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 paper, a self-scheduling strategy for increasing the profits of wind resources is proposed, where a Generation Company (GenCo), who owns both wind and pumped-storage plants, selfschedules the integrated operation of them regarding the uncertainty of wind power generation.
117 citations
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TL;DR: The outcome is that a clever but simple implementation of the Benders approach can be very effective even without separability, as its performance is comparable and sometimes even better than that of the most effective and sophisticated algorithms proposed in the previous literature.
117 citations
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01 Jan 1972117 citations
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TL;DR: A risk-constrained scenario-based stochastic programming framework is proposed using the conditional value at risk method to address various uncertainties in a microgrid that includes renewable energy, diesel generators, battery storage, and various loads.
Abstract: This paper presents a novel energy-management method for a microgrid that includes renewable energy, diesel generators, battery storage, and various loads. We assume that the microgrid takes part in a pool market and responds actively to the electricity price to maximize its profit by scheduling its controllable resources. To address various uncertainties, a risk-constrained scenario-based stochastic programming framework is proposed using the conditional value at risk method. The designed model is solved by two levels of stochastic optimization methods. One level of optimization is to submit optimal hourly bids to the day-ahead market under the forecast data. The other level of optimization is to determine the optimal scheduling using the scenario-based stochastic data of the uncertain resources. The proposed energy management system is not only beneficial for the microgrid and customers, but also applies the microgrid aggregator and virtual power plant. The results are shown to prove the validity of the proposed framework.
117 citations
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TL;DR: In this paper, two dynamic programming models, one deterministic and one stochastic, are compared to generate reservoir operating rules. And the results show that the DPR generated rules are more effective in the operation of medium to very large reservoirs and the SDP generated rules were more effective for small reservoirs.
Abstract: Two dynamic programming models — one deterministic and one stochastic — that may be used to generate reservoir operating rules are compared. The deterministic model (DPR) consists of an algorithm that cycles through three components: a dynamic program, a regression analysis, and a simulation. In this model, the correlation between the general operating rules, defined by the regression analysis and evaluated in the simulation, and the optimal deterministic operation defined by the dynamic program is increased through an iterative process. The stochastic dynamic program (SDP) describes streamflows with a discrete lag-one Markov process. To test the usefulness of both models in generating reservoir operating rules, real-time reservoir operation simulation models are constructed for three hydrologically different sites. The rules generated by DPR and SDP are then applied in the operation simulation model and their performance is evaluated. For the test cases, the DPR generated rules are more effective in the operation of medium to very large reservoirs and the SDP generated rules are more effective for the operation of small reservoirs.
117 citations