Optimal Configuration of Energy Storage in Photovoltaic Park with Electric Vehicle Demand Response Based on Stackelberg Game and Information Gap Decision Theory

[Objective] As the penetration rate of photovoltaics （PVs） increases， their volatility and randomness lead to intensified peak and valley fluctuations in the user net load， resulting in an increase in electricity demand. Energy storage can reduce the demand for electricity by utilizing the characteristics of peak shaving and valley filling； however， the high initial investment in energy storage limits its large-scale application on the user side. [Methods] A photovoltaic park energy storage optimal configuration method based on Stackelberg game pricing and information gap decision theory （IGDT） with electric vehicle （EV） demand response is proposed. First， considering the uncertainty of the grid， time-of-use， demand， and purchase and sale electricity price of EVs and PV output， an energy storage configuration model based on IGDT and an optimized operation model for EV clusters were constructed. Second， with the park as the leader and EVs as followers， a Stackelberg game model is constructed to minimize the costs of the park and EVs. Then， the Stackelberg game model is transformed into a mixed-integer linear programming problem for a solution using Karush-Kuhn-Tucker （KKT） conditions and the dual theorem of linear programming. Finally， we analyzed a PV park in a certain region as the research object. [Results] The results show that the proposed strategy reduced the annual comprehensive cost of the park by 12.06% and the charging and discharging costs of EV users by 54.88%. Owing to the participation of EVs in park scheduling， the storage configuration capacity and power were reduced by 62.80%， and the on-grid power by 1.32%， which improved the local consumption rate of PV. Compared with the IGDT model proposed in this study， the park cost of the robust optimization model is 1.97% higher， which proves that the IGDT model is more economical. [Conclusions] The comparison shows that the strategy proposed in this study meets the charging demand of EVs while reducing the comprehensive cost of the park， reduces the charging cost of the owners， and realizes a mutual benefit and win-win situation on both sides of the game.