Lagrangian Relaxation-Based Unit Commitment Considering Fast Response Reserve Constraints

Unit commitment (UC) is to determine the optimal unit status and generation level during each time interval of the scheduled period. The purpose of UC is to minimize the total generation cost while satisfying system demand, reserve requirements, and unit constraints. Among the UC constraints, an adequate provision of reserve is important to ensure the security of power system and the fast-response reserve is essential to bring system frequency back to acceptable level following the loss of an online unit within a few seconds. In this paper, the authors present and solve a UC problem including the frequency-based reserve constraints to determine the optimal FRR requirements and unit MW schedules. The UC problem is solved by using Lagrangian Relaxation-based approach and compared with the actual system schedules. It is observed that favorable reserve and unit MW schedules are obtained by the proposed method while the system security is maintained.

基于足间距信息辅助的行人三维惯性定位算法

针对行人惯性导航系统误差随时间累积致使定位精度严重下降的问题,提出了一种基于足间距信息辅助的行人三维惯性定位算法。该算法在零速修正算法的基础上,利用足部安装的超声波测距模块实时测量行人双足相对距离,构建了基于超声测距的足间距约束模型,通过随机森林算法实现行人运动模式识别,并针对上下楼梯场景,利用台阶高度和足间距信息进行高度解算,最终实现行人三维惯性定位。在实际路线上开展了三维定位实验,数据显示,所提算法平面闭环误差为总路程的0.64%,与零速修正算法相比下降了55.56%,高度误差为0.06 m,与零速修正和气压计联合算法相比下降了64.70%,能够实现导航误差在总路程的0.50%以内的三维定位。实验结果表明,所提算法具有良好的工程应用价值。

基于边界法的大型机组组合模型的简化方法

随着电网规模的持续扩大,市场环境下考虑网络安全约束的机组组合(security-constrained unit commitment,SCUC)模型中的变量和约束显著增加,模型的求解性能变差。当模型规模过大时,会出现现有的商用求解器无法求解的状况,造成大规模模型求解困难的问题。为实现大规模机组组合模型的快速求解,从减少模型约束数量的角度出发,提出了一种基于边界法的线性约束简化方法。通过边界法剔除模型中冗余的线性约束,可以有效降低模型规模,实现模型的快速求解。基于IEEE-39、WECC 179和IEEE-118算例,在市场环境下进行日前SCUC测试。通过对比简化前后的求解时间,表明该方法能够显著提高模型的求解速率。

电力市场环境下可靠性机组组合机制

完善的双边现货市场是实现电力市场优化资源配置功能的关键,可靠性机组组合是双边现货市场环境下满足日内发电容量充裕度等可靠性要求的重要手段。探讨集中式市场环境下可靠性机组组合的组织方式、出清模型与结算机制。总结国内外各典型电力市场中可靠性机组组合相关机制设计,得到可靠性机组组合机制设计的一般思路。通过算例分析对比各可靠性机组组合出清模型下的市场绩效。基于数值仿真与理论分析结果为中国可靠性机组组合机制设计提供建议。

回波主动抑制的次级水声发射单元设计与优化

水下航行器的传统涂覆层主要依赖于材料更新及结构优化,难以应对低频主动声波探测。基于超磁致伸缩材料设计一款轻质、薄型、耐高压的低频宽带水声发射单元,着重分析核心部件的设计优化过程,并由PSV-400激光测振仪验证有限元仿真结果。对辐射面板固定方式的模态进行分析,确定主动发射单元的最优边界约束条件。与原始构型的发射单元性能进行对比发现:在水声发射保持良好指向性的前提下,基于有限元分析2000 Hz以下的最高共振频率降低超过10%;基于声振耦合分析主动发射单元在低频范围内辐射声源级最大为147.48 dB,提高4.65%;发射单元声源级高于100 dB的频率带宽超过1500 Hz,且声功率级更高。该发射单元可为后期阵列式应用于大尺度声学覆盖层提供技术支持,具有一定的学术研究价值与广阔的工程应用前景。

计及碳捕集的含新能源电网低碳调度策略

在碳达峰、碳中和背景下,碳捕集机组可有效降低电力系统碳排放。但是在含新能源的电网中,碳捕集机组频繁参与系统调峰会降低系统的经济性。为此,文中首先在分析灵活运行碳捕集机组运行原理和碳交易工作机制的基础上,提出引入抽水蓄能机组辅助碳捕集机组参与系统调峰,以促进风电消纳,使碳捕集机组更专注捕碳工作并降低系统碳排放量;然后针对风电并网后的不确定性,引入模糊理论,将系统功率约束中风电和负荷用模糊参数表示,使约束转变为基于可信性的模糊机会约束,运用清晰等价类将模糊机会约束清晰化;接着以系统净收益最高为目标函数,综合考虑机组上网收益、抽蓄收益、碳交易收益、运行成本以及系统安全性约束等因素,构建碳捕集-抽蓄联合运行模型;最后通过CPLEX求解模型。仿真结果表明,引入抽蓄机组后系统净收益提高7.62%,碳排放降低7.01%。引入抽水蓄能机组能够兼顾系统的经济性和环保性。

