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A novel two-stage stochastic programming model for uncertainty characterization in short-term optimal strategy for a distribution company

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ENERGY 2016年第Part1期117卷 1-9页

作者： Ahmadi, Abdollah Charwand, Mansour Siano, Pierluigi Nezhad, Ali Esmaeel Sarno, Debora Gitizadeh, Mohsen Raeisi, Fatima Univ New South Wales Australian Energy Res Inst Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Univ Salerno Dept Ind Engn Fisciano Italy Islamic Azad Univ Marvdasht Branch Dept Elect Engn Marvdasht Iran Hormozgan Univ Dept Management Bandarabass Iran

In order to supply the demands of the end users in a competitive market, a distribution company purchases energy from the wholesale market while other options would be in access in the case of possessing distributed generation units and interruptible loads. In this regard, this study presents a two stage stochastic programming model for a distribution company energy acquisition market model to manage the involvement of different electric energy resources characterized by uncertainties with the minimum cost. In particular, the distribution company operations planning over a day-ahead horizon is modeled as a stochastic mathematical optimization, with the objective of minimizing costs. By this, distribution company decisions on grid purchase, owned distributed generation units and interruptible load scheduling are determined. Then, these decisions are considered as boundary constraints to a second step, which deals with distribution company's operations in the hour-ahead market with the objective of minimizing the short-term cost. The uncertainties in spot market prices and wind speed are modeled by means of probability distribution functions of their forecast errors and the roulette wheel mechanism and lattice Monte Carlo simulation are used to generate scenarios. Numerical results show the capability of the proposed method. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Distribution system Distributed generation Interruptible load stochastic programming

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Evaluating the required scenario set size for stochastic programming in forest management planning: incorporating inventory and growth model uncertainty

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CANADIAN JOURNAL OF FOREST RESEARCH 2016年第3期46卷 340-347页

作者： Eyvindson, Kyle Kangas, Annika Univ Helsinki Dept Forest Sci Latokartanonkaari 7 POB 27 FIN-00014 Helsinki Finland

Developing a plan of action for the future use of forest resources requires a way to predict the development of the forest through time. These predictions require the use of inventory data and growth models that contain a large number of uncertainties. These uncertainties impact the quality of the predictions, and if not accounted for, they can lead to the selection of a suboptimal management plan. To account for and manage the uncertainties and associated risk, we have explored the use of stochastic programming. stochastic programming can integrate uncertainty into the optimization process by solving the problem for a large number of potential scenarios of the forests future development. The selection of an appropriately sized set of scenarios involves a trade-off between tractability issues and problem representation issues. In this paper, an analysis of the trade-offs is conducted. Two cases are studied, one in which only the uncertainty of the inventory data is included and a second in which both growth model and inventory data uncertainties are included. The impact of increasing the number of scenarios on the problem representation is examined through a simple even-flow problem.

关键词： stochastic programming sample average approximation even-flow forestry risk uncertainty

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The value of stochastic programming in day-ahead and intra-day generation unit commitment

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ENERGY 2016年 101卷 592-605页

作者： Schulze, Tim McKinnon, Ken Univ Edinburgh Sch Math Peter Guthrie Tait Rd Edinburgh EH9 3FD Midlothian Scotland

The recent expansion of renewable energy supplies has prompted the development of a variety of efficient stochastic optimization models and solution techniques for hydro-thermal scheduling. However, little has been published about the added value of stochastic models over deterministic ones. In the context of day-ahead and intra-day unit commitment under wind uncertainty, we compare two-stage and multi-stage stochastic models to deterministic ones and quantify their added value. We present a modification of the WILMAR scenario generation technique designed to match the properties of the errors in our wind forecasts, and show that this is needed to make the stochastic approach worthwhile. Our evaluation is done in a rolling horizon fashion over the course of two years, using a 2020 central scheduling model based on the British power system, with transmission constraints and a detailed model of pump storage operation and system-wide reserve and response provision. We show that in day-ahead scheduling the stochastic approach saves 0.3% of generation costs compared to the best deterministic approach, but the savings are less in intra-day scheduling. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： stochastic programming Unit commitment Hydro-thermal scheduling Wind forecast uncertainty

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stochastic Nonlinear programming Based on Uncertainty Analysis for DNAPL-Contaminated Aquifer Remediation Strategy Optimization

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2018年第1期144卷

作者： Hou, Zeyu Lu, Wenxi Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun 130021 Jilin Peoples R China Jilin Univ Coll Environm & Resources Changchun 130021 Jilin Peoples R China

