Multi-time scale energy management of microgrid considering the uncertainties in both supply and demand

Abstract

The uncertainty of supply and demand have different characteristics, which can adversely affect the stable operation of microgrid. This paper presents a multi-time scale scheduling strategy, which mainly consists of day-ahead scheduling layer and intraday adjustment layer. In the day-ahead scheduling layer, the Monte Carlo method and demand response are used to deal with the uncertainty of both supply and demand, and a stochastic optimal model is established by considering the uncertainty in the microgrid. In the first stage, the unit commitment decisions are derived in a stochastic optimization framework to achieve minimum operating cost. In the second stage, the day-ahead economic power scheduling is achieved by using a deterministic optimization framework. In the intraday adjustment layer, a rolling optimization algorithm is applied to adjust the day-ahead economic power scheduling plan, which does not change the unit commitment decisions. The power fluctuations caused by inaccurate day-ahead forecast data are effectively smoothed with intraday rolling optimization. The simulation results show that the proposed model and strategy can effectively reduce the impact of uncertainty on the optimal scheduling of the microgrid. In addition, comparing the scheduling results for one month, it can be seen that the proposed strategy has better economy in the long-time operation of the microgrid.

Keywords

• Microgrid
• Multi-time scale
• Two-stage optimization scheduling
• Rolling optimization
• Uncertainty
• Economic operation