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Annual Meeting of the IEEE-Industry-Applications-Society (IAS)

作者： Tang, Lei Xu, Wei Islam, Md Rabiul Xi An Jiao Tong Univ Sch Elect Engn Sch Management Xian 710049 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia

ISBN: (纸本)9781728164014

Model predictive thrust control (MPTC) is one of the most effective approaches for linear induction motor (LIM) drive system. It can achieve the optimization of multiple objectives. However, the process of tuning the weighting factors in the objective function is the main drawback of MPTC. It greatly increases the computation burden. In this paper, the tuning process of weighting factors is regarded as a random sampling process. Then, a novel weighting factor optimization method based on the stochastic programming technique is proposed to select the suitable control action for LIM. It will optimize with flux and thrust together to avoid the adjustment of weighting factor. In this paper, the optimal model can be solved by Monte Carlo simulation. At last, the simulation results have shown better dynamic and steady state performance of the proposed method.

关键词： Linear induction machine (LIM) stochastic programming model predictive control (MPC) weighting factors fuzzy optimization

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stochastic programming-based mathematical model and solution strategy for chemical production scheduling with processing time uncertainty

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COMPUTERS & CHEMICAL ENGINEERING 2022年 168卷

作者： Gao, Jingxing Liu, Linlin Dong, Yachao Zhang, Lei Zhuang, Yu Du, Jian Dalian Univ Technol Frontiers Sci Ctr Smart Mat Oriented Chem Engn Sch Chem Engn Inst Chem Proc Syst Engn Dalian 116024 Peoples R China

Scheduling of multipurpose batch chemical plant is always affected by uncertain factors, including processing time of tasks. When the processing time deviates from its nominal value, the task sequence and executing time based upon the original schedule may become suboptimal or even infeasible. To address this issue, an optimization model based on stochastic programming is proposed for the short-term scheduling of multipurpose batch chemical plant, by introducing task sequence variables and new logical constraints relating multiple binary variables. Additionally, a network-based decomposition solution strategy, accounting for different situations of profits and shared units, is proposed to solve the large-scale problems, which has been shown to provide high quality solutions while consuming substantially less solution time than solving the entire process directly.

关键词： Multipurpose batch scheduling stochastic programming Processing time uncertainty Decomposition solution strategy

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Power Control in Local 5G/Beyond Systems Utilizing Spectrum Database and Monte Carlo stochastic programming 12

Power Control in Local 5G/Beyond Systems Utilizing Spectrum ...

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IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Taniguchi, Tetsuki Fujii, Takeo Univ Electrocommun UEC Adv Wireless & Commun Res Ctr AWCC Tokyo 1828585 Japan

ISBN: (纸本)9781665483032

In 5G / beyond, local communication systems with small cells managed by micro operators are collecting attentions, where interference mitigation and power saving is considered as an important topic. This paper presents an interference management method aided by spectrum database based on stochastic programming assuming non-line of sight (NLOS) environment. Regarding analysis of signal to interference plus noise ratio (SINR) under multiuser Rayleigh channel, density functions of SINR are approximated by exponential distributions. Then chance constraints in stochastic programming concerning outage SINR which are hard to deal with are converted to Monte Carlo expression for approximating predetermined outage SINR inequalities to improve the feasibility of the problem. Computer simulations show the proposed approach utilizing spectrum database is effective for performance improvement in small cellular system by micro operator.

