## 1 Introduction  
In order to fully develop the benefits of renewable energy generation systems, the integration and optimization of microgrids have become hotspots of recent research [1]. The microgrid is a new type of network supply and management technology, which can provide convenient access to renewable energy systems. Today, it plays an increasingly important role as the supplementary of the main grid [2; 3]. However, with the rapid development of the industry, the electrical demands of a distributed network are increased sharply, which makes it difficult to meet network peak demand, especially in the evening. To cope with the rapid growth of network electrical demands, it was the first time that a portable energy resource system was introduced in [4] to regulate peak demand and to participate in system demand response program. By adding an extra portable energy system, this can increase system peak load regulating capacity; however, this method can result in the increase of system uncertainty and this method also neglects system thermal demand. As for the increase of system uncertainty, it is mainly caused by the added renewable energy generators. As reported in [5; 6; 7], it is very difficult to predict wind output because of the high randomness of the wind speed, which makes it troublesome to accurately control the small-scaled wind turbine system. Moreover, scattered solar radiation increases the difficulty in predicting photovoltaic (PV) panel output, which is clearly demonstrated in [8; 9; 10]. Therefore, after adding a small-scaled portable wind turbine and a set of PV panels to the distributed microgrid, system uncertainty can be increased to some extent. Additionally, by simply adding an extra portable energy system to the distributed network, [PERSON]. [PERSON] et al. neglect the influence of the newly introduced portable energy system on heat loads [4]. Considering that sizing and controlling combined heat and power (CHP) units are mainly dependent on the heat loads [11], simply introducing a portable energy system can lead to the CHP generating redundant heat or power [12]. In [13], an economic operation model of the CHP is established, and it demonstrates that the output power of the CHP is constrained by the thermal demand, which can affect the peak load regulating capacity of the CHP. In [14], it is reported that the coupling effect of heat and power can cause the wind turbine power curtailment condition, which reduces energy efficiency. Therefore, it is quite uneconomic if the portable energy resource system is directly connected to the distributed network without decoupling heat and power. To solve system uncertainty, many existing mathematical models have been developed in previous literature; for example, the expected value model, fuzzy programming and chance constrained programming, and so forth [15; 16; 17]. Compared with other models, chance constrained programming is more flexible, and it can coherently consider the uncertainty variables in the objective function and the constraints. Therefore, in this paper, chance constrained programming is applied to model the economy and safety operation of the distributed network. In previous literature, many optimization algorithms have been raised to solve chance constrained programming; for example, the genetic algorithm, the ant colony algorithm, and the particle swarm optimization [18; 19; 20]. Compared with other algorithms, the particle swarm optimization (PSO) algorithm has the obvious advantages of easy implementation, fewer parameters, and better optimization ability. However, the PSO algorithm needs fixed parameters and it cannot be used to deal with the stochastic variables in an efficient way. Therefore, the stochastic variables need to be converted to the fixed variables before implementing the PSO.