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

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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A stochastic optimization framework for planning of waste collection and value recovery operations in smart and sustainable cities

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WASTE MANAGEMENT 2018年 78卷 104-114页

作者： Shah, Parth Jatinkumar Anagnostopoulos, Theodoros Zaslaysky, Arkady Behdad, Sara Univ Buffalo Buffalo NY 14260 USA Univ West Attica Athens Greece CSIRO Data61 Clayton Australia

The concept of City 2.0 or smart city is offering new opportunities for handling waste management practices. The existing studies have started addressing waste management problems in smart cities mainly by focusing on the design of new sensor-based Internet of Things (loT) technologies, and optimizing the routes for waste collection trucks with the aim of minimizing operational costs, energy consumption and transportation pollution emissions. In this study, the importance of value recovery from trash bins is highlighted. A stochastic optimization model based on chance-constrained programming is developed to optimize the planning of waste collection operations. The objective of the proposed optimization model is to minimize the total transportation cost while maximizing the recovery of value still embedded in waste bins. The value of collected waste is modeled as an uncertain parameter to reflect the uncertain value that can be recovered from each trash bin due to the uncertain condition and quality of waste. The application of the proposed model is shown by using a numerical example. The study opens new venues for incorporating the value recovery aspect into waste collection planning and development of new data acquisition technologies that enable municipalities to monitor the mix of recyclables embedded in individual trash bins. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： loT-enabled waste collection and recovery Smart cities End-of-life recovery chance-constrained programming

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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年第Feb.26期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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Rough-fuzzy quadratic minimum spanning tree problem

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EXPERT SYSTEMS 2019年第2期36卷 e12364-e12364页

作者： Majumder, Saibal Kar, Samarjit Pal, Tandra Natl Inst Technol Durgapur Dept Comp Sci & Engn Durgapur India Natl Inst Technol Durgapur Dept Math Durgapur 713209 W Bengal India

A quadratic minimum spanning tree problem determines a minimum spanning tree of a network whose edges are associated with linear and quadratic weights. Linear weights represent the edge costs whereas the quadratic weights are the interaction costs between a pair of edges of the graph. In this study, a bi-objective rough-fuzzy quadratic minimum spanning tree problem has been proposed for a connected graph, where the linear and the quadratic weights are represented as rough-fuzzy variables. The proposed model is formulated by using rough-fuzzy chance-constrained programming technique. Subsequently, three related theorems are also proposed for the crisp transformation of the proposed model. The crisp equivalent models are solved with a classical multi-objective solution technique, the epsilon-constraint method and two multi-objective evolutionary algorithms: (a) nondominated sorting genetic algorithm II (NSGA-II) and (b) multi-objective cross-generational elitist selection, heterogeneous recombination, and cataclysmic mutation (MOCHC) algorithm. A numerical example is provided to illustrate the proposed model when solved with different methodologies. A sensitivity analysis of the example is also performed at different confidence levels. The performance of NSGA-II and MOCHC are analysed on five randomly generated instances of the proposed model. Finally, a numerical illustration of an application of the proposed model is also presented in this study.

关键词： chance-constrained programming epsilon-constraint method MOCHC NSGA-II quadratic minimum spanning tree rough-fuzzy variable

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Simulation-based inexact chance-constrained nonlinear programming for eutrophication management in the Xiangxi Bay of Three Gorges Reservoir

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2012年 108卷 54-65页

作者： Huang, Y. L. Huang, G. H. Liu, D. F. Zhu, H. Sun, W. Univ Regina Fac Engn & Appl Sci Regina SK S4S 0A2 Canada China Three Gorges Univ Coll Hydraul & Environm Engn Yichang City 443002 Hubei Province Peoples R China

Although integrated simulation and optimization approaches under stochastic uncertainty have been applied to eutrophication management problems, few studies are reported in eutrophication control planning where multiple formats of uncertainties and nonlinearities are addressed in forms of intervals and probabilistic distributions within an integrated framework. Since the impounding of Three Gorges Reservoir (TGR), China in 2003, the hydraulic conditions and aquatic environment of the Xiangxi Bay (XXB) have changed significantly. The resulting emergence of eutrophication and algal blooms leads to its deteriorated water quality. The XXB becomes an ideal case study area. Thus, a simulation-based inexact chance-constrained nonlinear programming (SICNP) model is developed and applied to eutrophication control planning in the XXB of the TGR under uncertainties. In the SICNP, the wastewater treatment costs for removing total phosphorus (TP) are set as the objective function;effluent discharge standards, stream water quality standards and eutrophication control standards are considered in the constraints;a steady-state simulation model for phosphorus transport and fate is embedded in the environmental standards constraints;the interval programming and chance-constrained approaches are integrated to provide interval decision variables but also the associated risk levels in violating the system constraints. The model results indicate that changes in the violating level (q) will result in different strategy distributions at spatial and temporal scales;the optimal value of cost objective is from [2.74, 13.41] million RMB to [2.25, 13.08] million RMB when q equals from 0.01 to 0.25;the required TP treatment efficiency for the Baisha plant is the most stringent, which is followed by the Xiakou Town and the Zhaojun Town, while the requirement for the Pingyikou cement plant is the least stringent. The model results are useful for making optimal policies on eutrophication control pl

