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Network-Constrained Joint Energy and Flexible Ramping Reserve Market Clearing of Power- and Heat-Based Energy Systems: A Two-Stage Hybrid IGDT-stochastic Framework

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IEEE SYSTEMS JOURNAL 2021年第2期15卷 1547-1556页

作者： Mirzaei, Mohammad Amin Nazari-Heris, Morteza Mohammadi-Ivatloo, Behnam Zare, Kazem Marzband, Mousa Shafie-Khah, Miadreza Anvari-Moghaddam, Amjad Catalao, Joo P. S. Univ Tabriz Fac Elect & Comp Engn Tabriz *** Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Northumbria Univ Fac Engn & Environm Newcastle Upon Tyne NE1 8ST Tyne & Wear England King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah 21589 Saudi Arabia Univ Vaasa Sch Technol & Innovat Vaasa 65200 Finland Aalborg Univ Dept Energy Technol DK-9920 Aalborg Denmark Univ Porto Fac Engn P-4200465 Porto Portugal INESC TEC P-4200465 Porto Portugal

This article proposes a new two-stage hybrid stochastic-information gap-decision theory (IGDT) based on the network-constrained unit commitment framework. The model is applied for the market clearing of joint energy and flexible ramping reserve in integrated heat- and power-based energy systems. The uncertainties of load demands and wind power generation are studied using the Monte Carlo simulation method and IGDT, respectively. The proposed model considers both risk-averse and risk-seeker strategies, which enables the independent system operator to provide flexible decisions in meeting system uncertainties in real-time dispatch. Moreover, the effect of feasible operating regions of the combined heat and power (CHP) plants on energy and flexible ramping reserve market and operation cost of the system is investigated. The proposed model is implemented on a test system to verify the effectiveness of the introduced two-stage hybrid framework. The analysis of the obtained results demonstrates that the variation of heat demand is effective on power and flexible ramping reserve supplied by CHP units.

关键词： Cogeneration Uncertainty Load modeling Wind power generation Wind turbines Hybrid power systems Combined heat and power (CHP) flexible ramping reserve hybrid IGDT-stochastic information gap-decision theory (IGDT) market clearing stochastic programming

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Economic Dispatch for EV Energy Storage-Integrated Power Systems 14

Economic Dispatch for EV Energy Storage-Integrated Power Sys...

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14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022

作者： Satchidanandan, Bharadwaj Dahleh, Munther A. Massachusetts Institute of Technology Laboratory for Information and Decision Systems United States

ISBN: (纸本)9781665421041

Several economic and environmental considerations have made the transition to electric mobility inevitable. The power system can derive substantial benefits from this transition since Electric Vehicles (EVs) that are not in transit can be used as energy storage resources in the grid. Successful integration of EV energy storage into the grid requires the accompaniment of certain reforms in grid and market operations, one of which pertains to economic dispatch. In this paper, we address the problem of economic dispatch for a power system that has EV energy storage integrated into it. A quintessential feature that distinguishes EV storage from other forms of energy storage is that EV storage is unreliable: An EV could connect and disconnect from the grid at random times and so the availability of an EV's battery to the grid is intermittent and random. Consequently, the system operator cannot bank on the energy that it stores in an EV to be available for discharge at a future time. These aspects necessitate a rethinking of the economic dispatch problem when EV storage is integrated into the grid. The economic dispatch problem must account not simply for the availability of storage, but also the uncertainty in the availability. In this paper, we model these aspects and formulate the EV storage-integrated Economic Dispatch (EV-ED) problem. We also present an optimal, albeit computationally intensive approach to solving it. Methods to overcome the computational difficulty are briefly discussed. © 2022 IEEE.

