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Minimizing the Expected Renewable Resource Costs in a Project with stochastic Resource Availability

ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2024年第1期41卷 2350008-2350008页

作者： Moghaddaszadeh, Hossein Ranjbar, Mohammad Jamili, Negin Ferdowsi Univ Mashhad Fac Engn Dept Ind Engn Mashhad Iran Erasmus Univ Rotterdam Sch Management Rotterdam Netherlands

In this paper, we study the well-known resource availability cost problem with stochastic resource availability. The objective is to determine the initial levels of all renewable resources and establish a schedule corresponding to each scenario such that the expected resource availability cost is minimized. We assume that resource shortfalls can be compensated externally but at a noticeable higher cost. We formulate the problem as a two-stage stochastic programming model (TSSPM). We also develop an exact decomposition-based algorithm (DBA) for the particular case of the problem with at most two resources, which also functions as a heuristic for the original problem. Since the number of scenarios influences the performance of the developed solution approaches, we utilize a fast scenario reduction method to reduce the number of scenarios. Computational results indicate that the DBA outperforms the TSSPM formulation in solution quality and CPU runtime.

关键词： Project scheduling resource availability cost problem two-stage stochastic programming

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System-level analysis of atmospheric water extraction with MIL-100 (Fe) for design and optimal site selection using meteorological characteristics

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ENERGY 2024年 299卷

作者： Kim, Jinsu Jamdade, Shubham Yuan, Yanhui Realff, Matthew J. Georgia Inst Technol Dept Chem & Biomol Engn Atlanta GA 30332 USA Avnos Inc Los Angeles CA 90046 USA

This study presents a system-level atmospheric water extraction (AWE) analysis using MIL-100 (Fe) as a representative material. This study considers a range of system features, including (i) modeling the isotherm across a wide temperature range based on the experimental data, (ii) conceptualizing process types and their operating conditions, (iii) analyzing the thermodynamics and economics, and (iv) optimizing the system operation under meteorological variations in temperature and humidity through two-stage stochastic programming. The swing capacity analysis revealed that a vacuum pressure below 0.1 bar and a temperature swing degree of 20-30 degrees C are necessary for feasible MIL-100 (Fe) AWE operation. Energy analysis shows that higher degrees of vacuum with moderate temperature swings result in lower energy consumption, requiring 4.6 and 1.6 MJ kg H2O - 1 for thermal and electrical energy consumption. The estimated unit harvesting cost was 0.058 US$ kg H2O - 1 , with the potential for cost reduction through material and process development. We make contributions in several key areas: (i) facilitating the selection of optimal sites for pilot or commercial process implementation, (ii) offering insights into process design and operation, (iii) identifying and addressing potential bottlenecks for future process development, and (iv) providing a generalized platform for material screenings for comparative analysis.

关键词： Atmospheric water extraction Thermodynamic analysis Meteorological analysis Techno-economic analysis two-stage stochastic programming

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Handling forecast uncertainty and variability in solar generation to mitigate schedule deviation penalties

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SOLAR ENERGY 2024年 271卷

作者： Raj, Raviraj P. Kowli, Anupama Indian Inst Technol Elect Engn Dept Mumbai India

With increasing installed renewable capacity the uncertainty and variability poses many challenges to planners and operators of the power systems in terms of generators deviating from commitments due to imperfect forecasts as well as fluctuations due to weather phenomena. These schedule deviations cause power imbalances and increase the balancing/regulating resources requirements. A possible alternative approach is to compensate for fluctuations locally: this study is geared towards exploring this approach. A multitimescale scheduling framework is presented herein which ensures high degree of compliance between the day -ahead grid commitments and the actual grid injections. Battery is deployed as a local hedging device to provide compensating measures against uncertainty and variability. A two -stage recourse -based stochastic optimization framework for day -ahead scheduling is presented to tackle deviations due to forecast uncertainty. And model predictive control strategy is employed to tackle fluctuations due to short-term variability. Since the two approaches work on different time -scales, an effective temporal link between these two approaches is established and the energy management system for batteries is presented to reflect this link. A case study using on -field recorded solar generation data set for a PV producer is presented to showcase the effectiveness of the proposed approach.

