Aiming at the power allocation problem of multiple energy storage power stations distributed at different locations in the regional power grid participating in frequency modulation services, a frequency modulation pow...
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The scale of distributed energy resources is increasing,but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor *** address this issue,the concept of cleanness value of d...
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The scale of distributed energy resources is increasing,but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor *** address this issue,the concept of cleanness value of distributed energy storage(DES)is proposed,and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and *** on this,an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the powersystem ***,an optimal low-carbon dispatching for a virtual power plant(VPP)with aggregated DES is constructed,where-in energy value and cleanness value are both *** achieve the goal,a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network(DN)and DES behavior,but as a cost,it brings multiple nonlinear ***,a solution method based on the convex envelope(CE)linear re-construction method is proposed for the multivariate nonlinear programming problem,thereby improving solution efficiency and ***,the simulation verification based on the IEEE 33-bus DN is *** simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to ***,resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.
Generating molecular structures with certain desired properties is a fundamental problem for material science research. Deep learning models have demonstrated big potential to identify novel materials by exploring lar...
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Recently,high-frequency oscillation of themodularmultilevel converter(MMC)based high-voltage direct current(HVDC)projects has attracted great *** order to analyze the small-signal stability,this paper uses the harmoni...
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Recently,high-frequency oscillation of themodularmultilevel converter(MMC)based high-voltage direct current(HVDC)projects has attracted great *** order to analyze the small-signal stability,this paper uses the harmonic state-space(HSS)method to establish a detailed frequency domain impedance model of the AC-side of the HVDC transmission system,which considers the internal dynamic *** addition,the suggested model is also used to assess the system’s high-frequency oscillationmechanism,and the effects of the MMC current inner loop control,feedforward voltage links,and control delay on the high-frequency impedance characteristics and the effect of higher harmonic ***,three oscillation suppression schemes are analyzed for the oscillation problems occurring in actual engineering,and a simplified impedance model considering only the highfrequency impedance characteristics is established to compare the suppression effect with the detailed impedance model to prove its reliability.
High availability of wind power data is the basis for wind power research, but there are a large number of abnormal data in actual collected data, which seriously affects analysis of wind power law and reduces predict...
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High availability of wind power data is the basis for wind power research, but there are a large number of abnormal data in actual collected data, which seriously affects analysis of wind power law and reduces prediction accuracy. Measured power data of wind farm are analyzed, influence of wind speed fluctuation characteristics on wind power is discussed, and abnormal points are identified for data of different wind types. The Cluster-Based Local Outlier Factor (CLOF) algorithm based on K-means is used to identify outlier abnormal points, and conditional constraints based on physical background are used to identify accumulation abnormal points. Reconstructed data segment is divided according to fluctuation of wind speed. The Bidirectional Gate Recurrent Unit (BiGRU) model with wind speed as input reconstructs fluctuation segment data, and bi-directional weighted random forest model reconstructs stationary segment data. Based on analysis of measured data of a wind farm, results show the method can effectively identify various abnormal data, and complete high-quality reconstruction of data, thereby improving accuracy of wind power prediction.
Long-term operation or external forces may cause damage to the outer sheath of the cable, allowing moisture to enter the cable body and affecting the insulation and dielectric performance of the main XLPE insulation o...
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To address the frequent failure issues caused by defects in the buffer layer of high-voltage cross-linked polyethylene (XLPE) cables, research on buffer layer has become a hot topic at present. This paper first introd...
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The recently proposed ambient signal-based load modeling approach offers an important and effective idea to study the time-varying and distributed characteristics of power ***,it also brings new *** the load model par...
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The recently proposed ambient signal-based load modeling approach offers an important and effective idea to study the time-varying and distributed characteristics of power ***,it also brings new *** the load model parameters of power loads can be obtained in real-time for each load bus,the numerous identified parameters make parameter application *** order to obtain the parameters suitable for off-line applications,load model parameter selection(LMPS)is first introduced in this ***,the convolution neural network(CNN)is adopted to achieve the selection purpose from the perspective of short-term voltage *** begin with,the field phasor measurement unit(PMU)data from China Southern power Grid are obtained for load model parameter identification,and the identification results of different substations during different times indicate the necessity of ***,the simulation case of Guangdong power Grid shows the process of LMPS,and the results from the CNNbased LMPS confirm its effectiveness.
With the increasing penetration of renewable energy in powersystem,renewable energy power ramp events(REPREs),dominated by wind power and photovoltaic power,pose significant threats to the secure and stable operation...
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With the increasing penetration of renewable energy in powersystem,renewable energy power ramp events(REPREs),dominated by wind power and photovoltaic power,pose significant threats to the secure and stable operation of power *** paper presents an early warning method for REPREs based on long short-term memory(LSTM)network and fuzzy ***,the warning levels of REPREs are defined by assessing the control costs of various powercontrol ***,the next 4-h power support capability of external grid is estimated by a tie line power predictionmodel,which is constructed based on the ***,considering the risk attitudes of dispatchers,fuzzy rules are employed to address the boundary value attribution of the early warning interval,improving the rationality of power ramp event early *** results demonstrate that the proposed method can generate reasonable early warning levels for REPREs,guiding decision-making for control strategy.
Convergence speed, accuracy, generalization and decision transparency are very important indices for transient stability preventive control generation methods based on AI. This paper proposes a transient stability pre...
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