Photovoltaic-Based Residential Direct-Current Microgrid and Its Comprehensive Performance Evaluation

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This study offers a fresh concept for the use of PV technology. The concept behind this research can serve as a model for the creation and application of other new energy sources.

Abstract

The “dual carbon” strategy has drawn attention to distributed PV systems for their flexibility and variability, but the rising need for direct-current (DC) loads on the load side has created additional difficulties for microgrid system upgrades. In this article, a PV-based microgrid design approach for residential buildings is suggested, working on the assumption that distributed PV systems are given top priority to handle domestic DC needs. The residential DC microgrid system’s overall design concept is first put out, and the circuit system is then concentrated to supply the main idea for the ensuing verification of the system’s viability. Secondly, the actual power generation in the selected area was clarified by testing, and then the electricity consumption of DC loads accounted for about 20.03% of the total power consumption according to the survey of 100 users. In addition, the circuit system is subjected to spectral model measurements and physical measurements to verify the operational performance of the circuit system; the feasibility of the PV microgrid system is further verified using dual testing of the PV system and the circuit system. The test results show that the proposed DC microgrid system can accurately provide the required voltage for small household DC appliances, such as 24 V, 14 V, 5 V, etc. Finally, the system economics were analyzed, and the equipment payback years were estimated. The supply and demand of PV power generation and DC appliances can be balanced via the construction of a microgrid. This study offers a fresh concept for the use of PV technology. The concept behind this research can serve as a model for the creation and application of other new energy sources.

1. Introduction

The development and utilization of renewable energy technologies in buildings has been becoming an important way to achieve the “dual carbon goals” in China. Among various renewable energy utilization technologies, the solar utilization technologies have been widely concerned because of the huge potential [1,2,3,4,5]. According to the relevant statistical data in 2022, China’s installed capacity of grid-connected solar power has reached about 392.61 million kilowatts, an increase of 28.1% year-on-year [6]. It is clear that the potential and prospects of solar PV technology are vast. With the development of centralized PV power plants, distributed PV systems has been becoming more attractive due to the proximity to the load side with short transmission and distribution distances and system flexibility [2,7,8]. According to statistics, the area of urban rooftop PV in mainland China can reach about 3.35 billion square meters and be used for the installation of PV panels [9].

In the utilization of distributed PV systems, the microgrid should be optimized with the electricity consumption characteristics of the building, such as electricity consumption and electricity consumption categories, so as to reach its full potential [10,11,12]. Nowadays, with the upgrading and development of power electronics technology, the application of DC electrical appliances is increasing year by year, which poses new challenges to the traditional photovoltaic grid-connected mode [13]. Therefore, the PV power generation is directly connected with the electrical appliances to form a DC-DC microgrid system, which can improve solar energy utilization more effectively than a DC-AC-DC microgrid system used to serve DC appliances [14,15,16,17,18]. With the energy conversion process simplified, the efficiency of energy conversion and the efficiency of DC appliances operating at the appropriate voltage can be increased at the same time [19]. However, different DC devices require different voltages. Therefore, it is very important to design an effective circuit system to solve the mismatch between the output of a photovoltaic system and the demand of DC equipment economically and effectively.

At present, the research on the adaptation of DC microgrid can be roughly divided into two research directions, namely: power electronic buck–boost adaptation and energy management to improve power supply and distribution adaptation. In the research of power electronic step-down low-voltage DC equipment, in the literature [20,21], a 48 V and 72 V DC power terminal is proposed to supply power to the DC load, which can save costs and realize the matching of photovoltaic output and application. However, it is still DC-DC-DC, and further step-down is still needed until all kinds of DC equipment operate normally. In Reference [22], a 12 V or 24 V DC voltage algorithm combining PI and MPPT is proposed for fixed lighting. However, DC electrical equipment is still growing, except for lighting, and the use range of single fixed voltage output is limited. Reference [23] proposed two feasible architectures to compare AC distribution in the home: one architecture is powered by a low-power DC load, while the traditional AC distribution is powered by a residual load, and the other architecture increases the level of DC distribution and load installation to the highest possible level. But multiple load distribution systems will undoubtedly increase the impact on the power grid, and from a practical point of view, large-scale circuit transformation is also difficult to implement. Reference [24] proposed the use of a supercapacitor auxiliary circuit to realize DC-DC voltage regulation. Reference [25] shows that the use of a buck–boost voltage regulator can make the extraction power and tracking efficiency have a larger working area and better performance. However, the above methods are all aimed at a single voltage output, and the adaptation range has not been improved.

