Finally, simulation experiments are conducted in AnyLogic, and the results demonstrate the superiority of the algorithm over the classical algorithm. In the second stage, a genetic algorithm is used to accurately determine the time interval between charging and discharging to ensure the maximum benefit of EV owner. The most popular machine learning library for Python, scikit-learn, was used. Pypeline is a connector library for AnyLogic, and it allowed the easy and smooth creation of an interface between AnyLogic and Python. The software advanced and is capable of handling and simulating large projects. After these changes, the predictive model could then predict new behavior on energy savings in the building by an individual. Not many available tools can handle agent based simulation. In the first stage, a priority scheduling algorithm is proposed to emphasize the fairness of EV charging. Also, agent based simulating is a significant feature. The second is the scheduling center agent (SCA), which is used to solve the insufficient battery energy problem due to EVs’ random departures. Download scientific diagram Electricity demand model in the Anylogic using system dynamics from publication: Supply optimization based on society’s cost of electricity and a calibrated demand. The first is the power distribution center agent (PDCA), which is used to coordinate the energy output of photovoltaic (PV), energy storage system (ESS), and distribution station (DS) to solve the problem of grid overload. Cloud updates gave organizations that use simulation modeling an integrated environment for model management and execution. Based on this simulation model, we propose an EV scheduling algorithm. AnyLogic completes 2020 on version 8.7.2 with material handling additions, an integrated AI experiment, speed improvements and social distancing. The more metrics and data available for analysis, the more informed decision making becomes. Insight and planning are crucial factors in managing change. This study uses the simulation software AnyLogic to build a commercial parking lot multi-agent simulation model, and the agent-based model can fully reflect the autonomy of individual EVs. How to manage the transition to electric vehicle fleets. This deviation can lead to insufficient battery energy when the EVs leave the parking lot. There may also be a deviation in the departure time of charged and discharged EVs in commercial parking lots. YG Karpov, RI Ivanovski, NI Voropai, DB Popov. As the number of electric vehicles (EVs) increases, massive numbers of EVs have started to gather in commercial parking lots to charge and discharge, which may significantly impact the operation of the grid. Hierarchical modeling of electric power system expansion by anyLogic simulation software.
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