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stochastic programming OF TIME-CONSISTENT EXTENSIONS OF AVaR

SIAM JOURNAL ON OPTIMIZATION 2014年第3期24卷 993-1010页

作者： Densing, M. Paul Scherrer Inst Energy Econ Grp CH-5232 Villigen Switzerland

We discuss multiperiod stochastic programming formulations of time-consistent extensions of average value-at-risk (AVaR);AVaR measures the risk of a random financial value. Multiperiod risk measures that are recursively defined over time are known to be time consistent. For a multiperiod extension of AVaR for stochastic value processes, we reformulate the recursion as a linear stochastic program, such that the extension can be applied in multiperiod mean-risk optimization. In the special case of risk measurement for a final random value at a time horizon, we give a lower bound in terms of AVaR.

关键词： multistage stochastic linear programming average value-at-risk conditional value-at-risk time consistency mean-risk optimization

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Dispatch planning using newsvendor dual problems and occupation times: Application to hydropower

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2013年第2期228卷 321-330页

作者： Densing, M. Paul Scherrer Inst Energy Econ Grp CH-5232 Villigen Switzerland

We consider price-driven dispatch planning under price uncertainty: A storable commodity is optimally sold and purchased over time. First, we consider models where the storage level is constrained in expectation. The dual of the corresponding optimization problem is related to the newsvendor problem. Exact solutions of bang-bang type are given. The second methodology is for high-frequency dispatch decisions in multistage stochastic programming models: To overcome the curse of dimensionality, prices are modeled by occupation times at price levels. In a case study, we consider a pumped-storage hydropower plant: Numerical solutions are given, which have similar patterns as for the first, exactly solvable problems. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Dispatch planning Bang-bang control Newsvendor problem Occupation time multistage stochastic linear programming Hydropower optimization

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Coping with Risk Factors in Energy System Transformations - Climate Change Impacts on Nuclear Power Plant Availability in Europe 20

Coping with Risk Factors in Energy System Transformations - ...

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20th International Conference on the European Energy Market (EEM)

作者： Schmitz, Richard Frischmuth, Felix Braun, Martin Haertel, Philipp Fraunhofer IEE Energy Econ & Syst Anal Kassel Germany Univ Kassel Energy Management & Power Syst Operat Kassel Germany Fraunhofer IEE Grid Planning & Grid Operat Kassel Germany

ISBN: (纸本)9798350381757;9798350381740

Nuclear power is currently being discussed in many places as it could contribute to a low-carbon energy transformation. However, the projected effects of climate change, including high cooling water temperatures and intensifying droughts, may increasingly lead to future problems at nuclear power plants. This work establishes and quantifies the relationship between the probability of extreme weather situations leading to lower nuclear availability in certain countries and the expected costs of transforming the energy system. We analyse changes in the recourse decisions, including shifted technology investments and alterations in system operation. Given potentially larger nuclear outages, we expose more robust transformation pathways to a climate-neutral system and show how the new modelling and optimisation framework EMPRISE can provide better decision support. We run the analysis for multiple historical meteorological years and combine several of those in a comprehensive scenario tree. A higher probability for reduced nuclear availability, e.g. 50% instead of 5% within one meteorological year, could increase the overall costs of the energy system by only up to 0.4 %, whereas the choice of the meteorological year itself, e.g. 2012 instead of 2010, can increase these costs by up to 4 %.

关键词： multistage stochastic linear programming Integrated Energy System Planning Nuclear Power Climate Change Climate Neutrality

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stochastic planning of energy system transformation pathways under uncertain industry demands 20

Stochastic planning of energy system transformation pathways...

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20th International Conference on the European Energy Market (EEM)

作者： Frischmuth, Felix Schmitz, Richard Braun, Martin Haertel, Philipp Fraunhofer IEE Energy Econ & Syst Anal Kassel Germany Univ Kassel Energy Management & Power Syst Operat Kassel Germany Fraunhofer IEE Grid Planning & Grid Operat Kassel Germany

ISBN: (纸本)9798350381757;9798350381740

The transition of industrial sectors to achieve climate neutrality is imperative for effectively meeting the energy and climate policy objectives. Transforming these diverse sectors faces uncertainties, resulting in diverging end-use demands and effects on optimal transformation pathways. We use a scalable stochastic planning framework for optimising robust transformation pathways in a two- and multi-stage setup. By analysing changes in recourse decisions, including shifts in technology investments and modifications in system operation, such as imports of renewable hydrogen, we enhance decision support when facing the considered industry sector uncertainties. Our findings indicate that robust investments lead to higher lifetime system costs. Industrial demand variations affect electrolyser and storage capacities more than other capacities in the system. Moreover, weather uncertainty has a larger impact on system costs than industry uncertainties.

关键词： multistage stochastic linear programming Integrated Energy System Planning Industry demand Uncertainty Climate Neutrality

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stochastic Decomposition Algorithms for Risk-Averse multistage stochastic linear Programs and Applications

Stochastic Decomposition Algorithms for Risk-Averse Multista...

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作者： Parab, Prasad Texas A&M University

学位级别：Ph.D., Doctor of Philosophy

Mean-risk stochastic linear programming provides a framework for controlling cost variability in problems involving sequential decision making under uncertainty. It goes beyond the classical expected value framework by including risk measures in the objective function and aims at controlling cost variability in the solution. This allows for modeling risk averseness in variety of applications such as long-term financial planning, scheduling of power systems, supply chain management and portfolio optimization. In this dissertation, we derive stochastic decomposition algorithms for solving mean-risk two-stage stochastic linear programs (MR-SLP) and mean-risk multistage stochastic linear programs (MR-MSLP) with deviation and quantile risk measures. stochastic decomposition(SD) is a type of internal sampling method and at every iteration of algorithm only one linear problem is solved for approximating the recourse function. A salient feature of the SD algorithm is that the number of samples is not fixed a priori, which allows to obtain good candidate solutions early in the procedure. We also report on a computational study to evaluate the empirical performance of the SD algorithms for MR-SLP and MR-MSLP with expected excess (EE), quantile deviation (QDEV) and conditional value-at-risk (CVaR) as risk measures. The goal of the study was to analyze for a given instance how SD algorithm performs across different levels of risk, investigate the effect of different risk measures and understand when it is appropriate to use the risk-averse approach. For MR-SLP, the SD algorithm is implemented and applied to standard test instances and it shows that the risk measure QDEV has more impact on expected cost and the cost associated with extreme scenarios compared to the impact of CVaR and EE. We also observed that for higher target values, the risk measure EE becomes effective only for a relatively small number of scenarios and has little to no-effect on the optimal solution for smal

关键词： multistage stochastic linear programming Mean-risk Two-stage stochastic linear programming Expected excess Conditional value at risk Quantile deviation stochastic decomposition Long term hydro-thermal scheduling

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