In the study of energy management and simulation, Reference [26] proposed the concept of combining artificial intelligence with the Internet of Things to coordinate the energy management of microgrids and the energy use habits of households, so as to meet the voltage requirements of various DC loads. Reference [27] proposed a distributed optimal small-scale photovoltaic energy system scale strategy to reduce energy costs by combining the users’ demand for energy, but this undoubtedly requires the transformation of the existing circuit structure, the actual operating cost is too high, and the equipment recovery time is too long. Reference proposed a maximum power calculation method based on Newton Raphson. By comparison, it is verified that the proposed method has fast convergence, less calculation, and high precision. Based on the concept of DC microgrid, Reference [28] adds a short-term energy storage system to make the system run more smoothly when the photovoltaic intensity changes, but the upgrade of the power supply side cannot improve the fluctuation of the power supply side.

This paper aims at meeting the variable voltage demand for different DC appliances and supplying power for different low-voltage DC loads through the same circuit. This paper first puts forward the overall design idea of residential DC microgrid, determines the system optimization goal with investigation, then tests the system, and makes the circuit in kind to verify the feasibility of photovoltaic microgrid. Finally, the equipment recovery period is estimated. Without changing the original main circuit of the building, the photovoltaic power generation system is used to accurately supply power for different low-voltage DC loads through the same circuit, and the DC power supply system has a higher priority, which further avoids the influence of insufficient photovoltaic power generation on the use of DC equipment, and provides a new idea for the photovoltaic application of solar energy.

2. DC Microgrid System Based on Distributed PV Technology

2.1. Principle of Distributed PV Microgrid System Proposed

The block diagram of a distributed PV microgrid system is shown in Figure 1, which mainly includes the PV power generation system, circuit system, and various DC appliances. The common drawback of renewable energy sources is their intermittency [29], so this project considers the inclusion of PV batteries to reduce the intermittency of the system. After that, the PV system includes PV panels, an MPPT tracking system [30], DC detector, the circuit system includes boost, drive, buck, and an STM32 control system. This control method was selected for this project to achieve control of the boost and buck circuits because the inherent control algorithm of PWM maximizes the energy delivered to the battery pack, thus increasing its battery life [31,32].

The basic working principle is that the entire microgrid system sends out DC power through the PV system, which is boosted by the boost circuit in the circuit system, and outputs different voltages through the STM32-controlled pulse width duty cycle (PWM) to meet the voltage requirements of different DC appliances.

2.2. Survey of DC Appliances

In order to clarify the current residential building electricity consumption and DC electrical type, the project investigated the electricity consumption of 100 households in Qingdao, and the results showed that the average daily electricity consumption was 7.14 kWh. Also, the types of household DC appliances, the average number of devices per household, and the average running time were investigated, as shown in

Table 1. Summary of domestic DC appliances.

	Residential Lighting	Cell Phone	Microwave Oven	Laptop
Average power consumption (W)	25	80	1500	65
Average number of devices per household	8	3	1	1
Average hours of use per day (hours)	3	2.5	0.017	1.5

.

The daily power consumption of household DC appliances is shown in Figure 2, where “other” includes a small amount of household dry battery charging as well as the total power consumption of the DC line itself.

According to the survey results, the commonly used DC appliances in residential households include laptops, lightings, mobile phones, etc. Every type of electrical appliance requires the corresponding different voltage. At present, the commonly used voltages for DC electrical appliances are 5 V, 12 V, 14 V, and 24 V.

2.3. DC Circuit Design

Due to the wide output voltage range of PV power generation systems, it is generally necessary to lift and lower the power output from PV to meet the voltage requirements of DC loads. Capacitor-based buck and boost circuits have the advantages of small size, light weight, low cost, high conversion efficiency, and a wide regulation range [33,34,35]. This paper designs an adjustable boost circuit, as shown in Figure 3. The left diagram is the schematic diagram of the boost circuit, and the right diagram is the printed circuit board diagram of the boost circuit. According to the basic principle of step-down and boost converter, using Altium Designer software 2021 v21.2.2, the adjustable buck circuit shown in Figure 4 is designed, in which the left diagram is the schematic diagram of the buck circuit and the right diagram is the printed circuit board diagram of the buck circuit.

The principle of an adjustable boost circuit is to control the storage and release of energy from the inductor via opening and closing the switching tube, and the circuit employs 2101 and 75N75 combinations to form a boost-controllable circuit. An adjustable buck circuit then employs the form of differential sampling current and voltage, where R1, R4, R6, R2, R5, and R7 have different resistance values depending on the actual situation.

2.4. PV Circuit Design

The equipment used for the PV system tests is shown in

Table 2. Summary of devices required for field measurement of PV power generation.

