Simulation of PSDF (Photovoltaic, Storage, Direct Current and Flexibility) Energy System for Rural Buildings

Abstract

The PSDF (photovoltaic, storage, direct current, and flexibility) energy system represents an innovative approach aimed at achieving carbon neutrality. This study focused on rural buildings and utilized Modelica to develop a dynamic simulation model of the PSDF system. The research introduced a framework for direct current distribution microgrid systems with flexible regulatory mechanisms, employing a virtual inertia control strategy to provide stable adjustments for flexible operations and support integration with local grids. Case simulation results indicated that the system equipped with a water tank saved 3.15 kWh compared to the system without a water tank, resulting in an energy savings rate of 22.14%. Compared to traditional photovoltaic systems, the PSDF system significantly enhanced energy management flexibility and system reliability through the integration of thermal storage and battery management. This research made significant contributions to the fields of renewable energy and building energy systems by offering a scalable and practical solution suitable for rural contexts.

1. Introduction

1.1. Motivation

Upon setting the “Dual Carbon” goal, China has embarked on a new phase of energy structure upgrades and industry reform. The construction industry alone contributes up to 51.3% of national carbon emissions, with carbon emissions from both construction materials production and operation stages accounting for approximately 28% and 21% of national emissions, respectively [1]. Thus, reducing carbon emissions in the construction industry presents immense challenges [2].

Domestic scholars have proposed passive building solutions to reduce operational energy consumption, but these buildings have failed to meet standards [3,4]. Subsequently, photovoltaic building integration has been suggested [5]. Its combination of photovoltaics and building materials creates new materials, while the integration of photovoltaic systems and buildings is a useful alternative. However, these approaches face issues, such as instability, susceptibility to weather, and energy storage challenges. Academician Yi Jiang proposed the “photovoltaic, storage, direct current, and flexibility” (PSDF) system building [6], which integrates photovoltaic equipment with existing buildings. Thanks to rapid development in electrochemical energy storage, electric vehicles, and residential electrification, the PSDF building is presently considered the optimal solution for achieving the “carbon peak” [7,8,9,10].

The PSDF building system is expected to support a new power system based on zero-carbon electricity and provide significant energy storage capacity for this new system. The development of the PSDF system facilitates the direct current adaptation of electrical appliances in buildings, optimizes the distribution capacity of building power systems, enhances asset utilization efficiency, reduces operational electricity costs, and even enables potential revenue generation from electricity sales.

Due to the generally larger land area and relatively fewer building obstructions in rural areas, the photovoltaic, storage, direct current (PSDF) system can effectively utilize solar energy, providing clean energy for rural buildings. This system can significantly improve energy efficiency, enhance residents’ quality of life, and address issues of insufficient grid coverage or unstable power supply in rural areas, thereby promoting sustainable development in rural regions. Academician Yi Jiang proposed that the development of a new rural energy system based on rooftop photovoltaic systems is a key breakthrough and focal point for building a new power system and achieving low-carbon transformation of the energy system in China [11]. Wang Chaoliang et al. conducted a comparative analysis of two PSDF system topologies suitable for rural residential buildings, evaluating system performance and economic efficiency under different photovoltaic and storage capacity configurations [12]. Chen Wenbo et al. analyzed the economic and social benefits of implementing the PSDF system in rural areas, using the construction of such a system in Dongyao Village, Ruicheng County, Shanxi Province, as a case study. A preliminary exploration of the business model was also conducted [13].

1.2. Related Work

Although some prior research has not explicitly defined the concept of PSDF, a substantial body of work has explored PSDF from multiple aspects. This study mainly reviews related works in terms of capacity configuration optimization and control strategy optimization for PSDF.

