检索结果-内蒙古大学图书馆

Distributionally Robust chance-constrained AC-OPF for Integrating Wind Energy Through Multi-Terminal VSC-HVDC

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2020年第3期11卷 1414-1426页

作者： Yao, Li Wang, Xiuli Li, Yujun Duan, Chao Wu, Xiong Xi An Jiao Tong Univ Sch Elect Engn Shannxi Key Lab Smart Grid Xian 710049 Peoples R China

Using the multi-terminal VSC-HVdc (MTDC) system to collect far-distance offshore wind power into the main onshore ac grid has become one of the ideal transmission approaches for renewable energy integration. However, with traditional fixed dc droop control in MTDC system, the lack of the capability of regulating power flow might pose a great challenge to the system operation. Accordingly, a novel MTDC model has been established in this paper which establishes that the output power of onshore converters can be formulated as a linear function of the control variables, namely power initial set point of each onshore converter. Based on this model, considering the uncertainty from wind power forecast error, a distributionally robust chance-constrained AC-OPF model is proposed for the hybrid AC/MTDC system. Without any presumption on the probability distribution of uncertainty, this model ensures the secure operation by enforcing chance constraints under the worst-case probability distribution over an ambiguity set based on the Wasserstein-Moment metric. Aiming at obtaining a tractable chance-constraint reformulation, we first employ the partial linearization of ac power flow equations to yield a linear model which represents the system response under uncertainties. We then develop an efficient and scalable approach to derive the linear analytical reformulation of distributionally robust chance constraints. The performance of the proposed model is firstly tested on the 14-bus system for an illustrative purpose, and then on the 1354-bus system for demonstrating scalability.

关键词： Uncertainty Mathematical model Wind farms Wind power generation Measurement Matrix converters Probability distribution Distributionally robust optimization (DRO) chance-constrained programming Multi-terminal VSC-HVdc (MTDC) AC-OPF

来源：评论

学校读者我要写书评

暂无评论

Power-Efficient Positioning for Visible Light Systems via chance constrained Optimization

引用

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2020年第5期56卷 4124-4131页

作者： Yazar, Onurcan Keskin, Musa Furkan Gezici, Sinan Bilkent Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden

The problem of minimizing total power consumption in light-emitting diode transmitters is investigated for achieving power efficient localization in a visible light communication and positioning system. A robust power allocation approach based on stochastic uncertainties is proposed for total power minimization in the presence of localization accuracy, power, and illumination constraints. Specifically, the power consumption minimization problem is formulated under a chance constraint on the probability of Cramer-Rao lower bound exceeding a tolerable limit, which is a computationally intractable constraint. The sphere bounding method is used to propose a safe convex approximation to this intractable constraint, which makes the resulting problem suitable for standard convex optimization tools. Numerical results demonstrate the advantages of the proposed robust solution over the nonrobust solution and uniform power allocation in the presence of stochastic uncertainty.

关键词： Light emitting diodes Receivers Optical transmitters Visible light communication Power demand Resource management Uncertainty chance-constrained programming convex optimization power efficiency robust design visible light communication and positioning (VLCP)

来源：评论

学校读者我要写书评

暂无评论

Data quality aware chance-constrained DC-OPF: a variational Bayesian Gaussian mixture approach

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第17期14卷 3412-3421页

作者： Wu, Xiong Wang, Xiuli Duan, Chao Dang, Can Yao, Li Fan, Yue Song, Rui Xi An Jiao Tong Univ Sch Elect Engn Shannxi Key Lab Smart Grid Xian 710049 Peoples R China State Grid Qinghai Elect Power Co Xining 810008 Peoples R China State Grid Qinghai Elect Power Corp Elect Power Res Inst Xining 810008 Peoples R China

The contamination of outliers severely damages the data quality, resulting in the inaccurate data-driven optimisation model. This study proposes a data quality aware chance-constrained model for the direct current optimal power flow (DC-OPF) problem under uncertainties. Under the framework of Bayesian statistics, the variational Bayesian Gaussian mixture model (VBGMM) is employed to extract the probabilistic information from the available historical data, i.e. realisations of random variables. VBGMM can identify the outliers by capturing their probability characteristics, in which way improving the data quality. Notably, VBGMM automatically determines the number of components, which is a remarkable difference from the conventional Gaussian mixture model. In addition, based on the affine policy, a method integrating VBGMM with chance-constrained programming is proposed to make VBGMM scalable. The proposed method is firstly tested on a 6-bus system for an illustrative purpose, and then on a 118-bus system for validating the potential practical application. Comparative studies verify the effectiveness of the proposed method.

