## 1 Introduction
A closed-loop V2G control to achieve both the frequency regulation and the EV charging demands was studied by a hierarchical control structure considering the uncertain dispatch in Ref. [11]. In Ref. [12], an optimal energy management scheme to charge or discharge EVs efficiently was developed by considering vehicle-to-building (V2B), renewable energy source (RES) and energy storage system (ESS) considering driver behavior. A robust stochastic shortest path model to stochastically match the EV charging load with the wind supply was studied in Ref. [13]. Ref. [14] developed a multi-objective model for Multi-Energy Complementary System (MECS) operation considering different operation constraint conditions under three objective functions. Ref. [15] analyzed the unbalance of plug-in EVs home charging in distribution network by stochastic load model. In Ref. [16], an Eco-town based MES in UK was simulated to research the optimal size and operation strategy of mixed technologies such as fuel cell, CHP, gas boiler and PV to meet the electrical and thermal loads. Ref. [17] developed a method for scheduling EVs charging based on probabilistic models. The authors in [18] studied a stochastic model of the home energy management (HEM) considering the uncertainties of EVs availability and small-scale renewable energy generation, as well as the customers' satisfaction by developing a response fatigue measure. Ref. [19] proposed leveraging day-ahead power market and time-of-use electricity, and use stochastic programming to address the uncertainties in EV charging demand. In the above literature on integrated electricity and the heat system, the operational optimization modelling of electricity and heat energy as a whole, considering various CHP units with complementary properties, is limited. It is well known that, in MES, the electricity demand is independent from heat demand; on the contrary, the electricity generation is coupled with heat production due to the constraints on operational characteristics of CHP units, which makes the coordinated optimization intractable under the constraints of multi-energy supply/demand balance. Consequently, how to coordinately schedule the multi-energy generation in MES with various CHPs, i.e., the fixed or variable heat-to-electricity ratio, is imperative. Moreover, on the uncertainties of EVs included in MES, some work used the deterministic modeling to formulate the scheduling of MES, without further discussing the stochastic model of EV availability and driving pattern [7; 9; 11]; in other work, although the stochastics were considered, the coordination between electricity and thermal multi-energy was involved simply. As for the costs of EVs' batteries degradation, little research included it in the coupled scheduling optimization of multi-energy, which is important for realizing the high efficiency operation control for CHP units with the unmatched heating and electric power demands [12; 13; 14; 15; 16; 17; 18; 19]. As the driver information and the stochastic model of EVs availability are not easily exploited by classical control strategies, model predictive control (MPC) appears to be suitable for this purpose. Recently, MPC has drawn the attention of the power system community and consequently the research to show the advantages of applying MPC to dynamic economic dispatch with the uncertainties considered have been implemented. Initially, the EVs and CHP power coordination is made for the MES in order to create a power balance between power generation and demand. Then, this method optimizes a control sequence over a receding horizon to predict the future system behavior [20]. MPC is based on iterative, finite-horizon optimization of the controlled model. In MPC, at each time step, the solution to the optimal control problem is solved over a certain pre-defined horizon using the current state of the system as the initial state.