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Optimal water resource management for sustainable development of the chemical industrial park under multi-uncertainty and multi-pollutant control

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2018年第27期25卷 27245-27259页

作者： He, Li Chen, Yizhong Kang, Yu Tian, Peipei Zhao, Honghai Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China North China Elect Power Univ Sch Renewable Energy Beijing 102206 Peoples R China

Two major concerns over the chemical industrial park (CIP) operations are high consumption of water resources and large amount of pollutant emissions. This study develops an interval chance-constrained programming model for industrial water resources management (ICCP-IWM) with consideration of multi-uncertainty and multi-environmental constraints. Uncertainties expressed as intervals and probability distributions are merged in the ICCP-IWM framework. The developed model is used to solve a real-world water resource management problem in the Shenyang Chemical Industrial Park to demonstrate its capacity and effectiveness, where the objective is to minimize the system cost of water pathways and pollutant-emission control under a series of constraints. Interval solutions with respect to water resources allocation, wastewater management, and pollutant emissions could be generated. Results indicate that a lower violation risk leads to an increased strictness of the constraints, then to a higher system cost;conversely, a higher violation risk results in a lower system cost, at the expense of an increase in the risk. These findings would be recommended by the decision-makers because of their applicability for practical decision process providing the optimal strategy for sustainable water resource management under multiple uncertainties.

关键词： Chemical industrial park Water resources Violation risk chance-constrained programming Sustainable

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Iterative Solution Process for Multiple Objective Stochastic Linear programming Problems Under Fuzzy Environment

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FUZZY INFORMATION AND ENGINEERING 2018年第4期10卷 435-451页

作者： Garai, Arindam Mandal, Palash Roy, Tapan Kumar Sonarpur Mahavidyalaya Dept Math Kolkata India IIEST Dept Math Sibpur India

This article presents one interactive algorithm, and thereby determines the Pareto optimal solution to multi-objective stochastic linear programming (MOSLP) problems in real-life oriented fuzzy environment. Among the various objective functions, there always exists one objective function, referred to as the main objective function in this article, to multi-objective models, whose optimal value is most vital to decision-makers. When the optimal value to main objective function meets the pre-determined aspiration level, and the corresponding values to other objective functions are satisfactory in nature, that Pareto optimal solution is acceptable to decision-makers. Again, in several existing interactive fuzzy optimisation methods to MOSLP models, all reference membership levels of expectations to objective functions are considered as a unity. However, this seems to be less rational that the expectation of each conflicting objective function simultaneously attains the individual goal. So, the present article proposes to employ the trade-off ratios of membership functions to analytically determine reference membership levels in a fuzzy environment. Numerical applications further illustrate this algorithm. Finally, conclusions are drawn.

关键词： Main objective function trade-off ratio chance-constrained programming expectation model fuzzy multi-objective optimisation stochastic optimisation

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chance-constrained DATA ENVELOPMENT ANALYSIS MODELING WITH RANDOM-ROUGH DATA

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RAIRO-OPERATIONS RESEARCH 2018年第1期52卷 259-284页

作者： Shiraz, Rashed Khanjani Tavana, Madjid Di Caprio, Debora Univ Tabriz Sch Math Sci Tabriz Iran La Salle Univ Business Syst & Analyt Dept Business Analyt Philadelphia PA 19141 USA Univ Paderborn Fac Business Adm & Econ Business Informat Syst Dept D-33098 Paderborn Germany York Univ Dept Math & Stat Toronto ON M3J 1P3 Canada Polo Tecnol IISS G Galilei Via Cadorna 14 I-39100 Bolzano Italy

Data envelopment analysis (DEA) is a useful management tool for measuring the relative efficiency of decision making units (DMUs) which consumes multiple inputs to produce multiple outputs. Although precise input and output data are fundamentally indispensable in classical DEA models, real-world problems often involve random and/or rough input and output data. We present a chance-constrained DEA model with random and rough (random-rough) input and output data and propose a deterministic equivalent model with quadratic constraints to solve the model. The main contributions of this paper are fourfold: (3.1) we propose a DEA model for problems characterized by random-rough variables;(3.2) we transform the proposed chance-constrained model with random-rough variables into a deterministic equivalent non-linear form that could be simplified as a deterministic model with quadratic constraints;(3.3) we perform sensitivity analysis to investigate the stability and robustness of the proposed model;and (3.4) we use a numerical example to demonstrate the feasibility and richness of the obtained solutions.

