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A shared-mobility-based framework for evacuation planning and operations under forecast uncertainty

IISE TRANSACTIONS 2023年第10期55卷 971-984页

作者： Moug, Kati Jia, Huiwen Shen, Siqian Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. We optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution.

关键词： stochastic programming evacuation planning ridesharing chance-constrained programming conditional value at risk (CVaR)

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stochastic optimal transactive energy management with cloud energy storage using artificial neural networks

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IET GENERATION TRANSMISSION & DISTRIBUTION 2023年第23期17卷 5252-5272页

作者： Salehi, Mohammad Kazem Rastegar, Mohammad Shiraz Univ Sch Elect & Comp Engn Dept Power & Control Engn Shiraz Iran

Residential buildings may use energy storage, flexible loads, and renewable energy sources to reduce energy consumption and increase demand side flexibility. The flexibility of a single building can be coordinated with other facilities in a transactive energy (TE) market to reduce energy costs. In addition, cloud energy storage (CES) has been proposed to provide storage services for residential buildings with more economic benefits than individual energy storage units in recent years. Although the TE market and CES implementation have received much attention in previous works, a suitable structure for CES participation in TE market has not been addressed. Furthermore, previous studies ignored all or some sources of uncertainties in the TE decision making process. This paper presents a stochastic optimization model in a transactive energy framework based on a distributed optimization algorithm for peer-to-peer energy trading using the alternating direction method of multipliers in the presence of CES. This paper considers the uncertainties of the inflexible load demand, renewable energy generations, and market prices using an artificial neural network-based scenario generation and reduction methodology. Numerical results show improvements toward addressing the challenges of the uncertainties while maximizing the CES's owner revenue and minimizing the customers' costs in the proposed model. This paper presents a distributed stochastic optimization model for peer-to-peer energy trading within a transactive energy framework. The model incorporates distributed energy resources (e.g., solar photovoltaic systems, wind turbines, electric vehicles) and cloud energy storage (CES). To handle uncertainties in load demand, renewable energy generations, and market prices, an artificial neural network (ANN)-based scenario generation and reduction methodology is employed. image

关键词： energy management systems energy storage MATLAB renewable energy sources stochastic programming

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Parametric Algorithm for Finding a Guaranteed Solution to a Quantile Optimization Problem

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AUTOMATION AND REMOTE CONTROL 2023年第8期84卷 848-857页

作者： Ivanov, S. V. Kibzun, A. I. Akmaeva, V. N. Natl Res Univ Moscow Aviat Inst Moscow Russia

The problem of stochastic programming with a quantile criterion for a normal distribution is studied in the case of a loss function that is piecewise linear in random parameters and convex in strategy. Using the confidence method, the original problem is approximated by a deterministic minimax problem parameterized by the radius of a ball inscribed in a confidence polyhedral set. The approximating problem is reduced to a convex programming problem. The properties of the measure of the confidence set are investigated when the radius of the ball changes. An algorithm is proposed for finding the radius of a ball that provides a guaranteeing solution to the problem. A method for obtaining a lower estimate of the optimal value of the criterion function is described. The theorems are proved on the convergence of the algorithm with any predetermined probability and on the accuracy of the resulting solution.

关键词： stochastic programming quantile criterion confidence method quantile optimization guaranteeing solution

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Achieving robustness in the capacitated vehicle routing problem with stochastic demands

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2023年第3期15卷 254-268页

作者： Bernardo, Marcella Du, Bo Matias, Amanda Bezerra Univ Wollongong SMART Infrastruct Facil Wollongong NSW 2522 Australia Univ Wollongong Sch Civil Min & Environm Engn Wollongong NSW Australia Inst Fed Educ Ciencia & Tecnol Piaui Inst Fed Piaui Teresina Brazil

stochastic demands can impact the quality and feasibility of a solution. Robust solutions then become paramount. One way to achieve robustness in the Capacitated Vehicle Routing Problem with stochastic Demands (CVRPSD) is to add a measure of the second-stage (recourse) distance to the objective function of the deterministic problem. We adopt variance as a measure of the recourse distance and propose a Mean-Variance (MV) model. To solve the model, a Hybrid Sampling-based solution approach is developed. Numerical experiments are conducted on benchmark instances and a selective waste collection system in Brazil. We compare our model with others from literature which also use a measure of the second-stage distance to attain robustness. The numerical results show that our model generates the most robust solutions. The comparison provides detailed features of each model and their advantages and disadvantages, helping decision-makers decide which model to utilize based on their different needs and priorities.

