Development of electric load factor optimization technique with multi criteria decision making

• Main Author: Khoshsolat, Seyed Amir Hossein
• Format: Thesis
• Language: English
• Published: 2014
• Online Access: http://psasir.upm.edu.my/id/eprint/48184/1/FK%202014%2054R.pdf; http://psasir.upm.edu.my/id/eprint/48184/

Electric energy has an important role in daily human life. This energy, is the infrastructure for the electric energy industry. It is conveyed by three stages, namely generation, transmission and distribution systems for use by consumers. Consumers do not have a constant energy consumption over a daily hour and yearly days. The average and peak load of electric energy will be different. Load factor is defined as an average load divided by peak load. This study is concerned to modify this factor so that it could come close to unity (1) and the closer the better. Therefore the capacity and investment in electric infrastructures must be evaluated in coordination with peak load. In order to optimize the load factor different potential alternatives usage on the demand side like renewable energy, combined cooling, heat and power (CCHP) systems, fuel cell, tariff management and other will be analyzed. However, some criteria for each alternative must be considered under the process of substitution. In order to select the suitable alternatives, this research used Fuzzy Multi Criteria Decision Making technique to develop the load factor optimization method. This method was used to evaluate data for local area of Tenaga National Berhad in Malaysia and West Mazandaran Regional Electric Company in Iran. Among the Multi Criteria Decision Making (MCDM) techniques, Topsis method was selected to develop the coding using MATLAB software. Furthermore, sensitivity analysis was used to analyze and verify the out put data and the logic of the optimization methodology through graphic user interface (GUI). The results of three specific case study show that the 10%, 15% and 20% aimed in the improvement of load factor gives us a sort of priorities from the best to the worst one. Depending on the condition of alternatives and those criteria, the output could be different. This shows the flexibility and compatibility of the optimization method which have been developed in this study. Other studies and demand side management (DSM) methods just were able to consider limited alternatives or criteria in load mitigation. However,the proposed method is able to make solution for any dimensions of decision making matrix in load factor optimization (LFO) course. It can be concluded that this method, by the first time, made a direct bridge between electric LFO and decision making in demand side management.