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stochastic scheduling of generating units with weekly energy storage: A hybrid decomposition approach

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年 145卷

作者： Constante-Flores, Gonzalo E. Conejo, Antonio J. Lima, Ricardo M. Ohio State Univ Dept Elect & Comp Engn 2015 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Integrated Syst Engn 1971 Neil Ave Columbus OH 43210 USA King Abdullah Univ Sci & Technol KAUST Comp Elect & Math Sci & Engn Div Thuwal 239556900 Saudi Arabia

We propose a solution method for the large-scale stochastic unit commitment (SUC) problem with weekly -dispatched energy storage and significant weather-dependent stochastic generating capacity. Weekly storage facilities that mostly charge during weekends and discharge during weekdays require a weekly scheduling of generating units, which result in a large-scale optimization problem. This SUC problem is formulated as a two -stage stochastic model and we use the conditional value-at-risk as a risk measure. Using a Benders framework, the proposed solution method decomposes the problem into a mixed-integer linear master problem and linear and continuous subproblems. The master problem corresponds to the first-stage decisions throughout the week and includes all the commitment (binary) variables and their corresponding constraints. The subproblems correspond to the actual dispatch of the generating units on a weekly basis. Based on the success of column-and -constraint generation algorithms to solve robust optimization problems, we improve the low communication between the master problem and the subproblems in the standard Benders decomposition by adding primal variables and constraints from the subproblems to the master problem, which provides a better approximation of the recourse function. Our computational experiments demonstrate the effectiveness of the proposed decomposition method using an instance of the South Carolina synthetic system with 90 generating units under 40 scenarios.

关键词： Benders decomposition Column-and-constraint generation algorithm Energy storage Risk control two-stage stochastic programming Unit commitment

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Deep learning-aided joint DG-substation siting and sizing in distribution network stochastic expansion planning

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FRONTIERS IN ENERGY RESEARCH 2023年 10卷

作者： Han, Zhentao Li, Jianfeng Wang, Qixiang Lu, Hao Xu, Siyu Zheng, Weiye Zhang, Zixin State Grid Liaoning Elect Power Co Ltd Econ Res Inst Shenyang Peoples R China State Grid Liaoning Elect Power Supply Co LTD Shenyang Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China

The rapid growth of distributed generation (DG) and load has highlighted the necessity of optimizing their ways of integration, as their siting and sizing significantly impact distribution networks. However, little attention has been paid to the siting and sizing of new substations which are to be installed. This paper proposes deep learning-aided joint DG-substation siting and sizing in distribution network stochastic expansion planning. First, as the model depends on an accurate forecast, Long Short-Term Memory (LSTM) deep neural network is used to forecast DG output and load, where electricity growth rate, bidding capacity of the electric expansion, and industrial difference are all considered. Then, a two-stage stochastic mixed integer bilinear programming model was established for joint DG-substation siting and sizing under uncertainties, where multiple objective functions are comprehensively addressed. By using the Fortuny-Amat McCarl Linearization, the resultant bilinear model is equivalently transformed into a mixed integer linear program, which can be efficiently solved. Finally, stochastic power flow calculation in the IEEE 69-node system is conducted to analyze the influence of electric expansion and DG integration on the node voltage and power flow distribution of the power system. The effectiveness of the proposed method is also verified by simulation tests.

关键词： LSTM network load forecasting business and industrial expansion renewable energy integration two-stage stochastic programming distribution network planning

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A bi-objective two-stage stochastic optimization for designing a resilient humanitarian relief chain considering hybrid contracts, public donations, and item perishability

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 205卷

作者： Taghvaei, Dorsa Ghods, Tina Rabbani, Masoud Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

