1. Introduction
1.1 Background and Significance of Photovoltaic Power Generation
Since the first Industrial Revolution, the human demand for fossil energy has been increasing. However, with the reduction of its exploitable years and the negative impact of greenhouse gas emissions on the environment, the development and utilization of clean energy has become more and more urgent. In 2021, China’s 14th Five-Year Plan clearly stated the goal of achieving “carbon peak” by 2030 and “carbon neutrality” by 2060. In the report of the 20th National Congress of the Communist Party of China, it further clarified the need to actively and steadily promote “carbon peak”. The development and utilization of clean energy can not only effectively avoid the energy crisis, but also play a role in environmental protection. Among many clean energy sources, solar power is considered as the most likely to be utilized on a large scale because it is not limited by resource distribution. China is rich in solar energy resources, with an annual reserve of about 1.7 × 10^12 MJ, especially in the western region with abundant sunshine, which is suitable for the development of photovoltaic power generation. The annual total solar radiation in China is shown in the figure.
With the emphasis on solar energy by the Party and the state, various departments in China have also actively responded. Article 5.2.1 of the “General Code for Energy Conservation in Buildings and Utilization of Renewable Energy” (GB55015 – 2021) issued by the Ministry of Housing and Urban – Rural Development of the People’s Republic of China points out that “new buildings should install solar energy systems”, which further strengthens the promotion of clean energy such as solar energy in urban construction, and also means that more and more relatively small photovoltaic power generation systems will be applied in urban construction. According to the statistics of the National Energy Administration, from 2010 to 2022, China’s cumulative installed photovoltaic capacity was 392.61 GW; in 2022, China’s new installed photovoltaic capacity was 87.41 GW, an increase of 59.3% year – on – year, making it the fastest – growing power source in China; during the same period, the European Union, the United States, and India added 41.4 GW, 18.6 GW, and 13.96 GW respectively, with year – on – year increases of 54.4%, – 30.9%, and 35.5% respectively. The “Guiding Opinions on Energy Work in 2023” issued by the National Energy Administration also set a target for China’s solar power generation installed capacity in 2023, requiring it to reach about 490 million kilowatts.
1.1.2 Significance of the Research
At present, photovoltaic cells, as the carrier of converting solar energy into electrical energy, how to improve their conversion efficiency has always been a hot issue. In recent years, with the continuous development of traditional silicon materials, the power generation efficiency of photovoltaic cells has increased to about 15%, and the more efficient ones have reached 20%, but there is still a big gap from the theoretical upper limit of 29.1%. In addition to the research and development of solar cell materials to improve the conversion efficiency of photovoltaic power generation, proposing a more mature maximum power point tracking control strategy through the limited material conversion efficiency can also give full play to the conversion ability of photovoltaic cells.
However, the output power of a single photovoltaic cell is relatively low. In order to meet the power demand, photovoltaic cells are usually connected in series and parallel to form a photovoltaic array. With the rapid popularization of photovoltaic arrays in urban construction, their working environment is becoming more and more complex, and the situation of partial shading (Partial Shade Condition, PSC) is more likely to occur. When there is partial shading in the photovoltaic array, the bypass diode will short – circuit the shaded components to protect them, resulting in the output characteristic curve of the photovoltaic array showing a multi – peak shape. The traditional maximum power point tracking algorithm only works well in single – peak tracking, but it is easy to fall into the local optimal solution (Local Maximum Power Point, LMPP) and fail in the case of multiple peaks, reducing the power generation efficiency. Therefore, studying the maximum power point tracking control strategy of the photovoltaic array under partial shading is of great significance for improving the power generation efficiency.
Compared with traditional fossil energy power generation, photovoltaic power generation is intermittent and volatile, and its stability is poor. Therefore, it is necessary to configure an energy storage system as a buffer. When the power generation of photovoltaic power cannot meet the power demand, the energy storage system needs to make up the difference; when the power generation of photovoltaic power meets the power demand, the energy storage system enters the charging standby state. As an indispensable link in the photovoltaic power generation grid connection, the research on its application can minimize the negative impact of the instability of photovoltaic power generation and is of great significance for promoting the development of photovoltaic power generation.
1.2 Research Status at Home and Abroad
1.2.1 Research Status of Maximum Power Point Tracking Technology at Home and Abroad
Photovoltaic maximum power point tracking technology is the key technology to keep the photovoltaic array generating power efficiently, which can make the photovoltaic array maintain the maximum power output. With the continuous research of scholars at home and abroad, the control strategies mainly include traditional maximum power point tracking control strategies, maximum power point tracking control strategies based on intelligent algorithms, and maximum power point tracking control strategies based on fusion algorithms.
The traditional maximum power point tracking control strategies include the constant voltage method, the conductance increment method, and the perturbation observation method. The constant voltage method does not consider the influence of temperature, and the tracking accuracy is poor, so it is used less compared to the other two control methods. The perturbation observation method and the conductance increment method have similar working principles. Although the comprehensive tracking performance of the conductance increment method is slightly stronger than that of the perturbation observation method, the perturbation observation method is relatively simple in principle, and the required hardware cost is low in practical applications, so it is the most widely used among the three control methods.
Many studies have been conducted to improve the traditional maximum power point tracking control methods. For example, a new variable step size conductance increment method has been proposed, which does not require a proportional controller and adjusts the step size according to the calculated value of dP/dU, effectively improving the tracking speed and accuracy, and reducing the steady – state oscillation. Another example is the introduction of fuzzy logic to the conductance increment method, taking the slope of the P – U curve as the fuzzy input source, effectively reducing the power oscillation after stabilization.
