With the improvement of people’s living standards, there is a serious problem of insufficient supply and demand in microgrids during peak power loads. In order to solve this problem, power loads are subject to power restrictions, which affects the reliability of microgrid power supply. Relying solely on increasing the installed capacity of generator sets cannot optimally solve the reliability and economy of microgrid power supply. When the power load is low, there will be large batches of idle power units, which reduces the utilization rate of power units and also reduces the economic efficiency of microgrids. The use of Battery Energy Storage System (BESS) for peak shaving and valley filling of power loads alleviates supply and demand shortages, reduces the voltage regulation capacity of microgrids during peak power load periods, increases the utilization rate of generator sets, improves the efficiency of peak shaving and valley filling of microgrid power loads, and reduces the cost of battery energy storage systems for microgrids.
There are two main methods to achieve peak shaving and valley filling. One is to use corresponding economic incentives on the user side to shorten the peak valley difference by changing the electricity usage period of flexible loads, and the other is to use energy storage on the grid side to shorten the peak valley difference. The traditional method of energy storage is pumped storage, which stores energy in the form of potential energy. Modern methods of energy storage include flywheel storage, superconducting electromagnetic storage, and battery energy storage systems storage in the form of chemical energy; Both convert electrical energy into other forms of energy for storage. battery energy storage systems has the characteristics of high energy density, fast charging and discharging, and has become a hot research topic for scholars in recent years. When the power load is low, charge the battery energy storage systems to improve the utilization rate of the generator set and reduce the startup and shutdown of the unit; During peak power loads, the discharge of battery energy storage systems ensures the reliability and stability of microgrid power supply. Therefore, the application of battery energy storage systems is vigorously promoted both domestically and internationally. Foreign countries such as Japan, Europe and America have established a large number of battery energy storage systems. In 2020, the National Energy Administration implemented the pilot demonstration and solicitation work for energy storage projects in China. It stated that by analyzing the successful experience and existing problems of energy storage projects, the development direction of energy storage technology was summarized. The document pointed out that the energy storage industry is a key industry supported by the state.
In principle, the larger the capacity of battery energy storage systems configuration, the better the effect of power load peak shaving and valley filling. However, in practical applications, the cost of microgrids needs to be considered, so it is necessary to consider the capacity configuration and charging and discharging control strategy of battery energy storage systems. When battery energy storage systems performs peak shaving and valley filling in microgrids, its charging and discharging time, frequency, and power should be optimized and its capacity should be reasonably configured. Optimizing the charging and discharging control strategy and reasonable capacity configuration of battery energy storage systems can achieve good peak shaving and valley filling effects, and reduce the cost of microgrids.
Battery energy storage systems has a capacity ranging from 100 to megawatts, so it is applied in microgrids or distribution networks. Due to the unclear application scope of battery energy storage systems for peak shaving and valley filling, this article adopts battery energy storage systems for microgrid peak shaving and valley filling, and the operation mode of microgrid is island operation.
In recent years, with the rapid development of energy storage technology, the application of battery energy storage systems has become a hot topic. battery energy storage systems has extensive research on peak shaving and valley filling charging and discharging strategies and capacity configuration. This section mainly understands the research status of this topic from four aspects: the development and classification of energy storage technology, the current research status of battery energy storage systems, the current research status of battery energy storage systems capacity configuration, and the current research status of battery energy storage systems peak shaving and valley filling.
1. Development and classification of energy storage technology
(1) Development of energy storage technology
Compared with Japan and developed countries in Europe and America, China’s energy storage technology started relatively late but developed very quickly. According to the latest data analysis from the Global Energy Storage Database (DOE) of the US Department of Energy, as of the end of 2019, the cumulative installed capacity of 16 energy storage and power generation projects that have been built and operated worldwide was 183.1 gigawatts, an increase of 1.2% year-on-year. The installed capacity of the energy storage system is 183.1 gigawatts, an increase of 1.2% compared to the previous year. The maximum installed capacity of pumped energy storage accounts for 92.60%, electrochemical energy storage accounts for 5.2%, molten salt energy storage accounts for 1.7%, and compressed air energy storage accounts for 0.20%, as shown in Figure 1.1.
