In response to the issues of poor source-load matching and low local consumption rate of renewable energy caused by the large-scale
unordered integration of renewable energy generation and electric vehicles (EVs) into microgrids
a bi-level optimization model for microgrid adjustment and scheduling is proposed
considering demand response and orderly EV integration. The upper-level model combines price-based demand response(PBDR)and incentive-based demand response(IBDR)strategies
directly regulating the load curve obtained from time-of-use electricity pricing to minimize the peak-to-valley difference and net load variance of the microgrid. The lower-level model accounts for the V2G (vehicle-to-grid) characteristics of EVs
coordinating the scheduling of the microgrid's generation
grid
load
and storage to minimize operational costs and improve the local consumption rate of renewable energy. Typical case studies show that
based on the load curve adjusted by the upper-level model
microgrid scheduling can increase the renewable energy consumption rate by 23.3% and reduce operational costs by 3.6%. Additionally
considering the orderly integration of EVs can further increase the renewable energy consumption rate by 1.2% and reduce operational costs by 3.3%. The study demonstrates that the proposed bi-level optimization strategy for microgrid scheduling better matches the characteristics of renewable energy generation and load in the microgrid
while orderly EV integration can reduce grid operation costs and enhance the local consumption of renewable energy.
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