关键词： load flow Bayes methods optimisation Gaussian processes statistical analysis probability mixture models data quality aware chance-constrained DC-OPF variational Bayesian Gaussian mixture approach inaccurate data-driven optimisation model data quality aware chance-constrained model direct current optimal power flow Bayesian statistics variational Bayesian Gaussian mixture model historical data conventional Gaussian mixture model method integrating VBGMM chance-constrained programming VBGMM

来源：评论

学校读者我要写书评

暂无评论

A chance-constrained Decentralized Operation of Multi-Area Integrated Electricity-Natural Gas Systems With Variable Wind and Solar Energy

引用

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2020年第4期11卷 2230-2240页

作者： Qi, Feng Shahidehpour, Mohammad Li, Zhiyi Wen, Fushuan Shao, Changzheng Zhejiang Univ Sch Elect Engn Hangzhou 310027 Peoples R China IIT Galvin Ctr Elect Innovat Chicago IL 60616 USA King Abdulaziz Univ Jeddah 21589 Saudi Arabia Tallinn Univ Technol EE-19086 Tallinn Estonia

This article presents a decentralized decision-making strategy for the multi-area integrated electricity and natural gas system (IEGS). Independent system operators (ISOs) in the electric power sector and natural gas system operators (NGSOs) in the natural gas sector operate their respective systems independently with the consideration that a limited set of information is shared, and the information privacy and decision-making independence are preserved among operators. In this article, uncertainties associated with the proliferation of variable wind and solar generation as well as load forecast errors are addressed in a chance-constrained programming framework, and the loss of load probability (LOLP) as well as the transmission line overload probability (TLOP) are introduced to ensure a secure and economic operation of IEGS. The proposed multi-area & multi-layer problem is solved by a hierarchical iterative algorithm in which the inner loop deals with the collaboration between ISO and NGSO in each area, and the collaboration among areas is managed by the outer loop. Finally, case studies are carried out on two multi-area IEGSs with different sizes in which numerical results demonstrate the effectiveness of the proposed methods.

关键词： Natural gas Power systems Wind forecasting Decision making Collaboration Optimization Manganese Multi-area coordination integrated electricity and natural gas system (IEGS) variable wind and solar energy chance-constrained programming decentralized optimization

来源：评论

学校读者我要写书评

暂无评论

Supply chain network design considering carbon footprint, water footprint, supplier's social risk, solid waste, and service level under the uncertain condition

引用

CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2020年第2期22卷 337-370页

作者： Das, Rakhi Shaw, Krishnendu Irfan, Mohd. Indian Inst Technol ISM Dhanbad Dept Management Studies Dhanbad 826004 Bihar India

Supply chain network design (SCND) plays a crucial role in transforming a supply chain sustainable. Recently, various SCND models have been developed especially focusing on carbon footprint reduction. The current study argues that emphasizing only on carbon footprint cannot entirely transform a supply chain sustainable. There are other dimensions (water footprint, solid waste, and social factor), which need to be taken care of for implementing sustainability. Till now, most of the sustainable SCND models have been developed in deterministic conditions, and very few models have been reported in a stochastic environment. To fill the gaps of existing literature, the current study proposes a multi-product and multi-echelon SCND model by addressing carbon footprint, water footprint, solid waste, social sustainability, service level, different transportation modes and inventories in stochastic condition. The study intends to minimize the total cost and estimate the flow of materials across the various echelons of the supply chain. The model considers the demand and capacity as stochastic variables. Further, chance-constrained programming has been used to model the uncertainty of parameters. In the current research, an individual-level chance constraint with right-hand side uncertainty has been adopted. The applicability of the proposed model has been demonstrated with a numerical example and sensitivity analyses have been conducted by changing model parameters, like probability, carbon footprint, water footprint, solid waste, service level, and social factor. The suggested model facilitates decision-maker to estimate the optimum flow of material across the supply chain for delivering materials of predetermined carbon footprint, water footprint, solid waste, social sustainability, and service level. The model helps the manager to implement the sustainability holistically across the supply chain. [Graphics] .

