Dynamic economic emission dispatch based on group search optimizer with multiple producers

This paper presents a new method for dynamic economic emission dispatch (DEED) of power systems, using a novel multi-objective evolutionary algorithm, group search optimizer with multiple producers (GSOMP) that includes a constraint handling scheme introduced to deal with complex constraints. The DEED is divided into 24 decomposed DEEDs, which are then solved hour by hour in the time sequence. A technique for order preference similar to an ideal solution (TOPSIS), is then developed to determine the final solution from the Pareto-optimal solutions considering a decision maker's preference. The performance of GSOMP has been evaluated on the DEEDs of the IEEE 30-bus and 118-bus systems, respectively, in comparison with those of multi-objective particle swarm optimizer (MOPSO) and non-dominated sorting genetic algorithm-II (NSGA-II). The simulation results show that DEED is well solved by the proposed method as a set of widely distributed Pareto-optimal solutions can be obtained and that GSOMP has better convergence performance than MOPSO and NSGA-II and consumes much less time than NSGA-II. All the NOx, CO2 and SO2 are integrated into the emission objective function of the DEED, on which the solution obtained can have relatively low emission of each pollutant.

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► A novel model, dynamic economic emission dispatch (DEED).
► A group search optimizer with multiple producers (GSOMP).
► GSOMP has better performance of convergence and time consumption than other methods.
► DEED solution with widely spread Pareto fronts obtained.
► DEED solution with relatively low emission of all the NOx, CO2 and SO2.