Research on the Source-Load Collaborative Optimization Scheduling Strategy Based on Master-Slave Game
The increase in load diversity and the integration of a large number of distributed energy resources significantly increase the difficulty of power grid dispatch and control.In order to flatten the peak-valley difference of the power grid and increase the consumption rate of distributed energy resources such as wind and solar power,a source-load collaborative optimization strategy is proposed based on the master-slave game theory.At the same time,to address the uncertainty brought by the increase of load diversity and distributed energy resources to the participation of virtual power plants(VPP)in power grid dispatching,load aggregators(LA)and virtual power plant operators are introduced as representatives of the source and load sides of the power system to participate in the game,and the price and power consumption strategies are optimized through the game mechanism,so as to meet the maximum residual users on the load side and the maximum operating income on the source side under the requirements of carbon emission reduction.By adopting the distributed genetic joint quadratic programming algorithm for solution,and introducing a ladder-type carbon trading mechanism,the enthusiasm for energy conservation and emission reduction of the system is improved.Through case analysis and verification,this strategy can effectively reduce the load peak-valley difference,lower the system carbon emissions and the operation costs on both the source and load sides,achieving a win-win situation for the interests of both the source and load sides.
master-slave gamesource-load coordinationload aggregation operatorvirtual power plant operatorstaircase carbon trading
万方数据
维普