关键词： spectrum database micro operator small cell local 5G stochastic programming Monte Carlo

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Convex relaxation of two-stage network-constrained stochastic programming for CHP microgrid optimal scheduling

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ENERGY 2024年 308卷

作者： Mohtavipour, Seyed Saeid Univ Guilan Dept Elect Engn Rasht Iran

Different from most studies only incorporating economic aspect into operation scheduling, this paper develops a novel network-constrained framework that can address both economic and secure issues for combined heating and power (CHP) microgrids integrated with wind power considering uncertainties. According to quadratically constrained programs, a two-stage network-constrained stochastic programming (TSNCSP) model is formulated to improve economic benefits and meanwhile capture active/reactive power and off-nominal bus voltage constraints in the full decision variable domain. In the first stage, namely day-ahead, the schedule of transacted electricity with the upstream power grid and the generation cost of heat is determined according to the forecast information. In the second stage, namely real-time, a recourse function of adjustable resources is defined to reduce the expected cost incurred by the perturbation of random wind power outputs. Moreover, the original non-convex problem is innovatively transformed into a semidefinite programming through incorporation of demand response program (DRP), duality, complementary slackness, and relaxation techniques to improve the solving efficiency. Finally, a proposition is presented and proved that provides a sufficient condition for the exactness of the proposed convex relaxation. Numerical simulations on the 33-bus test system verify the effectiveness of the proposed framework in multi-period scenario-based scheduling problems.

关键词： CHP microgrid stochastic programming Demand response program Convex relaxation Wind power

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Emergency materials management of petrochemical accidents considering the randomness and uncertainty base on stochastic programming

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FRONTIERS IN ENERGY RESEARCH 2024年 12卷

作者： Jiang, Wei Yang, Yuxiang Hu, Ansen Liu, Yulin Hong, Siyan Dongying Hualian Petrochem Plant Co Ltd Dongying Shandong Peoples R China China Univ Petr Coll Safety & Ocean Engn Beijing Peoples R China Zhejiang Ocean Univ Sch Shipping & Maritime Zhoushan Zhejiang Peoples R China Zhejiang Ocean Univ Natl & Local Joint Engn Res Ctr Harbor Oil & Gas S Zhejiang Key Lab Petrochem Environm Pollut Control Zhoushan Peoples R China

Petroleum is the pillar industry of the national economy, but safety accidents are frequent all over the world. The government attaches more importance to the safety production management of enterprises to reduce the occurrence of accidents that infringe on personal safety. The management of emergency supplies, which can effectively respond to the occurrence of safety production accidents, is a key measure for handling emergency accidents. Rapid response to accidents means reducing accident rescue costs and protecting personal and property safety. This paper proposes a material stochastic model with the randomness of accident demand for materials. The enterprise and the government can obtain the material management scheme and the quantitative evaluation standard of accident preventive measures from the model results respectively. The model covers as many accident scenarios as possible through multi-scenario modeling to reduce the impact of accident uncertainty. Finally, the feasibility is proved by an example of a petroleum enterprise in Zhoushan City. When the accident demand fluctuates randomly between 80% and 120%, the model proposes a material management scheme that the dispatching time of materials and the cost in rescue work do not exceed 31.33 min and 11.68 million CNY respectively. With the assistance of the model, the enterprise saves the cost of safe production and improves the efficiency of rescue. The government has strengthened the supervision and evaluation of enterprise safety production management. Finally, the mission of protecting the property and life safety of the people will be realized.

关键词： emergency logistics stochastic programming multi-scenario modeling petrochemical accident disaster accident frequency

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A two-stage stochastic programming approach for dynamic OD estimation using LBSN data

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 158卷

作者： Lu, Qing-Long Qurashi, Moeid Antoniou, Constantinos Tech Univ Munich Chair Transportat Syst Engn Munich Germany Tech Univ Dresden Chair Transport Modeling & Simulat Dresden Germany

