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International Conference on Sustainable Power Generation and Supply

作者： Yuan, Yue Li, Qiang Wang, Weisheng Hohai Univ Sch Elect Engn Nanjing 210098 Peoples R China China Elect Power Res Inst Renewable Energy Dept Beijing 100192 Peoples R China

ISBN: (纸本)9781424449347

Large-scale wind power integration brings about many challenges to power system. Energy storage is regarded as an effective solution to the problems and some demonstration projects have been constructed. As the output power of wind turbines is a stochastic variable which is determined by wind velocity, it is very difficult to choose the operation strategy of energy storage. Probability method can be a wise selection of this kind of problem. Mathematical model for seeking the energy storage operation strategy is developed based on chance-constrained programming. Genetic algorithm is used to solve the optimization problem, and Monte-Carlo method is applied to deal with the chance-constrained question.

关键词： Energy storage operation strategy wind power integration chance-constrained programming

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Solving a multi-objective chance-constrained hub covering location problem by discrete invasive weed optimization

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COGENT ENGINEERING 2014年第1期1卷 1-N.PAG页

作者： Nikokalam-Mozafar, Seyed Hossein Ashjari, Behzad Tavakkoli-Moghaddam, Reza Omidvar, Aida Tafresh Univ Dept Ind Engn Tafresh Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Islamic Azad Univ Sch Ind Engn South Tehran Branch Tehran Iran

This paper presents a stochastic bi-objective model for a single-allocation hub covering problem (HCP) with the variable capacity and uncertainty parameters. Locating hubs can influence the performance of hub and spoke networks, as a strategic decision. The presented model optimizes two objectives minimizing the total transportation cost and the maximum transportation time from an origin to a destination simultaneously. Then, due to the NP-hardness of the multi-objective chance-constrained HCP, the presented model is solved by a well-known meta-heuristic algorithm, namely multi-objective invasive weed optimization. Additionally, the associated results are compared with a well-known multi-objective evolutionary algorithm, namely non-dominated sorting genetic algorithm. Furthermore, the computational results of the foregoing algorithms are reported in terms four well-known metrics, namely quality, spacing, diversification, and mean ideal distance. Finally, the conclusion is reported.

关键词： stochastic hub covering problem multi-objective optimization chance-constrained programming invasive weed optimization

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Distributionally Robust Slow Adaptive OFDMA with Soft QoS via Linear programming

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2013年第5期31卷 947-958页

作者： So, Anthony Man-Cho Zhang, Ying Jun (Angela) Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong CUHK BGI Innovat Inst Trans Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China

Being the predominant air interface of next-generation wireless standards, orthogonal frequency division multiple access (OFDMA) is well known for its flexibility in allocating subcarriers to different mobile users according to their different fast channel variations. Numerous research studies have demonstrated that OFDMA can bring substantial capacity gain when the subcarriers are optimally allocated. Nonetheless, practical systems can hardly afford optimal subcarrier allocation, because frequent re-optimization performed at the same timescale as fast fading variation would lead to excessively high computational and signaling costs. As a result, most practical systems settle for low-complexity schemes that operate far from the optimum, thus making them unable to enjoy the large capacity gain predicted by theoretical studies. To address this problem, we propose a novel alternative, termed the slow adaptive OFDMA, to drastically reduce the computational and signaling costs. The proposed scheme adapts subcarrier allocation at a much slower timescale than that of channel fading variation, yet achieves similar system capacity and quality of service (QoS) levels as the optimal fast adaptive OFDMA. Moreover, it possesses several attractive features. First, neither prediction of channel state information nor specification of channel fading distribution is needed for subcarrier allocation. As such, the algorithm is robust against any mismatch between actual channel state/distributional information and the one assumed. Secondly, although the optimization problem arising from our proposed scheme is non-convex in general, based on recent advances in chance-constrained optimization, we show that it can be approximated by a certain linear program with provable performance guarantees. In particular, we only need to handle an optimization problem that has the same structure as the fast adaptive OFDMA problem, yet we are able to enjoy lower computational and signaling costs. Last b

关键词： OFDMA Adaptive resource allocation Stochastic optimization chance-constrained programming

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An Inexact chance-constrained Quadratic programming Model for Stream Water Quality Management

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WATER RESOURCES MANAGEMENT 2009年第4期23卷 661-695页

作者： Qin, X. S. Huang, G. H. Univ Regina Ctr Energy & Environm Studies Regina SK S4S 0A2 Canada N China Elect Power Univ Chinese Res Acad Environm Sci Beijing 100012 Peoples R China Univ Regina Fac Engn Environm Syst Engn Program Regina SK S4S 0A2 Canada

