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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14 Apr 2013TL;DR: In this article, a framework for smart energy management based on the concept of quality-of-service in electricity (QoSE) is presented, where the resident electricity demand is classified into basic usage and quality usage.
Abstract: Microgrid (MG) is a promising component for future smart grid (SG) deployment. The balance of supply and demand of electric energy is one of the most important requirements of MG management. In this paper, we present a novel framework for smart energy management based on the concept of quality-of-service in electricity (QoSE). Specifically, the resident electricity demand is classified into basic usage and quality usage. The basic usage is always guaranteed by the MG, while the quality usage is controlled based on the MG state. The microgrid control center (MGCC) aims to minimize the MG operation cost and maintain the outage probability of quality usage, i.e., QoSE, below a target value, by scheduling electricity among renewable energy resources, energy storage systems, and macrogrid. The problem is formulated as a constrained stochastic programming problem. The Lyapunov optimization technique is then applied to derive an adaptive electricity scheduling algorithm by introducing the QoSE virtual queues and energy storage virtual queues. The proposed algorithm is an online algorithm since it does not require any statistics and future knowledge of the electricity supply, demand and price processes. We derive several "hard" performance bounds for the proposed algorithm, and evaluate its performance with trace-driven simulations. The simulation results demonstrate the efficacy of the proposed electricity scheduling algorithm.
142 citations
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TL;DR: In this article, the problem of evaluating and optimizing the probability of feasible operation for a design that is described by a nonlinear model is formulated as a sequence of optimization problems, which can be extended to design optimization problems for maximizing the stochastic flexibility subject to a cost contraint.
142 citations
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TL;DR: This study employs robust optimization tools to derive robust combined lot-sizing and cutting-stock models when production costs and product demands are uncertainty parameters, and provides some insights into the relationship between the budgets of uncertainty, fill rates and optimal values.
142 citations
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TL;DR: In this paper, the authors investigated a relief chain design problem where not only demands but also supplies and the cost of procurement and transportation are considered as the uncertain parameters, and the model considered uncertainty for the locations where those demands can arise and the possibility that a number of the facility could be partially destroyed by the disaster.
Abstract: Relief logistics is one of the most important elements of a relief operation. This paper investigates a relief chain design problem where not only demands but also supplies and the cost of procurement and transportation are considered as the uncertain parameters. Furthermore, the model considers uncertainty for the locations where those demands can arise and the possibility that a number of the facility could be partially destroyed by the disaster. The proposed model for this study is formulated as a mixed-integer nonlinear programming to minimize the sum of the expected total cost (which includes costs of location, procurement, transportation, holding, and shortage) and the variance of the total cost. The model simultaneously determines the location of relief distribution centers and the allocation of affected area to relief distribution centers. Furthermore, an efficient solution approach based on particle swarm optimization is developed in order to solve the proposed mathematical model. At last, computational results for several instances of the problem are presented to demonstrate the feasibility and effectiveness of the proposed model and algorithm.
141 citations
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TL;DR: Genetic programming is used to detect faults in rotating machinery to examine the performance of two-class normal/fault recognition and the results are compared with a few other methods for fault detection.
141 citations