Search

In the context of strong electromagnetic interference environments, the measurement accuracy of the capacitance parameters of transmission lines under power frequency measurement methods is not high. In this paper, a capacitance parameter anti-interference measurement method for transmission lines based on harmonic components is proposed to overcome the impact of power frequency interference. When applying this method, it is first necessary to open-circuit the end of the line under test. Subsequently, apply voltage to the head end of the tested line through a step-up transformer. Due to the saturation of the transformer during no-load conditions, a large number of harmonics are generated, primarily third harmonic. The third harmonic components of voltage and current on the tested transmission line are extracted using the Fourier transform. The proposed method addresses the influence of line distribution effects by establishing a distributed parameter model for long-distance transmission lines. The relevant transmission matrix for the zero-sequence distributed parameters is obtained by combining Laplace transform and similarity transform to solve the transmission line equations. Using synchronous measurement data from the third harmonic components of voltage and current at both ends of the transmission line, combined with the transmission matrix, this method accurately measures the zero-sequence capacitance parameters. The PSCAD/EMTDC simulation results and field test outcomes have demonstrated the feasibility and accuracy of the proposed method for measuring line capacitance parameters under strong electromagnetic interference. [ABSTRACT FROM AUTHOR]

In order to achieve peak carbon and carbon neutrality targets, a high number of distributed power sources have been connected to distribution networks. How to realize the planning of a distribution network containing integrated energy under the condition of carbon capture and complete the exceedance test of the distribution network under the condition of accessing a large number of distributed generators has become an urgent problem. To solve the above problem while promoting sustainable development, this work proposes an active distribution network risk-planning model based on multisource data from carbon capture and the Power Internet of Things. The model calculates the semi-invariants of each order of the node state vectors and branch circuit current vectors and then utilizes Gram–Charlier-level expansion to obtain the exceeding probability density function and the probability distribution functions of the node voltages and line powers in the distribution network. Combined with multisource data, an active distribution network with an integrated energy system designed for carbon capture was modeled. According to the risk scenario of the distribution network, the nonconvex constraints in the model were simplified by second-order cone relaxation, and the optimal planning scheme of the distribution network was solved by combining the Gurobi solver with the risk index as the first-level objective and the economic benefit as the second-level objective. The simulation results of a coupled network consisting of a 39-node distribution network and an 11-node transportation network verified the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Objective: Dietary antioxidants are associated with risk of death in cancer patients, and they were used to evaluate the prognosis of cancer patients. Dietary antioxidant index (DAI) can be used to evaluate dietary antioxidant content comprehensively; this study aimed to investigate the effect of preoperative DAI on health-related quality of life in patients with esophageal cell squamous carcinoma (ESCC). Methods: Data on dietary intakes were collected using a validated food-frequency questionnaire (FFQ). DAI was calculated for all study participants based on FFQ data of each participant. The study involved conducting several follow-up activities with patients diagnosed with ESCC to evaluate their quality of life. The approach employed in the study was to conduct a telephone interview. The EORTC Quality of Life Questionnaire-Core Questionnaire (EORTC QLQ-C30, version 3.0) and the Esophageal Cancer Module (EORTC QLQ-OES18) were used to collect data on the quality of life of the patients; all patients completed the full follow-up. Results: This prospective study was performed on 376 participants who were recruited from Fujian Cancer Hospital and First Hospital of Fujian Medical University. They all were diagnosed with ESCC. The results indicated that the time to deterioration of global health status (p = 0.043), cognitive functioning (p = 0.031), dry mouth (p = 0.019), and speech problems (p = 0.031) significantly delay in the high DAI group. Univariate and multivariate Cox regression analysis showed that global health status (HR = 0.718, 95% CI: 0.532–0.969), cognitive functioning (HR = 0.641, 95% CI: 0.450–0.913), dry mouth (HR = 0.637, 95% CI: 0.445–0.911), and speech problems (HR = 0.651, 95% CI: 0.449–0.945) were improved in the high DAI group. Conclusions: Prognostic value of preoperative DAI was significant for patients with ESCC who undergo surgical intervention. Its level was positively correlated with the postoperative quality of life of patients, which can delay and improve the occurrence of postoperative physical function and symptom deterioration. [ABSTRACT FROM AUTHOR]

