Endüstri Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/48

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Citation - WoS: 7
Citation - Scopus: 12
An Analysis of Price Spikes and Deviations in the Deregulated Turkish Power Market
(Elsevier, 2019) Gayretli, Gizem; Yücekaya, Ahmet; Bilge, Ayşe Hümeyra
The successful operation of a real time market is related to the planning in the day ahead market. We analyze the day ahead and real time market data for the Turkish power market for the period 2012-2015 to classify price spikes and their causes. We also focus on the levels of deviation between the day ahead market values and the real time market values. We define price deviation and load deviation ratios to measure the level of deviation both in price and demand. The analysis for the load is based on load shedding and cycling values. We analyze the mean and standard deviation in market prices and we determine the price spike as a two sigma deviation from the mean value. It is shown that 60% of the price deviation ratios are in the range of ( +/- 20%), while 44% are in the range of ( +/- 10%) and 35% are in the range of (+/- 5%). We also show that 56.9% of the spikes are due to problems in the generation of natural gas based power plants which affect the day ahead and real time prices. A total of 29.2% of the spikes are due to power plant and system failures that affect only real time prices. The share of high temperature based spikes is 13.9% which is a result of air conditioner usage.
Citation - WoS: 6
Citation - Scopus: 7
Assortment Optimization With Log-Linear Demand: Application at a Turkish Grocery Store
(Elsevier, 2019) Hekimoğlu, Mustafa; Sevim, İsmail; Aksezer, Çağlar Sezgin; Durmuş, İpek
In retail sector product variety increases faster than shelf spaces of retail stores where goods are presented to consumers. Hence assortment planning is an important task for sustained financial success of a retailer in a competitive business environment. In this study we consider the assortment planning problem of a retailer in Turkey. Using empirical point-of-sale data a demand model is developed and utilized in the optimization model. Due to nonlinear nature of the model and integrality constraint we find that it is difficult to obtain a solution even for moderately large product sets. We propose a greedy heuristic approach that generates better results than the mixed integer nonlinear programming in a reasonably shorter period of time for medium and large problem sizes. We also proved that our method has a worst-case time complexity of O(n 2 )while other two well-known heuristics’ complexities are O(n 3 )and O(n 4 ). Also numerical experiments reveal that our method has a better performance than the worst-case as it generates better results in a much shorter run-times compared to other methods. © 2019 Elsevier Ltd
Citation - WoS: 77
Citation - Scopus: 92
Forecasting Electricity Demand for Turkey: Modeling Periodic Variations and Demand Segregation
(Elsevier, 2017) Yükseltan, Ergün; Yücekaya, Ahmet; Bilge, Ayşe Hümeyra
In deregulated electricity markets the independent system operator (ISO) oversees the power system and manages the supply and demand balancing process. In a typical day the ISO announces the electricity demand forecast for the next day and gives participants an option to prepare offers to meet the demand. In order to have a reliable power system and successful market operation it is crucial to estimate the electricity demand accurately. In this paper we develop an hourly demand forecasting method on annual weekly and daily horizons using a linear model that takes into account the harmonics of these variations and the modulation of diurnal periodic variations by seasonal variations. The electricity demand exhibits cyclic behavior with different seasonal characteristics. Our model is based solely on sinusoidal variations and predicts hourly variations without using any climatic or econometric information. The method is applied to the Turkish power market on data for the period 2012-2014 and predicts the demand over daily and weekly horizons within a 3% error margin in the Mean Absolute Percentage Error (MAPE) norm. We also discuss the week day/weekend/holiday consumption profiles to infer the proportion of industrial and domestic electricity consumption. (C) 2017 Elsevier Ltd. All rights reserved.
Citation - WoS: 9
Citation - Scopus: 9
Managing Natural Gas Demand for Free Consumers Under Uncertainty and Limited Storage Capacity
(Elsevier, 2020) Aktunç, Esra Ağca; Yükseltan, Ergün; Yücekaya, Ahmet; Bilge, Ayşe Hümeyra
Demand for energy sources depends on several factors such as population growth, urbanization, industrialization, and climate. Among fundamental energy sources, natural gas is characterized by storage limitations and take-or-pay contracts, which makes it especially critical to forecast the demand accurately for cost management policies. Suppliers of natural gas require take-or-pay contracts to ensure that consumers pay for any unused amount up front; and if the demand exceeds the agreed amount, they pay for over-use as well. Consumers with a demand above the eligible consumer limit are categorized as free consumers; and they have to specify their daily, monthly, and annual demand in these take-or-pay contracts. In residential areas, natural gas is used predominantly for heating, hence its consumption has a strong seasonality. In winter, the variability in the atmospheric temperature leads to fluctuations in the demand, while in summer, weekend effects dominate. In order to take these features into account, a demand forecasting model based on a modulated expansion in Fourier series, supplemented by deviations from comfortable temperatures, is used in this study to determine the threshold value for the onset of natural gas usage for heating purposes. The upper and lower bounds for consumption are obtained as a function of temperature only, after analyzing the details of the temperature-consumption relationship using historical data. Moreover, a temperature-based simulation methodology is proposed and simulation results that provide guidelines to manage the costs of storage under uncertainty are presented by suggesting the minimum storage capacity required and showing the distribution of the costs.

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