关键词： Data envelopment analysis chance-constrained programming random and rough data alpha-optimistic alpha-pessimistic

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Day-ahead unit commitment method considering time sequence feature of wind power forecast error

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2018年 98卷 156-166页

作者： Wang, Chengfu Li, Xijuan Wang, Zhaoqing Dong, Xiaoming Liang, Zhengtang Liu, Xiaoyi Liang, Jun Han, Xueshan Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Shandong Peoples R China Shandong Elect Power Engn Consulting Inst Co Ltd Jinan 250013 Shandong Peoples R China State Grid Shandong Elect Power Res Inst Jinan 250002 Shandong Peoples R China

The variable and intermittent nature of large-scale wind power integration makes day-ahead unit commitment (UC) decision-making difficult. This paper establishes a novel and effective UC model with wind power integration by optimizing the utilization of the forecast error and reserve decision. First, considering the temporal feature of the UC model, a time sequence segment-fitting method (TSFM) for the wind power forecast error is presented, in which the non-parametric fitting method is used to address the 'fat-tail' effect of error distribution. Second, according to the probability intervals of the forecast error and characteristic of the reserve, a new reserve decision method is proposed to define three classes of reserve strategies and optimize the capacity for each type of reserve. Third, a UC model with time-varying confidence levels is established by introducing conditional value at risk (CVaR) and chance-constrained programming (CCP), and is linked with the TSFM. This novel model can balance the costs of fuel, various reserves, load shedding risk, and wind curtailment risk, which can improve the economy of the power grid operation. Finally, an improved hybrid particle swarm optimization algorithm with a heuristic searching strategy is applied to solve this multivariate mixed integer non-linear programming problem. The simulation results verify the effectiveness and practicality of the model proposed.

关键词： chance-constrained programming Forecast error Non-parametric Time sequence segment fit Time varying confidence level Unit commitment Wind power

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Optimal distributed generation planning in active distribution networks considering integration of energy storage

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APPLIED ENERGY 2018年 210卷 1073-1081页

作者： Li, Yang Feng, Bo Li, Guoqing Qi, Junjian Zhao, Dongbo Mu, Yunfei Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA Argonne Natl Lab Energy Syst Div Lemont IL 60439 USA Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China

A two-stage optimization method is proposed for optimal distributed generation (DG) planning considering the integration of energy storage in this paper. The first stage determines the installation locations and the initial capacity of DGs using the well-known loss sensitivity factor (LSF) approach, and the second stage identifies the optimal installation capacities of DGs to maximize the investment benefits and system voltage stability and to minimize line losses. In the second stage, the multi-objective ant lion optimizer (MOALO) is first applied to obtain the Pareto-optimal solutions, and then the 'best' compromise solution is identified by calculating the priority memberships of each solution via grey relation projection (GRP) method, while finally, in order to address the uncertain outputs of DGs, energy storage devices are installed whose maximum outputs are determined with the use of chance-constrained programming. The test results on the PG & E 69-bus distribution system demonstrate that the proposed method is superior to other current state-of-the-art approaches, and that the integration of energy storage makes the DGs operate at their pre-designed rated capacities with the probability of at least 60%.

