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• W4313654704 abstract "In this study, multi-objective optimal dispatching of demand response-enabled microgrid considering uncertainty in renewable energy generations is designed to obtain optimal energy point sets of microgrid. The proposed dispatching model contains upper-level model and lower-level model. The economic and environmental benefits of the microgrid are adopted as the optimization objectives in the upper-level model and the economic benefits under incentive-based demand response is taken as the optimization objective in the lower-level load demand. In the upper-level model, sequence operation theory is designed to deal with the uncertainty in renewable energy generations, and virtual real-time tariffs and electricity credibility of power consumption are utilized to make demand response by shifting load in the lower-level model. To optimally solve the proposed model, improved enhanced multi-objective optimization sparrow search algorithm (EMOSSA) based on sinusoidal search strategy, diversity variant processing by Cauchy variation and external archive updating mechanism is developed to optimize the objective functions of the upper-level model and mixed integer linear programming CPLEX is employed to determine the load demand for the lower-level model. After that, the power demand plan of the lower-level model and the real-time prices of the upper-level model are updated iteratively to obtain the optimal dispatching solutions using the real-time pricing mechanism. Eventually, experimental results of several operation scenarios illustrate the proposed optimal dispatching method can deal with the uncertainty of renewable energy generations and the randomness of load, thus, achieving a well supply and demand balance between microgrid and users." @default.
• W4313654704 created "2023-01-07" @default.
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• W4313654704 date "2023-12-01" @default.
• W4313654704 modified "2023-10-14" @default.
• W4313654704 title "Multi-objective optimal dispatching of demand response-enabled microgrid considering uncertainty of renewable energy generations based on two-level iterative strategy" @default.
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• W4313654704 doi "https://doi.org/10.1016/j.egyr.2022.12.149" @default.
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