A coordinated control method for distributed microgrid energy storage with multi-energy complementarity

Abstract

The present invention discloses a multi-energy complementary distributed microgrid energy storage coordinated control method, which relates to the field of microgrid technology, including collecting renewable energy and energy storage unit data and preprocessing, real-time monitoring of microgrid load changes and fluctuations in renewable energy power generation and prediction, generating short-term charging and discharging plans; adjusting the power distribution ratio of the energy storage unit in real time and dynamically adjusting the charging and discharging rate of the energy storage unit through a virtual navigator algorithm, and synchronizing the voltage and frequency between different energy storage units after the charging and discharging rate is adjusted. The present invention improves the accuracy of power and load prediction through the combination of LSTM model and ARIMA model, and dynamically adjusts the power distribution ratio and charging and discharging rate of the energy storage unit through a virtual navigator algorithm, ensuring the stable operation of the system under complex load fluctuations, and can adapt to real-time fluctuations in load and power generation, improving the operating efficiency of the energy storage system and the stability of the entire microgrid.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrids, and in particular to a distributed microgrid energy storage coordinated control method with multi-energy complementarity. Background Art

[0002] With the transformation of the global energy structure, the application of renewable energy has gradually become an important way for countries to achieve green and low-carbon development. Renewable energy such as photovoltaics and wind power has the significant advantages of environmental friendliness and sustainable development, but its intermittent and instability limit its application in large-scale power grids. In recent years, distributed microgrid technology has gradually emerged, which organically combines decentralized renewable energy systems with energy storage units, and realizes local energy self-sufficiency and complementary applications through reasonable scheduling and control. Under the architecture of distributed microgrids, regulating renewable energy through energy storage systems can effectively smooth out energy fluctuations and improve the stability and reliability of the system. However, the existing technology still has defects. The scheduling strategies of traditional energy management systems for energy storage units and renewable energy mostly rely on fixed rules, which cannot fully adapt to the real-time fluctuations of load and power generation. The existing control methods lack intelligent adjustment mechanisms for the power allocation and charge and discharge rate adjustment of energy storage units, and often rely only on simple power balance algorithms for processing, which not only affects the operating efficiency of the energy storage system, but also may cause the voltage and frequency between energy storage units to be out of sync, affecting the stability of the entire microgrid. Therefore, in the actual operation of microgrids, how to achieve coordinated optimization of multiple energy forms, especially balancing power generation and load demand through more intelligent and real-time control strategies, is the main challenge facing existing technologies. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing multi-energy complementary distributed microgrid energy storage coordinated control method, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is how to achieve coordinated optimization of multiple energy forms, especially balancing power generation and load demand through more intelligent and real-time control strategies, which is the main challenge faced by the existing technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a multi-energy complementary distributed microgrid energy storage coordinated control method, which includes collecting renewable energy and energy storage unit data and preprocessing them, monitoring the load changes of the microgrid and the fluctuations of renewable energy power generation in real time and predicting them, and generating a short-term charging and discharging plan;

[0006] The virtual navigator algorithm is used to adjust the power distribution ratio of the energy storage unit in real time and dynamically adjust the charge and discharge rate of the energy storage unit. After the charge and discharge rate is adjusted, the voltage and frequency between different energy storage units are synchronized.

[0007] Store the data generated during the coordination process and perform data backup.

[0008] As a preferred solution of the multi-energy complementary distributed microgrid energy storage coordinated control method described in the present invention, the collection of renewable energy and energy storage unit data and pre-processing thereof refers to deploying sensors to collect photovoltaic power generation and wind power generation data in the microgrid in real time, while collecting the charge state, power output, voltage and frequency of the energy storage unit, pre-processing the collected data, removing data noise and abnormal values, and performing smoothing.

[0009] As a preferred solution of the multi-energy complementary distributed microgrid energy storage coordinated control method of the present invention, wherein: the real-time monitoring of the load changes of the microgrid and the fluctuations of the renewable energy power generation and prediction refers to using the LSTM model to predict the microgrid load, using feature engineering to extract historical and real-time photovoltaic and wind power generation data features, and inputting the extracted historical data features as a training set into the LSTM model for model training, defining a loss function and an Adam optimizer to iteratively optimize the model parameters, and when the loss of the LSTM model no longer decreases significantly during the continuous iteration process, the iteration output model parameters are stopped to update the LSTM model, and the real-time photovoltaic and wind power generation data features are input into the LSTM model to obtain future photovoltaic and wind power generation data;

[0010] The ARIMA model is used to predict short-term load fluctuations of energy storage units. Historical load data is collected and input into the ARIMA model for model training. The model parameters are determined using maximum likelihood estimation. When the loss of the ARIMA model no longer decreases significantly during continuous iterations, the iteration is stopped and the model parameters are output to update the ARIMA model. The real-time load data of the energy storage unit is input into the ARIMA model to obtain future load data.