Capacity configuration optimization: Dou Xiaobo et al. proposed a capacity configuration method for grid-connected wind–solar storage microgrid systems, focusing on maximizing investment returns from the perspective of investors [14]. Zhang Lei et al. studied the capacity configuration methods for various key devices in PSDF building systems, enhancing the load supply rate and smoothing the electricity consumption curve [15]. Jiang Jianhui et al. classified the operating modes of household photovoltaic and energy storage systems into three categories: general mode, economic mode, and off-grid mode. Based on users’ electricity demands, local electricity prices, and subsidy policies, system capacity configurations for these three modes were proposed, followed by an economic analysis [16]. Zhao Anjun et al. developed a system optimization configuration model that incorporates market electricity pricing mechanisms and electric vehicle usage demands to establish a comprehensive operation mode for the new system. The optimal capacity configuration of distributed power sources in household new energy systems was determined using the particle swarm optimization algorithm [17]. Cervantes et al. proposed a stochastic mixed-integer optimization model for optimizing the scale of solar power generation systems and their battery storage, aiming to minimize the total costs associated with the investment in solar power generation systems as well as the electricity provided by the grid over the planning period [18]. Javeed et al. conducted a comparative study on the actual optimized sizing of grid-connected rooftop solar photovoltaic systems and battery storage systems, considering both unified and time-of-use electricity pricing options [19]. Using a rule-based novel energy management system, the optimal capacities of solar photovoltaic generation and battery storage are determined under both unified electricity pricing and time-of-use pricing conditions [20,21].

Control strategy optimization: Lindberg et al. optimized the investment and operation of energy technologies using thermal storage devices based on peak load electricity prices and grid feed-in tariffs, completing the cost design of the optimal energy system for Zero-Energy Buildings [22]. Huang Bixin et al. suggested that when integrating distributed photovoltaic power generation, both the integration system costs and the grid modification costs should be comprehensively considered. They advocated for selecting a scientific integration approach with the premise of optimizing overall costs [23]. Hofer et al. conducted a simulation study on the performance of a hybrid building microgrid that couples photovoltaic generation with alternating current and direct current loads in residential buildings [24]. Miglani et al. utilized a bi-level multi-objective optimization method to optimize building energy systems, aiming to minimize total costs and carbon dioxide emissions [25]. Zhang Tao et al. analyzed the system architecture and implementation methods of the vehicle–building–grid based on terminal energy demand and usage patterns [26]. Rong Xiuting et al. proposed a system architecture and a three-level control structure for DC commercial buildings, establishing three typical flexible operation modes of constant power extraction based on the coordination of photovoltaics, storage, and electric vehicles [27]. Wang Bingzheng integrated artificial intelligence and deep learning algorithms to develop a distribution system and dispatch strategy for PSDF buildings [28]. Luo Zhengyi et al. proposed a comprehensive definition of building demand flexibility and compared the flexibility capabilities and operational characteristics of major residential flexible loads [29]. Liang et al. adopted a novel data-driven model predictive control framework based on a physically consistent neural network model to optimize building energy flexibility [30]. Peng et al. proposed a multi-objective optimization scheduling framework for household energy systems, in which users’ demands for the optimization of household energy systems are categorized into different levels and priorities based on Maslow’s theory [31].

To investigate the use of a PSDF system for a rural building in Hangzhou and consider the electricity consumption characteristics of agricultural land, this study designed the system using the Dymola&Modelica experimental platform. Moreover, a virtual inertia control strategy is adopted to provide stable regulation for flexible operation inside the building [32]. The flexible effects of energy storage and thermal storage devices in the system are preliminarily analyzed.

2. Methodology

2.1. System Topological Structure

According to the conclusion of the research on the selection of topology of the PSDF system based on the low-carbon literature [6], this study adopts the single-ended radial topology. The topology structure uses dual-voltage levels of 375 V and 48 V in direct current. High-power loads, such as DC air conditioning, electric vehicle charging, and kitchen appliances, are connected to the DC 375 V busbar for maximum efficiency. Living area loads, such as mobile phones, computers, electric fans, and LED lighting, are connected to the 48 V busbar for enhanced safety. The topology structure schematic is shown in Figure 1.

2.2. System Model

Taking a building in Hangzhou, Zhejiang Province, as an example, a virtual simulation experiment was conducted using Dymola&Modelica. The weather data for Hangzhou are presented in

Table 1. Weather data for Hangzhou.

Month	Air Temperature (°C)	Sunshine Duration (hour)
January	5.1	135
February	7.1	117
March	11.7	140
April	17.4	153
May	22.2	174
June	25.4	162
July	29.9	232
August	29.0	243
September	24.8	161
October	19.5	162
November	13.5	149
December	7.4	150

, with the average annual temperature recorded at 17.8 °C.

