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.

A simulation-based two-stage interval-stochastic programming model for water resources management in Kaidu-Konqi watershed,China

This study presented a simulation-based two-stage interval-stochastic programming （STIP） model to support water resources management in the Kaidu-Konqi watershed in Northwest China. The modeling system coupled a distributed hydrological model with an interval two-stage stochastic programing （ITSP）. The distributed hydrological model was used for establishing a rainfall-runoff forecast system, while random parameters were pro- vided by the statistical analysis of simulation outcomes water resources management planning in Kaidu-Konqi The developed STIP model was applied to a real case of watershed, where three scenarios with different water re- sources management policies were analyzed. The results indicated that water shortage mainly occurred in agri- culture, ecology and forestry sectors. In comparison, the water demand from municipality, industry and stock- breeding sectors can be satisfied due to their lower consumptions and higher economic values. Different policies for ecological water allocation can result in varied system benefits, and can help to identify desired water allocation plans with a maximum economic benefit and a minimum risk of system disruption under uncertainty.

Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

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.

Relationship between Maximum Principle and Dynamic Programming in Stochastic Differential Games and Applications

This paper is concerned with the relationship between maximum principle and dynamic programming in zero-sum stochastic differential games. Under the assumption that the value function is enough smooth, relations among the adjoint processes, the generalized Hamiltonian function and the value function are given. A portfolio optimization problem under model uncertainty in the financial market is discussed to show the applications of our result.

Intelligent model of rehabilitation training program for stroke

In view of the uncertainty and complexity,the intelligent model of rehabilitation training program for stroke was proposed,combining with the case-based reasoning(CBR) and interval type-2 fuzzy reasoning(IT2FR).The model consists of two parts:the setting model based on CBR and the feedback compensation model based on IT2FR.The former presets the value of rehabilitation training program,and the latter carries on the feedback compensation of the preset value.Experimental results show that the average percentage error of two rehabilitation training programs is 0.074%.The two programs are made by the intelligent model and rehabilitation physician.That is,the two different programs are nearly identical.It means that the intelligent model can make a rehabilitation training program effectively and improve the rehabilitation efficiency.

考虑风光出力不确定性的电动汽车充电站多目标双层规划

【目的】为适应“双碳”目标下电动汽车充电站拓展规划需求,研究充电站选址定容对分布式电源消纳作用的影响。【方法】建立了综合考虑风光出力不确定性和相关性的多目标双层配置优化模型,并分析了分时电价激励下电动汽车用户充电行为变化对充电桩选址定容方案的影响。上层综合考虑经营者投资建设、运行维护、网损成本和用户充电、排队等效成本最小两个目标函数,采用改进多目标粒子群算法生成帕累托最优规划方案。下层模型以弃风弃光率最小为目标,对上层模型生成的帕累托解集进行校验。【结果】通过IEEE33节点路网图对所提模型进行验证,得到在风光不确定场景下的充电桩最优选址方案。

Enterprise resource planning implementation decision & optimization models

To study the uncertain optimization problems on implementation schedule, time-cost trade-off and quality in enterprise resource planning （ERP） implementation, combined with program evaluation and review technique （PERT）, some optimization models are proposed, which include the implementation schedule model, the timecost trade-off model, the quality model, and the implementation time-cost-quality synthetic optimization model. A PERT-embedded genetic algorithm （GA） based on stochastic simulation technique is introduced to the optimization models solution. Finally, an example is presented to show that the models and algorithm are reasonable and effective, which can offer a reliable quantitative decision method for ERP implementation.

电力系统中时序场景生成和约简方法研究综述

随着能源转型的推进,可再生能源以及新型负荷的渗透率不断提高,其引入的不确定性给电力系统安全与经济运行带来极大的挑战。对电力系统中不确定性因素精确建模是保证新一代电力系统安全稳定运行的基础。采用时序场景生成和约简技术对不确定性因素建模是推动电力系统不确定优化技术发展的关键。针对电力系统中时序场景生成和约简方法进行了系统性综述。首先,从研究对象和数学问题阐述了电力系统时序场景生成和约简的基本概念。其次,综述了电力系统中场景生成和约简的现有研究方法、评价指标以典型应用场景。最后对现阶段研究中存在的问题进行了归纳,并展望了未来的发展趋势和挑战。

