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Electricity swing option pricing by stochastic bilevel optimization: A survey and new approaches

Raimund M. Kovacevic, Georg Ch. Pflug
2014 European Journal of Operational Research  
We demonstrate how the pricing problem for electricity swing options can be considered as a stochastic bilevel program with asymmetric information.  ...  We also discuss some methods for nding numerical solutions of stochastic bilevel problems with a special emphasis on methods using duality gap penalizations.  ...  Stochastic bilevel problems A bilevel problem with random parameters is called a stochastic bilevel problem.  ... 
doi:10.1016/j.ejor.2013.12.029 fatcat:4rks2tnpc5fh5pveovymqtx7k4

Optimal Control of Conditional Value-at-Risk in Continuous Time [article]

Christopher W. Miller, Insoon Yang
2017 arXiv   pre-print
To resolve this challenge, we convert to an equivalent bilevel optimization problem in which the inner optimization problem is standard stochastic control.  ...  To broaden the applicability of the proposed algorithm, we propose convergent approximation schemes in cases where our key assumptions do not hold and characterize relevant suboptimality bounds.  ...  Our method can handle control problems with criteria including various combinations of (2.3), (2.4) , and (2.5).  ... 
arXiv:1512.05015v3 fatcat:gzhp2nbj3ndo7h7pcrxcnfqn34

Optimal Control of Conditional Value-at-Risk in Continuous Time

Christopher W. Miller, Insoon Yang
2017 SIAM Journal of Control and Optimization  
To resolve this challenge, we convert to an equivalent bilevel optimization problem in which the inner optimization problem is standard stochastic control.  ...  To broaden the applicability of the proposed algorithm, we propose convergent approximation schemes in cases where our key assumptions do not hold and characterize relevant suboptimality bounds.  ...  Our method can handle control problems with criteria including various combinations of (2.3), (2.4) , and (2.5).  ... 
doi:10.1137/16m1058492 fatcat:6sbskgzbkfewvojuig2rdtgzjm

Mathematical Modeling for Resources and Environmental Systems

Y. P. Li, G. H. Huang, S. L. Nie, B. Chen, X. S. Qin
2013 Mathematical Problems in Engineering  
bootstrap sample, such that the sampling distribution of design value was constructed; based on the sampling distribution, the uncertainty of quantile estimation could be quantified.  ...  The paper "Bilevel multiobjective programming applied to water resources allocation" by S. Q.  ...  bootstrap sample, such that the sampling distribution of design value was constructed; based on the sampling distribution, the uncertainty of quantile estimation could be quantified.  ... 
doi:10.1155/2013/674316 fatcat:n2bzvudydba7rfivnk6fr4hvb4

Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning [article]

Yufan Zhang, Honglin Wen, Qiuwei Wu, Qian Ai
2022 arXiv   pre-print
Compared with offline-trained methods, it obtains PIs with better quality and is more robust against concept drift.  ...  It relies on the online learning ability of reinforcement learning to integrate the two online tasks, i.e., the adaptive selection of probability proportion pairs and quantile predictions, both of which  ...  ACKNOWLEDGEMENT This work was supported by the National Natural Science Foundation of China (U1866206).  ... 
arXiv:2205.08698v1 fatcat:6t765kbvibe3lgu3q6miterzqi

Microgrid management with weather-based forecasting of energy generation, consumption and prices [article]

Jonathan Dumas
2021 arXiv   pre-print
Most of the generation technologies based on renewable sources are non-dispatchable, and their production is stochastic and complex to predict in advance.  ...  (2) How to make decisions with uncertainty using probabilistic forecasts?  ...  programming and approaches to handle uncertainty in the parameters with the stochastic programming and robust approach.  ... 
arXiv:2107.01034v7 fatcat:c5a7d2w2uzez3par3q6gs3elaq

A review of short‐term wind power probabilistic forecasting and a taxonomy focused on input data

Ioannis K. Bazionis, Panagiotis A. Karafotis, Pavlos S. Georgilakis
2021 IET Renewable Power Generation  
The short-term concept of forecasts is analysed in detail, along with the case studies and examples proposed by the reviewed literature.  ...  The improvement of the accuracy and efficiency of probabilistic forecasting models has been in the centre of attention of researchers in recent years, since the need to further comprehend and efficiently  ...  ACKNOWLEDGEMENTS This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH  ... 
doi:10.1049/rpg2.12330 fatcat:wk2piydl4fcrvd22jq5qsc7aem

Modeling the shelter site location problem using chance constraints: A case study for Istanbul

Ömer Burak Kınay, Bahar Yetis Kara, Francisco Saldanha-da-Gama, Isabel Correia
2018 European Journal of Operational Research  
In particular, we propose a maxmin probabilistic programming model that includes two types of probabilistic constraints: one concerning the utilization rate of the selected shelters and the other concerning  ...  By invoking the central limit theorem we are able to obtain an optimization model with a single set of non-linear constraints which, nonetheless, can be approximated using a family of piecewise linear  ...  Acknowledgments This research has been partially supported by the Turkish Academy of Sciences and by the Portuguese Science Foundation , projects UID/MAT/04561/2013 (CMAF-CIO/FCUL) and UID/MAT/00297/2013  ... 
doi:10.1016/j.ejor.2018.03.006 fatcat:xy7hhccnfzbwfa7gs6og75k5va

Challenges in the Application of Mathematical Programming in the Enterprise-wide Optimization of Process Industries

