Optimization of circulating cooling water systems based on chance constrained programming

Recent research on deterministic methods for circulating cooling water systems optimization has been well developed. However, the actual operating conditions of the system are mostly variable, so the system obtained under deterministic conditions may not be stable and economical. This paper studies the optimization of circulating cooling water systems under uncertain circumstance. To improve the reliability of the system and reduce the water and energy consumption, the influence of different uncertain parameters is taken into consideration. The chance constrained programming method is used to build a model under uncertain conditions, where the confidence level indicates the degree of constraint violation. Probability distribution functions are used to describe the form of uncertain parameters. The objective is to minimize the total cost and obtain the optimal cooling network configuration simultaneously.An algorithm based on Monte Carlo method is proposed, and GAMS software is used to solve the mixed integer nonlinear programming model. A case is optimized to verify the validity of the model. Compared with the deterministic optimization method, the results show that when considering the different types of uncertain parameters, a system with better economy and reliability can be obtained(total cost can be reduced at least 2%).

Chance-Constrained Approaches for Multiobjective Stochastic Linear Programming Problems

Multiple objective stochastic linear programming is a relevant topic. As a matter of fact, many practical problems ranging from portfolio selection to water resource management may be cast into this framework. Severe limitations on objectivity are encountered in this field because of the simultaneous presence of randomness and conflicting goals. In such a turbulent environment, the mainstay of rational choice cannot hold and it is virtually impossible to provide a truly scientific foundation for an optimal decision. In this paper, we resort to the bounded rationality principle to introduce satisfying solution for multiobjective stochastic linear programming problems. These solutions that are based on the chance-constrained paradigm are characterized under the assumption of normality of involved random variables. Ways for singling out such solutions are also discussed and a numerical example provided for the sake of illustration.

Reconstruction of geological surfaces using chance-constrained programming

Geological surface modeling is typically based on seismic data, well data, and models of regional geology. However, structural interpretation of these data is error-prone, especially in the absence of structural morphology information, Existing geological surface models suffer from high levels of uncertainty, which exposes oil and gas exploration and development to additional risk. In this paper, we achieve a reconstruction of the uncertainties associated with a geological surface using chance-constrained programming based on multisource data. We also quantifi ed the uncertainty of the modeling data and added a disturbance term to the objective function. Finally, we verifi ed the applicability of the method using both synthetic and real fault data. We found that the reconstructed geological models met geological rules and reduced the reconstruction uncertainty.

Chance-constrained programming (CCP)abatement of SO_2 emission for acid deposition control in Liuzhou City

A deterministic linear programming model which optimizes the abatement of each SO2 emission source, is extended into a CCP form by introducing equations of probabilistic constrained through the incorporation of uncertainty in the source-receptor-specific transfer coefficients. Based on the calculation of SO2 and sulfate average residence time for Liuzhou City, a sulfur deposition model has been developed and the distribution of transfer coefficients have been found to be approximately log-normal. Sulfur removal minimization of the model shows that the abatement of emission sources in the city is more effective, while control cost optimization provides the lowest cost programmes for source abatement at each allowable deposition limit under varied environmental risk levels. Finally a practicable programme is recommended.

A Dependent-Chance Programming Model for Proactive Scheduling

Proactive scheduling based on expected value model is an effective method to develop robust schedules in consideration of minimizing project cost caused by deviations between realized and planed activity starting times.However,these schedules may be realized with low probabilities.In this paper,a novel model based on dependent-chance programming(DCP) is proposed,considering probability as well as solution robustness.A hybrid intelligent algorithm integrating stochastic simulation and genetic algorithm(GA)is designed to solve the proposed model.Moreover,a numerical example is conducted to reveal the effectiveness of the proposed model and the algorithm.

Reducing Recidivism With the Chances Program:Immigrant Juvenile Delinquents in Israel

This study aimed to examine the effectiveness of the Chances program,a program that was established in 2004 in order to prevent juvenile delinquency among immigrant youth in Israel.Previous research showed that immediately after the program ended,all participants stopped their delinquent behavior.The current research tested the effectiveness of the program two and a half years later and explored the risk of or immunity to recidivism among graduates of the program.A sample of 145 graduates was asked to fill out a closed questionnaire examining the effectiveness of the program through self‐report questions about recidivism.The results were significant,showing that only three of the graduates(14%of the sample)continued to commit felons,while the great majority(86%)reintegrated into normative life within the community including school,employment,and army service.Fears of disappointing their tutor were found central to the discontinuation of delinquency.The graduates also attributed other elements of the Chances program to their normative behavior and reintegration into normative society.The implications of this study demonstrate that although the Chances program was designed to treat immigrant delinquent youth,its success can be relevant for treating native‐born delinquent youth as well.