According to data from the Global Energy Storage Project Library of the China Energy Storage Alliance (CNESA) in Zhongguancun, the installed capacity of energy storage systems worldwide increased by 103% from 1997 to 2019, reaching 183.1 gigawatts. The total installed capacity of energy storage systems increased every year, and from 2002 to 2007, the growth rate of the same year increased year by year, as shown in Figure 1.2. Nowadays, with the strong encouragement of local governments in various countries, the energy storage power station market has been actively and steadily developing, and the installed capacity market size of newly built large energy storage power stations worldwide has doubled.
(2) Classification of energy storage technologies
According to different forms of energy storage, energy storage technologies are mainly divided into three categories: electromagnetic energy storage, electrochemical energy storage, and mechanical energy storage.
2. Current research status of battery energy storage systems
In microgrids, battery energy storage systems has been widely used, such as power distribution, load backup, voltage regulation, peak shaving and valley filling, etc. The application of battery energy storage systems in microgrids can be divided into two aspects based on the length of time: power application and energy application; Power application refers to the ability of battery energy storage systems to achieve a large amount of power in a short period of time, while energy application refers to the long-term energy supply of battery energy storage systems. Large capacity electrical energy needs to be stored, so peak shaving and valley filling in microgrids belong to energy utilization.
With the rapid development of battery energy storage technology, new technological ideas and methods have been provided for achieving peak shaving and valley filling of power loads. In 2017, battery energy storage technology was first identified by the government as a renewable energy source, a component and key supporting technology of microgrids. battery energy storage systems has been widely applied in both microgrids and user sides. At present, the research on battery energy storage systems is divided into two aspects: firstly, directly optimizing the charging and discharging of battery energy storage systems to achieve the goal of peak shaving and valley filling; The second is the control of flexible loads and the time of use electricity price, indirectly achieving the goal of peak shaving and valley filling.
In the power system, the change in frequency caused by changes in power load is a major challenge for rapid compensation of generator sets. Load frequency control is not ideal for solving this problem, and the combination of battery energy storage systems and load frequency can better solve the problem of frequency fluctuations. battery energy storage systems improves the penetration rate of renewable energy and maintains system reliability. When a large number of distributed battery energy storage systems are connected to the grid, it can also affect the quality of battery energy storage systems. In order to improve the power quality of the microgrid, energy management is carried out on battery energy storage systems, and reasonable control is carried out based on the SOC of battery energy storage systems to extend the lifespan of battery energy storage systems and reduce the cost of the microgrid. Including energy storage photovoltaic power grid control, battery energy storage systems can effectively solve the problem of fluctuations in photovoltaic power generation units caused by light intensity and temperature fluctuations. This system can not only effectively achieve maximum power tracking but also improve the economic efficiency of the power grid. Using battery energy storage systems to achieve peak shaving and valley filling of microgrids, the relationship between the depth and frequency of charging and discharging of battery energy storage systems and the power balance are taken as constraints in modeling, ensuring good peak shaving and valley filling effect of power loads and good economic benefits of generator unit operation.
3. Current status of research on capacity configuration of battery energy storage systems
In microgrids, a reasonable capacity configuration of battery energy storage systems can not only improve the efficiency of peak load shaving and valley filling, but also reduce the cost of microgrids; The capacity of battery energy storage systems is closely related to the economy of microgrids. There are currently two methods for configuring battery energy storage systems capacity. One is traditional capacity allocation, such as random simulation production method; The second is to use intelligent algorithms to solve the objective function to obtain the capacity of battery energy storage systems. Reasonable battery energy storage systems capacity configuration is achieved by reducing the cost of microgrids while meeting the reliability of power supply.
