A comparative study of Lambda Iteration method and particle swarm optimization to solve Economic load dispatch (case study: TEST 30 BUS system)
Mukiibi, John Paul
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With the increasing demand for power today, it becomes necessary to operate power plants most economically. This gives rise to economic load dispatch. Economic load dispatch is the process for allocating the generation among the available generating units to fulfill the load demand in such a way to minimize the total generation cost and satisfying the equality and inequality constraints. On the other hand, the optimal power flow problem focuses on minimizing the generation cost considering the equality constraints, inequality constraints and the state and control variables. This project is focused on evaluating the lambda iteration and particle swarm optimization techniques that provide a solution to the economic load dispatch problem and analyzing the optimal power flow constraints of a test 30- bus system using the particle swarm optimization technique and the lambda iteration method. Using the particle swarm optimization technique in MATLAB and lambda iteration method in GAMS, we obtained the generation cost, system power losses and the respective power output of the generators. When optimal power flow is considered with more constraints such as the voltage constraints and the line limits, the cost of generation, power generated and the power loss, are observed to increase. This shows that, it is necessary to consider all the transmission constraints to obtain accurate optimization of a power system. It was observed that the total fuel cost slightly decreased when Economic load dispatch was solved using particle swarm optimization as compared to lambda iteration method. The particle swarm optimization technique provides a convergent solution which does not require an initial value of lambda whereas lambda iteration method depends on the selection of an initial value. In future work, ELD should try to ensure a reliable operation at the lowest cost rather than just provide economic optimization solutions.