The anti-vaccination movement was an ongoing issue in Malaysia, a Muslim-majority country, even before the COVID-19 pandemic. It is unclear whether the introduction of new COVID-19 vaccines would similarly provoke anti-vaccine sentiments. This study analyzed COVID-19 anti-vaccine sentiments in the Malaysian community. Anti-vaccine comments from Facebook page posts were extracted. The qualitative software QSR-NVivo 10 was used to manage, code and analyze the data. The fast-track COVID-19 vaccine evoked the fear of unknown long-term effects, safety, effectiveness and the duration of protection. The halal status of the COVID-19 vaccines is important. Although it is permissible to use vaccines that are not certified halal under the state of darurah (emergency), there was doubt that the current state has reached the stage of darurah that warrants the use of vaccines. COVID-19 vaccine microchip conspiracy theories were raised. COVID-19 is viewed as only severe for vulnerable populations, and hence vaccination is not needed for the healthy. There were opinions that coronavirus treatments would be more beneficial than vaccination. The anti-COVID-19 vaccine sentiments uncovered in this study provide important insights for the formulation of public health messages to instill confidence in new COVID-19 vaccines. Despite the pandemic being nearly over and many people worldwide having received COVID-19 vaccines, the findings provide important insight into potential issues regarding the introduction of new vaccines in the event of future pandemics. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore the relationship between lipids with different structural features and lung cancer (LC) risk and identify prospective biomarkers of LC. Univariate and multivariate analysis methods were used to screen for differential lipids, and two machine learning methods were used to define combined lipid biomarkers. A lipid score (LS) based on lipid biomarkers was calculated, and a mediation analysis was performed. A total of 605 lipid species spanning 20 individual lipid classes were identified in the plasma lipidome. Higher carbon atoms with dihydroceramide (DCER), phosphatidylethanolamine (PE), and phosphoinositols (PI) presented a significant negative correlation with LC. Point estimates revealed the inverse associated with LC for the n-3 PUFA score. Ten lipids were identified as markers with an area under the curve (AUC) value of 0.947 (95%, CI: 0.879–0.989). In this study, we summarized the potential relationship between lipid molecules with different structural features and LC risk, identified a panel of LC biomarkers, and demonstrated that the n-3 PUFA of the acyl chain of lipids was a protective factor for LC. [ABSTRACT FROM AUTHOR]

The large-scale integration of wind turbines (WTs) in renewable power generation induces power oscillations, leading to frequency aberration due to power unbalance. Hence, in this paper, a secondary frequency control strategy called load frequency control (LFC) for power systems with wind turbine participation is proposed. Specifically, a backpropagation (BP)-trained neural network-based PI control approach is adopted to optimize the conventional PI controller to achieve better adaptiveness. The proposed controller was developed to realize the timely adjustment of PI parameters during unforeseen changes in system operation, to ensure the mutual coordination among wind turbine control circuits. In the meantime, the improved particle swarm optimization (IPSO) algorithm is utilized to adjust the initial neuron weights of the neural network, which can effectively improve the convergence of optimization. The simulation results demonstrate that the proposed IPSO-BP-PI controller performed evidently better than the conventional PI controller in the case of random load disturbance, with a significant reduction to near 10 s in regulation time and a final stable error of less than 10 − 3 for load frequency. Additionally, compared with the conventional PI controller counterpart, the frequency adjustment rate of the IPSO-BP-PI controller is significantly improved. Furthermore, it achieves higher control accuracy and robustness, demonstrating better integration of wind energy into traditional power systems. [ABSTRACT FROM AUTHOR]

We conducted a case-control study (532 cases and 532 control) in Chinese adults to investigate the independent and interactive effects of dietary nutrients (pro- or anti-inflammation) on Esophageal Squamous Cell Carcinoma (ESCC) risk. Dietary data were collected using a food questionnaire survey that included 171 items. Two algorithms, the Least Absolute Shrinkage and Selector Operation (LASSO) and Bayesian Kernel Machine Regression (BKMR) were employed to select indicators and evaluate the interactive effect of nutrients' mixture on ESCC risk. Thirteen nutrients were selected, including three pro-inflammatory nutrients (protein, fat and carbohydrate) and ten anti-inflammatory nutrients (fiber, Vitamin A, riboflavin, niacin, Vitamin C, Fe, Se, MUFA, n-3 PUFA and n-6 PUFA). Single-exposure effects of fat, carbohydrate and fiber significantly contributed to ESCC risk. The pro-inflammatory nutrients' submodel discovered that the combined effect was statistically associated with increased ESCC risk. In addition, a higher fat level was significantly associated with ESCC risk. On the contrary, for fiber and riboflavin, the anti-inflammatory nutrients' submodel delineated a significant negative effect on the risk of ESCC. Our result implies that dietary nutrients and their inflammatory traits significantly impacted ESCC occurrence. Additional studies are warranted to verify our findings. [ABSTRACT FROM AUTHOR]

