A New Multi-objective Hybrid Algorithm For Dynamic Environmental Economic Dispatching Of Power System

Dynamic economic environment dispatch is a common multi-variable, strongly constrained, non-convex multi-objective optimization problem in power systems, and traditional algorithms are difficult to solve. Therefore, an improved IMOSSA-DE multi-objective optimization algorithm. First, initialize the...

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Vydáno v:2022 Power System and Green Energy Conference (PSGEC) s. 800 - 805
Hlavní autoři: Wang, Jie, Qian, Yuliang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.08.2022
Témata:
Costs
differential evolution algorithm
Heuristic algorithms
multi-objective optimization
Power system dynamics
Search problems
Simulation
Sociology
sparrow search algorithm
Valves
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Shrnutí:Dynamic economic environment dispatch is a common multi-variable, strongly constrained, non-convex multi-objective optimization problem in power systems, and traditional algorithms are difficult to solve. Therefore, an improved IMOSSA-DE multi-objective optimization algorithm. First, initialize the population with a chaotic map to improve the quality of the initial solution, and increase the diversity of the population. In order to further strengthen the discovery and development ability of the discoverer, the sine and cosine strategy is introduced. Secondly, the traditional sparrow search algorithm is easy to fall into the local optimal solution, and the convergence performance is poor. The differential evolution strategy is integrated into it can enhance the global search ability and convergence performance of the population. In addition, non-dominated sorting, a new crowding degree distance calculation strategy and elite retention strategy are introduced to make the algorithm capable of solving multi-objective optimization problems. The optimal compromise scheduling scheme is obtained by fuzzy decision theory. Finally, the simulation results of the 10-machine power system show that the proposed algorithm can optimize the two conflicting goals of emission and cost at the same time, and obtains a better Pareto frontier than other algorithms, verifying the effectiveness and superiority of the algorithm.
DOI:10.1109/PSGEC54663.2022.9880971