检索结果-内蒙古大学图书馆

Two-stage distributionally robust optimization for joint system design and maintenance scheduling in high-consequence systems

引用

IISE TRANSACTIONS 2024年第8期56卷 793-810页

作者： Zhang, Hanxiao Li, Yan-Fu Xie, Min Zhang, Chen City Univ Hong Kong Dept Syst Engn Hong Kong Peoples R China Hong Kong Sci Pk Ctr Intelligent Multidimens Data Anal Hong Kong Peoples R China Tsinghua Univ Dept Ind Engn Beijing Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China

The failures of high-consequence systems can cause serious harm to humans, including loss of human health, life security, finance, and even social chaos. To protect high-consequence systems, both optimal system design and maintenance activities contribute to improving system reliability and social safety. The existing works generally optimize these two problems sequentially and assume that the degradation process of components is precisely known. However, sequential optimization often results in significant losses due to redundancies, and such a presumption usually cannot be guaranteed in practice, due to limited historical data or a lack of expert knowledge, referred to as epistemic uncertainty. To fill this gap, in this article, we consider an integrated optimization of system design and maintenance scheduling for multi-state high-consequence systems in which the component's degradation is known with limited distributional information. To address this issue, we utilize the framework of distributionally robust optimization to provide a risk-averse decision to decision-makers even under the worst realizations of random parameters, and develop a two-stage integer distributionally robust model with moment-based ambiguity set to determine the system design and maintenance scheduling simultaneously. The proposed model can be converted to a tractable approximation as an integer linear stochastic programming problem. In order to solve large-scale problems, we develop a sample-based adaptive large neighborhood search algorithm to find the optimal system designs. In the numerical experiments, we present a case study on feedwater heating systems in nuclear power plants and demonstrate that an integrated optimization consideration creates significant benefits in profitability. We also present the out-of-sample performance of the distributionally robust design to avoid extreme risk.

关键词： Reliability optimization condition-based maintenance preventive maintenance uncertainty stochastic programming distributionally robust optimization

来源：评论

学校读者我要写书评

暂无评论

stochastic two-stage resilient scheduling of hydrogen system in power distribution grids with high penetration of solar PV systems: A convex worst case analysis

引用

SUSTAINABLE CITIES AND SOCIETY 2024年 112卷

作者： Liu, Xin Hua, Zhigang Yuan, Xiaolu Liu, Jianqi Jiangsu Univ Sci & Technol Sch Energy & Power Zhenjiang 212003 Peoples R China State Power Investment Grp Co Ltd Beijing 100029 Peoples R China Donghua Univ Coll Environm Sci & Engn Shanghai 201620 Peoples R China

The renewable distribution network holds tremendous potential for economic advantages and sustainability improvements within the power industry. However, resilient operations in post-disaster scenarios present significant challenges for the system. Operational risks arise from uncertain resources, making day-ahead scheduling complex for the distribution grid's operator. Therefore, real-time modeling is considered more suitable to effectively manage these uncertainties. The hydrogen system (HSYS) is also an emerging technology that improves the technical and operational characteristics of distribution networks, particularly in challenging and unpredictable situations. The study presents a comprehensive framework for evaluating HSYSs within a photovoltaic-focused power distribution network, focusing on day-ahead resiliency-oriented operations (RESOP) while considering risk factors associated with uncertain incidents. The effectiveness and feasibility of implementing the proposed HSYS in day-ahead risk-based resilient scheduling of the distribution network are analyzed using a specific distribution system. The model is formulated as a MILP problem, and the impacting uncertainties are modeled using stochastic programming. The primary risk associated with uncertainties is captured using the conditional value-at-risk (CVaR) approach, which involves the possible worst realizations of uncertainties in the model. The study's findings demonstrate that the integration of distributed HSYSs leads to significant cost savings of up to 16 % under normal conditions and 1.25 % when considering risk modeling.

关键词： Hydrogen system Resiliency -oriented operation stochastic programming Risk assessment Day -ahead scheduling

来源：评论

学校读者我要写书评

暂无评论

Towards financial resilience: Smart energy management in Virtual Power Plants using stochastic-Robust optimization

引用

JOURNAL OF CLEANER PRODUCTION 2024年 483卷

作者： Su, Yingjian Wu, Zhixin Liu, Jia Harbin Univ Sch Econ & Management Harbin 150086 Heilongjiang Peoples R China Zhejiang Univ Finance & Econ Dongfang Coll Coll Business Adm Haining 314408 Zhejiang Peoples R China Zhejiang Univ Finance & Econ Coll business Adm Hangzhou 310018 Zhejiang Peoples R China

