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17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2023

作者： Zahraoui, Younes Korotko, Tarmo Rosin, Argo Ahmadiahangar, Roya Tallinn University of Technology FinEst Centre for Smart Cities Tallinn Estonia Institute of Physical Energetics Smart Grid Research Centre Riga Latvia Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Estonia

ISBN: (纸本)9798350300048

As a result of the recent innovations in the deployment of distributed Energy Storage Systems (ESS) such as Battery Energy Storage Systems (BESS), this technology can play an important role as a distributed energy resource in supplying the system demand for the new generation of power systems. BESSs are considered promising technology in the microgrid to reduce reliance on fossil fuels and mitigate the intermittency from renewable energy sources in a microgrid in which operators adjust electricity procurement and storage decisions in dynamic response to changes in demand, supply, and pricing in real-time to increase the profit. Optimization approaches have been proposed as a way to increase the user's profits from microgrid participation. However, renewable energy resources are highly uncertain and cannot be effectively predicted which affects the operation of the microgrid-based BESS. Therefore, this study presents a layout design of energy planning for microgrids using the stochastic energy management model. The proposed management algorithm aims to find a day-ahead optimal operation of the microgrid including the photovoltaic (PV) system, and BESS considering demand response programs in the grid-connected mode. The stochastic model operates a grid-tied PV-BESS-based microgrid asset to inject the power into the main grid and store the surplus energy to maximize the profit. Besides, to create the stochastic energy management model, machine learning and deep learning models are used to generate proper and accurate scenarios for the PV system. Finally, a systematic simulation based on time-varying electricity prices was carried out using a day-ahead profile of a single load profile. © 2023 IEEE.

关键词： stochastic programming

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Integrated Hurricane Relief Logistics and Evacuation Planning under Forecast Uncertainty: A Case Study for Hurricane Florence

Integrated Hurricane Relief Logistics and Evacuation Plannin...

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IISE Annual Conference and Expo 2023

作者： Bhattarai, Sudhan Song, Yongjia Clemson University ClemsonSC United States

ISBN: (纸本)9781713877851

In this paper, we study an integrated hurricane relief logistics and evacuation planning (HRLEP) problem. We propose stochastic optimization models and methods that integrate the hurricane relief item pre-positioning problem and the hurricane evacuation planning problem, which are often treated as separate problems in the literature, by incorporating the forecast information as well as the forecast errors (FE). Specifically, we fit historical FE data into an auto-regressive model of order one (AR-1), from which we generate FE realizations to create evacuation demand scenarios. We compare a static decision policy based on the proposed stochastic optimization model with a dynamic policy obtained by applying this model in a rolling-horizon (RH) procedure. We conduct a preliminary numerical experiment based on real-world data to validate the value of stochastic optimization and the value of the dynamic policy based on the RH procedure. © 2023 IISE Annual Conference and Expo 2023. All rights reserved.

关键词： stochastic programming

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stochastic Distribution Network Reconfiguration Exploiting the Flexibilities of Data Center 7

Stochastic Distribution Network Reconfiguration Exploiting t...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Duan, Xumin Zheng, Weiye Zhu, Jizhong School of Electric Power Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350345094

The overvoltage issue has led to critical curtailment in renewable energy in the distribution network. Although distribution network reconfiguration can mitigate the problem, existing research has not fully utilized the flexibility resources. With the boost of the Internet, date centers with adjustable workloads offer a potential solution lately. To fully exploit their flexibilities, a data center operation model that comprehensively considers interactive workload allocation, batch workload delay, and batch workload shift is proposed. Then the model is integrated into distribution energy management, where we pick distribution network reconfiguration as a demonstrative example. The effectiveness is verified in a modified IEEE 33-bus system. Compared with the base case, the proposed method not only alleviates the overvoltage issue but also enhances renewable energy accommodation with fewer reconfiguration costs. © 2023 IEEE.

关键词： stochastic programming

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A stochastic mixed-integer convex programming model for long-term distribution system expansion planning considering greenhouse gas emission mitigation

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2019年第Jun.期108卷 86-95页

作者： Home-Ortiz, Juan M. Melgar-Dominguez, Ozy D. Pourakbari-Kasmaei, Mandi Sanches Mantovani, Jose Roberto Sao Paulo State Univ UNESP Elect Engn Dept BR-15385000 Ilha Solteira SP Brazil Aalto Univ Dept Elect Engn & Automat Maarintie 8 Espoo 02150 Finland

