## 1 Introduction
During the scheduling process of the ADN, demand response is always applied in combination with DG control. The source-load coordinated optimization scheduling in the distribution network mainly includes minimizing system operation costs [12], improving the utilization ratio of renewable energy and user satisfaction [13], smoothing renewable energy outputs [14], reducing renewable generation curtailment [15], and selecting the site and size of DGs for the purpose of coordinating multiple interests [16; 17]. Reference [18] focuses on the optimal intraday scheduling of a distribution system that includes renewable energy generation, energy storage systems, and thermostatically controlled loads. Reference [19] proposes a multiobjective dynamic economic scheduling model considering the EVs and uncertainties of wind power to minimize the total fuel cost and pollutant emissions. Reference [20] uses multiscene technology to deal with the uncertainty of intermittent DGs and loads, and a two-step optimal scheduling model of ADN considering day-ahead and real time is established. Reference [21] proposes a multi-timescale cost-effective power management algorithm for islanded microgrid operation targeting generation, storage, and demand management. However, in our study, the DR is implemented with responsive loads that consider the uncertainty of the load participation. Moreover, the satisfaction of users participating in the demand response is modeled. With the gradual application of ESSs, the management of demand-side loads also becomes possible [22]. In Reference [23], to minimize the cost of power loss, robust optimization is used to deal with the uncertainty of electricity price and the day-ahead scheduling problem of ESSs and responsive loads. Reference [24] comprehensively considers a variety of adjustable resources in the ADN, such as DGs, ESSs, voltage regulators, switchable capacitor banks, and interruptible loads,to minimize the total operating cost of the system in the scheduling cycle. To reduce curtailment from renewable distributed generation, reference [25] chooses minimum storage sizes at multiple locations in distribution networks. Considering the space-time relationship between ESSs and flexible loads as well as the influence of power flow, a multiobjective ADN optimization scheduling model is constructed in reference [26], and the synergy is quantified by setting the priority of the scheduling units of each generation unit. When it comes to research on optimal scheduling of source-network-load coordination, the single objective of the system operating cost is mainly considered. In order to realize the optimal control of DGs, networks, and demand-side loads, a smart distribution network optimization model with the minimum operating cost is proposed in reference [27]. Reference [28] proposes a comprehensive operational scheduling model to determine optimal decisions on active elements of the network, DGs, and responsive loads, seeking to minimize the day-ahead composite economic cost. Reference [29] presents a mixed-integer dynamic optimization model for the optimal scheduling of ADNs with the objective of minimizing the daily costs of electricity purchased from distribution substations. Reference [30] develops a framework for operational scheduling of distribution systems with dynamic reconfiguration considering coordinated integration of energy storage systems and demand response programs to minimize the total costs, including cost of total loss, switching cost, cost of bilateral contract with power generation owners and responsive loads, and cost of exchanging power with the wholesale market. Reference [31] introduces a multiagent system and multilayer electricity price response mechanism to construct an optimization model of the distribution network layer, direct coordination source-load layer, and indirect coordination microgrid layer.