Analysis of the flow pattern and periodicity of gas–liquid–liquid three-phase flow in a countercurrent mixer-settler

In contrast to the concurrent mixer-settler,the interaction between the mixing and settling chambers have to be taken into account in the simulation of the countercurrent mixer-settler,and no work has been reported for this equipment.In this work,a three-phase flow model based on the Eulerian multiphase model,coupled with a sliding mesh model is proposed for a countercurrent mixer-settler.Based on this,the dispersed phase distribution,flow pattern,and pressure distribution are investigated,which can help to fill the gap in the operation mechanism.In addition,the velocity vector distribution at the phase port shows an intriguing phenomenon that two types of vectors with opposite directions are distributed on the left and right sides of the same plane,which indicates that the material exchange in the mixing and settling chambers is simultaneous.Analysis of this variation at this location by a fast Fourier transform(FFT)method reveals that it is mainly influenced by the mixing chamber and is consistent with the main period of the outlet flow fluctuations.Therefore,by monitoring the fluctuation of the outlet flow and then analyzing it by the FFT method,the state of the whole tank can be determined,which makes it promising for the design of control systems for countercurrent mixer-settlers.

扩展的节点电价算法研究

首先回顾了最优潮流与安全约束经济调度问题,重点讨论了节点边际电价算法原理。着重分析了在非凸性条件下,基于安全约束机组组合与经济调度算法产生的节点边际电价机制存在的缺陷,阐述了如何利用扩展的节点边际电价机制,减少额外支付成本,促进实现激励相容的定价机制。然后,利用对偶原理,对于典型定价机制,即:Convex Hull Price、节点边际电价、整数松弛定价,产生的额外支付成本给出了严格的理论证明,并从几何角度说明其物理意义。最后,实现了混合整数规划与拉格朗日松弛2种机组组合优化算法。算例结果表明文中提出的节点边际电价与整数松弛定价的额外支付成本与Convex Hull Price最小额外支付成本之间关系原理的正确性。激励相容定价机制的研究将促进分布式可再生能源并网,从而推动电力系统向清洁、高效和智能的目标转型与升级。

Feature selection for probabilistic load forecasting via sparse penalized quantile regression

Probabilistic load forecasting(PLF)is able to present the uncertainty information of the future loads.It is the basis of stochastic power system planning and operation.Recent works on PLF mainly focus on how to develop and combine forecasting models,while the feature selection issue has not been thoroughly investigated for PLF.This paper fills the gap by proposing a feature selection method for PLF via sparse L1-norm penalized quantile regression.It can be viewed as an extension from point forecasting-based feature selection to probabilistic forecasting-based feature selection.Since both the number of training samples and the number of features to be selected are very large,the feature selection process is casted as a large-scale convex optimization problem.The alternating direction method of multipliers is applied to solve the problem in an efficient manner.We conduct case studies on the open datasets of ten areas.Numerical results show that the proposed feature selection method can improve the performance of the probabilistic forecasting and outperforms traditional least absolute shrinkage and selection operator method.

Mode splitting transmission effect of surface wave excitation through a metal hole array

The resonant frequencies of the excited surface waves on a metal hole array with respect to the incident angle were studied in the terahertz region.The experimental and theoretical results demonstrate that the resonant peak of surface wave excitation splits into two when transmitted through a metal hole array off-normally.The high-order mode with resonant frequency above the cutoff frequency fc(plasma frequency effect)has a shorter attenuation length than that of the low-order mode whose resonant frequency is below fc.The reason is that the high-order mode is a coupled mode consisting of surface wave and hole modes,while the low-order mode is just an excited surface wave(which can be considered as the spoof surface plasmons).Our investigation may open a door to distinguish the spoof surface plasmons and the coupled modes of surface waves and hole modes.

Month ahead average daily electricity price profile forecasting based on a hybrid nonlinear regression and SVM model:an ERCOT case study

With the deregulation of the electric power industry, electricity price forecasting plays an increasingly important role in electricity markets, especially for retailors and investment decision making. Month ahead average daily electricity price profile forecasting is proposed for the first time in this paper. A hybrid nonlinear regression and support vector machine(SVM) model is proposed. Offpeak hours, peak hours in peak months and peak hours in off-peak months are distinguished and different methods are designed to improve the forecast accuracy. A nonlinear regression model with deviation compensation is proposed to forecast the prices of off-peak hours and peak hours in off-peak months. SVM is adopted to forecast the prices of peak hours in peak months. Case studies based on data from ERCOT validate the effectiveness of the proposed hybrid method.