关键词： Scheduling framework two-stage stochastic programming Variable renewable energy Short-term variability Schedule deviations

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Integrated optimization of lane allocation and adaptive signal control at isolated intersection under stochastic demand

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 169卷

作者： Huang, Wei Cheng, Haofan Huang, Guoyu Li, Lubing Sun Yat sen Univ Sch Intelligent Syst Engn Guangdong Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China

Traffic arrival rate at intersections is usually fluctuating and random. This paper proposes an integrated optimization method for lane allocation and signal control under stochastic demand using the concept of level-of-service (LOS) reliability. First, we develop a two-stage stochastic programming model. In the first stage, a base lane allocation plan that takes account of the arrival uncertainty is derived. The objective is to minimize the expected average delay of the intersection. At the second stage, upon realizations of the random arrivals, the signal timing plan is adjusted in respond to the varying traffic conditions and address the occasional overflows, which may occur with the base lane allocation plan. In view of the intertwined two-stage decisions, we introduce the LOS reliability to decouple the model into two independent subproblems for solution efficiency. A decoupled model is then reconstructed with a LOS reliability constraint, which is associated with a fixed traffic arrival pattern. Then, a gradient descent algorithm is developed to solve the optimal LOS reliability level. Numerical experiments on both a test intersection and a real intersection are conducted and two benchmark methods are introduced for comparison. Experimental results demonstrate the effectiveness of the proposed LOS reliability-based optimization method in terms of reducing the expected average delay. With the benefits from a robust base lane allocation plan, the optimal signal timing plans can better adapt to random traffic demands.

关键词： Traffic signal control Lane allocation two-stage stochastic programming Demand uncertainty

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A holistic two-stage decision-making methodology for passive and active building design strategies under uncertainty

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BUILDING AND ENVIRONMENT 2024年 251卷

作者： Zong, Chujun Chen, Xia Deghim, Fatma Staudt, Johannes Geyer, Philipp Lang, Werner Tech Univ Munich Inst Energy Efficient & Sustainable Design & Bldg Arcisstr 21 D-80333 Munich Germany Leibniz Univ Hannover Sustainable Bldg Syst Herrenhauser Str 8 D-30419 Hannover Germany

In the last decade, many studies focused on finding optimal design solutions considering trade-offs between different aspects of building design. Accordingly, multi -objective optimization (MOO) approaches have been increasingly applied in the building industry. However, certain aspects must be deepened to ensure a more effective decision -making process in the early planning phase. On the one hand, uncertainties should be considered before making decisions to ensure the robustness of the optimal solutions;on the other hand, decisions are made at different times in building planning, and the sequential order of making decisions should be modeled. This paper presents a holistic two -stage multi -objective stochastic optimization (MOSO)-II framework to minimize the environmental impact of global warming potential (GWP) and cost throughout the entire life cycle of a building (phases of A1 -A3, B4, B6 and C3 -C4), considering passive and active design strategies in two consecutive stages, under uncertainty. Herein, individual/use and political/market uncertainties are considered. As a proof of concept, the proposed framework is applied in a case study for a generic zone in a multi -family terraced residential building type with solid brick construction. The advantages of the proposed framework are validated by comparing it with alternative single -stage MOSO frameworks. Results show that the proposed two -stage MOSO-II framework can deliver a smaller range of solutions with a better performance in terms of lower GWP and cost. It indicates that the proposed framework can effectively assist planners in decision -making by reducing the effort in choosing the proper solution. Secondly, the results also emphasize the importance of passive design strategies in sustainable building planning. In addition, the energy mix structure and cost of energy sources should be carefully adjusted in the future to promote a more ecologically sustainable building design.