关键词： The Xiangxi Bay Eutrophication management chance-constrained programming Uncertainty

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A generalized fuzzy chance-constrained energy systems planning model for Guangzhou, China

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ENERGY 2018年第PartA期165卷 191-204页

作者： Cai, Mengting Huang, Guohe Chen, Jiapei Li, Yunhuan Fan, Yurui North China Elect Power Univ UR NCEPU Inst Energy Environm & Sustainabil Res Beijing 102206 Peoples R China UR BNU Inst Energy Environm & Sustainable Communities 3737 Wascana Pkwy Regina SK S4S 0A2 Canada

In this study, a generalized fuzzy chance constrained programming method is developed for the energy system planning in Guangzhou under multiple uncertainties. Through integrating the generalized fuzzy programming and chance-constrained programming into an inexact optimization framework, this method can handle uncertainties expressed as probability distributions, fuzzy sets and fuzzy random variables. Solutions of energy supply, power generation, capacity expansion, air pollutant emissions, forest planning, and system cost under different levels of a-cutare obtained considering the constraint violation risk. The results show that the consumption of coal will decline gradually, while natural gas will become the main source of energy supply in the future;the power structure of the city changes from coal to clean energy (e.g., solar, wind, hydro and other renewable energy), and the city's energy supply security is enhanced by stimulating the utilization of renewable energy and reducing the utilization of imported energy. Moreover, a rational use of ecological land is of great significance. Forests can absorb carbon dioxide and will play a positive role in reducing greenhouse effects. When the preferred a value is predetermined by the decision makers, the energy selections can also be obtained directly from the resulting fuzzy membership function. The solutions obtained in the study will help managers to optimize the existing city energy structure, make decisions according to different preferences between system cost and the violation of the constraint, and thus reflect the corresponding energy supply security level. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： chance-constrained programming Energy model GHG mitigation Energy security Generalized fuzzy linear programming

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A Distributionally Robust chance-constrained MILP Model for Multistage Distribution System Planning With Uncertain Renewables and Loads

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IEEE TRANSACTIONS ON POWER SYSTEMS 2018年第5期33卷 5248-5262页

作者： Zare, Alireza Chung, C. Y. Zhan, Junpeng Faried, Sherif Omar Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK S7N 5A9 Canada

Successful transition to active distribution networks (ADNs) requires a planning methodology that includes an accurate network model and accounts for the major sources of uncertainty. Considering these two essential features, this paper proposes a novel model for the multistage distribution expansion planning (MDEP) problem, which is able to jointly expand both the network assets (feeders and substations) and renewable/conventional distributed generators. With respect to network characteristics, the proposed planning model employs a convex conic quadratic format of ac power flow equations that is linearized using a highly accurate polyhedral-based linearization method. Furthermore, a chance-constrained programming approach is utilized to deal with the uncertain renewables and loads. In this regard, as the probability distribution functions of uncertain parameters are not perfectly known, a distributionally robust (DR) reformulation is proposed for the chance constraints that guarantees the robustness of the expansion plans against all uncertainty distributions defined within a moment-based ambiguity set. Effective linearization techniques are also devised to eliminate the nonlinearities of the proposed DR reformulation, which yields a distributionally robust chance-constrained mixed-integer linear programming model for the MDEP problem of ADNs. Finally, the 24-node and 138-node test systems are used to demonstrate the effectiveness of the proposed planning methodology.