Battery energy storage systems can effectively reduce the fluctuations caused by renewable energy in the power grid. Considering the operating status of generators, a random simulation method is used to configure the capacity of battery energy storage systems, but the control strategy overly relies on the power load curve. In the integration of renewable energy and battery energy storage systems into microgrids, the load time shift method is introduced from the perspective of demand side response, and the problem of wind solar complementary battery energy storage systems capacity configuration is proposed. With the upper power limit and power balance of battery energy storage systems as constraints, an improved particle swarm optimization algorithm is used to configure the battery energy storage systems capacity. However, the control strategy does not fully consider the problem of battery energy storage systems overcharging and discharging, resulting in a smaller capacity configuration compared to actual demand. The reasonable allocation of energy storage capacity on the power grid and user sides is an important means to improve the economic efficiency of regional energy storage. Using game theory to optimize the capacity configuration of energy storage systems on the power grid and user sides. Considering the benefits of grid side energy storage investors and user side energy storage investors under power supply benefits and full life cycle costs, establish a profit model for grid side energy storage investors and user side energy storage investors. The cooperative game method established a capacity allocation model for the grid side and user side energy storage systems, and calculated the optimal capacity allocation strategy for the grid side and user side energy storage investors under the cooperative mode. The effectiveness and rationality of the configuration strategy were verified through a regional energy storage example, but the control strategy did not consider the overcharging and discharging issues of battery energy storage systems, nor did it consider the reliability of microgrid power supply. The optimization planning model for renewable energy storage systems takes the maximum comprehensive social benefits and system reliability as the objective function, considers the relationship between battery energy storage systems and demand side response, and configures the capacity of battery energy storage systems. The improved gravity search algorithm is used to optimize the capacity configuration, but the depreciation problem of battery energy storage systems is not considered. Reference proposes a new method for optimizing and modifying the energy storage capacity of user side batteries. Based on the energy storage structure of user side batteries, an improved particle swarm optimization algorithm is used to solve the objective function and obtain the optimal capacity configuration of battery energy storage systems. However, the control strategy does not consider the wind power grid connection situation.
Therefore, in a microgrid system with complementary wind and solar energy, this article fully considers the charging and discharging power of the battery energy storage system as constraints, the power load shortage rate, and the lifespan model of battery energy storage systems as constraints. An improved artificial bee colony algorithm is used to solve the objective function to obtain the capacity of battery energy storage systems in the microgrid.
4.Current status of research on peak shaving and valley filling in battery energy storage systems
With the rapid development of the economy, new energy microgrids have been widely used. The peak valley difference in microgrids is becoming increasingly apparent, and battery energy storage system’s peak shaving and valley filling can better solve this problem. There are two methods to achieve the goal of peak shaving and valley filling:
One is to indirectly achieve the goal of peak shaving and valley filling by controlling the output of flexible loads; The second is to directly achieve the goal of peak shaving and valley filling in the microgrid by controlling the charging and discharging process of battery energy storage systems. There are two types of methods for directly achieving peak shaving and valley filling: one is to control the charging and discharging process of battery energy storage systems through a constant power control strategy or a constant parameter power difference control strategy; The second is to obtain the charging and discharging power of battery energy storage systems by solving the objective function through intelligent algorithms, including genetic algorithm, simulated annealing algorithm, bat algorithm, etc.
Through government incentives and based on peak valley time of use electricity prices, the goal of battery energy storage systems peak shaving and valley filling is achieved to some extent. However, this method has strong randomness and uncertainty. The constant power charging and discharging control strategy is adopted to charge and discharge battery energy storage systems. A constant power rate optimization model for peak shaving and valley filling of battery energy storage systems is proposed. The model is optimized using a sequence quadratic programming method with multiple initial points. Finally, the effectiveness of the model is verified by Shenzhen Baoqing battery energy storage systems. However, the control strategy requires a high load prediction curve, and when the actual load curve differs significantly from the predicted load curve, it may exacerbate the peak valley difference in the microgrid. The constant parameter power difference control strategy is used for charging and discharging control of battery energy storage systems, but it is not written how to optimize the power difference control strategy. For the optimization problem under islanding operation, power difference control is used to control the charging and discharging of battery energy storage systems, with the SOC state and power balance of battery energy storage systems as constraints and economy as the goal. Particle swarm optimization algorithm is used to solve the objective function. This strategy not only reduces the cost of microgrids but also achieves the effect of peak shaving and valley filling in microgrids. However, this strategy does not optimize the particle swarm optimization algorithm, and sometimes battery energy storage systems overcharging and discharging problems may occur, which affects the efficiency of peak shaving and valley filling in microgrids.