Instrument Name	Model	Function
Solar battery	Colloidal battery (12 V/65 AH)	Storage of DC power from PV panels
DC Detector	Voltage 200 V Current 20 A	Total amount of electricity generated on the day of testing
Environmental testing system for PV power plants	FSZJ-J-1-01	Solar irradiance detection

.

The circuit diagram, field wiring diagram, and DC power tester actual measurement diagram of the PV system lab bench construction are shown in Figure 5 below.

3. Tests and Results

3.1. PV System Power Generation Test

3.1.1. Solar Irradiation Testing and Analysis

Factors affecting the performance of PV power generation mainly include solar radiation intensity and ambient temperature. Solar radiation intensity is the most important factor affecting the performance of PV power generation, while ambient temperature affects the operating performance of the electronic devices in the PV panels [36,37].

In this project, the data on annual irradiance and ambient temperature of Qingdao city in 2022 were tested using Jinzhou PV meteorological stations. The test device is shown in Figure 6 [38], the technical indicators are shown in

Table 3. Summary of technical indicators.

Meteorological Element	Resolution	Measuring Range	Precision
ambient temperature	0.1 °C	−40~80 °C	±0.1 °C
illuminance	1 LUX	0–200,000 LUX	±5%
radiation	1 W/m2	0~2000 W/m2	<5%

, and the daily average values of ambient temperature and daily solar radiation are shown in Figure 7 and Figure 8.

The weather station used in Figure 6 is equipped with an automatic tracking solar radiometer, an illuminance sensor, a total radiation meter, and other devices to detect the light intensity and temperature of Qingdao throughout the day and record historical data via its special software.

The day 1 in Figure 7 and Figure 8 represents 1 January, and day 365 represents 31 December. The yearly average of ambient temperature in Figure 7 is 14.0 °C, and the number of days close to it is 98–130 and 278–331. Figure 8 focuses on the solar radiation and its numerical fit; the mean value of solar radiation is 13.004 MJ/m², which is in the zone of average PV potential [39]. Comparing the fitting effect between days 98–130 and 278–331 in the two figures, we can see that the fitted value of days 98–130 is closer to the mean value and the fitting effect is better. Therefore, in order to simplify the test process without losing accuracy, the mean value of PV panel power generation from days 98–130 can be used to represent the mean value of annual power generation.

3.1.2. PV Power Generation Testing and Analysis

From the above actual measurement date selection, it is known that 98–130 days per year should be selected, and the specific test data are shown in Figure 9.

The daily power generation of PV panels is shown in Figure 9a, with the horizontal coordinates indicating the corresponding time periods and the vertical coordinates indicating the cumulative power generation of PV panels. Since there is no solar radiation at night, the cumulative daily power generation of the PV panels is 0.357 kWh, taking the period from 6:00 a.m. to 19:00 p.m. during daytime; the total daily power generation from days 98–130 is shown in Figure 9b, with the horizontal coordinates indicating the corresponding days and the vertical coordinates indicating the total daily power generation, and the average power generation is 0.286 kWh.

3.2. Circuit System Test Results

3.2.1. System Trajectory and Spectral Model Measurements

The transfer function is an important basis for analyzing the system’s performance and also plays a good auxiliary role in designing the system. The PI (proportional integral) transfer function is usually expressed as in Equation (1)

$$PI\left(s\right)=Kp+Ki/s$$

(1)

where Kp is the proportional gain, Ki is the integral gain, and s is the complex variable in Laplace transform. This means that the PI transfer function can provide proportional control and integral control, so as to achieve accurate control of the system; that is, the smaller the Kp, the faster the system response, the larger the Ki, the more stable the system.

The design of the PI controller [40] for the microgrid system is carried out via Matlab’s SISOTOOL tool, and the root trajectory and frequency domain characteristics of the designed system are shown in Figure 10.

According to the root trajectory and frequency domain characteristics of the designed system shown in Figure 10, the designed $PI$ controller transfer function for the microgrid system is obtained in Equation (2):

$$PI\left(s\right)=\frac{0.0035481 (s+952.2)}{s}$$

(2)

According to Equations (1) and (2), it is clear that $Kp$ is about 0.003548, and $Ki$ is about 3.38. From the $PI$ simulation of the system, it is clear that the proposed microgrid system is very responsive and more stable compared to other conventional systems.

3.2.2. Circuit System Principle and Output Test

The main control unit in the circuit system is the STM32 control system, and its specific control flow chart for the output voltage is shown in Figure 11. When the system is initialized by programming the four keys on the STM32 development board to set the reset button and the set button, the reset button accounts for one (called the reset key) and the set button accounts for three (called keys 1, 2, and 3). Due to the limitations of the development environment, the settings of this project are only for this experiment, and the keys of the control system can be added or subtracted later to further meet the voltage requirements of different household DC loads.

