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14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Bertrand, Gilles Papavasiliou, Anthony CORE UCLouvain Louvain La Neuve Belgium

ISBN: (纸本)9781665435970

The recent integration of renewable resources in electricity markets has increased the need for producers to correct their trading position close to real time in order to avoid volatile real-time prices. The last market to close before delivery is the Continuous Intraday Market. Therefore, this market is an interesting outlet for renewable units that aim at covering their forecast errors. As a starting point for tackling this problem, we characterize an optimal policy for trading a fixed quantity in a simplified market model. We use this analytical solution as a basis for developing an Approximate dynamic programming algorithm and an alternative stochastic dual dynamic programming that can trade under a more realistic set of assumptions.

关键词： stochastic optimal control continuous intraday market approximate dynamic programming stochastic dual dynamic programming

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Evaluation of scenario reduction methods for stochastic inflow in hydro scheduling models

Evaluation of scenario reduction methods for stochastic infl...

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PowerTech, 2015 IEEE Eindhoven

作者： Larsen, Camilla Thorrud Doorman, Gerard L. Mo, Birger NTNU Dept Elect Power Engn Trondheim Norway SINTEF Energy Res Ctr Trondheim Norway

ISBN: (纸本)9781479976959

The long-term hydropower scheduling problem is inherently stochastic due to uncertainty in future reservoir inflow. We use stochastic dual dynamic programming (SDDP) to solve this problem. This work evaluate and compare three scenario reduction methods used to construct a multistage scenario tree which represents the underlying stochastic inflow process in the SDDP model. A case study is carried out to numerically assess the performance of the different scenario reduction methods. The performance is measured using out-of-sample simulation, simulating the solution strategies obtain with the various scenario models on an exogenously given set of inflow scenarios. Our results show that the choice of scenario reduction method impacts the solution to a hydropower operation planning problem substantially.

关键词： Hydropower scenario reduction stochastic dual dynamic programming stochastic inflow

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On variance reduction of mean-CVaR Monte Carlo estimators

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COMPUTATIONAL MANAGEMENT SCIENCE 2015年第2期12卷 221-242页

作者： Kozmik, Vaclav Charles Univ Prague Fac Math & Phys Dept Probabil & Math Stat Prague Czech Republic

We formulate an objective as a convex combination of expectation and risk, measured by the CVaR risk measure. The poor performance of standard Monte Carlo estimators applied on functions of this form is discussed and a variance reduction scheme based on importance sampling is proposed. We provide analytical solution for random variables based on normal distribution and outline the way for the other distributions, either by analytical computation or by sampling. Our results are applied in the framework of stochastic dual dynamic programming algorithm. Computational results which validate the previous analysis are given.

关键词： Importance sampling Risk-averse optimization Monte Carlo sampling stochastic dual dynamic programming

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Assessment of the Long-Term Hydrothermal Scheduling Operation Polices with Alternative Inflow Modeling

Assessment of the Long-Term Hydrothermal Scheduling Operatio...

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2014 Power Systems Computation Conference (PSCC)

作者： de Matos, Vitor L. Larroyd, Paulo V. Finardi, Erlon C. Plan4 Engn SS Florianopolis SC Brazil Univ Fed Santa Catarina Florianopolis SC Brazil

ISBN: (纸本)9788393580132

The Long-Term Hydrothermal Scheduling (LTHS) problem plays an important role in power systems that rely heavily on hydroelectricity. The purpose of the LTHS problem is to define an optimal operation policy that uses stored water as inexpensively as possible. A popular solution approach to this problem is called stochastic dual dynamic programming (SDDP). To incorporate the inflow uncertainties, the LTHS problem is modeled as a multi-stage linear stochastic problem. Although the Periodic Auto Regressive (PAR) model is considered the best model to forecast inflows, the PAR model may require nonlinear transformations that include nonconvexities in the problem. In the Brazilian LTHS problem, some modifications are applied in the PAR model to avoid the nonlinear transformations and negative energy inflow generation. However, these adjustments can still add nonconvexities to the problem. As a result, this paper describes three approaches to generate inflow scenarios that maintain the convexity in the LTHS problem and are compatible with traditional solution algorithms. We show the results considering a small hydrothermal configuration, as well as the Brazilian hydrothermal power system.

