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chance-constrained Optimization in D2D-Based Vehicular Communication Network

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2019年第5期68卷 5045-5058页

作者： Liu, Zhixin Xie, Yuan'ai Chan, Kit Yan Ma, Kai Guan, Xinping Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China Curtin Univ Sch Elect Engn Comp & Math Sci Perth WA 6845 Australia Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

In this paper, a novel scheme is proposed to optimize the effectiveness of the device-to-device (D2D) enabled vehicular communications (D2D-V), which are underlaid in cellular networks, where the uplink channel allocated to one cellular user (CU) is reused by the multiple D2D-V links. To develop a well-functioning D2D-V system, the sum rate of all D2D-V links is necessary to be maximized, and also the reliability of the co-channel CU to infrastructure has to be guaranteed with the CU interference constraint. Unlike the traditional statical D2D systems, the D2D-V system suffers with high mobility and channel uncertainty. Therefore, the CU interference constraint is formulated as a probabilistic function by the Bernstein approximation, the constraint attempts to address the mobile channel fluctuations. Besides, the tractable approximate constraint is reformulated as two separable structure, this reformulated constraint attempts to determine the near-optimal solutions more efficiently. Since the objective function with a logarithmic form is nonconvex, successive convex approximation is applied to transform the nonconvex problem into the convex problem. The dual decomposition is used to determine the near-optimal solutions. After the problem is reformulated, a distributed robust power control algorithm is proposed to perform the chance-constrained optimization. Numerical simulations are used to evaluate the performance of the proposed scheme. The simulation results show the impacts of system performance when applying the proposed scheme in vehicular environments with high mobility. The proposed optimization schemes are further verified by comparing with the existing methods.

关键词： Device-to-device (D2D) communication power control vehicular communications channel uncertainty chance-constrained programming

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A chance-constrained portfolio selection model with random-rough variables

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NEURAL COMPUTING & APPLICATIONS 2019年第2-Sup期31卷 931-945页

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

Traditional portfolio selection (PS) models are based on the restrictive assumption that the investors have precise information necessary for decision-making. However, the information available in the financial markets is often uncertain. This uncertainty is primarily the result of unquantifiable, incomplete, imprecise, or vague information. The uncertainty associated with the returns in PS problems can be addressed using random-rough (Ra-Ro) variables. We propose a new PS model where the returns are stochastic variables with rough information. More precisely, we formulate a Ra-Ro mathematical programming model where the returns are represented by Ra-Ro variables and the expected future total return maximized against a given fractile probability level. The resulting change-constrained (CC) formulation of the PS optimization problem is a non-linear programming problem. The proposed solution method transforms the CC model in an equivalent deterministic quadratic programming problem using interval parameters based on optimistic and pessimistic trust levels. As an application of the proposed method and to show its flexibility, we consider a probability maximizing version of the PS problem where the goal is to maximize the probability that the total return is higher than a given reference value. Finally, a numerical example is provided to further elucidate how the solution method works.

关键词： Portfolio selection chance-constrained programming Quadratic programming Random-rough data Rough set theory

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A feasibility study of a risk-based stochastic optimization approach for radiation treatment planning under setup uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 135卷 67-78页

作者： Khabazian, Azin Zaghian, Maryam Lim, Gino J. Univ Houston Dept Ind Engn 4800 Calhoun Rd Houston TX 77204 USA Univ Texas MD Anderson Canc Ctr Houston TX 77030 USA

This article introduces a planner-driven flexible stochastic decision making model to develop radiation treatment plans for cancer patients under patient-setup uncertainty. The clinical goal is to deliver the prescribed amount of radiation dose to the target tissue(s) while sparing the organs nearby. However, it is difficult to achieve the goal because organs are often closely located in the body. Therefore, some tissues may receive a higher radiation dose than desired. To minimize such violations and allow to make a trade-off between tumor coverage and healthy tissue sparing, we present a chance constrained programming (CCP) optimization method. A planner can use the CCP approach to specify how much clinical violation can be allowed for a specific patient. Assuming that the uncertain dose distribution follows a known (or estimated) probability distribution function, the CCP model was tested using five clinical cases. The resulting treatment plans were compared with the plans generated by the conventional robust worst-case optimization method using dose-volume histograms. Our results support the CCP approach over the robust optimization method in terms of healthy tissues sparing and the clinical target dose requirements. Overall, the risk-based CCP model is not only flexible to accommodate the planner's risk profile and to meet patient specific treatment goals, but has potential to compromise for overly-conservative treatment plans generated by robust optimization methods.