New approach for uncertain random multi-objective programming problems based on C_(ESD) criterion

To overcome the defects that the traditional ap-proach for multi-objective programming under uncertain ran-dom environment(URMOP)neglects the randomness and uncer-tainty of the problem and the volatility of the results,a new ap-proach is proposed based on expected value-standard devi-ation value criterion(C_(ESD) criterion).Firstly,the effective solution to the URMOP problem is defined;then,by applying sequence relationship between the uncertain random variables,the UR-MOP problem is transformed into a single-objective program-ming(SOP)under uncertain random environment(URSOP),which are transformed into a deterministic counterpart based on the C_(ESD) criterion.Then the validity of the new approach is proved that the optimal solution to the SOP problem is also effi-cient for the URMOP problem;finally,a numerical example and a case application are presented to show the effectiveness of the new approach.

A Chance–Constrained Data Envelopment Analysis Approach to Problem Provincial Productivity Growth in Vietnamese Agriculture from 1995 to 2007

This study employs a chance-constrained data envelopment analysis (CDEA) approach with two models (model A and model B) to decompose provincial productivity growth in Vietnamese agriculture from 1995 to 2007 into technological progress and efficiency change. The differences between the chance - constrained programming model A and model B are assumptions imposed on the covariance matrix. The decomposition allows us to identify the contributions of technical change and the improvement in technical efficiency to productivity growth in Vietnamese production. Sixty-one provinces in Vietnam are classified into Mekong - technology and other -technology categories. We conduct a Mann-Whitney test to verify whether the two samples, the Mekong technology province sample and the other technology sample, are drawn from the same productivity change populations. The result of the Mann-Whitney test indicates that the differences between the Mekong technology category and the other technology category from two models are more significant. Two important questions are whether some provinces in the samples could maintain their relative efficiency rank positions in comparison with the others over the study period and how to further examine the agreements between the two models. The Kruskal - Wallis test statistic shows that technical efficiency from both models for some provinces are higher than those of them in the study period. The Malmquist results show that production frontier has contracted by around 1.3 percent and 0.31 percent from chance-constrained model A and model B, respectively, a year on average over the sample period. To examine the agreements or disagreements in the total factor productivity indexes we compute the correlation between Malmquist indexes, which is positive and not very high. Thus there is a little discrepancy between the two Malmquist indexes, estimated from the chance - constrained models A and B.

Estimation of CARA Preferences and Positive Mathematical Programming

The purpose of this paper is to combine the estimation of output price risk and positive mathematical programming (PMP). It reconciles the risk programming presented by Freund with a consistent estimate of the constant absolute risk aversion (CARA) coefficient. It extends the PMP approach to calibration of realized production outputs and observed input prices. The results of this specification include 1) uniqueness of the calibrating solution, 2) elimination of the tautological calibration constraints typical of the original PMP procedure, 3) equivalence between a phase I calibrating solution and a solution obtained by combining phase I and phase II of the traditional PMP procedure. In this extended PMP framework, the cost function specification involves output quantities and input prices—contrary to the myopic cost function of the traditional PMP approach. This extension allows for a phase III calibrating model that replaces the usual linear technology with relations corresponding to Shephard lemma (in the primal constraints) and the marginal cost function (in the dual constraints). An empirical example with a sample of farms producing four crops illustrates the novel procedure.