含聚合CSP和深度调峰火电机组的电力系统分布鲁棒机会约束优化调度方法

随着“双碳”目标不断推进,可再生能源的装机容量和发电占比不断增加。然而,以风电、光伏为代表的可再生能源所固有的不确定性和波动性,使得以火电机组深度调峰为主的传统运行方式的经济性难以得到保障。针对上述问题,提出一种含聚合光热发电(Concentrating Solar Power,CSP)和深度调峰火电机组的电力系统分布鲁棒机会约束优化调度方法。首先,分析火电机组的基本调峰和深度调峰能力,构建考虑火电机组进行基本调峰或深度调峰成本的深度调峰模型。其次,分析光热电站启动时的热量传递过程,构建考虑启动热量约束的CSP模型。在此基础上,采用基于数据驱动的分布鲁棒机会约束描述可再生能源出力的不确定性,构建以火电机组发电成本、购售电成本和储能使用成本之和最小为优化目标的调度模型。最后,以改进的IEEE 30节点系统为例验证了所提方法具有较好的经济性和鲁棒性。

A Quick Method for Judging the Feasibility of Security-Constrained Unit Commitment Problems within Lagrangian Relaxation Framework

Generally, the procedure for Solving Security constrained unit commitment (SCUC) problems within Lagrangian Relaxation framework is partitioned into two stages: one is to obtain feasible SCUC states;the other is to solve the economic dispatch of generation power among all the generating units. The core of the two stages is how to determine the feasibility of SCUC states. The existence of ramp rate constraints and security constraints increases the difficulty of obtaining an analytical necessary and sufficient condition for determining the quasi-feasibility of SCUC states at each scheduling time. However, a numerical necessary and sufficient numerical condition is proposed and proven rigorously based on Benders Decomposition Theorem. Testing numerical example shows the effectiveness and efficiency of the condition.

Constraint-induced movement therapy enhances angiogenesis and neurogenesis after cerebral ischemia/reperfusion

Constraint-induced movement therapy after cerebral ischemia stimulates axonal growth by decreasing expression levels of Nogo-A,RhoA,and Rho-associated kinase(ROCK)in the ischemic boundary zone.However,it remains unclear if there are any associations between the Nogo-A/RhoA/ROCK pathway and angiogenesis in adult rat brains in pathological processes such as ischemic stroke.In addition,it has not yet been reported whether constraint-induced movement therapy can promote angiogenesis in stroke in adult rats by overcoming Nogo-A/RhoA/ROCK signaling.Here,a stroke model was established by middle cerebral artery occlusion and reperfusion.Seven days after stroke,the following treatments were initiated and continued for 3 weeks:forced limb use in constraint-induced movement therapy rats(constraint-induced movement therapy group),intraperitoneal infusion of fasudil(a ROCK inhibitor)in fasudil rats(fasudil group),or lateral ventricular injection of NEP1-40(a specific antagonist of the Nogo-66 receptor)in NEP1-40 rats(NEP1-40 group).Immunohistochemistry and western blot assay results showed that,at 2 weeks after middle cerebral artery occlusion,expression levels of RhoA and ROCK were lower in the ischemic boundary zone in rats treated with NEP1-40 compared with rats treated with ischemia/reperfusion or constraint-induced movement therapy alone.However,at 4 weeks after middle cerebral artery occlusion,expression levels of RhoA and ROCK in the ischemic boundary zone were markedly decreased in the NEP1-40 and constraint-induced movement therapy groups,but there was no difference between these two groups.Compared with the ischemia/reperfusion group,modified neurological severity scores and foot fault scores were lower and time taken to locate the platform was shorter in the constraint-induced movement therapy and fasudil groups at 4 weeks after middle cerebral artery occlusion,especially in the constraint-induced movement therapy group.Immunofluorescent staining demonstrated that fasudil promoted an immune response of nerve-regeneration-related markers(BrdU in combination with CD31(platelet endothelial cell adhesion molecule),Nestin,doublecortin,NeuN,and glial fibrillary acidic protein)in the subventricular zone and ischemic boundary zone ipsilateral to the infarct.After 3 weeks of constraint-induced movement therapy,the number of regenerated nerve cells was noticeably increased,and was accompanied by an increased immune response of tight junctions(claudin-5),a pericyte marker(a-smooth muscle actin),and vascular endothelial growth factor receptor 2.Taken together,the results demonstrate that,compared with fasudil,constraint-induced movement therapy led to stronger angiogenesis and nerve regeneration ability and better nerve functional recovery at 4 weeks after cerebral ischemia/reperfusion.In addition,constraint-induced movement therapy has the same degree of inhibition of RhoA and ROCK as NEP1-40.Therefore,constraint-induced movement therapy promotes angiogenesis and neurogenesis after cerebral ischemia/reperfusion injury,at least in part by overcoming the Nogo-A/RhoA/ROCK signaling pathway.All protocols were approved by the Institutional Animal Care and Use Committee of China Medical University,China on December 9,2015(approval No.2015 PS326 K).