关键词： Hydrothermal Scheduling stochastic programming stochastic dual dynamic programming Periodic Auto Regressive Model

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Risk-averse medium-term hydro optimization considering provision of spinning reserves

Risk-averse medium-term hydro optimization considering provi...

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International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Abgottspon, Hubert Njalsson, Karl Bucher, Matthias A. Andersson, Goeran Swiss Fed Inst Technol Power Syst Lab Zurich Switzerland

ISBN: (纸本)9781479935611

This paper presents two algorithms for solving a medium-term hydro optimization. Considered are risk-averse operation, provision of spinning reserves as well as short-term production flexibility. Proposed is a variant of stochastic dual dynamic programming (SDDP) and stochastic dynamic programming as a benchmark. A risk measure is introduced in both methods. To deal with short-term production flexibility a decomposition of the problem into inter-and intrastage subproblems is performed. The provision of spinning reserves leads to non-convex value functions. To deal with it in SDDP a method based on Lagrangian relaxation was used which was further enhanced by locally valid cuts in order to find realistic water values.

关键词： hydro power scheduling medium-term planning stochastic programming spinning reserves risk measures intrastage subproblems stochastic dual dynamic programming stochastic dynamic programming convexification

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SDDP for multistage stochastic programs: preprocessing via scenario reduction

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COMPUTATIONAL MANAGEMENT SCIENCE 2017年第1期14卷 67-80页

作者： Dupacova, Jitka Kozmik, Vaclav Charles Univ Prague Dept Probabil & Math Stat Prague Czech Republic

Even with recent enhancements, computation times for large-scale multistage problems with risk-averse objective functions can be very long. Therefore, preprocessing via scenario reduction could be considered as a way to significantly improve the overall performance. Stage-wise backward reduction of single scenarios applied to a fixed branching structure of the tree is a promising tool for efficient algorithms like stochastic dual dynamic programming. We provide computational results which show an acceptable precision of the results for the reduced problem and a substantial decrease of the total computation time.

关键词： Multistage stochastic programs stochastic dual dynamic programming Multiperiod CVaR Scenario reduction

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Scheduling Battery Energy Storage Systems Under Battery Capacity Degradation Uncertainty

Scheduling Battery Energy Storage Systems Under Battery Capa...

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American Control Conference (ACC)

作者： Moring, Hannah Mathieu, Johanna L. Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

The increasing penetration of renewable energy resources and the decreasing cost of battery energy storage in recent years has led to a growing interest in using batteries to provide grid services like frequency regulation. In this paper, we discuss the advantages and disadvantages of different battery degradation models and the impacts that model choice can have on the assumed cost of energy capacity loss due to operation. We also explore the effects of modeling degradation as an uncertain process by extending a two-stage, multi-period optimization problem for scheduling the operation of a battery providing multiple services with risk aversion. We use stochastic dual dynamic programming to derive a policy for the problem. Case study results show that using a stochastic degradation model with risk aversion produces a policy for more conservative battery use and longer lifespan in comparison to that obtained with a deterministic degradation model.

关键词： Battery Energy Storage Capacity Degradation stochastic dual dynamic programming Degradation Uncertainty

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A Min-plus-SDDP Algorithm for Deterministic Multistage Convex programming 58

A Min-plus-SDDP Algorithm for Deterministic Multistage Conve...

