Safety and effectiveness of neuromuscular electrical stimulation in cardiac surgery:A systematic review

BACKGROUND Lack of mobilization and prolonged stay in the intensive care unit(ICU)are major factors resulting in the development of ICU-acquired muscle weakness(ICUAW).ICUAW is a type of skeletal muscle dysfunction and a common complication of patients after cardiac surgery,and may be a risk factor for prolonged duration of mechanical ventilation,associated with a higher risk of readmission and higher mortality.Early mobilization in the ICU after cardiac surgery has been found to be low with a significant trend to increase over ICU stay and is also associated with a reduced duration of mechanical ventilation and ICU length of stay.Neuromuscular electrical stimulation(NMES)is an alternative modality of exercise in patients with muscle weakness.A major advantage of NMES is that it can be applied even in sedated patients in the ICU,a fact that might enhance early mobilization in these patients.AIM To evaluate safety,feasibility and effectiveness of NMES on functional capacity and muscle strength in patients before and after cardiac surgery.METHODS We performed a search on Pubmed,Physiotherapy Evidence Database(PEDro),Embase and CINAHL databases,selecting papers published between December 2012 and April 2023 and identified published randomized controlled trials(RCTs)that included implementation of NMES in patients before after cardiac surgery.RCTs were assessed for methodological rigor and risk of bias via the PEDro.The primary outcomes were safety and functional capacity and the secondary outcomes were muscle strength and function.RESULTS Ten studies were included in our systematic review,resulting in 703 participants.Almost half of them performed NMES and the other half were included in the control group,treated with usual care.Nine studies investigated patients after cardiac surgery and 1 study before cardiac surgery.Functional capacity was assessed in 8 studies via 6MWT or other indices,and improved only in 1 study before and in 1 after cardiac surgery.Nine studies explored the effects of NMES on muscle strength and function and,most of them,found increase of muscle strength and improvement in muscle function after NMES.NMES was safe in all studies without any significant complication.CONCLUSION NMES is safe,feasible and has beneficial effects on muscle strength and function in patients after cardiac surgery,but has no significant effect on functional capacity.

Electric Vehicle Charging Load Optimization Strategy Based on Dynamic Time-of-Use Tariff

Electric vehicle(EV)is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future.However,a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff.Therefore,this paper proposes a dynamic time-of-use tariff mechanism,which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean(FCM)clustering algorithm,and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period.Based on the proposed tariff mechanism,an EV charging optimization model with the lowest cost to the users and the lowest variance of the grid-side load as the objective function is established.Then,a weight selection principle with an equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem.Finally,the EV charging load optimization model under three tariff strategies is set up and solved with the mathematical solver GROUBI.The results show that the EV charging load optimization strategy based on the dynamic time-of-use tariff can better balance the benefits between charging stations and users under different numbers and proportions of EVs connected to the grid,and can effectively reduce the grid load variance and improve the grid load curve.

Tail Quantile Estimation of Heteroskedastic Intraday Increases in Peak Electricity Demand

Modelling of intraday increases in peak electricity demand using an autoregressive moving average-exponential generalized autoregressive conditional heteroskedastic-generalized single Pareto (ARMA-EGARCH-GSP) approach is discussed in this paper. The developed model is then used for extreme tail quantile estimation using daily peak electricity demand data from South Africa for the period, years 2000 to 2011. The advantage of this modelling approach lies in its ability to capture conditional heteroskedasticity in the data through the EGARCH framework, while at the same time estimating the extreme tail quantiles through the GSP modelling framework. Empirical results show that the ARMA-EGARCH-GSP model produces more accurate estimates of extreme tails than a pure ARMA-EGARCH model.

