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6th International Conference for Convergence in Technology (I2CT)

作者： Reddy, Meegada Indeevar Saha, Radheshyam Valluru, Sudarshan K. Delhi Technol Univ Dept Elect Engn Delhi India

ISBN: (纸本)9781728188768

To promote investment in the electricity sector, the deregulated electricity market regime has created an enabling environment to accelerate the all-around development of power generation, transmission and distribution systems. RE-based power generation is proliferating in the power sectors worldwide. Participation of large numbers of market players, and massive penetration of RE-generation have created enough complexities and has made fundamental changes in the deregulated electricity market conditions. Small scale RE generating units have limited participation in the electricity markets due to the uncertainties. These units integrate with other fossil fuel plants and forms as Virtual Power Plants (VPPs). Increasing participation of RE based VPPs in the competitive electricity market, has brought out further complexity in market operation primarily in terms of its generation scheduling, economic profitability, etc. In this paper a two-stage stochastic programming approach for optimal scheduling of VPPs in the electricity markets is presented, along with modeling of uncertainties in the electricity market price, available level of stochastic renewable generation and the request for reverse deployment. These uncertainties are modeled using scenario bounds and are formulated using stochastic programming approach. Simulation results are carried out on 4-h planning horizon.

关键词： Electricity markets Renewable Energy Sources (RES) Virtual Power Plants stochastic programming Day Ahead Markets (DAM)

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Application of stochastic programming Technique to Solve Interval Quadratic programming Problem

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International Journal of Applied and Computational Mathematics 2021年第1期7卷 1-16页

作者： Kumari, Shaveta Srivastava, Saurabh Department of Mathematics Jaypee University of Information Technology Waknaghat Solan 173234 H.P. India

In this paper we developed a methodology to solve interval quadratic programming problem (IQPP). In the proposed work we convert interval quadratic programming problem (IQPP) into a non-interval stochastic programming problem. We transform the interval valued objective function into function of random variables and corresponding equality/inequality interval valued constraints into stochastic constraints using Gaussian distribution and 6σ-rule. Using this transformation method we tried to develop a solution of native interval quadratic programming problem. The methodologies are illustrated with numerical examples. © 2021, The Author(s), under exclusive licence to Springer Nature India Private Limited part of Springer Nature.

关键词： 6σ-rule gH-difference Interval quadratic programming problem (IQPP) stochastic programming

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Optimal sizing of an isolated microgrid with electric vehicles using stochastic programming

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2022年 32卷

作者： Soykan, Gurkan Er, Gulfem Canakoglu, Ethem Bahcesehir Univ Dept Energy Syst Engn Istanbul Turkey Bahcesehir Univ Dept Ind Engn Istanbul Turkey Amer Univ Middle East Coll Engn & Technol Kuwait Kuwait

An isolated microgrid system with renewable sources and energy storage systems ensures sustainable access to electricity and is especially suitable to handle inadequate electrical infrastructure in rural areas. The capacity configuration of the system depends on the uncertainties of renewable energy sources and home user behaviour, and the stochastic behaviour of electric vehicle (EV) users. This study determines the optimal configuration of the isolated microgrid that comprises wind and photovoltaic generation systems as well as batteries and EVs by proposing a two-stage stochastic programming -based multi-objective optimization problem where the objective functions are minimization of the life cycle cost and reliability. The problem is solved using the deterministic equivalent of the stochastic programming model to find the exact solution. The scenarios in the model are generated for one year period to consider the effects of uncertainties throughout seasons. Cases for different roles and configurations of electric vehicles are simulated and analysed to compare their impacts on the sizing of the microgrid. Also, the effects of charger size on the microgrid configuration are investigated through simulations. The simulation results show that a slight increase in LPSP values causes a drop in cost values of around 10%-20% in each EV scenario. The minimum cost is obtained when more efficient charge & discharge plugs for EVs are used. Furthermore, the robustness of the model results is observed through repeated experiments with different random parameters.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Capacity configuration Electric vehicle Microgrid Renewable energy stochastic programming

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A two-stage stochastic programming model for selective maintenance optimization

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年 223卷 1页

作者： Ghorbani, Milad Nourelfath, Mustapha Gendreau, Michel Interuniv Res Ctr Enterprise Networks Logist & Tr Montreal PQ Canada Laval Univ Fac Sci & Engn Dept Mech Engn Quebec City PQ Canada Polytech Sch Montreal Dept Math & Ind Engn Montreal PQ Canada

The problem of selective maintenance exists in many multicomponent systems carrying out an alternating sequence of missions, with scheduled breaks where only a limited number of components can be maintained due to time limit. In this paper, a stochastic programming approach is proposed for determining an optimum maintenance plan to minimize maintenance costs and expected failure costs, while maximizing the probability of successful accomplishment of the next mission under uncertainties in future operating conditions. Traditionally, future operating conditions that affect failure time distribution when calculating reliability in selective maintenance models were assumed as deterministic. In this study, future operating conditions are assumed to be uncertain. The system is subject to several uncertain condition scenarios of exposure, conditional, usage, stress, etc. Each scenario is modeled with its associated occurrence probability. The presented model is a two-stage stochastic mixed-integer nonlinear programming model with fixed recourse, where the first stage is associated with maintenance decisions made before uncertainties are revealed, and the second stage is modeled as a recourse function which is related to the occurrence probability of system failure. A numerical example of a series-parallel system is used to demonstrate the effectiveness of the suggested model.