考虑功率预测偏差和出力调节不确定性的风电集群功率分配策略

当因输电通道限制需对风电集群进行限电时,应考虑各风电场功率预测和调节能力的差异。考虑功率预测偏差和调节能力不确定性,构建机会约束规划和机会约束目标规划相结合的风电集群日前功率调度模型,并采用采样排序的方法将不确定变量转化为确定性变量对模型进行求解。对华北某地区风电集群进行案例分析,结果表明,在满足风电场间期望调度电量比例要求的基础上,相较于传统模型,所提模型能有效降低弃风率和系统负荷不平衡时的缺额电量。

考虑随机需求满足风险的第四方物流网络设计

电商模式的迅速发展为客户线上购物带来了便利,同时也使得物流供应链系统中客户需求的不确定性显著增强,考虑需求满足风险的网络设计问题成为第四方物流(fourth partylogistics,4PL)运作模式下的重要研究问题。针对这一问题,以概率视角刻画了需求满足风险,并构建了考虑需求均值的混合整数线性规划模型和考虑需求满足风险的机会约束规划模型,在需求正态分布的情况下,针对机会约束模型中非线性特征难以处理的问题,给出了一种基于需求正态分布的线性化方法,并给出了问题的等价线性模型。最后,通过数值实验对两种模型进行对比,验证了考虑需求满足风险的4PL网络设计模型的有效性和优越性,并探究了需求的随机性以及需求满足风险水平对网络设计的影响,给出了考虑需求满足风险的第四方物流网络设计的管理启示。

计及多元不确定性的综合能源系统优化配置

综合能源系统的优化配置关键在于设备选型与数量配置,而能源负荷负荷和可再生能源出力预测误差以及故障发生的不确定性将直接影响配置方案的合理性以及经济性.为此,本文提出一种考虑源–网–荷多元不确定性的综合能源系统多目标–机会约束规划方法.考虑可再生能源出力与负荷需求预测误差引起的不确定性,本文构建了满足置信概率的能量供需平衡约束;针对供能网络中设备N-1故障引起的不确定性,提出调整裕度模型,进而构建了调整裕度与N-1设备能量缺额的机会约束.对于获得的帕累托解集,采用信息熵与逼近理想排序法构建多准则评价模型,以确定最优的系统配置.将本文方法应用于某区域综合能源系统的最优结构设计,实验结果表明,本文方法的有效性与可靠性.

考虑多种工业负荷参与的多时间尺度源荷储优化调度

为解决大规模新能源接入导致传统电网中常规机组调节压力不断增大的问题,提出了一种考虑储能和多种工业负荷参与的多时间尺度优化调度策略。该策略通过协调荷侧电解铝、水泥、钢铁、非生产性负荷与储能以及源侧常规机组调用计划,有效缓解电网调节压力。首先,分析储能和多种工业负荷的调节特性,建立风-光-荷-储的滚动调节框架。然后,针对源荷不确定性,采用多场景随机规划与模糊机会约束目标规划相结合的方法,建立日前和日内阶段以系统经济性最优为目标、实时调度阶段兼顾安全性和经济性的多时间尺度调度模型。最后,通过新能源充足与不足典型日算例可知,本文所提调度策略能够充分发挥可调控资源的调节能力,在两种典型日的总成本较日前最优调度分别降低了17.08%、44.13%,弃新能源量降低39%、失负荷量降低23%,有效提高了电网运行经济性和安全性。

考虑交通流量和用户行为不确定性的加氢站选址优化模型

加氢站是氢能产业发展的重要基础设施,其布局选址对于氢能在交通领域的推广应用至关重要。为此,提出一种加氢站的选址优化方法。首先,分别以运营商追求捕获交通流量最大化、客户追求获得加氢服务可能性最大化为上、下层目标函数构建了双层规划模型,并引入机会约束规划理论处理了交通流量的不确定性。该模型考虑了客户路径选择的行为特性,能够为交通流量不确定性下的加氢站选址提供重要借鉴。其次,利用KKT条件和确定性等价将所提机会约束双层规划模型转化为了单层的混合整数二阶锥规划问题以实现高效求解。最后,为验证该模型的有效性,以长三角地区交通网络为案例进行了仿真分析。

基于改进p-有效点法的多风电场共享储能联合参与一次调频的容量优化

以大规模多风电场接入电力系统的一次调频备用容量为研究对象,首先通过引入风电出力预测误差的概率分布描述系统的不确定性因素,考虑风储一次调频备用总成本最小为目标;然后建立基于机会约束规划的多风电场联合共享储能参与一次调频的容量优化模型;最后提出基于快速傅里叶变换快速求解联合变量概率分布的改进p-有效点法通过数学解析将机会约束规划从形式上转化为确定性规划,再通过Yalmip调用Cplex实现模型的快速求解。算例结果表明,所提模型和方法可有效提高风储调频效果和收益,且具有更高的求解效率。

