## 1 Introduction  
Microgrids have played an important role in solving the global energy crisis issue and, in recent years, microgrids have received more and more attention [1; 2]. According to the definition by the U.S. Department of Energy, a microgrid is \"a group of interconnected loads and Distributed Energy Resources within clearly defined electrical boundaries, which is considered as a single controllable entity from the main grid point of view [3]\". A microgrid can either work in grid-connected mode or stand-alone mode. The microgrid is extremely appealing owing to its flexibility, economy, and environmental protection. In [4], an optimal energy planning of a stand-alone microgrid considering system uncertainties is studied to evaluate the system performance. In [5; 6], several real-time optimization and energy management approaches considering ESS lifetime are proposed for an isolated microgrid. A multi-layer ant colony algorithm is adopted to address the problem. In [7], a novel expert fuzzy system--a grey wolf optimization based intelligent meta-heuristic method for battery sizing and energy management--is proposed. In [8], a fuzzy multi-objective optimization model with corresponding constraints to minimize the economic cost and network loss of microgrid is presented. In [9], the optimal operation problem is decomposed into two sub-problems of unit combination and optimal power flow. Ref. [10] discusses the optimal scheduling problem of accessible energies for a stand-alone microgrid. Ref. [11] presents a model-based method to analyze the performance of a domestic photovoltaic power plant with ESS. Among the research, the operation strategy optimization is limited to stand-alone microgrid. However, once the load demand of the microgrid changes significantly, the system may not be able to meet the load demand and loses stability as a result. Ref. [12] investigates the coordinated optimal dispatch of ESSs to minimize the electricity costs in a group of grid-connected microgrids. In [13], a dynamic optimal operation strategy for a microgrid in both stand-alone and grid-connected mode is presented. Dynamic programming (DP) and equal incremental fuel cost method are employed to solve the problem. In [14], a central controller based on mixed-integer-linear programming (MILP) is proposed to solve the optimal dispatch problem of a stand-alone microgrid. In the above research, the operation models are all built on deterministic optimization. The predictive errors of renewable energies are ignored. However, the uncertainty of renewable energies has a great influence on the safe and economical operation of microgrid. A modeling method which integrates both the deterministic and the stochastic nature of renewable power plants using a novel methodology inspired from reliability engineering: the Stochastic Hybrid Fault Tree Automaton is proposed in [15]. A novel probabilistic optimization model considering random characteristics of renewables and load profile is developed in [16]. Chance constraint programming for bi-objective optimal scheduling is used to solve the problem. In [17; 18], MCS and Roulette mechanism are utilized to generate scenarios and a backward scenario reduction technique is applied to simplify the problem and reduce the computational burden. In [19], a multi-objective optimal dispatch model for a grid-connected microgrid is raised. The optimization model is solved by an improved Particle Swarm Optimization (PSO) algorithm. In [20], a distributed control method based on multi-agent is proposed, which is used to deal with the complex energy management problems in the microgrid and multi-agent coordination is realized by game theory. Ref. [21] proposes an optimization strategy to provide the required energy for the microgrid system. PSO and Gaussian mutation algorithm are used to solve this problem. The above heuristic algorithms are sensitive to initial guesses that they can hardly find the global optimal solution.