Data-driven Wasserstein distributionally robust chance-constrained optimization for crude oil scheduling under uncertainty

Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil refining.But uncertainties,including uncertain demands of crude distillation units(CDUs),might make the production plans made by the traditional deterministic optimization models infeasible.A data-driven Wasserstein distributionally robust chance-constrained(WDRCC)optimization approach is proposed in this paper to deal with demand uncertainty in crude oil scheduling.First,a new deterministic crude oil scheduling optimization model is developed as the basis of this approach.The Wasserstein distance is then used to build ambiguity sets from historical data to describe the possible realizations of probability distributions of uncertain demands.A cross-validation method is advanced to choose suitable radii for these ambiguity sets.The deterministic model is reformulated as a WDRCC optimization model for crude oil scheduling to guarantee the demand constraints hold with a desired high probability even in the worst situation in ambiguity sets.The proposed WDRCC model is transferred into an equivalent conditional value-at-risk representation and further derived as a mixed-integer nonlinear programming counterpart.Industrial case studies from a real-world refinery are conducted to show the effectiveness of the proposed method.Out-of-sample tests demonstrate that the solution of the WDRCC model is more robust than those of the deterministic model and the chance-constrained model.

Dynamics and adaptive control of a dual-arm space robot with closed-loop constraints and uncertain inertial parameters

A dynamics-based adaptive control approach is proposed for a planar dual-arm space robot in the presence of closed-loop constraints and uncertain inertial parameters of the payload. The controller is capable of controlling the po- sition and attitude of both the satellite base and the payload grasped by the manipulator end effectors. The equations of motion in reduced-order form for the constrained system are derived by incorporating the constraint equations in terms of accelerations into Kane＇s equations of the unconstrained system. Model analysis shows that the resulting equations perfectly meet the requirement of adaptive controller design. Consequently, by using an indirect approach, an adaptive control scheme is proposed to accomplish position/attitude trajectory tracking control with the uncertain parameters be- ing estimated on-line. The actuator redundancy due to the closed-loop constraints is utilized to minimize a weighted norm of the joint torques. Global asymptotic stability is proven by using Lyapunov＇s method, and simulation results are also presented to demonstrate the effectiveness of the proposed approach.

Modeling and analysis of rigid multibody systems with driving constraints and frictional translation joints

An approach is proposed for modeling and anal- yses of rigid multibody systems with frictional translation joints and driving constraints. The geometric constraints of translational joints with small clearance are treated as bilat- eral constraints by neglecting the impact between sliders and guides. Firstly, the normal forces acting on sliders, the driv- ing constraint forces （or moments） and the constraint forces of smooth revolute joints are all described by complementary conditions. The frictional contacts are characterized by a set- valued force law of Coulomb＇s dry friction. Combined with the theory of the horizontal linear complementarity problem （HLCP）, an event-driven scheme is used to detect the transi- tions of the contact situation between sliders and guides, and the stick-slip transitions of sliders, respectively. And then, all constraint forces in the system can be computed easily. Secondly, the dynamic equations of multibody systems are written at the acceleration-force level by the Lagrange multiplier technique, and the Baumgarte stabilization method is used to reduce the constraint drift. Finally, a numerical example is given to show some non-smooth dynamical behaviors of the studied system. The obtained results validate the feasibility of algorithm and the effect of constraint stabilization.

Centralized-local PV voltage control considering opportunity constraint of short-term fluctuation

This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute active cycle of the inverter and reactive outputs to reduce network loss and light rejection.In the second stage,the local control stabilizes the fluctuations and tracks the system state of the first stage.The uncertain interval model establishes a chance constraint model for the inverter voltage-reactive power local control.Second-order cone optimization and sensitivity theories were employed to solve the models.The effectiveness of the model was confirmed using a modified IEEE 33 bus example.The intraday control outcome for distributed power generation considering the effects of fluctuation uncertainty,PV penetration rate,and inverter capacity is analyzed.

