Research On Improved Artificial Bee Colony Algorithm For Large-scale Multi-objective Unit Combination Optimization

In order to keep up with the pace of national energy conservation and emission reduction and reduce the pressure of energy and environmental pollution,the environmental economic dispatching problem is an urgent problem to be solved in the current power system.Environmental pollution,electricity costs and dynamic factors are taken into account.For this kind of dynamic,multi-objective and high-dimensional optimization problem,artificial intelligence optimization algorithm has its advantages.Under this background,the paper studies the dynamic and multi-objective economic scheduling model for the environmental economic scheduling problem with environmental factors as the research object.In this paper,the environmental economic dispatching model is modeled.In this model,environmental factors and fuel cost are considered simultaneously,and the valve point effect and four optimization constraints are considered comprehensively.The constraint conditions include upper and lower limits of unit output,climbing rate,working dead zone and unit output power balance.In this paper,an improved artificial bee colony Algorithm(ABC)is designed after the colony size is dynamically adjusted.The algorithm adds memory feedback mechanism and introduces the concept of evolution rate,which can improve the efficiency of the algorithm and balance the exploration and development level of the algorithm.The paper carries on the simulation experiment.First,the improved artificial bee colony algorithm based on benchmark function is validated to verify the effectiveness of the algorithm in the optimization scheduling process.The second is to solve the environmental economic scheduling model.In addition,based on the weight coefficient method,the algorithm in this paper is used to solve the multi-objective transformation in the environmental economic scheduling problem.In order to verify the feasibility of the algorithm and the model,economic scheduling and multi-objective optimization simulation experiments were carried out for a large-scale example with up to 1000 units.The results show that the proposed algorithm can achieve good optimization effect in solving the dynamic multi-objective environment economic scheduling model.