A mobile energy storage system and a control method thereof

Abstract

The application discloses a mobile energy storage system and a control method thereof, and relates to the field of energy storage of power distribution networks. The power supply instability index is obtained by fusing all the power supply quality indexes of each monitoring point in all dimensions through a preset weight, and the energy storage equipment is distributed to the energy storage access points. The energy storage equipment is regulated and controlled by using a PID algorithm. The energy storage capacity demand degree of each energy storage access point is obtained according to the difference between the power supply instability indexes when the energy storage equipment is in the charging and discharging process and in the dormant state. The energy storage equipment is redistributed to each energy storage access point according to the energy storage capacity demand degrees of all the energy storage access points, and the weight of each monitoring point is updated. The energy storage equipment is redistributed by using the updated weight. The application makes the cooperation process of the energy storage equipment and the power distribution network adapt to the characteristics that the power supply conditions of the power distribution network at different monitoring points are different and complex, and further maintains the operation stability of the power distribution network.

Description

Technical Field

[0001] This invention relates to the field of energy storage in power distribution networks, specifically to a mobile energy storage system and its control method. Background Technology

[0002] As a crucial link in the power system facing end users, the power supply quality of the distribution network directly affects the normal operation of production and daily life, as well as the safety of electrical equipment. However, in actual operation, due to factors such as the randomness and asymmetry of loads and the integration of distributed power sources, the distribution network often faces typical instability problems such as voltage deviation, frequency fluctuation, and three-phase imbalance. These power quality problems not only lead to substandard power supply, failing to meet the needs of production and daily life, but also damage distribution lines, transformers, and other facilities.

[0003] To address these challenges, existing technologies commonly employ the method of installing fixed energy storage devices on the grid side or at key nodes. These energy storage devices can smooth power fluctuations and support voltage and frequency through rapid charging and discharging, thereby contributing to grid stability and improving power quality. However, this method has significant limitations. In distribution networks, the power consumption environment varies greatly across different locations and regions, resulting in complex and variable power usage. For example, some areas may be connected to a large number of variable frequency devices, such as variable frequency air conditioners and industrial inverters, whose operating characteristics easily trigger grid harmonics and frequency sensitivity issues; while other areas may be dominated by resistive loads, such as lighting and electric heating, whose load changes are more likely to cause voltage deviations. This diversity in power consumption environments means that the manifestations and dominant factors of grid instability vary at different monitoring points, making it difficult to accurately and reliably characterize these complex and variable regional features. It also hinders the accurate and reliable assessment of grid operation at different locations, making it impossible to develop new methods for adaptively and dynamically optimizing energy storage device configuration and control strategies based on the assessment results of grid power supply conditions, and consequently, failing to further maintain the stability of the distribution network. Summary of the Invention

[0004] The purpose of this invention is to provide a mobile energy storage system and its control method. The system screens energy storage access points by evaluating power supply instability indicators from multiple dimensions, regulates the charging, discharging and dormancy states of energy storage devices through a PID algorithm, and dynamically allocates devices and updates weights based on capacity demand to form a closed-loop optimization to ensure the stability of power supply in the distribution network.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a mobile energy storage control method, which includes the following steps:

[0006] S1: Evaluate several dimensions of power supply quality indicators based on the power data of each monitoring point in the distribution network. Use the preset weights of each monitoring point to merge all dimensions of power supply quality indicators of each monitoring point to obtain the power supply instability index of each monitoring point. Allocate and connect the energy storage device to the energy storage access point selected from all monitoring points.

[0007] S2: Use PID algorithm to regulate the discharge process of energy storage device to minimize the power supply instability index of each energy storage access point. When the power supply instability index is less than or equal to the preset threshold, charge the energy storage device. When the energy storage device is insufficient, enter the hibernation state.

[0008] S3: Obtain the energy storage capacity demand of each energy storage access point based on the difference in power supply instability indicators when the energy storage device is in the charging / discharging process and in the dormant state. Re-allocate energy storage devices to each energy storage access point based on the energy storage capacity demand of all energy storage access points and the capacity of all energy storage devices. Then, update the weight of each monitoring point based on the change in energy storage capacity demand before and after the reallocation of energy storage devices. Use the updated weight to merge the power supply quality indicators of all dimensions of each monitoring point to obtain the power supply instability indicators of each monitoring point again, and re-execute S2 and S3.

[0009] Preferably, the specific steps of evaluating several dimensions of power supply quality indicators based on the power data of each monitoring point in the distribution network, and merging all dimensions of power supply quality indicators of each monitoring point using preset weights to obtain the power supply instability index of each monitoring point are as follows:

[0010] The aforementioned dimensions include voltage deviation, frequency fluctuation, and three-phase imbalance. The power supply quality index corresponding to voltage deviation is obtained by the difference between the rated voltage and the measured voltage. The power supply quality index corresponding to frequency fluctuation is obtained by the difference between the rated frequency and the measured frequency. The power supply quality index corresponding to three-phase imbalance refers to the degree of three-phase imbalance between the zero-sequence component, the negative-sequence component, and the positive-sequence component of the voltage.

[0011] At each monitoring point, a weight is initialized for each dimension. The power supply quality indicators of all dimensions are weighted and summed using the weight of each dimension. The result is recorded as the power supply instability index of each monitoring point.

[0012] Preferably, the specific steps for allocating and connecting the energy storage device to the energy storage access point selected from all monitoring points are as follows:

[0013] The average value of the power supply instability index of each monitoring point at all times within the first preset time period is obtained and recorded as the instability level of each monitoring point. The n1 monitoring points with the highest instability levels are obtained and recorded as energy storage access points. An energy storage device is assigned to each energy storage access point for access, where n1 is a preset value. When the maximum value of the instability level of the n1 monitoring points is less than or equal to the preset access threshold, no energy storage access point is obtained.

[0014] Preferably, the specific steps of using the PID algorithm to regulate the discharge process of the energy storage device to minimize the power supply instability index of each energy storage access point are as follows:

[0015] The voltage output of the energy storage device is controlled by a PID algorithm. The target value of the PID algorithm is set as the optimal instability index, which is the minimum value of the power supply instability index.

