### System Planning and Configuration
#### 2.2.2 System Configuration Process and Method  
The configuration optimization and evaluation framework for the PEDF system consists of a dual-layer system configuration optimization model, which includes planning and operating, along with an evaluation model. Initially, the PEDF topology is established to determine the capacity range of photovoltaic and energy storage systems based on the building type and construction objectives. Various capacity configuration combinations are identified, serving as constraints for the planning layer. In the planning layer, these combinations are evaluated to minimize the total annual lifecycle cost, which is then passed down to the operating layer. The operating layer aims for three objectives: recurring electricity purchase cost, minimizing emissions, and ensuring reliability. A scheduling plan is created based on these goals and provides feedback on operating costs to the planning layer. This feedback is processed through a double-layer optimization algorithm to refine the optimal configuration plan. Finally, the evaluation indexes are established to assess the proposed configuration. Figure 4 illustrates the PEDF configuration optimization process. The dual-layer model separates the planning and operating layer, solved iteratively. The planning layer employs an intelligent optimization algorithm (such as Particle Swarm Optimization, Whale Optimization, Grey Wolf Optimization, or the Sinh Cosh optimizer) to solve the model. In contrast, the operating layer utilizes the Cplex solver for its computations. The optimization results inform the energy scheduling and configuration, leading to the optimal system configuration scheme. A multi-dimensional evaluation method for the PEDF system has been developed based on this approach. * PEDF topology configuration The process for configuring the topology of the PEDF system network is as follows: 1. Analyzing typical topology structures: The process begins with an examination of the typical network topologies used in the PEDF system. This analysis considers various economic, reliability, and social indexes relevant to the system [24; 25; 26]. The typical topology network structures of standard PEDF systems are illustrated in Figure 5, along with their characteristics and applicable scenarios, as detailed in Table 1. 2. Determining Voltage Levels: The PEDF system typically adopts single or multiple voltage conversion levels [11; 12; 27]. The recommended voltage levels are DC750V, DC375V, and DC48V, in accordance with TCABEE030-2022, titled \"The Design Standard for Direct Current Power Distribution of Civil Building\". Figure 4: Optimization process for the PEDF system configuration. * Load configuration The load configuration process of the PEDF system involves several steps: 1. Determine the typical type of electricity loads. Analyze load demand for various DC equipment types, including large, medium, and small. Figure 6 illustrates the distribution percentages of various types of DC electrical equipment in PEDF buildings [8]. 2. Identify flexible loads. Recognize and assess flexible loads, such as interruptible, transferable, and curtailable electrical equipment, focusing on their operating times and power needs. 3. Select modeling method. Choose a suitable model approach, often using machine learning-like methods for load prediction or clustering to define the power curve [28; 29; 30]. Figure 7 illustrates load curves for typical office and commercial buildings. * Photovoltaic configuration The photovoltaic configuration process of the PEDF system is as follows: 1. Potential evaluation of building photovoltaic utilization. The first step is to assess the potential for building photovoltaic use in the building, analyzing the layout and transportation systems to determine the suitability of rooftop or facade installations [31; 32; 33]. 2. Determination of Specifics affecting factors. Based on factors such as system load configuration, solar radiation intensity and available installation area, as well as