关键词： Passive design Life cycle assessment Decision-making Multi-objective optimization Uncertainty two-stage stochastic programming Machine learning

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Navigating the green shipping: stochastic hydrogen hub deployment in inland waterways

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2024年 129卷

作者： Shangguan, Yidan Tian, Xuecheng Pang, King-Wah Ruan, Qingfang Jin, Yong Wang, Shuaian Hong Kong Polytech Univ Dept Logist & Maritime Studies Hung Hom Kowloon Hong Kong Peoples R China Shenzhen Guangming Dist Inst Adm Shenzhen Peoples R China Hong Kong Polytech Univ Fac Business Sch Accounting & Finance Hung HomKowloon Hong Kong Peoples R China

The maritime industry is shifting toward sustainability to help combat climate change, given the sector's significant contribution to global greenhouse gas emissions. This study focuses on the strategic integration of hydrogen fuel-a zero -emission alternative-into inland waterway transport systems. We recognize the operational characteristics of inland shipping networks and the complexities of two -stage financial planning. Consequently, we propose a two -stage stochastic framework for the deployment of hydrogen refueling hubs, taking into account financial uncertainties in the future. This approach contrasts with a myopic strategy and a two -stage deterministic framework, addressing both immediate financial constraints and future uncertainties. We focus on the Yangtze River's shipping network as a case study, and our analysis validates the superior performance of our proposed two -stage stochastic strategy. This suggests its potential to accommodate volatile future financial uncertainties while ensuring the economic and operational viability of hydrogen integration.

关键词： Green shipping Hydrogen energy Hub deployment problem two-stage stochastic programming

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Optimal Day-Ahead Scheduling for Active Distribution Network Considering Uncertainty 10

Optimal Day-Ahead Scheduling for Active Distribution Network...

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10th IEEE International Conference on Power Electronics, Drives and Energy Systems (IEEE PEDES)

作者： Vemalaiah, Kasi Khatod, Dheeraj Kumar Padhy, Narayana Prasad Indian Inst Technol Roorkee Elect Engn Dept Roorkee India Malaviya Natl Inst Technol Jaipur Jaipur Rajasthan India Indian Inst Informat Technol Kota Jaipur Rajasthan India

ISBN: (纸本)9781665455664

In recent days, the adoption of the battery energy storage system has increased in the electric grid due to the massive integration of renewable energy sources. This paper proposes a two-stage stochastic optimal day-ahead scheduling of an active distribution network considering uncertainty. It seeks to define an optimal day-ahead dispatch of battery energy storage systems and switchable capacitor banks to diminish operational costs incurred. The scheduling problem includes second-order cone programming power flow to capture distribution network features and assures the global optimal solution. The normal and beta probability distribution functions have been employed to model the forecast error uncertainty of load demand and generation from renewable resources, respectively. The Monte Carlo simulation approach is utilized to generate a large number of scenarios. Further, to make the proposed algorithm computationally efficient, these large number of scenarios are reduced to a small number using the Kantorovich probability distance approach ensuring problem tractability. The proposed framework is developed as a mixed-integer second-order cone programming problem, coded in GAMS, and solved with a GUROBI solver. The presented results on the modified IEEE 33-bus distribution network show reduced operating costs, reduced energy losses, decreased peak demand, and a notably enhanced voltage profile.

关键词： Active distribution network day-ahead scheduling mixed-integer second-order cone program scenario generation and reduction two-stage stochastic programming

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Coordinating Electric Vehicle Flow Distribution and Charger Allocation by Joint Optimization

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第12期17卷 8112-8121页

作者： Bi, Xiaowen Chipperfield, Andrew John Tang, Wallace K. S. City Univ Hong Kong Dept Elect Engn Kowloon Tong Tat Chee Ave Hong Kong Peoples R China Univ Southampton Fac Engn & Phys Sci Sch Engn Southampton SO17 1BJ Hants England