[0016] Preferably, the specific steps for obtaining the energy storage capacity demand of each energy storage access point based on the difference in power supply instability indicators when the energy storage device at each access point is in the charging / discharging process and in a dormant state are as follows:

[0017] The average value of all power supply instability indicators obtained when each energy storage device is in the discharge and charging process is obtained, denoted as A1; the average value of all power supply instability indicators obtained when each energy storage device is in the dormant state is obtained, denoted as A2; the energy storage capacity demand coefficient of each energy storage access point is obtained, and the energy storage capacity demand coefficient is positively correlated with the difference between A2 and A1;

[0018] The energy storage capacity demand of each energy storage access point is positively correlated with the capacity of the energy storage equipment at each access point and the energy storage capacity demand coefficient.

[0019] Preferably, the specific steps of reallocating energy storage devices to each energy storage access point based on the energy storage capacity demand of all energy storage access points and the capacity of all energy storage devices are as follows:

[0020] Randomly pair energy storage access points and energy storage devices to obtain several matching pairs, each containing one energy storage access point and one energy storage device. Construct an objective function based on the difference between the capacity of the energy storage device and the energy storage capacity requirement of the energy storage access point in the same matching pair, as well as the distance of the shortest path between the energy storage device and the energy storage access point. Obtain all matching pairs that minimize the objective function. Among all matching pairs that minimize the objective function, connect the energy storage device of any matching pair to the energy storage access point in that matching pair.

[0021] Preferably, the specific steps for updating the weight of each monitoring point based on the change in energy storage capacity demand before and after the reallocation of energy storage devices at each energy storage access point are as follows:

[0022] For any energy storage access point, within the most recent second preset time period, the energy storage capacity demand obtained by the access point after each reallocation of energy storage devices is denoted as B1, and the energy storage capacity demand obtained by the access point before each reallocation of energy storage devices is denoted as B2. (B1-B2) / (B1+B2) is denoted as the rate of change of energy storage capacity demand after each reallocation of energy storage devices. Within the second preset time period, the average rate of change of energy storage capacity demand after all reallocations of energy storage devices is obtained and denoted as the average rate of change of capacity demand at the current moment, expressed as b. For any dimension weight at the energy storage access point, the weight of the energy storage access point is updated using the average rate of change of capacity demand b, wherein the update magnitude of the weight is positively correlated with the average rate of change of capacity demand b.

[0023] Preferably, the step of updating the weight of the energy storage access point using the average capacity demand change rate b, wherein the update magnitude of the weight is positively correlated with the average capacity demand change rate b, includes the following specific steps:

[0024] During the historical allocation of energy storage devices at this energy storage access point, the energy storage device allocation process corresponding to the minimum average capacity demand change rate is obtained, and the value of the weight used in this energy storage device allocation process is denoted as the optimal weight w0.

[0025] The updated weight w1 = (1-b)×w + b×w0; where w represents the weight value at the current time.

[0026] Preferably, the energy storage capacity demand of each energy storage access point is positively correlated with the capacity of the energy storage device at each energy storage access point and the energy storage capacity demand coefficient, as shown in the following specific formulas:

[0027] Where C represents the energy storage capacity demand of each energy storage access point, C0 represents the capacity of the energy storage equipment at each energy storage access point, and a represents the energy storage capacity demand coefficient.

[0028] Another embodiment of the present invention provides a mobile energy storage control system. The system includes several mobile energy storage devices, a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor runs the computer program, it executes all the steps of the mobile energy storage control method described above, for realizing the control of the working status and the allocation of access locations for the several mobile energy storage devices. The control of the working status includes the control of the discharge process, and the switching between the discharge process, the charging process, and the dormant state. The allocation of access locations refers to assigning the several mobile energy storage devices to different monitoring points and then connecting them to the power distribution network.

[0029] This mobile energy storage control system achieves dynamic protection of the power supply stability of the distribution network through the synergy of multi-dimensional sensing, precise control and adaptive optimization.

[0030] First, the system collects power data from various monitoring points in the distribution network, assessing power quality from key dimensions such as voltage deviation, frequency fluctuation, and three-phase imbalance. Then, it combines these multi-dimensional indicators with preset weights to form a power supply instability index, thereby quantifying the power supply risk at each monitoring point. Based on this, monitoring points with higher instability are selected as energy storage access points, and energy storage devices are initially allocated, laying the foundation for subsequent intervention.

[0031] During the real-time control phase, the system uses a PID algorithm to precisely control the discharge process of the energy storage device, adjusting the output voltage with the goal of minimizing power supply instability indicators, and quickly smoothing out power supply fluctuations. When the indicators drop to a preset threshold, the device switches to charging mode to replenish energy. If the remaining power is insufficient, it enters sleep mode to reduce ineffective energy consumption and ensure efficient utilization of the device.

[0032] To achieve continuous optimization, the system calculates the energy storage capacity demand at each access point by comparing the differences in power supply instability indicators between the energy storage devices during operation and in hibernation. Based on this, the system reallocates energy storage devices, balancing device capacity with access point requirements and travel path costs during the matching process to improve resource allocation efficiency. Simultaneously, based on the rate of change in capacity demand before and after device reallocation, the system dynamically updates the dimensional weights of each monitoring point, making the assessment of power supply instability indicators more closely aligned with actual needs. Through iterative iteration, the system can adapt to changes in the distribution network state, achieving intelligent scheduling of mobile energy storage devices and continuous improvement in power supply stability.

[0033] The beneficial effects of the technical solution of the present invention are:

[0034] This invention first integrates the power quality indicators of each monitoring point across all dimensions using preset weights, resulting in a power instability indicator for each monitoring point. Then, energy storage devices are allocated and connected to energy storage access points selected from all monitoring points. This process addresses the issue that different monitoring points have varying or unique power environments, conducting grid assessments from multiple dimensions to provide a comprehensive description of the power quality or supply situation at each monitoring point from multiple perspectives.