关键词： stochastic programming

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Time-dependent stochastic vehicle routing problem with random requests: Application to online police patrol management in Brussels

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期292卷 869-885页

作者： Saint-Guillain, Michael Paquay, Celia Limbourg, Sabine Catholic Univ Louvain Louvain Belgium Maastricht Univ Sch Business & Econ Maastricht Netherlands Univ Liege HEC Management Sch Liege Belgium

The Static and stochastic Vehicle Routing Problem with Random Requests (SS-VRP-R) describes realistic operational contexts in which a fleet of vehicles has to serve customer requests appearing dynamically. Based on a probabilistic knowledge about the appearance of requests, the SS-VRP-R seeks a priori sequences of vehicle relocations, optimizing the expected responsiveness to the requests. In this paper, an existing computational framework, based on recourse strategies, is adapted to meet the objectives of the SS-VRP-R. The resulting models are applied to a real case study of the management of police units in Brussels. In this context, the expected average response time is minimized. To cope with the reality of the urban context, a time-dependent variant is also studied (TD-SS-VRP-R) in which the travel time between two locations is a function that depends on the departure time at the first location. Experiments confirm the contribution and the adaptability of the recourse strategies to a real-life, complex operational context. Provided an adequate solution method, simulation-based results show the high quality of the a priori solutions designed, even when compared to those designed by field experts. Finally, the experiments provide evidence that there is no potential gain in considering time-dependency in such an operational context. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Routing stochastic programming On-demand transportation Optimization under uncertainty Recourse strategies

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Sustainable closed-loop supply chain network under uncertainty: a response to the COVID-19 pandemic

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年 1-17页

作者： Rafigh, Parisa Akbari, Ali Akbar Bidhandi, Hadi Mohammadi Kashan, Ali Husseinzadeh Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran Tarbiat Modares Univ Fac Ind & Syst Engn Tehran Iran

This study proposes a sustainable closed-loop supply chain under uncertainty to create a response to the COVID-19 pandemic. In this paper, a novel stochastic optimization model integrating strategic and tactical decision-making is presented for the sustainable closed-loop supply chain network design problem. This paper for the first time implements the concept of sustainable closed-loop supply chain for the application of ventilators using a stochastic optimization model. To make the problem more realistic, most of the parameters are considered to be uncertain along with the normal probability distribution. Since the proposed model is more complex than majority of previous studies, a hybrid whale optimization algorithm as an enhanced metaheuristic is proposed to solve the proposed model. The efficiency of the proposed model is tested in an Iranian medical ventilator production and distribution network in the case of the COVID-19 pandemic. The results confirm the performance of the proposed algorithm in comparison with two other similar algorithms based on different multi-objective criteria. To show the impact of sustainability dimensions and COVID-19 pandemic for our proposed model, some sensitivity analyses are done. Generally, the findings confirm the performance of the proposed sustainable closed-loop supply chain for the pandemic cases like COVID-19.

关键词： Sustainable supply chain stochastic programming Multi-objective optimization Whale optimization algorithm COVID-19 pandemic

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Mixed Integer programming models for planning maintenance at offshore wind farms under uncertainty

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2020年 112卷 180-202页

作者： Schrotenboer, Albert H. Ursavas, Evrim Vis, Iris F. A. Univ Groningen Fac Econ & Business Dept Operat Groningen Netherlands

We introduce the stochastic Maintenance Fleet Transportation Problem for Offshore wind farms (SMFTPO), in which a maintenance provider determines an optimal, medium-term planning for maintaining multiple wind farms while controlling for uncertainty in the maintenance tasks and weather conditions. Since the maintenance provider is typically not the owner of a wind farm, it needs to adhere minimum service requirements that specify the required service. We consider three of such settings: (1) perform all maintenance tasks, (2) allow for a fraction of unscheduled tasks, and (3) incentivize to perform maintenance rather quickly. We provide a two-stage stochastic mixed integer programming model for the three SMFTPO settings, and solve it by means of Sample Average Approximation. In addition, we provide an overview of the, what we discovered, non-aligned modeling assumptions in the literature regarding operational decisions. By providing a series of special cases of the second-stage problem resembling the different modeling assumptions, we aim to establish a common consensus regarding the key modeling decisions to be taken in maintenance planning problems for offshore wind farms. We provide newly constructed, and publicly available, benchmark sets. We extensively compare the different SMFTPO settings and its special cases on those benchmark sets, and we show that the special case reformulations are very effective for solving the second-stage problems. In addition, we find that for particular cases, established modeling techniques result in overestimations and increased running times.