不确定条件下的车流径路问题及其树形径路约束机制研究

针对既有车流径路研究中确定性建模参数的局限性以及对车流径路优化阶段是否应该考虑树形径路约束的争议,在确定性车流径路模型的基础上,引入模糊集理论和随机规划理论,分别采用三角模糊数和机会约束刻画车流量的波动和区间通过能力的弹性,为车流径路问题重构Hurwicz准则下的模糊机会约束规划模型,并将其转化为清晰等价形式。基于我国西南局部路网的案例研究树形径路约束对车流径路和编组计划的影响,对比分析确定性条件和不确定条件下车流径路的差别。实验结果表明,车流径路优化阶段强调树形结构径路会限制径路方案的灵活性并增加车流运输总成本;不确定条件下的车流径路模型在困难区段的识别方面更具优势。

考虑模糊工序工期的重复性项目时间费用权衡优化

为了减少不确定性对项目业绩目标的影响,提高进度计划的鲁棒性,研究模糊工序工期下的重复性项目离散时间费用权衡问题。通过模糊风险度量,建立考虑决策者风险偏好的模糊机会约束规划模型,目标是确定所有工序的最优执行模式(即模式列表),从而在满足事先设定的工期延误和费用超支风险水平条件下最小化项目预算。提出模式列表已知条件下,计算模糊总工期和模糊总费用隶属度函数的正向递归过程,并据此设计搜索最优模式列表的基于电磁机制的改进遗传算法(GA-EM)。利用一个实际工程案例验证算法的有效性,并通过数值实验分析算法的计算性能。结果表明,GA-EM能够给出满足给定工期延误和费用超支风险水平的模糊进度计划,预算的平均和最大百分比误差分别不大于0.096%和0.239%。

考虑新能源出力不确定性的风-光-柴-储系统调度策略

文章以风-光-柴-储系统为研究对象,为了研究新能源出力不确定性对该系统的影响,提出了一种新能源出力复合预测模型。为提高风-光-柴-储系统运行的经济性、环保性和安全性,提出了考虑新能源出力不确定性的风-光-柴-储系统调度模型,并采用了带有Monte Carlo模拟的遗传算法对模型进行求解。文章采用了负荷缺失率(load loss rate,LLR)和置信概率对系统的安全性进行评价,并分析了其对系统调度结果的影响。仿真结果表明,文中所提出的考虑新能源出力不确定性的风-光-柴-储系统调度模型,可以降低新能源出力不确定性对系统的影响,且该方法可以有效地平衡系统的经济性和安全性。

基于风、光联合出力仿真的多能联供系统鲁棒机会约束优化研究

为了有效规避风、光出力不确定性造成的供需失衡风险、促进多能联供系统的持续、稳定和健康发展,联合使用Copula理论、机会约束规划方法和鲁棒优化算法,构建了考虑风、光联合出力不确定性的多能联供系统鲁棒机会约束优化调度模型。结果显示:该模型不仅可以准确识别和表征风、光联合出力分布概率,制定低违约风险条件下的风、光出力策略,还可以通过调整供给侧能源输出结构,生成鲁棒性最优的多能联供系统运行方案,显著提高系统运行的稳定性,降低风、光不确定性造成的经济风险。

基于机会约束规划的含电动汽车市域铁路牵引供电系统优化运行

市域铁路为市区和郊区提供了便捷通道,沿线车站不仅可实现电动汽车停车换乘,也为电动汽车接入铁路牵引供电系统提供了友好接口。为了提升列车再生制动能量利用率,降低市域铁路运营成本,本文提出了一种含电动汽车的同相牵引供电系统优化运行模型,以牵引变电所日电费成本最低为目标,优化电动汽车和超级电容的充放电策略、牵引变电所功率调控策略。为应对电动汽车到达时间、离开时间和初始荷电状态的不确定性,使用机会约束规划,利用概率约束代替传统确定约束,保证电动汽车充电方案满足驾驶要求的置信水平高于预定的置信水平。使用样本均值法将机会约束转换为确定性约束,得到混合整数线性规划模型,然后采用CPLEX求解器求解。仿真分析表明,提出的模型可以有效降低牵引变电所日电费20.37%,体现出了电动汽车在参与牵引供电系统负荷调节中具有良好的灵活性,有效提高了系统的运行经济性。