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58th IEEE Conference on Decision and Control (CDC)

作者： Akian, Marianne Chancelier, Jean-Philippe Tran, Benoit Ecole Polytech INRIA Route Saclay F-91128 Palaiseau France Ecole Polytech CMAP Route Saclay F-91128 Palaiseau France Ecole Ponts ParisTech CERMICS 6 & 8 Av B Pascal F-77455 Marne La Vallee France Ecole Polytech INRIA 6 & 8 Av B Pascal F-77455 Marne La Vallee France Ecole Polytech CMAP 6 & 8 Av B Pascal F-77455 Marne La Vallee France

ISBN: (纸本)9781728113982

We consider discrete time optimal control problems with finite horizon involving continuous states and possibly both continuous and discrete controls, subject to non-stationary linear dynamics and convex costs. In this general framework, we present a stochastic algorithm which generates monotone approximations of the value functions as a pointwise supremum or infimum of basic functions (for example affine or quadratic) which are randomly selected. We give sufficient conditions on the way basic functions are selected in order to ensure almost sure convergence of the approximations to the value function on a set of interest. Then we study a linear-quadratic optimal control problem with one control constraint. On this toy example we show how to use our algorithm in order to build lower approximations, like the SDDP algorithm, as supremum of affine cuts and upper approximations, by min-plus techniques, as infimum of quadratic fonctions.

关键词： Deterministic multistage optimization problems min-plus algebra tropical algebra stochastic dual dynamic programming dynamic programming

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Multistage stochastic programming: Algorithms, Modelling and Applications

Multistage Stochastic Programming: Algorithms, Modelling and...

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作者： Siddig, Murwan Clemson University

学位级别：Ph.D., Doctor of Philosophy

This dissertation comprises four different topics related to multistage stochastic programming (MSP) algorithms, modeling, and applications. First, we extend the adaptive partition-based approach for solving two-stage stochastic programs with a fixed recourse matrix and a fixed cost vector to the MSP setting, where the stochastic process is assumed to be stage-wise independent. The proposed algorithms integrate the adaptive partition-based strategy with a popular approach for solving multistage stochastic programs, the stochastic dual dynamic programming (SDDP) algorithm, according to two main strategies. These two strategies are distinct from each other in the manner by which they refine the partitions during the solution process. In particular, we propose a refinement outside SDDP strategy whereby we iteratively solve a coarse scenario tree induced by the partitions and refine the partitions in a separate step outside of SDDP, only when necessary. We also propose a refinement within SDDP strategy where the partitions are refined in conjunction with the machinery of the SDDP algorithm. We then use, within the two different refinement schemes, different tree-traversal strategies, which allow us to have some control over the size of the partitions. Our numerical experiments on a hydro-thermal power generation planning problem show the effectiveness of the proposed algorithms in comparison to the standard SDDP algorithm. Moreover, the results show that the algorithms with the refinement outside SDDP strategy outperform the ones with the refinement within SDDP strategy. Second, we study an important question related to solving MSP problems with a large number of stages. A common approach to tackle MSP problems with a large number of stages is a rolling-horizon (RH) procedure, where one solves a sequence of MSP problems with a smaller number of stages. This leads to a delicate issue of how many stages to include in the smaller problems used in the RH procedure. We study

关键词： Multistage stochastic programming stochastic dual dynamic programming Algorithms Hydrothermal power generation Logistics costs Decision making Planning

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Multi-horizon modeling in hydro power planning 5

Multi-horizon modeling in hydro power planning

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5th International Workshop on Hydro Scheduling in Competitive Electricity Markets

作者： Abgottspon, Hubert Andersson, Goran Swiss Fed Inst Technol Power Syst Lab Zurich Switzerland

In this paper, a novel modeling framework is proposed, the multi-horizon modeling approach. This approach allows a very detailed and transparent modeling of many problems in hydro power planning by simultaneously being computationally very efficient. The model is applied to a complex pumped storage hydro power plant in a liberalized market environment in order to give decision support for the self-scheduling of it. The modeling framework is compared to three alternative state-of-the-art modeling approaches. The results suggest that multi-horizon models are especially valuable for the modeling of hydro power plants with different types of reservoirs. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： medium-term hydro scheduling multi-horizon stochastic dual dynamic programming

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