[0011] As a preferred solution of the multi-energy complementary distributed microgrid energy storage coordinated control method of the present invention, wherein: the generation of a short-term charging and discharging plan refers to calculating the power difference based on the power forecast result and the load forecast result, and determining the charging and discharging state of the energy storage system. If the power difference is greater than or equal to 0, it means that there is excess power generation, and the system will charge the energy storage unit. If the power difference is less than 0, it means that there is insufficient power generation, and the system needs to discharge the energy storage unit.

[0012] As a preferred solution of the multi-energy complementary distributed microgrid energy storage coordinated control method of the present invention, wherein: the real-time updating of the power distribution of the energy storage unit and the dynamic adjustment of the charge and discharge rate of the energy storage unit by the virtual navigator algorithm refers to calculating the total power demand that the energy storage unit needs to bear in the current time period according to the microgrid load demand predicted in the short-term discharge plan and the power of renewable energy;

[0013] Establish a communication network between energy storage units, and allocate the preliminary power demand to each energy storage unit according to the calculated total power demand and the SOC state of each energy storage unit;

[0014] Define the communication weight between energy storage unit i and energy storage unit j, select the energy storage unit with the SOC state closest to as the leader, and set the selected leader as B = 1, set the remaining energy storage units as followers, and set 0 < B ≤ 1. Each energy storage unit shares its status information through the communication network between energy storage units;

[0015] After the communication network is established, under the guidance of the leader, continuously adjust the power allocation of each energy storage unit. The power allocation update formula for each energy storage unit is:

[0016] ,

[0017] where, is the power allocation ratio of energy storage unit i at the (k + 1)-th moment, is the communication weight between energy storage unit i and j, B is the leader indication parameter, is the coupling coefficient affected by the leader, is the power allocation adjustment amount of energy storage unit i in the k-th iteration;

[0018] Each energy storage unit adjusts its power output according to the information transmitted by the communication network and the guidance of the leader for iteration. Set the iteration number threshold of power allocation as Q. When the iteration reaches the threshold Q, output the final power allocation ratio of each energy storage unit;

[0019] Convert the allocation ratio into the power allocation value of each energy storage unit through the proportional allocation algorithm;

[0020] Calculate the charge and discharge rate I of each energy storage unit according to the calculated power allocation result:

[0021] ,

[0022] where, W is the power output, A is the voltage, is the maximum state of charge of the energy storage unit, is the current state of charge of the energy storage unit;

[0023] Adjust the actual power output situation of the energy storage unit according to the calculated charge and discharge rate.

[0024] As a preferred solution of the multi-energy complementary distributed microgrid energy storage coordinated control method of the present invention, wherein: the synchronization of the voltage and frequency between different energy storage units after the charge and discharge rate adjustment refers to collecting the current frequency and voltage of each energy storage unit after completing the power distribution and the charge and discharge rate adjustment, and calculating the frequency synchronization adjustment input according to the frequency difference between energy storage unit i and energy storage unit j through the consensus algorithm. and voltage adjustment input :

[0025] ,

[0026] ,

[0027] In the formula, is the coupling weight between energy storage units i and j, and is the frequency of energy storage units i and j, is the power of the frequency difference, is the adjustment factor for the phase angle difference, is the hyperbolic tangent function, and is the phase angle of energy storage units i and j, and is the power output of energy storage units i and j, is the attenuation coefficient of power loss, and is the voltage of energy storage units i and j, is the adjustment coefficient of the voltage phase angle, and is the voltage phase angle of energy storage units i and j, is the regulation coefficient of reactive power synchronization, and is the reactive power of energy storage units i and j, is the maximum voltage adjustment range;

[0028] The frequency and voltage of the energy storage unit are adjusted in real time according to the calculated synchronization input, the frequency and voltage of the energy storage unit are updated, and the frequency and voltage between different energy storage units are gradually synchronized.