关键词： Vehicle routing problem stochastic demand robustness mean-variance model stochastic programming selective waste collection kerbside recycling collection

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Robust capacity planning for sterilisation department of a hospital

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第3期61卷 726-740页

作者： Gokalp, Elvan Sanci, Ece Univ Bath Informat Decis & Operat Div Sch Management Bath BA2 7AY Avon England

Sterile services departments are special units designed to perform sterilisation operations in an efficient way within a hospital. The delays in sterilisation services cause significant disruptions on surgery schedules and bed management. To prevent the delays, an upper time limit can be imposed on the time spent in the sterilisation services. In this paper, we propose a mathematical modelling approach for the optimum capacity planning of a sterilisation service unit considering the uncertainties in the sterilisation process. The model aims to find the optimum capacity on four tandem steps of the sterilisation whilst at the same time minimising the total cost and keeping the maximum time in the system below a limit. Assuming general distributions for service and interarrival times, an approximation structure based on robust optimisation is used to formulate the maximum time spent in the system. We analysed the structural property of the resulting model and found that the relaxed version of the model is convex. The real data from a large sterilisation services unit is used for computational experiments. The results indicated that the approximation fits well against the simulated maximum time in the system. Other experiments revealed that an upper limit of 7 hours for the sterilisation services balances the cost vs. robustness trade-off.

关键词： stochastic programming robust optimisation sterilisation services queuing theory hospital operations

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How to Assess Uncertainty-Aware Frameworks for Power System Planning?

IEEE TRANSACTIONS ON ENERGY MARKETS POLICY AND REGULATION

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IEEE TRANSACTIONS ON ENERGY MARKETS POLICY AND REGULATION 2024年第4期2卷 436-448页

作者： Spyrou, Elina Hobbs, Ben Chattopadhyay, Deb Mukhi, Neha Imperial Coll London London SW7 2BX England Johns Hopkins Univ Baltimore MD 21218 USA World Bank Grp Washington DC 20433 USA

Computational advances along with the profound impact of uncertainty on power system investments have motivated the creation of power system planning frameworks that handle long-run uncertainty, large number of alternative plans, and multiple objectives. Planning agencies seek guidance to assess such frameworks. This article addresses this need in two ways. First, we augment previously proposed criteria for assessing planning frameworks by including new criteria such as stakeholder acceptance to make the assessments more comprehensive, while enhancing the practical applicability of assessment criteria by offering criterion-specific themes and questions. Second, using the proposed criteria, we compare two widely used but fundamentally distinct frameworks: an 'agree-on-plans' framework, Robust Decision Making (RDM), and an 'agree-on-assumptions' framework, centered around stochastic programming (SP). By comparing for the first time head-to-head the two distinct frameworks for an electricity supply planning problem under uncertainties in Bangladesh, we conclude that RDM relies on a large number of simulations to provide ample information to decision makers and stakeholders, and to facilitate updating of subjective inputs. In contrast, SP is a highly dimensional optimization problem that identifies plans with relatively good probability-weighted performance in a single step, but even with computational advances remains subject to the curse of dimensionality.

关键词： Planning Uncertainty Stakeholders Investment Mathematical models Power system planning Decision making Deep uncertainty power system planning risk analysis robust decision making stochastic programming

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A parallel hub-and-spoke system for large-scale scenario-based optimization under uncertainty

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MATHEMATICAL programming COMPUTATION 2023年第4期15卷 591-619页

作者： Knueven, Bernard Mildebrath, David Muir, Christopher Siirola, John D. D. Watson, Jean-Paul Woodruff, David L. L. Natl Renewable Energy Lab Computat Sci Ctr Golden CO 80401 USA Rice Univ Dept Computat & Appl Math Houston TX 77005 USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA Sandia Natl Labs Ctr Comp Res Albuquerque NM 87185 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp & Global Secur Directorate Livermore CA 94550 USA Univ Calif Davis Grad Sch Management Davis CA 95616 USA

Practical solution of stochastic programming problems generally requires the use of parallel computing resources. Here, we describe the open source package mpi-sppy, in which efficient and scalable parallelization is a central feature. We report computational experiments that demonstrate the ability to solve very large stochastic programming problems-including mixed-integer variants-in minutes of wall clock time, efficiently leveraging significant parallel computing resources. We report results for the largest publicly available instances of stochastic mixed-integer unit commitment problems, solving to provably tight optimality gaps. In addition, we introduce a novel software architecture that facilitates combinations of methods for accelerating convergence that can be combined in plug-and-play manner. The mpi-sppy package is written in Python, leverages the widely used Pyomo (http://***) library for modeling mathematical programs, builds on existingMPI implementations to ensure efficiency and scalability, and is available via http://***/Pyomo/mpi-sppy.