[0035] Based on this, the present invention utilizes a PID algorithm to regulate the discharge process of the energy storage device, thereby minimizing the power supply instability indicators at each energy storage access point. This process, through the regulation of the energy storage device, enables it to eliminate or mitigate the problem of low power supply quality at the energy storage access point, thus initially maintaining the stability of the distribution network.

[0036] Furthermore, this invention obtains the energy storage capacity demand of each energy storage access point based on the differences in power supply instability indicators when the energy storage device is in the charging / discharging process and in a dormant state. Then, based on the energy storage capacity demand of all energy storage access points and the capacity of all energy storage devices, energy storage devices are reallocated to each energy storage access point. This process achieves the goal of real-time updating of the energy storage devices connected to each energy storage access point, ensuring that the actual capacity of each energy storage device matches the required capacity of the energy storage access point as closely as possible. This allows for the adaptive connection of appropriate energy storage devices according to the operating conditions of the distribution network in the area, further maintaining the stability of the distribution network.

[0037] Furthermore, this invention updates the weights of each monitoring point based on the change in energy storage capacity demand before and after the reallocation of energy storage devices at each energy storage access point. Using the updated weights, the power quality indicators across all dimensions of each monitoring point are integrated to re-obtain the power instability index for each monitoring point. This process ensures that the power instability index accurately describes the power supply situation at different monitoring points or energy storage access points, further optimizing the method of periodically reallocating energy storage devices at each access point. This allows the PID control process and allocation process of the energy storage devices to adapt as closely as possible to the complex and variable power supply conditions at different energy storage access points in the distribution network, further maintaining the stability of the distribution network in the area.

[0038] In summary, by dynamically updating the fusion weights of multi-dimensional power grid assessment results and the access location of energy storage devices, the coordination process between energy storage devices and the distribution network can adapt to the characteristics of the power supply situation at different monitoring points of the distribution network, which is both unique and complex, thereby further maintaining the operational stability of the distribution network. Attached Figure Description

[0039] Figure 1This is a flowchart of the steps of a mobile energy storage control method according to the present invention. Detailed Implementation

[0040] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a mobile energy storage system and its control method according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0042] The following description, in conjunction with the accompanying drawings, details a specific scheme for a mobile energy storage system and its control method provided by the present invention.

[0043] Example 1:

[0044] Please see Figure 1 The diagram illustrates a flowchart of a mobile energy storage control method according to an embodiment of the present invention, the method comprising the following steps:

[0045] Step S101: Evaluate the power quality indicators of several dimensions based on the power data of each monitoring point, and integrate the power quality indicators of all dimensions of each monitoring point using the weight of each monitoring point to obtain the power instability index.

[0046] For any given distribution area in the power distribution network, there are a large number of electricity users, such as residential users, business users, and charging stations. Multiple monitoring points are artificially set up within the distribution network of this area. In this embodiment, 30 monitoring points are set up. Each monitoring point contains three-phase power lines of the distribution network. A multi-functional power meter is installed at each monitoring point to measure the power data of the three-phase power lines. The power data includes the voltage and frequency of the three-phase power lines. In this embodiment, every 0.1 seconds is recorded as a time interval, and power data is collected once at each time interval.

[0047] This embodiment assesses power quality or supply status based on power data from each monitoring point. Considering that the power environment (e.g., power equipment, power consumption behavior) at different monitoring points varies, this embodiment requires a comprehensive assessment from multiple dimensions to obtain power quality indicators for each dimension. Specifically, this embodiment assesses power quality from three dimensions: voltage deviation, frequency fluctuation, and three-phase imbalance.

[0048] As an example, methods for obtaining power quality metrics such as voltage offset include:

[0049] Calculate the difference between the rated voltage (or nominal voltage) and the effective value of the measured voltage of each phase line at each monitoring point. The ratio of this difference to the rated voltage is recorded as the voltage deviation rate of each phase line. Specifically, when the voltage deviation rate is less than or equal to 0, the voltage deviation rate is set to 0.

[0050] The average voltage deviation rate of all phase lines at each monitoring point is used as the power supply quality index in the dimension of voltage deviation.

[0051] As an example, methods for obtaining power quality metrics such as frequency fluctuation include:

[0052] Calculate the absolute value of the difference between the rated frequency (in this embodiment, the rated frequency is equal to 50Hz) of each phase line at each monitoring point and the measured frequency of the phase line. The ratio of the absolute value of the difference to the rated frequency is recorded as the frequency offset rate of each monitoring point.

[0053] The average frequency offset rate of all phase lines at each monitoring point is used as the power supply quality index for the dimension of frequency fluctuation.

[0054] As an example, methods for obtaining power quality indicators in the dimension of three-phase imbalance include:

[0055] The zero-sequence component, negative-sequence component, and positive-sequence component of the voltage of the three-phase wires at each monitoring point are obtained. The method for obtaining the zero-sequence component, negative-sequence component, and positive-sequence component from the three-phase voltage is well known and will not be described in detail in this embodiment.

[0056] The formula for obtaining the three-phase imbalance is as follows:

[0057]

[0058] in , , These represent the positive-order component, the negative-order component, and the zero-order component, respectively. This indicates the degree of three-phase imbalance.

[0059] In this embodiment, the three-phase imbalance is used as the power supply quality indicator for the dimension of three-phase imbalance.

[0060] Thus, this embodiment has evaluated power quality from multiple dimensions. The power quality indicators obtained in real time at each monitoring point can describe the power quality or power supply status at each monitoring point. The higher the power quality indicator, the more unqualified the power supply status and the lower the quality of power supply at each monitoring point.

[0061] In other embodiments, the power data also includes current, current frequency, power factor, etc., and the dimensions in other embodiments also include current offset, power factor offset, etc. The calculation methods of the corresponding power quality indicators are the same as the calculation methods of the power quality indicators of the voltage offset dimension mentioned above, and will not be described in detail in this embodiment.

[0062] Furthermore, at each monitoring point, this embodiment uses an expert scoring method to assign a weight to each dimension (satisfying that the sum of the weights of all dimensions equals 1), and uses the weight of each dimension to perform a weighted summation of the power supply quality indicators of all dimensions, and the result is recorded as the power supply instability index of each monitoring point.

