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Fuzzy-stochastic distributional robust approach for microalgae-based bio-plastic supply chain with new sustainability aspects

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JOURNAL OF CLEANER PRODUCTION 2024年 476卷

作者： Mondal, Arijit Roy, Sankar Kumar Vidyasagar Univ Dept Appl Math Midnapore 721102 West Bengal India

The current study offers a sustainable supply chain model that can be used to identify opportunities and constraints for the future of the microalgae-based bio-diesel and bio-plastic industry. The network's carbon emissions are restricted by an uncertain constraint that allows for flexibility in controlling emissions as needed. The social constraints of the model provides a transparent depiction of labour needs in dynamic business landscape and foster women's empowerment through a preference coefficient. The proposed model introduces a new upside risk measures under fuzzy-stochastic setting to quantify the upward financial risk. To characterize and manage the model uncertainty, a fuzzy-stochastic distributional robust model is presented. Credibility distribution is utilized to handle fuzzy uncertainty, while box and polyhedral uncertainty sets are included to handle stochastic uncertainty. After that, tractable deterministic formulations of the uncertain model for two ambiguity sets are presented to solve the model. Finally, a number of insights are obtained by means of five test problems that are implemented in Python and resolved by one exact and five meta-heuristic algorithms. Experiments indicate that, when faced with uncertainty, two robust models have produced more stable solutions (16.23% and 19.67% higher stability, respectively) than nominal stochastic model. Further, a 4.68% decline in total cost and 3.27% increment in bio-diesel production can be achieved by increasing bio-diesel conversion rate to 10%. In addition, a 9.09% downturn in total cost and 10.44% rise in bio-plastic production can be obtained by 10% increment in bio-plastic conversion rate. Moreover, 45.38% more woman labours can be recruited by increasing the preference coefficient from -0.2 to +0.2.

关键词： Bio-diesel and bio-plastic supply chain Risk measure Flexible carbon constraint Fuzzy-stochastic distributional robust optimization

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A column generation approach for the quay crane allocation problem considering uncertain operation efficiency

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 190卷

作者： Tang, Xufeng Li, Xinqi Yin, Ming Shanghai Maritime Univ Coll Transportat & Commun Shanghai 201306 Peoples R China Shanghai Univ Sch Management Shanghai 200444 Peoples R China

This study constructs a mixed integer programming model for the integrated quay crane allocation problem to minimize vessels' waiting time and delay time cost. Taking the uncertainty of quay crane operation efficiency into consideration, this study constructs a stochastic programming model and a distributional robust chance constraint (DRCC) model. In addition, column generation is applied to solve the three proposed models. Moreover, this paper uses real-world data from Shanghai Yangshan Port to verify the effectiveness of proposed model and algorithm. To prove the robustness of the DRCC model, this study conducts an uncertain parameters sensitivity analysis, and analyzes the performance of the models proposed above. The results show that the proposed DRCC model has better anti-interference ability. The conclusion drawn from this study has potential implications for resource allocation within port operations.

关键词： Port Arrangement Quay Crane Allocation Problem distributional robust optimization Stochastic Programming Column Generation

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Two-stage optimization model for day-ahead scheduling of electricity- heat microgrids with solid electric thermal storage considering heat flexibility

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JOURNAL OF ENERGY STORAGE 2024年 95卷

作者： Ma, Yinghao Wang, Shuai Yang, Hejun Zhang, Dabo Shen, Yuming Hefei Univ Technol Anhui Prov Key Lab Renewable Energy Utilizat & Ene Hefei 230009 Peoples R China State Grid Anhui Elect Power Co Ltd Econ & Technol Res Inst Hefei 230071 Peoples R China

Traditional electricity-heat microgrid (EHM) is limited in flexibility due to real-time balancing between the supply and demand of electric and heat system. The heat flexibility (HF) can be released by thermal inertia and heat storage characteristics of district heating systems (DHS) and heat storage units, and it can be used to increase the electric power flexibility (EF) of EHM. In this paper, the quantitative relationship between HF and EF is investigated, and a two-stage optimization model for day-ahead scheduling of EHM with solid electric thermal storage (SETS) is established. Firstly, the electric-heat flexibility conversion coefficient (EHFC) is developed to decide the allocation of HF among different electricity-heat coupling units, such as CHP and SETS, and EF supply model of EHM based on HF is built. Secondly, a two-stage optimization model is proposed for day-ahead scheduling of EHM to improve EF and economy of EHM by optimizing the heating power and HF's allocation. Thirdly, the equivalent energy storage of the DHS is quantitatively analyzed, providing a reference for planning and operating of EHM's flexibility resource. Finally, the case study shows that by considering HF and deploying SETS, the total cost can be reduced by 18.8 % and 22.6 %, respectively.