This paper presents a bi-objective, two-stage stochastic programming (TSSP) model for designing a resilient humanitarian relief chain (HRC). The model considers uncertainties in demand, public donations, and disruption risks while integrating decisions in both the pre- and post-crisis stages. This model optimizes critical decisions such as supplier selection, establishing contracts, pre-positioning items, inventory management, and distribution of perishable and non-perishable items. It aims to minimize the total network costs and the maximum average travel time to each demand point. To tackle disruptions in network facilities and links, several resilience strategies were adopted, including inventory pre-positioning and holding safety stock, as well as backup suppliers. Additionally, a hybrid supply contract was established, consisting of quantity flexibility contracts (QFCs) and option contracts (OPCs) between the governmental relief organization (GRO) and the main and backup suppliers, ensuring an acceptable level of responsiveness in the relief network. The epsilon-constraint method is utilized in GAMS software to illustrate different numerical instances and a case study of Tabriz City in Iran, demonstrating its applicability. The findings reveal that resilience strategies significantly enhance cost efficiency and response times, with pre-positioning improving overall costs by 28.42% and response times by 84.79% as the most influential strategy. The results review and sensitivity analysis provided valuable managerial insights for designing a functional and effective HRC.

关键词： Humanitarian logistics Disruption risks Resilient relief supply chain Public donation Perishability Hybrid contracts two-stage stochastic programming

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Production planning decisions in the broiler chicken supply chain with growth uncertainty

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OPERATIONS RESEARCH PERSPECTIVES 2023年 10卷

作者： Solano-Blanco, Alfaima L. Gonzalez, Jaime E. Medaglia, Andres L. Univ Andes Ctr Optimizac & Probabil Aplicada COPA Dept Ingn Ind Cra 1 Este 19A-40 Bogota Colombia Polytech Montreal Dept Math & Ind Engn 2900 Blvd Edouard Montpetit Montreal PQ H3T 1J4 Canada

Broiler chickens are specially raised for meat production and their supply chain is composed of breeders, hatcheries, feed mills, farms, slaughterhouses, wholesalers, and retailers. The coordination between supply chain players has great potential to streamline production and increase competitiveness in the market. A critical element in the decision-making process of this agri-system is the weight gain of broiler chickens. Variability in chicken growth patterns can affect production plans in practice. The most efficient weight estimation models require sensors in real-time, which are not available in many companies. Thus, some small farms require a simpler approach to adjust production planning based on the uncertainty of chickens' weight while they transition to new technologies. This research proposes an optimization-based methodology for the integration of farms and slaughterhouses to plan production under chicken growth uncertainty. The methodology includes two models: (i) a two-stage stochastic model that supports lot-sizing and inventory management decisions while considering scenarios of chicken growth uncertainty;and (ii) a mixed-integer linear programming model that supports lot allocation. We present the results of implementing the methodology in a poultry company in Santa Marta (Colombia), where we improved costs by 8.6% while meeting tactical, biological, and biosecurity constraints.

关键词： Poultry supply chain two-stage stochastic programming Lot sizing decisions Growth uncertainty

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stochastic RWA and Lightpath Rerouting in WDM Networks

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INFORMS JOURNAL ON COMPUTING 2022年第5期34卷 2700-2719页

作者： Daryalal, Maryam Bodur, Merve HEC Montreal Dept Decis Sci Montreal PQ H3T 2A7 Canada Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3GH Canada

In a telecommunication network, routing and wavelength assignment (RWA) is the problem of finding lightpaths for incoming connection requests. When facing a dynamic traffic, greedy assignment of lightpaths to incoming requests based on predefined deterministic policies leads to a fragmented network that cannot make use of its full capacity because of stranded bandwidth. At this point, service providers try to recover the capacity via a defragmentation process. We study this setting from two perspectives: (i) while granting the connection requests via the RWA problem and (ii) during the defragmentation process by lightpath rerouting. For both problems, we present the first two-stage stochastic integer programming model incorporating incoming request uncertainty to maximize the expected grade of service. We develop a decomposition-based solution approach, which uses various relaxations of the problem and a newly developed problem-specific cut family. Simulation of two-stage policies for a variety of instances in a rolling-horizon framework of 52 stages shows that our stochastic models provide high-quality solutions when compared with traditionally used deterministic ones. Specifically, the proposed provisioning policies yield improvements of up to 19% in overall grade of service and 20% in spectrum saving, while the stochastic lightpath rerouting policies grant up to 36% more requests, using up to just 4% more bandwidth spectrum.