A two-stage stochastic programming model is established to minimize EV's expected total journey time under stochastic traffic conditions, by jointly optimizing the allocation of chargers and the distribution of EV flows. Based on sample average approximation, a feasible deterministic equivalent of the original stochastic problem is obtained. Then, a hybrid solution method, composing of a Tabu-based search and sequential quadratic programming (SQP), is proposed. The Tabu heuristic manages the charger allocation problem, where each solution candidate undergoes a second-stage EV flow optimization. SQP is applied to optimally distribute the EV flows, which is proved to be a convex problem. Extensive simulations are carried out using the eastern Massachusetts highway network. Results show that the proposed algorithm outperforms existing approaches by finding high-quality charger allocation and optimal flow distribution policies under different traffic conditions. Additionally, the two-stage model designates charging resource sufficiency by statistically estimating a lower bound for the number of chargers to allocate, which in practice helps to prevent over-investment on charging resources.

关键词： Manganese stochastic processes Resource management Charging stations Optimization Roads Informatics Charger allocation convex optimization electric vehicle (EV) traffic flow distribution two-stage stochastic programming

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Decomposition methods for Wasserstein-based data-driven distributionally robust problems

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OPERATIONS RESEARCH LETTERS 2021年第5期49卷 696-702页

作者： Gamboa, Carlos Andres Valladao, Davi Michel Street, Alexandre Homem-de-Mello, Tito Pontifical Catholic Univ Rio de Janeiro PUC Rio Dept Ind Engn Rio De Janeiro Brazil Pontifical Catholic Univ Rio de Janeiro PUC Rio Dept Elect Engn Rio De Janeiro Brazil Univ Adolfo Ibanez Sch Business Santiago Chile

We study decomposition methods for two-stage data-driven Wasserstein-based DROs with right-hand-sided uncertainty and rectangular support. We propose a novel finite reformulation that explores the rectangular uncertainty support to develop and test five new different decomposition schemes: ColumnConstraint Generation, Single-cut and Multi-cut Benders, as well as Regularized Single-cut and Multi-cut Benders. We compare the efficiency of the proposed methods for a unit commitment problem with 14 and 54 thermal generators whose uncertainty vector differs from a 24 to 240-dimensional array. (C) 2021 Elsevier B.V. All rights reserved.

关键词： two-stage stochastic programming Decomposition methods Distributionally robust optimization

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Designing a competitive photovoltaic supply chain under uncertainty: An efficient L-shape algorithm

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JOURNAL OF CLEANER PRODUCTION 2024年 434卷

作者： Kousha, Atieh Rahmani, Donya Dehghani, Ehsan K N Toosi Univ Technol Dept Ind Engn Tehran Iran Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

Due to the special features of the photovoltaic system, it is crucial yet feasible to efficiently manage and coordinate its supply chain. In this study, a competitive photovoltaic supply chain network is devised in two phases. The first phase denominates three strategies of the game-theoretic approach to take into account the competition between the suppliers. In the second phase, the obtained results are included in a novel mixed-integer programming model as input parameters to design a photovoltaic supply chain. To increase the adaptability to real -world conditions, the parameters of the proposed model are considered to be imbued with uncertainty. Addi-tionally, two-stage stochastic programming is exploited to withstand uncertainty. Due to the NP-Hard nature of the developed model, an L-shape method has been employed to solve it. The computational results corroborate the effectiveness of the solution approach. Eventually, sensitivity analyses are carried out to provide valuable managerial insights. The results indicated that the significance of the game theory approach in capturing strategic interaction among players in a competitive environment and the practicality of two-stage stochastic programming in addressing uncertainties in decision-making can optimally boost photovoltaic supply chain network design. Additionally, the L-shape method exhibits lower gaps than the benders decomposition algorithm and achieves better solutions within the same CPU time.

关键词： Solar energy Photovoltaic supply chain Electric vehicle charging station Competitive supply chain two-stage stochastic programming L-shaped method

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