Bilijipub publisherAdvances in Engineering and Intelligence Systems2821-02630010120220401Electricity market management through optimum installation of distributed generation sources and optimum placement based on LMP and ISC14753410.22034/aeis.2022.147534ENMuhammad Hammad SaeedDepartment of Electrical Engineering, Mehran University of Engineeringand Technology, SZAB Campus, Khairpur Mirs66020,PakistanSajid IqbalDepartment of Mechatronics and Control Engineering, University of Engineering and Technology, Lahore 54890, PakistanBasheer Ahmed KalwarDepartment of Electrical Engineering, Mehran University of Engineeringand Technology, SZAB Campus, Khairpur Mirs66020,PakistanJournal Article20220115In this paper, a novel method based on LMP with the title of system cost index is presented for optimum placement of distributed generation (DG) sources in the electricity market based on optimum power flow. Along with optimum placement, the optimum size of these sources is also calculated. Desirable locations are determined for optimum order of DGs based on local margin price (LMP). The LMP index is defined in the Lagrange coefficient of active power flow in each bus. Another index used to find desirable locations for DG placement is the customer payment (CP) index, which can be calculated for each bus by multiplying of LMP in busload. The proposed method is implemented on the modified IEEE 9-bus system. The simulation results suggest that the proposed method satisfies the engineering aspect of operation and the economic aspect of the process in the market. The optimum placement of DGs in the market environment leads to a decrease in the system cost and management of line congestion.https://aeis.bilijipub.com/article_147534_4642a8ce148ec2e9cb2654af34a4b133.pdfBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02630010120220401Combinatorial price offer for a wind turbine with flexible load considering the uncertainty to increase the benefit of wind turbine14797110.22034/aeis.2022.147971ENBehnam SobhaniSchool of Electrical Engineering, Iran University of Science and Technology, Tehran, IranSadegh AfzalDepartment of Mechanical engineering, University of Mohaghegh Ardabili, Ardabil, IranJournal Article20220103Wind energy is one of the most important renewable energies in the present and future. However, one of the significant challenges is offering amount of generation and generation price in the next-day market because, on one hand, this energy has nonlinear behavior, and on the other hand any imbalance between forecast and generation results in the generation unit being fined. In this paper, a method is presented to propose wind turbine generation by increasing the forecast accuracy and combination with flexible load and uncertainties. This increases the revenue of the wind generator companies and consumers and guarantees the connection of wind generators. In fact, in this paper, the agreed price between the wind and load is flexible so that this agreed performance will be beneficial to both wind generation and the elastic load. In this modeling, first, the system equations in the market are written and merged. To increase the revenue, the uncertainties are considered, and it is shown that their effect can change the gain in wind turbine and load. The price and wind prediction will be conducted using one of the previous methods, and the primary purpose here is to increase the system revenue. In sum up, this paper introduces the integrated offering strategy model for wind turbines and demand response to support of wind turbines in power market is proposed. the main of this strategy is increasing of renewable generations and demands profits in day ahead market which in this paper the suggestions including; integrated offering of power, uncertainties modelling, using neural fuzzy model for predictions, flexible loads and etc are proposed to achieve the optimal profits. Modeling is done using the MATLAB application to calculate Spain's wind and price information in Spain, and the performance output results are demonstrated thoroughly.https://aeis.bilijipub.com/article_147971_92e6979102122b782dc074729dac03bd.pdfBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02630010120220401A Reliable Approach for Solving Transmission Network Expansion Planning with Objective of Planning Cost Reduction14801210.22034/aeis.2022.148012ENYongqiu LiuSchool of Electrical and Mechanical Engineering, Guangdong University of Science & Technology,Dongguan,523083,ChinaJournal Article20220105This article presents a multi-objective optimization framework for transmission expansion planning using AC optimal power flow to identify the most suitable set of projects and their scheduling along the planning horizon. The candidate plans are evaluated using a fitness function that considered objective function for transmission expansion planning problem is composed of two terms. The first term is related to the sum of investment costs which is the construction cost of new lines; the second term is related to the expected operation costs, which is the expected cost of generation in the power system. The third term is related to the cost of load curtailment. The optimization problem represented in this paper is a large-scale non-convex mixed integer nonlinear programming problem with multiple local minima. The transmission expansion planning procedure is formulated as an optimization problem to overcome the difficulties in solving the non-convex and mixed-integer nature of the optimization problems. The particle swarm optimization algorithm searches for optimal planning to reach the fitness requirement. transmission expansion planning problem involves a decision on the location and number of new transmission lines. In optimization process all constrains are modeled beside problem which should be considered in investment. The proposed transmission expansion planning model has been applied to the well-known IEEE 30-bus test system. In order to illustrated the performance of the proposed method, we consider three scenarios as fix load and generation, fixed load and variable generation and variable load and generation. The detailed results of the case study are presented and thoroughly analyzed. The obtained transmission expansion planning results show the efficiency of the proposed algorithm.https://aeis.bilijipub.com/article_148012_b5059544974b6502cc6ad9f1c602d2de.pdfBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02630010120220401Employment of vehicle to grid technology to decrease the economic-environmental costs equipped with mixed-integer non-linear programming approach14817210.22034/aeis.2022.148172ENJinkui LiSchool of Economics and Management, Guangdong Songshan Polytechnic College, Shaoguan, Guangdong , 512000, ChinaHuahui LiSchool of Management, Universiti Sains Malaysia, Minden, Penang 11800, MalaysiaJournal Article20220108In the present work, the programming of thermal production units is adopted by vehicle to grid (V2G) technology. The suggested approach solution is made by the mixed-integer non-linear programming (MINLP) method in the GAMS simulation environment. The main objective of this study is to obtain an answer to minimize the considered objective function (OF). Some limitations are also considered in this optimization problem that should be met by the proposed method. The proposed method of this work is evaluated to validate its efficiency. In this regard, this proposed technique is tested on IEEE 10-unit case study that contains 5000 EVs (electrical vehicles). According to the obtained results, the utilized V2G can considerably influence the unit commitment (UC) problem. EVs bring on new loads to the electrical network that grows the expenditure of power production. Nevertheless, the coordinated charging method along with rational utilization of V2G power can decrease this expenditure. Also, take into considering the minimizing operating expenses as the programing goal presents the better overall economic and environmental performance in the thermal unit with V2G cases.https://aeis.bilijipub.com/article_148172_910da303a5e593f799c8107b57c90382.pdfBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02630010120220401Determining the amount of earthquake displacement using differential synthetic aperture radar interferometry (D-InSAR) and satellite images of Sentinel-1 A: A case study of Sarpol-e Zahab city14830410.22034/aeis.2022.148304ENJacob CherianCollege of Business, Abu Dhabi University, Abu Dhabi P.O. Box 59911, United Arab EmiratesJournal Article20220117One of the effects of an earthquake is the creation of displacement on the land surface. It’s important to determine displacement due to natural disasters such as earthquakes. So, it’s essential to identify height changes occurring on Earth due to these movements. Detecting these changes in the extent of field operations requires a lot of time and money; hence, satellite technology can be used to eliminate the limitations of field operations. This study aimed to determine the trend and rate of land surface changes in the Sarpol-e Zahab earthquake using Sentinel-1 A and D-InSAR method. To this end, two radar images of SAR from the Sentinel-1 A satellite were prepared before and during the earthquake. Then, these two images were registered based on a prepared radar file to produce the interferogram of study area. After removing the topography phase, the removal of existing noises was done by the interferogram generated from the Goldstein filter. Then, to determine the real phase difference, the produced phases were corrected. Before changing phase to displacement, to improve the processing results, the refining phase and applying multiple corrections and absolute generated phase were changed to displacement. The displacement map of Sarpol-e Zahab city resulted from an earthquake of 7.3 magnitudes showing displacement between -1.6 and 68 centimeters. Also, the results of this study showed that maximum displacement occurred in the north and northwest of the city, namely the villages of Dasht-e Zahab, Sarpol suburb, and Posht-tang, because of the adjacency of the earthquake center. Given the advantages of using remote sensing data, such as the ability to check the displacement between any desired point with the proper precision on the interferogram and the extensive coverage of SAR images, it’s suggested to use radar data such as Sentinel-1 to investigate displacement of the land surface.https://aeis.bilijipub.com/article_148304_e1a0f941b5a3212b47d3152eea929858.pdfBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02630010120220401A novel hybrid radial basis function method for predicting the fresh and hardened properties of self-compacting concrete14830510.22034/aeis.2022.148305ENZhangabay NurlanDepartment - INDUSTRIAL, CIVIL AND ROAD BUILDING
М.AUEZOV SOUTH KAZAKHSTAN STATE UNIVERSITYJournal Article20220119It is observed from the published literature that there were so limited studies concentrating on predicting both fresh or hardened properties of self-compacting concrete (SCC). Hence, it is tried to develop models for predicting the fresh and hardened properties of SCC by the optimized radial basis function neural network (RBFNN) method. The RBFNN method's key parameters are optimized using ant-lion optimization (ALO) and biogeography optimization (BBO) algorithms. The considered properties of SCC in the fresh phase are the L-box test, V-funnel test, slump flow, and compressive strength (CS) in the hardened phase. Results demonstrate powerful potential in the learning section as well as approximating in the testing phase. It means that the correlation between observed and predicted properties of SCC from hybrid models is acceptable so that it represents high accuracy in the training and approximating process. Regarding D flow, L-box, V-funnel, and CS, the results of ALO-RBFNN were better than BBO-RBFNN and literature. Overall, the RBFNN model developed by ALO outperforms others, which depicts the capability of the ALO algorithm for determining the optimal parameters of the considered method.https://aeis.bilijipub.com/article_148305_21659be7154e5d5318968017d3943b1f.pdfBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02630010120220401Allocation of Interline Power Flow Controller based Congestion Management in Deregulated Power System14847810.22034/aeis.2022.148478ENMuhammad Safdar SialDepartment of Management Sciences, COMSATS University Islamabad (CUI), Islamabad 44000, PakistanQinghua FuDepartment of Business Administration, Moutai Institute Zunyi City 563000, ChinaTalles Vianna BrugniAccounting Department, FUCAPE Business School, Av. Fernando Ferrari, 1358, Boa Vista, Vitória–ES 29075-505, BrazilJournal Article20220116The present paper provides an optimal location of the Interline Power Flow Controller (IPFC) method in the power system using the Sperm Whales Swarm Optimization (SWSO) algorithm. The main aim of the IPFC optimal location is to achieve an appropriate structure for congestion management in restructured power systems. The IPFC model changes the power flow by injecting power into the system. One of the most important issues to reduce power losses and improve the voltage profile, which leads to a reduction in the generation and congestion costs, is determining the appropriate location for installing IPFC. Therefore, an objective function is defined, including the stated parameter, minimizing the generation cost, congestion costs, power losses, and improving the voltage profile. Using the upgraded SWSO algorithm, a new approach to the optimal location of IPFC is presented. For validation, the proposed method has been implemented on the IEEE 14 bus restructured power system. By examining the obtained results, the performance of the proposed algorithm is better than the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. It causes a further reduction in the generation cost, congestion costs, power losses, and improving voltage profile. Therefore, it is suggested that the proposed method be applied to the actual world restructured power system.https://aeis.bilijipub.com/article_148478_0f59355cba331417a8e75fb92264e23d.pdf