## 1 Introduction  
Nowadays, great concerns about energy crisis and environment problems are encouraging more efficient energy management and consumption. Residential and commercial buildings consume about 32% of the global energy use, in which usually there are multi-energy demands, such as electricity and heat [1]. It is particularly important to change the existing energy systems which are individually designed to operate separately, and to build various energy systems as an integrated whole in order to improve the overall energy utilization efficiency and lower environmental pollution. Therefore, some coupled energy systems, such as the multiple-energy carriers system (MCES) [2] and the multi-energy system (MES) [3], have been investigated in recent years. The essential characteristics of these systems is the interaction of diverse forms of energy, such as heat power, electrical power, nature gas, renewable energies and so on. However, the uncertainty and randomness of the MES considering electric vehicles (EVs) and renewable energy are still a challenge for scheduling optimization in MES. The integration of EVs and combined heat and power (CHP) units have an increasing impact on the distribution grid and will change the buildings' electricity consumption and supply. In general, EVs are parked for more than 90% of the time and EVs charging/discharging flexibility can be utilized fully, although the charging/discharging is limited by battery limitations, the mobility objectives,the available power ratings and battery state of charge (SOC). Consequently, as one of the most important components in energy systems due to their advantages of flexible charging/discharging and zero emission, EVs can connect to the MES in the form of Grid-to-Vehicle (G2V) or Vehicle-to-Grid (V2G) [4]. However, on one hand, the electricity consumption of EVs in G2V mode may lead to peak loads, higher resistive losses, voltage deviations, and phase unbalance in the grid [5]; on the other hand, in contrast to other electrical equipment, the integration of EVs simultaneously connected in parking stations could be considered as a potential source for the demand respond in the MES. However, the uncertain arrival/departure time induced stochastic availability of aggregated EVs should be considered carefully. As a significant means to improve energy efficiency, CHP units have received increasing attention owing to their ability in providing electrical and thermal power simultaneously, but they have to remain on certain constrained electric power output to meet the heat loads' demand. Therefore, the scheduling coordination between the aggregated EVs and CHP units in MES is crucial to improve the system operational flexibility and enhance energy efficiency. Extensive research has been conducted on the optimal operation of MES, which is modeled based on the integration of EVs, CHPs, or other renewable energies. Ref. [6] presented an approach to solve the CHP unit commitment problem in the presence of plug-in EVs as small portable generation units in addition to typical generation constraints. In Ref. [7], a multi-objective optimization algorithm was proposed for minimization of energy cost and emissions for residential microgrids considering combined cooling heating and power (CCHPs), electric and thermal energy storage and EVs. A two-stage stochastic programing was used to minimize the operational cost of a microgrid by using scenario generation and a reduction technique [8]. Ref. [9] investigated the optimization and energy management in smart homes considering photovoltaic, wind, and battery storage systems with integration of EVs through a heuristic technique. Ref. [10] provided a number of stochastic programming models for optimal decision-making under uncertainty in electricity markets and electric energy systems with a large integration of non-dispatchable sources.