This paper proposes a multistage convex distribution system planning model to find the best reinforcement plan over a specified horizon. This strategy determines planning actions such as reinforcement of existing substations, conductor replacement of overloaded feeders, and siting and sizing of renewable and dispatchable distributed generation units. Besides, the proposed approach aims at mitigating the greenhouse gas emissions of electric power distribution systems via a monetary form. Inherently, this problem is a non-convex optimization model that can be an obstacle to finding the optimal global solution. To remedy this issue, convex envelopes are used to recast the original problem into a mixed integer conic programming (MICP) model. The MICP model guarantees convergence to optimal global solution by using existing commercial solvers. Moreover, to address the prediction errors in wind output power and electricity demands, a two-stage stochastic MICP model is developed. To validate the proposed model, detail analysis is carried out over various case studies of a 34-node distribution system under different conditions, while to show its potential and effectiveness a 135-node system with two substations is used. Numerical results confirm the effectiveness of the proposed planning scheme in obtaining an economic investment plan at the presence of several planning alternatives and to promote an environmentally committed electric power distribution network.

关键词： Conic programming Distributed energy Multistage distribution system expansion planning Renewable energy sources stochastic programming

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stochastic Generation and Transmission Expansion Planning using Sample Average Approximation 13

Stochastic Generation and Transmission Expansion Planning us...

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13th International Symposium on Advanced Topics in Electrical Engineering, ATEE 2023

作者： Altun, Osman Karatepe, Engin Dokuz Eylul University Department of Electrical - Electronics Engineering Izmir Turkey Institute of Solar Energy Ege University Izmir Turkey

ISBN: (纸本)9798350331936

The issue of modeling and incorporating uncertainty considering tractability in the planning of electrical power systems has become important for planners to maintain a reliable system. In this study, the generation and transmission expansion planning is performed considering the uncertainty in load and wind generation. The sample average approximation (SAA) method is used to obtain deterministic equivalent of stochastic optimization problems in a mixed integer linear programming (MILP) framework. The problem is formulated with a large number of independent samples, each small in size, to reduce computational effort. The results are presented by comparing them with investment decisions made using a single large sample size. It is observed that the presented approach can be used as an alternative in terms of sufficiently operationalizable to produce optimal investment decisions in mathematical programming. © 2023 IEEE.

关键词： stochastic programming

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stochastic approaches for multi-echelon disassembly lot-sizing under random lead times. 50

Stochastic approaches for multi-echelon disassembly lot-sizi...

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50th International Conference on Computers and Industrial Engineering: Sustainable Digital Transformation, CIE 2023

作者： Slama, Ilhem Ben-Ammar, Oussama Thevenin, Simon Dolgui, Alexandre UniversitÃl' de Technologie de Troyes LIST3N 12 rue Marie Curie Troyes10010 France EuroMov Digital Health in Motion Univ Montpellier IMT Mines Ales Ales France IMT Atlantique LS2N-CNRS la Chantrerie 4 rue Alfred Kastler - B.P. 20722 Nantes44307 France

ISBN: (纸本)9781713886952

With the increasing focus on environmental issues, manufacturers are facing pressure to adopt end-of-life item collection practices and maximize the reuse of components or sub-assemblies to their fullest potential. The decision-making processes for disassembly planning and components inventory management become critical as they directly impact a company's production costs and service level. In this particular context, Reverse Material Requirements Planning (RMRP) is a widely utilized method to determine the optimal lot sizes of endof-life (EoL) products for disassembly, ensuring sufficient component supply. Traditionally, these lot sizes are calculated based on deterministic and dynamic demand assumptions when disassembly lead times are either zero or deterministic. However, in this work, we investigate optimization approaches for RMRP systems that consider disassembly lead time uncertainties, in a disassembly multi-echelon lot-sizing problem. We proposed stochastic programming models based on a Monte Carlo simulation approach whose performance is validated that are validated Their performance is validated on several numerical tests. © 2023 Computers and Industrial Engineering. All rights reserved.

关键词： stochastic programming

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A Practical Method for Generating Day-Ahead Output Probabilistic Scenarios of Multiple Wind Farms Based on K-Shape Clustering 13

A Practical Method for Generating Day-Ahead Output Probabili...

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13th International Conference on Power and Energy Systems, ICPES 2023

作者： Bo, Lin Xu, Taishan Tu, Mengfu Lu, Jinjun Nari Group Co. Ltd. State Key Laboratory of Smart Grid Protection and Control Nanjing China

ISBN: (纸本)9798350345063

The volatility and uncertainty of wind power bring severe challenges to the power system, and the modeling of multi-wind farm output scenarios considering the uncertainty of wind power is of great significance in power system dispatching decision-making. Especially for area/ province with abundant wind power resources, where the reliability of the dispatching plan can be strongly affected by the volatility of large amount of wind power. In this paper, a practical method of generating probabilistic scenarios for day-ahead wind power output is proposed for provincial power grid with high proportion of wind power. Firstly, the K-Shape clustering algorithm is used to cluster the wind farm stations, and then the output scenario are generated by using the total wind power forecast value and forecast error probability distribution characteristics of each aggregated station. After arrangement and combination of the aggregated stations' scenes, the scenario minimization based on probability distance was conducted for clustering reduction to obtain the typical output scenes, and then the typical output scenes were assigned to each station according to the forecast value of the station to obtain the probability output scenes of each station. It is verified by actual power grid data that the scenarios generated by this method can combine with wind power predictions to well depict the random fluctuation of multi-wind farms output and has a good application prospect in the practice of day-ahead dispatching decision making. © 2023 IEEE.

