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Consumers' Flexibility Estimation at the TSO Level for Balancing Services

IEEE TRANSACTIONS ON POWER SYSTEMS 2019年第3期34卷 1918-1930页

作者： De Zotti, Giulia Pourmousavi, S. Ali Morales, Juan M. Madsen, Henrik Poulsen, Niels Kjolstad Tech Univ Denmark DK-2800 Lyngby Denmark Univ Queensland St Lucia Qld 4072 Australia Univ Malaga Malaga 29016 Spain

Demand flexibility will be an inevitable part of the future power system operation to compensate stochastic variations of ever-increasing renewable generation. One way to achieve demand flexibility is to provide time-varying prices to customers at the edge of the grid. However, appropriate models are needed to estimate the potential flexibility of different types of consumers for day ahead and real-time ancillary services (AS) provision. The proposed method should account for rebound effect and variability of the customers' reaction to the price signals. In this study, an efficient algorithm is developed for consumers' flexibility estimation by the transmission system operator (TSO) based on offline data. No aggregator or real-time communication is involved in the process of flexibility estimation, although real-time communication channels are needed to broadcast price signals to the end-users. Also, the consumers' elasticity and technical differences between various types of loads are taken into account in the formulation. The problem is formulated as amixed-integer linear programming (MILP) problem, which is then converted to a chance-constrained programming to account for the stochastic behavior of the consumers. Simulation results show the applicability of the proposed method for the provision of AS from consumers at the TSO level.

关键词： Rational end-users transmission system operator flexibility resources ancillary services chance-constrained programming

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An integrated chance-constrained stochastic model for a mobile phone closed-loop supply chain network with supplier selection

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JOURNAL OF CLEANER PRODUCTION 2019年 226卷 988-1003页

作者： Ahmadi, Shahrzad Amin, Saman Hassanzadeh Ryerson Univ Dept Mech & Ind Engn Toronto ON Canada

One of the important concerns in the world is electronic waste (e-waste). Ending up e-waste in the landfill and inappropriate disposing of it are hazardous to the environment. The goal of this research is to design and optimize a multi-period, multi-product, multi-echelon, and multi-customer Closed-Loop Supply Chain (CLSC) network for a mobile phone network considering different types of product returns. Commercial, end of life, and end-of-use returns are well-known in practice. In this research, a multiobjective mixed-integer linear programming formulation with stochastic demand and return is proposed to maximize the total profit in the mobile phone CLSC network, alongside maximizing the weights of eligible suppliers which are estimated based on a fuzzy method for efficient supplier selection and order allocation. The goal is to determine the appropriate location and number of different facilities including suppliers, manufacturers, retailers, drop-off centres, and consolidation centres as well as the amount of required materials to produce a mobile phone, and the number of products that should be transported between various facilities. The application of the proposed mathematical model is illustrated in Toronto, Canada using real maps. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Closed-loop supply chain (CLSC) chance-constrained programming Supplier selection Order allocation Mobile phone Uncertainty

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chance-constrained Surgery Planning Under Conditions of Limited and Ambiguous Data

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INFORMS JOURNAL ON COMPUTING 2019年第3期31卷 559-575页

作者： Deng, Yan Shen, Siqian Denton, Brian Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

Surgery planning decisions include which operating rooms (ORs) to open, allocation of surgeries to ORs, sequence, and time to start each surgery. They are often made under uncertain surgery durations with limited data that lead to unknown distributional information. Moreover, cost parameters for criteria such as overtime and surgery delays are often difficult or impossible to estimate in practice. In this paper, we formulate distributionally robust (DR) chance constraints on surgery waiting and OR overtime, which recognize practical limitations on data availability and cost parameter accuracy. We use phi-divergence measures to build an ambiguity set of possible distributions of random surgery durations, and derive a branch-and-cut algorithm for optimizing a mixed-integer linear programming reformulation based on finite samples of the random surgery durations. We test instances generated from real hospital-based surgery data. The results show computational efficacy of our approaches, and provide insights for DR surgery planning.

关键词： allocation and scheduling chance-constrained programming distributionally robust optimization phi-divergence mixed-interger linear programming branch-and-cut

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Optimal pricing decision for supply chains with risk sensitivity and human estimation

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第7期10卷 1717-1730页

作者： Ma, Weimin Cheng, Rong Ke, Hua Zhao, Zhang Tongji Univ Sch Econ & Management Tongji Bldg ASiping Rd 1500 Shanghai 200092 Peoples R China Yunnan Univ Sch Business & Tourism Management Kunming Yunnan Peoples R China

In reality, consumption markets are changeable and uncertain in terms of consumer demands, product manufacturing costs, sales costs and so on. When facing these markets, the small and medium manufacturers are usually weak and risk sensitive vis-a-vis their counterpart power retailers, like Walmart, Carrefour, and TESCO. Therefore, human estimations on uncertain market information given by experienced experts and risk sensitivity of the weak manufacturers are of great significance for the optimal pricing strategies. Accordingly, this paper investigates the pricing decision problem in an uncertain supply chain where two risk-sensitive manufacturers distribute substitutable products into the same market through a common dominant and risk-neutral retailer. Uncertainty theory is employed to deal with human-estimation information and chance-constrained programming models are proposed to formulate the pricing decision problem with risk sensitivity. Moreover, numerical experiments are provided to validate the effectiveness of the proposed model. Interestingly, we show that the results critically depend on the comparison between how much the demands and manufacturing costs of two competing products change with the manufacturers' risk-sensitivity levels. Correspondingly, based on the estimated uncertainty distributions of uncertain variables by experts, the weak manufacturers are suggested to adjust their risk-sensitivity levels strategically for their profitability.