关键词： Electricity-heat microgrids Solid electric thermal storage distributional robust optimization Flexibility

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IMPLEMENTATION AND optimization OF PROBABILISTIC AND MATHEMATICAL STATISTICAL ALGORITHMS UNDER DISTRIBUTIVE ARCHITECTURE

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第5期25卷 3841-3849页

作者： Li, Shengbiao Peng, Jiankui Lanzhou Univ Arts & Sci Sch Educ Lanzhou 730000 Peoples R China

Statistical methods must be developed and optimized in distributed systems due to the increasing amount of data and processing demands in modern applications. The application and optimization of mathematical and probabilistic statistical methods in distributed computing settings is the main topic of this study. Algorithms like these have the potential to improve performance, scalability, and parallel processing abilities when integrated into distributed systems. We commence our investigation by reviewing current mathematical and probabilistic statistical algorithms, determining their advantages and disadvantages, and evaluating their suitability for distributed architectures. We then suggest new approaches for their smooth incorporation into distributed computing structures, making use of distributed storage and parallel processing to effectively manage massive datasets. Improving these algorithms' performance in distributed environments is the focus of this research's refinement phase. We seek to optimize the use of distributed infrastructures by minimizing latency and maximizing computational resources by investigating efficient communication protocols, load balancing mechanisms, and parallelization approaches. The suggested algorithms are put into practice inside a distributed structure for empirical confirmation, and their effectiveness is evaluated in comparison to more conventional, non-distributed competitors. We test the scaling, precision, and effectiveness of the methods in practical scenarios using a variety of datasets and use cases.

关键词： probabilistic optimization stochastic optimization robust optimization distributional robust optimization chance constrained optimization energy management smart grid

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distributionally robust Federated Learning for Differentially Private Data 42

Distributionally Robust Federated Learning for Differentiall...

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42nd IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Shi, Siping Hu, Chuang Wang, Dan Zhu, Yifei Han, Zhu Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Wuhan Univ Dept Comp Sci Wuhan Peoples R China Shanghai Jiao Tong Univ UM SJTU Joint Inst Shanghai Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Local differential privacy (LDP) is a prominent approach and widely adopted in federated learning (FL) to preserve the privacy of local training data. It also nicely provides a rigorous privacy guarantee with computational efficiency in theory. However, a strong privacy guarantee with local differential privacy can degrade the adversarial robustness of the learned global model. To date, very few studies focus on the interplay between LDP and the adversarial robustness of federated learning. In this paper, we observe that LDP adds random noise to the data to achieve privacy guarantee of local data, and thus introduces uncertainty to the training dataset of federated learning. This leads to decreased robustness. To solve this robustness problem caused by uncertainty, we propose to leverage the promising distributionally robust optimization (DRO) modeling approach. Specifically, we first formulate a distributionally robust and private federated learning problem (DRPri). While our formulation successfully captures the uncertainty generated by the LDP, we show that it is not easily tractable. We thus transform our DRPri problem to another equivalent problem, under the Wasserstein distance-based uncertainty set, which is named the DRPri-W problem. We then design a robust and private federated learning algorithm, RPFL, to solve the DRPri-W problem. We analyze RPFL and theoretically show it satisfies differential privacy with a robustness guarantee. We evaluate algorithm RPFL by training classifiers on real-world datasets under a set of well-known attacks. Our experimental results show our algorithm RPFL can significantly improve the robustness of the trained global model under differentially private data by up to 4.33 times.

