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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. [ABSTRACT FROM AUTHOR]

In order to satisfy the growing demands of control performance and operation efficiency in the automatic generation control (AGC) system of a grid, a novel, intelligent predictive controller, combined with predictive control and neural network ideas, is proposed and applied to the AGC systems of thermal power units. This paper proposes a Bayesian neural network identification model for typical ultra-supercritical thermal power units, which was found to be accurate and can be used as a simulation model. Based on the model, this paper develops an intelligent predictive control for the AGC of thermal power units, which improves unit load operation and constitutes a novel, closed-loop AGC structure based on online control performance standard (CPS) evaluations. Intelligent predictive control is mainly improved because the neural network rolling optimization model replaces the traditional rolling optimization model in the rolling optimization module. The simulation results indicate that the intelligent predictive controller developed in the two-area interconnected power grid under CPS can, on the one hand, improve the load tracking performance of AGC thermal power units, and, on the other hand, the controller has strong robustness. Whether the system parameters change considerably or the AGC has different grid disturbances, the new type of the loop AGC system can still sufficiently meet the control requirements of the power grid. [ABSTRACT FROM AUTHOR]