Management of Peak Loads in an Emerging Electricity Market

An electricity market is a trading platform provided by the actors in the electricity sector to sell and buy electricity while maintaining the stability of the transmission network and minimizing energy losses.The management of electrical energy for rational use consists of all the operations that the consumers can carry out in order to minimize their electricity bill,while the producers optimize their benefits and the transmission infrastructure.The reduction of active and reactive power consumption and the smoothing of daily and yearly load profiles are the main objectives in this work.Many developed countries already have properly functioning electricity markets,but developing countries are still in their infancy of deregulated electricity markets.The major tools used in smoothing the load profiles include decentralized generation,energy storage and demand response.A load power smoothing control strategy is proposed to smooth the load power fluctuations of the distribution network.The required power change is determined by evaluating the power fluctuation rate of the load,and then the required power change is allocated to some generators or to some stored reserves.Otherwise,the consumers are made to curtail their power consumption.The ideas proposed in this work provide important opportunities for energy policy makers and regulators.These ideas would only be feasible if there exists real-time communication among the actors in the electricity market.The results indicate that as much as 1100 Megawatt-hours of energy can be stored for smoothing the load profile,when applied to the Southern Interconnected Grid of the Cameroon power system;and that Time of Use(TOU)pricing could be used instead of rotating blackouts in case of energy shortage.

National Electricity Generation,Electricity Consumption and Peak Load by Grid (March 2006)

Statistics on electricity generation and time of frequency over-limit;Statistics on electricity consumption and peak load.

National Electricity Generation,Electricity Consumption and Peak Load by Grid (April 2006)

Statistics on electricity generation and time of frequency over-limit;Statistics on electricity consumption and peak load.

Application of a Bayesian Network Complex System Model Examining the Importance of Customer-Industry Engagement to Peak Electricity Demand Reduction

This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand Model (REPDM). The REPDM is based on a multi-disciplinary perspective designed to solve the complex problem of residential peak energy demand. The model provides a way to conceptualise and understand the factors that shift and reduce consumer demand in peak times. To gain insight into the importance of customer-industry engagement in affecting residential peak demand, this research investigates intervention impacts and major influences through testing five scenarios using different levels of customer-industry engagement activities. Scenario testing of the model outlines the dependencies between the customer-industry engagement interventions and the probabilities that are estimated to govern the dependencies that influence peak demand. The output from the model shows that there can be a strong interaction between the level of CIE activities and interventions. The influence of CIE activity can increase public and householder support for peak reduction and the model shows how the economic, technical and social interventions can achieve greater peak demand reductions when well-designed with appropriate levels of CIE activities.

Estimate of China's energy carbon emissions peak and analysis on electric power carbon emissions

China＇s energy carbon emissions are projected to peak in 2030 with approximately 110% of its 2020 level under the following conditions： 1） China＇s gross primary energy consumption is 5 Gtce in 2020 and 6 Gtce in 2030; 2） coal＇s share of the energy consumption is 61% in 2020 and 55% in 2030; 3） non-fossil energy＇s share increases from 15% in 2020 to 20% in 2030; 4） through 2030, China＇s GDP grows at an average annual rate of 6%; 5） the annual energy consumption elasticity coefficient is 0.30 in average; and 6） the annual growth rate of energy consumption steadily reduces to within 1%. China＇s electricity generating capacity would be 1,990 GW, with 8,600 TW h of power generation output in 2020. Of that output 66% would be from coal, 5% from gas, and 29% from non-fossil energy. By 2030, electricity generating capacity would reach 3,170 GW with 11,900 TW h of power generation output. Of that output, 56% would be from coal, 6% from gas, and 37% from non-fossil energy. From 2020 to 2030, CO2 emissions from electric power would relatively fall by 0.2 Gt due to lower coal consumption, and rela- tively fall by nearly 0.3 Gt with the installation of more coal-fired cogeneration units. During 2020--2030, the portion of carbon emissions from electric power in China＇s energy consumption is projected to increase by 3.4 percentage points. Although the carbon emissions from electric power would keep increasing to 118% of the 2020 level in 2030, the electric power industry would continue to play a decisive role in achieving the goal of increase in non-fossil energy use. This study proposes countermeasures and recommendations to control carbon emissions peak, including energy system optimization, green-coal-fired electricity generation, and demand side management.

Aggregator-based demand response mechanism for electric vehicles participating in peak regulation in valley time of receiving-end power grid

With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this problem while considering the potential of the large-scale charge load of electric vehicles(EVs), an aggregator-based demand response(DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study. In this aggregator-based DR mechanism, the profits for the power grid’s operation and the participation willingness of the EV owners are considered. Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling, a rolling unit commitment model with the DR is established to maximize the social welfare. In addition, to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator(ISO) and the aggregators, the clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.

