Journal of Global Optimization
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch
IEEE Transactions on Evolutionary Computation
| Hi-index | 12.05 |
Daily optimum economic emission scheduling of hydrothermal systems is an important task in the operation of power systems. Many heuristic techniques such as differential evolution, and particle swarm optimization have been applied to solve this problem and found to perform better in comparison with classical techniques. But a very common problem with these methods is that they often converge to sub-optimal solution prematurely. A reliable and efficient method termed as self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) is presented in this paper to avoid premature convergence. A multi-chain cascaded hydrothermal system with non-linear relationship between water discharge rate, power generation and net head is considered in this paper. The water transport delay between connected reservoirs is also taken into consideration. The problem is formulated considering both cost and emission as competing objectives. The effect of valve point loading is also taken into account in the present problem formulation. The feasibility of the proposed method is demonstrated on a sample test system. The results of the proposed technique are compared with other heuristic techniques. It is found that the results obtained by the proposed technique are superior in terms of fuel cost, emission output etc.