## 1 Introduction
Reference [32] proposes an optimal scheduling model aiming to find the lowest operating cost of a complete scheduling period, and taking controllable DGs and tie switches as control means. The impact of electricity price and the adjustment of tie switches on the operating cost is considered, and the energy conservation and capacity constraints of the ESSs throughout the scheduling period are guaranteed. Reference [33] proposes a source-network-load coordinated economic scheduling method in an ADN considering the electricity purchase cost, power loss cost, and demand-side management cost and taking DGs, ESSs, flexible network topologies, interruptible loads, and transferable loads as the control means. To summarize, in the present research on optimal ADN scheduling, there are mainly the following problems:  
* The abundant controllable resources and diverse scheduling means are not fully considered, which is mainly limited to some aspect of source-network-load, such as the source-load interaction. * The scheduling model tends to aim for economic optimization while ignoring the uncertainty of WTs, PV cells, and loads. * The scheduling model has only one objective and fails to fully consider the interaction between the source-network-load, which cannot guarantee that multiple aspects will be simultaneously optimal. This paper comprehensively utilizes the controllable elements of DGs, ESSs, switches, and interruptible loads in the ADN, fully considers the uncertainties of renewable energy and demand-side load response, and establishes a multiobjective scheduling model with coordinated source-network-load, which aims at finding the lowest operating cost, the highest renewable energy utilization rate, and the highest user satisfaction in the scheduling cycle. The NSGA3 algorithm is used to solve the three-objective scheduling model, and the performance of the algorithm is compared with another algorithm. The main contributions of the paper include the following:  
* Considering the deficiency of control measures for existing scheduling strategies, the proposed method fully considers the controllable resources of the source-network-load, including the DGs, switches, responsive loads, and storage systems. The specific control method includes DG control, network reconfiguration, and demand-side management. * Different from previous single-objective models, a three-objective scheduling model with coordinated source-network-load is proposed, considering the lowest operating cost, the highest renewable energy utilization rate, and the highest user satisfaction
* Different from the method of transforming three objectives into one single objective by weight coefficients, this paper uses the NSGA3 algorithm to calculate the Pareto solution set of the optimization model and uses a fuzzy decision-making method to filter the solution set. The remainder of this work is organized as follows. In Section 2, we propose the scheduling strategy of source-network-load in the ADN. In Section 3, we analyze the uncertainty of scheduling in the ADN. In Section 4, we establish the multiobjective optimization scheduling model of the ADN. In Section 5, we introduce the reference point-based many-objective evolutionary algorithm (NSGA3). Results are presented in Section 6, and conclusions are drawn in Section 7.