[0029] As a preferred solution of the multi-energy complementary distributed microgrid energy storage coordination control method described in the present invention, the data generated in the storage coordination process refers to real-time recording of the microgrid coordination control process to generate an operation log, and the collected energy storage unit data, photovoltaic power generation and wind power generation data and the coordinated control data generated by analysis are stored in a database, and the database is sorted in chronological order and marked with corresponding tags.

[0030] As a preferred solution of the multi-energy complementary distributed microgrid energy storage coordinated control method described in the present invention, the data backup refers to storing the data in the database while using the log file system and the memory database for synchronous data storage, setting a regular backup strategy, and regularly backing up the historical data generated daily, weekly, and monthly to off-site storage and cloud storage platforms.

[0031] A computer device comprises: a memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of a distributed microgrid energy storage coordinated control method with multi-energy complementarity are implemented.

[0032] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a distributed microgrid energy storage coordinated control method for multi-energy complementarity are implemented.

[0033] The beneficial effects of the present invention are as follows: the present invention improves the accuracy of power and load forecasting through the combination of the LSTM model and the ARIMA model, and dynamically adjusts the power allocation ratio and the charge and discharge rate of the energy storage unit through the virtual navigator algorithm to ensure the stable operation of the system under complex load fluctuations, and can adapt to real-time fluctuations in load and power generation, thereby improving the operating efficiency of the energy storage system and the stability of the entire microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0035] Figure 1 The figure is a flow chart of a distributed microgrid energy storage coordinated control method based on multi-energy complementarity. DETAILED DESCRIPTION

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0037] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0038] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0039] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a multi-energy complementary distributed microgrid energy storage coordinated control method, and the multi-energy complementary distributed microgrid energy storage coordinated control method comprises the following steps:

[0040] S1. Collect and pre-process data on renewable energy and energy storage units, monitor and predict the load changes of microgrids and fluctuations in renewable energy generation in real time, and generate short-term charging and discharging plans;

[0041] Specifically, collecting renewable energy and energy storage unit data and preprocessing it means deploying sensors to collect real-time photovoltaic and wind power generation data in the microgrid, while collecting the charge state, power output, voltage and frequency of the energy storage unit, preprocessing the collected data, removing data noise and outliers, and performing smoothing.

[0042] Through the deployment of high-precision sensors, real-time monitoring of each energy node in the microgrid can be achieved to ensure the timeliness and accuracy of data collection, thereby providing reliable basic data for energy scheduling. Sensors automatically collect data without frequent human intervention, which greatly improves the efficiency of data collection and reduces the errors that may be caused by manual operations. By removing noise and outliers, errors in sensors or communication processes are eliminated to ensure data accuracy. Data smoothing further reduces data volatility, making the system control process more stable. Preprocessed data can more realistically reflect the state of the microgrid, avoiding wrong decisions caused by data noise or errors, thereby improving the system's response speed and scheduling efficiency.

[0043] Furthermore, real-time monitoring of the load changes of the microgrid and the fluctuations of renewable energy power generation and prediction refers to using the LSTM model to predict the microgrid load, using feature engineering to extract historical and real-time photovoltaic and wind power generation data features, inputting the extracted historical data features as training sets into the LSTM model for model training, defining the loss function and Adam optimizer for iterative optimization of model parameters, stopping the iteration and outputting the model parameters to update the LSTM model when the loss of the LSTM model no longer decreases significantly during the continuous iteration process, and inputting the real-time photovoltaic and wind power generation data features into the LSTM model to obtain future photovoltaic and wind power generation data;

[0044] The ARIMA model is used to predict short-term load fluctuations of energy storage units. Historical load data is collected and input into the ARIMA model for model training. The model parameters are determined using maximum likelihood estimation. When the loss of the ARIMA model no longer decreases significantly during continuous iterations, the iteration is stopped and the model parameters are output to update the ARIMA model. The real-time load data of the energy storage unit is input into the ARIMA model to obtain future load data.