关键词： two-stage stochastic programming routing and wavelength assignment lightpath rerouting optical networks

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A stochastic programming model with endogenous uncertainty for incentivizing fuel reduction treatment under uncertain landowner behavior

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期277卷 699-718页

作者： Bhuiyan, Tanveer Hossain Moseley, Maxwell C. Medal, Hugh R. Rashidi, Eghbal Grala, Robert K. Univ Tennessee Dept Ind & Syst Engn Knoxville TN 37996 USA Mississippi State Univ Dept Ind & Syst Engn Mississippi State MS 39762 USA Santa Clara Univ Dept Informat Syst & Analyt Santa Clara CA 95053 USA Mississippi State Univ Dept Forestry Mississippi State MS 39762 USA

Reducing the potential damage caused by a wildfire is a problem of significant importance to land and fire managers. Fuel reduction treatment is a well-known method of reducing the risk of fire occurrence and spread on landscapes. However, officials seeking fuel reduction treatments on privately owned lands can only encourage it through incentive programs such as cost-share programs. This research developed a methodology that provides the basis for a decision-making tool to help managers allocate limited cost-share resources among a set of landowners to maximize wildfire risk reduction by implementing a hazardous fuel reduction treatment. A key feature of the methodology is that it incorporates uncertainty in the landowners' decision of whether or not to implement treatment on their lands. The methodology is based on a stochastic programming model with endogenous uncertainty where the probability that a landowner accepts a cost-share offer to implement a fuel reduction treatment on their land depends on the offer amount. To estimate the probability that a landowner accepts a given cost-share offer amount, we used a predictive modeling technique to analyze landowner survey data. The results provide insight about the effects of different cost-share allocation strategies on the expected damage. Numerical experiments show that the risk-based allocation provides up to 37.3% more reduction in damage compared to other strategies that allocate equal cost-share amounts among landowners. Additionally, the results show that the solution quality is substantially sensitive to changes in the number of resource allocation levels. (C) 2019 Elsevier B.V. All rights reserved.

关键词： OR in natural resources two-stage stochastic programming Predictive modeling Monte Carlo simulation Wildfire hazard

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A stochastic programming Approach for Electric Vehicle Charging Network Design

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第5期20卷 1870-1882页

作者： Faridimehr, Sina Venkatachalam, Saravanan Chinnam, Ratna Babu Wayne State Univ Dept Ind & Syst Engn Detroit MI 48202 USA

The advantages of electric vehicles (EVs) include reduction of greenhouse gas and other emissions, energy security, and fuel economy. The societal benefits of large-scale adoption of EVs cannot be realized without adequate deployment of publicly accessible charging stations. We propose a two-stage stochastic programming model to determine the optimal network of charging stations for a community, considering uncertainties in the arrival and dwell times of vehicles, the state of charge of arriving vehicles' batteries, drivers' walking ranges and charging preferences, demand during weekdays and weekends, and the community's rate of EV adoption. We conducted studies using the sample average approximation method, which asymptotically converges to an optimal solution for a two-stage stochastic problem. However, this method is computationally expensive for large-scale instances. Therefore, we also developed a heuristic to produce nearly optimal solutions quickly for our data instances. We conducted computational experiments using various publicly available data sources and evaluated the benefits of the solutions for a given community, both quantitatively and qualitatively.