Allocation of Repairable and Replaceable Components for a System Availability Using Selective Maintenance with Probabilistic Maintenance Time Constraints

In this paper, we obtain optimum allocation of replaceable and repairable components in a system design. When repair and replace time are considered as random in the constraints. We convert probabilistic constraint into an equivalent deterministic constraint by using chance constrained programming. We have used the selective maintenance policy to determine how many components to be replaced & repaired within the limited maintenance time interval and cost. A Numerical example is presented to illustrate the computational procedure and problem is solved by using LINGO Software.

考虑交通网络不确定性及SESS与MESS空间配合的分布式储能防灾应急规划

为在大规模停电后提供紧急电力支持,提出一种考虑交通网络不确定性及固定储能系统(SESS)和移动储能系统(MESS)空间配合的分布式储能(DES)防灾应急规划方法。基于正态分布区间数,建立通行时间不确定的MESS调度交通网络模型,提出考虑SESS与MESS空间配合效应的联合应急供电策略。采用机会约束描述交通网络不确定性,构建考虑供电可靠性最优的DES选址定容模型。将机会约束转换为确定性等价类,针对DES防灾应急系统多个供电可靠性衡量指标间的冗余问题,提出基于规格化法平面约束法的多目标优化求解方法。采用改进的IEEE 30节点系统及交通网络进行验证,结果表明所提方法能够实现多区域最优配置,验证了SESS与MESS的空间配合效应,并反映了交通网络不确定性对规划结果的影响。

Superlinear Convergence of a Smooth Approximation Method for Mathematical Programs with Nonlinear Complementarity Constraints

Mathematical programs with complementarity constraints(MPCC) is an important subclass of MPEC.It is a natural way to solve MPCC by constructing a suitable approximation of the primal problem.In this paper,we propose a new smoothing method for MPCC by using the aggregation technique.A new SQP algorithm for solving the MPCC problem is presented.At each iteration,the master direction is computed by solving a quadratic program,and the revised direction for avoiding the Maratos effect is generated by an explicit formula.As the non-degeneracy condition holds and the smoothing parameter tends to zero,the proposed SQP algorithm converges globally to an S-stationary point of the MPEC problem,its convergence rate is superlinear.Some preliminary numerical results are reported.

A dynamical neural network approach for distributionally robust chance-constrained Markov decision process

In this paper,we study the distributionally robust joint chance-constrained Markov decision process.Utilizing the logarithmic transformation technique,we derive its deterministic reformulation with bi-convex terms under the moment-based uncertainty set.To cope with the non-convexity and improve the robustness of the solution,we propose a dynamical neural network approach to solve the reformulated optimization problem.Numerical results on a machine replacement problem demonstrate the efficiency of the proposed dynamical neural network approach when compared with the sequential convex approximation approach.

利用CNC多维约束的S型速度规划

经典控制模型中存在时间过耦合,而速度规划与实时轨迹控制都与时间关联,导致实时计算量、规划与控制协调性等不足的问题,提出利用CNC多维约束的S型速度规划方法.首先建立双重反馈的控制模型,内反馈继承经典控制模型将时间反馈到控制器,外反馈将非时间反馈到规划器;然后将影响速度规划的因素分为时间相关与时间无关,与时间相关因素在控制器中实时控制,与时间无关因素在规划器中进行非时间速度规划;在规划器中建立基于轨迹路径长度、轨迹几何特性、运动参数以及程序行号等信息的多维约束模型;最后以路径长度代替时间作为运动参考量,结合多维约束模型,考虑轨迹的几何特性与路径长度映射关系,使轨迹特性与非时间建立具有空间性关联.通过MATLAB仿真对控制模型的抗干扰能力进行验证,并通过CODESYS软件编程和实验的结果表明,所提方法是可行和有效的.