The application of intelligent algorithms in maximum power point tracking has also been widely studied. Many intelligent algorithms, such as genetic algorithm, gray wolf algorithm, butterfly optimization algorithm, and particle swarm optimization algorithm, have been applied to solve the problem of the failure of the traditional MPPT control strategy under partial shading. However, these intelligent algorithms also have their own shortcomings, such as slow tracking speed, large power oscillation, and low accuracy, and cannot guarantee convergence to the global maximum power point.
The control strategy based on the fusion of different algorithms has also been a research hotspot. By combining the advantages of different algorithms, the tracking performance can be improved. For example, the combination of the overall distribution algorithm and the particle swarm optimization algorithm can effectively improve the tracking speed of the particle swarm optimization algorithm. The combination of the particle swarm optimization algorithm and the genetic algorithm can greatly improve the tracking accuracy and speed of the algorithm.
1.2.2 Research Status of Energy Storage Technology at Home and Abroad
Due to the volatility and randomness of photovoltaic power generation and its unstable output power, it is necessary to connect an energy storage device to the DC side to alleviate the uncertainty of new energy power generation and make its output tend to be smooth. Energy storage technology is also the core link of distributed power generation, which not only has the function of power peak shaving and suppressing the output power fluctuation of photovoltaic power generation, but also has the function of providing voltage support and improving power quality.
Energy storage devices are mainly divided into three categories according to the energy storage method: physical energy storage, electromagnetic energy storage, and electrochemical energy storage. Physical energy storage stores electrical energy through mechanical movement, and the more common ones are pumped storage and flywheel energy storage; electromagnetic energy storage stores energy through the mutual conversion between electrical energy and electromagnetic field energy, and the more common ones are supercapacitors and superconducting energy storage; electrochemical energy storage realizes energy conversion through chemical reactions, and the most common chemical energy storage is various types of batteries.
Energy storage devices can also be divided into two categories according to their energy storage characteristics: power density type and energy density type. Power density type energy storage has the characteristics of high power density and fast response, and is suitable for repeated charging and discharging occasions, but its energy density is relatively small and not suitable for large – scale energy storage; energy density type energy storage has high energy density and can store a large amount of energy, but its power density is relatively small and not suitable for applications that require high – power output in a short time. A comparison of common energy storage devices is shown in the table.
In practical applications, in order to maintain the stable output power of distributed power generation, the energy storage system often needs to charge and discharge power frequently. Therefore, the energy storage system needs to have both high power density and high energy density. Currently, no single energy storage device can meet such requirements, and usually energy storage devices with different characteristics are combined to form a hybrid energy storage system. In the hybrid energy storage system, the supercapacitor is used to smooth the instantaneous power fluctuation, and the battery is used to provide long – term power support. The combination of the two can give full play to their respective advantages, and can also cooperate with each other within the hybrid energy storage system to smooth the output of the battery and extend the service life of the battery. At present, the hybrid energy storage system is widely used in electric vehicles and is still in its infancy in distributed power generation applications.
Many studies have shown that the application of hybrid energy storage can effectively extend the service life of the battery and improve the stability of the power system. For example, in wind power generation, the use of hybrid energy storage can make the output of the battery more smooth and reduce the impact of frequent charging and discharging on the battery. In photovoltaic power generation, the reasonable allocation of the energy allocation strategy can effectively stabilize the power fluctuation and improve the dynamic response capability of the system.
1.3 Main Work and Organizational Structure
In this paper, aiming at the problem that the perturbation observation method cannot balance the tracking accuracy and speed, a partitioned variable step size perturbation observation method is proposed; aiming at the problem that the perturbation observation method and its improvements fail under partial shading and the shortcomings of the particle swarm optimization algorithm applied in this scenario, an MPPT control strategy based on the fusion of the adaptive particle swarm optimization algorithm based on the natural selection strategy and the partitioned variable step size perturbation observation method is proposed; and through MATLAB/Simulink simulation analysis, the improvement of the tracking performance of the proposed strategy is verified; in addition, a three – phase photovoltaic grid – connected system based on hybrid energy storage is built through MATLAB/Simulink to study the application of hybrid energy storage in photovoltaic grid connection. The main research contents are as follows:
(1) Analyze and compare the constant voltage method, the conductance increment method, and the perturbation observation method. Aiming at the problem that the perturbation observation method cannot balance the tracking speed and accuracy, a partitioned variable step size perturbation observation method is proposed, and the effectiveness of the algorithm improvement is verified through simulation comparison experiments.
(2) Aiming at the problem that the perturbation observation method and its improved algorithms fail under partial shading, the particle swarm optimization algorithm is studied and applied in the MPPT control strategy. In order to improve the tracking speed of the particle swarm optimization algorithm, reduce the power oscillation, and improve the tracking accuracy, an adaptive particle swarm optimization algorithm based on the natural selection strategy is proposed, and the effectiveness of the proposed improvement is verified through simulation. An MPPT control strategy based on the fusion of the adaptive particle swarm optimization algorithm based on the natural selection strategy and the partitioned variable step size perturbation observation method is proposed, and the effectiveness of the proposed control strategy is verified through simulation comparison.
(3) A three – phase photovoltaic grid – connected system and a hybrid energy storage system are built, and the two are combined to form a three – phase photovoltaic grid – connected system based on hybrid energy storage. The application and advantages of hybrid energy storage in photovoltaic grid connection are studied through simulation analysis.