Energy storage optimization method for large-scale wind and solar grid connected power generation system based on quantum genetic algorithm
Large scale wind and solar grid connected power generation has obvious intermittency and randomness,and there are significant differences in the power generation characteristics of wind and solar energy itself.Configuring energy storage is an effective method to solve the above problems.Transforming uncertain wind and solar output scenarios into a small-scale scenario set with typi-cal characteristics,and based on this scenario set,configuring energy storage for wind and solar combined power generation systems to suppress fluctuations in new energy output,a quantum genetic algorithm based energy storage optimization method for large-scale wind and solar grid connected power generation systems is proposed.Under the docking of the energy storage system with the wind so-lar complementary power generation system using a bidirectional DC/DC converter,a stable scenario is generated with the objective function of the maximum number of charges and discharges after the battery is connected to the docking system;The output power of grid connected power generation and energy storage shall not exceed the maximum power as a constraint condition;Using quantum ge-netic algorithm to collect feedback on the state of charge of the battery,encoding chromosomes through the probability amplitude of quantum bits,increasing the number of optimization parameters through non gate mutation,updating the quantum phase using the ro-tation gate method,and combining gradient function and first-order difference to find the stable input power of the battery under stable conditions,achieving stable energy storage capacity regulation.The experimental results show that the power deviation value of the proposed method is small,and the energy loss rate is also low,so the energy storage optimization effect is good.
wind and solar grid connectionpowerenergy storage capacitybattery charge and dischargechromosomesquan-tum genetic algorithm
万方数据
维普
