Multi-objective Optimization Scheduling Method Of Peak Load Regulation Based On Improved Grey Particle Swarm Algorithm

Currently,the development of China's new energy industry has been listed on the top of the world.Also,China has become the country with largest scale in wind power and the fastest growth rate in photovoltaic power.However,The randomness and intermittence of the output of distributed generation(DG)has caused a big problem that the traditional operation of peak adjustement system can not accomplish the new peak adjustment job brought up by large scale new power integration,leading to severe abandon of the wind power and photovoltaic power.Under the current circumstance that traditional peak adjustment way can't be totally adapted to new change coming with large scale grid in new power,this dissertation will explore feasible options in which users' participation in peak adjustment project will be given more consideration.Firstly,the dissertation briefly introduces the traditional peak-shunting scheduling model and analyzes the advantages and disadvantages of its peak-shunting scheduling model.This dissertation analyzes the impact of the new energy large-scale accessing the power grid,according to the new energy power generation characteristics.Aiming at the new energy large-scale accessing the power grid at present,the traditional peak load regulation operation mode can not meet the consumption of the new energy output.This dissertation research demand side management?look for the peaking resources in the grid load side and put forward scheduling model which a variety of distributed generation and different types of distributed energy resource both participate to reduce the system peak load regulation.Secondly,for the difficult to manage these different types of distributed energy(DER)such as DG,energy storage unit,controllable load,interrupt load,etc.due to that the number and type of DER is numerous and the distribution of DER is scattered,this dissertation put forward a strategy of virtual power plant(VPP),which consisting of various types of DER will participate peak load regulation.With goal of minimizing the load variance and operating costs in each period,an multi-objective peak load regulation modes of virtual power plant is established.Aiming at the shortcomings of the traditional multi-objective particle swarm optimization(MOPSO)algorithm,which is easy to fall into the local optimal solution?the update strategy is random and the global optimal solution of population is difficult to choose,which makes the optimization of the objective and credibility insufficient.this dissertation proposed multi-objective particle swarm optimization algorithm based on gray relational degree.Finally,take the IEEE33 node distribution system as an example to simulate.The results show that the proposed method can effectively reduce the running cost and improve the capacity of peaking the load.