Abstract
Economic Load Dispatch (ELD) is a key issue in power systems and its goal is to achieve minimum economic costs by allocating the output of generator units when satisfying the load demands and the operating constraints. As the dimension of the variables and the constraints increase, the traditional mathematical method is gradually not suitable for the ELD. This paper proposes an Improved Bird Swarm Algorithm (IBSA) to solve the ELD problem of a power system. By introducing the nonlinear cognitive and social coefficients, the proportion of individual learning and social learning of birds can be dynamically adjusted. In addition, the Levy flight strategy is added to the group between producers and beggars to increase the randomness. The performance of IBSA is verified via two systems consisting of 6 and 15 units, respectively, that take into account generation limitation, ramp rate limit, and prohibited operating zones. From the simulation results, the IBSA has shown excellent performance and robustness, which can be considered as a reliable solution for the ELD.
Highlights
With the continuous growth of the global economic scale, the consumption of traditional fossil energy and the demand for energy are gradually increasing year by year
Through a detailed analysis of test results and comparison with other algorithms, it is demonstrated that the Improved Bird Swarm Algorithm (IBSA) can provide a stable and economic dispatching scheme for the power system
30 trials, the total time spent on IBSA and Bird Swarm Algorithm (BSA) is almost the same, which indicates that the improved strategy does not increase the amount of computation of the original algorithm
Summary
With the continuous growth of the global economic scale, the consumption of traditional fossil energy and the demand for energy are gradually increasing year by year. ELD is an effective dispatching strategy to enhance economic benefits under the condition of ensuring the safety and stability of power systems. With the expansion of the power grid and increase of the constraint variables, the traditional mathematical programming method is prone to the problem of dimensionality disaster. In this case, intelligent algorithms gradually replace traditional mathematical methods. Intelligent algorithms have become common and effective approaches to solving the ELD problem [7].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