关键词： chance-constrained programming distributionally robust optimization mixed-integer linear programming (MILP) multistage distribution expansion planning (MDEP)

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Short-Term Line Maintenance Scheduling of Distribution Network With PV Penetration Considering Uncertainties

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IEEE ACCESS 2018年 6卷 33621-33630页

作者： Bao, Zhejing Gui, Canzhi Guo, Xiaogang Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

In this paper, the methodology for short-term line maintenance scheduling in distribution network with PV penetration is proposed, in which the mixed randomness and fuzziness of solar power generation concerning the available cloud amount forecasting, along with other random or fuzzy factors, such as electricity demand and component historical failure rate, are considered. First, based on the obtained historical data from NASA, the empirical mapping from cloud amount to solar irradiance can be established where the uncertainty is represented by combining randomness and fuzziness. Second, the short-term line maintenance scheduling of distribution network with uncertainties is modeled by using random fuzzy chance-constrained programming aiming at minimizing the pessimistic value in terms of economics and reliability subject to the chance constraints. Finally, a hybrid intelligent algorithm with two-layer optimization is implemented to solve the model. Simulation experiments are carried out on the IEEE 33-Bus and IEEE RBTS 2-Bus systems, and the results demonstrate the effectiveness of the proposed short-term line maintenance scheduling solution for distribution network with the mixed uncertainties of randomness and fuzziness.

关键词： chance-constrained programming fuzzy maintenance scheduling random renewable energy uncertainty

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Robust optimization: Lessons learned from aircraft routing

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COMPUTERS & OPERATIONS RESEARCH 2018年 98卷 165-184页

作者： Marla, Lavanya Vaze, Vikrant Barnhart, Cynthia Univ Illinois Champaign IL 61820 USA Dartmouth Coll Hanover NH 03755 USA MIT Cambridge MA 02139 USA

Building robust airline scheduling models involves constructing schedules and routes with reduced levels of flight delays as well as fewer passenger and crew disruptions. In this paper, we study different classes of models to achieve robust airline scheduling solutions, with a focus on the aircraft routing problem. In particular, we compare one domain-specific approach and two general paradigms of robust models, namely, (i) an extreme-value based or robust optimization-based approach, and (ii) a chance-constrained or stochastic optimization-based approach. Our modeling and solution approach demonstrates the creation of data-driven uncertainty sets for aircraft routing using domain-specific knowledge and develops a completely data-driven simulation-based validation and testing approach. We first demonstrate that additional modeling, capturing domain knowledge, is required to adapt these general robust modeling paradigms to the aircraft routing problem, in order to meaningfully add robustness features specific to aircraft routing. However, we show that these models in their naive forms, still face issues of tractability and solution quality for the large-scale networks which are representative of real-world airline scheduling problems. Therefore, we develop and present advanced models that address these shortcomings. Our advanced models can be applied to aircraft routing in multiple ways, through varied descriptions of the uncertainty sets;and moreover, are generally applicable to linear and binary integer programming problems. Through our detailed computational results, we compare the performance of solutions arising from these different robust modeling paradigms and discuss the underlying reasons for their performance differences from a data-driven perspective. Published by Elsevier Ltd.

关键词： Robust airline scheduling Aircraft routing Robust optimization chance-constrained programming

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chance constrained dynamic optimisation method for AGC units dispatch considering uncertainties of the offshore wind farm

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JOURNAL OF ENGINEERING-JOE 2019年第16期2019卷 2112-2119页

作者： Zhao, Xia Ye, Xiaobin Yang, Lun Zhang, Rongrong Yan, Wei Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing Peoples R China State Grid Fujian Fuzhou Elect Power Supply Co Fuzhou Fujian Peoples R China State Grid Jiangxi Nanchang Elect Power Supply Co Nanchang Jiangxi Peoples R China

The continuing growth of an offshore wind farm integrated into a power grid via high-voltage DC has posed great challenges for automatic generation control (AGC) of the power system. To address these challenges, a new concept of dynamic dispatch of AGC units (DDA)' under control performance standards from the view of economic dispatch (ED) has been proposed in the previous work, and proved to be an effective technique to co-operate the AGC units with different ramping rates and to fill the gap between ED and AGC. However, the existing DDA model is deterministic in nature, which can hardly deal with the uncertain forecasting error of the offshore wind power output. A novel stochastic DDA model based on chance-constrained programming is proposed considering a random offshore wind power forecasting error, and a hybrid algorithm combining the evolutionary programming algorithm and point estimate method is then developed to solve the stochastic model. Numerical results from a two-area test system with additional offshore wind power generation demonstrate the accuracy and computation efficiency of the hybrid algorithm and the benefits offered by the stochastic AGC dispatch method.

关键词： power generation control evolutionary computation power generation dispatch power generation economics power generation scheduling power generation reliability stochastic processes wind power offshore installations wind power plants power grids optimisation additional offshore wind power generation hybrid algorithm stochastic AGC dispatch method dynamic optimisation method AGC units offshore wind farm continuing growth power grid high-voltage DC great challenges automatic generation control power system dynamic dispatch control performance standards economic dispatch existing DDA model uncertain forecasting error offshore wind power output novel stochastic DDA model chance-constrained programming random offshore wind power forecasting error evolutionary programming algorithm point estimate method

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