关键词： Active distribution network Distributed generation planning Two-stage optimization Energy storage chance-constrained programming Multi-objective ant lion optimize

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Optimization of Value-at-Risk: computational aspects of MIP formulations

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2018年第5期69卷 676-690页

作者： Pavlikov, Konstantin Veremyev, Alexander Pasiliao, Eduardo L. Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA Air Force Res Lab Munit Directorate Eglin AFB FL USA

Optimization of Value-at-Risk is an important problem both from theoretical and practical standpoints. It can be represented through a class of chance-constrained optimization problems, which are generally hard to solve. Mixed integer problem formulations with big M constants is a standard way to approach such problems, where tightness of these constants is a crucial factor for good performance of a solver. This study aims to improve the tightness of existing big Ms by explicitly incorporating bounds on the optimal value of VaR into the problem formulation. Moreover, the lower bound is demonstrated to play an especially important role in obtaining tight big M constants, and a procedure to lift this bound is discussed. Finally, a two-stage solution approach is proposed, where the first stage solely deals with tightening the bounds, and the second stage employs improved bounds to redefine big Ms and solves the problem to optimality. Numerical experiments suggest that proposed solution methods can decrease solution time by up to 75% compared to the most recent benchmark, which may allow to handle larger problem instances while using the same hardware.

关键词： chance-constrained programming percentile minimization combinatorial optimization Value-at-Risk Conditional Value-at-Risk

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Optimal Latin hypercube sampling-based surrogate model in NAPLs contaminated groundwater remediation optimization process

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WATER SCIENCE AND TECHNOLOGY-WATER SUPPLY 2018年第1期18卷 333-346页

作者： Luo, Jiannan Ji, Yefei Lu, Wenxi Wang, He Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Coll Environm & Resources 2519 Jiefang Rd Changchun 130021 Jilin Peoples R China Jilin Univ Construct Engn Coll 6 Ximinzhu St Changchun 130026 Jilin Peoples R China Minist Water Resources Songliao Water Resources Commiss 4188 Jiefang Rd Changchun 130021 Jilin Peoples R China Jilin Jinrun Environm Technol Serv Co Ltd 888 Guigu St Changchun 130015 Jilin Peoples R China

A surrogate model based groundwater optimization model was developed to solve the non-aqueous phase liquids (NAPLs) contaminated groundwater remediation optimization problem. To illustrate the impact of sampling method improvement to the surrogate model performance improvement, aiming at a nitrobenzene contaminated groundwater remediation problem, optimal Latin hypercube sampling (OLHS) method was introduced to sample data in the input variables feasible region, and a radial basis function artificial neural network was used to construct a surrogate model. Considering the surrogate model's uncertainty, a chance-constrained programming (CCP) model was constructed, and it was solved by genetic algorithm. The results showed the following, for the problem considered in this study. (1) Compared with the Latin hypercube sampling (LHS) method, the OLHS method improves the space-filling degree of sample points considerably. (2) The effects of the two sampling methods on surrogate model performance were analyzed through comparison of goodness of fit, residual and uncertainty. The results indicated that the OLHS-based surrogate model performed better than the LHS-based surrogate model. (3) The optimal remediation strategies at 99%, 95%, 90%, 85%, 80% and 50% confidence levels were obtained, which showed that the remediation cost increased with the confidence level. This work would be helpful for increasing surrogate model performance and lowering the risk of a groundwater remediation strategy.

关键词： chance-constrained programming groundwater contamination remediation NAPLs optimal Latin hypercube sampling surrogate model uncertainty

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Outage protection for cellular-mode users in device-to-device communications through stochastic optimization

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COMPUTER NETWORKS 2018年 132卷 145-160页

作者： Quang-Tuan Thieu Hsieh, Hung-Yun Natl Taiwan Univ Grad Inst Commun Engn Taipei 106 Taiwan Natl Taiwan Univ Dept Elect Engn Taipei 106 Taiwan

