Topic
Stochastic programming
About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.
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TL;DR: In this article, a two-stage stochastic programming model for the short- or mid-term cost-optimal electric power production planning is developed, where the power generation in a hydro-thermal generation system under uncertainty in demand (or load) and prices for fuel and delivery contracts is considered.
Abstract: A two-stage stochastic programming model for the short- or mid-term cost-optimal electric power production planning is developed. We consider the power generation in a hydro-thermal generation system under uncertainty in demand (or load) and prices for fuel and delivery contracts. The model involves a large number of mixed-integer (stochastic) decision variables and constraints linking time periods and operating power units. A stochastic Lagrangian relaxation scheme is designed by assigning (stochastic) multipliers to all constraints that couple power units. It is assumed that the stochastic load and price processes are given (or approximated) by a finite number of realizations (scenarios). Solving the dual by a bundle subgradient method leads to a successive decomposition into stochastic single unit subproblems. The stochastic thermal and hydro subproblems are solved by a stochastic dynamic programming technique and by a specific descent algorithm, respectively. A Lagrangian heuristics that provides approximate solutions for the primal problem is developed. Numerical results are presented for realistic data from a German power utility and for numbers of scenarios ranging from 5 to 100 and a time horizon of 168 hours. The sizes of the corresponding optimization problems go up to 400.000 binary and 650.000 continuous variables, and more than 1.300.000 constraints.
103 citations
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TL;DR: In this article, the authors investigated the demand side management (DSM) in a commercial building microgrid with solar generation, stationary battery energy management system (BESS) and gridable (V2G) electric vehicle (EV) integration.
103 citations
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TL;DR: A new decision making model is proposed to maximize both possibility and probability, which is based on possibilistic programming and stochastic programming, and an interactive algorithm is constructed to obtain a satisficing solution satisfying at least weak Pareto optimality.
103 citations
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29 Nov 1993TL;DR: This work uses second order local trajectory optimization to generate locally optimal plans and local models of the value function and its derivatives, and maintains global consistency of the local Models of thevalue function, guaranteeing that the locally optimal Plans are actually globally optimal.
Abstract: Dynamic programming provides a methodology to develop planners and controllers for nonlinear systems. However, general dynamic programming is computationally intractable. We have developed procedures that allow more complex planning and control problems to be solved. We use second order local trajectory optimization to generate locally optimal plans and local models of the value function and its derivatives. We maintain global consistency of the local models of the value function, guaranteeing that our locally optimal plans are actually globally optimal, up to the resolution of our search procedures.
103 citations
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TL;DR: This paper describes a framework for modeling significant financial planning problems based on multi-stage optimization under uncertainty based on interior-point methods and possesses a special structure that lends itself to parallel and distributed optimization algorithms.
103 citations