Multi-time-scale energy management for microgrid using expected-scenario-oriented stochastic optimization

Abstract

The uncertainty of renewable distributed energy sources, load demand and electric vehicles bring great difficulties to the optimal scheduling of the grid-connected microgrid, this paper proposes a multi-time scale stochastic optimization model for microgrid with low operation cost and high reliability against these uncertainties. In the day-ahead scheduling stage, the Monte Carlo method is used to generate stochastic scenarios and simulate the uncertainty of the microgrid. The day-ahead stochastic optimization model is established considering the energy balance in the expected-scenario, the operation cost of distributed energy sources, the charging and discharging characteristics of electric vehicles, and the stable operation under multiple stochastic scenarios. Then the hourly robust scheduling decisions are obtained by solving the day-ahead stochastic optimization model to achieve the optimization objective of expected optimal and stochastic feasibility. In the intraday ultra-short-term scheduling stage, according to the updated minute forecast information, a rolling optimization strategy is carried out and the intraday optimal power allocation is obtained to minimize the operation cost in the prediction time domain. Even if the prediction error of the ultra-short-term forecast data is very small, it still adversely affects the stability of the system in the real-time operation stage. To eliminate the random fluctuations of renewable energy output and load demand in the real-time operation, the power deviation is smoothed through interacting with the power grid. Compared to the deterministic scheduling model, simulation results demonstrate that the proposed stochastic scheduling model and multi-time-scale optimization strategy are superior in minimizing the operation cost and reducing the negative impact of uncertainty on the economic operation.