[0063] It should be noted that since the power consumption environments of different monitoring points are different or have different characteristics, the weights allocated to the same dimension at different monitoring points may be different. For example, a monitoring point connected to a large number of frequency converters and a monitoring point connected to a large number of resistive loads are in different power consumption environments and have different power consumption characteristics. The focus of attention on dimensions such as frequency fluctuation and voltage deviation is different at these two monitoring points, and the weights allocated are also different.

[0064] In other embodiments, for convenience and speed, a weight can be randomly assigned to each dimension at each monitoring point.

[0065] The power supply instability index obtained above provides a comprehensive description of the power quality or power supply situation at each monitoring point from multiple dimensions. The larger the power supply instability index, the lower the overall power quality, the more unqualified the power supply situation, and the more unstable the distribution network status at each monitoring point.

[0066] Step S102: Select energy storage access points from all monitoring points based on power supply instability indicators, and connect energy storage devices to each energy storage access point.

[0067] In the above steps, a power supply instability index is calculated for each monitoring point at each time point. The average value of the power supply instability index of each monitoring point at all times within the most recent first preset time period is obtained and recorded as the instability level of each monitoring point. The instability level is used to describe the stability of the distribution network in the most recent time period. The larger the value, the worse the stability of the distribution network and the lower the power supply quality in the most recent time period. In this embodiment, the length of the first preset time period is set to half an hour.

[0068] The n1 monitoring points with the highest instability are identified and designated as energy storage access points. Each energy storage access point is randomly assigned an energy storage device for connection. The energy storage device injects active power into the phase lines or outputs a suitable voltage to avoid or mitigate problems such as low power quality or instability in the distribution network due to voltage deviation, harmonic interference, and three-phase imbalance. In this embodiment, the energy storage device is a portable battery, such as a vehicle-mounted lithium battery; the energy storage device discharges and charges with the distribution network through an energy storage converter.

[0069] As an example, n1 equals the number of available energy storage devices in the distribution area; specifically, when the number of available energy storage devices in the distribution area is greater than the number of monitoring points, let n1 equal the number of monitoring points.

[0070] It should be noted that when the maximum value of the instability of the n1 monitoring points is less than or equal to the access threshold, there are no energy storage access points, and no energy storage device is connected in this case; this embodiment uses an access threshold of 0.2 as an example. Furthermore, the energy storage devices available in this embodiment do not include those with insufficient remaining power.

[0071] Step S103: Use the PID algorithm to regulate the power output of the energy storage device during the discharge process, so as to minimize the power supply instability index of each energy storage access point. When the power supply instability index of each energy storage access point is less than the preset threshold, charge the energy storage device.

[0072] In this embodiment, it is considered that the power data of each energy storage access point changes in real time. In order for the energy storage device to eliminate or reduce the problem of low power supply quality and instability at the energy storage access point, it is necessary to control the output of the energy storage device in real time to adapt to the changes in the power data of the energy storage access point.

[0073] Specifically, during the discharge process, the energy storage device uses a PID algorithm to control the voltage output of the energy storage device on each phase of the power line. The principle of the PID algorithm is to continuously adjust the parameters (that is, the voltage output of the energy storage device in this embodiment) so that the power distribution network approaches and stabilizes at the target state (or stabilizes at the target value of the PID algorithm). The target state refers to the state where the power supply is qualified and the power supply quality is high (that is, the state when the power supply instability index of each energy storage access point is minimized). In this embodiment, when using the PID algorithm to regulate the output voltage of the energy storage device, the target value of the PID algorithm is set to the optimal instability index. The purpose is that during the discharge process, the energy storage device connected to each energy storage access point can change the power data of each energy storage access point by injecting active power into the energy storage access point, so that the power supply instability index of the energy storage access point approaches and stabilizes at the optimal instability index. The optimal instability index is the minimum value of the power supply instability index. In some optional examples, the optimal instability index is set to 0, which means that there is no voltage deviation, frequency fluctuation, or three-phase imbalance. In some preferred examples, considering that the voltage deviation, frequency fluctuation, and three-phase imbalance in the distribution network cannot be completely eliminated, the optimal instability index is set to be greater than and close to 0. Its preferred value range is (0, 0.2], for example, the optimal instability index is set to 0.1.

[0074] In summary, the energy storage device in this embodiment uses PID control to ensure that each energy storage access point approaches and stabilizes in a high-quality power supply state where voltage deviation, frequency fluctuation, and three-phase imbalance are absent or minimal. It should be noted that the principle of the PID algorithm is a well-known technique, and the proportional, integral, and derivative control parameters used are pre-tuned; therefore, this embodiment will not elaborate on them in detail.

[0075] Furthermore, during the discharge process of the energy storage device, the power supply instability index of each energy storage access point at each moment is calculated by measuring the power data of each energy storage access point at each moment (see step S101 for details). It should be noted that under the control of the PID algorithm, the power supply instability index will approach the optimal instability index. After each acquisition of the power supply instability index, the average value of all power supply instability indices obtained in the most recent 2 seconds is obtained. When the average value is less than or equal to the preset threshold th, it indicates that the power supply of the energy storage access point has been restored to stability (that is, in a state where voltage deviation, frequency fluctuation, and three-phase imbalance are absent or not obvious), and the distribution network charges the energy storage device. When the average value is greater than the preset threshold th, it indicates that the power supply of the energy storage access point has not been restored to stability or has become unstable again (it may still be in a state where voltage deviation, frequency fluctuation, and three-phase imbalance are absent), and the energy storage device continues to discharge, and its discharge process is still controlled by PID. This embodiment takes th=0.2 as an example for description, and the preferred value range of th is [0.1, 0.4].

