Journal ArticleDOI
Short-term optimal operation of water systems using ensemble forecasts
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TLDR
In this paper, a tree-based model predictive control (TB-MPC) is used to set up a multistage stochastic programming, which finds a different optimal strategy for each branch and enhances the adaptivity to forecast uncertainty.About:
This article is published in Advances in Water Resources.The article was published on 2014-09-01. It has received 75 citations till now. The article focuses on the topics: Ensemble forecasting & Forecast verification.read more
Citations
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Effect of Streamflow Forecast Uncertainty on Real-Time Reservoir Operation
TL;DR: In this article, the authors modeled the dynamic evolution of uncertainties involved in the various forecast products and explored their effect on real-time reservoir operation decisions, showing that forecast uncertainty exerts significant effects.
Ensemble flood forecasting: A review
TL;DR: The scientific drivers of this shift towards ‘ensemble flood forecasting’ and the literature evidence of the ‘added value’ of flood forecasts based on EPS are reviewed.
Journal ArticleDOI
Model predictive control of urban drainage systems: A review and perspective towards smart real-time water management
Nadia Schou Vorndran Lund,Anne Katrine Falk,Morten Borup,Henrik Madsen,Peter Steen Mikkelsen +4 more
TL;DR: A terminology for MPC of urban drainage systems and a hierarchical categorization where there is a large variety of both convex and non-linear optimization models and optimization solvers as well as constructions of the internal MPC model is proposed.
Journal ArticleDOI
Complex relationship between seasonal streamflow forecast skill and value in reservoir operations
Sean W. D. Turner,Sean W. D. Turner,James C. Bennett,James C. Bennett,David E. Robertson,Stefano Galelli +5 more
TL;DR: In this paper, the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives is examined.
Journal ArticleDOI
On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid
TL;DR: In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained, is presented.
References
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Journal ArticleDOI
River flow forecasting through conceptual models part I — A discussion of principles☆
J.E. Nash,J.V. Sutcliffe +1 more
TL;DR: In this article, the principles governing the application of the conceptual model technique to river flow forecasting are discussed and the necessity for a systematic approach to the development and testing of the model is explained and some preliminary ideas suggested.
Journal ArticleDOI
Survey Constrained model predictive control: Stability and optimality
TL;DR: This review focuses on model predictive control of constrained systems, both linear and nonlinear, and distill from an extensive literature essential principles that ensure stability to present a concise characterization of most of the model predictive controllers that have been proposed in the literature.
Journal ArticleDOI
On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
TL;DR: A comprehensive description of the primal-dual interior-point algorithm with a filter line-search method for nonlinear programming is provided, including the feasibility restoration phase for the filter method, second-order corrections, and inertia correction of the KKT matrix.
Book
Predictive Control With Constraints
TL;DR: A standard formulation of Predictive Control is presented, with examples of step response and transfer function formulations, and a case study of robust predictive control in the context of MATLAB.
BookDOI
Introduction to Stochastic Programming
John R. Birge,Franois Louveaux +1 more
TL;DR: This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability to help students develop an intuition on how to model uncertainty into mathematical problems.