2.2.1. Architectural Overview

The selected building is shown in Figure 2, Figure 3 and Figure 4. The building has two floors, including a bedroom, living room, study, bathroom, etc., with a total heating area of approximately 225 m²; the first floor is 120 m², the second floor is 105 m², and the building faces north and south, with a glazing-to-wall ratio of 0.2. The external wall and the roof are, respectively, insulated with 60 mm and 80 mm thick benzene panels, the external door is insulated with 30 mm thick material, the external window is insulated with a hollow plastic steel window, and the glass curtain wall is insulated with hollow argon-filled glass. Heat transfer coefficients for the building envelope are shown in

Table 2. Heat transfer coefficient of building envelope.

Wall U Value	Door U Value	Window U Value	Floor U Value	Roof U Value
W/m2·K	W/m2·K	W/m2·K	W/m2·K	W/m2·K
0.384	1.7	1.27	0.431	0.411

.

The roof selected for the installation of photovoltaic panels is a flat roof. In this study, the PV panels were installed at a fixed inclination angle, optimized based on site conditions to maximize solar energy capture. The panels are strategically positioned on the roof’s most effective areas to fully utilize the available surface.

In order to gain insight into the local residents’ electricity usage habits, the researchers conducted a questionnaire survey in this study. The survey elicited information on the daily electrical equipment used by residents and their electrical habits. Statistical analysis was performed to identify users’ electricity usage patterns, which were then used to develop an appropriate photovoltaic design plan. Given the rising number of household electrical devices, the survey took into account the electricity usage situation of the following devices: 1. stage-based electrical devices (rice cookers, kettles, etc.); 2. long-term electrical appliances (refrigerators, water heaters, etc.); 3. periodic electrical appliances (electric vehicles, etc.); 4. seasonal electrical appliances (air conditioners, etc.); and 5. personal electricity usage habits. Due to the significant variations in household size and electrical appliances among different families, we assumed a household structure of 2 + 2 and selected identical specifications for the electrical appliances. Based on the survey results, the design was calculated, and the normal (no heating or cooling) and high-power electricity consumption patterns were analyzed.

The following assumptions were made regarding electricity consumption patterns: 1. It was assumed that users’ electricity usage habits remained consistent, meaning they used similar appliances at the same time each day. 2. It was assumed that electricity demand was unaffected by changes in electricity prices; i.e., users’ consumption behavior did not change in response to price fluctuations. These assumptions helped to simplify complex consumption behaviors and reduce uncertainty in the modeling process, making the research or simulations more feasible to conduct.

The electricity consumption patterns of users are shown in Figure 5. The primary consumption periods for the building are between 7:00 and 8:00 a.m., 11:00 and 12:00 p.m., and 6:00 and 10:00 p.m. As illustrated in Figure 5a, consumers with low energy consumption experience their peak power consumption around 6:00 p.m., with a maximum power consumption of 1.42 kW and a total daily consumption of 3.85 kWh. Figure 5b shows that consumers with high energy consumption reach their peak power consumption around 7:00 p.m., with a maximum power consumption of 2.33 kW and a total daily consumption of 9.81 kWh. For rural buildings, the photovoltaic power generation during the day supports the daytime electricity load of the building, and the excess electricity is stored in batteries or fed into the municipal power grid. At night, the stored electricity in batteries or the grid can be used to meet power supply needs.

2.2.2. Simulation Model

To evaluate the performance of the proposed system, a Modelica-based dynamic simulation model was developed using Dymola (Dymola—Dassault Systèmes^®, Paris, France) [33]. Modelica is a high-level declarative language used to describe mathematical behavior, and Dymola is a commercial simulation environment that can perform symbolic transformations for large systems and real-time applications. The simulation model was developed mainly based on the Modelica Standard Library (MSL) and the Modelica Buildings library [34]. The Modelica Buildings library is a free open-source library with dynamic simulation models for building and district energy and control systems.

The proposed PSDF system for this rural building in Hangzhou was designed using the Dymola platform, as illustrated in Figure 6, Figure 7, Figure 8 and Figure 9. As shown in Figure 6, the system comprises a photovoltaic module, battery, battery control system (Figure 7), various household electrical equipment (Figure 8), the heat pump unit (Figure 9), building and heating system, among others. The building is equipped with a radiator heating system and a heat storage tank. The photovoltaic module has a total power of 16,240 Wp, based on the optimal panel installation angle and the available roof area. The battery has a capacity of 5 kWh, while the heat storage tank has a capacity of 1000 L. A DC air source heat pump (air-to-water) was identified based on the cooling and heating loads of the existing building and a 5 hp unit was selected (COP of 3.32). The daily electricity consumption patterns of various household electrical equipment were set based on the survey questionnaire results. The State Graphic was employed to regulate the charging and discharging of the battery, as well as the circulating water pump’s operation.