Prediction and assessment of demand response potential with coupon incentives in highly renewable power systems

Demand Response(DR)provides both operational and financial benefits to a variety of stakeholders in the power system.For example,in the deregulated market operated by the Electric Reliability Council of Texas(ERCOT),load serving entities(LSEs)usually purchase electricity from the wholesale market(either in day-ahead or real-time market)and sign fixed retail price contracts with their end-consumers.Therefore,incentivizing end-consumers’load shift from peak to off-peak hours could benefit the LSE in terms of reducing its purchase of electricity under high prices from the real-time market.As the first-of-its-kind implementation of Coupon Incentive-based Demand Response(CIDR),the EnergyCoupon project provides end-consumers with dynamic time-of-use DR event announcements,individualized load reduction targets with EnergyCoupons as the incentive for meeting these targets,as well as periodic lotteries using these coupons as lottery tickets for winning dollar-value gifts.A number of methodologies are developed for this special type of DR program including price/baseline prediction,individualized target setting and a lottery mechanism.This paper summarizes the methodologies,design,critical findings,as well as the potential generalization of such an experiment.Comparison of the EnergyCoupon with a conventional Time-of-Use(TOU)price-based DR program is also conducted.Experimental results in the year 2017 show that by combining dynamic coupon offers with periodic lotteries,the effective cost for demand response providers in EnergyCoupon can be substantially reduced,while achieving a similar level of demand reduction as conventional DR programs.

Advances in cochlear implantation for hereditary deafness caused by common mutations in deafness genes

The pathogenic factors of deafness are complex;more than 50%of cases are caused by genetic factors.Between 75%and 80%of cases of hereditary hearing impairment are autosomal recessive,15%to 25%are autosomal dominant,and 1%to 2%are mitochondrial or X-linked.Cochlea implantation is the main method for treating severe and extremely severe bilateral sensorineural deafness and it is widely used in clinical treatment.As clinical cases of cochlea implantation accumulate,differences in the efficacy of implantation in individuals are emerging and attracting attention.In addition to residual hearing level,implantation age,and other factors,gene mutation is an important factor influencing postoperative rehabilitation in patients.With continuous progress in genetic testing technology for deafness,genetic diagnosis has become an important tool in preoperative evaluation and postoperative effect prediction in patients undergoing cochlear implantation.This article reviews the current status and future development of cochlear implantation in the treatment of hereditary deafness resulting from mutations in common deafness-causing genes.

Implications of COVID-19 for the Electricity Industry: A Comprehensive Review

As COVID-19 sweeps through the whole world,human activities have been changed significantly.Under such circumstances,the electricity sector is deeply affected and faced with great challenges.This paper provides a comprehensive review of the impacts that the pandemic has caused on the electricity sector.Electricity demand has dropped sharply as governments around the world executed lockdown restrictions,while the load composition and daily load profile have also changed.The share of renewable generation has increased against the decline of the total electricity generation.Changed power balance situation and increased uncertainty of demand have posed higher pressure on system operators,along with voltage violation issue and challenges for system maintenance and management.The electricity market is also substantially influenced,while longterm investment in clean energy is expected to be stable.The externality such as emission reduction is also discussed.

Synthetic PMU Data Creation Based on Generative Adversarial Network Under Time-varying Load Conditions

In this study,a machine learning based method is proposed for creating synthetic eventful phasor measurement unit(PMU)data under time-varying load conditions.The proposed method leverages generative adversarial networks to create quasi-steady states for the power system under slowly-varying load conditions and incorporates a framework of neural ordinary differential equations(ODEs)to capture the transient behaviors of the system during voltage oscillation events.A numerical example of a large power grid suggests that this method can create realistic synthetic eventful PMU voltage measurements based on the associated real PMU data without any knowledge of the underlying nonlinear dynamic equations.The results demonstrate that the synthetic voltage measurements have the key characteristics of real system behavior on distinct time scales.