Necroptosis is a newly developed cell death pathway that differs from necrosis and apoptosis; however, the potential mechanism of necroptosis-related genes in EAC and whether they are associated with the prognosis of EAC patients remain unclear. We obtained 159 NRGs from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and performed differential expression analysis of the NRGs in 9 normal samples and 78 EAC tumor samples derived from The Cancer Genome Atlas (TCGA). Finally, we screened 38 differentially expressed NRGs (DE-NRGs). The results of the GO and KEGG analyses indicated that the DE-NRGs were mainly enriched in the functions and pathways associated with necroptosis. Protein interaction network (PPI) analysis revealed that TNF, CASP1, and IL-1B were the core genes of the network. A risk score model based on four DE-NRGs was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) regression, and the results showed that the higher the risk score, the worse the survival. The model achieved more efficient diagnosis compared with the clinicopathological variables, with an area under the receiver operating characteristic (ROC) curve of 0.885. The prognostic value of this model was further validated using Gene Expression Omnibus (GEO) datasets. Gene set enrichment analyses (GSEA) demonstrated that several metabolism-related pathways were activated in the high-risk population. Single-sample GSEA (ssGSEA) provided further confirmation that this prognostic model was remarkably associated with the immune status of EAC patients. Finally, the nomogram map exhibited a certain prognostic prediction efficiency, with a C-index of 0.792 and good consistency. Thus, the prognostic model based on four NRGs could better predict the prognosis of EAC and help to elucidate the mechanism of necroptosis-related genes in EAC, which can provide guidance for the target prediction and clinical treatment of EAC patients. [ABSTRACT FROM AUTHOR]

We aimed to investigate whether the age-adjusted Charlson comorbidity index (ACCI) can predict the postoperative overall survival (OS) and cancer-specific survival (CSS) of esophageal squamous cell carcinoma (ESCC) patients. Between 1 July 2015 and 31 July 2021, a retrospective cohort study was conducted among patients with primary ESCC who underwent radical esophagectomy. A total of 352 patients were included, with median age of 63.00 (IQR (interquartile range) 56.00–68.00). The patients were divided into low (n = 300) and high (n = 52) ACCI groups based on the optimal cut-off value of 5 points. Chronic pulmonary disease (38.4%) was the most common comorbidity. The results of the multivariate Cox regression showed that the ACCI (HR = 1.63, 95%CI: 1.04–2.56), tumor size (HR = 1.67, 95%CI: 1.05–2.66), pTNM (II vs. I, HR = 4.74, 95%CI: 1.82–12.32; III vs. I, HR = 6.08, 95%CI: 2.37–15.60), and postoperative chemotherapy (HR = 0.60, 95%CI: 0.40–0.91) were significantly associated with the OS. Furthermore, the ACCI, tumor size, pTNM, and postoperative chemotherapy were also significantly associated with the CSS. Interactions were identified between the ACCI and postoperative chemotherapy, pTNM stage, and tumor size in relation to the OS and CSS. In conclusion, the ACCI may be an independent prognostic factor affecting the long-term prognosis of patients after radical esophagectomy. [ABSTRACT FROM AUTHOR]

With the high penetration of wind turbines, many issues need to be addressed in relation to load frequency control (LFC) to ensure the stable operation of power grids. The particle swarm optimization-based model predictive control (PSO-MPC) approach is presented to address this issue in the context of LFC with the participation of wind turbines. The classical MPC model was modified to incorporate the particle swarm optimization algorithm for the power generation model to regulate the system frequency. In addition to addressing the unpredictability of wind turbine generation, the presented PSO-MPC strategy not only addresses the randomness of wind turbine generation, but also reduces the computation burden of traditional MPC. The simulation results validate the effectiveness and feasibility of the PSO-MPC approach as compared with other state-of-the-art strategies. [ABSTRACT FROM AUTHOR]