关键词： chance-constrained programming Radiation treatment planning Risk-based approach Setup uncertainty Stochastic optimization

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Optimized Operation of Hybrid System Integrated With MHP, PV and PHS Considering Generation/Load Similarity

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IEEE ACCESS 2019年 7卷 107793-107804页

作者： Li, Tao Hu, Weihao Xu, Xiao Huang, Qi Chen, Gang Han, Xiaoyan Chen, Zhe Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Sichuan Peoples R China State Grid Sichuan Elect Power Res Inst Chengdu 610041 Sichuan Peoples R China State Grid Sichuan Elect Power Co Chengdu 610041 Sichuan Peoples R China Aalborg Univ Dept Energy Technol DK-9100 Aalborg Denmark

Renewable energy is widely used as a clean energy in the world, yet the intermittent of the power generation from renewable energy power plants, resulting in poor quality of power supply. However, the hybrid energy system and some energy storage devices can be installed to mitigate the power fluctuation, to achieve power smoothing and maximize the profit, a double-objective optimization model of hybrid system integrated with micro-hydro power (MHP), photovoltaic (PV), and pumped hydro storage (PHS) is established in this study. The day-ahead optimized operation strategy of the PHS based-on hour-level data of seven days is obtained, which aims to maximize the similarity value (SIM) between the power generation curve and load profile, and the economic revenue (ER) of the PHS. Optimized schedule is obtained by means of chance-constrained programming (CCP) and modified multi-objective particle swarm optimization (MOPSO). The performance of the model is evaluated by using the meteorological, the historical MHP power generation and the load data obtained in Xiaojin County, Sichuan province, China. The simulation results reveal that the best trade-off value between SIM and ER can be obtained on the Pareto front with comprehensive consideration of the system operation strategy and target. Furthermore, the generation/load similarity can be increased 7.89% under the participating of the PHS.

关键词： Micro-hydro power pump-hydro storage chance-constrained programming Pareto front

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Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design

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APPLIED ENERGY 2019年 236卷 183-195页

作者： Khishtandar, Soheila Islamic Azad Univ Qazvin Branch Dept Ind Management Qazvin Iran

This study proposes a fuzzy chance-constrained programming model to include uncertainty in the biogas supply chain design problem. Uncertain parameters of the model are available workforce, biomass demand, available biomass and biomass price. A hybrid solution framework consisting of Monte Carlo simulation and evolutionary algorithms (genetic algorithm and differential evolution) is put forward to find the exact and near global optimal solution for the fuzzy chance-constrained model. The results of the test problems show that evolutionary algorithms can effectively solve the mixed integer nonlinear model of biogas location allocation within a reasonable computational time. Also, validation of the hybrid solution framework at different confidence levels is verified. The impacts of uncertainty in available biomass, biomass demand and available workforce on the overall cost of biogas supply chain are studied through sensitivity analysis. A real-world case study with real-life data available from the Province of Khorasan Razavi is performed. This is the first study that designs a biogas supply chain for a province of Iran.

关键词： Biogas supply chain Facility location allocation Uncertainty Monte Carlo simulation Evolutionary algorithms chance-constrained programming

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Size optimization and economic analysis of a coupled wind-hydrogen system with curtailment decisions

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2019年第36期44卷 19658-19666页

作者： Jiang, Yuewen Deng, Zhihong You, Shi Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Fujian Peoples R China Tech Univ Denmark Dept Elect Engn DK-2800 Lyngby Denmark

In order to maximize the return on equity (ROE) of wind-hydrogen system investors, take full advantage of the wind power as well as smooth the wind power output of a wind farm, an optimal sizing model of a coupled wind-hydrogen system (CWHS) is established considering the requirements of wind power grid-connection technology. The fluctuating cost of wind power is calculated by an "equal-kWh following load" method and the chance-constrained programming is introduced to deal with the uncertainty generated by the wind power. The optimal capacity of each unit for a CWHS, including the wind power transmission project, electrolyser, compressor and so on, is acquired and the economic analysis is evaluated under comprehensive aspects, e.g. wind curtailment decisions, hydrogen prices, the correlations between wind power output and system load, and the fluctuation degrees of wind power generation. The simulation is established by the realistic historical data from a wind farm in Fujian, China. When the confidence level is 92%, the capacity of electrolysers increases with the increase of the hydrogen price when it is larger than the equivalent value 4.34 (sic)/kg. In addition, the smaller the correlation between wind power output and load and the bigger the volatility index of wind power output, the less the smoothing benefit of the CWHS, where the smaller capacity of the transmission project and bigger capacities of electrolysers and compressors are required. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Coupled wind-hydrogen system (CWHS) Size optimization Smoothing benefit chance-constrained programming Return on equity

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A chance-constrained programming Based Renewable Resources Included Generation Expansion Planning Method And Its Application

A Chance-Constrained Programming Based Renewable Resources I...

