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IEEE/PES Transmission and Distribution Conference and Exposition (T and D)

作者： Liang, Junkai Tang, Wenyuan North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9781728145471

Power-to-gas technology provides an effective form of long-term electric storage, which improves the renewable energy utilization. Moreover, power-to-gas can capture the carbon dioxide from the air and reduce the total carbon emission. In this paper, we propose a multistage stochastic expansion co-planning model for the integrated energy systems with power-to-gas storage. The value of the multistage program is verified in the case study. Moreover, the case study evaluates the economic value of power-to-gas in the long-term expansion co-planning, especially when the renewable penetration is high.

关键词： Integrated energy system multistage stochastic programming power-to-gas uncertainty

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Evaluating the potential for modal shift in last-mile cargo distribution through stochastic programming

MARITIME TRANSPORT RESEARCH

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MARITIME TRANSPORT RESEARCH 2020年 1卷

作者： Ormevik, Andreas Breivik Fgerholt, Kjetil Erikstad, Stein Ove Norwegian Univ Sci & Technol Dept Ind Econ & Technol Management NO-7491 Trondheim Norway Norwegian Univ Sci & Technol Dept Marine Technol NO-7491 Trondheim Norway

This paper presents a multistage stochastic programming model used to evaluate the potential for obtaining a modal shift from road to sea in the cargo distribution between a regional port and the surrounding customer regions, each with a given (and uncertain) future demand for cargo. We use the port of Bergen in Norway as a case study as it has recently been decided to relocate the main cargo terminal from the city center in 2025. Analyses with the model indicate that implementing a multimodal distribution network with small electric vessels 10 years ahead will lead to slight cost reductions compared with using only trucks and reduce the road transportation need by 23%. A modal shift can be initiated at an earlier stage and reduce the road transportation further if modal shift targets are specified, without resulting in significant cost increases. What-if analyses have been conducted and indicate robustness in the obtained results.

关键词： Maritime transportation Modal shift Multimodal distribution network multistage stochastic programming Electric vessels

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A stochastic programming model for dynamic portfolio management with financial derivatives

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JOURNAL OF BANKING & FINANCE 2022年第0期140卷

作者： Barro, Diana Consigli, Giorgio Varun, Vivek Univ CaFoscari Venice Dept Econ Dorsoduro 3246 I-30123 Venice Italy Khalifa Univ Sci & Technol Dept Math POb 127788 Abu Dhabi U Arab Emirates Univ Bergamo Dept Econ Via Caniana 2 I-24127 Bergamo Italy

stochastic optimization models have been extensively applied to financial portfolios and have proven their effectiveness in asset and asset-liability management. Occasionally, however, they have been applied to dynamic portfolio problems including not only assets traded in secondary markets but also derivative contracts such as options or futures with their dedicated payoff functions. Such extension allows the construction of asymmetric payoffs for hedging or speculative purposes but also leads to several mathematical issues. Derivatives-based nonlinear portfolios in a discrete multistage stochastic programming (MSP) framework can be potentially very beneficial to shape dynamically a portfolio return distribution and attain superior performance. In this article we present a portfolio model with equity options, which extends significantly previous effort s in this area, and analyse the potential of such extension from a modeling and methodological viewpoints. We consider an asset universe and model portfolio set-up including equity, bonds, money market, a volatility-based exchange-traded-fund (ETF) and over-the-counter (OTC) option contracts on the equity. Relying on this market structure we formulate and analyse, to the best of our knowledge, for the first time, a comprehensive set of optimal option strategies in a discrete framework, including canonical protective puts, covered calls and straddles, as well as more advanced combined strategies based on equity options and the volatility index. The problem formulation relies on a data-driven scenario generation method for asset returns and option prices consistent with arbitrage free conditions and incomplete market assumptions. The joint inclusion of option contracts and the VIX as asset class in a dynamic portfolio problem extends previous effort s in the domain of volatility-driven optimal policies. By introducing an optimal trade-off problem based on expected wealth and Conditional Value-at-Risk (CVaR), we formula

关键词： multistage stochastic programming Option strategies Equity and volatility risk Financial engineering Optimal risk control Derivatives pricing

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multistage stochastic capacity planning of partially upgraded bitumen production with hybrid solution method

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OPTIMIZATION AND ENGINEERING 2019年第4期20卷 1193-1226页

作者： Shahandeh, Hossein Nasab, Farough Motamed Li, Zukui Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada

Partial upgrading technologies have received a great amount of attention as a promising and economic solution for Canadian oil sands bitumen processing. In this paper, the optimal planning of capital and expansion capacities for partial upgrading of bitumen is studied. We propose a multistage stochastic planning model with consideration of various sources of uncertainties. The main challenge of the stochastic model is the presence of terms in which an uncertain parameter is multiplied by an adjustable dynamic decision variable. To solve the problem, two hybrid methods are proposed: in method 1, the uncertain parameter is modeled with an uncertainty set and the dynamic variable is modeled as scenario dependent variables;in method 2, the uncertain parameter is modeled with samples and the dynamic variable is modeled using the decision rule-based approximation. Finally, results obtained from both hybrid models are compared based on different criteria: (1) computational time, (2) solution performance, and (3) solutions for representative scenarios.