The dynamic coordination of Distributed Energy Resources (DERs) and price-responsive demands, orchestrated as a Virtual Power Plant (VPP), stands as a pivotal challenge in the electricity industry. This paper presents a novel contribution through the application of stochastic-robust optimization to address the intricate uncertainties associated with stochastic DERs, fluctuating energy prices, and varying Micro-Grid (MG) component availability. Leveraging smart grid technology enables real-time monitoring of the VPP, facilitating dynamic adjustments to energy management decisions in response to uncertainties. The focus is on maximizing the financial profit of the VPP, achieved through seamless integration of solar power stations, storage units, and price-responsive demands within a MG. The paper employs interval- and scenario-based forecasting to systematically model continuous random variables (e.g., energy price and solar generation) and discrete random variables (e.g., MG component availability). Through an in-depth case study, the proposed stochastic-robust optimization is rigorously analyzed, demonstrating superior performance, particularly during contingencies on days with highly volatile energy prices. This research provides valuable insights and a robust framework for navigating the intricate landscape of VPP energy management amid evolving industry dynamics and technological advancements. The stochastic robust model obtained in average 12.5% more profit in comparison with other models. Moreover, regarding the conditional value at risk in the healthy condition of MG, the profit reduced about 43.7% and 12.6% compared to deterministic with confidence levels 85% and 95%, respectively.

关键词： Energy management Virtual power plant Smart grid Robust optimization stochastic programming

来源：评论

学校读者我要写书评

暂无评论

A stochastic optimisation model to support cybersecurity within the UK national health service

引用

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2024年

作者： Grass, Emilia Pagel, Christina Crowe, Sonya Ghafur, Saira Karlsruhe Inst Technol Karlsruhe Germany UCL London England Imperial Coll London London England

Over the past decade, the adoption of new digital technologies in healthcare has surged, significantly enhancing care delivery and accessibility. However, this digital transformation has been accompanied by a sharp increase in cyber-attacks, posing severe risks to hospital functionality and patient safety. To address the challenge of planning for uncertain future cyber incidents, we propose a two-stage stochastic model designed to bolster the cyber resilience of healthcare providers by selecting optimal countermeasures in preparation for upcoming cyber incidents. Numerical tests demonstrate the model's effectiveness, with the Value of the stochastic Solution showing a 21% improvement over a deterministic approach. To be optimally equipped even for low-probability high-impact attacks we incorporate the risk measure Conditional Value-at-Risk. The corresponding countermeasure solution led to 44% fewer rejected patients in a worst-case scenario. The robustness of the proposed solution is underscored by its consistent performance across various scenarios, budget levels, and risk preferences, making it a reliable tool for enhancing cybersecurity in healthcare. These results highlight the importance of tailored, robust cybersecurity strategies in healthcare, ensuring preparedness for a wide range of potential threats.

关键词： stochastic programming cybersecurity risk measure CVaR health services NHS

来源：评论

学校读者我要写书评

暂无评论

A smart building energy management incorporating clustering-based tariffs in the presence of domestic solar energy, battery, and electric vehicle

引用

SOLAR ENERGY 2024年 279卷

作者： Alilou, Masoud Mohammadi-ivatloo, Behnam Mohammadpourfard, Mousa Univ Tabriz Fac Elect & Comp Engn Tabriz Iran LUT Univ Sch Energy Syst Dept Elect Engn Lappeenranta Finland Univ Tabriz Fac Chem & Petr Engn Tabriz Iran Izmir Inst Technol Dept Energy Syst Engn Izmir Turkiye

Smart buildings play a crucial role in optimizing energy management within the power network. As end-users of the power network, they have the ability to not only reduce economic costs for householders but also modify the technical indices of the power network. To promote efficient device management in smart homes (SH), demand response programs are recommended for consumers. This research investigates the application of clusteringbased electricity pricing strategy aimed at effectively managing the energy devices of a residential smart home. The utilized method categorizes the electricity tariff into five rates according to the clustering of the realtime pricing program. Ward's clustering method is utilized to cluster and determine new electricity tariffs. The primary goal of the energy management program is to minimize the building's energy cost, which is accomplished through the utilization of the multi-verse optimizer. The smart home consists of essential and manageable appliances, a photovoltaic panel (PV), a sodium-sulfur (NaS) battery, and an electric vehicle (EV). The initial parameters of the PV and EV are modeled stochastically by their probability distribution functions and calculated using the Latin hypercube sampling algorithm. The smart building's performance is assessed by taking into account various demand response programs. The numerical results present that the application of the clusteringbased management method has resulted in a significant reduction of 23-43 % in the electricity cost of smart homes. Additionally, the smart home exhibits a more linear consumption pattern when considering the electricity tariffs based on the clustering approach.

