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A stochastic prepositioning model for distribution of disaster supplies considering lateral transshipment

SOCIO-ECONOMIC PLANNING SCIENCES 2021年 74卷 100930-100930页

作者： Wang, Yusheng Dong, Zhijie Sasha Hu, Shaolong Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Texas State Univ 601 Univ Dr San Marcos TX 78666 USA Southwest Jiaotong Univ Sch Econ & Management Chengdu Peoples R China

This paper focuses on addressing uncertainties in disasters when considering lateral transshipment opportunities for pre-positioning relief supplies. To deal with uncertain demands the problem is formulated as a two-stage stochastic programming model, which decides simultaneously on the locations of relief facilities and the allocations of relief supplies to demand nodes. Meanwhile, different damage levels caused by disasters are considered and reflected by a survival rate of usable stocked relief items. Multiple types of supplies with various priorities, values and spaces are explored. A real-world case study based on the Gulf Coast region of the United States is conducted to illustrate the application of the developed model. By comparison with the direct shipment solution, the lateral transshipment solution is demonstrated to be more cost-effective and flexible. The sensitivity analysis of out-of-stock penalty cost and maximum travel distance provides managerial insights for relief agencies.

关键词： Pre-positioning strategy Lateral transshipment stochastic programming Disaster uncertainty Freight transportation

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Hybrid Power Plant Offering Strategy to Deal with the stochastic Nature and Outage of Wind Generators 20

Hybrid Power Plant Offering Strategy to Deal with the Stocha...

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20th IEEE International Conference on Electrical Machines and Systems (ICEMS)

作者： Ghavidel, Sahand Barani, Mostafa Azizivahed, Ali Ghadi, M. Jabbari Li, Li Zhang, Jiangfeng Univ Technol Sydney Fac Engn & Informat Technol POB 123 Broadway NSW 2007 Australia Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

ISBN: (纸本)9781538632468

The stochastic nature of wind power generators and their possible outage are crucial issues which make them difficult to participate in electricity markets. However, demand side as a decent balancing resource can be used to compensate the challenges of lack of supply-demand balance or state of outage for wind generators. This paper firstly models the outage of wind generators. Then an offering strategy with a three-stage stochastic programming is presented for a hybrid power plant which includes a wind power producer and a demand response provider. Three electricity markets are considered including day-ahead, adjustment and balancing market. The conditional value-at-risk is also added to the offering strategy to control the profit risk The offering strategy is tested in a wind farm and electricity market located in Spain. The result shows that the hybrid power plant offering strategy can effectively assist with the balancing and outage problem of the wind power producer and increase the overall profit of the joint operation.

关键词： Hybrid power plant Wind power producer Demand response provider Conditional value-at-risk stochastic programming

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Enhancing disaster resilience: optimizing debris management for end-of-life buildings amidst uncertainty

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2024年

作者： Nayeem, Moddassir Khan Abbaas, Omar Univ Texas San Antonio Dept Mech Aerosp & Ind Engn 1 UTSA CircleEB 3-04-54 San Antonio TX 78249 USA

This study develops a stochastic Mixed Integer Linear programming (SMILP) model to optimize the reverse logistics network for debris generated from proactive demolition of end-of-life buildings. Unlike most existing research, this study addresses proactive strategies for the Mitigation stage of disaster management. Our model identifies the optimal number of landfilling areas and sorting facilities, factoring in uncertainties in debris quantity and material quality. It incorporates environmental constraints, such as mandatory sorting processes and recycling thresholds. Multiple scenarios are considered, each with unique parameter values and occurrence probabilities, with the overall objective of minimizing net costs across all scenarios. A realistic case study is used to illustrate the model, demonstrating its capacity to reduce post-disaster recovery costs, improve operational efficiency, and balance financial and environmental considerations. This study offers insights for decision-makers, advocating proactive end-of-life buildings' management as a disaster preparedness and sustainable practices.

关键词： Disaster management end-of-life building stochastic programming recycling reverse logistics

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Cash versus in-kind transfer programs in humanitarian operations: An optimization program and a case study

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SOCIO-ECONOMIC PLANNING SCIENCES 2022年第PartA期82卷

作者： Rafiei, Rezvan Huang, Kai Verma, Manish McMaster Univ DeGroote Sch Business Hamilton ON Canada

Humanitarian organizations, mandated with responding to emergencies, generally provide food assistance via in-kind and/or cash transfers. Although cash and in-kind transfers have had varying effects across different regions, the superiority of one over the other has been debated. This study considers a mixed strategy that includes both cash and in-kind transfer in the presence of finite budget, given a slow-onset disaster such as famine or drought. Importantly, it proposes an evidence-based framework based on the given data. Specifically, a two-stage stochastic program with recourse is proposed, where uncertainty stems from the slow-onset disaster that has non-uniform impact across a given geographical region. The proposed program is first used to study the slow-onset disaster situation in Kenya, and then to evaluate the performance of the cash versus in-kind transfer programs. We also solved larger size problem instances using the sample average approximation (SAA) algorithm, and the resulting analyses underscore the deductions of the case study that although cash transfer is more efficient than in-kind transfer, however, the latter is inevitable due to local unavailability of certain commodities.

