Chance-constrained bi-level optimal dispatching model and benefit allocation strategy for off-grid microgrid considering bilateral uncertainty of supply and demand

Off-grid microgrids lack of backup of the main network, which poses a higher challenge to the effective energy supply, how to improve the matching degree of supply and demand has become a popular direction of current research. Therefore, firstly, on the basis of considering the bilateral uncertainty of supply and demand, based on the demand response model, a chance-constrained bi-level optimal dispatching model is constructed to promote the linkage of demand-supply; Then, considering that the demand side participation in the demand response on the energy supply side can bring incremental benefits, an benefit allocation strategy for both sides of supply and demand is constructed; Finally, an off grid microgrid in Northwest China is selected as the research background, using scenario generation and opportunity constraints to deal with uncertainty, and using an improved genetic algorithm to solve the bi-level optimal dispatching model. The results show that: The proposed two-level un-certainty processing model can effectively reduce the energy supply deviation; The optimal dispatching model proposed can realize demand-supply interaction and reduce the load shedding rate; The installed capacity of units can be increased to further reduce the load shedding, but it will cause higher curtailment of clean energy, consideration can be given to implementing demand response or increasing energy conversion units to enhance efficiency of clean energy utilization; The improved Shapley value method can improve the willingness of the demand side to actively participate in the demand response of the supply side, and improve the satisfaction of the cooperative alliance.

Automated summary

This study addresses the uncertainty in supply and demand of off-grid microgrids and proposes a chance-constrained bi-level optimal dispatching model, along with a benefit allocation strategy for both sides of supply and demand. Using scenario generation and an improved genetic algorithm, the research effectively reduces energy supply deviation and achieves demand-supply interaction to reduce load shedding. The results suggest that increasing installed capacity, implementing demand response, or adding energy conversion units can enhance supply-demand matching and clean energy utilization efficiency.