通信受限下T-S模糊网络控制系统L_(1)动态输出反馈控制

针对通信受限的非线性网络控制系统,为兼顾系统性能和节约利用网络资源,引入事件触发通信机制(ETCM),利用时延建模方法和并行分布补偿(PDC)技术,将连续控制系统建模为一个采样数据误差依赖的非线性网络化系统模型;构建保守性低的时滞依赖和模糊基依赖的Lyapunov-Krasovskii泛函,给出增广系统稳定性和鲁棒性结果,得到鲁棒控制器存在的充分条件,提出一种基于线性矩阵不等式(LMIs)的事件触发参数,以及全局模糊L 1动态输出反馈控制器参数的协同设计方法。采用永磁同步电动机模型仿真验证,结果表明该设计方法可减少网络资源占用,达到闭环控制系统的性能要求。

Smooth constraint inversion technique in genetic algorithms and its application to surface wave study in the Tibetan Plateau

Smooth constraint is important in linear inversion, but it is difficult to apply directly to model parameters in genetic algorithms. If the model parameters are smoothed in iteration, the diversity of models will be greatly suppressed and all the models in population will tend to equal in a few iterations, so the optimal solution meeting requirement can not be obtained. In this paper, an indirect smooth constraint technique is introduced to genetic inversion. In this method, the new models produced in iteration are smoothed, then used as theoretical models in calculation of misfit function, but in process of iteration only the original models are used in order to keep the diversity of models. The technique is effective in inversion of surface wave and receiver function. Using this technique, we invert the phase velocity of Raleigh wave in the Tibetan Plateau, revealing the horizontal variation of S wave velocity structure near the center of the Tibetan Plateau. The results show that the S wave velocity in the north is relatively lower than that in the south. For most paths there is a lower velocity zone with 12-25 km thick at the depth of 15-40 km. The lower velocity zone in upper mantle is located below the depth of 100 km, and the thickness is usually 40-80 km, but for a few paths reach to 100 km thick. Among the area of Ando, Maqi and Ushu stations, there is an obvious lower velocity zone with the lowest velocity of 4.2-4.3 km/s at the depth of 90-230 km. Based on the S wave velocity structures of different paths and former data, we infer that the subduction of the Indian Plate is delimited nearby the Yarlung Zangbo suture zone.

Lie symmetry and Hojman conserved quantity of a Nielsen equation in a dynamical system of relative motion with Chetaev-type nonholonomic constraint

The Lie symmetry and Hojman conserved quantity of Nielsen equations in a dynamical system of relative motion with nonholonomic constraint of the Chetaev type are studied. The differential equations of motion of the Nielsen equation for the system, the definition and the criterion of Lie symmetry, and the expression of the Hojman conserved quantity deduced directly from the Lie symmetry for the system are obtained. An example is given to illustrate the application of the results.

稀疏广义S变换及其在储层地震低频异常检测中的应用

时频分析(TF)是地震资料处理与解释中非常重要的方法之一,时频分辨率是高精度储层预测的关键参数。常规S变换及广义S变换的时频分辨率已难以满足高精度储层预测的需求。为此,将稀疏约束的思想引入TF中,在利用广义S变换参数可灵活调节的基础上,通过优化窗矩阵构建一种稀疏广义S变换方法。合成信号的对比分析结果表明,稀疏广义S变换方法能够获得时频分辨率更高、能量聚集性更好的时频分布,在高频和低频部分均能保持较高的时间分辨率。在实际地震数据的低频阴影检测中,该方法能更清楚地刻画油气储层的空间展布,有利于减小油气储层检测的多解性。

Assimilating temperature and salinity profiles using Ensemble Kalman Filter with an adaptive observation error and T-S constraint

Temperature （T） and salinity （S） profiles from conductivity-temperature-depth data collected during the Northern South China Sea Open Cruise from August 16 to September 13, 2008 are assimilated using Ensemble Kalman Filter （EnKF）. An adaptive observational error strategy is used to prevent filter from diverging. In the meantime, aiming at the limited improvement in some sites caused by the T and S biases in the model, a T-S constraint scheme is adopted to improve the assimilation performance, where T and S are separately updated at these locations. Validation is performed by comparing assimilated outputs with independent in situ data （satellite remote sensing sea level anomaly （SLA）, the OSCAR velocity product and shipboard ADCP）. The results show that the new EnKF assimilation scheme can significantly reduce the root mean square error （RMSE） of oceanic T and S compared with the control run and traditional EnKF. The system can also improve the simulation of circulations and SLA.

