An optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaic

Renewable energy, such as wind and photovoltaic (PV), produces intermittent and variable power output. When superimposed on the load curve, it transforms the load curve into a 'load belt', i.e. a range. Furthermore, the large scale development of electric vehicle (EV) will also have a significant impact on power grid in general and load characteristics in particular. This paper aims to develop a controlled EV charging strategy to optimize the peak-valley difference of the grid when considering the regional wind and PV power outputs. The probabilistic model of wind and PV power outputs is developed. Based on the probabilistic model, the method of assessing the peak-valley difference of the stochastic load curve is put forward, and a two-stage peak-valley price model is built for controlled EV charging. On this basis, an optimization model is built, in which genetic algorithms are used to determine the start and end time of the valley price, as well as the peak-valley price. Finally, the effectiveness and rationality of the method are proved by the calculation result of the example.