“双碳”目标下我国电力系统灵活性资源发展策略研究

大力发展新能源是实现“双碳”目标的重要途径,但新能源的随机性、波动性和间歇性特点给电力系统供需平衡及稳定性带来严峻挑战,亟需发展多元灵活性资源来保障系统的安全稳定运行。本文综述了我国碳中和目标下电力需求及电源结构发展路线的相关研究情况,深入分析了不同风光发电量比例下电力系统对灵活性调节的不同需求,并从高峰能力(充足性)、爬坡灵活性、稳定性、惯性4个方面阐述了未来所需要的“源网荷储”各类灵活性资源特性;结合国际经验,提出了保障安全、低碳发展、经济最优的灵活性资源发展原则,并在分析我国灵活性资源发展存在问题的基础上,提出了与我国电力行业减排目标和中长期电力结构变化趋势相一致的灵活性资源发展路线;最后从电源、电网、负荷、储能、市场机制5个方面提出了发展灵活性资源的重点举措。

建设以新能源为主体的新型电力系统自动化课程改革

培养新型电力系统自动化建设人才对实现新能源大规模并网至关重要。原有的电力系统自动化课程大都围绕火电和水电展开教学,涉及新能源的新型电力系统关键技术和知识点都有所欠缺。本次教学改革采用“讲授课+翻转课堂+研讨课”的形式,致力于培养面向“双碳”目标的新型电力系统自动化专业人才。

考虑多类型主体的日前能量-调峰联合市场优化

新型电力系统中,风电渗透率持续攀升,调峰需求随之增加,有必要通过日前能量-调峰联合出清调动市场主体调峰意愿,支撑大规模风电消纳。在对燃煤机组、风电场与储能等多类型市场主体调峰能力与报价规则进行分析的基础上,建立综合考虑燃煤机组深度调峰、启停调峰,风电场主动“弃风”调峰与储能充电调峰的日前能量-调峰联合市场优化模型。模型以能量与调峰辅助服务总采购成本最小为优化目标,同时考虑各市场主体技术约束、投标约束与电网潮流约束,是大规模混合整数规划问题,可采用CPLEX求解器求解。基于改进IEEE 14节点系统的仿真实验验证该文所提日前能量-调峰联合市场优化模型与求解方法的有效性。此外,结合仿真实验对各市场主体参与调峰辅助服务的收益进行详细分析。

适用于电动汽车的单开关动作双向固态断路器

设计了一种适用于电动汽车的单开关动作直流固态断路器,该断路器在切除短路故障时只需要通过控制一个开关的通断实现短路故障的隔离与短路能量的耗散。这里对该固态断路器的不同工作状态进行分析,建立相应的数学模型,并以此作为参数设计的理论依据,设计完成后在电动汽车动力电池输出400 V/20 A工作模式下进行短路切除实验,实验结果表明:电动汽车没有发生短路故障时,单开关动作直流固态断路器不影响电路的正常运行;当电动汽车发生短路故障后,该断路器能在5 ms内自动关断,且可以平滑的吸收故障电流使固态开关所承受的峰值电压小于550 V,对电动汽车具有可靠的保护作用。

考虑运营成本和用户体验的电动汽车充电站布局优化模型

针对电动汽车充电站布局优化问题,综合考虑充电站建设运营成本和交通流量变化情况下的用户体验,据此建立充电站布局优化模型。先分析充电站城市土地转让成本、基建及充电设备成本、运营成本各项构成要素,并建立相应的数学模型,再结合交通拥堵指数建立用户体验数学模型,最终以满足充电站建设运营成本最低和用户体验最优为预期目标,建立充电站布局优化模型。引入Tent混沌映射对初始解空间进行均布化,采用AVOA优化算法对该模型进行问题求解。仿真结果表明:与对比文献模型相比,新的充电站选址方案与随机产生的需求点距离更近、更平均;在典型测试场景下,新选址方案可有效降低配电网络的网损率、提升充电站效能,并能为用户提供更优的用户体验,充分说明该模型能够较好解决充电站优化布局问题。