关键词： Clustering-based electricity tariff Smart home Solar energy stochastic programming Demand response program Electric vehicle

来源：评论

学校读者我要写书评

暂无评论

Optimal Operation of Fast Charging Station Aggregator in Uncertain Electricity Markets Considering Onsite Renewable Energy and Bounded EV User Rationality

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第11期20卷 13384-13395页

作者： Chen, Yuanyi Zheng, Yanchong Hu, Simon Xie, Shiwei Yang, Qiang Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ ZJU UIUC Inst Haining 314400 Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310027 Peoples R China Alibaba Zhejiang Univ Joint Res Inst Frontier Technol Hangzhou 310058 Peoples R China Fuzhou Univ Sch Elect Engn & Automat Fuzhou 350108 Peoples R China

The increasing proliferation of electric vehicles (EVs) and renewable energy sources (RESs) poses challenges to the operation of coupled power and transportation networks due to their uncertainties, where EV users' routing and charging behaviors are subjected to their complex decision-making rationality. To economically manage numerous fast charging stations with onsite RESs, this article focuses on the optimal day-ahead bidding and intraday scheduling strategies of a fast charging station aggregator (FCSA) to maximize its profit in the electricity market. Traffic simulation based on boundedly rational dynamic user equilibrium is presented to model charging demand under bounded rationality of EV users. To efficiently manipulate large-scale EVs, a group charging scheduling framework is proposed to reduce decision variables. Uncertainties in electricity prices, RES generation, traffic demand, and user rationality are addressed by stochastic programming. Case studies have validated the effectiveness of the proposed method in reducing the FCSA's operational costs and RES curtailment.

关键词： Bounded rationality charging dispatch dynamic user equilibrium electricity market fast charging station aggregator (FCSA) stochastic programming

来源：评论

学校读者我要写书评

暂无评论

A comprehensive stochastic-based adaptive robust model for transmission expansion planning

引用

ELECTRIC POWER SYSTEMS RESEARCH 2024年 234卷

作者： Alnowibet, Khalid A. Alrasheedi, Adel F. Alshamrani, Ahmad M. King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia

To address various uncertainties appearing in the transmission expansion planning (TEP) problem, appropriate tools such as stochastic programming (SP) or robust optimization (RO) are required. On the other hand, both long-term (LT) and short-term (ST) uncertainties must be well captured to acquire the most reliable and robust solution. The current works model the LT uncertainty by RO and the ST uncertainty by SP, forming a two -stage adaptive robust optimization problem. In contrast, this paper presents a novel approach in which the SP and RO are simultaneously utilized to cope with each type of uncertainty, i.e. , LT and ST uncertainty. Unlike the current methods with bilinear terms appearing in the subproblems, the trilinear terms appear in the objective function of the proposed approach, which are linearized accordingly. In addition, we propose a mixed -integer bilinear programming (MIBP) model to specify the rectangles used as uncertainty sets, which is further solved using Konno ' s algorithm. A standard column -and -constraint (CCG) generation solves the established tri-level structure. Two case studies are used to implement the formulation developed in this work. Results signify the effectiveness of the proposed method.

关键词： Column -and -constraint generation Long and short-term uncertainty Mixed -integer bilinear programming Robust optimization stochastic programming Transmission expansion planning

来源：评论

学校读者我要写书评

暂无评论

Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem

引用

ARTIFICIAL INTELLIGENCE REVIEW 2024年第6期57卷 1-50页

作者： Aydin, Nezir Murat, Alper Mordukhovich, Boris S. Yildiz Tech Univ Dept Ind Engn TR-34349 Besiktas Istanbul Turkiye Hamad Bin Khalifa Univ Coll Sci & Engn Doha Qatar Wayne State Univ Dept Ind & Syst Engn Detroit MI 48202 USA Wayne State Univ Dept Math Detroit MI 48202 USA