Estimating origin-destination (OD) demand is essential for urban transport management and traffic control systems. With the ubiquity of smartphones, location based social networks (LBSN) data has emerged as a new rich data source with broad urban spatial and temporal coverage highly suitable for OD estimation. Its nature of confirmed trip purpose (activity) and activity chain host makes it more advantageous than other data (e.g., household travel surveys and traffic network detection). On the other hand, LBSN data is a more direct and accurate representation of demand patterns and can remove the significant burden of developing traffic models and estimating simulation-based objective functions. However, thus far, most LBSN-based estimation models only focus on static (day-level) OD estimation, making less use of those characteristics. To this end, this paper establishes a two-stage stochastic programming (TSSP) framework integrating the activity chains to model activity-level dynamic mobility flows using LBSN data. The first-stage model aims to minimize the errors introduced by the inter-zone OD flows alongside the expected errors of the check-in patterns. The second-stage model attempts to minimize the errors produced by the considered check-in pattern scenarios. Markov chain Monte Carlo (MCMC) sampling is used to generate plausible check-in scenarios. A generalized Benders decomposition (GBD) algorithm is presented to solve the two-stage stochastic programming model. We conduct the experiments on the case study of Tokyo, Japan, under the employment of the generalized least squares (GLS) estimator. The results show that algorithm convergence can be guaranteed within several iterations. The approach can provide satisfactory estimations of check-in patterns, zonal production and attraction, and OD flows. Furthermore, multiple objective function states are tested for evaluating the completeness of the proposed framework and exploring its potential for simplification a

关键词： OD estimation Demand estimation stochastic programming LBSN data Activity chain

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Predictive stochastic programming

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COMPUTATIONAL MANAGEMENT SCIENCE 2022年第1期19卷 65-98页

作者： Deng, Yunxiao Sen, Suvrajeet Univ Southern Calif Daniel J Epstein Dept Ind & Syst Engn Los Angeles CA 90089 USA

Several emerging applications call for a fusion of statistical learning and stochastic programming (SP). We introduce a new class of models which we refer to as Predictive stochastic programming (PSP). Unlike ordinary SP, PSP models work with datasets which represent random covariates, often refered to as predictors (or features) and responses (or labels) in the machine learning literature. As a result, these PSP models call for methodologies which borrow relevant concepts from both learning and optimization. We refer to such a methodology as Learning Enabled Optimization (LEO). This paper sets forth the foundation for such a framework by introducing several novel concepts such as statistical optimality, hypothesis tests for model-fidelity, generalization error of PSP, and finally, a non-parametric methodology for model selection. These new concepts, which are collectively referred to as LEO, provide a formal framework for modeling, solving, validating, and reporting solutions for PSP models. We illustrate the LEO framework by applying it to a production-marketing coordination model based on combining a pedagogical production planning model with an advertising dataset intended for sales prediction.

关键词： stochastic programming Fusion with statistical learning Model assessment

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A two-stage stochastic programming approach for electric energy procurement of EV charging station integrated with BESS and PV

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 232卷

作者： Kazemtarghi, Abed Mallik, Ayan Arizona State Univ Polytech Sch 6075 Innovat Way W Mesa AZ 85212 USA

Energy procurement of an electric vehicle charging station (EVCS) needs medium-term decisions, which depend on the short-term energy transactions of the EVCS in real-time market. However, the energy exchange in real-time operation is affected by uncertainties related to the pool prices, electric vehicle (EV) load demand, and photovoltaic (PV) generation level. To comprehensively include the real-time considerations in the decision framework, this paper proposes a two-stage stochastic programming approach to formulate EVCS energy procurement and optimal equipment dispatch as a single problem. The objective is to minimize the total operational cost of EVCS. First, a decision-making framework is developed that integrates EVCS energy procurement, charging pricing, and equipment dispatch in order to make energy procurement decisions based on random nature of electricity pool prices, EV loads, and PV generation in real-time operations. Next, the developed decision framework is formulated as a two-stage stochastic program with risk modeling using Conditional value-at-risk (CVaR) index. The first-stage variables include medium-term bilateral contracts and EV charging prices, while the second-stage variables encompass energy dispatch of battery energy storage systems (BESS) and energy transactions with the main grid in real-time pool. In addition, uncertainties related to electricity pool prices, EV loads, and PV generation levels are characterized by stochastic scenarios generated based on historical data. The simulation results for an EVCS equipped with DC fast chargers and integrated with BESS and PV generation demonstrate the effectiveness of the proposed approach in achieving efficient and profitable EVCS energy procurement. It is observed that expected profit for beta = 0 is $11,308 while none of the bilateral contracts are selected in this case because the pool prices are expected to be cheaper. On the other hand, expected profit for beta = 100 is $9900 where some par