[0076] When the remaining power of the energy storage device is insufficient, the device stops discharging to avoid over-discharging and affecting its service life. In this embodiment, the energy storage device is considered to have insufficient remaining power when its remaining power is less than 15%. It should be noted that when the energy storage device stops discharging, if the average value mentioned above is less than or equal to a preset threshold th, the device is charged. If the average value is greater than the preset threshold th, the device does not operate and remains in a dormant state. When the remaining power of the energy storage device is sufficient (i.e., when the remaining power is greater than or equal to 15%), it exits the dormant state and continues the discharging or charging process according to the above method.

[0077] So far, the control methods for energy storage devices and the switching methods for charging and discharging operating states have been described. As the energy storage devices at each energy storage access point operate, each energy storage device may undergo several charging, discharging, or dormant processes, so that with the cooperation of the energy storage devices, the stability of the power distribution network and the healthy operation of the energy storage devices can be guaranteed as much as possible.

[0078] Step S104: The difference in power supply instability index between the energy storage device at each energy storage access point and when it is in the charging / discharging process and in the dormant state is taken as the energy storage capacity demand of each energy storage access point.

[0079] During the operation of energy storage devices, whether the actual capacity of each energy storage device matches the capacity required by the energy storage access point directly affects the coordination between the energy storage device and the distribution network. Specifically, when the actual capacity of the energy storage device does not match the capacity required by the energy storage access point, for example, if the capacity of the energy storage device is too small, it may cause the energy storage access point to enter a dormant state too often (or frequently) before it has recovered to stability (it may be in a state of voltage deviation, frequency fluctuation, or three-phase imbalance). This will prevent the energy storage device of that capacity from stabilizing the distribution network after it is connected to the access point. On the other hand, if the current energy storage device capacity is too large, although it helps the current energy storage access point to recover to stability, if the capacity required by other energy storage access points does not match the actual capacity of the connected energy storage devices, the current energy storage devices will not be fully utilized, affecting the stable recovery of the distribution network at other energy storage access points.

[0080] When the actual capacity of the energy storage device matches the capacity required by the energy storage access point, it means that the energy storage device connected to each energy storage access point can regulate the power data of each energy storage access point through the discharge and charging process. This can minimize the problem of the energy storage device entering a dormant state too often (or too frequently) before the energy storage access point has recovered its stability. As a result, with the cooperation of the energy storage devices connected to all energy storage access points, the distribution network in the distribution area can operate as stably as possible.

[0081] Based on this, this embodiment obtains the energy storage capacity demand of each energy storage access point according to the difference in power supply instability index when the energy storage device is in the charging and discharging process and in the dormant state. The energy storage capacity demand is used to reflect the demand of each energy storage access point for the capacity of the energy storage device. The larger the energy storage capacity demand, the more energy storage capacity each energy storage access point needs.

[0082] As an example, the energy storage capacity demand of each energy storage access point is obtained based on the difference in power supply instability indicators when the energy storage device is in the charging / discharging process and in a dormant state. The method includes:

[0083] When the energy storage device at each energy storage access point starts timing from the time it is connected to the distribution network, after the energy storage device has been working for a certain number of days (e.g., after working for one day), the average value of all power supply instability indicators obtained when each energy storage device is in the discharge and charging process is obtained, denoted as A1; the average value of all power supply instability indicators obtained when each energy storage device is in the dormant state is obtained, denoted as A2.

[0084] Obtain the energy storage capacity demand coefficient for each energy storage access point. The energy storage capacity demand coefficient is used to reflect the relative amount of energy storage capacity that each energy storage access point still lacks, based on the capacity of the energy storage devices connected to each energy storage device. The energy storage capacity demand coefficient is positively correlated with the difference between A2 and A1. When the difference between A2 and A1 is larger, it indicates that, based on the capacity of the energy storage devices connected to each energy storage device, the energy storage access point has entered a dormant state too much before it has recovered to a stable state (it may also be in a state of voltage deviation, frequency fluctuation, or three-phase imbalance), indicating that the energy storage access point still lacks a lot of energy storage capacity.

[0085] The energy storage capacity demand of each energy storage access point is positively correlated with the capacity of the energy storage equipment and the energy storage capacity demand coefficient of each energy storage access point. The larger the capacity of the energy storage equipment and the larger the energy storage capacity demand coefficient, the more the distribution network can not be guaranteed to be stable with the cooperation of the energy storage equipment. In this case, the energy storage capacity demand of each energy storage access point is larger than the actual capacity of the energy storage equipment.

[0086] As an optional example, the energy storage capacity demand coefficient is positively correlated with the difference between A2 and A1, as shown in the following formula:

[0087]

[0088] Where 'a' represents the energy storage capacity demand coefficient. Specifically, when 'a' is less than or equal to 0, it indicates that the instability in the power distribution network when the energy storage device enters dormancy is relatively insignificant and can be ignored. In this case, let... .

[0089] As a preferred example, the energy storage capacity demand coefficient is positively correlated with the difference between A2 and A1, as shown in the following formula:

[0090]

[0091] Where 'a' represents the energy storage capacity demand coefficient, and 'm1' represents the concern coefficient for grid instability during dormancy. This example uses... Let's take an example. Specifically, when a is less than or equal to 0.1, it indicates that the instability in the power distribution network when the energy storage device enters dormancy is relatively minor and can be ignored. In this case, let... .

[0092] As an example, the energy storage capacity demand of each energy storage access point is positively correlated with the capacity of the energy storage device at each access point and the energy storage capacity demand coefficient, as shown in the following formula:

[0093]

[0094] Where C represents the energy storage capacity requirement of each energy storage access point, and C0 represents the capacity of the energy storage equipment at each energy storage access point.

[0095] Step S105: Based on the energy storage capacity requirements of all energy storage access points and the capacity of all energy storage devices, reallocate energy storage devices to each energy storage access point.

[0096] After any energy storage device has been operating for a certain number of days (e.g., after one day), its corresponding monitoring point is temporarily no longer considered an energy storage access point. At this time, for all monitoring points other than the energy storage access points, energy storage access points are re-selected from all monitoring points other than the energy storage access points according to the method in step S102. After at least one energy storage access point is selected, energy storage devices are reallocated to each energy storage access point based on the energy storage capacity requirements of all energy storage access points and the capacity of all energy storage devices. This process, by reallocating energy storage devices to each energy storage access point, ensures that the actual capacity of each energy storage device and the capacity required by the energy storage access point are matched as closely as possible, thereby ensuring the stability of the distribution network with the cooperation of energy storage devices.