According to the types of DC electrical appliances in Figure 2, the initial voltage of the circuit system is set to 12 V (reset voltage), and the set voltages of 5 V, 14 V, and 24 V are controlled by setting buttons 1, 2, and 3, respectively, and the specific detection values are shown in Figure 12.

The test results of the boost circuit and buck circuit in the circuit system are shown in

Table 4. Summary of measured data of boost and buck circuits.

Circuits	Set Voltage (V)	Measured Voltage (V)
Buck Circuit	5	5.091
	12	11.98
	14	14.126
Boost Circuit	24	24.208

. The physical diagram of the circuit system (including the STM32 microcontroller, boost circuit, driver circuit, and buck circuit) is shown in Figure 13.

In order to further illustrate the advantages of the proposed DC microgrid system, the performance of the proposed system is compared with that of the existing DC microgrid system [20,21,22,23]. The comparison results are shown in

Table 5. Comparison of the performance of the DC microgrid system and the existing system.

	Refs. [20,21]	Ref. [22]	Ref. [23]	Proposed Method	Proposal
circuitry reconstruction	Yes	Yes	Yes	No	No
multiplexed output	No	No	No	Yes	Yes
Precise power supply	No	Yes	No	Yes	Yes
stored energy	Yes	Yes	No	Yes	Yes
energy conservation	Yes	Yes	Yes	Yes	Yes

.

4. System Economic Analysis

At present, equipment costs have become one of the main factors restricting PV power generation [41,42]. In order to prove the practicability of the proposed DC microgrid, the cost of test equipment will be counted. The main economic investment in this project comes from the initial microgrid system build, and the test build physical selling price is shown in

Table 6. Breakdown of initial project investment.

Serial Number	Equipment Name	Quantity	Specification	Factory	Selling Price (RMB)
1	PV components	1 (set)	Monocrystalline silicon (80 W)	Hekate	545
2	PV battery	1 (pc)	Colloidal battery (60 AH)	Hekate	330
3	Circuit Boards	4 (kinds)	100 mm × 10 mm	JiaLiChuang	120
4	All kinds of electronic devices	4 (sets)	Chips and Components	Your Cee	300
5	Shipping Fee				85
Total					1380

.

The recovery period can be estimated as in Equation (3)

$$PB=\frac{Ci}{DCC\times AED\times 365}$$

(3)

where PB represents the recovery period, Ci represents the initial investment, DCC represents the DC power consumption, and AED represents the local average electricity price.

According to the above test results, it can be seen that the daily electricity generated by the monocrystalline PV panel with an area of 0.46 m² is 0.286 kWh. According to the electricity consumption statistics, the average daily DC power required per household is 1.43 kWh, so the area of PV panels required is 2.3 m². According to the market price of about 600 RMB/m², the initial investment per household is about 1380 yuan. The daily power generation is 1.43 kWh, the average price of electricity in the Qingdao area is 0.55 RMB per kWh, the daily saving is 0.79 RMB, and the cost recovery is calculated to be completed in about 4.8 years.

5. Conclusions

Under the background of the “dual carbon” strategy, with the application of distributed PV systems and the increasing electrification of buildings, facing the high level of application of DC appliances in the future, it is important to solve the matching problem between solar PV power generation and user load effectively with microgrid design for the application of solar energy in buildings. This project investigates the use of domestic DC loads in the Qingdao area, proposes a PV-based design of a domestic DC microgrid with local solar resources, and conducts practical tests on the system. The results show that the proposed DC microgrid system can accurately provide the voltage required for small household DC appliances, such as 24 V, 12 V, 5 V, 3.3 V, etc., and the direct supply of DC appliances using solar photovoltaics can currently reduce about 20% of power consumption. The payback period of the equipment is about 4.8 years. The project can be widely used in all kinds of buildings including small DC equipment, such as residential, office, laboratory, and so on. The innovation points of this project are as follows:

(1)

Realizing a single circuit to supply different household DC loads, and reducing the number of converters at the equipment end, with fast response and stable circuits;

(2)

It enhances the direct and efficient use of PV with DC loads in buildings without changing the original grid circuit.

This study only considers the power consumption of existing small DC electrical equipment, and does not consider that the proportion of DC electrical equipment will increase in the future, which may lead to the fact that the existing photovoltaic power supply equipment will not generate enough power to supply the new DC electrical equipment in the future. In the subsequent research, photovoltaic power supply equipment can be installed according to the proportion of annual increase in DC electrical equipment.