关键词： Partial upgrading Canadian bitumen Capacity and expansion planning multistage stochastic programming Robust optimization

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Utilising demand response for distribution service restoration to achieve grid resiliency against natural disasters

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第14期13卷 2942-2950页

作者： Hafiz, Faeza Chen, Bo Chen, Chen de Queiroz, Anderson Rodrigo Husain, Iqbal North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA Argonne Natl Lab Div Energy Syst 9700 S Cass Ave Argonne IL 60439 USA North Carolina Cent Univ Sch Business Dept Decis Sci Durham NC USA North Carolina State Univ Civil Construct & Environm Engn Dept Raleigh NC USA

The increased frequency of power outages due to natural disasters in recent years has highlighted the urgency of enhancing distribution grid resilience. The effective distribution service restoration (DSR) is an important measure for a resilient distribution grid. In this work, the authors demonstrate that DSR can be significantly improved by leveraging the flexibility provided by the inclusion of demand response (DR). The authors propose a framework for this by considering integrated control of household-level flexible appliances to vary the load demand at the distribution-grid level to improve DSR. The overall framework of the proposed system is modelled as a three-step method considering three optimization problems to (i) calculate feasible controllable aggregated load range for each bus, (ii) determine candidate buses to perform DR and their target load demand, and (iii) maintain the load level in each house through home energy management during the DSR, considering uncertainties in load and solar generation sequentially. The optimization problems are formulated as linear programming, mixed-integer linear programming, and multistage stochastic programming (solved using the stochastic dual dynamic programming) models. Case studies performed in the IEEE 123-node test feeder show improvements in resilience in terms of energy restored compared to the restoration process without DR.

关键词： optimisation power grids energy management systems power distribution control power system restoration demand side management domestic appliances demand response grid resiliency power outages distribution grid resilience resilient distribution grid integrated control household-level flexible appliances distribution-grid level optimisation problems aggregated load target load demand mixed-integer linear programming distribution service restoration IEEE 123-node test feeder system stochastic dual dynamic programming multistage stochastic programming solar generation home energy management

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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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DISTRIBUTIONALLY ROBUST stochastic DUAL DYNAMIC programming

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SIAM JOURNAL ON OPTIMIZATION 2020年第4期30卷 2841-2865页

作者： Duque, Daniel Morton, David P. Northwestern Univ Ind Engn & Management Sci Evanston IL 60208 USA

We consider a multistage stochastic linear program that lends itself to solution by stochastic dual dynamic programming (SDDP). In this context, we consider a distributionally robust variant of the model with a finite number of realizations at each stage. Distributional robustness is with respect to the probability mass function governing these realizations. We describe a computationally tractable variant of SDDP to handle this model using the Wasserstein distance to characterize distributional uncertainty.

关键词： distributionally robust optimization multistage stochastic programming stochastic dual dynamic programming

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stochastic multistage co-planning of integrated energy systems considering power-to-gas and the cap-and-trade market

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年 119卷 105817-000页

作者： Liang, Junkai Tang, Wenyuan North Carolina State Univ Raleigh NC 27695 USA

This paper develops an expansion co-planning model for integrated power and gas systems where uncertainties in both systems are considered. The power-to-gas technology is modeled and considered as a potential solution for some worldwide societal goals such as decarbonized economy and 100% renewable penetration. Compared with expansion planning models for power systems, the complexity of expansion co-planning models is severely exacerbated by random variables in gas systems. A scenario based decomposition scheme called branch-and-price is presented. The numerical results demonstrate the effectiveness of the proposed method and show the economic value of power-to-gas technology.

关键词： Branch-and-price algorithm multistage stochastic programming Expansion co-planning for integrated power and gas systems Power-to-gas

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stochastic Design Optimization Of Modular, Reconfigurable, Persistent Support Platforms In Earth Orbit

Stochastic Design Optimization Of Modular, Reconfigurable, P...

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作者： Jeff Perez University of Illinois – Urbana-Champaign

学位级别：硕士

This thesis focuses on the design and optimization of modular, reconfigurable, hosted payload platforms operating in Earth orbit. Recent advancements in on-orbit servicing technologies and robotics are creating a market for hosted-payload platforms which can support multiple payloads with varying requirements. Such platforms can employ on-orbit servicing and robotic manipulation to repair or replace modules, enhance the platformâs capabilities over time, and reconfigure modules to optimize performance. Traditional spacecraft design is often driven largely by payload requirements. For the case of persistent platforms, however, not all payloads will be known in the initial design phase. This presents a unique challenge to designers, who must account for the uncertainty of future payloads by trading off between the costs of adding more capability to the platform initially, which assumes the risk of wasted costs due to over-designing the platform, and the costs of utilizing an on-orbit servicer to add capability as needed. The hosted payload platforms considered in this thesis consist of platform modules and payload modules and uses a standardized interface for intermodular and customer payload connection. Each platform module contains a critical satellite subsystem that is necessary for on-orbit functionality. As payloads are added to the platform over time, their demands may exceed the current capability of the platform, at which point additional platform modules can be added to increase the platforms capabilities. This thesis proposes an approach using a multi-stage stochastic programming method to create an initial platform design that is robust and flexible enough to support a wide range of payloads and minimizes the expected costs of future platform additions. Probability distributions for future payload selections are created based on a survey of active satellites. These distributions are then used to create samples of payload selection scenarios. Using a simpl

关键词： stochastic design optimization hosted payload platform persistent support platform modular satellite spacecraft design optimization multistage stochastic programming reconfigurable on-orbit satellite servicing servicing spacecraft robotic servicer reconfigurable satellite

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