[0097] As an example, the method of reallocating energy storage devices to each energy storage access point based on the energy storage capacity demand of all energy storage access points and the capacity of all energy storage devices includes:

[0098] In the preferred example, for energy storage access points already connected to energy storage devices, the connection to the energy storage devices is maintained, and no new energy storage devices are assigned to them. For energy storage access points whose connected energy storage devices have been operating for several days, and for energy storage access points that have not yet been connected to energy storage devices, energy storage devices need to be reassigned to them. Energy storage devices that have been operating for more than a certain number of days and those that have not yet been connected to energy storage access points are considered as available energy storage devices.

[0099] In an optional example, energy storage devices can be reassigned to all energy storage access points, and all energy storage devices can be made available.

[0100] For energy storage access points that need to be reallocated, and for all available energy storage devices, the energy storage access points and available energy storage devices are randomly paired to obtain several matching pairs, each pair containing one energy storage access point and one energy storage device. It should be noted that once an energy storage access point or energy storage device appears in any matching pair, it will not appear in any other matching pair. The number of matching pairs is denoted as N.

[0101] Construct the following objective function P using several matching pairs:

[0102]

[0103] in, This represents the capacity of the energy storage device in the nth matching pair. This represents the energy storage capacity requirement of the energy storage access point in the nth matching pair. This represents the shortest path distance from the energy storage device in the nth matching pair to the location of the energy storage access point in the nth matching pair. The shortest distance is obtained by map navigation software, and will not be described in detail in this embodiment; m2 represents a preset distance attention coefficient. In one example, m2 is set to a value greater than 0, for example, m2=0.1; in other examples, m2 can be set to 0, indicating that the road transportation distance from the energy storage device to the energy storage access point is not considered.

[0104] It should be noted that in this embodiment, the unit of capacity of the energy storage device is megawatt-hour, and the unit of distance is kilometer. When the physical quantities related to capacity and distance in the above objective function are used in the calculation, numerical values ​​are used and the dimensions are not considered. Since the objective function does not have a specific physical meaning, and only the magnitude of the objective function is considered, the use of numerical values ​​in the calculation does not affect the implementation of this embodiment.

[0105] It should be further noted that if the energy storage access point in the nth matching pair is a newly selected point in this step, has not yet participated in the operation of step S104, and has never obtained the energy storage capacity demand, then its energy storage capacity demand is initialized to 0.

[0106] Thus, after all energy storage access points and all available energy storage devices are paired and all matching pairs are obtained, the above objective function can be obtained. The genetic algorithm is used to obtain all matching pairs that minimize the objective function. For these matching pairs, the energy storage device in any matching pair is connected to the energy storage access point in that matching pair, thereby realizing the reallocation of energy storage devices for each energy storage access point.

[0107] When the objective function is minimized (i.e., when the difference between the capacity of the matched energy storage device and the energy storage capacity requirement of the energy storage access point is minimized, and the distance is minimized), it can ensure that the capacity of the available energy storage device in the matched pair is adapted to the capacity requirement of the energy storage access point, and can also ensure that the energy storage device is quickly transported to the corresponding energy storage access point, which helps to maintain the stability of the distribution network in the distribution area in a timely manner.

[0108] Step S106: Based on the change in energy storage capacity demand before and after the reallocation of energy storage devices at each energy storage access point, update the weight of each monitoring point, and use the updated weight to re-integrate the power supply quality indicators of all dimensions of each monitoring point, and obtain the power supply instability indicators of each monitoring point again.

[0109] After each energy storage access point is reassigned energy storage devices, steps S103, S104, and S105 are executed cyclically. This achieves an iterative process of controlling the reassigned energy storage devices, calculating the energy storage capacity demand of the energy storage access point, and reassigning energy storage devices again. During this process, the same energy storage access point will be reassigned energy storage devices periodically, achieving the goal of real-time updating of the energy storage devices connected to each energy storage access point. This allows for the adaptive connection of appropriate energy storage devices based on the operation of the distribution network in the distribution area, further maintaining the stability of the distribution network. In addition, when adding or removing energy storage devices in the distribution area, it can still ensure that all energy storage devices in the distribution area are reasonably connected to the distribution network, making the application of mobile energy storage devices more flexible.

[0110] However, this embodiment further considers that the power environment of each monitoring point or energy storage access point is relatively complex. For any dimension weight at each monitoring point (or energy storage access point) (see step S101 for details), when using fixed or inappropriate weights to obtain the power supply instability index of each monitoring point (or energy storage access point), the power supply instability index may not accurately describe the power supply situation of different monitoring points (or energy storage access points). This directly affects the regulation process of the energy storage device in step S103, as well as the redistribution process of the energy storage device in steps S104 and S105. As a result, the above-mentioned method of periodically redistributing and controlling the energy storage device for each energy storage access point is not optimal and has limited ability to maintain the stable operation of the distribution network.

[0111] For any dimension of the weight at each energy storage access point, this embodiment updates the weight of each monitoring point based on the change in energy storage capacity demand before and after the redistribution of energy storage devices at each energy storage access point. The updated weights are then used to re-integrate the power supply quality indicators of all dimensions at each monitoring point, and the power supply instability indicators of each monitoring point are obtained again. Then, steps S103, S104, and S105 are executed cyclically to ensure that the redistributed energy storage devices can be adapted to the complex and ever-changing power supply conditions at different energy storage access points in the distribution network, thereby further maintaining the stability of the distribution network.

