Name
Abstract | ||
|---|---|---|
The economic load dispatch (ELD) optimization is an important approach to decrease the power consumption. In this paper, a network-plant hierarchical economic load dispatch mode is proposed based on the conventional load dispatch mode. The concept of plant coal consumption is proposed, as well as the method to obtain it. The model to achieve the least coal consumption in the full network depending on the plant coal consumption characteristic is also proposed. And it is theoretically proved that the optimization result of hierarchical ELD is consistent with that of conventional ELD. The chaotic particle swarm optimization (CPSO) algorithm is used to solve the optimal dispatch problem, adopting the adaptive inertia weight to accelerate the convergence speed. The hybrid optimization algorithm is improved from particle swarm optimization (PSO) algorithm by chaotic searching in the neighborhood to avoid getting into the local optimum, with the algorithm steps listed in the paper. A numerical example is done and analyzed, verifying the validity of the hierarchical optimization mode and CPSO. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICNC.2013.6818031 | ICNC |
Keywords | Field | DocType |
hybrid optimization algorithm,hierarchical eld,economic load diapatch,hierarchical optimization mode,pso algorithm,power consumption,chaotic-particle swarm optimization,chaos,particle swarm optimisation,power generation dispatch,cpso algorithm,chaotic-partical swarm optimization,optimal dispatch problem,economic load dispatch optimization,plant coal consumption characteristic,power generation economics,adaptive inertia weight,hierarchical optimization,hierarchical economic load dispatch,steam power stations,network-plant hierarchical economic load dispatch mode,particle swarm optimization,power generation,economics,optimization,coal,mathematical model | Convergence (routing),Particle swarm optimization,Chaotic particle swarm optimization,Mathematical optimization,Computer science,Local optimum,Multi-swarm optimization,Inertia,Chaotic,Metaheuristic | Conference |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Zhu | 1 | 0 | 0.34 |
| Qianjun Li | 2 | 0 | 0.34 |
| Yong-Xin Feng | 3 | 6 | 8.27 |
| Weiming Han | 4 | 0 | 0.34 |
| Feilong Liu | 5 | 429 | 15.52 |
| Chaobing Han | 6 | 0 | 0.34 |
| Jianxin Zhou | 7 | 0 | 0.34 |
| Fengqi Si | 8 | 3 | 3.45 |