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Offshore oilfield development planning under uncertainty and fiscal considerations

OPTIMIZATION AND ENGINEERING 2017年第1期18卷 3-33页

作者： Gupta, Vijay Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

The development planning of an offshore oil and gas field infrastructure involves critical investment and operating decisions at an early stage of the project that impact the overall project profitability. These strategic/tactical decisions need to be made considering sufficient reservoir details, fiscal contracts with the government and uncertainty in the field parameters to be useful in practice. However, it makes the optimization problem difficult to model and solve. With this motivation, the objective of this paper is to present a comprehensive review of a unified modeling framework and solution strategies to address the issues of complex fiscal rules and endogenous uncertainties in the development planning of offshore oil and gas field infrastructure that relies on our recent work in this area. In particular, the paper emphasizes the need to have as a basis an efficient deterministic model that can account for various alternatives in the decision making process for a multi-field site incorporating sufficient level of details, while being computationally tractable for large instances. Consequently, such a model can be effectively extended to include other complexities, for instance a production sharing agreement and endogenous uncertainties. Computational results on the deterministic as well as multistage stochastic instances of the problem are discussed.

关键词： Oil and gas exploration Production sharing agreements Ringfencing multistage stochastic programming Endogenous uncertainties Lagrangian decomposition

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Quantitative stability of multistage stochastic programs via calm modifications

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OPERATIONS RESEARCH LETTERS 2018年第5期46卷 543-547页

作者： Jiang, Jie Chen, Zhiping Xi An Jiao Tong Univ Sch Math & Stat Xian Shaanxi Peoples R China

In this paper, we revisit the quantitative stability of multistage stochastic programs. Different from the single calm modification used in Ktichler (2008), we introduce two types of calm modifications which leads to a much simpler proof and tighter upper bound for the difference of optimal values of multistage stochastic programs under different stochastic processes than those of Kuchler (2008). In addition, we avoid those restrictive assumptions in Kuchler (2008) and the filtration distance in Heitsch et al. (2006). Finally, we illustrate our results with two numerical examples. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multistage stochastic programming Calm modification Filtration distance Quantitative stability

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Time consistency of dynamic risk measures

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OPERATIONS RESEARCH LETTERS 2012年第6期40卷 436-439页

作者： Shapiro, Alexander Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

In this paper we discuss time consistency of risk averse multistage stochastic programming problems. We show, in a framework of finite scenario trees, that composition of law invariant coherent risk measures can be la... 详细信息

关键词： multistage stochastic programming Risk averse optimization Dynamic programming Time consistency Coherent risk measures

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Day-Ahead Optimal Dispatch Strategy for Active Distribution Network Based on Improved Deep Reinforcement Learning

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IEEE ACCESS 2022年 10卷 9357-9370页

作者： Li, Xiaoyu Han, Xueshan Yang, Ming Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

To effectively deal with the challenge of optimal dispatch caused by uncertainties such as renewable energy in active distribution network, a day-ahead optimal dispatch strategy for active distribution network based on improved deep reinforcement learning is proposed. First, the day-ahead optimal dispatch problem for the active distribution network is modeled as a multistage stochastic programming model. multistage models attempt to capture the dynamics of unfolding uncertainties over time, so that the interaction between decision making and uncertainty unfolding is represented more accurately and realistically, and the decision can be adjusted dynamically and adaptively. Second, with the help of deep neural network to approximate the power flow model of the active distribution network in a data-driven manner, an improved deep reinforcement learning algorithm is proposed to solve the corresponding multistage stochastic programming problem. The proposed improved deep reinforcement learning algorithm effectively solves the problem that trial-and-error information acquisition methods are difficult to apply to high-cost power systems. Finally, the effectiveness of the proposed day-ahead optimization dispatch strategy for active distribution network based on improved deep reinforcement learning is verified by a modified IEEE33 case.