[0112] As an example, the weight of each monitoring point is updated based on the change in energy storage capacity demand before and after the reallocation of energy storage devices at each energy storage access point. The method includes:

[0113] For any energy storage access point, assuming that at the current moment, the energy storage device has been reallocated to the energy storage access point according to step S105, and the energy storage capacity demand of the energy storage access point has been obtained again according to steps S103 and S104, the following processing is performed:

[0114] Within the most recent second preset time period, the energy storage capacity demand obtained by the energy storage access point after each reallocation of energy storage equipment is denoted as B1, and the energy storage capacity demand obtained by the energy storage access point before each reallocation is denoted as B2. (B1-B2) / (B1+B2) is recorded as the rate of change of energy storage capacity demand after each reallocation. Within the most recent second preset time period, when the number of times the energy storage access point reallocates energy storage equipment is greater than n2, the average rate of change of energy storage capacity demand after all reallocations is obtained and recorded as the average rate of change of capacity demand at the current moment, denoted as b. Here, B1+B2 is used as the denominator to remove dimensions and orders of magnitude.

[0115] In this embodiment, the length of the second preset time period is set to one week, and n2=2 is used as an example for description.

[0116] A larger average capacity demand change rate indicates a significant increase in the energy storage capacity demand at the energy storage access point as energy storage devices are periodically allocated. Multiple allocations of energy storage devices cannot meet the capacity requirements of the access point, which is detrimental to the stability of the distribution network. This is because the power supply instability indicators of the energy storage access point cannot accurately describe its power supply environment, causing the PID control process and allocation process of the multiple allocations of energy storage devices to be unsuitable for the power supply conditions of the distribution network at that access point.

[0117] Furthermore, for any dimension of the energy storage access point, this embodiment updates the weight of the energy storage access point using the average capacity demand change rate at the current moment, wherein the update magnitude of the weight is positively correlated with the average capacity demand change rate.

[0118] The weights of all dimensions at the energy storage access point are updated according to this method.

[0119] After the current moment, when the energy storage device is redistributed to the energy storage access point again (that is, when steps S103, S104, and S105 are executed again), the power supply quality indicators of all dimensions at the energy storage access point are weighted and summed using the updated weights to obtain the power supply instability indicator again. Then, the power supply instability indicator is used to control the energy storage device at the energy storage access point and to calculate the subsequent energy storage capacity demand.

[0120] Specifically, when the number of times an energy storage access point redistributes energy storage devices is no greater than n², the weight of that energy storage access point is not updated. When the average capacity demand change rate is less than or equal to 0, the average capacity demand change rate is set to 0, indicating that the PID control process and allocation process of the energy storage devices in multiple allocations are adapted to the power supply situation of that energy storage access point. In this case, the weight of each energy storage access point is not updated.

[0121] As an example, the weight of the energy storage access point is updated using the average capacity demand change rate at the current moment, where the update magnitude of the weight is positively correlated with the average capacity demand change rate. The methods include:

[0122] As can be seen from the above process, each time an energy storage device is reallocated to this energy storage access point, an average capacity demand change rate can be obtained using the above method. During the historical allocation of energy storage devices at this energy storage access point, the energy storage device allocation process corresponding to the minimum average capacity demand change rate (including the average capacity demand change rate at the current moment) is obtained, and the weight used in this energy storage device allocation process is denoted as the optimal weight w0.

[0123] The updated weight w1 = (1-b)×w + b×w0. Where b represents the average rate of change of capacity demand at the current moment, and w represents the weight used in the allocation of energy storage devices at the current moment (that is, the value of the weight at the current moment).

[0124] The larger the value of b, the more the updated weights reflect the weights used in the historical allocation of energy storage devices that were most suitable for the power supply conditions of the energy storage access point (i.e., the optimal weights). This is equivalent to a significant update of the weights. Conversely, the smaller the value of b, the more the current energy storage device allocation results are suitable for the power supply conditions of the energy storage access point. There is no need to overemphasize the weights used in the historical allocation of energy storage devices (i.e., the optimal weights). This is equivalent to a smaller update of the weights, especially when b=0, which is equivalent to no weight update.

[0125] Specifically, during the historical allocation of energy storage devices at this energy storage access point, if the number of times the energy storage access point is allocated energy storage devices is less than or equal to 2, then the average of the weights allocated to all monitoring points in step S101 is used as the initial value of the optimal weight w0.

[0126] In summary, each time energy storage devices are redistributed to the same energy storage access point, the weight of the same access point is dynamically updated based on the weight of the previously redistributed devices and the change in energy storage capacity demand (or average capacity demand change rate) before and after the redistribution. In subsequent redistribution processes, the updated weights are used to comprehensively describe the power supply situation of each access point (i.e., the updated weights are used to calculate the power supply instability index). This ensures that the power supply instability index accurately describes the power supply situation of different monitoring points (or energy storage access points), further optimizing the method of periodically redistributing energy storage devices to each access point. This allows the PID control process and allocation process of the energy storage devices to adapt as closely as possible to the complex and variable power supply situations at different energy storage access points in the distribution network, further maintaining the stability of the distribution network in the area.

[0127] Example 2:

[0128] This embodiment provides a mobile energy storage control system. The system includes several mobile energy storage devices, a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor runs the computer program, it executes the steps of all the above embodiments to realize the working status control and access location allocation of the several mobile energy storage devices. The working status control includes PID control of the discharge process and switching between the discharge process, the charging process, and the dormant state. The access location allocation refers to assigning the several mobile energy storage devices to different monitoring points and then connecting them to the power distribution network.