A Comprehensive Power Flow Approach for Multi-terminal VSC-HVDC System Considering Cross-regional Primary Frequency Responses

For the planning,operation and control of multiterminal voltage source converter(VSC)based high-voltage direct current(HVDC)(VSC-MTDC)systems,an accurate power flow formulation is a key starting point.Conventional power flow formulations assume the constant frequencies for all asynchronous AC systems.Therefore,a new feature about the complex coupling relations between AC frequencies,DC voltages and the exchanged power via VSC stations cannot be characterized if VSC-MTDC systems are required to provide cross-regional frequency responses.To address this issue,this paper proposes a comprehensive frequency-dependent power flow formulation.The proposed approach takes the frequencies of asynchronous AC systems as explicit variables,and investigates the novel bus models of the interlinking buses of VSC stations.The proposed approach accommodates different operation modes and frequency droop strategies of VSC stations,and considers the power losses of VSC stations.The effectiveness and generality of the developed approach are validated by a 6-terminal VSC-HVDC test system.The test system presents the characteristics of the coexistence of numerous VSC operation modes,the absence of slack buses in both AC and DC subsystems,and diversified grid configurations such as point-to-point integration of renewable energy sources and one AC system integrated with multiple VSC stations.

Impact of power system network topology errors on real-time locational marginal price

This paper examines the impact of power transmission network topology change on locational marginal price(LMP) in real-time power markets. We consider the case where the false status of circuit breakers(CBs) that bypass topology error processing can generate an incorrect power system network topology, subsequently distorting the results of the state estimation and economic dispatch.The main goal of this paper is to assess the economic impact of this misconfigured network topology on realtime LMP in an entire power system with network congestion. To this end, we start with our prior result, a simple and analytical congestion price equation, which can be applied to any single line congestion scenario. This equation can be extended to better understand the degree to which the LMP at any bus changes due to any line status error. Furthermore, it enables a rigorous analysis of the relationship between the change in LMP at any bus with respect to any line error and various physical/economical grid conditions such as the bidding prices for marginal generators and the locations of the congested/erroneous lines. Numerical examples on the impact analysis of this topology error are illustrated in IEEE 14-bus and 118-bus systems.

A Reliability Model for Integrated Energy System Considering Multi-energy Correlation

An integrated energy system (IES) is a regional energy system incorporating distributed multi-energy systems to serve various energy demands such as electricity, heating, cooling, and gas. The reliability analysis plays a key role in guaranteeing the safety and adequacy of an IES. This paper aims to build a capacity reliability model of an IES. The multi-energy correlation in the IES can generate the dependent capacity outage states, which is the distinguished reliability feature of an IES from a generation system. To address this issue, this paper presents a novel analytical method to model the dependent multi-energy capacity outage states and their joint outage probabilities of an IES for its reliability assessment. To model the dependent multi-energy capacity outage states, a new multi-dimensional matrix method is presented in the capacity outage probability table (COPT) model of the generation system. Furthermore, a customized multi-dimensional discrete convolution algorithm is proposed to compute the reliability model, and the adequacy indices are calculated in an accurate and efficient way. Case studies demonstrate the correctness and efficiency of the proposed method. The capacity value of multi-energy conversion facilities is also quantified by the proposed method.

A novel and effective hyperthermia method for Schistosomiasis japonica prevention and treatment

The emerging resistance to schistosoma has been paid much attention and is in urgent need for a novel strategy to control the prevalent parasitic zoonosis. In this study, the efficiency of hyperthermia therapy was investigated by the skin hyperthermia device. The survival rate of cercariae decreased from 68.15 %(37 °C, 5 min) to 0(49 °C, 10 min) with the thermal dosages increased, which proved the preventing effect of hyperthermia therapy(P \ 0.05). Therapeutic effects were assessed in Schistosoma japonicum-infected BALB/c mice. When the cercarial contact region of the skin was treated at 45–49 °C for5 min within 8 h of infection, worm reduction rate(WRR)reached 74 %–83 %(P \ 0.01). The sensitivity of adultschistosoma to heat was also investigated using microwave intraperitoneal hyperthermia(thermal dosages 42–43 °C,20 min). The WRR, hepatic shift rates and egg reduction rates were 23.7 %, 40 % and 30 %, respectively, comparing with 80.2 %, 59.6 % and 53.9 % of praziquantel(PZQ)-treated group. Encouraging results have been obtained that hyperthermia can effectively kill schistosomula, especially with the appearance of cercarial dermatitis, while PZQ lacks efficacy against the cercariae. Thus,hyperthermia therapy would show significant benefit in preventing and treatment of schistosoma, especially in the early stage.