关键词： Supply chain network design Sustainability Water footprint Solid waste Carbon emission chance-constrained programming

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Envelope constrained Multiperiod Stochastic Emergency Medical Services Location Model and Decomposition Scheme

引用

TRANSPORTATION SCIENCE 2020年第6期54卷 1471-1494页

作者： Peng, Chun Delage, Erick Li, Jinlin HEC Montreal Montreal PQ H3T 2A7 Canada GERAD Montreal PQ H3T 2A7 Canada Beijing Inst Technol Beijing 100081 Peoples R China

This paper considers a multiperiod emergency medical services (EMS) location problem and introduces two two-stage stochastic programming formulations that account for uncertainty about emergency demand. Whereas the first model considers both a constraint on the probability of covering the realized emergency demand and minimizing the expected cost of doing so, the second one employs probabilistic envelope constraints that allow us to control the degradation of coverage under the more severe scenarios. These models give rise to large mixed-integer programs, which can be tackled directly or by using a conservative approximation scheme. For the former, we implement the branch-and-Benders-cut method, which improves significantly the solution time when compared with using both a recently proposed state-of-the art branch-and-bound algorithm and the CPLEX solver. Finally, a practical study is conducted using historical data from the Northern Ireland Ambulance Service and sheds some light on optimal EMS location configuration for this region and on necessary trade-offs that must be made between emergency demand coverage and expected cost. These insights are confirmed through an out-of-sample performance analysis.

关键词： facility location two-stage stochastic programming chance-constrained programming probabilistic envelope constraint emergency medical services location branch-and-benders-cut

来源：评论

学校读者我要写书评

暂无评论

chance-constrained models for transactive energy management of interconnected microgrid clusters

引用

JOURNAL OF CLEANER PRODUCTION 2020年 271卷 122177-122177页

作者： Daneshvar, Mohammadreza Mohammadi-Ivatloo, Behnam Asadi, Somayeh Anvari-Moghaddam, Amjad Rasouli, Mohammad Abapour, Mehdi Gharehpetian, Gevork B. Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Penn State Univ Dept Architectural Engn State Coll PA 16801 USA Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark Behrend Coll Penn State Erie Erie PA 16563 USA Amirkabir Univ Technol Dept Elect Engn Tehran Iran

Transactive energy as an emerging approach and sustainable technology can provide an exceptional opportunity for microgrids to exchange energy with each other for greater benefits in the cluster mode. In this mode of operation, some collective and individual interests can be realized for the microgrids based on transactive energy management. This paper proposes mathematical models for microgrid clusters using a transactive energy structure to manage energy exchange in the smart grid. In order to make an informed decision for the operation of microgrid clusters, chance-constrained programming is employed to consider the uncertainties in balancing collective and individual interests under the transactive energy management. In this research, sixteen commercial microgrids are considered in the process of evaluating the efficiency of the proposed models using the chance-constrained programming method. Simulation results prove the effectiveness of the transactive energy approach accompanying the implementation of chance-constrained programming in energy management of the microgrid clusters. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Transactive energy chance-constrained programming Microgrid clusters Smart grid Energy management Uncertainty handling

来源：评论

学校读者我要写书评

暂无评论

Locating electric vehicle charging stations under uncertain battery energy status and power consumption

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2020年 149卷 106752-106752页

作者： Kchaou-Boujelben, Mouna Gicquel, Celine UAE Univ Coll Business & Econ POB 15551 Al Ain U Arab Emirates Paris Sud Univ LRI F-91405 Orsay France

Fostering the adoption of electric vehicles (EVs) by private drivers requires the development of a wide network of fast charging stations in which drivers traveling long-distance trips will be able to easily recharge their battery within a few minutes. However, due to their high installation costs, the number of stations that can actually be deployed with the available investment budget is strongly limited. It is thus necessary to carefully choose their location so that the charging demand satisfaction is maximized. This leads to the formulation of a facility location problem known as the flow refueling location problem (FRLP). In this paper, we study the FRLP and seek to take into account uncertainties on the vehicle driving range in the problem modeling. We propose to relax several modeling assumptions previously used in the literature to handle this problem. First, we allow the power consumption on a road segment to depend on the crossing direction. Second, we take into account uncertainties related to the energy available in the battery after recharging at a station as well as uncertainties related to the power consumption on each portion of the road network. Finally, we consider statistical dependencies between the stochastic power consumption on different arcs of the network. We focus on the chance-constrained flow refueling location model, which seeks to maximize the number of drivers for whom the probability of running out of fuel when carrying out their trip is below a certain threshold. To solve the resulting stochastic optimization problem, we propose to use a solution approach based on a partial sample approximation of the stochastic parameters and compare its performance with the one of a previously published approach based on Bonferroni's inequality. We carry out numerical experiments on a set of medium-size randomly generated and real life instances. Our results show that the proposed partial sample approximation approach outperforms the Bonferroni ap

