Energy management of wind turbine-based DC microgrid utilizing modified differential evolution algorithm

An energy management optimization model of a wind turbine-based DC microgrid with batteries as the energy storage system is proposed. With introduction of the state of health of battery in this model, the aging process of the storage system is measured. In addition, an improved differential evolution algorithm is proposed which uses the group fitness variance as parameter adjustment indicator and is applied to solve the optimization problem of the DC microgrid system model. In order to verify the effectiveness of the proposed energy management method, simulations are conducted with results showing that the proposed energy management model can reduce the operation cost caused by the aging of batteries and correct in advance the foreseeable power fluctuations, thus meets the requirement of stably and economically operation of the DC microgrid.