, with an average age of 70.61 years (SD 8.02). ROC analysis suggested that the capacity of MEWS in predicting in-hospital mortality was as good as the APACHE II, SOFA, PSI and qSOFA (Difference in AUROC: MEWS vs. APACHE II, −0.025 (95% CI [−0.075 to 0.026]); MEWS vs. SOFA, −0.013 (95% CI [−0.049 to 0.024]); MEWS vs. PSI, −0.015 (95% CI [−0.065 to 0.035]); MEWS vs. qSOFA, 0.024 (95% CI [−0.029 to 0.076]), all P > 0.05), but was significantly higher than SIRS and CURB-65 (Difference in AUROC: MEWS vs. SIRS, 0.218 (95% CI [0.156–0.279]); MEWS vs. CURB-65, 0.064 (95% CI [0.002–0.125]), all P < 0.05). Logistic regression models implied that the male patients (≥75 years) had higher risk of death than the other older adults (estimated coefficients: 1.16, P = 0.044). Our analysis further suggests that the cut-off points of the MEWS score for the male patients (≥75 years) subpopulation and the other elderly patients should be 2.5 and 3.5, respectively. Conclusions MEWS is an efficient tool for rapid assessment of elderly COVID-19 patients. MEWS has promising performance in predicting in-hospital mortality and identifying the high-risk group in elderly patients with COVID-19.

Introduction With intense deficiency of medical resources during COVID-19 pandemic, risk stratification is of strategic importance. Blood glucose level is an important risk factor for the prognosis of infection and critically ill patients. We aimed to investigate the prognostic value of blood glucose level in patients with COVID-19.Research design and methods We collected clinical and survival information of 2041 consecutive hospitalized patients with COVID-19 from two medical centers in Wuhan. Patients without available blood glucose level were excluded. We performed multivariable Cox regression to calculate HRs of blood glucose-associated indexes for the risk of progression to critical cases/mortality among non-critical cases, as well as in-hospital mortality in critical cases. Sensitivity analysis were conducted in patient without diabetes.Results Elevation of admission blood glucose level was an independent risk factor for progression to critical cases/death among non-critical cases (HR=1.30, 95% CI 1.03 to 1.63, p=0.026). Elevation of initial blood glucose level of critical diagnosis was an independent risk factor for in-hospital mortality in critical cases (HR=1.84, 95% CI 1.14 to 2.98, p=0.013). Higher median glucose level during hospital stay or after critical diagnosis (≥6.1 mmol/L) was independently associated with increased risks of progression to critical cases/death among non-critical cases, as well as in-hospital mortality in critical cases. Above results were consistent in the sensitivity analysis in patients without diabetes.Conclusions Elevation of blood glucose level predicted worse outcomes in hospitalized patients with COVID-19. Our findings may provide a simple and practical way to risk stratify COVID-19 inpatients for hierarchical management, particularly where medical resources are in severe shortage during the pandemic.

Wind speed and wind power are two important indexes for wind farms. Accurate wind speed and power forecasting can help to improve wind farm management and increase the contribution of wind power to the grid. However, nonlinear and non-stationary wind speed and wind power can influence the forecasting performance of different models. To improve forecasting accuracy and overcome the influence of the original time series on the model, a forecasting system that can effectively forecast wind speed and wind power based on a data pre-processing strategy, a modified multi-objective optimization algorithm, a multiple single forecasting model, and a combined model is developed in this study. A data pre-processing strategy was implemented to determine the wind speed and wind power time series trends and to reduce interference from noise. Multiple artificial neural network forecasting models were used to forecast wind speed and wind power and construct a combined model. To obtain accurate and stable forecasting results, the multi-objective optimization algorithm was employed to optimize the weight of the combined model. As a case study, the developed forecasting system was used to forecast the wind speed and wind power over 10 min from four different sites. The point forecasting and interval forecasting results revealed that the developed forecasting system exceeds all other models with respect to forecasting precision and stability. Thus, the developed system is extremely useful for enhancing forecasting precision and is a reasonable and valid tool for use in intelligent grid programming.

Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes of clouds. Ground-based remote sensing with high temporal and spatial resolution may have potential for solar irradiation forecasting, especially under cloudy conditions. To this end, we established two ultra-short-term forecasting models of global horizonal irradiance (GHI) using Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN), respectively, based on the observation of a ground-based sky imager (TSI-880, Total Sky Imager) and a radiometer at a PV plant in Dunhuang, China. Sky images taken every 1 min (minute) were processed to determine the distribution of clouds with different optical depths (thick, thin) for generating a two-dimensional cloud map. To obtain the forecasted cloud map, the Particle Image Velocity (PIV) method was applied to the two consecutive images and the cloud map was advected to the future. Further, different types of cloud fraction combined with clear sky index derived from the GHI of clear sky conditions were used as the inputs of the two forecasting models. Limited validation on 4 partly cloudy days showed that the average relative root mean square error (rRMSE) of the 4 days ranged from 5% to 36% based on the TLR model and ranged from 12% to 32% based on the BPN model. The forecasting performance of the BPN model was better than the TLR model and the forecasting errors increased with the increase in lead time.

The effect of Sn foil layers on the mechanical properties and microstructure of friction stir lap welded 5052 aluminium alloy and AZ31B magnesium alloy was explored in this study. Models of numerous joints were set by different rotational (700, 900, 1100, 1300 and 1500 rpm) and welding speeds (50 mm min ^−1 ). Mg/Al dissimilar lap joints with and without Sn interlayer were produced by friction stir lap welding. The results suggest that Mg _2 Sn intermetallic compounds formed instead of Mg _17 Al _12 and Mg _2 Al _3 intermetallic compounds. In direct welding, the joint are only connected by metallurgical bonding between atoms (Mg/Al). In solder joints with Sn interlayer, the joint is connected by the combined effect of metallurgical bonding between atoms (Mg/Sn, Al/Sn) and interface mechanical coupling. For the joint with Sn interlayer, the maximum fracture load of the joint with the Sn interlayer reached 3.72 kN at a rotational speed of 900 rpm. As the rotational speed is increased from 1300 rpm to 1500 rpm, the Sn content on the the advancing side and the retreating side is more, resulting in more Mg _2 Sn crystal content, increased crack content. The joint performance gradually decreases. For the joint without a Sn interlayer, the microscopic morphology was a river-like pattern, which was characterized by brittle fracture. For a joint with a Sn interlayer, the microscopic morphology contained micro-dimples and a small quantity of inclusions, which were characterized by mixed fracture.