An improved integer coded genetic algorithm for power generation dispatch problem

Golestani, S.; Taddayon, M.

doi:10.1109/td-asia.2009.5356900

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reasonable planning and optimization of power system operation can effectively realize the safe and stable operation of power industry while achieving the goal of high efficiency and low emission [1][2] . The scheduling of power systems is a typical multi-objective optimization problem, and the existing solution algorithms include backtracking search optimization algorithm [3] , multi-objective evolutionary algorithm [4][5][6] , multi-objective particle swarm optimization algorithm [7][8][9] , genetic algorithm [10][11] , etc. In summary, most of the existing studies are less concerned with the quantitative impact analysis and comprehensive optimization of energy storage on multiple objectives such as environment.…”

Section: Introductionmentioning

confidence: 99%

Low carbon optimal dispatch of power systems with energy storage and wind power

Li,

Jiang,

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

View full text Add to dashboard Cite

Using a regional integrated energy system with the advantages of multi-energy complementarity and coupling, it can comprehensively enhance the optimal allocation of multiple energy consumption. This paper proposes a multi-objective low-carbon optimal dispatch model for a regional integrated energy system , which fully exploits the advantages of heat storage devices, electric boilers and electric energy storage in regional energy systems to promote the grid-connected consumption of wind power. By establishing a load elastic demand response mechanism, the peak-to-valley difference level of system load is reduced to further enhance the grid access space of wind power. Finally, the proposed scheduling model is optimally solved using the NSGA-II algorithm under the improved elite strategy.

show abstract