Ignacio E. Grossmann
2014 Теоретические основы химической технологии  
in the information societyShunei Norikumo Robustness in stochastic programs with the first order stochastic dominance and/or probabilistic constraints Milos Kopa, Jitka Dupacova 2 -Chance constrained  ...  problem, where the technology coefficients in the stochastic constraints are normally distributed random variables Andras Prekopa, Tamas Szantai 2 -Solution of probabilistic constrained stochastic programming  ...  OEE simulation Werner Schroeder, Markus Gram -A Two Stage Solution Procedure of Stochastic Programming Problem for Production Planning with Advance Demand Information Nobuyuki Ueno, Koji Okuhara, Takashi  ... 
doi:10.7868/s0040357114050054 fatcat:kli7aeuyxbaplfhup2t6nmuyxq

Challenges in the application of mathematical programming in the enterprise-wide optimization of process industries

Ignacio E. Grossmann
2014 Theoretical foundations of chemical engineering  
in the information societyShunei Norikumo Robustness in stochastic programs with the first order stochastic dominance and/or probabilistic constraints Milos Kopa, Jitka Dupacova 2 -Chance constrained  ...  problem, where the technology coefficients in the stochastic constraints are normally distributed random variables Andras Prekopa, Tamas Szantai 2 -Solution of probabilistic constrained stochastic programming  ...  OEE simulation Werner Schroeder, Markus Gram -A Two Stage Solution Procedure of Stochastic Programming Problem for Production Planning with Advance Demand Information Nobuyuki Ueno, Koji Okuhara, Takashi  ... 
doi:10.1134/s0040579514050182 fatcat:3ra5yqooyzgmroo5qccbnauftm

Optimization of real asset portfolio using a coherent risk measure: application to oil and energy industries

Sergio Vitor de Barros Bruno, Claudia Sagastizábal
2010 Optimization and Engineering  
We consider the problem of optimally determining an investment portfolio for an energy company owning a network of gas pipelines, and in charge of purchasing, selling and distributing gas.  ...  As a consequence, state of the art planning models are stochastic, and usually consider some kind of risk measure as well as financial instruments to hedge the investment portfolio.  ...  Fig. 5 5 CPU time of equivalent deterministic problem versus number of scenarios. Fig. 6 6 Asset allocation with the deterministic and stochastic models.  ... 
doi:10.1007/s11081-010-9127-x fatcat:7tyrior32zcwzi4zejfw7ilc64

On Exponential Utility and Conditional Value-at-Risk as Risk-Averse Performance Criteria [article]

Kevin M. Smith, Margaret P. Chapman
2021 arXiv   pre-print
For EU, the transformation is φ(y) = exp(-θ/2y), and under certain conditions, the quantity φ^-1(E(φ(Y))) can be approximated by a linear combination of the mean and variance of Y.  ...  Here, we study the applications of risk-averse functionals to controller synthesis and safety analysis through the development of numerical examples, with emphasis on EU and CVaR.  ...  ACKNOWLEDGMENTS The authors thank Laurent Lessard, Claire Tomlin, Marco Pavone, and Chuanning Wei for fruitful discussions.  ... 
arXiv:2108.01771v1 fatcat:cq7xitkkqbg3lha76pg74pw4l4

An approach to the distributionally robust shortest path problem [article]

Sergey S. Ketkov, Oleg A. Prokopyev, Evgenii P. Burashnikov
2020 arXiv   pre-print
Under some additional assumptions the resulting distributionally robust shortest path problem (DRSPP) admits equivalent robust and mixed-integer programming (MIP) reformulations.  ...  The ambiguity set is formed by all distributions that satisfy prescribed linear first-order moment constraints with respect to subsets of arcs and individual probability constraints with respect to particular  ...  The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (Sections 1-4) and RFBR grant 20-37-90060 (Sections 5-6).  ... 
arXiv:1910.08744v3 fatcat:ewhvgxddxbgwdd3hten37rk4fe

The Vessel Schedule Recovery Problem (VSRP) – A MIP model for handling disruptions in liner shipping

Berit D. Brouer, Jakob Dirksen, David Pisinger, Christian E.M. Plum, Bo Vaaben
2013 European Journal of Operational Research  
of planning and vehicle-routing models, d) optimization of oilfield infrastructures under uncertainty that lead to multistage stochastic programming problems with endogenous uncertain parameters.  ...  We illustrate the application of these ideas in four major problems: a) integration of planning and scheduling in batch processes that lead to large-scale mixed-integer linear programs, b) optimization  ...  to solve a stochastic program with individual probabilistic constraints.  ... 
doi:10.1016/j.ejor.2012.08.016 fatcat:c27kagfnxnhjfbil2rydhjhomm

Energy and reserve scheduling under correlated nodal demand uncertainty: An adjustable robust optimization approach

Alexandre Moreira, Alexandre Street, Jose M. Arroyo
2014 2014 Power Systems Computation Conference  
The resulting problem is formulated as a trilevel program and solved by means of Benders decomposition.  ...  In this model, up-and down-spinning reserves provided by generators are endogenously defined as a result of the optimization problem.  ...  ACKNOWLEDGMENT The authors would like to thank FICO (Xpress-MP developer) for the academic partnership program with the Electrical Engineering Department of the Pontifical Catholic University of Rio de  ... 
doi:10.1109/pscc.2014.7038415 dblp:conf/pscc/MoreiraSA14 fatcat:yriybdcb4zddvcgoh2pklm4y4y
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