Robust modeling and evidence-based evaluation method for a active distribution network with EVs and CHPs.

Promoting the increase of the proportion of new energy into the power grid and improving the utilization rate of energy has become the urgent needs of the world. To reduce the impact of renewable energy output prediction errors on the distribution network, this paper considers the charging and discharging behaviors of electric vehicles (EVs) and demand responses, and proposes a robust optimization model for the distribution network with a three-stage framework. In the day-ahead stage, a day-ahead optimization operation model is constructed by promoting load shifting through price-based demand response, minimizing daily operation costs while accounting for new energy uncertainty via penalty costs for supply-demand imbalances. In the intraday stage, an intraday rolling optimization model is developed by integrating EV charging/discharging responses, refining the day-ahead plan with higher-resolution forecasts to enhance renewable energy absorption and load balancing. In the real-time stage, a real-time adjustment model utilizes incentive-based demand response to smooth short-term fluctuations in renewable output, ensuring voltage stability and operational flexibility. Experimental simulations on the IEEE 33-bus test system demonstrate that the proposed three-stage cooperative operation strategy significantly promotes renewable energy consumption, reduces operational costs, and improves the reliability of active distribution network (ADN) operation.