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Öğe Determining Spot Price and Economic Dispatch in Deregulated Power Systems(Assoc Sci Res, 2010) Ürkmez, Abdullah; Çetinkaya, NurettinThis paper presents a new formula for determining spot price and a new algorithm for economic dispatch in deregulated power systems. According to participant's biddings, an independent system operator (ISO) manages deregulated power systems (DPS) and decides transaction between participants. ISO aims to obtain minimum cost for entire power system. However each participant aims to work with minimum cost for only themselves. Each participant in DPS shares expensive operating and upkeep costs. Energy sources are used more efficient. Energy prices are determined according to the costs. The proposed algorithm considers price / power bids, generating / demand balance and generating units' constraints. The results are shown on the IEEE 30 bus standart test system.Öğe Improving an Expert-Supported Dynamic Programming Algorithm and Adaptive-Neuro Fuzzy Inference System for Long-Term Load Forecasting(2017) Çetinkaya, NurettinLoad forecasting is very important to manage the electrical power systems. Load forecasting can be analyzed in three different ways as short-term, medium-term and long-term. Long-term load forecasting (LTLF) is inneed to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression analysis. In this study, a new effective expert-supported dynamic programming algorithm (ESDP) has been improved. Additionally, adaptive neuro-fuzzy inference system (ANFIS) and mathematical modeling (MM) are used to forecast long term energy demand. ANFIS is one of the famous artificial intelligence and has widely used to solve forecasting problemsin literature. In addition to numerical inputs, ANFIShas linguistics inputs. The results obtained from ESDP, ANFIS and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and mean absolute error(MAE) are used. The obtained results show that the proposedalgorithms are available.Öğe Improving of renewable energy support policy and a performance analysis of a grid-connected 1 MWP PV power plant in Konya(Selçuk Üniversitesi Mühendislik Fakültesi, 2017) Çetinkaya, NurettinEver increasing energy needs in today's world no longer corresponds to the continuous production of new solutions are required. Supporting renewable energy sources has been required to remedy this need. Renewable energy support for the world-wide accepted feed-in tariff (FiT) policy is being used effectively. FiT support policies, structure and application vary between countries. In this study a new support model is proposed. The most important aim of this model is to obtain highest benefit for both the government and the investors. The proposed flexible pricing model is depending on a country's economic status, the energy market and energy production plants technical data. Efficiency increasing of the photovoltaic (PV) systems and reduction of the costs has increased the choice of renewable energy. Thanks to the spread of environmentally friendly solutions and production, load flow problems in electric power systems can be solved and losses can be reduced. There are many areas in Konya to establish solar power plant. In this study a performance analysis of the 1 MW grid-connected PV power plants which was founded in Konya was conducted. In addition, proposals have been made for investors and legislators for more efficient and less costly energy production.Öğe Long-term Load Forecasting Based on Adaptive Neural Fuzzy Inference System Using Real Energy Data(Elsevier Science Bv, 2012) Akdemir, Bayram; Çetinkaya, NurettinEnergy production and distributing have critical importance for all countries especially developing countries. Studies about energy consumption, distributing and planning have much importance at the present day. In order to manage any power plant or take precautions about energy subject, many kinds of observations are used for short, mid and long term forecasting. Especially long term forecasting is in need to plan and carry on future energy demand and investment such as size of energy plant and location. Long term forecasting often includes power consumption data for past years, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Long term forecasting data vary from one month to several years. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression models. In this study, artificial intelligence is used to forecast long term energy demand. Artificial intelligences are widely used for engineering problems to solve and obtain valid solutions. Adaptive neural fuzzy inference system is one of the most famous artificial intelligence methods and has been widely used in literature. In addition to numerical inputs, Adaptive neural fuzzy inference system has linguistics inputs such as good, bad and ugly. Adaptive neural fuzzy inference system is used to obtain long term forecasting results and the results are compared to mathematical methods to show validity and error levels. In order to show error levels, mean absolute error and mean absolute error percentage are used. Mean absolute error and mean absolute error percentages are very common and practical methods in literature. The obtained error results, from 2003 to 2025, mean absolute error and mean absolute percentage error are 1.504313 and 0.82439, respectively. Success of Adaptive neural fuzzy inference system for energy demand forecasting is 99.17%. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the organizing committee of 2nd International Conference on Advances in Energy Engineering (ICAEE).