Economic dispatch of distribution network considering EV integration: A three-stage robust optimization method for multiple uncertainties

With the high penetration of renewable energy and large-scale access of electric vehicles (EVs), the economic dispatch of the distribution network (DN) is faced with the severe challenge of multiple uncertainties in source-price. To tackle such challenges, this study prioritizes the detailed modeling of EV charging loads and embeds them within DN economic dispatch. To quantify the impact of electricity price and renewable generation uncertainties on DN economic dispatch, this paper formulates a three-stage robust optimization (THSRO) model. The first stage optimizes the start/stop of units, the charging plan of private cars, and the vehicle-to-grid (V2G) capacity configuration. In the second stage, the V2G dispatch and EV charging plan are developed within the uncertainty set of electricity price. In the third stage, the operational strategies for DN are formulated to minimize the operation cost under the renewable generation uncertainty. For the solution challenge of the three-stage mixed integer nonlinear programming (MINLP) model, the two-layer column and constraint generation (TL-C&CG) algorithm is developed to decompose the problem solving process into three levels of loops. Simulation results in a modified IEEE-33 bus system show that THSRO model can reduce the peak charging load by 33% and guarantee stable DN operation in the worst scenario, although the total cost of THSRO rises by 18% compared with the deterministic model. Compared with existing two-stage robust optimization (TSRO) models, the proposed THSRO delivers lower operational costs while eliminating the coupling effects of renewable generation on EV user decisions. Besides, scalability analysis conducted on an extended IEEE-69 bus system confirms the THSRO model’s practical applicability in larger-scale DN.