Browsing by Subject "Multi-Stage Stochastic Programming"

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Open Access
Multi-stage stochastic programming for demand response optimization
(2018-07) Şahin, Munise Kübra
The increase in the energy consumption puts pressure on natural resources and environment and results in a rise in the price of energy. This motivates residents to schedule their energy consumption through demand response mechanism. We propose a multi-stage stochastic programming model to schedule di erent kinds of electrical appliances under uncertain weather conditions and availability of renewable energy. We incorporate appliances with internal batteries to better utilize the renewable energy sources. Our aim is to minimize the electricity cost and the residents' dissatisfaction. We use a scenario groupwise decomposition approach to compute lower and upper bounds for instances with a large number of scenarios. The results of our computational experiments show that the approach is very e ective in nding high quality solutions in small computation times. We provide insights about how optimization and renewable energy combined with batteries for storage result in peak demand reduction, savings in electricity cost and more pleasant schedules for residents with di erent levels of price sensitivity.
Open Access
Stochastic shelter site location under multi-hazard scenarios
(2018-06) Özbay, Eren
In some cases, natural disasters happen successively (e.g. a tsunami following an earthquake) in close proximity of each other, even if they are not correlated. This study locates shelter sites and allocates the a ected population to the established set of shelters by considering the aftershock(s) following the initial earthquake, via a three-stage stochastic mixed-integer programming model. In each stage, before the uncertainty, which is the number of a ected people, in the corresponding stage is resolved, shelters are established, and after the uncertainty is resolved, a ected population is allocated to the established set of shelters. To manage the inherent risk related to the uncertainty, conditional value-atrisk is utilized as a risk measure in allocation of victims to the established set of shelters. Computational results on the Istanbul dataset are presented to emphasize the necessity of considering secondary disaster(s), along with a heuristic method to improve the solution times and qualities. During these computational analyses, it is observed that the original single-objective model poses some obstacles in parameter selection. As in humanitarian operations, choosing parameters may cause con ict of interests and hence may be criticized, a multi-objective framework is developed with various formulations. Some generalizations regarding the performance and applicability of the developed formulations are discussed and nally, another heuristic for the multi-objective formulation is presented to tackle the curse of dimensionality and improve the solution times.