The wide spread of district heating in Denmark offers a massive potential for flexibility in an energy system with intermittent renewable energy production. To leverage this potential, a cost-efficient power market in...
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The wide spread of district heating in Denmark offers a massive potential for flexibility in an energy system with intermittent renewable energy production. To leverage this potential, a cost-efficient power market integration of combined heat and power (CHP) units in district heating systems is important. We propose a stochastic program optimising block bids to the day-ahead market for CHP units in district heating systems under uncertain power prices. Block bids allow the internalisation of start-up costs. Based on the stochastic program, we develop a solution approach based on sample average approximation (SAA) to solve the stochastic program for a large number of price scenarios. We present results for a case study from Middelfart, Denmark. The system consists of two sub-networks that have lately been connected. We analyse the block bidding behaviour with and without connection using real data from different seasons. The results show that the bidding varies significantly depending on seasons and the layout of the network. Furthermore, the results show that the solution approach based on SAA reduces computation time significantly while maintaining solution quality.& COPY;2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
We address optimization problems with uncertain objective functions, given by discrete probability distributions. Within this setting, we investigate the so-called K-adaptability approach: the aim is to calculate a se...
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Bi-level stochastic programming is a process of taking the optimal value of the lower level as the feedback to the upper level, and the constraint that the lower level has one single optimal solution can be relaxed in...
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A stochastic programming model of the operation of energy plants with the introduction of photovoltaic generation and a storage battery is developed. The uncertainty of the output of the photovoltaic generation is rep...
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A stochastic education resources management (SERM) model has been proposed to solve resource-allocation problems in education under uncertainty. The advantages of SERM are as follows: (1) it is capable for dealing wit...
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This study addresses primal-dual dynamics for a stochastic programming problem for capacity network design. It is proven that consensus can be achieved on the here and now variables which represent the capacity of the...
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In most sport leagues, a schedule is announced before the start of the season. However, due to unexpected events (e.g. bad weather conditions), some games cannot be played on the announced date. To handle this, before...
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In most sport leagues, a schedule is announced before the start of the season. However, due to unexpected events (e.g. bad weather conditions), some games cannot be played on the announced date. To handle this, before the start of the season, empty so-called catch-up rounds are positioned in the schedule as a buffer. During the season, games can then be rescheduled to these catch-up rounds. We develop a two-stage stochastic programming approach to determine where to position the catch-up rounds in order to maintain the quality of the realized schedule. While our method is generally applicable, we demonstrate its use with soccer. Scenarios and their probabilities are deduced from historical data from 10 major European soccer leagues. We study the impact of the number of catch-up rounds and costs on the positions of catch-up rounds and compare our method with other proactive strategies from the literature. We conclude with a case study based on the English Premier League. In particular when many games cannot be played as planned and few catch-up rounds are available, our stochastic programming approach outperforms existing methods with respect to maintaining a fair ranking and avoiding cancelled games.
We propose a computational framework to quantify (measure) and to optimize the reliability of complex systems. The approach uses a graph representation of the system that is subject to random failures of its component...
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Cost-efficient selection and scheduling of a subset of geographically distributed resources to meet the demands of a scientific workflow is a challenging problem. The problem is exacerbated by uncertainties in demand ...
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In this work, we present different tools of mathematical modeling that can be used in oil and gas industry to help improve the decision-making for field development, production optimization and planning. Firstly, we f...
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In this work, we present different tools of mathematical modeling that can be used in oil and gas industry to help improve the decision-making for field development, production optimization and planning. Firstly, we formulate models to compare simultaneous multiperiod optimization and sequential single period optimization for the maximization of net present value and the maximization of total oil production over long term time horizons. This study helps to identify the importance of multiperiod optimization in oil and gas production planning. Further, we formulate a bicriterion optimization model to determine the ideal compromise solution between maximization of the two objective functions, the net present value (NPV) and the total oil production. To account for the importance of hedging against uncertainty in the oil production, we formulate a two-stage stochastic programming model to compute an improved expected value of NPV and total oil production for uncertainties in oil prices and productivity indices.
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