[0129] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A mobile energy storage control method, characterized in that, The method includes the following steps: S1: Evaluate several dimensions of power supply quality indicators based on the power data of each monitoring point in the distribution network. Use the preset weights of each monitoring point to merge all dimensions of power supply quality indicators of each monitoring point to obtain the power supply instability index of each monitoring point. Allocate and connect the energy storage device to the energy storage access point selected from all monitoring points. S2: Use PID algorithm to regulate the discharge process of energy storage device to minimize the power supply instability index of each energy storage access point. When the power supply instability index is less than or equal to the preset threshold, charge the energy storage device. When the remaining power of the energy storage device is insufficient, enter the sleep state. S3: Obtain the energy storage capacity demand of each energy storage access point based on the difference in power supply instability indicators when the energy storage device is in the charging / discharging process and in the dormant state. Re-allocate energy storage devices to each energy storage access point based on the energy storage capacity demand of all energy storage access points and the capacity of all energy storage devices. Then, update the weight of each monitoring point based on the change in energy storage capacity demand before and after the re-allocation of energy storage devices. Use the updated weight to merge the power supply quality indicators of all dimensions of each monitoring point to obtain the power supply instability indicators of each monitoring point again, and re-execute S2 and S3. Step S3 obtains the energy storage capacity demand of each energy storage access point based on the differences in power supply instability indicators when the energy storage device is in the charging / discharging process and in a dormant state. The specific steps include the following: The average value of all power supply instability indicators obtained when each energy storage device is in the discharge and charging process is obtained, denoted as A1; the average value of all power supply instability indicators obtained when each energy storage device is in the dormant state is obtained, denoted as A2; the energy storage capacity demand coefficient of each energy storage access point is obtained, and the energy storage capacity demand coefficient is positively correlated with the difference between A2 and A1; The energy storage capacity demand of each energy storage access point is positively correlated with the capacity of the energy storage equipment at each energy storage access point and the energy storage capacity demand coefficient. Step S3 involves reallocating energy storage devices to each energy storage access point based on the energy storage capacity demand of all energy storage access points and the capacity of all energy storage devices. The specific steps are as follows: Randomly pair energy storage access points and energy storage devices to obtain several matching pairs, each pair containing one energy storage access point and one energy storage device. Construct an objective function based on the difference between the capacity of the energy storage device and the energy storage capacity demand of the energy storage access point in the same matching pair, as well as the distance of the shortest path between the energy storage device and the energy storage access point. Obtain all matching pairs that minimize the objective function. Among all matching pairs that minimize the objective function, connect the energy storage device of any matching pair to the energy storage access point in that matching pair. Step S3 updates the weight of each monitoring point based on the change in energy storage capacity demand before and after the reallocation of energy storage devices at each energy storage access point. The specific steps include the following: For any energy storage access point, within the most recent second preset time period, the energy storage capacity demand obtained by the access point after each reallocation of energy storage devices is denoted as B1, and the energy storage capacity demand obtained by the access point before each reallocation of energy storage devices is denoted as B2. (B1-B2) / (B1+B2) is denoted as the rate of change of energy storage capacity demand after each reallocation of energy storage devices. Within the second preset time period, the average rate of change of energy storage capacity demand after all reallocations of energy storage devices is obtained and denoted as the average rate of change of capacity demand at the current moment, expressed as b. For any dimension weight at the energy storage access point, the weight of the energy storage access point is updated using the average rate of change of capacity demand b, wherein the update magnitude of the weight is positively correlated with the average rate of change of capacity demand b.

2. The mobile energy storage control method according to claim 1, characterized in that, Step S1 evaluates several dimensions of power supply quality indicators based on the power data of each monitoring point in the distribution network. Then, using the preset weights for each monitoring point, the power supply quality indicators of all dimensions for each monitoring point are merged to obtain the power supply instability index for each monitoring point. The specific contents include the following: The aforementioned dimensions include voltage deviation, frequency fluctuation, and three-phase imbalance. The power supply quality index corresponding to voltage deviation is obtained by the difference between the rated voltage and the measured voltage. The power supply quality index corresponding to frequency fluctuation is obtained by the difference between the rated frequency and the measured frequency. The power supply quality index corresponding to three-phase imbalance refers to the degree of three-phase imbalance between the zero-sequence component, the negative-sequence component, and the positive-sequence component of the voltage. At each monitoring point, a weight is initialized for each dimension. The power supply quality indicators of all dimensions are weighted and summed using the weight of each dimension. The result is recorded as the power supply instability index of each monitoring point.

3. The mobile energy storage control method according to claim 1, characterized in that, Step S1 involves allocating and connecting the energy storage devices to the energy storage access points selected from all monitoring points. The specific steps are as follows: The average value of the power supply instability index of each monitoring point at all times within the first preset time period is obtained and recorded as the instability level of each monitoring point. The n1 monitoring points with the highest instability level are obtained and recorded as energy storage access points. An energy storage device is assigned to each energy storage access point for access, where n1 is a preset value. When the maximum value of the instability level of the n1 monitoring points is less than or equal to the preset access threshold, no energy storage access point is obtained.

4. The mobile energy storage control method according to claim 1, characterized in that, Step S2 uses a PID algorithm to regulate the discharge process of the energy storage device, minimizing the power supply instability index of each energy storage access point. The specific steps are as follows: The voltage output of the energy storage device is controlled by a PID algorithm. The target value of the PID algorithm is set as the optimal instability index, which is the minimum value of the power supply instability index.

5. The mobile energy storage control method according to claim 1, characterized in that, The step of updating the weight of the energy storage access point using the average capacity demand change rate b, wherein the update magnitude of the weight is positively correlated with the average capacity demand change rate b, includes the following specific steps: During the historical allocation of energy storage devices at this energy storage access point, the energy storage device allocation process corresponding to the minimum average capacity demand change rate is obtained, and the value of the weight used in this energy storage device allocation process is denoted as the optimal weight w0. The updated weight w1 = (1-b)×w + b×w0; where w represents the weight value at the current time.

6. The mobile energy storage control method according to claim 1, characterized in that, The energy storage capacity demand of each energy storage access point is positively correlated with the capacity of the energy storage device at each access point and the energy storage capacity demand coefficient, as shown in the following specific formulas: ； in, This indicates the energy storage capacity requirement of each energy storage access point. This represents the capacity of the energy storage device at each energy storage access point, and 'a' represents the energy storage capacity demand coefficient.

7. A mobile energy storage control system, comprising a plurality of mobile energy storage devices, and further comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor runs the computer program, it executes all the steps of the mobile energy storage control method according to any one of claims 1-6, which is used to realize the working status control and access location allocation of several mobile energy storage devices. The working status control includes the regulation of the discharge process and the switching between discharge, charging and dormancy states. The access location allocation refers to allocating several mobile energy storage devices to different monitoring points and then connecting them to the power distribution network.

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