检索结果-内蒙古大学图书馆

Optimal Scheduling of Distribution Network With Autonomous Microgrids: Frequency Security Constraints and Uncertainties

引用

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2023年第1期14卷 613-629页

作者： Zheng, Shunwei Liao, Kai Yang, Jianwei He, Zhengyou Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

Widespread integration of autonomous microgrids (MGs) into the distribution network (DN) makes the scheduling of distribution systems challenging. Especially, the uncertainties of renewable energy sources (RESs) and load demand put the MG frequency at the risk of violation for its low inertia and the high penetration of RESs. To address the schedule problems associated with uncertainties and frequency security, a day-ahead scheduling optimization model is proposed based on the chance-constrained programming (CCP) theory. The frequency security constraints and uncertainties of RESs/load demand associated with MGs are considered during the development of the proposed model. The total operation cost of the distribution system consisting of DN and MGs is minimized, while the MG frequency deviations caused by uncertainties are restricted in the predefined safe range by the proposed CCP-based model. Besides, a linearization method is presented and used to transform the proposed model into a mixed-integer linear program to determine the efficiency of the CCP-based model. Finally, numerous case studies are conducted to illustrate the effectiveness and good scalability of the proposed model and understand the advantages of using the model. The RT-LAB hardware-in-loop tests are carried out to study the real-time performance under the scheduling solved by the proposed model. Simulation and experimental results validate that the proposed model can be used to minimize the total operation cost of the DN system integrated with MGs while ensuring that the MG frequency deviations are operated within an acceptable range.

关键词： Distribution network microgrids uncertainties frequency security constraints chance-constrained programming optimal scheduling

来源：评论

学校读者我要写书评

暂无评论

Combined optimal dispatching of wind-light-fire-storage considering electricity price response and uncertainty of wind and photovoltaic power

引用

ENERGY REPORTS 2023年 9卷 790-798页

作者： Zhang, Mingguang Xu, Weiqiang Zhao, Wenyuan Lanzhou Univ Technol Lanzhou Gansu Peoples R China

The high proportion of renewable energy connected to the power grid puts enormous pressure on the power system for peaking. To reduce the peak-to-valley load difference, reduce the abandoned wind and light rate, and improve the economy of power system peaking, this paper constructs a wind-light-fire-storage joint optimal dispatching model based on electricity price response and uncertainty of wind and photovoltaic power. First, the model adds electricity price response to guide customers to change their electricity consumption habits by adjusting electricity prices at different times, bringing economic benefits to customers while shifting part of the peak load to the trough to achieve the purpose of peak shaving and valley filling;Secondly, considering the impact of wind and photovoltaic output uncertainty on power grid peaking, chance-constrained programming is added to the model, which is based on the simulation method, using the Latin hypercube sampling method, eventually transformed into a mixed integer linear programming model for solution;Finally, simulation is carried out in the modified IEEE30 node system to verify the feasibility and effectiveness of the model. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Wind and Photovoltaic power consumption Cost analysis Deep peak shaving Electricity price response chance-constrained programming

来源：评论

学校读者我要写书评

暂无评论

Distributionally Robust Unit Commitment Considering Unimodality-Skewness Information of Wind Power Uncertainty

引用

IEEE TRANSACTIONS ON POWER SYSTEMS 2023年第6期38卷 5420-5431页

作者： Zhou, Anping Yang, Ming Zheng, Xiaodong Yin, Shengfei Southern Methodist Univ Dept Elect & Comp Engn Dallas TX 75205 USA Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

The deepening penetration of renewable energy sources (RESs) into power grids imposes remarkable challenges that merit special attention. In this paper, we work out a distributionally robust chance-constrained (DRCC) day-ahead unit commitment problem while the stochasticity and variability arising from RESs are taken into consideration. The developed framework optimizes the expected objective function and stipulates that the DRCCs will hold within a moment-based ambiguity set, which additionally incorporates the unimodality information and mode skewness. The employed ambiguity set can not only account for the wind power uncertainty but also expressly alleviate the conservatism compared with the single moment metric. By deriving efficient mathematical reformulations to tackle the expected objective function and DRCCs, the proposed model is reduced to an exact mixed-integer second-order cone programming problem that can readily be implemented. Numerical experiments are carried out on two representative test systems to demonstrate the effectiveness and efficiency of the suggested methodology.