关键词： stochastic programming Decomposition strategies Parallel computing Progressive hedging

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Analytics with stochastic optimisation: experimental results of demand uncertainty in process industries

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第19期61卷 6501-6518页

作者： Gupta, Narain Dutta, Goutam Mitra, Krishnendranath Tiwari, M. K. Management Dev Inst Gurgaon Gurugram India Indian Inst Management Prod & Quantitat Methods Area Ahmadabad Gujarat India Univ Calcutta Dept Business Management Kolkata W Bengal India Natl Inst Training Ind Engn Mumbai Maharashtra India

This study reports the test results of a two-stage stochastic linear programming (SLP) model with recourse using a user-friendly generic decision support system (DSS) in a North American steel company. This model has the flexibility to configure multiple material facilities, activities and storage areas in a multi-period and multi-scenario environment. The value of stochastic solution (VSS) with a real-world example has a potential benefit of US$ 24.61 million. Experiments were designed according to the potential joint probability distribution scenarios and the magnitude of demand variability. Overall, 144 SLP optimisation model instances were solved across four industries, namely, steel, aluminium, polymer and pharmaceuticals. The academic contribution of this research is two-fold: first, the potential contribution to profit in a steel company using an SLP model;and second, the optimisation empirical experiments confirm a pattern that the VSS and expected value of perfect information (EVPI) increase with the increase in demand variability. This study has implications for practicing managers seeking business solutions with prescriptive analytics using stochastic optimisation-based DSS. This study will attract more industry attention to business solutions, and the prescriptive analytics discipline will garner more scholarly and industry attention.

关键词： Decision support system iron and steel industry planning with optimisation stochastic programming EVPI VSS

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Sizing a grid-connected photovoltaic system under economic and environmental uncertainties

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SOLAR ENERGY 2023年 265卷

作者： Attia, Ahmed M. Darghouth, Mohamed N. Ghaithan, Ahmed M. Mohammed, Awsan King Fahd Univ Petr & Minerals Dept Ind & Syst Engn Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Dept Construct Engn & Management Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Smart Mobil & Logist Dhahran Saudi Arabia

Several looming challenges confront the implementation of PV systems. The decision-maker must consider technical, economic, and environmental uncertainties to maximize the PV systems' efficiency under specific operating constraints. In this research, we have developed a two-stage stochastic programming model using a multiobjective optimization technique, extending the approach originally introduced by Attia et al. (2021), to optimize the performance of a grid-coupled PV system considering economic and environmental uncertainties. The capacity of a PV system is determined by deciding the number of PV arrays, the amount of energy imported from the grid, and the amount exported to external utilities. The inflation rate, hourly irradiation, ambient temperature, and energy demand are considered uncertain parameters associated with economic and environmental factors. Uncertain parameters are characterized by possible scenarios associated with the corresponding probability of occurrence. A case study of sizing a grid-connected PV system to provide power to the housing area is presented to demonstrate the usefulness of the proposed technique. A sensitivity analysis is conducted by changing the levels of the primary parameters to grasp the tradeoffs that govern the research framework. It is found that the system can meet the entire demand using 1,566 PV arrays at an annual cost of M$1.47 and a reduction of CO2 emissions by 96.32%.

关键词： Grid-connected PV system Sizing planning Multi-criteria decision making Multiobjective optimization stochastic programming

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Unit Commitment Problem with Energy Storage Under Correlated Renewables Uncertainty

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OPERATIONS RESEARCH 2023年第6期71卷 1960页

作者： Cordera, Felipe Moreno, Rodrigo Ordonez, Fernando Univ Chile Fac Phys & Math Sci Santiago 8330111 Chile MIT Operat Res Ctr Cambridge MA 02142 USA Inst Sistemas Complejos Ingn Santiago 8370398 Chile

The extensive integration of renewable generation in electricity systems is significantly increasing the variability and correlation in power availability and the need for energy storage capacity. This increased uncertainty and storage capacity should be considered in operational decisions such as the short-term unit commitment (UC) problem. In this work, we formulate a day-ahead UC problem with energy storage, considering multistage correlated uncertainty on renewables' power availability. We solve this multistage stochastic unit commitment (MSUC) problem with integer variables in the first stage using a new variant of SDDP that can explicitly deal with temporal correlations. Our computational results on the IEEE 118-bus system demonstrate the significance of considering multistage uncertainty and correlations, comparing our solution with other multistage solutions, two-stage solutions, and deterministic solutions typically used by industry. We also solve the MSUC problem for a representation of the Chilean power system, finding superior UC solutions for scenarios where adapting generation to the unfolding uncertainty is costly. Finally, we demonstrate that the MSUC approach can be used to define a more efficient deterministic UC solution, outperforming the current industry practice.

关键词： energy natural resources stochastic programming large-scale systems linear programming

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