Research on the demand response characteristics of emerging users based on Monte Carlo simulation

As environmental problems become more serious, the trend of “carbon peaking and carbon neutral” has become necessary. However, the disorderly entry of large-scale EVs into the grid has threatened the security of the grid. The purpose of this paper is to study the optimization strategy of EVs to improve the economy and stability of distributed energy. Firstly, the EV user behavior model is constructed to study the charging and discharging behavior influencing factors, and the EV charging and discharging loads are simulated using Monte Carlo simulation. Secondly, we build a hierarchical scheduling optimization strategy based on EV user satisfaction using an improved AntLion optimizer, finally, the load peaks of the distributed energy system are suppressed and the satisfaction of EV customers is significantly improved; in the process of EV scheduling optimization at the source storage layer, EVs fully consume renewable energy output and the comprehensive operating costs of the distributed energy system are reduced. The conclusions are verified and the system is optimized, resulting in improved user satisfaction and optimized system economy and stability.

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“…The flexibility and randomness of EVs are shown in the parking time, travel moment, and driving range (Junhui et al, 2021).…”

Section: Ev Modeling Analysismentioning

confidence: 99%

Research on EV Scheduling Optimization Strategy Based on Monte Carlo and AntLion Optimizer

Peng

Zhao

et al. 2022

Front. Energy Res.

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Research on the demand response characteristics of emerging users based on Monte Carlo simulation

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Research on EV Scheduling Optimization Strategy Based on Monte Carlo and AntLion Optimizer

Research on EV Scheduling Optimization Strategy Based on Monte Carlo and AntLion Optimizer

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