Surrogate-based simulation-optimization techniques are widely applied in developing optimal remediation strategies that increase the efficiency and reduce the cost of surfactant-enhanced aquifer remediation (SEAR) when clearing dense nonaqueous phase liquids (DNAPLs). In such processes, there are many uncertainty factors that may greatly affect the optimization outcome of a SEAR strategy selection. However, previous research on this subject rarely incorporates an uncertainty analysis. This paper presents an uncertainty analysis of both the simulation model and the ensemble surrogate model used to optimize SEAR strategies. Set pair analysis (SPA) and kriging methods were used to build the ensemble surrogate model. The probability distributions of the residuals of the outputs between the ensemble surrogate model and the simulation model, run on 100 testing samples, were analyzed to ascertain the uncertainty of the surrogate model, which was found to be less than 1.5%. The uncertainty of the simulation model was derived by combining a Monte Carlo random simulation with the Sobol' global sensitivity analysis. Finally, a stochastic nonlinear programming model was established to compute the optimal remediation strategies for the remediation target under different confidence levels. This research will allow decision makers to more confidently select the optimal remediation strategy for a given scenario by balancing the reliability of model prediction with the cost of the remediation strategy according to the demands of the project.

关键词： Dense nonaqueous phase liquids (DNAPLs) Simulation-optimization Ensemble surrogate model Uncertainty analysis stochastic programming

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A comment on "computational complexity of stochastic programming problems"

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MATHEMATICAL programming 2016年第1-2期159卷 557-569页

作者： Hanasusanto, Grani A. Kuhn, Daniel Wiesemann, Wolfram Ecole Polytech Fed Lausanne Risk Analyt & Optimizat Chair Lausanne Switzerland Imperial Coll London Imperial Coll Business Sch London England

Although stochastic programming problems were always believed to be computationally challenging, this perception has only recently received a theoretical justification by the seminal work of Dyer and Stougie (Math Program A 106(3):423-432, 2006). Amongst others, that paper argues that linear two-stage stochastic programs with fixed recourse are #P-hard even if the random problem data is governed by independent uniform distributions. We show that Dyer and Stougie's proof is not correct, and we offer a correction which establishes the stronger result that even the approximate solution of such problems is #P-hard for a sufficiently high accuracy. We also provide new results which indicate that linear two-stage stochastic programs with random recourse seem even more challenging to solve.

关键词： stochastic programming Complexity theory Two-stage problems

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Evacuation planning for disaster responses: A stochastic programming framework

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2016年第Aug.期69卷 150-172页

作者： Wang, Li Yang, Lixing Gao, Ziyou Li, Shukai Zhou, Xuesong Beijing fiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Arizona State Univ Sch Sustainable Engn & Built Environm Tempe AZ 85287 USA

Some disasters such as earthquakes, floods and hurricanes may result in evacuation for people in an affected area. This paper focuses on finding the a priori evacuation plans by considering side constraints and scenario-based stochastic link travel times and capacities. Hence a stochastic programming framework is developed so as to provide a reorganization of the traffic routing for a disaster response. Considering the different preferences of decision-makers, three evaluation criteria are introduced to formulate the objective function. Crisp linear equivalents for different evacuation strategies are further deduced to simplify solution methodologies. A heuristic algorithm combining the Lagrangian relaxation-based approach with K-shortest path techniques is designed to solve the expected disutility model. The experimental results indicate that the algorithm can solve large-scale instances for the problem of interest efficiently and effectively. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Evacuation stochastic programming Side constraint Relaxation-based heuristic

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Minimum cardinality non-anticipativity constraint sets for multistage stochastic programming

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MATHEMATICAL programming 2016年第1期157卷 69-93页

作者： Boland, Natashia Dumitrescu, Irina Froyland, Gary Kalinowski, Thomas Univ Newcastle Univ Dr Callaghan NSW 2308 Australia IBM Res Australia Carlton Vic Australia Univ Melbourne Melbourne Vic Australia Univ New S Wales Sydney NSW Australia Univ Newcastle Callaghan NSW 2308 Australia Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

We consider multistage stochastic programs, in which decisions can adapt over time, (i.e., at each stage), in response to observation of one or more random variables (uncertain parameters). The case that the time at which each observation occurs is decision-dependent, known as stochastic programming with endogeneous observation of uncertainty, presents particular challenges in handling non-anticipativity. Although such stochastic programs can be tackled by using binary variables to model the time at which each endogenous uncertain parameter is observed, the consequent conditional non-anticipativity constraints form a very large class, with cardinality in the order of the square of the number of scenarios. However, depending on the properties of the set of scenarios considered, only very few of these constraints may be required for validity of the model. Here we characterize minimal sufficient sets of non-anticipativity constraints, and prove that their matroid structure enables sets of minimum cardinality to be found efficiently, under general conditions on the structure of the scenario set.