关键词： two-stage stochastic programming electric vehicle charging network sample average approximation

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A decomposition approach for the stochastic asset protection problem

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COMPUTERS & OPERATIONS RESEARCH 2022年 138卷 105591-105591页

作者： Nuraiman, Dian Ozlen, Melih Hearne, John RMIT Univ Sch Sci Melbourne Vic Australia UIN Sunan Gunung Djati Dept Math Bandung Indonesia

The deterministic Asset Protection Problem (APP) involves deploying firefighting resources of various capabilities to service as many community assets as possible within time windows determined by an advancing wildfire. A common situation arising during these situations is a wind change. Forecasts of changes in wind velocity (i.e. direction and speed) are reasonably accurate but there is some uncertainty around the time of a wind change. The timing has implications for which areas, and hence which assets, will be impacted by the wildfire. This presents a difficult problem for an Incident Management Team (IMT) operating under severe time pressure as the wildfire sweeps across the landscape. In this study, we extend the spatial decomposition-based math-heuristic originally developed for the deterministic APP to handle large real life sized stochastic APPs in operational time. This is achieved by regarding the deterministic and stochastic components as a coupled system. A two-stage stochastic programming (TSSP) model is thus only used for a smaller sub-problem around the uncertainty. We found this approach outperformed other methods when tested on a new set of benchmarks. More importantly, the accuracy and solution times make the method suitable for operational purposes.

关键词： Wildfires Asset protection problem Team orienteering problem two-stage stochastic programming Math-heuristic Spatial decomposition

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Multiperiod refinery optimization for mitigating the impact of process unit shutdowns

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COMPUTERS & CHEMICAL ENGINEERING 2022年 164卷

作者： Boucheikhchoukh, Ariel Berger, Valentin Swartz, Christopher L. E. Deza, Antoine Nguyen, Alexander Jaffer, Shaffiq McMaster Univ Dept Chem Engn 1280 Main St W Hamilton ON L8S 4L7 Canada McMaster Univ Dept Comp & Software 1280 Main St W Hamilton ON L8S 4L7 Canada Inst Polytech Paris Ecole Polytech CNRS LIX Palaiseau France TOTAL Amer Serv Inc 82 South St Hopkinton MA 01748 USA

Process unit shutdowns, whether planned or unplanned, disrupt the normal operation of process plants. The negative impact of such disruptions can often be mitigated by the strategic use of material inventories, coupled with redistribution of material flows. In this paper, an optimization framework is proposed for a multiperiod refinery planning model in which a system of inventory tanks is used to mitigate the impact of process unit shutdowns. An economic objective is optimized over a planning horizon, subject to operating, design, and product quality constraints. two formulation paradigms are considered - an operating problem in which optimal material flow trajectories are determined in response to a shutdown scenario, and a retrofit design problem in which the optimal location and sizing of inventory tanks are determined, the latter formulated as a two-stage stochastic programming problem. The utility of the formulation to shutdown planning is shown through case studies of the operational and design problems applied to a simple refinery scheme. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： MILP Multiperiod planning Petroleum refinery Inventory management Shutdowns Uncertainty two-stage stochastic programming

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Simultaneous optimization of design and operation of an air-cooled geothermal ORC under consideration of multiple operating points

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COMPUTERS & CHEMICAL ENGINEERING 2022年 161卷

作者： Langiu, Marco Dahmen, Manuel Mitsos, Alexander [a]Forschungszentrum Julich GmbH Institute of Energy and Climate Research Energy Systems Engineering (IEK-10) Julich Germany [b]RWTH Aachen University Germany [c]JARA-ENERGY Julich 52425 Germany [d]RWTH Aachen University Process Systems Engineering (AVT.SVT) Aachen 52074 Germany

We simultaneously optimize both the design and operation of an air-cooled geothermal organic Rankine cycle, maximizing total annual return (TAR), while considering multiple operating scenarios based on different ambient temperatures. In order to accurately capture realistic off-design behavior of the heat exchangers and turbine, as well as for the overall system, we incorporate component models that consider performance variations with both size and operating conditions. We employ a hybrid mechanistic data-driven modeling approach, involving artificial neural networks (ANNs) as surrogate models for accurate fluid properties, as well as for intermediate expressions for which ANNs improve tractability of the optimization problem. We demonstrate the importance of considering multiple operating conditions within design problems and propose a methodology for formulating and solving such problems globally, using our open-source solver MAiNGO. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Design and operation ORC Optimization two-stage stochastic programming Artificial neural networks

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