| In order to achieve the great national strategic goal of peak carbon dioxide emissions and carbon neutrality as soon as possible,and make China enter a new stage of green and sustainable development,the widespread use of renewable energy has become an irresistible trend.However,large-scale integration of wind power,photovoltaic and other renewable energy sources,which are affected by the environment and lead to output fluctuations,will inevitably bring great pressure to the stability and economy of power grid operation.VPP integrate distributed generator sets,energy storage systems and controllable loads to carry out collaborative optimal scheduling by means of communication and convergence technology,which provides an effective solution for distributed power sources to connect to the power grid on a large scale and participate in the power market competition.This thesis focuses on the aggregation and scheduling optimization model of VPP,and the main work is summarized as follows:（1）The aggregation problem of a single VPP is studied.The uncertainty of source and load exists in the virtual power plant,which will cause VPP to face certain risk loss while gaining profit.To solve this problem,VPP can avoid risk loss and maximize the income of VPP by selecting the units with suitable adjustable capacity from the existing multi-power units.Based on conditional value-at-risk（CVa R）risk control,the aggregation optimization model of multi-power VPP was established by using CVa R as the risk measurement index and aiming at maximization of operating income and minimization of risk loss.Firstly,scene technology is used to simulate the uncertainty of source load in VPP.Then the influence of risk preference of VPP managers on VPP unit selection and the influence of environmental penalty cost and electricity purchase price on VPP unit selection is studied.The simulation results show that VPP can choose the most suitable unit from many units,and get higher income and face lower risk loss.（2）The aggregation problem of multiple VPPs is studied.By introducing controllable unit lessee,the lessee guides controllable unit with appropriate leasing capacity of VPP to optimize operation by adjusting unit leasing price,so as to maximize the overall income.At the upper level,the objective is to maximize the revenue obtained by leasing units,and at the lower level,the objective is to minimize the operating cost of VPPs.The two-level optimization model of VPPs aggregation for market transaction is constructed.On this basis,the linearization technique and complementary principle are used to transform the lower level problem into equilibrium constraint,and then the original nonlinear bilayer optimization model is transformed into a single-layer mixed integer linear programming model based on strong duality theory.Finally,by comparing the simulation results of the three scenarios,it can be seen that this model has the highest overall income.（3）The point-to-point energy trading problem of multiple VPPs is studied.The introduction of electricity retailer,retailer through price discrimination,to determine the purchase and sale of electricity prices to encourage VPPs to participate in energy coordination management,and VPPs through direct energy transactions with neighboring VPPs to respond to the electricity prices set by retailer.Upper with retailer to obtain the maximum profit as the goal,to lower VPPs operation cost minimizing the goal,in order to improve VPPs to participate in the enthusiasm of point-to-point energy trading,on the basis of generalized Nash game,fair distribution obtained by energy exchange earnings,build the meter and price discrimination swarm optimization scheduling of VPPs double model.Then,the two-layer nonlinear model is transformed into a single-layer mixed integer linear programming model by KKT,Big-M method and strong duality theory,and solved by commercial solver.The simulation results show that the model can make VPPs gain extra income and improve the ability to resist risks. |
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