The power generation of a photovoltaic panel can be determined by Equation (1):

$$P_{\mathrm{pv}}=\eta f_{\mathrm{pv}}{A}_{\mathrm{pv}}G$$

(1)

where P_pv is the power generation of photovoltaic panel, W; η is the photovoltaic panel efficiency; f_pv is the photovoltaic module effective area ratio; A_pv is the photovoltaic panel area, m²; G is the total solar irradiance per unit area, which is the sum of direct and diffuse irradiation, W/m². The model takes into account the location and the orientation of the photovoltaic panel, specified by the surface tilt, latitude, and azimuth.

The model of the battery is represented by Equations (2)–(4):

$$P_{\mathrm{b}}={\displaystyle \frac{U^2}{R}}$$

(2)

$$E=P_{\mathrm{b}}t$$

(3)

$$\mathrm{SOC}={\displaystyle \frac{E}{E_{\max }}}$$

(4)

where P_b is the power stored in the battery (if positive), or extracted from the battery (if negative), W; U is the voltage difference across the pins, V; R is the resistance of the battery, Ω; E is available charge of the battery, J; t is the charging time, s; E_max is maximum available charge of the battery, J; SOC is state of charge of the battery, which should be between zero and one.

Dymola layout for battery model can be seen in Figure 10. The control of the battery is achieved through the State Graphic. The State Graphic provides a description of the battery’s behavior under different operating conditions, allowing for precise monitoring and control of the charging and discharging processes of the battery. The battery is charged at 6:00 a.m. until it is full. At 18:00, it is discharged until it is empty. This control is implemented using a finite state machine. The charging and discharging power is assumed to be controlled to a constant value.

Dymola layout for heat pump model can be seen in Figure 11. Using the equation fit method, the reversable heat pump model takes the set point for the leaving fluid temperature as an input. This reversable heat pump can be operated either in heating mode or in cooling mode. It is typically used for a water-to-water heat pump, but if the performance data are set up for other media, they can also be used for such applications. The heat exchanger at medium 1 is to be connected to the building load and the other heat exchanger to the heat source or sink, such as a geothermal loop. If, in heating mode, the heat exchanger at medium 1 operates as a condenser, then, in cooling mode, it operates as an evaporator.

The heat pump performance in cooling and heating mode is predicted by four non-dimensional equations or curves, which are given by Equations (5)–(8) [35]. The methodology involved using the generalized least square method to generate a set of performance coefficients from the catalog data at indicated reference conditions. Then, the respective coefficients and indicated reference conditions are used in the model to simulate the heat pump performance. The governing equations for the cooling and heating mode are as follows:

$${\displaystyle \frac{Q_{\mathrm{c}}}{Q_{\mathrm{c},\mathrm{ref}}}}=(A_1+A_2{\displaystyle \frac{T_{\mathrm{a}}}{T_{\mathrm{a},\mathrm{ref}}}}+A_3{\displaystyle \frac{T_{\mathrm{b}}}{T_{\mathrm{b},\mathrm{ref}}}}+A_4{\displaystyle \frac{m_{\mathrm{a}}}{m_{\mathrm{a},\mathrm{ref}}}}+A_5{\displaystyle \frac{m_{\mathrm{b}}}{m_{\mathrm{b},\mathrm{ref}}}})$$

(5)

$${\displaystyle \frac{P_{\mathrm{c}}}{P_{\mathrm{c},\mathrm{ref}}}}=(B_1+B_2{\displaystyle \frac{T_{\mathrm{a}}}{T_{\mathrm{a},\mathrm{ref}}}}+B_3{\displaystyle \frac{T_{\mathrm{b}}}{T_{\mathrm{b},\mathrm{ref}}}}+B_4{\displaystyle \frac{m_{\mathrm{a}}}{m_{\mathrm{a},\mathrm{ref}}}}+B_5{\displaystyle \frac{m_{\mathrm{b}}}{m_{\mathrm{b},\mathrm{ref}}}})$$

