Chance-constrained scheduling model of grid-connected microgrid based on probabilistic and robust optimisation

This study presents a chance-constrained scheduling model based on probabilistic and robust optimisation to handle the uncertainty of renewable energy generation and loads in microgrids. In order to generate appropriate scenarios, a large number of scenarios are generated by a Latin hypercube sampling Monte Carlo method and reduced by a fast forward selection algorithm. With the aggregated scenarios, a probabilistic scheduling model is established to obtain the expectation of schedules in different probability scenario. Aiming at taking full use of the generated scenarios, a robust optimisation is applied to the probabilistic model to consider the worst situations. The scheduling model proposed in this study combines the probabilistic and robust optimisation, in which the probabilistic one utilises the aggregated scenarios to introduce the probability characteristic of uncertainty and the robust one utilises the eliminated scenarios to consider the worst case of uncertainty. Finally, the proposed scheduling model is applied to a designed grid-connected microgrid, and the simulation results demonstrate the effectiveness of the proposed scheduling model.