关键词： chance-constrained programming conic reformulation day-ahead unit commitment distributionally robust optimization renewable energy

来源：评论

学校读者我要写书评

暂无评论

A decision making framework for joint replenishment and delivery scheduling problems under mixed uncertainty

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2024年 187卷

作者： Wang, Guang Zhou, Jian Pantelous, Athanasios A. Liu, Yuanyuan Li, Youwei Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China Shanghai Univ Sch Management Shanghai 200444 Peoples R China Univ Bahrain Confucius Inst Zallaq Bahrain Monash Univ Monash Business Sch Dept Econometr & Business Stat 20 Chancellors WalkWellington Rd Clayton Vic 3800 Australia Shangdong Univ Finance & Econ Sch Management Sci & Engn Jinan 250014 Peoples R China Univ Hull Hull Univ Business Sch Cottingham Rd Kingston Upon Hull HU6 7RX England 99 Shangda Rd Shanghai Peoples R China

Concerning the essence of risk, a joint replenishment and delivery scheduling problem with fuzzy cost -related parameters and random number of imperfect quality items is developed to make it suitable for the inherent uncertainties of procurement-shipment process. The mathematical modelling-based decision system is formulated as a chance-constrained programming with the idea of embedding decision makers' risk tolerance. Following this notion, the model is translated into an equivalent non-linear counterpart and a neighbourhood heuristic search is designed based on the properties of the cost function. We introduce an integrated cross -entropy algorithm, incorporating the heuristic in the cross-entropy framework, to solve it. The numerical results demonstrate that ICE is quite effective in comparison to state-of-the-art algorithms. Our framework is helpful for decision makers to determine economically acceptable performance objectives in the presence of uncertain issues, and thus to build resilience in supply chain.

关键词： Supply chain resilience Joint replenishment Mixed uncertainty chance-constrained programming Cross-entropy algorithm

来源：评论

学校读者我要写书评

暂无评论

Grey Wolf Optimizer and Whale Optimization Algorithm for Stochastic Inventory Management of Reusable Products in a Two-Level Supply Chain

引用

IEEE ACCESS 2023年 11卷 40278-40297页

作者： Sadeghi, Amir Hossein Bani, Erfan Amani Fallahi, Ali Handfield, Robert North Carolina State Univ Dept Ind & Syst Engn Raleigh NC 27606 USA Sharif Univ Technol Dept Ind Engn Tehran *** Iran North Carolina State Univ Dept Business Management Raleigh NC 27607 USA

Product reuse and recovery is an efficient tool that helps companies to simultaneously address economic and environmental dimensions of sustainability. This paper presents a novel problem for stock management of reusable products in a single-vendor, multi-product, multi-retailer network. Several constraints, such as the maximum budget, storage capacity, number of orders, etc., are considered in their stochastic form to establish a more realistic problem. The presented problem is formulated using a nonlinear programming mathematical model. The chance-constrained approach is suggested to deal with the constraints' uncertainty. Regarding the nonlinearity of the model, grey wolf optimizer (GWO) and whale optimization algorithm (WOA) as two novel metaheuristics are presented as solution approaches, and the sequential quadratic programming (SQP) exact algorithm validates their performance. The parameters of algorithms are calibrated using the Taguchi method for the design of experiments. Extensive analysis is established by solving several numerical results in different sizes and utilizing several comparison measures. Also, the results are compared statistically using proper parametric and non-parametric tests. The analysis of the results shows a significant difference between the algorithms, and GWO has a better performance for solving the presented problem. In addition, both algorithms perform well in searching the solution space, where the GWO and WOA differences with the optimal solution of the SQP algorithm are negligible.

