The developed strategy corresponds to a multi-objective and dynamic real-time optimization (MOO-DRTO) framework applied to a postcombustion MEA-based CO 2 capture process from a baseload coal-fired power plant under cycling conditions. The aim in this work is to develop an optimization framework to address cycling of baseload energy systems due to the penetration of renewables into the grid. Finally, this demonstrates that the zigzag search is easy to implement and is superior to other multi-objective (MO) techniques in both accuracy and = , Comparisons are made with algorithms which have been widely used in literatures, such as the genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate that the proposed zigzag search algorithms have the ability to deal with relevant power system problems. Case studies are carried out with the IEEE 30-bus system and IEEE 118-bus system.
A set of non-dominant solutions can be obtained to form the Pareto front. The problem is formulated as a non-linear multi-objective optimization model taking energy constraints, generation limits, and transmission constraints into consideration. Here, the zigzag search algorithm is introduced, modified with enhancement, and effectively applied to solve an economic emission dispatch problem and to demonstrate its practicability in power systems. The zigzag search algorithm has been applied in engineering fields, such as oil well placement, with satisfactory results.
0 kommentar(er)
