What Is The Implementation Of MFO To Solve Multi-Area Economic Dispatch Problem?

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In this thesis implementation of MFO to solve multi-area economic dispatch problems is presented. The practical nonlinear generator constrained like VPL and POZ along with tie-lines power flow constraints are also considered in this study. The results obtained by MFO are compared with recently reported results to validate and to show superior performance for the solution of MAED problems with different dimensions and complexity levels. It was established that the MFO algorithm provides good solution in terms of convergence rate and optimum cost with less number of control parameters. The MFO is found to be a promising approach for real-world problems.
Some conclusions can be drawn from the obtained results & cost convergence
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The thesis has presented a new procedure for solving the Multi-Area Economic Dispatch problem using MFO algorithm. The procedure is shown to be very fast, robust, and extensible to include a large class of utility problems.

• The MAED problem is much complex due to the practical operational constraints such as valve point loading effect(VPL) ,prohibited operating zones (POZ) along with tie line power flow limit constraints, which make the system highly nonlinear. Therefore it requires a powerful optimization approach to solve these types of problems.
• The project successfully applied MFO for the solution of classical MAED problem with security consideration and without security consideration. The aims of the project were to introduce renewable energy to a classical MAED problem and minimize the production costs while taking into consideration the transmission lines, tie lines and bus voltage constraints. The proposed algorithm was successfully tested on a five-area system. Introduction of security aspects to the classical MAED problem resulted in an increase in the total production cost of power.
• Moth-Flame optimization (MFO) Algorithm, is applied for constrained optimization and engineering design problems. A comparative analysis of MFO algorithm expresses the optimum functional value in term of accuracy and standard deviation over rest of well-known constraint

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