Modeling and Solution of Economic Dispatch Problem for GTCC Units

Economic dispatch problem lies at the kernel among different issues in GTCC units’ operation, which is about minimizing the fuel consumption for a period of operation so as to accomplish optimal load dispatch among units. This paper has analyzed the load dispatch model of gas turbine combined-cycle (GTCC) units and utilizes a quantum genetic algorithm to optimize the solution of the model. The performance of gas turbine combined-cycle units varies with many factors and this directly leads to variation of model parameters. To solve the dispatch problem, variable constraints are adopted to correct the parameters influenced by ambient conditions. In the simulation, comparison of dispatch models for GTCC units considering and not considering the influence of ambient conditions indicates that it is necessary to adopt variable constraints for the dispatch model of GTCC units. To optimize the solution of the model, a Quantum Genetic Algorithm is used considering its advantages in searching performance. QGA combines the quantum theory with evolutionary theory of genetic algorithm. It is a new kind of intelligence algorithm which has been successfully employed in optimization problems. Utilizing quantum code, quantum gate and so on, QGA shows flexibility, high convergent rate, and global optimal capacity and so on. Simulations were performed by building up models and optimizing the solutions of the models by QGA. QGA shows better effect than equal micro incremental method used in the previous literature. The operational economy is proved by the results obtained by QGA. It can be concluded that QGA is quite effective in optimizing economic dispatch problem of GTCC units.

基于自适应步长约束的行人导航系统误差修正算法

基于惯性传感器的行人导航系统定位精度随时间累积下降,根据捷联惯导理论和人体运动学特征,提出基于自适应步长约束的行人导航误差修正算法。所提算法首先利用零速检测划分行人运动区间,其次根据加速度信息利用自适应步长估计模型计算各区间内步长,最后通过零速修正与步长约束模型修正导航误差。实验将WT901BC姿态仪固定于行人脚跟,并围绕闭环路径行走进行算法验证。实验结果表明,相比于零速修正,经过自适应步长约束算法修正后,行进240 m后起点、终点间距离误差平均值由2.50 m下降至0.18 m,导航闭环误差平均值由1.04%D下降至0.07%D,有效提高行人导航系统的定位精度。

Models for Ordering Multiple Products Subject to Multiple Constraints, Quantity and Freight Discounts

One of the most important responsibilities of a supply chain manager is to decide “how much” (or “many”) of inventory items to order and how to transport them. This paper presents four mixed-integer linear programming models to help supply chain managers make these decisions for multiple products subject to multiple constraints when suppliers offer quantity discounts and shippers offer freight discounts. Each model deals with one of the possible combinations of all-units, incremental quantity discounts, all-weight and incremental freight discounts. The models are based on a piecewise linear approximation of the number of orders function. They allow any number of linear constraints and determine if independent or common (fixed) cycle ordering has a lower total cost. Results of computational experiments on an example problem are also presented.

基于伞约束的安全约束机组组合迭代求解方法

为了提高电力市场出清结果的可靠性,在传统安全约束机组组合(security constrained unit commitment, SCUC)问题的基础上根据N-1原则考虑了故障态安全约束;为了不影响出清效率,基于伞约束原理提出了迭代式安全约束筛选方法。以机组组合(unit commitment, UC)模型为迭代起点,计算基态和故障态下的线路潮流,添加越限的所有基态安全约束,对于故障态潮流越限的线路,每条线路只保留越限最严重的故障态,并且给每轮添加的故障态约束数量设置上限,迭代求解直至无越限约束。分别对IEEE118系统和Polish2383系统进行仿真,与直接求解完整模型相比,求解速度分别提升了72.6%和88.4%。仿真结果表明,根据伞约束原理进行约束筛选后再迭代求解,能够显著提高SCUC模型的求解效率,尤其是对于考虑故障态安全约束的大规模SCUC问题,提速效果尤为明显。

