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1.6.3 A Systems Decision Modeling Approach to Electrical Generation Capacity Planning

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INCOSE International Symposium 2002年第1期12卷 1033-1039页

作者： Leeprechanon, N. Moorthy, S.S. Brooks, R.D. Ellis, M. Sappakitkamjorn, J. RMIT University MelbourneVIC3001 Australia

This paper presents modeling techniques for planning electrical generation capacity where demand is uncertain. We present traditional solutions to the capacity generation problem using a deterministic programming model based on both simple and complex demand scenarios. We then present a systems approach, employing a unique two-stage stochastic programming model that employs random parameters, which operate on deterministic model data, over the range of all possible scenarios. This systems model enables the expected cost of uncertain demand information to be calculated (ECII), and candidate stochastic solutions (VSS) to the capacity generation problem to be evaluated. © 2002 The Authors.

关键词： stochastic programming

Strong convexity in stochastic programs with deviation risk measures

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arXiv 2018年

作者： Claus, Matthias Schultz, Rüdiger Spürkel, Kai University of Duisburg-Essen Thea-Leymann-Str. 9 EssenD-45127 Germany

We give sufficient conditions for the expected excess and the upper semideviation of recourse functions to be strongly convex. This is done in the setting of two-stage stochastic programs with complete linear recourse and random right-hand side. This work extends results on strong convexity of risk-neutral models. Copyright © 2018, The Authors. All rights reserved.

关键词： stochastic programming

Adaptability via Sampling

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Adaptability via Sampling

Proceedings of the 46th IEEE Conference on Decision and Control, Pages 3220-3677

作者： Dimitris Bertsimas Constantine Caramanis Sloan School of Management Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering University of Texas Austin Austin TX USA

ISBN: (纸本)9781424414970;1424414970

There has recently been considerable attention devoted to sample-based approaches to chance constraints in stochastic programming, and also multi-stage optimization formulations. In this short paper, we consider the merits of a joint approach. A specific motivation for us, is the possibility of developing techniques suitable for integer-constrained future stages. We propose a technique based on structured adaptability, and some recent sampling techniques, that results in sample complexity that is polynomial in the number of stages. Thus we circumvent a difficulty that has traditionally plagued sample-based approaches for multi-stage formulations. This allows us to provide a hierarchy of adaptability schemes, not only for continuous problems, but also for discrete problems.

关键词： Sampling sample complexity Social Hierarchy Polynomial Joints Sampling techniques stochastic programming stochastic models Probability

Multistage stochastic Program for Mitigating Power System Risks under Wildfire Disruptions

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arXiv 2023年

作者： Yang, Hanbin Rhodes, Noah Yang, Haoxiang Roald, Line Ntaimo, Lewis School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China College of Engineering University of Wisconsin Madison MadisonWI United States Industrial & Systems Engineering Texas A&M University College StationTX United States

The frequency of wildfire disasters has surged fivefold in the past 50 years due to climate change. Preemptive de-energization is a potent strategy to mitigate wildfire risks but substantially impacts customers. We propose a multistage stochastic programming model for proactive de-energization planning, aiming to minimize economic loss while accomplishing a fair load delivery. We model wildfire disruptions as stochastic disruptions with varying timing and intensity, introduce a cutting-plane decomposition algorithm, and test our approach on the RTS-GLMC test case. Our model consistently offers a robust and fair de-energization plan that mitigates wildfire damage costs and minimizes load-shedding losses, particularly when pre-disruption restoration is considered. Copyright © 2023, The Authors. All rights reserved.

关键词： stochastic programming

Efficient algorithms for distributionally robust stochastic optimization with discrete scenario support

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arXiv 2019年

作者： Zhang, Zhe Ahmed, Shabbir Lan, Guanghui Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States

Recently, there has been a growing interest in distributionally robust optimization (DRO) as a principled approach to data-driven decision making. In this paper, we consider a distributionally robust two-stage stochastic optimization problem with discrete scenario support. While much research effort has been devoted to tractable reformulations for DRO problems, especially those with continuous scenario support, few efficient numerical algorithms were developed, and most of them can neither handle the non-smooth second-stage cost function nor the large number of scenarios K effectively. We fill the gap by reformulating the DRO problem as a trilinear min-max-max saddle point problem and developing novel algorithms that can achieve an O(1/Q) iteration complexity which only mildly depends on K. The major computations involved in each iteration of these algorithms can be conducted in parallel if necessary. Besides, for solving an important class of DRO problems with the Kantorovich ball ambiguity set, we propose a slight modification of our algorithms to avoid the expensive computation of the probability vector projection at the price of an O(√K) times more iterations. Finally, preliminary numerical experiments are conducted to demonstrate the empirical advantages of the proposed algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： stochastic programming

A branch-and-Benders-cut algorithm for a bi-objective stochastic facility location problem