关键词： Supply chains Inventory management Uncertainty Stochastic processes Metaheuristics Whale optimization algorithms programming Reuse and recovery chance-constrained programming grey wolf optimizer whale optimization algorithm Taguchi method

来源：评论

学校读者我要写书评

暂无评论

Data-Driven chance-constrained Planning for Distributed Generation: A Partial Sampling Approach

引用

IEEE TRANSACTIONS ON POWER SYSTEMS 2023年第6期38卷 5228-5244页

作者： Jiang, Shiyi Cheng, Jianqiang Pan, Kai Qiu, Feng Yang, Boshi Hong Kong Polytech Univ Dept Logist & Maritime Studies Hung Hom Hong Kong Peoples R China Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA Argonne Natl Lab Energy Syst Div Lemont IL 60439 USA Clemson Univ Sch Math & Stat Sci Clemson SC 29634 USA

The planning of distributed energy resources has been challenged by the significant uncertainties and complexities of distribution systems. To ensure system reliability, one often employs chance-constrained programs to seek a highly likely feasible solution while minimizing certain costs. The traditional sample average approximation (SAA) is commonly used to represent uncertainties and reformulate a chance-constrained program into a deterministic optimization problem. However, the SAA introduces additional binary variables to indicate whether a scenario sample is satisfied and thus brings great computational complexity to the already challenging distributed energy resource planning problems. In this paper, we introduce a new paradigm, i.e., the partial sample average approximation (PSAA) using real data, to improve computational tractability. The innovation is that we sample only a part of the random parameters and introduce only continuous variables corresponding to the samples in the reformulation, which is a mixed-integer convex quadratic program. Our extensive experiments on the IEEE 33-Bus and 123-Bus systems show that the PSAA approach performs better than the SAA because the former provides better solutions in a shorter time in in-sample tests and provides better guaranteed probability for system reliability in out-of-sample tests. All the data used in the experiments are real data acquired from Pecan Street Inc. and ERCOT. More importantly, our proposed chance-constrained model and PSAA approach are general enough and can be applied to solve other valuable problems in power system planning and operations.

关键词： Planning distributed energy resources renewable distributed generation energy storage stochastic programming chance-constrained programming data-driven

来源：评论

学校读者我要写书评

暂无评论

Closed-Loop Dynamic Blending Optimization Based on Variational Bayesian and its Application in Industry

引用

IEEE ACCESS 2023年 11卷 494-505页

作者： Chen, Yu Jin, Wenbing Zheng, Limin Xiu, Xiaojie Huang, Yudong Qiu, Weijie Li, Yudong Hangzhou Polytech Sch Internet Things Technol Hangzhou 311402 Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang 110167 Peoples R China

There exist uncertainties in raw material components and distribution parameters in blending process. Considering the dynamic uncertainty of distribution parameters and the dynamic change of raw material inventory, a general closed-loop dynamic blending optimization (CDBO) is proposed. The proposed method consists of both data-driven system and model-based system. In the data-driven system, the feedback optimization problem is reconstructed into a linear regression problem, and the variational Bayesian is proposed to obtain the mean and variance of the distribution. Then, according to the incoming raw materials, the mean value of each distribution parameter is adjusted by expert rules. Last, in the model-based system, blending ratio is obtained by chance-constrained programming. To verify the effectiveness of CDBO, a detailed derivation process of variable Bayesian, expert rule and chance-constrained programming are established for zinc smelting process. Numerical studies and an industrial application for zinc smelting process are presented to demonstrate the advantages of the proposed method. Compared with the manual blending, the volatility index of chemical tests data is greatly reduced and the compliance rates of zinc and lead is increased by 6.7% and 3.3%, respectively.