(6)

$${\displaystyle \frac{Q_{\mathrm{h}}}{Q_{\mathrm{h},\mathrm{ref}}}}=(C_1+C_2{\displaystyle \frac{T_{\mathrm{a}}}{T_{\mathrm{a},\mathrm{ref}}}}+C_3{\displaystyle \frac{T_{\mathrm{b}}}{T_{\mathrm{b},\mathrm{ref}}}}+C_4{\displaystyle \frac{m_{\mathrm{a}}}{m_{\mathrm{a},\mathrm{ref}}}}+C_5{\displaystyle \frac{m_{\mathrm{b}}}{m_{\mathrm{b},\mathrm{ref}}}})$$

(7)

$${\displaystyle \frac{P_{\mathrm{h}}}{P_{\mathrm{h},\mathrm{ref}}}}=(D_1+D_2{\displaystyle \frac{T_{\mathrm{a}}}{T_{\mathrm{a},\mathrm{ref}}}}+D_3{\displaystyle \frac{T_{\mathrm{b}}}{T_{\mathrm{b},\mathrm{ref}}}}+D_4{\displaystyle \frac{m_{\mathrm{a}}}{m_{\mathrm{a},\mathrm{ref}}}}+D_5{\displaystyle \frac{m_{\mathrm{b}}}{m_{\mathrm{b},\mathrm{ref}}}})$$

(8)

where Q_c is the load side heat transfer rate in cooling mode, W; P_c is the power consumption in cooling mode, W; Q_h is the load side heat transfer rate in heating mode, W; P_h is the power consumption in heating mode, W; T_a is the entering source side temperature, K; T_b is the entering load side temperature, K; m_a is the source side volumetric flow rate, m³/s; m_b is the load side volumetric flow rate, m³/s; A₁-D₅ are the equation fit coefficients for the cooling and heating mode.

3. Results

3.1. Power Production of the PSDF System

The PSDF system’s performance was evaluated based on the different seasons. In spring, for instance, the photovoltaic panel angle was adjusted to 30°, while the total photovoltaic power generated during a specific day and the interaction between the PSDF and the power grid are illustrated in Figure 12. The blue curve in the figure represents the photovoltaic power generation over the course of a day, with the power reaching its peak around noon at approximately 12:00, with a maximum value of 9.05 kW. The red curve illustrates the power interaction with the grid. When the power is greater than zero, it indicates surplus electricity being fed into the grid, while a power value less than zero represents electricity consumption from the grid. From the figure, it is evident that the photovoltaic power generation significantly exceeds the user’s consumption. As shown in Figure 13, the total photovoltaic power generation on a typical day is 58.81 kWh. According to Figure 5, a customer with high energy consumption uses 9.81 kWh on a typical day. After subtracting the battery capacity of 5 kWh, the system generates a surplus of 44 kWh.

The total photovoltaic power generation of the building in different seasons, as obtained through simulations and analyses, is presented in

Table 3. The total photovoltaic power generation in different seasons.

Season	The Total Photovoltaic Power Generation (kWh)
Spring	5265.85
Summer	5244.03
Autumn	4785.2
Winter	3125.02

. As shown in the table, the photovoltaic power generation is significantly influenced by seasonal and weather data, with higher total generation during spring and summer, while winter shows a lower total generation. The total annual power generation is calculated to be 18,420.1 kWh. This annual power generation far surpasses the residential electricity consumption. If all buildings in this rural village converted to photovoltaic energy storage structures and their individual systems were integrated in parallel to connect to the electrical grid, it would create a village-level photovoltaic power station that could generate a significant amount of electricity.

3.2. Impacts of the Battery on PSDF System

The battery is a crucial component of the PSDF system, playing a vital role in ensuring its efficient operation. During the photovoltaic power generation period, any excess electricity generated will be stored in the battery if the building consumes less electricity than the photovoltaic power generated. Conversely, when the photovoltaic power generation is insufficient to meet the building’s electricity demand, the battery will discharge the stored electricity to supplement the shortage. This effectively balances the power supply and demand, ensuring the stable operation of the system. Additionally, the battery also serves as a backup power source during power outages, ensuring uninterrupted power supply for the building.

Figure 14 presents a comparative analysis of the daily operations of the PSDF systems with and without battery storage. In the figure, the red line represents the power curve of the PSDF system equipped with a battery, while the black line indicates the power curve of the PSDF system without a battery. A comparison reveals that the presence of the battery significantly reduces the user’s electricity consumption from the grid. The PSDF system with a battery stores excess power generated during peak photovoltaic production and discharges it during periods of higher night-time load, thereby enhancing the operational flexibility of the system, increasing the utilization of renewable energy, and achieving the effect of “peak shaving and valley filling”.