关键词： Conditional value-at-risk (CVaR) Electric energy procurement Electric vehicle charging station stochastic programming

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Considering the circular economy for designing closed-loop supply chain under hybrid uncertainty: A robust scenario-based possibilistic-stochastic programming

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期238卷

作者： Dehshiri, Seyyed Jalaladdin Hosseini Amiri, Maghsoud Allameh Tabatabai Univ Fac Management & Accounting Dept Ind Management Tehran Iran

Due to the significance of environmental and economic topics and limited resources, integrating Circular Economy (CE) principles is necessary for Supply Chain (SC) to improve sustainable competitive advantage. The integration of CE in SCs leads to a Closed-Loop Supply Chain Network Design (CLSCND) and a circular SC to achieve the economic, environmental, and social aspects of outputs and processes. On the other hand, the integration of CE in CLSCND faces hybrid uncertainties in different time horizons and scenarios due to the strategic and long-term nature of decisions. Due to the impact of random and cognitive uncertainties in the long term, it is necessary to consider these factors in the integration of CE in CLSCND. So, the aim of the present study is CLSCND to achieve the principles of CE and provide a robust scenario-based possibilistic-stochastic programming approach to consider cognitive and random uncertainties simultaneously. The contributions and innovations of the present study are the integration of CE in CLSCND, considering cognitive and random uncertainties at the same time, developing the Me criterion to achieve flexible solutions based on the convex combination of opinions of experts, and proposing the use of the absolute possible deviation to consider the possibilistic deviation. A case study was investigated for CLSCND in the paper industry to assess the presented approach, and the results indicated the accuracy and robustness of the solutions. The numerical simulation results demonstrated the appropriate performance of the proposed method, that the lowest average and standard deviation values of the constraints violation were 601 and 48, respectively, in the developed approach. Analytical findings show that implementing CE in the paper CLSCND provides insights for managers in demand and capacity constraint violations based on different risk levels. Also, this study offers a comprehensive framework for presenting robustness and flexible solutions i

关键词： Circular economy Mixed integer linear programming Possibilistic programming Closed -loop supply chain stochastic programming Robust optimization

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Optimal bond issuance with cost and liquidity constraints for Chinese local governments: a multi-period stochastic programming approach

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EMPIRICAL ECONOMICS 2022年第5期63卷 2605-2632页

作者： Yang, Ruicheng Li, Li Jiang, Qi Qi, Ji Inner Mongolia Univ Finance & Econ Sch Finance Hohhot Peoples R China Marist Coll Sch Management Poughkeepsie NY 12601 USA

In recent years, China's local governments have issued numerous bonds to support the country's economic development. However, as total debt accumulates, the pressure on debt repayments is gradually increasing. To increase the sustainability of local government debt, we propose a multi-period stochastic optimization-based approach to determining the portfolio composition of issued bonds, with the goal of minimizing the expected cost under the constraints of liquidity risk and cost deviation risk. Liquidity risk is measured by conditional payment-at-risk (CPaR), and cost deviation risk is measured by conditional value-at-risk (CVaR). By bounding CVaR and CPaR, local governments can control the levels of cost deviation risk and liquidity risk. To alleviate future liquidity risk, which is caused by the issuance of a large number of long-term bonds to deliberately reduce repayment pressure within a debt planning horizon, we consider an extended liquidity planning horizon to manage both current and future liquidity risk. Based on this, we analyze the efficient frontier and portfolio compositions of issued bond under the constraints of different CVaR and CPaR levels. Compared with actual Chinese local government bond portfolios, the efficient frontier performs better for different issuance strategies.

关键词： Local government bonds Bond cost Liquidity risk stochastic programming

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