The plant-derived macrocyclic resin glycoside ipomoeassin F (Ipom-F) binds to Sec61α and significantly disrupts multiple aspects of Sec61-mediated protein biogenesis at the endoplasmic reticulum, ultimately leading to cell death. However, extensive assessment of Ipom-F as a molecular tool and a therapeutic lead is hampered by its limited production scale, largely caused by intramolecular assembly of the macrocyclic ring. Here, using in vitro and/or in cellula biological assays to explore the first series of ring-opened analogues for the ipomoeassins, and indeed all resin glycosides, we provide clear evidence that macrocyclic integrity is not required for the cytotoxic inhibition of Sec61-dependent protein translocation by Ipom-F. Furthermore, our modeling suggests that open-chain analogues of Ipom-F can interact with multiple sites on the Sec61α subunit, most likely located at a previously identified binding site for mycolactone and/or the so-called lateral gate. Subsequent in silico-aided design led to the discovery of the stereochemically simplified analogue 3 as a potent, alternative lead compound that could be synthesized much more efficiently than Ipom-F and will accelerate future ipomoeassin research in chemical biology and drug discovery. Our work may also inspire further exploration of ring-opened analogues of other resin glycosides. [ABSTRACT FROM AUTHOR]

Featured Application: A state-of-the-art fog-cloud hierarchical energy management structure embedded with artificial intelligence can be applied in the next-generation distribution network with prosumers and large-scale Internet of Things devices. This work makes a contribution to the construction of a real-time optimal energy management and decision-making system of the distribution network. With the increasing penetration of Internet of Things devices and distributed energy resources in the next-generation distribution network, the efficient energy management for system operation are facing new challenges. One reason is that the large-scale resources cannot be all connected to the supervisory control and data acquisition system, which have limited storage and computation capabilities. In order to adapt to the new energy management requirements of next-generation distribution networks, a state-of-the-art energy management method called cloud-fog hierarchical architecture is proposed in this work. Based on this architecture, we established a utility and revenue model for various stakeholders, including normal customers, prosumers, and distribution system operators. Furthermore, by embedding an artificial intelligence module in the proposed architecture, energy management could be implemented automatically. In this work, neural network are used at fog computing layers to achieve regression prediction of energy usage behavior and power source output. Moreover, based on the maximizing utility objective function, the amount of energy consumption of customers and prosumers in the distribution network was optimized with a genetic algorithm at cloud layer. The proposed methods were tested with a set of normal customers and prosumers in a general distribution network, and the results, including the captured usage patterns of the customers and revenues of various stakeholders, verify the effectiveness of the proposed method. This work provides an effective reference for the development of real-time energy management systems for the next-generation distribution network. [ABSTRACT FROM AUTHOR]

Purpose: This study analyzed the insights and sentiments of COVID-19 anti-vaccine comments from Instagram feeds and Facebook postings. The sentiments related to the acceptance and effectiveness of the vaccines that were on the verge of being made available to the public. Patients and methods: The qualitative software QSR-NVivo 10 was used to manage, code, and analyse the data. Results: The analyses uncovered several major issues concerning COVID-19 vaccine hesitancy. The production of the COVID-19 vaccine at an unprecedented speed evoked the fear of skipping steps that would compromise vaccine safety. The unknown long-term effects and duration of protection erode confidence in taking the vaccines. There were also persistent concerns with regard to vaccine compositions that could be harmful or contain aborted foetal cells. The rate of COVID-19 death was viewed as low. Many interpreted the 95% effectiveness of the COVID-19 vaccine as insufficient. Preference for immunity gains from having an infection was viewed as more effective. Peer-reviewed publication-based data were favoured as a source of trust in vaccination decision-making. Conclusions: The anti-COVID-19 vaccine sentiments found in this study provide important insights for the formulation of public health messages to instill confidence in the vaccines. [ABSTRACT FROM AUTHOR]