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arXiv 2020年

作者： Parragh, Sophie N. Tricoire, Fabien Gutjahr, Walter J. Institute of Production and Logistics Management Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria Department of Statistics and Operations Research University of Vienna Oskar-Morgenstern-Platz 1 Vienna1090 Austria

In many real-world optimization problems, more than one objective plays a role and input parameters are subject to uncertainty. In this paper, motivated by applications in disaster relief and public facility location, we model and solve a bi-objective stochastic facility location problem. The considered objectives are cost and uncovered demand, whereas the demand at the different population centers is uncertain but its probability distribution is known. The latter information is used to produce a set of scenarios. In order to solve the underlying optimization problem, we apply a Benders’ type decomposition approach which is known as the L-shaped method for stochastic programming and we embed it into a recently developed branch-and-bound framework for bi-objective integer optimization. We analyze and compare different cut generation schemes and we show how they affect lower bound set computations, so as to identify the best performing approach. Finally, we compare the branch-and-Benders-cut approach to a straight-forward branch-and-bound implementation based on the deterministic equivalent formulation. Copyright © 2020, The Authors. All rights reserved.

关键词： stochastic programming

A stochastic Unit Commitment Policy for Wind Power Uncertainty Integrating Corrective Actions

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A Stochastic Unit Commitment Policy for Wind Power Uncertain...

IEEE Power and Energy Society General Meeting

作者： E. Pereira-Bonvallet C. Suazo-Martínez R. Palma-Behnke Department of Electrical Engineering Universidad de Chile 8370451 Santiago

ISBN: (纸本)9781479913022

Due to its natural variability and limited predictability, wind generation compared with other energy resources involves additional uncertainties that lead to challenges to current system planning and operation practices. This paper presents a novel stochastic unit commitment policy, named CORE, to incorporate wind power uncertainty explicitly. The main contribution is the integration of typical intraday operator decisions into the day-ahead scheduling process by relaxing a subset of the non-anticipativity constraints. Fast start unit commitment as well as switching operation modes of combined cycle gas turbines are discussed in detail. The proposal is intended to guide ISO intraday decisions. Numerical results indicate that CORE policy weights better probability of occurrence, increasing market planning efficiency by reducing expected operating costs.

关键词： Day ahead unit commitment stochastic programming wind power operating reserves operating reserve Wind energy Unit commitment gas turbine cycle Energy sources core Systems planning stochastic programming stochastic models CORRECTIVE ACTIONS Policies

Modeling Sustainability and Resilience of Supply Chains Under Uncertainty: An Analysis of Quantity-Based Strategies

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SSRN

SSRN 2024年

作者： Saidi, Dounia Hlyal, Mustapha El Alami, Jamila Bassou, Aziz Ait LASTIMI Laboratory High School of Technology in Sale Mohammed V University in Rabat Morocco Casablanca Morocco

In a globalized context, integrating resilience and sustainability into supply chains incurs significant costs. This study explores their impact on quantity-based strategies in supply chain networks. A conceptual framework focuses on a two-echelon network with a central supplier and multiple stores. A stochastic model addresses demand uncertainty, considering sustainability and resilience. Suppliers and stores negotiate competitively for profit maximization. Two store configurations, non-cooperative and cooperative, are explored. The supplier enhances resilience with security stocks and diversified sourcing. Sustainability efforts are considered by both. Findings show that demand following a uniform distribution benefits both store and supplier, with cooperation among stores proving more profitable. Sustainability efforts affect profits, with security stocks benefiting supplier profitability. The use of foreign products negatively impacts profits, highlighting the importance of government regulation. The paper concludes by discussing integrating sustainability and resilience in supply chains, with considerations for future research. © 2024, The Authors. All rights reserved.

关键词： stochastic programming

Network reconfiguration impact on renewable energy system and energy storage system in day-ahead scheduling

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arXiv 2021年

作者： Ramesh, Arun Venkatesh Li, Xingpeng Department of Electrical and Computer Engineering University of Houston Houston TX United States

Renewable energy sources (RES) has gained significant interest in recent years. However, due to favorable weather conditions, the RES is installed in remote locations with limited transmission capacity. As a result, it can lead to major curtailments of the free resource when the network is congested. Therefore, energy storage system (ESS) is considered as a viable solution to store energy and address the intermittent nature of RES though ESS is often distributed and may not be geographically close to RES. Therefore, ESS may also suffer from the limited transmission capacity due to network congestion. Currently, grid operators overlook network flexibility as a congestion management tool in day-ahead scheduling. This paper addresses these issues and studies the benefits of introducing network reconfiguration (NR) as a preventive and corrective action for transmission flexibility in day-ahead stochastic security-constrained unit-commitment (SSCUC-PC) while considering a multi-scenario RES output. Simulation results demonstrate that NR can lower total system cost, reduce RES curtailments and utilize ESS for better impact by alleviating network congestion in both base-case and post-contingency networks. Copyright © 2021, The Authors. All rights reserved.

关键词： stochastic programming