Water quality management is complicated with a variety of uncertainties and nonlinearities. This leads to difficulties in formulating and solving the resulting inexact nonlinear optimization problems. In this study, an inexact chance-constrained quadratic programming (ICCQP) model was developed for stream water quality management. A multi-segment stream water quality (MSWQ) simulation model was provided for establishing the relationship between environmental responses and pollution-control actions. The relationship was described by transformation matrices and vectors that could be used directly in a multi-point-source waste reduction (MWR) optimization model as water-quality constraints. The interval quadratic polynomials were employed to reflect the nonlinearities and uncertainties associated with wastewater treatment costs. Uncertainties associated with the water-quality parameters were projected into the transformation matrices and vectors through Monte Carlo simulation. Uncertainties derived from water quality standards were characterized as random variables with normal probability distributions. The proposed ICCQP model was applied to a water quality management problem in the Changsha section of the Xiangjiang River in China. The results demonstrated that the proposed optimization model could effectively communicate uncertainties into the optimization process, and generate inexact solutions containing a spectrum of wastewater treatment options. Decision alternatives could then be obtained by adjusting different combinations of the decision variables within their solution intervals. Solutions from the ICCQP model could be used to analyze tradeoffs between the wastewater treatment cost and system-failure risk due to inherent uncertainties. The results are valuable for supporting decision makers in seeking cost-effective water management strategies.

关键词： Interval quadratic programming chance-constrained programming Water quality management Uncertainty

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Dynamic stochastic fractional programming for sustainable management of electric power systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第Dec.期53卷 553-563页

作者： Zhu, H. Huang, G. H. Univ Regina Inst Energy Environm & Sustainable Communities Regina SK S4S 0A2 Canada North China Elect Power Univ SC Inst Energy Environm & Sustainabil Res Resources & Environm Res Acad Beijing 102206 Peoples R China Univ Regina Regina SK S4S 0A2 Canada

A dynamic stochastic fractional programming (DSFP) approach is developed for capacity-expansion planning of electric power systems under uncertainty. The traditional generation expansion planning focused on providing a sufficient energy supply at minimum cost. Different from using least-cost models, a more sustainable management approach is to maximize the ratio between renewable energy generation and system cost. The proposed DSFP method can solve such ratio optimization problems involving issues of capacity expansion and random information. It has advantages in balancing conflicting objectives, handling uncertainty expressed as probability distributions, and generating flexible capacity-expansion strategies under different risk levels. The method is applied to an expansion case study of municipal electric power generation system. The obtained solutions are useful in generating sustainable power generation schemes and capacity-expansion plans. The results indicate that DSFP can support in-depth analysis of the interactions among system efficiency, economic cost and constraint-violation risk. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Sustainability Generation expansion planning Fractional programming chance-constrained programming Renewable energy Stochastic uncertainty

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A Stochastic chance-constrained programming Model for Time-Cost Trade-off Problem

A Stochastic Chance-Constrained Programming Model for Time-C...

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第七届中国不确定系统年会

作者： Hua Ke,Weimin Ma School of Economics and Management,Tongji University,Shanghai 200092,China

In real projects,both the trade-off between the project cost and the project completion time,and the uncertainty of the environment are considerable for *** the existing models for the stochastic time-cost trade-off problem,optimizing the expected value of the objective is the exclusive decision-making *** this paper,an alternative stochastic model is proposed,in which the philosophy of chance-constrained programming is adopted for decision *** addition,stochastic simulation and genetic algorithm are integrated to design a hybrid intelligent algorithm to search the quasi-optimal schedule.

关键词： Time-cost trade-off chance-constrained programming Genetic algorithm

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Integer feasibility of random polytopes: random integer programs 14

Integer feasibility of random polytopes: random integer prog...

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Proceedings of the 5th conference on Innovations in theoretical computer science

作者： Karthekeyan Chandrasekaran Santosh S. Vempala Harvard Cambridge MA USA Georgia Tech Atlanta GA USA