关键词： Cash and in-kind transfers Humanitarian operations stochastic programming Slow-onset disaster Sample average approximation

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The promotion of community energy projects in Chile and Scotland: An economic approach using biform games

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ENERGY ECONOMICS 2020年第0期86卷 104677-000页

作者： Fuentes Gonzalez, Fabian Hendrik van der Weijde, Adriaan Sauma, Enzo Univ Edinburgh Sch Engn Inst Energy Syst Kings BldgMayfield Rd Edinburgh EH9 2JL Midlothian Scotland Pontificia Univ Catolica Chile UC Energy Res Ctr Dept Ind & Syst Engn Av Vicuna Mackenna 4860 Santiago 4860 Chile Alan Turing Inst British Lib 96 Euston Rd London NW1 2DB England

We model simple and novel three-player bi-form coalitional games to analyse community energy projects in Chile and Scotland. We take into account two methods based on biform games, which deal with games with a non-empty Core and an empty Core, respectively, and construct models based on real-world data on community energy projects, net billing (or distributed generation) schemes, and ordinary utility contracts. We then use these to derive insights about the economic-strategic viability of community energy projects, in the sense of stability within the projects or coalitions and competitiveness versus the other schemes. Under some mild assumptions, we find that community energy projects can be the best strategy to follow for residential electricity customers in Scotland and Chile. Cost subsidisation can further improve community energy incentives. Moreover, after a statistical simulation, we find that community energy projects present more opportunities to be implemented in comparison with net billing schemes in both countries. We use these results to draw conclusions for the community energy sector and show that biform games can be a valuable tool to analyse increasingly complex electricity markets. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Community energy stochastic programming Biform Games Scotland Chile

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L-shaped Algorithm for stochastic Disassembly Line Balancing Problem

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IFAC Proceedings Volumes 2013年第9期46卷 407-411页

作者： M.L. Bentaha O. Battaïa A. Dolgui École Nationale Supérieure des Mines EMSE-FAYOL CNRS UMR6158 LIMOS F-42023 Saint-Étienne France

Disassembly Line Balancing Problem considered here both chooses the best disassembly alternative for an end of life product and assigns the corresponding disassembly tasks to the workstations of the line with the aim to reduce the line cost. Precedence and cycle time constraints are observed. Task times are assumed stochastic with known normal probability distributions. The line cost includes the investment and operation costs for workstations as well as penalty costs generated by the cycle time constraints violations. To deal with uncertainties, a stochastic linear mixed integer formulation is developed. To solve efficiently the problem, L-shaped algorithm combined with Latin Hypercube Sampling is proposed.

关键词： Disassembly Disassembly Line Design DLBP stochastic programming Sample Average Approximation L-shaped Method LHS Sustainable manufacturing

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Short-term risk management of electricity retailers under rising shares ofdecentralized solar generation

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ENERGY ECONOMICS 2022年 109卷 1页

作者： Russo, Marianna Kraft, Emil Bertsch, Valentin Keles, Dogan NEOMA Business Sch Dept Finance 1 rue Marechal Juin F-76130 Mont St Aignan France Norwegian Univ Sci & Technol NTNU Business Sch Trondheim Norway Karlsruhe Inst Technol KIT Inst Ind Prod IIP Chair Energy Econ Karlsruhe Germany Ruhr Univ Bochum Chair Energy Syst & Energy Econ Bochum Germany Tech Univ Denmark DTU Dept Technol Management & Econ Lyngby Denmark

Electricity retailers face increasing uncertainty due to the ongoing expansion and self-consumption ofunpredictable distributed generation in the residential sector. We analyze how increasing levels of households'solar PV self-generation affect the short-term decision-making and associated risk exposure of electricityretailers in the German day-ahead and intraday markets. First, we develop a stochastic model accountingfor correlations between solar load, residual load and price in sequentially nested wholesale spot marketsacross seasons and type of day. Second, we develop a computationally tractable two-stage stochastic mixed-integer optimization model to investigate the trading portfolio and risk optimization problem faced by *** conditional value-at-risk we assess the retailers' profitability and risk exposure to different levels ofPV self-generation by assuming different retail tariff schemes. We find risk-hedging trading strategies andtariffs to have greater impact in Summer and with low levels of residual load in the system, i.e. when thesolar generation uncertainty affects more the households' demand to be served and the wholesale spot *** study is innovative in unveiling the potential of dynamic electricity tariffs, which are indexed to spotprices, to sustain a high penetration of renewable energy source while promoting a fair risk sharing betweenretailers, regular consumers and prosumers (consumers with self-generation). Our findings have implicationsfor electricity retailers facing load and revenue risks in wholesale spot markets, likewise for regulators andpolicy-makers interested in electricity market design.

