Research On Bilevel Inverse Robust Optimization Dispatch And Strategy Adjustment Of Power Systems With Wind Power

Wind power access network has the positive significance on improving the economy of power system operation and reducing the energy consumption.Because of the characteristics of wind power generation such as anti-peaking and intermittent characteristics,the peaking difficulties and the deficiency of line transmission capacity are generated when the wind power is placed massively.Finally the phenomenon of wind abandoning is generated.Wind abandoning results in the waste of energy,and that has a strong impact on the economy of wind farm operation.Therefore,it is of immediate research value that formulating the reasonable optimization scheduling policy improves the capacity of wind power utilization and reduces the impact of wind power fluctuation.Based on this,the robust optimization theory is improved and the subsequent adjustment scheme is given in this paper.The following studies are carried out as follows.Firstly,for improving the insufficient of subjective error and solving too conservative of the traditional robust optimization dispatch method,the bilevel inverse multi-objective robust optimization model is established.Based on the principle of topological manifold,the optimal inverse robust index is formulated.The calculation scheme of optimal inverse robust index is given by combining boundary correspondence principle and dichotomy.Then the scheme is implanted in the grid multi-objective bacterial colony chemotaxis(GMOBCC)algorithm.It aims at ensuring the synchronization of the calculation process and improving the operation speed.The numerical examples are applied for verifying the effectiveness of the proposed method.Secondly,the bilevel inverse robust optimization method is presented for dealing with wind power uncertainty.In order to improve wind power accommodation,pumping and generating conditions of pumped storage power station are optimized.The bilevel inverse robust optimized wind power dispatch model is proposed.The inner layer of model aims for total generating cost optimum.According to the ideal disturbance constraint of objective function,the outer layer introduces wind power optimal inverse robust index for analyzing limit constraint relation between total generating cost and wind power accommodation.The economy and effectiveness of proposed method is validatedby the example.Thirdly,the virtual power plant(VPP)is adopted for packing the intelligent management managing to charge-discharge of vehicle to grid(V2G)and wind power generation.The bilevel inverse robust scheduling optimization model containing VPP is established based on the wind power uncertainty and V2 G uncertainty.The inner layer of model aims at the optimum of gross profit.The outer layer sets the ideal perturbation constraints according to the requirements of the decision maker.The optimal inverse robust index of the wind power output and charge-discharge of V2 G are introduced.The economy and effectiveness of proposed method is validated by the example.Ultimately,for improving the prcticability of scheduling strategy,it is established a multi-objective low-carbon/economic bilevel inverse robust optimization scheduling adjustment strategy with the optimal objectives of system loss,fuel cost,system equilibrium and carbon emission considering the uncertainty of wind power and load.The bilevel inverse robust optimization method is used for improving the capacity of resisting disturbance of daily dispatch schedule.Based on the indifference surface cluster and the minimum enclosing ball,the optimal coordinated operation space-domain are formed.It provides the reference for intraday adjustment.Based on the shortest distance grid method,the optimal scheduling strategy is established.The validity of proposed method is verified by the modified IEEE30 node test system.