关键词： stochastic programming Selective maintenance Uncertain future operating conditions System failure cost Imperfect maintenance

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A stochastic programming approach using multiple uncertainty sets for AC robust transmission expansion planning

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2022年 30卷

作者： Alnowibet, Khalid A. El-Meligy, Mohammed A. King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia King Saud Univ Coll Engn Ind Engn Dept POB 800 Riyadh 11421 Saudi Arabia

We present a transmission expansion planning model to find the most desirable configuration of transmission grid under the robust optimization. Unlike the common existing approaches where only one uncertainty set is considered, we propose to model multiple uncertainty sets each with its own probability. In this regard, we devise a method to construct the corresponding uncertainty sets from the historical data based on minimum covering circle. Moreover, we introduce a new criterion for uncertainty budget based on which the planner can efficiently control the conservative level of the optimization problem. Using this new criterion, the planner is capable to make a trade-off between the tractability and solution quality. The robust planning method is formulated as a tri-level min-max-min optimization model in which the classic column-and-constraint generation technique is used to solve the problem. The proposed strategy is implemented on the IEEE 118-bus power system to show the effectiveness of the model. The results indicate the short-term uncertainties in demand and intermittent renewable generation can be efficiently captured by the presented strategy. The comparable results also corroborate the superiority of the model over the single uncertainty-set-based models as well as over the pure stochastic models. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Conservative level Minimum covering circle Multiple uncertainty sets Robust optimization stochastic programming Transmission expansion planning

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A Nationwide Multi-Location Multi-Resource stochastic programming Based Energy Planning Framework

arXiv

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arXiv 2023年

作者： Al-Lawati, Razan A.H. Faiz, Tasnim Ibn Noor-E-Alam, Md Dept. of Mechanical and Industrial Engineering Northeastern University 360 Huntington Avenue BostonMA02115 United States

With the escalating global energy demands, nations face greater urgency in diversifying their electricity market portfolio with various energy sources. The efficient utilization of these fuel resources necessitates the integrated optimization of the entire energy portfolio. However, the inherent uncertainty of variable renewable energy generators renders traditional deterministic planning models ineffective. Conversely, expansive stochastic models that capture all the complexities of the power generation problem are often computationally intractable. This paper introduces a scalable energy planning framework tailored for policymakers, which is adaptable to diverse geopolitical contexts, including individual countries, regions, or coalitions formed through energy trade agreements. A two-stage stochastic programming approach, including a scenario-based Benders decomposition method, is employed to achieve computational efficiency. Scenarios for representing parameter uncertainty are developed using a k-means clustering algorithm. An illustrative case study is presented to show the practical application of the proposed framework and its ability to inform policymaking by identifying actionable cost-reduction strategies. The findings underscore the importance of promoting resource coordination and demonstrate the impact of increasing interchange and renewable energy capacity on overall gains. Notably, the study shows the superiority of stochastic optimization over deterministic models in energy planning. © 2023, CC BY.

关键词： stochastic programming

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On the Convergence of L-shaped Algorithms for Two-Stage stochastic programming

arXiv

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arXiv 2023年

作者： Birge, John R. Lu, Haihao Zhou, Baoyu The University of Chicago Booth School of Business United States University of Michigan Department of Industrial and Operations Engineering United States

In this paper, we design, analyze, and implement a variant of the two-loop L-shaped algorithms for solving two-stage stochastic programming problems that arise from important application areas including revenue management and power systems. We consider the setting in which it is intractable to compute exact objective function and (sub)gradient information, and instead, only estimates of objective function and (sub)gradient values are available. Under common assumptions including fixed recourse and bounded (sub)gradients, the algorithm generates a sequence of iterates that converge to a neighborhood of optimality, where the radius of the convergence neighborhood depends on the level of the inexactness of objective function estimates. The number of outer and inner iterations needed to find an approximate optimal iterate is provided. More specifically, suppose ϵ¯ is the error level of the function evaluation, we show that the L-shaped method with constant step-size can find an ϵ¯-optimal solution within O(ϵ¯13) total iterations. Suppose the optimal objective is known, we propose a Polyak-type step-size that can find an ϵ¯-optimal solution within O(ϵ¯12) total iterations. It turns out that the rate O(ϵ¯12) also matches with the complexity lower bound for first-order non-smooth optimization even in the exact situation. Furthermore, assuming the problem satisfies the sharpness condition, which holds when the support of the scenario is finite, then we can improve the complexity of the algorithm with constant step-size to O(1ϵ¯) and Polyak-type step-size to O(log (1ϵ¯)). Finally, we show a sample complexity result for the algorithm with a Polyak-type step-size policy that can be extended to analyze other situations. We also present a numerical study that verifies our theoretical results and demonstrates the superior empirical performance of our proposed algorithms over classic solvers. Copyright © 2023, The Authors. All rights reserved.