计及储能荷电状态约束的输配协同机组组合优化

为解决传统输配割裂机组组合运行方式导致的电网堵塞问题,提出一种考虑储能荷电状态约束的输配协同机组组合优化模型。首先,基于分布式优化理论将模型分解成输电网机组组合模型和配电网经济调度模型。然后,采用增量分段方法和二阶锥技术将模型中的非线性项转化为混合整数线性规划问题,并利用交替方向乘子法(ADMM)对所提模型进行迭代求解。最后,通过算例分析表明所提算法和优化模型有助于提高配电网对新能源的消纳能力,保证了输配电网运行的经济性。

炼油企业实现能效约束标杆值策略研究

为实现国家发展与改革委员会等部门对重点领域的能效约束指标标杆值要求,A炼油企业对标行业能效领跑者和集团公司内炼油企业重点用能参数,分析用能差距,结合企业发展规划,制定了能效提升策略。该策略制定思路分全厂用能规划、能量集成、单元强化三个层次,应用了能量三环节理论,对标分析差距,采用了多种能效提升技术,从加强节能管理、生产操作优化、生产流程优化、生产工艺改进、生产设备提效、公用工程优化等方面制定了多项能效提升措施,预期能够达到能效约束标杆值目标,助力企业实现降碳目标。

基于图神经网络的起作用安全约束辨识方法

在安全约束机组组合问题中含有大量冗余安全约束,如何高效辨识起作用的安全约束对提高求解速度具有重要意义。构建电网拓扑图、节点与支路特征后,输入至图卷积网络,对基态和故障态下支路潮流约束是否起作用进行分类,在IEEE RTS 79系统中迭代求解安全约束机组组合。结果表明,所提方法可以有效降低求解迭代次数和计算时间。所提方法可大幅提高求解效率,对将机器学习应用于辅助优化模型求解具有启示作用。

基于同步相量测量的电力系统网络拓扑分析方法

现代电力系统对网络拓扑分析的实时性和准确性要求越来越高,相量测量装置广泛地应用于电力系统中。分析了厂站母线之间的关联关系,基于同步相量测量装置获取的实时进出线电压、电流相量数据和基本电路定理,创新性地提出了一种判断厂站内等效节点数以及判断进出线运行状态的方法,由此可以获得全网的拓扑结构。结果表明,所提算法有效地采用了同步相量测量的模拟量信息,能在开关发生变化时快速获取新的网络拓扑。通过不同类型的仿真场景验证了所提方法的准确性和适用性,并且进一步采用实测数据验证说明了所提方法能在实际电网中起到重要作用。

考虑新能源极限并网强度约束的常规机组最小开机方式优化方法

新能源集群并网系统中需保证一定数量的常规电源提供短路容量以支撑系统电压,而开机容量过大导致新能源可消纳空间减少,故合理优化常规机组开机方式对于兼顾新能源消纳与电网强度至关重要。针对该问题,基于短路比指标计算方法,以临界短路比指标约束建立了常规机组开机方式与新能源并网系统强度之间的耦合模型;然后在满足新能源设备极限并网强度要求的前提下,提出以新能源最大化消纳为目标的机组最小开机方式优化模型,并采用遗传算法和CPLEX求解器相结合的混合策略进行求解。最后以我国三北地区某新能源汇集送端系统工程为例,验证了所提方法的有效性及优越性。

联合Huber核函数与可切换约束算法改进的视觉惯性SLAM方法

针对目前VINS-Fusion双目视觉及惯性同步定位与建图(SLAM)在后端优化时存在误匹配而导致系统定位精度低、鲁棒性差的问题,提出了一种联合Huber核函数与可切换约束算法改进的双目视觉及惯性SLAM方法。在VINS-Fusion双目视觉与惯性SLAM框架的基础上,利用Huber核函数的权值重新构建并求解状态优化中IMU残差的代价函数,降低优化中过大的误差项;同时使用可切换约束算法控制环路闭合因子实现动态协方差矩阵的缩放,剔除闭环检测的异常值实现准确的后端收敛。利用不同场景的公开数据集EuRoc中进行了对比验证实验,结果表明联合改进方法的和方差降低了2.416 m,系统精度和鲁棒性都有所提高;同时在实际场景中的实验也验证了改进方法的可行性。

计及线路优化投切限流的含风电电网扩展规划方法

为应对以风电为代表的新能源大规模接入电力系统带来的新挑战,提出了一种计及线路优化投切限流的含风电电网扩展规划模型。首先,从系统负荷变化引起常规火电机组启停的角度出发,将典型场景下的短路电流约束转化为对应机组启停状态下的约束,通过对风电场集电系统的结构等值研究风电场的等效表达式,引入考虑风电接入的短路电流约束。然后,确定电网扩展规划总体思路,以总投资最小为目标函数,建立综合考虑储能、需求侧响应以及短路电流约束的电网规划混合整数线性规划模型并求解。最后,以IEEE-RTS 24节点系统为例,通过设置不同的案例对比分析,验证了所提方法的有效性。