This study investigates the interface bond strength and anchorage performance of steel bars within prefabricated concrete. Twenty-two specimens were designed and manufactured to study the interface bond behavior of deformed and plain steel bars under a larger cover thickness. Diameter of steel bars, strength grade of concrete, and anchorage length were considered influential factors. The finite element method (ABAQUS) was used for the validation of experimental results. The interface bond's failure mechanism and the anchorage length in the prefabricated concrete under different concrete strength levels were explored and compared to national and international codes. A suitable value of the basic anchoring length for the prefabricated structure was recommended. The results show that the interface bond strength of prefabricated bridge members is directly proportional to the strength grade of the concrete, inversely proportional to the reinforcement diameter, and less related to anchorage length. The effect of the cover thickness of the surrounding concrete is negligible. Conversely, the bearing capacity of prefabricated bridge members depends on the strength of the concrete, the diameter of the steel bar, and the anchorage length. Furthermore, it is concluded that the mechanical bond strength accounts for 88% of the bond strength within prefabricated concrete. [ABSTRACT FROM AUTHOR]

Data-driven methods using synchrophasor measurements have a broad application prospect in Transient Stability Assessment (TSA). Most previous studies only focused on predicting whether the power system is stable or not after disturbance, which lacked a quantitative analysis of the risk of transient stability. Therefore, this paper proposes a two-stage power system TSA method based on snapshot ensemble long short-term memory (LSTM) network. This method can efficiently build an ensemble model through a single training process, and employ the disturbed trajectory measurements as the inputs, which can realize rapid end-to-end TSA. In the first stage, dynamic hierarchical assessment is carried out through the classifier, so as to screen out credible samples step by step. In the second stage, the regressor is used to predict the transient stability margin of the credible stable samples and the undetermined samples, and combined with the built risk function to realize the risk quantification of transient angle stability. Furthermore, by modifying the loss function of the model, it effectively overcomes sample imbalance and overlapping. The simulation results show that the proposed method can not only accurately predict binary information representing transient stability status of samples, but also reasonably reflect the transient safety risk level of power systems, providing reliable reference for the subsequent control. [ABSTRACT FROM AUTHOR]

With the tight coupling of multi-energy systems, accurate multiple-load forecasting will be the primary premise for the optimal operation of integrated energy systems. Therefore, this paper proposes a Copula correlation analysis combined with deep bidirectional long and short-term memory neural network forecasting model. First, Copula correlation analysis is used to conduct correlation analysis on multiple loads and various influencing factors. The influencing factors that have a great correlation with multiple loads were screened out as the input feature set of the model to eliminate the influence of interfering factors. Then, a deep bidirectional long and short-term memory neural network was constructed. Combined with the input feature set screened by the Copula correlation analysis method, the useful information contained in the historical data was more comprehensively learned from the forward and backward directions for training and forecasting. Through the actual calculation example analysis and comparison with other models, the forecasting accuracy of the method presented in this paper was improved to a certain extent. [ABSTRACT FROM AUTHOR]

There is increasing evidence that nonalcoholic steatohepatitis (NASH) is a risk factor for hepatocellular carcinoma (HCC) in the absence of cirrhosis, a phenomenon termed noncirrhotic HCC. Early diagnosis of HCC is critical to a favorable prognosis. We tested the hypothesis that hydroxyproline content of liver biopsy samples is diagnostic for HCC in murine models of NASH induced by diet or by diet and chemicals. The training set comprised mice fed a standard diet or a fast-food diet with or without administration of thioacetamide. At harvest, livers from the modified diet cohort exhibited NASH with a subset of NASH livers exhibiting HCC. Hydroxyproline content was measured in liver biopsy samples with tissue in the NASH+HCC cohort sampled from the remote, nontumor parenchyma. Plotting the receiver operating characteristics (ROC) with hydroxyproline as the continuous variable against the absence or presence of HCC yielded an area under ROC of 0.87, a threshold of >0.18 μg hydroxyproline/mg liver and sensitivity of 91% with a specificity of 83.3%. The use of liver hydroxyproline content as a diagnostic for HCC in a test set comprising healthy, NASH and NASH+HCC livers proved 87% accurate. [ABSTRACT FROM AUTHOR]