[0045] Feature engineering extracts key features from these historical and real-time data to ensure that the LSTM model can receive effective input. The combination of real-time monitoring and data extraction not only improves the real-time performance of the system, but also provides a reliable foundation for subsequent power forecasting and load scheduling. The LSTM model has the advantage of processing long-term series dependencies, so it can better capture the long-term and short-term load change trends in the microgrid. Using the historical and real-time data extracted by feature engineering, the LSTM model can accurately predict the power generation and load demand in the future. In addition, the introduction of the Adam optimizer accelerates the convergence speed of model training, ensures that the LSTM model reaches the optimal state in a shorter time, and reduces the prediction error. The ARIMA model accurately models and predicts the short-term load fluctuations of the energy storage unit, optimizes the model parameters through maximum likelihood estimation, and ensures that the model fits the historical load data best. This model can quickly identify the changes in power demand in the energy storage system, and then provide an important reference for the generation of charging and discharging plans. By combining the prediction results of the LSTM and ARIMA models, the system can generate accurate short-term charging and discharging plans to ensure that the energy storage unit is charged when there is excess power generation and discharged when there is insufficient power generation. The accuracy of the charging and discharging plan directly determines the operating efficiency of the energy storage system and the rational allocation of power resources.

[0046] Furthermore, generating a short-term charging and discharging plan means calculating the power difference based on the power forecast results and the load forecast results to determine the charging and discharging status of the energy storage system. If the power difference is greater than or equal to 0, it means that there is excess power generation and the system will charge the energy storage unit. If the power difference is less than 0, it means that there is insufficient power generation and the system needs to discharge the energy storage unit.

[0047] Through load forecasting, the growth of future load demand can be known in advance. The energy storage system can schedule the charging state of the energy storage unit in advance according to the forecast results to ensure that there is enough electricity available during the peak load period. By calculating the power difference, the energy storage system can determine in real time whether it is necessary to store excess electricity or release the stored energy to supplement the insufficient load demand. When the power difference is positive, the energy storage system will automatically enter the charging state and store the excess electricity, avoiding energy waste caused by excess power generation. When the power difference is negative, the energy storage system enters the discharging state, releasing the stored energy to make up for the insufficient power supply and ensure that the load demand is continuously and stably met. Through the real-time calculation of the power difference, the dynamic adjustment of the energy storage system is realized, so that the microgrid system can cope with rapid load changes and power generation fluctuations, and improve the response speed of the system. The short-term charging and discharging plan predicts future power generation and load demand, and reasonably plans the working state of the energy storage unit, avoiding frequent charging and discharging of the energy storage system in unnecessary periods, extending the service life of the equipment, and improving energy utilization efficiency.

[0048] S2. Use the virtual navigator algorithm to adjust the power allocation ratio of the energy storage unit in real time and dynamically adjust the charge and discharge rate of the energy storage unit, and synchronize the voltage and frequency between different energy storage units after the charge and discharge rate is adjusted;

[0049] Specifically, the real-time update of the power distribution of the energy storage unit and the dynamic adjustment of the charge and discharge rate of the energy storage unit through the virtual navigator algorithm refers to the calculation of the total power demand that the energy storage unit needs to bear in the current time period based on the microgrid load demand and the power of renewable energy predicted in the short-term discharge plan:

[0050] ,

[0051] Where D is the total power demand that the energy storage unit needs to bear at time t, is the load demand at time t, indicating the total power consumption of the microgrid during this period, and is the predicted value of photovoltaic and wind power generation at time t;

[0052] Establishing a communication network between the energy storage units, and allocating preliminary power requirements to each energy storage unit according to the calculated total power requirement and the SOC state of each energy storage unit;

[0053] Define the communication weight between energy storage unit i and energy storage unit j. (The size of the communication weight depends on factors such as the physical distance between the energy storage units, device performance, and the difference in their respective SOC states, ensuring that more information is transmitted between energy storage units that are close or have similar states.) Select the energy storage unit with the closest SOC state. The energy storage unit is the leader, and the selected leader is set to B = 1. The remaining energy storage units are set as followers with 0 < B ≤ 1. Each energy storage unit shares its status information through the communication network among the energy storage units;

[0054] When the energy storage unit i is the leader, its influence on power distribution is completely dominant. When the energy storage unit i is a follower, the influence of B is weak, but it can still make reasonable power distribution adjustments through the reference information of the virtual leader and communication with neighboring units;

[0055] After the communication network is established, according to the guidance of the leader, continuously adjust the power distribution of each energy storage unit. The power distribution update formula for each energy storage unit is:

[0056] ,

[0057] In the formula, is the power distribution ratio of the energy storage unit i at the k + 1th time, is the communication weight between the energy storage units i and j, indicating the influence degree of the information received by the energy storage unit i from j, which is allocated through the network algorithm. B is the leader indication parameter, indicating whether the energy storage unit i is the leader, is the coupling coefficient of the leader's influence, indicating the influence degree of the leader on the follower energy storage units. It is used to control the guiding strength of the leader on other energy storage units in the system and is usually set by the system design and adjusted according to the overall load status and scheduling requirements of the system, is the power distribution adjustment amount of the energy storage unit i in the kth iteration, indicating the change amount of the power output of the energy storage unit. Through iterative calculation, it is dynamically adjusted each time according to real-time information such as the SOC state, load demand, and power generation prediction of the energy storage unit;

[0058] Each energy storage unit adjusts its power output according to the information transmitted by the communication network and the guidance of the leader for iteration. The iteration times threshold for power distribution is set to Q by the gradient descent method. When the iteration reaches the threshold Q, the final power distribution ratio of each energy storage unit is output;

[0059] Convert the distribution ratio into the power distribution value of each energy storage unit through the proportional distribution algorithm (simple proportional algorithm);

[0060] Calculate the charge and discharge rate I of each energy storage unit according to the calculated power distribution result:

[0061] ,

[0062] In the formula, W is the power output, A is the voltage, is the maximum state of charge of the energy storage unit, is the current state of charge of the energy storage unit;

[0063] The actual power output of the energy storage unit is adjusted according to the calculated charge and discharge rates.

[0064] Through the virtual navigator algorithm, the system can reasonably allocate the power output of each energy storage unit according to the real-time load demand and the SOC status of the energy storage unit. Compared with the traditional static allocation method, the virtual navigator algorithm has the characteristics of dynamic response, which can quickly adjust the power allocation ratio when the load fluctuates and the renewable energy generation changes, thereby effectively reducing the power imbalance phenomenon. This dynamic adjustment mechanism improves the collaboration between energy storage units and optimizes the operation efficiency of the power grid. It is especially suitable for scenarios with large fluctuations in renewable energy. The virtual navigator algorithm considers the SOC status of each energy storage unit when selecting power allocation. By allocating loads to energy storage units with a more moderate SOC status, the system can avoid excessive use or idleness of some energy storage units, thereby balancing the charging and discharging behavior of each unit. This allocation strategy significantly reduces the frequent charging and discharging operations of a single energy storage unit, extends the service life of the energy storage equipment, and reduces the maintenance and replacement costs. Since each energy storage unit in the system shares status information and adjusts power allocation through the communication network, the energy storage system can achieve stronger fault tolerance. When the state of a certain energy storage unit is abnormal or the power output fluctuates, other units can make adjustments quickly based on the shared information to ensure the power stability of the entire microgrid. This distributed coordinated control method reduces the dependence on a single node, improves the robustness of the system, and avoids the risk of system crash caused by single point failure. Through multiple iterations of the power allocation update formula, the system can achieve accurate power allocation between multiple energy storage units. Specifically, the communication weight in the power allocation formula reflects the collaborative relationship between the energy storage units, and the guiding role of the navigator ensures that the power allocation of the entire system is gradually optimized. This precise allocation strategy ensures that the power output of the energy storage unit accurately matches the actual load demand, avoids energy waste, and improves the accuracy and efficiency of energy management. The charge and discharge rate of each energy storage unit is further determined by calculating the power allocation results. The charge and discharge rate of each energy storage unit can be accurately calculated based on the power output and current voltage of the energy storage unit, as well as the difference between the maximum state of charge and the current state of charge. This dynamic adjustment method based on power demand and SOC status not only optimizes the energy flow of the energy storage unit, but also prevents the energy storage unit from overcharging or discharging, thereby protecting the health of the battery. It can dynamically adjust power distribution and charging and discharging rates in real time, reducing power waste, and reasonably allocating power demand during load peaks, thereby improving the utilization rate of renewable energy.