In device-to-device (D2D) communications, the stochastic nature of the wireless channel causes the coexistence problem between cellular-mode and D2D-mode users a nontrivial task, especially when protection of cellular-mode users is strictly required. While related work has investigated the interference management problem in different scenarios, most approaches have considered only the long-term channel gain without explicitly addressing the randomness of the channel. In this paper, we aim to investigate the problem of D2D-mode users sharing the same radio resource blocks with the cellular-mode user under Rayleigh channel fading through stochastic optimization. Our objective is to determine the transmission powers of all D2D-mode users for optimizing their sum data rates while ensuring the outage probability of data sent by the cellular-mode user to stay below the desired protection threshold. We first introduce a new technique to transform both the objective and constraint functions involving random variables into equivalent yet deterministic forms. Since the formulated problem is non-linear and non-convex, we further tighten the upper bound of the transmission power constraint to reduce the complexity and uncertainty of searching for the optimal solution. To solve the formulated problem, we propose two different algorithms: the first algorithm reformulates and solves the problem as a linear programming (LP) problem while the second algorithm directly solves the non-linear problem based on the meta-heuristic of differential evolution (DE). Simulation results demonstrate that using the proposed algorithms the outage probability of the cellular-mode user can be maintained below the desired threshold despite the uncertainty of channel conditions, while the sum rate of D2D-mode users outperforms baseline methods under the same constraint. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Device-to-device (D2D) communication Power control Interference management chance-constrained programming Rayleigh fading

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Vehicle routing with probabilistic capacity constraints

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第2期270卷 544-555页

作者： Noorizadegan, Mandi Chen, Bo Univ Warwick Warwick Business Sch Coventry CV4 7AL W Midlands England

In this paper, we study chance-constraint vehicle routing with stochastic demands. We propose a set partitioning formulation for the underlying problem and solve it via a branch-and-price method. Our method is flexible in modeling different types of demand randomness while ensuring that the resulting problem is tractable. An extensive computational analysis, which includes simulation tests and a sensitivity analysis, is carried out to investigate the solution quality and computational efficiency. Some large instances of the underlying problems from the VRP library are solved to optimality for the first time. Our sensitivity analysis provides some useful insights about the impact of the probability of route failure on the decision variables, the expected cost and the route reliability. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： Routing chance-constrained programming Set-partitioning formulation

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An Interval-based Fuzzy chance-constrained Irrigation Water Allocation model with double-sided fuzziness

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AGRICULTURAL WATER MANAGEMENT 2018年 210卷 22-31页

作者： Zhang, Chenglong Engel, Bernard A. Guo, Ping China Agr Univ Coll Water Resources & Civil Engn Ctr Agr Water Res China Tsinghuadong St 17 Beijing 100083 Peoples R China Purdue Univ Dept Agr & Biol Engn W Lafayette IN 47907 USA

This study presents an Interval-based Fuzzy chance-constrained Irrigation Water Allocation (IFCIWA) model with double-sided fuzziness for supporting irrigation water management. It is derived from incorporating double-sided chance-constrained programming (DFCCP) into an interval parameter programming (IPP) framework. The model integrates interval linear crop water production functions into its general framework for irrigation water allocation. Moreover, it can deal with uncertainties presented as discrete intervals and fuzziness. It can also allow violation of system constraints with double-sided fuzziness, where each confidence level consists of two reliability scenarios (i.e. minimum and maximum reliability scenarios). To demonstrate its applicability, the model is then applied to a case study in the middle reaches of the Heihe River Basin, northwest China. Therefore, optimal solutions have been generated for irrigation water allocation under uncertainty. The results indicate that planning under a lower confidence level and a minimum reliability scenario can provide maximized system benefits. System benefits under the high water level are [2.659, 7.913] x 10(9) Yuan when alpha = 0, [2.650, 7.822] x 10(9) Yuan when alpha = 0.5 and [2.642, 7.734] x 10(9) Yuan when alpha = 1.0 under the minimum reliability scenario. Furthermore, the results can support in-depth analysis of interrelationships among system benefits, confidence levels, reliability levels and risk levels. These results can effectively provide decision support for managers identifying desired irrigation water allocation plans in study area.

关键词： Irrigation water Management Interval crop water production function Fuzzy chance-constrained programming Decision-making

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