考虑生态环境的灌区双水源区间两阶段模糊可信性约束模型

对水资源进行合理的优化配置是缓解水资源短缺问题和遏制生态环境持续恶化的重要途径之一。为了实现水资源的可持续利用和生态环境的良性循环,该研究针对灌区水资源配置系统中存在的复杂性和不确定性,以黑龙江省建三江垦区为研究区,将水质污染治理成本(生态补偿费用)与系统经济收益相结合作为目标函数,引入离散区间、概率分布函数及模糊数表示系统中的多重不确定性,通过设置不同的可信性置信水平解决带有违规风险概率的模糊风险问题,进而建立考虑生态环境的区间两阶段模糊可信性约束规划模型。通过对模型求解,实现地表地下水资源在典型作物间合理的优化配置,得到不同来水情境下生态补偿费用、不同水源不同作物的最优配水方案及不同可信性置信水平下系统最大经济效益,结果表明:模型能够反映系统中的不确定性,有效平衡系统收益与违规风险,并通过满足生态需水和农业排污约束以促进系统经济效益和生态环境的协调发展;当来水水平分别为低、中、高3种流量时,研究区地表水生态补偿费用和地下水生态补偿费用均呈现下降的趋势;当可信性置信水平为1.0时,系统经济收益为29.88×10^(8)~52.18×10^(8)元,当可信性置信水平为0.5时,系统经济收益为31.17×10^(8)~53.44×10^(8)元,系统收益值随着可信性置信水平的降低而升高,管理者可根据实际情况和风险偏好选择科学合理的决策方案,以实现水资源的高效利用与可持续发展。该研究所构建的不确定性优化模型和作物配水结果可为类似地区的水资源优化配置提供参考。

Multi-stage Co-planning Model for Power Distribution System and Hydrogen Energy System Under Uncertainties

The increased deployment of electricity-based hydrogen production strengthens the coupling of power distribution system(PDS)and hydrogen energy system(HES).Considering that power to hydrogen(PtH)has great potential to facilitate the usage of renewable energy sources(RESs),the coordination of PDS and HES is important for planning purposes under high RES penetration.To this end,this paper proposes a multi-stage co-planning model for the PDS and HES.For the PDS,investment decisions on network assets and RES are optimized to supply the growing electric load and PtHs.For the HES,capacities of PtHs and hydrogen storages(HSs)are optimally determined to satisfy hydrogen load considering the hydrogen production,tube trailer transportation,and storage constraints.The overall planning problem is formulated as a multistage stochastic optimization model,in which the investment decisions are sequentially made as the uncertainties of electric and hydrogen load growth states are revealed gradually over periods.Case studies validate that the proposed co-planning model can reduce the total planning cost,promote RES consumption,and obtain more flexible decisions under long-term load growth uncertainties.

Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

The networking of microgrids has received significant attention in the form of a smart grid.In this paper,a set of smart railway stations,which is assumed as microgrids,is connected together.It has been tried to manage the energy exchanged between the networked microgrids to reduce received energy from the utility grid.Also,the operational costs of stations under various conditions decrease by applying the proposed method.The smart railway stations are studied in the presence of photovoltaic(PV)units,energy storage systems(ESSs),and regenerative braking strategies.Studying regenerative braking is one of the essential contributions.Moreover,the stochastic behaviors of the ESS’s initial state of energy and the uncertainty of PV power generation are taken into account through a scenario-based method.The networked microgrid scheme of railway stations(based on coordinated operation and scheduling)and independent operation of railway stations are studied.The proposed method is applied to realistic case studies,including three stations of Line 3 of Tehran Urban and Suburban Railway Operation Company(TUSROC).The rolling stock is simulated in the MATLAB environment.Thus,the coordinated operation of networked microgrids and independent operation of railway stations are optimized in the GAMS environment utilizing mixed-integer linear programming(MILP).

不确定条件下考虑横纵联盟策略的船期协调研究

为探究港口与航运公司及港口间形成横纵向联盟时,集装箱船船期计划与港航双方收益间的关系,结合船舶在港时间不确定因素,考虑某一航运公司多条航线经挂靠港的船期协调优化问题。以航运公司和港方收益最大化为双目标,构建港口横纵向联盟下集装箱船船期协调的混合整数非线性规划模型,并设计改进的带有精英策略的非支配排序遗传算法(NSGA-Ⅱ)进行求解。以高丽海运公司多条航线挂靠上海港、舟山港和南通港为例,在不同情形下进行模拟验证。结果表明:与不考虑港口间联盟相比,港口横纵向联盟下航运公司收益增加6.2%,港方收益增加25.9%,航运公司、计划港和合作港在联盟下均获益;不同航线数或到离港时间情形下,模型仍可获得稳定的船期计划。研究表明,该模型和算法可为航运公司和港口方在不同情形下做出合理船期计划提供参考。

基于不确定度连续传递模型的标准曲线双误差拟合程序

"不确定度连续传递模型"是建立在"约克方程"基础上的双误差标准曲线回归新理论。文章简单介绍了这一理论的基本思路与数学模型以及根据该模型设计出的计算机程序。该程序可进行标准曲线的一次曲线、二次曲线、三次曲线、指数曲线和对数曲线的简单回归、一次曲线的Y-单误差(含相对误差和绝对误差)回归,一次曲线x、y双误差(含相对误差和绝对误差)回归,并可根据回归曲线计算出给定允许最大误差时的检出限。程序功能齐全、界面友好、使用方便,是化学检测实验室必备的工具软件之一。

