Ümmühan Başaran Filik

Solving Unit Commitment Problem Using Modified Subgradient Method Combined with Simulated Annealing Algorithm

This paper presents the solving unit commitment (UC) problem using Modified Subgradient Method (MSG) method combined with Simulated Annealing (SA) algorithm. UC problem is one of the important power system engineering hard-solving problems. The Lagrangian relaxation (LR) based methods are commonly used to solve the UC problem. The main disadvantage of this group of methods is the difference between the dual and the primal solution which gives some significant problems on the quality of the feasible solution. In this paper, MSG method which does not require any convexity and differentiability assumptions is used for solving the UC problem. MSG method depending on the initial value reaches zero duality gap. SA algorithm is used in order to assign the appropriate initial value for MSG method. The major advantage of the proposed approach is that it guarantees the zero duality gap independently from the size of the problem. In order to show the advantages of this proposed approach, the four-unit Tuncbilek thermal plant and ten-unit thermal plant which is usually used in literature are chosen as test systems. Penalty function (PF) method is also used to compare with our proposed method in terms of total cost and UC schedule.

A Comparative Study of Three Different Mathematical Methods for Solving the Unit Commitment Problem

The unit commitment (UC) problem which is an important subject in power system engineering is solved by using Lagragian relaxation (LR), penalty function (PF), and augmented Lagrangian penalty function (ALPF) methods due to their higher solution quality and faster computational time than metaheuristic approaches. This problem is considered to be a nonlinear programming-(NP-) hard problem because it is nonlinear, mixed-integer, and nonconvex. These three methods used for solving the problem are based on dual optimization techniques. ALPF method which combines the algorithmic aspects of both LR and PF methods is firstly used for solving the UC problem. These methods are compared to each other based on feasible schedule for each stage, feasible cost, dual cost, duality gap, duration time, and number of iterations. The numerical results show that the ALPF method gives the best duality gap, feasible and dual cost instead of worse duration time and the number of iterations. The four-unit Tuncbilek thermal plant which is located in Kutahya region in Turkey is chosen as a test system in this study. The programs used for all the analyses are coded and implemented using general algebraic modeling system (GAMS).

Hourly Forecasting of Long Term Electric Energy Demand Using a Novel Modeling Approach

In this study, hourly forecasting of long term electric energy demand is proposed using a novel modeling approach. Previous works regarding energy demand forecasting either treated the problem of long term prediction over yearly averages, or considered hourly prediction using a very short term time lag, such as a few hours. The proposed method produces predictions with hourly accuracy; however the proposed time lag is years, making the model suitable for long term prediction. The model is constructed and verified using 4-year-long real-life hourly load data obtained from Turkish Electric Power Company. The method consists of a nested combination of three sub-sections for modeling. The first section is the coarse level for modeling yearly average loads. The second section refines this structure by modeling weekly residual load variations within a year. The final section reaches to the hourly variations within a week, using a novel 2-D mathematical representation at this resolution. Consequently, the combination yields a unified model for short-, medium-, and long-term hourly load forecasting.

Feasible Modified Subgradient Method for Solving the Thermal Unit Commitment Problem as a New Approach

The Lagrangian relaxation- (LR-) based methods are commonly used to solve the thermal unit commitment (UC) problem which is an important subject in power system engineering. The main drawback of this group of methods is the difference between the dual and the primal solutions which gives some significant problems on the quality of the feasible solutions. In this paper, a new approach, feasible modified subgradient (F-MSG) method which does not require finding an unconstrained global minimum of the Lagrangian function and knowing an optimal value of the problem under consideration in order to update dual variables at the each iteration, is firstly used for solving the thermal UC problem. The major advantage of the proposed approach is that it guarantees the zero duality gap and convergence independently from the size of the problem. In order to discuss the advantages of this method, the four-unit Tuncbilek thermal plant, which is located in Kutahya region in Turkey, is chosen as a small test system. The numerical results show that F-MSG gives better solutions as compared to the standard LR method.

A New Hybrid Approach for Wind Speed Prediction Using Fast Block Least Mean Square Algorithm and Artificial Neural Network

A new hybrid wind speed prediction approach, which uses fast block least mean square (FBLMS) algorithm and artificial neural network (ANN) method, is proposed. FBLMS is an adaptive algorithm which has reduced complexity with a very fast convergence rate. A hybrid approach is proposed which uses two powerful methods: FBLMS and ANN method. In order to show the efficiency and accuracy of the proposed approach, seven-year real hourly collected wind speed data sets belonging to Turkish State Meteorological Service of Bozcaada and Eskisehir regions are used. Two different ANN structures are used to compare with this approach. The first six-year data is handled as a train set; the remaining one-year hourly data is handled as test data. Mean absolute error (MAE) and root mean square error (RMSE) are used for performance evaluations. It is shown for various cases that the performance of the new hybrid approach gives better results than the different conventional ANN structure.

Short-term wind speed prediction using several artificial neural network approaches in Eskisehir

Due to the negative environmental impact of using fossil energy sources and depletion of fossil fuels, the alternative energy sources are being searched all over the world. Since wind energy is clean and renewable, the penetration of wind energy for electricity generation is increasing day by day. Wind power plants require continuous and appropriate intensity winds. In terms of the reliability and power quality of the power system, the variability of wind energy has led to problems. To minimize these problems, highly accurate wind speed prediction method should be used. In this study, accurate short term wind speed prediction approach is aimed for increasing efficiency of wind energy production. The short term wind speed prediction approached is trained/tested with real three years hourly averaged wind speed values from Eskisehir region of Turkey. Feed forward backpropagation network and Levenberg-Marquardt algorithms are used for analyzing and the identified four network model are compared in terms of mean square error values.

EFFICIENCY ANALYSIS OF THE SOLAR TRACKING PV SYSTEMS IN ESKISEHIR REGION

In this study, efficiency analysis of the tracking solar photovoltaic (PV) system in Eskisehir region is considered for onsite real solar power generation values. It is shown that the tracking system’s power generations are always higher than fixed one, but increased yield ratio is changing according to weather conditions (sunny or cloudy times). In order to make a fair analysis/comparison, all the instantaneous real solar power generations between 1 July and 30 October 2016 are used to figure out the ratio of sunny and cloudy days of whole year. The averaged total generation values are calculated and it is revealed that tracking system provides approximately 33 % higher yield from same capacity fixed PV panels for Eskisehir region.

Parameters and Power Flow Analysis of the 380 -kV Interconnected Power System in Turkey

This paper presents all the general overview of the interconnected power system in Turkey which consists of 30 generation and 35 load buses, totaling 65 buses connected each other with 380-kV power transmission lines. Also the power flow analysis implemented using MATLAB is made to find optimal operating points of the system and to make power systems generation planning. All data used in this analysis is taken from TEIAS (transmission system operator of Turkey) and EUAS (electricity generation Co. Inc.).

Optimal Power Flow Analysis of the 22-Bus 380-kV Interconnected Power System in Turkey

In this paper, the Newton based optimal power flow analysis (OPF) implemented using MATLAB is applied for the 22-bus interconnected power system in Turkey which consists of 8 generation and 14 load bus connected each other with 380-kV power transmission lines. The results are given in the tables. All data used this analysis is taken from TEIAS (Transmission System Operator of Turkey) and EUAS (Electricity Generation Co. Inc.).