Selecting facility locations requires significant investment to anticipate and prepare for disruptive events like earthquakes, floods, or labor strikes. In practice, location choices account for facility capacities, which often cannot change during disruptions. When a facility fails, demand transfers to others only if spare capacity exists. Thus, capacitated reliable facility location problems (CRFLP) under uncertainty are more complex than uncapacitated versions. To manage uncertainty and decide effectively, stochastic programming (SP) methods are often employed. Two commonly used SP methods are approximation methods, i.e., Sample Average Approximation (SAA), and decomposition methods, i.e., Progressive Hedging Algorithm (PHA). SAA needs large sample sizes for performance guarantee and turn into computationally intractable. On the other hand, PHA, as an exact method for convex problems, suffers from the need to iteratively solve numerous sub-problems which are computationally costly. In this paper, we developed two novel algorithms integrating SAA and PHA for solving the CRFLP under uncertainty. The developed methods are innovative in that they blend the complementary aspects of PHA and SAA in terms of exactness and computational efficiency, respectively. Further, the developed methods are practical in that they allow the specialist to adjust the tradeoff between the exactness and speed of attaining a solution. We present the effectiveness of the developed integrated approaches, Sampling Based Progressive Hedging Algorithm (SBPHA) and Discarding SBPHA (d-SBPHA), over the pure strategies (i.e. SAA). The validation of the methods is demonstrated through two-stage stochastic CRFLP. Promising results are attained for CRFLP, and the method has great potential to be generalized for SP problems.

关键词： stochastic programming Capacitated reliable facility location Hybrid algorithm Progressive hedging Sample average approximation

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Distributionally Robust Optimization Approach for Optimal Sizing of Hybrid Renewable Energy Systems

引用

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2024年第2期203卷 2055-2082页

作者： Keyvandarian, Ali Saif, Ahmed Dalhousie Univ Dept Ind Engn 5269 Morris St Halifax NS B3H 4R2 Canada

Hybrid renewable energy systems (HRESs) that integrate conventional and renewable energy generation and energy storage technologies represent a viable option to serve the energy demand of remote and isolated communities. A common way to capture the stochastic nature of demand and renewable energy supply in such systems is by using a small number of independent discrete scenarios. However, some information is inevitably lost when extracting these scenarios from historical data, thus introducing errors and biases to the design process. This paper proposes two frameworks, namely robust-stochastic optimization and distributionally robust optimization, that aim to hedge against the resulting uncertainty of scenario characterization and probability, respectively, in scenario-based HRES design approaches. Mathematical formulations are provided for the nominal, stochastic, robust-stochastic, distributional robust, and combined problems, and directly-solvable tractable reformulations are derived for the stochastic and the distributional robust cases. Furthermore, an exact column-and-constraint-generation algorithm is developed for the robust-stochastic and combined cases. Numerical results obtained from a realistic case study of a stand-alone solar-wind-battery-diesel HRES serving a small community in Northern Ontario, Canada reveal the performance advantage, in terms of both cost and utilization of renewable sources, of the proposed frameworks compared to classical deterministic and stochastic models, and their ability to mitigate the issue of information loss due to scenario reduction.

关键词： Hybrid renewable energy systems Distributionally robust optimization stochastic programming Robust-stochastic optimization Optimization under uncertainty

来源：评论

学校读者我要写书评

暂无评论

Privacy-preserving adaptive traffic signal control in a connected vehicle environment

引用

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 158卷

作者： Tan, Chaopeng Yang, Kaidi Natl Univ Singapore Dept Civil & Environm Engn 1 Engn Dr 2 Singapore 117576 Singapore

Although Connected Vehicles (CVs) have demonstrated tremendous potential to enhance traffic operations, they can impose privacy risks on individual travelers, e.g., leaking sensitive information about their frequently visited places, routing behavior, etc. Despite the large body of literature that devises various algorithms to exploit CV information, research on privacy preserving traffic control is still in its infancy. In this paper, we aim to fill this research gap and propose a privacy-preserving adaptive traffic signal control method using partially connected vehicle data. Specifically, we proposed a privacy-preserving mechanism to protect CV data against three types of attacks: CV collusion attacks, database attacks, and inference attacks. The mechanism leverages secure Multi-Party Computation and differential privacy to aggregate individual-level CV data to calculate key traffic parameters without compromising the privacy of CV users. For seamless integration with the privacy-preserving mechanism, we develop a traffic signal optimization model and an arrival rate estimator relying only on aggregated CV data, being applied to both undersaturated and oversaturated traffic conditions. The optimization model is further extended to a stochastic programming problem to explicitly handle the noises added by the privacy-preserving mechanism. Evaluation results show that the linear optimization model preserves privacy with a marginal impact on control performance, and the stochastic programming model can significantly reduce residual queues compared to the linear programming model, with almost no increase in vehicle delay. Overall, our methods demonstrate the feasibility of incorporating privacy-preserving mechanisms in CV-based traffic modeling and control, which guarantees both utility and privacy.

关键词： Traffic signal control Privacy Connected vehicles Secure multi-party computation Differential privacy stochastic programming

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：