3.3. Impacts of the Thermal Inertia on PSDF System

The building under study uses an air source heat pump as the heat source. To utilize the system’s thermal inertia to absorb the electricity generated by the PSDF system, a water tank was added to the system for thermal storage. The water tank is capable of storing the surplus electricity generated by the PSDF system during the period of peak photovoltaic power generation. This stored electricity can then be used to heat water for use in the building. This thermal storage system can effectively reduce the peak load of the PSDF system and improve its stability. Additionally, it can also reduce the building’s energy consumption during peak hours, further enhancing the system’s energy efficiency.

Figure 15 compares the operational performance of heat pump systems with and without a water tank. The blue line represents the heat pump’s power consumption over time. The two systems show distinct operational differences; the heat pump in the system without a water tank undergoes frequent start–stop cycles, while the system with a water tank leverages the tank’s thermal storage capacity, concentrating the heat pump’s operation within a specific time frame. This difference in operation leads to varying electricity consumption between the two systems over a day. The red line illustrates total daily electricity consumption, showing that the system without a water tank uses 14.23 kWh, whereas the system with a water tank consumes only 11.08 kWh, saving 3.15 kWh, resulting in 22.14% energy savings. Furthermore, the temperature variation curves show that the system with a water tank maintains more stable indoor temperatures, with less fluctuation, compared to the system without a water tank.

4. Conclusions

The present study built a PSDF virtual simulation system based on the Modelica simulation language and the Dymola simulation platform, and we conducted initial system analysis. The system includes PV, battery, building system, and air source heat pump heating equipment, where the building system comprises various household appliances and radiator heating systems. After preliminary calculations and analysis, the following conclusions can be drawn:

1. The PSDF system can significantly reduce the energy consumption of rural buildings and improve energy utilization efficiency. Compared to other energy-saving and emission-reducing measures, building a PSDF system in rural areas is convenient, simple, and adaptable. By transforming traditional buildings from “consumers” to “producers”, each household can produce much more electricity than they consume, contributing to the development of renewable energy in rural areas.

2. The PSDF system with batteries can enhance the system’s utilization of photovoltaic power and improve its flexibility and adjustment capabilities. With the presence of a battery, excess electricity generated during the peak photovoltaic power generation period can be stored and discharged at night, achieving “peak shaving and valley filling” and ensuring the system’s flexibility.

3. Adding a thermal storage tank to the PSDF system can utilize the system’s thermal inertia and provide more stable heating loads. The thermal storage tank can store the excess electricity generated by the PSDF system during the peak photovoltaic power generation period and use it later to heat water for the building’s use, effectively reducing the system’s peak load and improving its stability. Case simulations demonstrate that the system equipped with a water tank saves 3.15 kWh of energy compared to the system without a water tank, achieving an energy savings rate of 22.14%.

4. Compared to traditional photovoltaic systems, the PSDF system significantly enhances energy management flexibility and system reliability through the integration of thermal storage and battery management. Traditional photovoltaic systems rely heavily on battery capacity, whereas the PSDF system expands energy utilization by incorporating thermal storage, reducing the frequency of battery charging and discharging. This approach extends battery life and lowers system maintenance costs.

Overall, the results suggest that the PSDF system, with its various components and functionalities, offers a promising alternative for renewable energy production and energy-efficient building operations in rural areas. Although this study provides a theoretical foundation for the design and optimization of building PSDF systems, several limitations remain, which also point to important directions for future research. Firstly, due to certain constraints, the simulation model developed in this study has not been experimentally validated. Future research should focus on verifying the accuracy of the simulation results and further exploring the dynamic operational characteristics of the PSDF system under different environmental conditions to optimize its energy efficiency and overall performance. Secondly, this study’s modeling is based on the climate conditions of Hangzhou, and future research should extend simulations to different geographical and climatic conditions to assess the system’s adaptability on a global scale. Lastly, although the present study does not provide an in-depth economic analysis, we acknowledge its critical importance for the adoption and application of PSDF systems. Future studies should conduct economic analyses to evaluate the return on investment of the PSDF system and determine its economic feasibility across various scenarios. These studies will be essential for the system’s practical application and widespread deployment.