关键词： Mixed Integer programming stochastic programming Offshore wind Transportation Service logistics Maintenance

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A data-driven approach for a class of stochastic dynamic optimization problems

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2021年第3期80卷 687-729页

作者： Silva, Thuener Valladao, Davi Homem-de-Mello, Tito Pontifical Catholic Univ Rio de Janeiro PUC Rio Dept Ind Engn Rua Marques de Sao Vicente 225 BR-22451900 Rio De Janeiro RJ Brazil Univ Adolfo Ibanez Sch Business Diagonal Torres 2640 Santiago Chile

Dynamic stochastic optimization models provide a powerful tool to represent sequential decision-making processes. Typically, these models use statistical predictive methods to capture the structure of the underlying stochastic process without taking into consideration estimation errors and model misspecification. In this context, we propose a data-driven prescriptive analytics framework aiming to integrate the machine learning and dynamic optimization machinery in a consistent and efficient way to build a bridge from data to decisions. The proposed framework tackles a relevant class of dynamic decision problems comprising many important practical applications. The basic building blocks of our proposed framework are: (1) a Hidden Markov Model as a predictive (machine learning) method to represent uncertainty;and (2) a distributionally robust dynamic optimization model as a prescriptive method that takes into account estimation errors associated with the predictive model and allows for control of the risk associated with decisions. Moreover, we present an evaluation framework to assess out-of-sample performance in rolling horizon schemes. A complete case study on dynamic asset allocation illustrates the proposed framework showing superior out-of-sample performance against selected benchmarks. The numerical results show the practical importance and applicability of the proposed framework since it extracts valuable information from data to obtain robustified decisions with an empirical certificate of out-of-sample performance evaluation.

关键词： stochastic programming Distributionally robust dynamic optimization Hidden Markov models Risk constraints stochastic dual dynamic programming

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Optimal investment for a retirement plan with deferred annuities allowing for inflation and labour income risk

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期295卷 1132-1146页

作者： Owadally, Iqbal Jang, Chul Clare, Andrew Cass Business Sch 106 Bunhill Row London EC1Y 8TZ England Hanyang Univ 55 Hanyangdeahak Ro Ansan 15588 South Korea

We construct an optimal investment portfolio model for an individual investor saving in a retirement plan. The investor earns stochastic labour income with both permanent and temporary shocks, and has access to equity, conventional bond, inflation-indexed bond and cash, as well as two types of deferred annuities: nominal and inflation-protected. The objective function consists of power utility in terms of real retirement income from the annuities as well as bequest from remaining wealth in tradable securities. Asset returns are represented by a vector autoregressive model underpinned by Nelson-Siegel real and nominal yield curves. The optimization problem is solved numerically using multi-stage stochastic programming with a hybrid scenario structure combining a scenario tree with scenario fans. Our numerical results show that deferred annuities are bought early and in increasing amounts during the working lifetime of the investor, with portfolio risk declining with age. Welfare is diminished by 40% if deferred annuities are not available. Inflation-protected deferred annuities are marginally more important in the presence of real labour income risk, but nominal deferred annuities are bought as a cheaper alternative if real yields are low or negative. Portfolio composition and annuity allocation vary depending on financial market expectations, but our central result about the importance of deferred annuities is robust to a variety of financial market conditions. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Finance stochastic programming Inflation-protected annuities Interest rate model Scenario fans

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stochastic single-allocation hub location

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期289卷 1087-1106页

作者： Rostami, Borzou Kaemmerling, Nicolas Naoum-Sawaya, Joe Buchheim, Christoph Clausen, Uwe Wilfrid Laurier Univ Lazaridis Sch Business & Econ Waterloo ON Canada Polytech Montreal Excellence Res Chair DataScience Realtime Decis M Montreal PQ Canada TU Dortmund Univ Inst Transport Logist Dortmund Germany Western Univ Ivey Business Sch London ON Canada TU Dortmund Univ Fak Math Dortmund Germany TU Dortmund Univ Inst Transport Logist Dortmund Germany