关键词： Flow refueling location model Electric vehicle charging station network design Stochastic driving range Stochastic power consumption chance-constrained programming Partial sample approximation

来源：评论

学校读者我要写书评

暂无评论

Integrated multi-objective stochastic fuzzy programming and AHP method for agricultural water and land optimization allocation under multiple uncertainties

引用

JOURNAL OF CLEANER PRODUCTION 2019年 210卷 12-24页

作者： Ren, Chongfeng Li, Zhehao Zhang, Hongbo Minist Educ Key Lab Subsurface Hydrol & Ecol Effect Arid Reg Xian Shaanxi Peoples R China Changan Univ Sch Environm Sci & Engn YantaSt 126 Xian 710054 Shaanxi Peoples R China

Optimization allocation of agricultural water and land is a very complex system involves multiple objectives. Moreover, weights of each objective were signed by subjective judgement of decision makers is unreasonable. Furthermore, uncertainties are inevitable in the optimization allocation of irrigation water and land. In order to solve the above problems, this paper developed an improved multi-objective stochastic fuzzy programming method. The developed model was then applied to a case study in Wuwei City, Gansu Province, China. Maximum net benefit, maximum agricultural water productivity, and minimum irrigation area were regarded as planning objectives. A series of optimal irrigation and planting structure schemes were obtained under multiple uncertainties. From the results, water resources shortage in Wuwei city is very severely and it could not satisfy Wuwei's water demand even if the risk probability P-i reaches 0.25. Moreover, water and irrigation area would vary in different crops. Such changes would mainly take place in potato, vegetable and cucurbit, which have the same characteristics with higher yield, lower cost or lower irrigation quota than other crops. Furthermore, the decision makers could make reasonable decisions on the optimal use of irrigation water and land resources under multiple objective and uncertainties based on the obtained results. (C) 2018 Published by Elsevier Ltd.

关键词： Irrigation water optimal allocation Multi-objective programming Analytic hierarchy process method chance-constrained programming Fuzzy sets Multiple uncertainties

来源：评论

学校读者我要写书评

暂无评论

A simulation-based bi-level multi-objective programming model for watershed water quality management under interval and stochastic uncertainties

引用

JOURNAL OF ENVIRONMENTAL MANAGEMENT 2019年 245卷 418-431页

作者： Rong, Qiangqiang Cai, Yanpeng Su, Meirong Yue, Wencong Yang, Zhifeng Dang, Zhi Dongguan Univ Technol Res Ctr Ecoenvironm Engn Dongguan 523808 Peoples R China Guangdong Univ Technol Inst Environm & Ecol Engn Guangzhou 510006 Guangdong Peoples R China South China Univ Technol Sch Environm & Energy Guangzhou 510006 Guangdong Peoples R China

A simulation-based interval stochastic bi-level multi-objective programming (SISBLMOP) model was proposed in this research, through integrating the global nutrient export from watersheds model, interval parameter programming and stochastic chance-constrained programming into a general bi-level multi-objective programming framework. The SISBLMOP model can handle multiple uncertainties expressed as discrete intervals and probability density functions in both the simulation and optimization processes. System complexities, including the hierarchy structure of upper- and lower-level decision makers, can also be addressed in the model. The proposed model is applied to a real-world case study of the Xinfengjiang Reservoir Watershed in South China to identify the satisfactory implementation levels of multiple best management practices (BMPs). The model results show that multiple BMP schemes for water quality management can be obtained under different upper- and lower level decision-making and risk-violation scenarios, reflecting the cooperation and gaming results of the two-level decision makers. Consequently, the corresponding BMP implementation costs are acceptable to both the upper and lower-level decision makers. The model is widely applicable and can be effectively used for water quality management under multiple uncertainties and complexities.

关键词： Water quality management Bi-level multi-objective programming Interval parameter programming chance-constrained programming Nutrient export from watersheds

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：