关键词： Uncertainty Optimization Renewable energy sources Reinforcement learning Heuristic algorithms Adaptation models Active distribution networks Active distribution network multistage stochastic programming optimal dispatch data driven deep learning deep reinforcement learning

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A general primal bounding framework for large-scale multistage stochastic programs under endogenous uncertainties

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2019年 141卷 464-480页

作者： Zeng, Zuo Cremaschi, Selen Auburn Univ Dept Chem Engn Auburn AL 36849 USA

multistage stochastic programming is an approach for modeling and solving optimization problems under uncertainty. Such problems are commonly seen in production planning and scheduling in chemical process industry. This paper considers large-scale multistage stochastic programs under a specific type of uncertainty, i.e., endogenous uncertainty, and develops a general primal-bounding framework based on extending the concepts of expected value solution and value of stochastic solution from multistage stochastic programs under exogenous uncertainties. Under traditional decision-making process with uncertainties, decision makers utilize known information to-date along with the expected results for unrealized information to take action, realize some uncertainty, and repeat this process along the planning horizon. The bounding framework introduced here fits this decision-making process. It yields a tight feasible bound and an implementable solution for multistage stochastic programs under endogenous uncertainties, which we call the absolute expected value solution (AEEV). The framework is tested on three planning problems with up to 16,384 scenarios. It yielded primal bounds within 1% of the true solutions for all tested cases, and generated these implementable solutions up to four orders of magnitude faster than solving the original multistage stochastic programs. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： multistage stochastic programming Endogenous uncertainty Absolute expected value solution Primal bounds Planning under uncertainty

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Incentive-Based Coordination Mechanism for Renewable and Conventional Energy Suppliers

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IEEE TRANSACTIONS ON POWER SYSTEMS 2019年第3期34卷 1761-1770页

作者： Babaei, Sadra Zhao, Chaoyue Fan, Lei Liu, Tieming Oklahoma State Univ Sch Ind Engn & Management Stillwater OK 74074 USA Siemens Ind Inc Minnetonka MN 55305 USA

This paper proposes an incentive-based coordination mechanism between a wind energy supplier (WES) and a conventional energy supplier (CES) to achieve a Pareto improvement. To comply with its day-ahead schedules and hedge against the intermittent wind energy generation, the WES is allowed to outsource a back up power capacity from the CES via making a bilateral contract. However, unanimous agreement cannot always be achieved since each party plays on its own interest. We employ the concept of swing option contracts to further encourage the suppliers to reach an agreement of the contract. On the one hand, the WES can leverage the uncertainty of wind output by covering possible energy shortage from the CES. On the other hand, the CES can optimally allocate its energy capacity by participating into the electricity market and offering capacity to backup the shortage of energy from the WES. The bidding problem for each supplier is formulated as a multi-stage stochastic programming model, with the objective of maximizing the expected profit while maintaining a low level of risk. Unlike the traditional two-stage approach, the proposed multi-stage model can effectively capture the impact of rebidding process in the real-time market. We incorporate conditional-value-at-risk as a risk measure to characterize the effect of risk perception of suppliers on their bidding decisions. Meanwhile, a game theory based approach is developed to obtain the contract items between the suppliers. Implementation results on real cases are provided to illustrate the effectiveness of our proposed framework.

关键词： Bilateral contracts conditional-value-at-risk game theory multistage stochastic programming self-scheduling wind energy

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Integrated multiperiod power generation and transmission expansion planning with sustainability aspects in a stochastic environment

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ENERGY 2015年 86卷 9-18页

作者： Seddighi, Amir Hossein Ahmadi-Javid, Amir Amirkabir Univ Technol Dept Ind Engn Tehran Iran

This paper presents a multistage stochastic programming model to address sustainable power generation and transmission expansion planning. The model incorporates uncertainties about future electricity demand, fuel prices, greenhouse gas emissions, as well as possible disruptions to which the power system is subject. A number of sustainability regulations and policies are considered to establish a framework for the social responsibility of the power system. The proposed model is applied to a real-world case, and several sensitivity analyses are carried out to provide managerial insights into different aspects of the model. The results emphasize the important role played by sustainability policies on the configuration of the power grid. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Electrical power systems Generation expansion planning Transmission expansion planning Social responsibility Sustainable development multistage stochastic programming

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Assessing the Cost of Network Simplifications in Long-Term Hydrothermal Dispatch Planning Models

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2022年第1期13卷 196-206页

作者： Rosemberg, Andrew W. Street, Alexandre Garcia, Joaquim Dias Valladao, Davi M. Silva, Thuener Dowson, Oscar Pontifical Catholic Univ Rio de Janeiro PUC Rio Lab Appl Math Programming & Stat LAMPS PUC Rio BR-22251030 Rio De Janeiro Brazil Pontifical Catholic Univ Rio de Janeiro PUC Rio Dept Elect Engn BR-22251030 Rio De Janeiro Brazil PSR BR-22250145 Rio De Janeiro Brazil Pontificia Univ Catolica Rio de Janeiro LAMPS PUC Rio BR-22251030 Rio de Janeiro Brazil Pontificia Univ Catolica Rio de Janeiro Dept Ind Engn BR-22251030 Rio de Janeiro Brazil Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA