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Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Sun Yingyun Han Tao Ashfaq, Ahsan N China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing Peoples R China

ISBN: (纸本)9781457705472;9781457705465

Traditional generation expansion planning is based on the principle of certainty and not suitable for renewable energy resources (RES) included planning. The paper proposed a chance-constrained programming model to deal with the uncertainty caused by RES, and a simulation-base PSO algorithm is adopted to solve the model. The model and algorithm is used in an actual case which lie in the west of china, and the results show the effectiveness of the model and algorithm.

关键词： Generation Expansion Planning chance-constrained programming Renewable Energy Resources particl-swarm optimization(PSO)

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Probabilistic planning of electric vehicles charging stations in an integrated electricity-transport system

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ELECTRIC POWER SYSTEMS RESEARCH 2020年 189卷 106698-106698页

作者： Aghapour, Raziye Sepasian, Mohammad Sadegh Arasteh, Hamidreza Vahidinasab, Vahid Catalao, Joao P. S. Shahid Beheshti Univ Abbaspour Sch Engn Dept Elect Engn Tehran Iran Niroo Res Inst Power Syst Planning & Operat Grp Tehran Iran Newcastle Univ Sch Engn Newcastle Upon Tyne Tyne & Wear England Univ Porto Fac Engn Porto Portugal INESC TEC Porto Portugal

One of the most important aspects of the development of Electric Vehicles (EVs) is the optimal sizing and allocation of charging stations. Due to the interactions between the electricity and transportation systems, the key features of these systems (such as traffic network characteristics, charging demands and power system constraints) should be taken into account for the optimal planning. This paper addressed the optimal sizing and allocation of the fast-charging stations in a distribution network. The traffic flow of EVs is modeled using the User Equilibrium-based Traffic Assignment Model (UETAM). Moreover, a stochastic framework is developed based on the Queuing Theory (QT) to model the load levels (EVs' charging demand). The objective function of the problem is to minimize the annual investment cost, as well as the energy losses that are optimized through chance-constrained programming. The probabilistic aspects of the proposed problem are modeled by using the point estimation method and Gram-Charlier expansion. Furthermore, the probabilistic dominance criteria are employed in order to compare the uncertain alternatives. Finally, the simulation results are provided for both the distribution and traffic systems to illustrate the performance of the proposed problem.

关键词： Fast charging station Queuing theory Traffic assignment chance-constrained programming Point estimation Probabilistic dominance criteria

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chance-constrained Optimal Dispatch of Integrated Electricity and Natural Gas Systems Considering Medium and Long-term Electricity Transactions

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CSEE Journal of Power and Energy Systems 2019年第3期5卷 315-323页

作者： Gang Wu Yue Xiang Junyong Liu Xin Zhang Shuoya Tang College of Electrical Engineering Sichuan UniversityChengdu 610065China Energy and Power Theme School of WaterEnergy and EnvironmentCranfield UniversityCranfieldMK430ALUK

A novel stochastic optimal dispatch model is proposed considering medium and long-term electricity transactions for a wind power integrated energy system using chance constrained *** electricity contract decomposition problem is introduced into the day-ahead optimal dispatch plan formulation *** the case that decomposition results may be not executable in the dispatch plan,a coordinated optimization strategy based on the Lagrange multiplier is proposed to eliminate the non-executable electric *** the same time,the uncertainties and correlation of wind power are considered in the dispatch model,and the original stochastic dispatch problem is transformed into a mixed integer second-order cone programming problem using second-order cone relaxation and sample average approximation *** study results demonstrate the validity of the proposed method.

关键词： chance-constrained programming electricity transaction uncertainties wind power

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A shared-mobility-based framework for evacuation planning and operations under forecast uncertainty

A I I E Transactions

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A I I E Transactions 2023年第10期55卷

作者： Kati Moug Huiwen Jia Siqian Shen Department of Industrial and Operations Engineering University of Michigan at Ann Arbor MI USA

To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. We optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution.

关键词： Stochastic programming evacuation planning ridesharing chance-constrained programming conditional value at risk (CVaR)

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