Therefore, this article adopts an improved particle swarm optimization algorithm to optimize the constant parameter power difference control strategy, with the standard deviation of peak shaving and valley filling as the minimum objective function, and the constraints of preventing overcharging and discharging of the battery energy storage system, power balance, and upper limit of charging and discharging power as constraints, to control the charging and discharging of battery energy storage systems, achieving good peak shaving and valley filling effects and reducing the cost of the microgrid.
As the difference between peak and valley power loads becomes increasingly apparent, increasing the assembly capacity of generators during peak power loads can solve the problem of insufficient power in microgrids; Reducing the output of generator units during low power loads reduces equipment utilization, while also reducing the economy of microgrids. In order to better meet the power generation requirements during peak and low periods of power load, using battery energy storage systems can effectively solve the problem of increasing the capacity of generator units and reducing their output, achieving the goal of peak shaving and valley filling. Based on the analysis of the working principle, capacity configuration, and charging and discharging control strategy of battery energy storage systems, this article proposes a study on the optimization control strategy of battery energy storage systems participating in peak shaving and valley filling of isolated microgrids.
Introduce the background, significance, and applicable scenarios of battery energy storage system peak shaving and valley filling optimization control strategy. The research background points out the role of battery energy storage systems in microgrids, and summarizes the problems in capacity configuration and peak shaving and valley filling control strategies of battery energy storage systems at China and abroad.
Study the architecture of the microgrid and select the AC/DC microgrid architecture. Then analyze different energy storage systems, select battery energy storage systems as the energy storage system of the microgrid, and build a large capacity battery energy storage systems composition and simplified model. Introduce the working principle of battery energy storage systems and establish evaluation indicators for peak shaving and valley filling in microgrids.
The research is about how to allocate capacity reasonably for battery energy storage systems. Firstly, it introduces the shortcomings of traditional capacity allocation methods and introduces the method of using intelligent algorithms to solve the objective function to allocate capacity reasonably for battery energy storage systems. A microgrid economic benefit model has been established with the goal of microgrid economy, the charging and discharging power of battery energy storage systems as limitations, the power load vacancy rate, and the overcharging and discharging of battery energy storage systems as constraints. The artificial bee colony algorithm with dynamic parameter improvement is used to solve and obtain the optimal configuration of the capacity of the microgrid battery energy storage system.
The main research focuses on the battery energy storage systems peak shaving and valley filling charge and discharge control strategy. Analyze the problem of sudden changes in load under constant power control strategy, leading to instability of microgrids and overcharging and discharging of battery energy storage systems. Considering the economy of microgrids while meeting the requirements of peak shaving and valley filling, an objective function with the minimum standard deviation of peak shaving and valley filling was constructed. The constraint conditions of preventing overcharging and discharging of battery energy storage systems, power balance, and upper limit of charging and discharging power were taken as constraints, and particle swarm optimization algorithm was used to solve the problem. Adopting constant parameter power and constant parameter power difference control strategy without load jump, reducing overcharging and discharging phenomena, and extending the service life of battery energy storage system. Compared with the constant power control strategy, the constant parameter power difference control strategy effectively solves the problem of overcharging and discharging in battery energy storage systems. Using the ergodicity of chaos theory to solve the local optimization problem of particle swarm optimization algorithm, and optimizing the charging and discharging power of the battery energy storage system through an improved particle swarm algorithm. The improved particle swarm optimization algorithm with constant parameter power difference control strategy has a better effect on peak shaving and valley filling of power loads, further reducing overcharging and discharging phenomena in battery energy storage systems.