碳中和目标下电力需求预测体系构建及华东区域电力需求发展趋势研究

碳达峰、碳中和将加速全社会各行各业,尤其是能源系统的转型,而电力行业作为能源枢纽,能源领域在双碳转型过程中将对电力需求带来不可忽视的影响。该文立足于分析双碳目标对于能源电力的影响,挖掘双碳和电力需求的关联性,提出碳中和目标下分行业发展趋势分析思路,构建了一套适应于双碳目标下的电力电量预测方法体系;以华东区域作为典型案例,研究了华东区域碳达峰至碳中和过程中的电力需求发展趋势。研究结果表明,所构建的分析思路和方案体系科学可行,预测结论合理。

考虑源网荷效益的峰谷电价与峰谷时段双层优化模型

用户响应行为测算需求解峰谷时段与峰谷电价之间的双线性项,目前仅考虑负荷曲线的数值大小对峰谷时段进行单独划分,分时电价实行效果受限。提出峰谷时段与峰谷电价的双层优化模型,考虑风电不确定性下的规划成本与发电成本,反映分时电价的实际经济效用;考虑用户响应的不确定性,保证分时电价实行后的源网荷效益在一定范围内变化;峰谷时段划分与峰谷电价设计均以系统成本最低为目标优化得到,充分考虑峰谷时段划分、峰谷电价设计和用户响应行为之间的交互机理。仿真结果表明,与单独划分峰谷时段的模型相比,所提模型能够制定更优、科学性更强的峰谷时段和峰谷电价,进一步降低系统总成本。

“双碳”背景下我国低碳电力发展研究

在“十四五”规划中提出的“碳中和”以及“碳达峰”目标对电力行业各方面的发展产生了深层次的影响,电力行业向低碳方向转型已成为主流趋势。通过阐述低碳电力体系中的低碳电厂、低碳电网、低碳能源消费以及相关评价技术,说明了该体系在节能减排、环境保护方面有着高效、清洁、可循环的低碳属性。同时,分析了国际低碳形式变化对我国的影响以及“双碳”政策具体落实过程中的相关问题,为“双碳”战略的执行以及电力行业的低碳转型提供了新的科研思路。

碳达峰约束下电动汽车在线充电调度算法

随着电动汽车数量的增加,电动汽车充电对社区电网总负荷和碳排放量产生了很大的影响,导致社区电网不太稳定,降低了电能质量。文中基于碳达峰的约束条件,研究了未提前知晓电动汽车到达时间、出发时间和充电需求等情况下的电动汽车充电碳排放问题。首先,建立了电动汽车充电碳排放问题,并在未来信息未知的情况下进行了研究。针对电动汽车充电行为的不确定性,提出了一种改进型演员-评论家的智能充电碳排放算法。该算法采用的是连续碳排放动作的电动汽车充电碳排放策略,而不是离散近似碳排放动作。仿真结果表明,相比OA和AEM两个基准算法,所提算法能够降低电动汽车预期成本约24.03%和21.49%。

参与电网削峰调节的电动重卡换电站调度策略

换电站因其快速、便捷的特点获得了迅猛的推广和发展。在电动重卡领域,换电站建设已初具规模并保持高速发展。如何对电动重卡换电站进行充电调度以实现电网友好,是一个重点研究方向。首先,针对电动重卡换电需求在时间上分布规律较弱的特点,使用统计分析法分析换电站历史运行数据,得到一天内各时段的换电需求预测值;其次,提出基于换电需求预测的日内调度策略和基于实际换电需求的日内实时修正策略,以确定电池充电数量和允许充电时间;然后,建立以电网削峰为目标的时间-功率模型,结合电网背景负荷曲线,使用差分算法对电池的充电功率和实际充电时间进行求解;最后,通过算例证明了调度策略和时间-功率模型的有效性。

深度调峰背景下火电机组热电解耦技术路径对比分析

新能源发电在出力和发电负荷上具有波动大、不稳定的特点,为解决新能源机组上网后带来的上述问题,利用多种热电解耦方式对火电机组进行改造,并对不同技术路径进行对比分析,探究火电机组解耦潜力与调峰能力。采用EBSILON Professional软件进行模拟仿真,对不同热电解耦方案下调峰负荷空间进行评估和对比分析。计算结果表明,低压缸零出力热电解耦改造方案可以使机组的调峰能力得到最大的改善。在原最大供热负荷304.60 MW下,最小发电量下降172.21 MW,最低电负荷率下降32.70%。火电机组经过热电解耦改造可显著提高热电机组与新能源机组的协调调峰能力,提高发电单元整体发电的稳定性。