ISBN: (纸本)9781450326988

We study the chance-constrained Integer Feasibility Problem, where the goal is to determine whether the random polytope P(A,b)={x ϵ Rn : Aix ≤ bi, i ϵ [m]} obtained by choosing the constraint matrix A and vector b from a known distribution is integer feasible with probability at least 1-ε. We consider the case when the entries of the constraint matrix A are i.i.d. Gaussian (equivalently are i.i.d. from any spherically symmetric distribution). The radius of the largest inscribed ball is closely related to the existence of integer points in the polytope. We find that for m up to 2O(√n) constraints (rows of A), there exist constants c0 < c1 such that with high probability (ɛ = 1 /poly(n)), random polytopes are integer feasible if the radius of the largest ball contained in the polytope is at least c1√log(m/n)); and integer infeasible if the largest ball contained in the polytope is centered at (1/2,...,1/2) and has radius at most c0√log(m/n)). Thus, random polytopes transition from having no integer points to being integer feasible within a constant factor increase in the radius of the largest inscribed ball. Integer feasibility is based on a randomized polynomial-time algorithm for finding an integer point in the *** main tool is a simple new connection between integer feasibility and linear discrepancy. We extend a recent algorithm for finding low-discrepancy solutions to give a constructive upper bound on the linear discrepancy of random Gaussian matrices. By our connection between discrepancy and integer feasibility, this upper bound on linear discrepancy translates to the radius bound that guarantees integer feasibility of random polytopes.

关键词： probabilistic instances chance-constrained programming integer programming discrepancy random matrices

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Stochastic Optimal Reactive Power Dispatch Method Based on Point Estimation Considering Load Margin

Stochastic Optimal Reactive Power Dispatch Method Based on P...

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IEEE Power and Energy Society General Meeting

作者： Sidun Fang Haozhong Cheng Yue Song Pingliang Zeng Liangzhong Yao Masoud Bazargan Dept. Of Electrical Engineering Shanghai Jiao tong University China Electric Power Research Institute ALSTOM Grid Research & Technology

ISBN: (纸本)9781479964161

Conventional optimal reactive power dispatch approaches operate mostly in deterministic form where the power injections are fixed. In practice, however, power injections, especially from intermittent renewable sources, and demand are of uncertainties. To address this problem, in this paper, we develop a load margin constrained stochastic optimal reactive power dispatch (LMC-SORPD) method. We first formulated the considered problem into a chance-constrained programming, which is then solved through genetic algorithm and stochastic power flow based on point estimation. Simulation results on several cases demonstrate that the proposed method is able to prevent the risk of under and over-voltage and increase load margin at a cost of a small but acceptable increase of active power loss. Specified chance-constrained handling techniques are adopted to improve the computational speed. Numerical examples validate the effectiveness of those techniques.

关键词： LMC-SORPD chance-constrained programming Point estimation Stochastic power flow Genetic algorithm

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A STOCHASTIC programming METHOD FOR UNIT COMMITMENT OF WIND INTEGRATED POWER SYSTEM

A STOCHASTIC PROGRAMMING METHOD FOR UNIT COMMITMENT OF WIND ...

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2nd International Conference on Energy and Environmental Protection (ICEEP 2013)

作者： Shao Jian Zhang Buhan Deng Weisi Zhang Kaimin Jin Binjie Ge Tengyu Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9783037857434

This paper presents a stochastic programming method that can assess the impact of wind generation uncertainties on unit comment (UC) problem. To model the uncertainty of wind genration, scenarios of wind speed are generated based on the known probability interval of forecasted wind speed and a scenario reduction technique limits the number of scenarios. The UC problem is modeled as a stochastic programming problem based on chance-constrained programming, and is decomposed into two embedded optimization sub-problems: the unit on/off status schedule problem and the load economic dispatch problem. Discrete particle swarm optimization (DPSO) and the equal incremental principle are used to solve the stochastic UC problem. The numerical results indicate that the proposed stochastic model is more suitable for wind integrated system with uncertainty.

关键词： unit commitment scenario analysis chance-constrained programming discrete particle swarm optimization

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A stochastic programming Optimization Model of Container Shipping of sea-carriage

A stochastic Programming Optimization Model of Container Shi...

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2nd International Conference on Information Technology and Management Innovation (ICITMI 2013)

作者： Wang, Bin Yang, Tao Shanghai Second Polytech Univ Sch Econ & Management Shanghai Peoples R China

ISBN: (纸本)9783037858646

The paper dose research about the optimization of container shipping of sea carriage for meeting the goods transport requirement by use of stochastic programming. Both laden and empty containers are combined into a system. In particular, the effect of special laden container shipping capacity on the shipping plan is investigated. In the model, the objective function is to maximize the total profit of container shipping. The profit caused by laden container shipping minus the cost caused by both laden and empty container shipping equal to the total profit. The constraints to the model include meeting the need of both laden and empty containers, shipping limit to both common and special laden containers, the number of empty container supported. The number of empty containers is stochastic and the model is transmitted to an integer programming. Lingo9.0 is used to solve the model and shipping methods in varied parameters are shown by simulation. The aim of the paper is to provide a reasonable plan of container shipping of sea-carriage, so the container shipping cost of a shipping company can be reduced and the its profit made by container shipping are maximized.

关键词： line container special container chance-constrained programming simulation

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