Constraint Stabilization and One-Step Correction Method for Dynamical Systems

A computational method of constraint stabilization and correction is introduced. The method is based on the Baumgart＇s one-step method. Constraint conditions are addressed to stabilize and correct the solution. Two examples are given to illustrate the results of the method.

基于改进Benders分解的配电网扩展规划机会约束优化方法

为了解决已有配电系统规划中非线性潮流模型建模问题和不确定性优化效率问题,文中提出了基于双线性Benders分解的配电网扩展规划机会约束优化方法。通过在规划投资层面和运行优化层面进行分阶段,构建了两阶段随机混合整数二阶锥规划模型;为了避免极端场景下导致的高昂投资成本,进一步扩展了传统的Benders分解方法,设计了基于双线性Benders分解的机会约束优化方法。通过三个算例系统验证了所提方法具有强健的鲁棒性。

考虑稳态和动态数据的S-型生化系统参数估计方法

针对具有稳态实验数据和动态实验数据的一类S-型生化系统的参数估计问题,以浓度误差、斜率误差与稳态误差之和为极小化目标,构建了一种参数估计优化模型。为了求解参数估计问题,利用四阶龙格库塔离散化格式,将优化问题中的微分方程转化为代数方程,同时将所构建的优化模型转化为稳态约束条件下的非线性规划问题。为了求解上述非线性规划问题,应用样条插值估计实验值的速率。为了说明算法的有效性可行性,将建立的优化模型与求解方法应用到已有的S-型生化系统中,并绘制了仿真结果的图像。与已有方法比较,数值结果表明,加入稳态误差优化与稳态约束后,可获得更为精确的参数估计结果。

A predictive chance constraint rebalancing approach to mobility-on-demand services

This paper considers the problem of supply-demand imbalances in Mobility-on-Demand(MoD)services.These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehicles to areas where the demand is high.To achieve this,we propose a method that takes into account uncertainties of predicted travel demand while minimizing pick-up time and rebalance mileage for autonomous MoD ride-hailing.More precisely,first travel demand is predicted using Gaussian Process Regression(GPR)which provides uncertainty bounds on the prediction.We then formulate a stochastic model predictive control(MPC)for the autonomous ride-hailing service and integrate the demand predictions with uncertainty bounds.In order to guarantee constraint satisfaction in the optimization under estimated stochastic demand prediction,we employ a probabilistic constraining method with user-defined confidence interval,using Chance Constrained MPC(CCMPC).The benefits of the proposed method are twofold.First,travel demand uncertainty prediction from data can naturally be embedded into the MoD optimization framework,allowing us to keep the imbalance at each station below a certain threshold with a user-defined probability.Second,CCMPC can be relaxed into a Mixed-Integer-Linear-Program(MILP)and the MILP can be solved as a corresponding Linear-Program,which always admits an integral solution.Our transportation simulations show that by tuning the confidence bound on the chance constraint,close to optimal oracle performance can be achieved,with a median customer wait time reduction of 4%compared to using only the mean prediction of the GPR.

基于Stackelberg博弈的多站融合微电网运行策略研究

微电网技术将许多小规模分布式能源并入电网,由于可再生能源的高可变性,将对电网调度策略提出新的挑战。为此,提出一种具有机会约束的双层斯坦伯格随机模型用于描述不确定性的影响。通过机会约束能够有效考虑多站融合微电网生产能力的不确定性对智能电网的影响,利用析取约束、强对偶定理等方法,将双层模型重新表述为混合整数二次规划,以利于有效求解。通过算例证明所提方法的有效性。