The impact of electricity market liberalization in Turkey: “Free consumer” and distributional monopoly cases

Electricity sector has grown substantially in Turkey since the early 1960s as a result of rapid industrialization and urbanization. The vertically integrated state-owned company had a legally established monopoly on the generation, transmission and distribution of electricity in Turkey. With the support and encouragement of international organizations like the World Bank, Turkey has initiated a comprehensive program to liberalize and privatize the electricity market in 2001. The liberalization of the electricity market in Turkey started in the distribution side of the market. The distribution network was divided into 21 distribution regions and in each of these, separate – initially state-owned – distribution companies have been authorized to distribute and sell electricity. The plan envisaged to complete privatization of all distribution companies until the end of 2006. This study compares the welfare implication of privatization of the distribution networks by comparing two extreme cases, a pure regional distributional monopoly case and a representative pure “free” consumer case, with a benchmark case of administered price regulation. For this purpose, we develop a simulation model of the Turkish electricity system, and use the data on generation and distribution costs. Our simulation analysis shows that substantial welfare losses occur if the distributional companies behave as regional monopolists. Our findings reiterate the importance of regulation and market design.     

Battle royale optimization algorithm

Neural Computing and Applications - Recently, several metaheuristic optimization approaches have been developed for solving many complex problems in various areas. Most of these optimization...     

Application of response surface methodology to optimize and investigate the effects of operating conditions on the performance of DMFC

In this study, the response surface methodology (RSM) has been applied to optimize the operating conditions of direct methanol fuel cell (DMFC). A quadratic model was developed through RSM in terms of related independent variable to describe the current as the response. The input data required in this model has been obtained experimentally. For this purpose, an experimental set up for testing of direct methanol fuel cell has been established to investigate the effects of temperature and flow rate parameters on the cell performance. Two different analyses for operating conditions were performed applying the response surface method to obtain the maximum power. These analyses were based on the unlimited and minimum methanol consumptions. Methanol flow rate, oxygen flow rate, methanol temperature, humidification temperature and cell temperature were the main parameters considered that they were varied between 2 and 50 ml/min, 100-1000 ml/min, 30-70 °C, 30 70 °C and 30-80 °C in the analyses respectively. The maximum current under the unlimited and minimum methanol consumptions was found as 1230 mA and 582 mA based on the contour plots and variance analysis.     

Numerical study of assembly pressure effect on the performance of proton exchange membrane fuel cell

The performance of the fuel cell is affected by many parameters. One of these parameters is assembly pressure that changes the mechanical properties and dimensions of the fuel cell components. Its first duty, however, is to prevent gas or liquid leakage from the cell and it is important for the contact behaviors of fuel cell components. Some leakage and contact problems can occur on the low assembly pressures whereas at high pressures, components of the fuel cell, such as bipolar plates (BPP), gas diffusion layers (GDL), catalyst layers, and membranes, can be damaged. A finite element analysis (FEA) model is developed to predict the deformation effect of assembly pressure on the single channel PEM fuel cell in this study. Deformed fuel cell single channel model is imported to three-dimensional, computational fluid dynamics (CFD) model which is developed for simulating proton exchange membrane (PEM) fuel cells. Using this model, the effect of assembly pressure on fuel cell performance can be calculated. It is found that, when the assembly pressure increases, contact resistance, porosity and thickness of the gas diffusion layer (GDL) decreases. Too much assembly pressure causes GDL to destroy; therefore, the optimal assembly pressure is significant to obtain the highest performance from fuel cell. By using the results of this study, optimum fuel cell design and operating condition parameters can be predicted accordingly.     

Experimental investigation on water and heat management in a PEM fuel cell using response surface methodology

Water and heat management are the most critical issues for the performance of proton exchange membrane (PEM) fuel cells. They can be provided by keeping hydrogen flow rate, oxygen flow rate, cell temperature and humidification temperature under control. In this study, the effects of these parameters on the power density of proton exchange membrane (PEM) fuel cell which has 25 cm² active area have been examined experimentally using hydrogen on the anode side and oxygen on the cathode side. Response Surface Methodology (RSM) has been applied to optimize these operation parameters of proton exchange membrane (PEM) fuel cell. The test responses are the maximum output power density. ANOVA (analysis of variance) analyses are used to compute the effects and the contributions of the various factors to the fuel cell maximal power density. The use of this design shows also how it is possible to reduce the number of experiments. Hydrogen flow rate, oxygen flow rate, humidification temperature and cell temperature were the main parameters to have been varied between 2.5–5 L/min, 3–5 L/min, 40–70 °C and 40–80 °C in the analyses. The maximum power density was found as 241.977 mW/cm².     

Prediction of convection heat transfer in converging–diverging tube for laminar air flowing using back-propagation neural network

The ability of an artificial neural network (ANN) model for heat transfer analysis in a converging–diverging tube is studied. Back propagation learning algorithm, the most common method for ANNs, was used in training and testing/validation the network. It is trained with selected values of the Reynolds numbers (Re), Prandtl numbers (Pr), half taper angle (θ), aspect ratio (L_cyc/D_max), and Nusselt number (Nu). The trained network is the used to make predictions of the Nusselt numbers. The accuracy between selected data and ANNs results was achieved with a mean absolute relative error less than 1.5%. This shows that well trained neural network model provided fast, accurate and consistent results.