Day-ahead dispatch of novel battery charging and swapping station based on distributionally robust optimization

Battery swapping station (BSS) is a promising way to support the proliferation of electric vehicles (EVs). This paper upgrades BSS to a novel battery charging and swapping station (NBCSS) with wind power, photovoltaic power, energy storage and gas turbine integrated, which is equivalent to a microgrid with flexibility further enhanced. An integrated model of batteries based on the state of charge (SOC) interval is put forward to release the complexity of separate modeling of each battery, where the charging and discharging priorities are embedded. Then, a distributionally robust optimization (DRO) model for the day-ahead dispatch of NBCSS is presented considering the uncertainties of wind power, photovoltaic power and load. This model minimizes the worst-case expected total cost over a family of distributions characterized by an ambiguity set. By employing the affine decision rules, the primitive two-stage DRO model can be eventually reformulated as a tractable mixedinteger linear program. Finally, case studies are conducted to demonstrate the effectiveness of the proposed method. The results show that the charging and discharging freedom of batteries enhances the operational flexibility of NBCSS and reduces the 22.9 % of the total cost. And the proposed method has the superiority of decision-making over the deterministic and adaptive robust optimization ones.

Automated summary

Battery swapping station is upgraded to a battery charging and swapping station integrated with wind power, photovoltaic power, energy storage and gas turbine, creating a microgrid with enhanced flexibility. An integrated model of batteries based on the state of charge interval is introduced, simplifying the modeling process. A distributionally robust optimization model is presented to optimize the day-ahead dispatch considering uncertainties. Case studies demonstrate the effectiveness of the proposed method.