## 1 Introduction  
High proportion renewable energy sources (RES) integration can effectively solve many environmental issues; thus, it will be one of the characteristics in future power systems [1]. However, the rapid development of RES has brought about several problems in matching the supply and demand in power grids [2]. The intermittent and stochastic characteristics of RES require adequate mechanical inertia and reserves in the whole system, while a large number of conventional generators have to be replaced by RES generators; this is a paradox and causes obstacles to RES integration. Demand response (DR) has been proven to be a very useful and crucial regulation technology in high proportion RES integration power systems [3; 4]. Due to the high proportion of RES in power systems, the basic characteristic of DR will be its large scale, and how to effectively manage a large number of wide-area demand-side resources is the main challenge [5; 6]. Current DR schemes can be categorized into price-based DR and incentive-based DR [7]. In price-based DR (PDR) schemes, price fluctuations in the wholesale market are imposed upon retail customers in a mandatory manner through several retail types, such as time-of-use pricing (TOU), critical peak pricing (CPP) and real-time pricing (RTP) [8; 9]. These schemes depend on the retail rate; thus, the utility just needs to publish the same signal to large numbers of customers, which is highly efficient in terms of communication and computation. There are a few studies ([10; 11; 12; 13]) that introduced several PDR pilots that have successful results in small-scale DR scenarios. However, there are three potential issues with PDR. First, most of the PDR schemes are involuntary for customers to regulate their electric consumption, whereas voluntary approaches are more recommended in many electric markets [14]. Additionally, the major concern of PDR is its instability, including price volatility and electric consumption rebounds [15; 16]. In practice, it is very hard for a utility to estimate a suitable retail rate to achieve accurate peak load reduction or shifting because of limited customers' preference parameters and the bounded rationality of customers. At the same time, most customers tend to avoid risk in decision-making [17], but retail rates will be highly dynamic in large-scale DR, which means PDR may, in turn, undermine the customer base. In addition, a dynamic retail rate requires customers to have smart energy scheduling systems, such as [18; 19; 20]. But it will take a long time for numerous customers to have such systems, especially in some developing countries. As a result, PDR may be more suitable in small-scale DR but not at a large-scale, and indeed PDR accounts for a smaller part of total DR resources than other DR schemes in recent years [21; 22]. In Incentive-based DR (IDR) schemes, customers are rewarded if they respond to the DR signal, such as interruptible load control (ILC) [23; 24] direct load control (DLC) [25; 26] and peak time rebate (PTR) [27; 28]. To overcome the limitations of a pre-determined fixed incentive rate, a novel incentive-based DR scheme, called coupon incentive-based DR (CIDR), is proposed [29; 30; 31]. This paper also focuses on issues related to IDR. The study of IDR focuses on how to operate DR resources and how to measure DR performance [32]. For the former, recent studies can be divided into the individual level and the aggregated level. On the individual level, home energy management systems need to schedule flexible loads to respond to the DR signals based on optimization models [16; 33; 34]. On the aggregated level, load serving entities (LSEs) allocate DR resources according to customer models, such as the utility function [9; 35; 36] and the price elasticity matrix [37; 38], in order to maximize LSEs' welfare or social welfare. To measure DR performance, most studies are based on the customer baseline load (CBL), which is a predicted load curve of the customer in the absence of a DR event.