Research On Dynamic Economic Dispatch Optimization Method For A Class Of Power Systems

With the implementation of China's sustainable development strategy,the state's emphasis on clean energy applications has gradually increased.But as clean energy is used to generate electricity,the problem of matching multiple resources is gradually emerging.But as clean energy is used to generate electricity,the problem of matching multiple resources is gradually emerging.How to properly regulate various energy sources for power generation is urgently needed.The requirement for power systems in China is to ensure power quality.The power quality includes voltage,frequency,and waveform.Only economic scheduling is pre-emptive in the control measures to ensure frequency quality.The economic dispatching of the power system is based on the predicted load curve,and the output plan of each unit is specified.Under the premise of meeting the balance of supply and demand of the power system,the objective function is optimized.This objective function is generally an indicator of economics and environmental.It can be seen that the economic dispatch of the power system is very meaningful in reducing economic costs.The main content of this paper is to conduct dynamic economic dispatching for power systems to cope with the challenges faced by the system and reduce economic costs.This paper first establishes a power system combining hydropower and thermal power and abstract it as a mathematical problem.The valve point effect of a thermal power generator is considered in a thermal power generation system.The hydropower system is a four-stage hydroelectric power station.And in it should consider the constraints of hydropower,thermal power and power balance.Then,an improved inertia weight particle swarm optimization algorithm is proposed for economic scheduling.The inertia weight is divided into linear part and adjustment part.The improved algorithm is applied to the previous model and compared with the linear inertia weight particle swarm algorithm.The results show that the improved algorithm is stronger than the linear inertia weight particle swarm optimization algorithm in terms of convergence speed and optimality of finding the optimal solution.With the development of society and the advancement of science and technology,China's power system is striding towards the direction of smart grid.There are a large number of distributed grid-connected and mobile load grids connected to the smart grid.In order to adapt to the development of smart grid,this paper also established a distributed grid-connected power system.In this power system,there is no longer centralized scheduling,but distributed scheduling.With the development of society and the advancement of science and technology,China's power system is stridingtowards the direction of smart grid.There are a large number of distributed grid-connected and mobile load grids connected to the smart grid.In order to adapt to the development of smart grid,this paper also established a distributed grid-connected power system.In this power system,there is no longer centralized scheduling,but distributed scheduling is applied.In this system,there will also be power balance,unit output constraint,mobile load grid connection,topology change and distributed power supply plug-and-play.Then,for this system,use system containing principle.The paired subsystems are decomposed and the improved consistency algorithm based on the system containing principle is obtained.The simulations are carried out for the above mentioned problems.The results show that the improved consistency algorithm can satisfy the unit constraints and power balance,and can be well adapted to the mobile load grid-connected,topology changes and plug-and-play of distributed power grids.