关键词： stochastic programming Endogeneous uncertainty Multistage stochastic programming

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Multi-objective dynamic cell formation problem: A stochastic programming approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年第0期98卷 323-332页

作者： Zohrevand, A. M. Rafiei, H. Zohrevand, A. H. Univ Tehran Coll Engn Sch Ind & Syst Engn POB 11155-4563 Tehran Iran Univ Tehran Fac Engn Coll Farabi Dept Ind Engn Tehran 14174 Iran Sharif Univ Technol Grad Sch Management & Econ Tehran Iran

This paper addresses dynamic cell formation problem (DCFP) which has been explored vastly for several years. Although a considerable body of literature in this filed, two remarkable aspects have been significantly ignored so far, as uncertainty and human-related issues. In order to compensate such a shortage, this paper develops a bi-objective stochastic model. The first objective function of the developed model seeks to minimize total cost of machine procurement, machine relocation, inter-cell moves, overtime utilization, worker hiring/laying-off, and worker moves between cells;while the second objective function maximizes labor utilization of the cellular manufacturing system. In the developed model, labor utilization, worker overtime cost, worker hiring/laying off, and worker cell assignment are considered to tackle some of the most notable human-related issues in DCFP. Considering the complexity of the proposed model, a hybrid Tabu Search-Genetic Algorithm (TS-GA) is proposed whose strength is validated to obtain optimal and near optimal solutions through conducted experimental results. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Dynamic cell formation Fuzzy sets theory Human-related issues Multi-objective optimization stochastic programming

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A risk-based interactive multi-stage stochastic programming approach for water resources planning under dual uncertainties

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ADVANCES IN WATER RESOURCES 2016年第0期94卷 217-230页

作者： Wang, Y. Y. Huang, G. H. Wang, S. Li, W. Guan, P. B. North China Elect Power Univ Environm Res Acad Beijing 102206 Peoples R China Univ Regina Fac Engn & Appl Sci Regina SK S4S 0A2 Canada

In this study, a risk-based interactive multi-stage stochastic programming (RIMSP) approach is proposed through incorporating the fractile criterion method and chance-constrained programming within a multistage decision-making framework. RIMSP is able to deal with dual uncertainties expressed as random boundary intervals that exist in the objective function and constraints. Moreover, RIMSP is capable of reflecting dynamics of uncertainties, as well as the trade-off between the total net benefit and the associated risk. A water allocation problem is used to illustrate applicability of the proposed methodology. A set of decision alternatives with different combinations of risk levels applied to the objective function and constraints can be generated for planning the water resources allocation system. The results can help decision makers examine potential interactions between risks related to the stochastic objective function and constraints. Furthermore, a number of solutions can be obtained under different water policy scenarios, which are useful for decision makers to formulate an appropriate policy under uncertainty. The performance of RIMSP is analyzed and compared with an inexact multi-stage stochastic programming (IMSP) method. Results of comparison experiment indicate that RIMSP is able to provide more robust water management alternatives with less system risks in comparison with IMSP. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Multi-stage stochastic programming Dual uncertainties Interactive Random interval Risk analysis Water resources

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A Novel stochastic-programming-Based Energy Management System to Promote Self-Consumption in Industrial Processes

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ENERGIES 2018年第2期11卷 441页

作者： Barrientos, Jorge David Lopez, Jose Valencia, Felipe Univ Antioquia UDEA Fac Engn SISTEMIC Calle 70 52-21 Medellin 1226 Colombia Univ Chile Fac Math & Phys Sci Energy Ctr Santiago 8370451 Chile

The introduction of non-conventional energy sources (NCES) to industrial processes is a viable alternative to reducing the energy consumed from the grid. However, a robust coordination of the local energy resources with the power imported from the distribution grid is still an open issue, especially in countries that do not allow selling energy surpluses to the main grid. In this paper, we propose a stochastic-programming-based energy management system (EMS) focused on self-consumption that provides robustness to both sudden NCES or load variations, while preventing power injection to the main grid. The approach is based on a finite number of scenarios that combines a deterministic structure based on spectral analysis and a stochastic model that represents variability. The parameters to generate these scenarios are updated when new information arrives. We tested the proposed approach with data from a copper extraction mining process. It was compared to a traditional EMS with perfect prediction, i.e., a best case scenario. Test results show that the proposed EMS is comparable to the EMS with perfect prediction in terms of energy imported from the grid (slightly higher), but with less power changes in the distribution side and enhanced dynamic response to transients of wind power and load. This improvement is achieved with a non-significant computational time overload.

关键词： non-conventional energy sources energy management system stochastic programming industrial processes

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