Robust Optimal Dispatch of Microgrid Considering Flexible Loads and Step Carbon Trading

In this paper, a two-stage robust optimization method is proposed to address the uncertainties in renewable energy output and carbon emissions in microgrids under the low-carbon back-ground. Our method is a two-phase robust optimal method incorporating both the step carbon trading mechanism and the flexible loads which participate in the regulation. A step carbon trading mechanism was introduced into the dispatch model to limit the carbon emissions of the system and compensate for the power shortage caused by the renewable energy output fluctuations through the regulation of flexible loads, further reducing the carbon emissions and dispatch costs of the microgrid. In addition, the uncertain parameter was incorporated into the model to adjust the conservativeness of the system. In the day-ahead stage, a scheduling plan was formulated based on the forecast data and worst scenarios that the system may suffer from within a day. In the intra-day stage, the sub-optimization was provided based on the day-ahead scheduling plan; an intra-day control scheme of the system was further presented for worst-case scenario. The scheduling model was solved using the nested C&CG algorithm. The simulation results of the example demonstrate that, on average, the total dispatch cost of the robust optimization method was reduced by 3.1% compared with that of the deterministic optimization method. The inclusion of the carbon trading mechanism resulted in a 17.7% reduction in carbon emissions and a 3.7% decrease in the total operating cost of the system in a single day. With the consideration of the flexible loads which participate in the dispatch, the carbon emissions and total dispatch cost of the system in a single day operation were reduced by 26% and 24.3%, respectively, compared to those without flexible loads. The effectiveness of the optimization model proposed in this paper is thus verified.