考虑光伏接入不确定性的主动配电网有功?无功可控资源优化配置

为考虑光伏接入的不确定性对主动配电网的影响,并分析建模,首先通过光伏?负荷的连续概率密度函数,将概率性场景进行最优多状态建模以抽取代表性时序场景;然后面向所抽取的场景集合,基于模糊层次分析法量化考量电压质量、经济效益、自治能力3个主动配电网运行规划性能要素。采用双层机会约束规划,构建了针对光伏接入不确定性的有功-无功可控资源优化配置模型。运用嵌入机会约束的混沌粒子群优化算法求解,实现了时序运行与配置方案协同的微型燃气轮机和电容器组的协调部署。某地实际馈线算例验证了所提模型与方法的有效性和实用性,同时该方法提高了计算精度。

基于不确定客流的高速铁路列车停站方案优化方法

利用不确定变量刻画高铁车站客流需求不确定性的基础上,以最小化区段内开行列车的总停站次数为优化目标,以客流需求得到满足应达到的置信水平、列车的最大停站次数及车站所需最少停站列车数量等为约束条件,构建不确定客流条件下高速铁路列车停站方案设计问题的机会约束规划模型,并利用不确定理论的相关知识将其转化为确定性的等价类模型,通过MATLAB平台调用GUROBI优化软件对模型进行求解和计算分析。最后,以武广段高速铁路作为实际背景,在假设列车开行对数和开行区间已知的前提下,通过上述方法求得最优列车停站方案。算例结果验证本文所提方法具有良好的优化质量和计算效率。

不确定性下的多矩阵博弈模型及求解算法

为解决多矩阵博弈中每个支付值存在多种选择的问题,提出1种多矩阵博弈模型。定义了最优矩阵的概念。以多个矩阵的形式表述支付矩阵的支付值不确定问题。通过引入二进制变量,将混合整数规划的多矩阵博弈模型转化为标准的线性规划模型进行求解。根据二进制变量的取值搜索出了最优矩阵。

战时多阶段备件供应保障优化

战时备件供应保障优化研究战时备件在供应保障网络中如何配置以及储运的问题。根据网络的层次性及作战的时序性,抽象出一个典型的多阶段三级战时备件供应保障系统的规划模型。针对战时作战单元对备件需求的不确定性,在确定性规划模型基础上给出了战时备件供应保障系统的机会约束规划模型,最后给出了采用随机模拟的遗传算法求解模型的步骤,并给出了应用实例。

基于区间多阶段随机规划模型的灌区多水源优化配置

灌区多水源灌溉系统中存在许多不确定性因素,随着系统环境的变化及不确定性因素的影响,导致其配水过程具有动态特征。针对灌区多水源灌溉系统的配水特点,该文建立基于区间多阶段随机规划的灌区多水源优化配置模型。同时,考虑灌溉水对作物产量的影响,引入水分敏感指数权重系数,并以黑龙江省和平灌区水稻不同生育阶段灌溉水资源优化配置进行实例研究。结果表明,在不同来水情境下,管理者可根据各个生育阶段水分敏感指数权重系数,判断作物不同生育阶段的需水敏感程度,当来水情境的来水量多时,会产生余水量,可调配给下一生育阶段;当来水情境的来水量少时,管理者可在减少灌溉水量与增加外调水之间进行权衡,并根据需水关键期与需水非关键期做出决策,使水资源在作物生育阶段间及作物生育阶段内进行分配,实现灌区多水源灌溉系统的动态配水。该模型的应用在确保作物产量的同时,使灌溉水资源在作物各个生育阶段进行合理配置,有效地避免了水资源浪费,对提高灌溉水利用效率、保证水资源的可持续利用具有重要意义。

考虑风险价值的不确定性水资源优化配置

保障区域农业用水的可持续发展,对水资源进行优化配置至关重要。由于粮食主产区水资源配置过程中存在许多不确定性因素,在追求最小用水成本时,也存在着较大的风险,因此该文以三江平原涵盖的七台河、佳木斯、双鸭山、鹤岗和鸡西5个重要粮食主产区为研究区域,以区间两阶段随机规划模型为基础,引入风险偏好,构建地表水和地下水优化配置模型。结果表明,双鸭山和鸡西的配水过程中地表水缺水量很大,主要开采利用地下水;佳木斯作为粮食生产面积较大的行政区,需要外来水进行补给;七台河和鹤岗的种植面积较小,综合考虑引水成本和粮食收益,引用较少的外来水来降低成本;最后得出不同来水水平下,各种风险偏好下水资源优化配置的最小成本的变化规律,即在低来水水平下,用水成本从344.2×108~355.4×108元增加到411.5×108~430.7×108元;在高来水水平下,总用水成本从422.5×108~435.3×108元降低到351.7×108~365.3×108元;在中来水水平时,总用水成本则呈现出先增加后减少的规律。该模型兼有区间两阶段和风险价值模型的特点,综合衡量成本和风险,可有效节约用水成本,并能增强水资源系统规避风险的能力,并以佳木斯2014年实际用水为例,计算得到相对误差在15%以内,较为真实地反映水资源优化配置过程中的不确定性和风险性,为提高水资源利用效率和区域水资源规划管理提供依据。