关键词： stochastic programming

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Multi-Objective Distribution System Planning Considering Non-Utility-Owned Distributed Generation and CO2Emissions Costs

Multi-Objective Distribution System Planning Considering Non...

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Mejia, Mario A. Franco, John F. Macedo, Leonardo H. Muñoz-Delgado, Gregorio Contreras, Javier São Paulo State University Department of Electrical Engineering Ilha Solteira Brazil São Paulo State University Department of Engineering Rosana Brazil Universidad de Castilla-La Mancha Escuela Técnica Superior de Ingeniería Industrial Ciudad Real Spain

ISBN: (纸本)9798350332308

Distribution systems planning (DSP) has become increasingly challenging due to the growing adoption of renewable distributed generation (DG) aimed at reducing CO2 emissions, particularly when the utility does not own these units. Therefore, this paper proposes a multi-objective stochastic strategy for DSP that considers non-utility-owned renewable DG. Under this approach, the utility makes investment decisions for assets such as conductors and voltage control equipment. Furthermore, the utilization of a multi-objective approach enables a sensitivity analysis, which assists both the utility and DG owner in reaching a consensus on the type, size, and location of renewable DG units. The strategy proposes minimizing the net present value of the investment and operational costs for both parties, with the additional goal of reducing the cost of CO2 emissions from the network. A scenario-based stochastic programming framework is used to characterize the behavior of uncertain parameters. The model is written in the AMPL language and solved using the CPLEX solver. Tests are conducted using a 69-node system, revealing that the total costs for both parties vary depending on the size and location of the DG as well as the cost of energy sold from the DG to the network. © 2023 IEEE.

关键词： stochastic programming

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A Survey of Modeling the Healthcare Inventory for Emerging Infectious Diseases 6th

A Survey of Modeling the Healthcare Inventory for Emerging I...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Limsakul, Tatitayakorn Taladgaew, Sompoap Department of Teacher Training in Mechanical Engineering Faculty of Technical Education King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9783031501579

Inventory management is a critical process in the healthcare industry. Challenges to the healthcare industry, such as supply shortages or overstocking, especially during the pandemic, make healthcare inventory management highly important. Efficient inventory management can help ensure quality patient care while reducing inventory costs. Over the years, several approaches and methods for modeling healthcare inventory management have been developed by researchers. This paper aims to provide an overview of inventory management (modeling) to handle inventory management problems under several constraints, such as limited budget and resources in both deterministic and probabilistic demand scenarios by focusing on the challenges posed by the COVID-19 pandemic. In this paper, techniques and methods, including Economic Order Quantity, Mathematical Optimization Models, stochastic programming, and Metaheuristics are presented and critically reviewed as guidance for future research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： stochastic programming

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Nursing rescheduling problem with multiple rescheduling methods under uncertainty

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COMPLEX & INTELLIGENT SYSTEMS 2022年第6期8卷 4557-4569页

作者： Long, Zhiren Wen, Xianxiu Lan, Mei Yang, Yongjian Chengdu 8th Peoples Hosp Chengdu Elderly Serv Demonstrat Training Ctr Chengdu 610503 Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Dept Nursing Chengdu 610072 Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Dept Endocrinol Chengdu 610072 Peoples R China Chinese Acad Sci Sichuan Translat Med Res Hosp Chengdu 610072 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Management & Econ Chengdu 611731 Peoples R China

The nursing rescheduling problem is a challenging decision-making task in hospitals. However, this decision-making needs to be made in a stochastic setting to meet uncertain demand with insufficient historical data or inaccurate forecasting methods. In this study, a stochastic programming model and a distributionally robust model are developed for the nurse rescheduling problem with multiple rescheduling methods under uncertain demands. We show that these models can be reformulated into an integer program. To illustrate the applicability and validity of the proposed model, a study case is conducted on three joint hospitals in Chengdu, Chongzhou, and Guanghan, Sichuan Province. The results show that the stochastic programming model and the distributionally robust model can reduce the cost by 78.71% and 38.92%, respectively. We also evaluate the benefit of the distributionally robust model against the stochastic model and perform sensitivity analysis on important model parameters to derive some meaningful managerial insights.

关键词： Nurse rescheduling problem stochastic programming Distributionally robust optimization

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