关键词： Uncertainty theory Risk-averse manufacturer Human estimation chance-constrained programming Pricing decision

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Improving IoT Data Quality in Mobile Crowd Sensing: A Cross Validation Approach

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IEEE INTERNET OF THINGS JOURNAL 2019年第3期6卷 5651-5664页

作者： Luo, Tie Huang, Jianwei Kanhere, Salil S. Zhang, Jie Das, Sajal K. ASTAR Inst Infocomm Res Singapore Singapore Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

Data quality, or sometimes referred to as data credibility, is a critical issue in mobile crowd sensing (MCS) and more generally Internet of Things (IoT). While candidate solutions, such as incentive mechanisms and data mining have been well explored in the literature, the power of crowds has been largely overlooked or under-exploited. In this paper, we propose a cross validation approach which seeks a validating crowd to ratify the contributing crowd in terms of the sensor data contributed by the latter, and uses the validation result to reshape data into a more credible posterior belief of the ground truth. This approach consists of a framework and a mechanism, where the framework outlines a four-step procedure and the mechanism implements it with specific technical components, including a weighted random oversampling (WRoS) technique and a privacy-aware trust-oriented probabilistic push (PATOP(2)) algorithm. Unlike most prior work, our proposed approach augments rather than redesigning existing MCS systems, and requires minimal effort from the crowd, making it conducive to practical adoption. We evaluate our proposed mechanism using a real-world MCS IoT dataset and demonstrate remarkable (up to 475%) improvement of data quality. In particular, it offers a unified solution to reconciling two disparate needs: reinforcing obscure (weakly recognizable) ground truths and discovering hidden (unrecognized) ground truths.

关键词： chance-constrained programming crowdsourcing data quality exploration-exploitation tradeoff Internet of Things (IoT) Kullback-Leibler divergence privacy trust

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Iterative Pinch Analysis to address non-linearity in a stochastic Pinch problem

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JOURNAL OF CLEANER PRODUCTION 2019年 227卷 543-553页

作者： Arya, Deepika Bandyopadhyay, Santanu Indian Inst Technol Dept Energy Sci & Engn Mumbai 400076 Maharashtra India

Pinch Analysis is a widely used technique for resource conservation. It minimizes the requirement of the external resource through efficient use of internally available sources. These networks are designed based on the flow and quality parameters of internal sources and demands. However, Pinch Analysis is applicable only for the linear problems and fails if the problem becomes non-linear under certain conditions. The uncertainty in the quality parameter of sources in a resource allocation problem is one such situation where the problem becomes non-linear. The quality parameter is considered to be normally distributed with known mean and standard deviation to incorporate uncertainty. In such a case, the quality load constraint is probabilistic, which on conversion to its deterministic equivalent using chance-constrained programming, becomes non-linear. The previous works adopted a conservative approach to linearize the constraint and to solve it using Pinch Analysis. This leads to a higher value of resource requirement than resource targeted using non-linear formulation. This difference in the resource requirements, obtained from the two formulations, can be extremely high in certain cases. In this work, an iterative Pinch Analysis methodology is proposed to bridge this gap between the two formulations that helps in reducing the resource requirement while maintaining the desired demand reliability. An approximated Pinch problem is solved in each iteration to reach the optimal resource. This work extends the applicability of Pinch Analysis to the source-sink problems with non-linear constraints. The applicability of the methodology has been demonstrated through three different examples. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Stochastic Pinch analysis chance-constrained programming Non-linearity Iterative Pinch Analysis Resource conservation networks

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Integrated control of voltage regulators and distributed generation inverters

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ELECTRIC POWER SYSTEMS RESEARCH 2019年 169卷 45-52页

作者： Ibrahim, Sarmad Cramer, Aaron M. Liao, Yuan Univ Kentucky Lexington KY 40506 USA Univ Babylon Dept Elect Engn Hilla Iraq

High penetration of distributed generation (DG) can cause fast voltage changes in distribution systems. Inverter-based DG can be a significant source of reactive power, which brings new opportunities to improve the performance of the distribution system. An integrated control strategy is formulated for the coordinated control of both distribution system equipment and inverter-based DG. The control strategy combines the use of inverter reactive power capability with the operation of voltage regulators in order to improve the expected value of a desired figure of merit (e.g., system losses) while maintaining appropriate system voltage magnitudes, by formulating chance constraints on the voltage magnitudes. The effectiveness of the proposed method is validated using the IEEE 123-node radial distribution system.

关键词： Distributed power generation chance-constrained programming Reactive power control Voltage control

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chance-constrained Optimization in D2D-Based Vehicular Communication Network

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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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