关键词： stochastic programming

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A strategic bi-level framework for wind power investment considering grid strength index: A stochastic programming approach

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2022年 31卷

作者： Alnowibet, Khalid A. King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia

Increasing the penetration level of renewable energy sources (RESs) has been a key energy policy for international societies seeking to reduce carbon dioxide emissions. However, the growing integration of wind power into the electric network would alter the system characteristics since the inverter-based RESs weaken the grid strength. Therefore, it becomes necessary to include the system strength in the wind power investment problem (WPIP). In this regard and with being subject to wind production and electric load uncertainty, this paper presents a stochastic programming approach for WPIP considering the grid strength index. For this purpose, a bi-level model is presented whose upper level seeks to maximize the profit of a private investor while respecting the system strength requirement. In the lower level, the market-clearing problem is formulated under different operating conditions. Unlike the common approaches, which employ a DC model to represent the electric network, an approximated yet efficient AC model is utilized. Karush-Kuhn-Tucker (KKT) conditions are employed to transform the stochastic bi-level problem into a bilinear single-level problem which is solved using Konno's cutting plane algorithm. IEEE 118-bus system is used to exhibit the efficiency of the proposed model for wind power investment. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Bi-level model KKT conditions stochastic programming System strength Wind power investment Mixed-integer linear/quadratic programming

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Optimising the implementation of BIM: A 2-stage stochastic programming approach

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AUTOMATION IN CONSTRUCTION 2022年第0期136卷 104170-104170页

作者： Hong, Ying Hammad, Ahmed W. A. Nezhad, Ali Akbar Griffith Univ Sch Engn & Built Environm Gold Coast Qld Australia Univ New South Wales UNSW Sydney Sch Built Environm Sydney NSW Australia Boral Sydney NSW Australia

A challenge faced by contractors attempting to implement Building Information Modelling (BIM) is deciding on the functions that BIM needs to be implemented for, along with the associated level of development (LOD). This study proposes a 2-stage stochastic optimisation model for planning the BIM implementation at the strategic level for organisations. The proposed approach accounts for learning curve effects associated with acquiring BIM skills and the uncertain demand for BIM usage due to other emerging technologies. The results indicated that BIM implementation can reduce the technology costs by up to 200%, in contrast to not implementing BIM;however, organisations are likely to experience a 5% cost increment at the beginning until 50 learning cycles. The proposed method is designed to assist decision-makers on whether BIM should be implemented at their firms, and if so, what functions to implement and what LOD to associate with developed models.

关键词： BIM implementation stochastic programming BIM cost Mathematical optimisation

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Integrating stochastic programming and reliability in the optimal synthesis of chemical processes

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COMPUTERS & CHEMICAL ENGINEERING 2022年 157卷 107616-107616页

作者： Chen, Ying Ye, Yixin Yuan, Zhihong Grossmann, Ignacio E. Chen, Bingzhen Tsinghua Univ Dept Chem Engn Beijing 100084 Peoples R China SINOPEC Res Inst Petr Proc Beijing 100083 Peoples R China Carnegie Mellon Univ Ctr Adv Proc Decis Making Dept Chem Engn Pittsburgh PA 15213 USA

Plant availability and operating uncertainties are critical considerations for the design and operation of chemical processes as they directly impact service level and economic performance. This paper proposes a two-stage stochastic programming GDP (Generalized Disjunctive programming) model with reliability constraints to deal with both the exogenous and endogenous uncertainties in process synthesis, where the reliability model is incorporated into the flowsheet superstructure optimization. The proposed stochastic programming model anticipates the market uncertainties through scenarios for selecting the optimal flowsheet topology, equipment sizes and operating conditions, while considering the impact of selecting parallel units for improving plant availability. An improved logic-based outer approximation algorithm is applied to solve the resulting hybrid GDP model, which effectively avoids numerical difficulties with zero flows and provides high quality design solutions. The applicability of the proposed modeling framework and the efficiency of solution strategy are illustrated with two well-known conceptual design case studies: methanol synthesis process and toluene hydrodealkylation process. The model, which integrates reliability (endogenous uncertainty) and exogenous uncertainty, shows the best economic performance with the increasing operational flexibility and plant availability. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Reliability-based superstructure optimization stochastic programming Endogenous and exogenous uncertainties Logic-based outer approximation algorithm

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