Collagenofibrotic glomerulopathy, a collagen type III kidney disease, is associated with proteinuria and accumulation ofcollagen type III in the glomerulus specifically the mesangium and/or capillary walls. The puromcyin aminonucleoside (PAN) nephropathy model was evaluated to examine the relation between COL3A1 mRNA and proteinuria. In Wistar rats administered PAN, a robust increase in proteinuria was accompanied by glomerular hypertrophy and expansion of both the Bowman's capsule and Bowman's space. An ~4-fold increase in renal COL3A1 mRNA was observed in the PAN cohort with urine protein exhibiting a direct (r = 0.8) and significant correlation with kidney COL3A1 mRNA level. Both Picrosirius red polarized microscopy and immunohistochemical analysis showed localization of collagen type III to the glomerular mesangium. Gene ontology-driven transcriptomic analysis reveals a robust COL3A1 network in the glomerular compartment. [ABSTRACT FROM AUTHOR]

In order to improve the dynamic stability of multi-area microgrid (MG) clusters in the autonomous mode, this study proposes a novel fuzzy-based dynamic inertia control strategy for supercapacitors in multi-area autonomous MG clusters. By virtue of the integral manifold theory, the interactive influence of inertia on dynamic stability for multi-area MG clusters is explored in detail. The energy function of multi-area MG clusters is constructed to further analyze the inertia constant. Based on the analysis of the mechanism, a control strategy for the fuzzy-based dynamic inertia control of supercapacitors for multi-area MG clusters is further proposed. For each sub-microgrid (sub-MG), the gain of the fuzzy-based dynamic inertia control is self-tuned dynamically, with system events being triggered, so as to flexibly and robustly enhance the dynamic performance of the multi-area MG clusters in the autonomous mode. To verify the effectiveness of the proposed control scheme, a three-area photovoltaic (PV)-based MG cluster is designed and simulated on the MATLAB/Simulink platform. Moreover, a comparison between the dynamic fuzzy-based inertial control method and an additional droop control method is finally presented to validate the advantages of the fuzzy-based dynamic inertial control approach. [ABSTRACT FROM AUTHOR]

This paper presents a passive wireless measurement system based on wireless power transfer (WPT) technology. It does not require separate information and power transmission circuits. The data receiver only needs to send a short signal to the data collector through WPT, and then the information of the measured environment can be obtained by analyzing the feedback signal from the data collector. Three concepts are included in this system, namely (1) the constant oscillation period of oscillation attenuation waveforms; (2) the characteristics of inductive coupling WPT; and (3) the relationship between sensitive resistances and environmental parameters. It is very suitable for measuring the parameters in an internal or closed space. The data collector is small in size and simple in structure, and no power is needed. It has stable performance after implantation and can be used permanently. Results obtained from simulations and experiments are included. They verify the measurement process and measurement results meet the requirements of passive wireless measurement, and the measurement error is less than 1.5%. [ABSTRACT FROM AUTHOR]

In order to coordinate the economic desire of microgrid (MG) owners and the stability operation requirement of the distribution system operator (DSO), a multi-market participation framework is proposed to stimulate the energy transaction potential of MGs through distributed and centralized ways. Firstly, an MG equipped with storage can contribute to the stability improvement at special nodes of the distribution grid where the uncertain factors (such as intermittent renewable sources and electric vehicles) exist. The DSO is thus interested in encouraging specified MGs to provide voltage stability services by creating a distribution grid service market (DGSM), where the dynamic production-price auction is used to capture the competition of the distributed MGs. Moreover, an aggregator, serving as a broker and controller for MGs, is considered to participate in the day-ahead wholesale market. A Stackelberg game is modeled accordingly to solve the price and quantity package allocation between aggregator and MGs. Finally, the modified IEEE-33 bus distribution test system is used to demonstrate the applicability and effectiveness of the proposed multi-market mechanism. The results under this framework improve both MGs and utility. [ABSTRACT FROM AUTHOR]

Wind power intervals with different confidence levels have an impact on both the economic cost and risk of dispatch plans for power systems with wind power integration. The higher the confidence level, the greater the bandwidth of corresponding intervals. Thus, more reserves are needed, resulting in higher economic cost but less risk. In order to balance the economic cost and risk, a unit commitment model based on the optimal wind power confidence level is proposed. There are definite integral terms in the objective function of the model, and both the integrand function and integral upper/lower bound contain decision variables, which makes it difficult to solve this problem. The objective function is linearized and solved by discretizing the wind power probability density function and using auxiliary variables. On the basis, a rolling dispatching model considering the dynamic regulation costs among multiple rolling plans is established. In addition to balancing economic cost and risk, it can help to avoid repeated regulations among different rolling plans. Simulations are carried on a 10-units system and a 118-bus system to verify the effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]