### Background and Literature Review  
Traditional consumers have been gradually transforming towards prosumers for both consuming and producing energy. With the deployment of advanced metering and smart management technologies, different demand-side flexible resources can contribute significantly to DR implementation. Since most prosumers are small in size and cannot directly participate in flexibility or electricity markets, a prosumer aggregator is required to facilitate such transactive interactions. The most common DR framework for prosumer aggregators can be categorized into price-maker or price-taker [4]. The former one indicates that prosumer aggregators perform flexible DR to determine the market clearing price and quantities. In [5], a two-stage stochastic optimal bidding strategy is proposed for prosumer aggregators to participate in the tertiary reserve market under the pay-as-bid remuneration scheme. In [6], an optimal bidding strategy of prosumer aggregators is proposed based on their bottom-up responsiveness. The latter one indicates that prosumer aggregators act as retailers in order to economically optimize the DR based on retail tariffs. Generally, time-varying retail tariffs are adopted, such as time-of-use pricing (TOU) [7] and real-time pricing (RTP) [8]. They are all used to encourage the participation of prosumer aggregators in distribution system flexibility or electricity services. Based on the operational time scales of prosumer aggregators, DR can be divided into two types. The first one is off-line DR. Heating ventilation air conditioning (HVAC) is a commonly used load, which is controllable and flexible resource that participates in day-ahead DR [9]. In [10], a resistance capacity model is adopted to simulate the thermal dynamics of a HVAC-based area, and its operational potential is evaluated based on substantial amounts of smart meter data. In [11], an interrelated indoor thermal response and HVAC are coordinated to day-ahead electricity prices. Other demand-side flexible resources, including electric vehicles (EVs), also can participate in the DR. EV users and an EV aggregator are investigated in [12] based on their stochastic reality needs. The existing off-line DR schemes [9; 10; 11; 12] of prosumer aggregators assume the perfect forecasting of uncertain factors, including renewable generations, ambient temperature, and EV charging needs, which pose difficulties in practical applications. The other one is the on-line DR of prosumer aggregators. HVAC can quickly respond to the price signal or renewable energy fluctuations, which can serve as flexible resources in real-time operation. In [13], a resistance-capacity model for HVAC is reviewed and a transactive building-aggregator-grid framework is developed via a robust model predictive control (MPC) algorithm. In [14], a unified appliance model is proposed to aggregate diverse types of flexible resources, and a dynamic energy management framework is developed based on MPC. By augmenting it using a rule-based method, real-time electrical and multi-energy DR are investigated in [15; 16]. In [17], a robust multi-time-scale model is proposed for price-based and incentive-based multi-energy DR. In [18], the real-time multi-energy DR of aggregators are optimized via a hierarchical distributed structure. Nevertheless, the existing on-line DR schemes [13; 14; 15; 16; 17; 18] of prosumer aggregators assume that all flexible resources are equipotent and equally willing to participate in the DR program, which is impractical. The transactive DR framework for the multi-time-scale proactive participation of multi-energy prosumer aggregators with 100% renewables has not been involved yet. The concept of flexibility has, in general, been introduced to understand the function mechanism of and improvements made in DR. During the practical implementation of DR, a reasonable and accurate evaluation of the flexibility potential can provide a certain number of dispatch references for prosumer aggregators [1; 19].