Abstract
As China proposes to achieve carbon peak by 2030 and carbon neutrality by 2060, as well as the huge pressure on the power grid caused by the load demand of the energy supply stations of electric vehicles (EVs), there is an urgent need to carry out comprehensive energy management and coordinated control for EVs’ energy supply stations. Therefore, this paper proposed a two-step intelligent control method known as ISOM-SAIA to solve the problem of the 24 h control and regulation of green/flexible EV energy supply stations, including four subsystems such as a photovoltaic subsystem, an energy storage subsystem, an EV charging subsystem and an EV battery changing subsystem. The proposed control method has two main innovations and contributions. One is that it reduces the computational burden by dividing the multi-dimensional mixed-integer programming problem of simultaneously optimizing the 24 h operation modes and outputs of four subsystems into two sequential tasks: the classification of data-driven operation modes and the rolling optimization of operational outputs. The other is that proper carbon transaction costs and carbon emission constraints are considered to help save costs and reduce carbon emissions. The simulation analysis conducted in this paper indicates that the proposed two-step intelligent control method can help green/flexible EV energy supply stations to optimally allocate energy flows between four subsystems, effectively respond to peak shaving and valley filling of power grid, save energy costs and reduce carbon emissions.
Highlights
With the increasingly prominent problems of environmental pollution and fossil fuel depletion, countries all over the world have designed different low-carbon development routines
In order to meet the needs of different electric vehicles (EVs) users as well as increasing operational flexibility through low-carbon measures, some EVs’ energy supply stations provide charging and battery changing services simultaneously, and are equipped with photovoltaic systems and energy storage devices
Overall architecture of two-stepintelligent intelligent control based on improved self-organizing map neural network (ISOM)-SAIA
Summary
With the increasingly prominent problems of environmental pollution and fossil fuel depletion, countries all over the world have designed different low-carbon development routines. In order to meet the needs of different EV users as well as increasing operational flexibility through low-carbon measures, some EVs’ energy supply stations provide charging and battery changing services simultaneously, and are equipped with photovoltaic systems and energy storage devices. The dynamic characteristics of distributed energy sources including photovoltaic, energy storage, charging and battery changes in the EVs’ “Photovoltaic-StorageCharging-Change” stations are different from each other in terms of time-scale, and are mostly nonlinear and multi-dimensional This increases the complexity of the optimization control and regulation problem. Proposed a multi-agent-based energy management deep reinforcement learning method, established a distributed power generation model for EV charging stations containing photovoltaic systems and energy storage systems, and obtained a targeted power load dispatching plan to reduce operating costs [11]. Where Pes (i )—total charging power of the battery changing system at time i, kW; n—the number of chargers in the battery changing system; Pn,es (i )—the charging power of the nth charger at time i, kW; λces —the charging efficiency of the charging compartment to the battery to be replaced; ωn,es (i )—the state of the nth charger at time i, charging is 1 and discharging is −1, otherwise it is 0; N—the number of EVs that need to be replaced; S N,es (i )—the EV’s electric quantity that is replaced at time i; Qes0 —initial power; Ses (i )—total power of the EV that is replaced at time i; Stes (i + 1)—total amount of change electricity required by the EV at time i + 1; Stes (i )—total amount of change electricity required by the EV at time i; ∆T—time interval, min
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