Genetic Algorithm in Determining Wheeling Cost Allocation Using LRMC and MW-Mile
Abstract
Electricity deregulation has occurred in many countries. This deregulation primarily aims to introduce competitions to increase the efficiency and quality of service in the electricity supply industry. Generation values and transmission line functions will change significantly. Customers will welcome the free market, causing many companies to build their own generators in a wheeling operation scheme to meet their needs. Wheeling is the solution to this problem. The power flow method was used after adding wheeling to the system. This method was used to determine the system conditions after wheeling was added, considering that power flow map will change when there is a wheeling costumer. The study of the power flow method provides information on the amount of total power generated by the generator yet does not provide information on the power supplied by the generator in each transmission network. To address this shortcoming, the power tracing method was used. This method can provide information on the allocation of power supplied by generators in each transmission network in the system. This research discusses the power tracing method using the genetic algorithm (AG) method. AG is one of several optimization methods; it assumes the allocation of power flowing by the generator as the problem to be optimized. The wheeling pricing used the long run marginal cost (LRMC) method. This method projects future costs by taking into account changes in expenses that occur at any time within a specified period. In this study, the LRMC method was compared with another wheeling costing method, namely the MW-Mile method. The results showed that the LRMC method was cheaper than the MW-Mile method. From an economic perspective, the wheeling costs determination using the LRMC method is 14%-20% cheaper than the MW-Mile method.
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