关键词： Optimization Bayes methods Zinc Uncertainty Smelting Raw materials Closed loop systems Closed-loop dynamic blending optimization under uncertainty Variational Bayesian chance-constrained programming

来源：评论

学校读者我要写书评

暂无评论

A polynomial-time algorithm for a nonconvex chance-constrained program under the normal approximation

引用

OPTIMIZATION LETTERS 2023年第2期17卷 265-282页

作者： Mildebrath, David Rice Univ Dept Computat & Appl Math 3100 Main St MS-134 Houston TX 77005 USA

We study a chance-constrained optimization problem where the random variable appearing in the chance constraint follows a normal distribution whose mean and variance both depend linearly on the decision variables. Such structure may arise in many applications, including the normal approximation to the Poisson distribution. We present a polynomial-time algorithm to solve the resulting nonconvex optimization problem, and illustrate the efficacy of our method using a numerical experiment.

关键词： chance-constrained programming Reverse convex programming Nonlinear optimization Polynomial-time algorithms

来源：评论

学校读者我要写书评

暂无评论

Scale elasticity and technical efficiency analysis in the European forest sector: a stochastic value-based approach

引用

EUROPEAN JOURNAL OF FOREST RESEARCH 2023年第6期142卷 1293-1311页

作者： Amirteimoori, Alireza Allahviranloo, Tofigh Zadmirzaei, Majid Istinye Univ Fac Engn & Nat Sci Istanbul Turkiye Univ Guilan Fac Nat Resources Dept Forestry Rasht Iran

This paper is the first to examine both technical efficiency and scale elasticity of the forest sector across different European (EU) regions in an uncertain environment. In doing so, a dataset from 29 EU forest sectors have been collected;where the input and output data have been assumed to be random while their prices (or importance weights) were known. A chance-constrained data envelopment analysis approach was derived from several directional value-based models aimed at evaluating technical efficiency in the first stage. The results indicated that the number of efficient EU forest sectors were reduced substantially with extremely weak efficiency dispersion (about 18%), and the total average technical efficiency scores were moderately reduced by 0.74 as well. However, in the second stage, the findings of the modified cost/revenue-based scale elasticity model interestingly revealed that almost 7% of the EU forest sectors are in increasing returns to scale regions, which means they are still attractive acquisition targets and have the opportunity to increase the level of outputs and shift to the fully efficient border and finally increase their overall efficiency. As the major conclusion, all of our applied stochastic data envelopment analysis models (specially the cost/revenue-based approach) have high discriminating power in order to clearly distinguish the efficiency of the EU forest sector.

关键词： chance-constrained programming Stochastic data envelopment analysis Risk management in European forest sector Scale elasticity

来源：评论

学校读者我要写书评

暂无评论

Achieving Real-Time Spectrum Sharing in 5G Underlay Coexistence With Channel Uncertainty

引用

IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第4期22卷 1922-1937页

作者： Li, Shaoran Huang, Yan Li, Chengzhang Hou, Y. Thomas Lou, Wenjing Jalaian, Brian A. Russell, Stephen Virginia Tech Blacksburg VA 24061 USA NVIDIA Corp Santa Clara CA 95051 USA CCDC Army Res Lab Adelphi MD 20783 USA

Underlay coexistence is a spectrum efficient mechanism to roll out 5G picocells within a macrocell on the same spectrum. Due to a lack of cooperation between the primary users (PUs) in the macrocell and secondary users (SUs) in the picocells, it is impossible to have complete knowledge of channel conditions between them. Under such a circumstance, chance-constrained programming (CCP) has been shown to be an ideal optimization tool to address such a channel uncertainty. However, solutions to CCP are computationally intensive and cannot meet 5G's timing requirement (125 mu s). To address this problem, we propose a novel scheduler called GPU-based Underlay Coexistence (GUC) with the goal of finding an approximate solution to CCP in real-time. The essence of GUC is to decompose the original optimization problem into a large number of small subproblems that are suitable for parallel computation on GPU platforms. By selecting a subset of promising subproblems and solving them in parallel with fast algorithms, we are able to leverage GPU parallel computing and develop a real-time solution. Through extensive experiments, we show that GUC meets the 125 mu s requirement while achieving 90% optimality on average.

关键词： Graphics processing units 5G mobile communication Macrocell networks Real-time systems Timing Uncertainty Optimization 5G spectrum sharing underlay coexistence channel uncertainty real-time GPU chance-constrained programming scheduling power control

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：