关键词： Electricity markets stochastic modeling stochastic programming Retailers uncertainty modeling Risk management

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An integrated decision approach for energy procurement and tariff definition for prosumers aggregations

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ENERGY ECONOMICS 2021年 97卷 105034-105034页

作者： Ferrara, Massimiliano Violi, Antonio Beraldi, Patrizia Carrozzino, Gianluca Ciano, Tiziana Mediterranean Univ Reggio Calabria Dept Law Econ & Human Sci Via Univ 25 I-89100 Reggio Di Calabria RC Italy Univ Sannio Dept Law Econ Management & Quantitat Methods Piazza Arechi 2 I-82100 Benevento BN Italy Univ Calabria Dept Mech Energy & Management Engn Via P Gucci 41C I-87036 Arcavacata Di Rende CS Italy

The paper deals with the energy procurement and economic management problem for an aggregation of prosumers at a strategic/tactical level. This decision process, usually in charge of the ?aggregator?, the entity which coordinates market operations and resource management for the entire coalition, consists into the definition of the optimal mix of energy to procure from the available sources (bilateral contracts, self-production, day ahead market) and the tariff scheme to offer to the members of the coalition for buying and selling energy. This problem is made more complex by the presence of several sources of uncertainty, like market prices, overall demand of the coalition and production from renewable systems. To the best of our knowledge, even if several contributions have been proposed to deal with the energy procurement and tariff definition problems separately, none of them has addressed the decision process as a whole. In this paper, we propose a multiperiod 2-stage stochastic programming approach, which models the strict relations between the decisions to be made and controls risk exposure by a mean-risk objective function with the Conditional Value at Risk as risk measure. Moreover, the proposed approach aims at defining 2-components tariffs, with a variable part that is related to the random evolution of market prices, in order to enhance prosumers' responsiveness. Preliminary computational results show the effectiveness of the proposed approach as a decision support tool to guarantee the economic sustainability of the coalition and the convenience of single prosumers. ? 2020 Elsevier B.V. All rights reserved.

关键词： Energy procurement Tariff definition Energy coalition stochastic programming Risk management

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Multi-class EV charging and performance-based regulation service in a residential smart parking lot

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2020年 22卷

作者： Khalkhali, Hassan Hosseinian, Seyed Hossein Amirkabir Univ Technol Dept Elect Engn Tehran Iran

Electric vehicles are becoming an indispensable part of modern cities and power grids through their versatile and fast-response charge and discharge characteristics. Currently, smart parking lots in residential areas mainly provide slow-charging services and possibly participate in demand response programs. However, the fast emerging of EV fast-charge has brought the necessity to provide new installations and planning frameworks to cater a high level of energy within a small non-shiftable time fraction for this type of power demand. Also, due to its random nature and high power demand, fast charge may inflict heavy fluctuations to the grid. In this paper, the EV aggregator's task in a parking lot is modeled to also incorporate fast-charge services. Through using the aggregate capacity of slow-charged EVs in the parking lot as a sourcing-sinking resource, the fast-charge demand fluctuations are guaranteed to be safely absorbed. Besides, the flexibility of slow-charged EVs is also used to perform the newly-restructured regulation services which credits the participants not only based on the offered capacities but also on their real-time regulation performance. A real-time stochastic approach is introduced to take care of the aggregator's extensive planning for both slow and fast-charging EVs besides procuring dynamic regulation to the grid. This approach significantly lowers charging costs for aggregators in exchange for sophisticated scheduling task to use the available resources more efficiently. The effectiveness of the proposed method in handling the energy and regulation management is demonstrated through several case studies. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Conditional neutrality Electric vehicles Fast-charge Performance-based regulation service Real-time planning stochastic programming

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Towards smart transportation: A learning-based data-driven optimization approach for electric taxi dispatch problem

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INTERNET TECHNOLOGY LETTERS 2021年第1期4卷

作者： Li, Xiaoming Zhao, Liang Shenyang Aerosp Univ Sch Comp Sci Shenyang Liaoning Peoples R China Concordia Univ Dept Informat & Syst Engn Quebec City PQ Canada Ericsson INC Global Artificial Intelligence Accelerator GAIA I Quebec City PQ Canada

Electric taxi dispatch problem (ETDP) is one of the key issues in smart transportation. Existing study in the context of centralized optimization adopts either deterministic optimization, regular stochastic programming (SP) or simulation technique. Nevertheless, in data-driven environment, the real passenger demands normally follow complicating probability distribution which cannot be described exactly by the parametric approaches. Hence, we propose a novel data-driven optimization framework that integrates robust kernel density estimation (RKDE) and the two-stage SP modeling technique. In particular, the probability distributions of customer demands are derived from historical data by RKDE, and the ETDP is formulated as a two-stage SP model with the input parameters from RKDE. Meanwhile, a Monte Carlo method called sample average approximation is introduced to reformulate and solve the SP model. Finally, the experimental results show that the proposed approach outperforms the deterministic counterpart with the average demands as the input.

关键词： electric taxi dispatch robust kernel density estimation stochastic programming

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