The sustainable utilization of hydro energy relies on accurate estimates of the opportunity cost of the water. This value is calculated through long-term hydrothermal dispatch problems (LTHDP), and the recent literature has raised awareness about the consequences of modeling simplifications in these problems. The inaccurate representation of Kirchhoff's voltage law under the premise of a DC power flow is an example. Under a non-linear AC model, however, the LTHDP becomes intractable, and the literature lacks an accurate evaluation method of different modeling alternatives. In this paper, we extend the state-of-the-art cost-assessment framework of network approximations for LTHDP and bring relevant and practical new insights. First, we increase the quality of the assessment by using an AC power flow to simulate and compare the performance of five policies based on different network approximations. Second, we find that the tightest network relaxation (based on semidefinite programming) is not the one exhibiting the best performance. Results show that the DC power flow with quadratic losses approximation exhibits the lowest expected cost and inconsistency gaps. Finally, its computational burden is lower than that exhibited by the semidefinite relaxation, whereas market distortions are significantly reduced in comparison to previously published benchmarks based on DC power flow.

关键词： Long-term hydrothermal dispatch multistage stochastic programming network model relaxations optimal power flow time consistency

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Airport terminal capacity planning

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2009年第6期43卷 659-676页

作者： Solak, Senay Clarke, John-Paul B. Johnson, Ellis L. Univ Massachusetts Dept Finance & Operat Management Isenberg Sch Management Amherst MA 01003 USA Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

The airport terminal capacity planning problem deals with determining the optimal design and expansion capacities for different areas of the terminal in the presence of uncertainty with regards to future demand levels and expansion costs. Analytical modeling of passenger flow in airport terminals under transient demand patterns is especially difficult due to the complex structure of a terminal. Because of this difficulty, the airport terminal capacity planning problem has not been studied in a holistic fashion, such that studies in this area either do not account for expandability or focus only on one particular area of the terminal. In this study, we consider the airport terminal capacity planning problem as a whole. In this regard, we first derive time functions to approximate maximum delays in passageways and processing stations of an airport terminal. We then use these delay functions to develop a multistage stochastic programming model based on a multicommodity flow network representation of the whole airport terminal. The solution of the model, for which we develop an efficient solution algorithm, provides optimal capacity requirements for each area in an airport terminal during the initial building phase, as well as the optimal expansion policy under stochastic future demand. The results of the study are applicable to all similar queuing networks, including other types of passenger terminals. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Passenger terminal design Airport planning Capacity expansion multistage stochastic programming

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The value of flexibility for pulp mills investing in energy efficiency and future biorefinery concepts

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2014年第14期38卷 1864-1878页

作者： Svensson, Elin Berntsson, Thore Stromberg, Ann-Brith Chalmers Environm & Energy Dept SE-41296 Gothenburg Sweden Chalmers Dept Math Sci SE-41296 Gothenburg Sweden Univ Gothenburg SE-41296 Gothenburg Sweden

Changing conditions in biomass and energy markets require the pulp and paper industry to improve energy efficiency and find new opportunities in biorefinery implementation. Considering the expected changes in the pulp mill environment and the variety of potential technology pathways, flexibility should be a strong advantage for pulp mills. In this context, flexibility is defined as the ability of the pulp mill to respond to changing conditions. The aim of this article is to show the potential value of flexibility in the planning of pulp mill energy and biorefinery projects and to demonstrate how this value can be incorporated into models for optimal strategic planning of such investments. The paper discusses the requirements on the optimization models in order to adequately capture the value of flexibility. It is suggested that key elements of the optimization model are multiple points in time where investment decisions can be made as well as multiple scenarios representing possible energy price changes over time. The use of a systematic optimization methodology that incorporates these model features is illustrated by a case study, which includes opportunities for district heating cooperation as well as for lignin extraction and valorization. A quantitative valuation of flexibility is provided for this case study. The study also demonstrates how optimal investment decisions for a pulp mill today are influenced by expected future changes in the markets for energy and bioproducts. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： energy efficiency forest biorefinery process integration investment under uncertainty multistage stochastic programming energy price scenarios

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