This paper considers the single allocation hub location problem under demand uncertainty where the allocation of the spokes to the hubs is optimized as second stage decision after the uncertainty in the demand is realized. We refer to this case as the variable allocation case, meaning that the allocation of the spokes to the hubs can be altered after the uncertainty is realized. This is in contrast to the fixed allocation case that is addressed in the literature, where the spokes are allocated to the chosen hubs before the uncertainty is realized. As shown in the paper, the fixed allocation case can be solved as a deterministic problem using the expected values of the random variables. However, the variable allocation model is a two-stage stochastic program that is challenging to solve. An alternative convex mixed-integer nonlinear formulation is presented for the variable allocation and a customized solution approach based on cutting planes is proposed to address the computational challenges. The proposed solution approach is implemented in a branch-and-cut framework where the cut-generating subproblems are solved combinatorially, i.e. without an optimization solver. Extensive computational results on the single allocation hub location problem and two of its variants, the capacitated case and the single allocation p-median problem are presented. The proposed cutting plane approach outperforms the direct solution of the problem using the state-of-the-art solver GUROBI as well the L-shaped decomposition, which is a common approach for addressing two-stage stochastic programs with recourse. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Single allocation hub location Demand uncertainty stochastic programming Outer approximation Cutting planes

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Two-Stage stochastic Resilience Optimization of Converter Stations Under Uncertain Mainshock-Aftershock Sequences

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IEEE Transactions on Power Systems 2025年

作者： Wang, Kai Xue, Zhihang Cao, Di Liu, Yu Fang, Yi-Ping University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Center for System Reliability and Safety Chengdu611731 China Université Paris-Saclay Laboratoire Génie Industriel CentraleSupélec Gif-sur-Yvette91190 France

Reducing vulnerability and enhancing resilience of power systems, particularly converter stations, against earthquakes are crucial for maintaining their safe operation. Nevertheless, earthquakes are often accompanied by aftershocks and encompass plenty of uncertainties, posing significant challenges for the development of a joint pre-earthquake preparation and post-earthquake restoration strategy. In this article, a novel two-stage stochastic programming model is put forth to bolster the resilience of converter stations under uncertainties associated with mainshock-aftershock sequences. The model is bifurcated into two pillars: the first stage focuses on designing equipment hardening strategy (EHS) and spare parts strategy (SPS) for converter stations before earthquakes. The second stage is dedicated to the optimization of recovery scheduling (RS) in the wake of mainshock-aftershock sequences. To mitigate the endogenous uncertainties of the model, a tolerated random number generation method is employed to generate deterministic scenarios in the second stage. The resulting optimization model is, then, efficiently solved using a combination of the sample average approximation and progressive hedging algorithm. To evaluate the performance of the proposed resilience optimization method, comparative studies are carried out on a real ±800 kV converter station located in the city of Yibin, Sichuan Province, P.R. China. Results from this application demonstrate that the EHS pre-dominantly focuses on hardening the equipment of the same circuit to guarantee the low transmission capacity under diverse earthquake scenarios, while the SPS is proven to be more effective than the EHS in scenarios with high values of peak ground acceleration (PGA) of mainshock and occurrence intensity of aftershocks. © 1969-2012 IEEE.

关键词： Converter station endogenous uncertainty mainshock-aftershock sequences resilience stochastic programming

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A stochastic Capacity Expansion Model for Enhanced Power Grid Resilience to Extreme Weather

A Stochastic Capacity Expansion Model for Enhanced Power Gri...

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IISE Annual Conference and Expo 2022

作者： Sahin, Berk Hasenbein, John Kutanoglu, Erhan The University of Texas at Austin AustinTX United States

ISBN: (纸本)9781713858072

Capacities of lines and generators in a power grid are regularly increased to satisfy the increasing demand due to electrification or population growth. Since some lines and generators go out of service in extreme weather events, grid resources and therefore the power flow are impacted during such events. Proactive capacity expansion might provide much needed slack that helps the grid satisfy the power demand effectively during these events. The proactive allocation of capacity expansion resources with the goal of increasing extreme weather resilience of the power grid has become increasingly critical given its age and vulnerabilities. In this paper, we propose a twostage stochastic programming model for the optimal expansion of generators and lines in the power grid and optimal rehabilitation of aging substations, in order to increase resilience by minimizing the load shed across a variety of extreme weather scenarios. We present computational results on a problem instance representative of the Texas grid using hurricaneinduced flood events as scenarios. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

关键词： stochastic programming

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