关键词： Federated learning local differential privacy distributional robust optimization

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Probabilistic optimization Techniques in Smart Power System

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ENERGIES 2022年第3期15卷

作者： Riaz, Muhammad Ahmad, Sadiq Hussain, Irshad Naeem, Muhammad Mihet-Popa, Lucian COMSATS Univ Dept Elect & Comp Engn Wah Campus Wah 47040 Pakistan Univ Engn & Technol Peshawar Fac Elect & Comp Engn Peshawar 25000 Pakistan Oestfold Univ Coll Fac Informat Technol Engn & Econ N-1757 Halden Norway

Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. robust optimization is the most advanced approach to optimize a system under uncertainty, in which a deterministic, set-based uncertainty model is used instead of a stochastic one. The computational complexity of stochastic programming and the conservativeness of robust optimization are both reduced by distributionally robust *** constrained algorithms help in solving the constraints optimization problems, where finite probability get violated. This review paper discusses microgrid and home energy management, demand-side management, unit commitment, microgrid integration, and economic dispatch as examples of applications of these techniques in smart power systems. Probabilistic mathematical models of different scenarios, for which deterministic approaches have been used in the literature, are also presented. Future research directions in a variety of smart power system domains are also presented.

关键词： probabilistic optimization stochastic optimization robust optimization distributional robust optimization chance constrained optimization energy management smart grid

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robust optimization of the active distribution network involving risk assessment

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Zhang, Yumin Yang, Zizhen Ji, Xingquan Zhang, Xuan Yu, Zihan Wu, Fucheng Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Peoples R China

Inherent dynamic constraints of distributed generations (DGs) and the correlation between injected variables bring great challenges to distribution network operation. In order to improve the degree of coupling and interconnection coordination between different energy devices, improve the ability of the distribution network to cope with the uncertainty of DGs, achieve low-carbon operation, and improve the environmental friendliness of distribution network operation, this article proposes a robust optimization approach involving risk assessment. The semi-invariant method and scene clustering are used to deal with the uncertainty of DGs and load, thus formulating a robust optimization model for distribution network distribution based on risk indices. To address the time-varying constraints of energy storage systems (ESSs) and gas turbines, a two-stage box-based decomposition model is established. Dynamic constraints are included in the first stage to constrain the operating state and operating domain of the unit and ESSs. In the second stage, the multi-timescale optimization problem is transformed into multiple single-timescale optimization problems, which are solved by the column and constraint generation (C & CG) algorithm to improve the solution efficiency. The feasibility of the comprehensive optimization model based on dynamic reconfiguration and distributed robust optimization (DRO) is demonstrated with the PG & E 69 bus system.

关键词： risk assessment distributional robust optimization semi-invariant method clustering two-stage box-based decomposition

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robust Integrated optimization of Active Distribution Network Based on System Risk Index

Robust Integrated Optimization of Active Distribution Networ...

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3rd IEEE Student Conference on Electrical Machines and Systems (SCEMS)

作者： Zhang, Yumin Gao, Haishu Ji, Xingquan Liu, Qi Yu, Yixiao Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Peoples R China Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin Peoples R China Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China

ISBN: (纸本)9781728156224

This paper presents a comprehensive distribution robust optimization model based on the distribution network risk index to address the distributional uncertainties of distributed generations (DGs) and loads. To analyze the security of active distribution network (ADN) systematically, a risk assessment method based on improved semi-invariant method and clustering technique is proposed. The risk assessment method is performed on the integrated dynamic reconfiguration and reactive power optimization model. This method can consider the correlation between distributed power sources, and when the injected power is large, the calculation results with higher accuracy can be obtained Case studies on the PG&E 69-bus system verify the robustness and feasibility of the proposed model.

关键词： active distribution network distributional robust optimization probability distribution risk assessment

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Research on Mortgage-Backed Securitization Structure

Research on Mortgage-Backed Securitization Structure

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2016 International Conference on Applied Mathematics and Data Science (ICAMDS2016)

作者： Yang Yue Yu Bo Department of Mathematics Dalian Naval Academy Department of Mathematics Dalian University of Technology

Asset securitization is an important way to improve bank asset liability structure, and can offer investors financial tools with high yields. Owing to the rapid and stable development of the financial market, the large-scale asset securitization age is on the way. To establish a complete and reasonable mortgage-backed securitization structuring model, this paper firstly introduced a method to estimate the repayment default rate based on distributional robust optimization. Then it improved a credit rating system by containing an option on the mortgage market value. Finally it proposed to use the dynamic programming model to structure the mortgage-backed securities. It is supposed to make a contribution to the financial innovation in China and put forward fresh ideals.

关键词： Dynamic Programming Mortgage-Backed Securitization distributional robust optimization Default Rate Credit Rating

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