Research On Unit Commitment Optimization Scheduling Considering New Energy

In view of the adverse impact of fossil fuel thermal power plants emitting a large amount of greenhouse gases and pollutants on environmental issues,the massive popularity of renewable generations(RGs)and plug-in electric vehicles(PEV).The large-scale use of vehicle has played a positive role in mankind’s response to climate change challenges.However,the large-scale integration of PEV and RGs directly affects the balance of power supply and demand,which brings challenges to the safe and economic operation of power system.On the one hand,due to the randomness of PEV users’ charging behavior,their large-scale access to the power grid will bring great uncertain load to the power grid,thus bringing challenges to the safety and stability of the power grid operation.On the other hand,because RGs is largely dependent on the characteristics of weather conditions,the uncertainty of RGs also brings great challenges to the operation and control of the power grid.Therefore,new energy factors such as RGs and PEV undoubtedly greatly increase the complexity of power system scheduling problems,and how to solve such problems well is an inevitable requirement to comply with the general trend of green energy development in the new era.Therefore,a new parallel social learning particle swarm optimization(SLSPO)framework is proposed to solve the large-scale power system scheduling problem with RGs and PEV integrated access to the grid.This framework combines the real values with binary decision variables and proposes corresponding scheduling strategies for RGs and PEV respectively,aiming at solving the large-scale mixed integer unit commitment(UC)problem considering the charge and discharge management of PEV integrated with RGs.To verify the effectiveness of the algorithm,numerical examples of RGs and PEV under different conditions and the number of multi-scale units are analyzed.The results show that the proposed framework of parallel social learning particle swarm optimization algorithm has good performance in solving UC problems with the influence of new energy factors.In addition,the cases study shows that intelligent charge and discharge management strategies for PEV and rescheduling strategies for RGs have great potential in alleviating the negative impact of power grid load and intermittent wind power generation on power grid operation and can bring considerable economic benefits.The details of this thesis are as follows:(1)A new large-scale UC model considering the influence of new energy factors such as RGs and PEV was proposed.The model not only considered the negative impact of the uncertainty of RGs on the power grid,but also considered the intelligent charge and discharge management of PEV.(2)In order to solve the above problems,a new binary and real-value parallel framework based on social learning particle swarm optimization is established.In the parallel framework,the dispatching of unit output and intelligent management of PEV charge and discharge are realized.(3)Considering the uncertainty of RGs,an active rescheduling model of power system considering real-time wind power generation is established.The evaluation index of wind power uncertainty in different application scenarios is designed,and the power system is rescheduled based on it,so as to alleviate the negative impact of RGs uncertainty on power system scheduling.(4)Finally,considering different application scenarios of RGs and PEV,the effectiveness of the proposed algorithm framework and scheduling strategy is verified through a large number of experiments,and the impact of RGs and PEV on reducing power grid load and economic cost is analyzed.