## 1 Introduction
The DR program was adopted to increase the total expected profit and decrease the total operational cost. Most models in this context have optimized wind power utilization by responsive loads. However, the scheduling characteristics of loads were not considered, and responsive loads were not divided finely. Different types of responsive loads have different characteristics [17]. Therefore, different loads can participate in the optimized operation in different ways [18], which may lead to better optimized results. In this study, according to their regulating characteristics, loads are grouped into two types, including the high-penetation energy load (HL) and the shiftable load (SL). The HL possesses both adjustable and interruptible characteristics, which can achieve power control in the range of 0%-100%. The regulate action can be completed in an instant. The HL is suitable for increasing wind power consumption because we can raise its power when the wind power is high and reduce its power in the opposite case. The use of an SL implies shifting the load from one period to another to maintain the electricity consumption. Loads can be cut out or put in at any periods of the whole cycle. Generally, an SL is put in during the peak wind power periods and cut out in trough wind power periods to increase the wind power utilization. HLs and SLs are shown in Figure 1.  
The two types of responsive load participate in the optimal scheduling with conventional power, developing a new scheduling mode based on source-load coordinate optimal operation. However, the dispatch of responsive loads will damage the users' benefit. Economic compensation needs to be made for them, which incurs additional costs. Thus, besides the amount of consumption of wind power, the cost of system operation should be considered as well. The model contains two objectives: to obtain the maximum capacity of wind power accommodation and to minimize the system operation costs. A multi-objective optimal model is established in this study. There are many algorithms that can be used to solve the multi-objective optimal problem, for example: the Non-dominated sorting genetic algorithm II (NSGA-II) [19], the Strength pareto evolutionary algorithm (SPEA) [20], the Pareto archived evolution strategy (PAES) [21] and the Multi-objective differential evolution (MODE) [22]. In [19], NSGA-II, SPEA2 and PAES were designed for multi-objective problems, and NSGA-II achieved the best results in terms of Pareto front spread. In [22], NSGA-II, MODE and multi-objective particle swarm optimization (MOPSO) algorithms are compared in accuracy and computational time. The results show that the obtained Pareto Front of MODE is more accurate and faster. It can be concluded that MODE performs better among these algorithms. So we adopted MODE algorithm to solve the multi-objective model in this paper. The remaining part of this paper is organized as follows. Section 2 describes the problem with chance constraints. Section 3 presents the solution methods which including SAA and MODE algorithms. Section 4 reports the computational experiments for the power system of Yancheng City of Jiangsu province in China. Section 5 concludes the study.