[0065] Furthermore, synchronizing the voltage and frequency between different energy storage units after the charge and discharge rate adjustment means collecting the current frequency and voltage of each energy storage unit after completing the power distribution and charge and discharge rate adjustment, and calculating the adjustment input of the frequency synchronization according to the frequency difference between energy storage unit i and energy storage unit j through the consensus algorithm. and voltage adjustment input :

[0066] ,

[0067] ,

[0068] In the formula, is the coupling weight between energy storage units i and j, indicating the information exchange intensity between nodes, and is the frequency of energy storage units i and j, It is the power of the frequency difference, used to adjust the nonlinear response of the frequency difference, and is determined through system experiments or debugging. It is the adjustment coefficient of the phase angle difference, which is used to control the influence of the phase angle difference on the frequency adjustment and is determined through experimental optimization. is the hyperbolic tangent function, dealing with phase angle differences, and is the phase angle of energy storage units i and j, collected by a phase meter, and is the power output of energy storage units i and j, is the attenuation coefficient of power loss, which is used to control the influence of power difference on frequency synchronization and is determined by experiment or design. and is the voltage of energy storage units i and j, which is the main variable for voltage synchronization. is the voltage phase angle adjustment coefficient, which controls the impact of voltage phase angle difference on synchronous adjustment. and is the voltage phase angle of energy storage units i and j, collected by the voltage phase angle sensor, It is the adjustment coefficient of reactive power synchronization, which is used to adjust the impact of reactive power difference on voltage synchronization. and is the reactive power of energy storage units i and j, max(0,...) ensures that the voltage synchronization adjustment value is not negative to prevent excessive voltage drop. Make sure the adjustment value does not exceed the maximum voltage adjustment range allowed by the system , avoid over-tuning the system, is the maximum voltage adjustment range;

[0069] Frequency synchronization in existing technologies mainly relies on consensus algorithms, which gradually adjust the frequencies of energy storage units i and j by comparing the frequency difference between them until consistency is reached:

[0070] ,

[0071] This method uses a simple linear frequency difference to make adjustments. The frequency synchronization process responds to the frequency difference more simply. In the case of a large frequency difference, the adjustment process may be too drastic, resulting in oscillation or system instability. In order to improve the linear response problem to the frequency difference in the prior art, a power response term is introduced. , which is used to perform nonlinear processing on the frequency difference, making the system smoother for small frequency fluctuations, and speeding up the synchronization process for large frequency differences, avoiding long-term frequency imbalance and phase angle differences between energy storage units. It will also affect the synchronization speed of the system. Therefore, by introducing the hyperbolic tangent function tanh() to deal with the phase angle difference, more flexible synchronization adjustment can be achieved. In the distributed energy storage system, the power output difference of the energy storage unit It will also affect the frequency synchronization process. In order to further optimize the synchronization process, an exponential decay term related to the power difference is added to the formula: By introducing dynamic adjustment of power difference, frequency synchronization not only considers frequency and phase angle differences, but also adjusts synchronization speed according to power difference. This helps the system to quickly adjust frequency when load fluctuates and maintain system stability;

[0072] In the prior art, voltage synchronization control is usually achieved through the following consensus algorithm:

[0073] ,

[0074] This is a linear formula. The core idea is to calculate the voltage difference of each energy storage unit. Perform weighted average adjustment to ensure that the voltages between energy storage units are consistent. Each energy storage unit adjusts its own voltage output according to the voltage difference with other units. The processing of voltage difference and reactive power difference is too simple and cannot provide accurate control in a complex fluctuating environment. Therefore, a more flexible adjustment mechanism needs to be introduced to enable the energy storage unit to adjust adaptively according to system changes.

[0075] To avoid over-adjustment when the voltage difference is large, we introduce an exponential decay term of the phase angle difference This item is used to smooth the adjustment amplitude, so that when the phase angle difference is large, the adjustment is more gentle, avoiding the system oscillation problem caused by large phase angle difference;

[0076] In existing energy storage control technologies, simple linear or weighted sum methods are usually used to deal with reactive power differences. ,Reactive power regulation is an important part of voltage stability, but the processing means are usually relatively simple. ,Use exponential function to deal with reactive power difference. ,When the reactive power difference is small, the adjustment amplitude is large to ensure that the system can respond quickly. ,When the reactive power difference is large, the adjustment amplitude is gradually reduced to prevent unnecessary ,overadjustment due to large reactive power difference. ,In traditional voltage synchronization control, the range of adjustment value usually has no strict ,upper and lower limits, or is limited in the form of a simple saturation function. ,We use the control mechanism of upper and lower limits to ensure that the adjustment value is within ,a safe range and avoid negative adjustment;

[0077] The frequency and voltage of the energy storage unit are adjusted in real time according to the calculated synchronization input, the frequency and voltage of the energy storage unit are updated, and the frequency and voltage between different energy storage units are gradually synchronized.

[0078] Since the frequency and voltage of the energy storage unit are synchronized, the imbalance in the power grid is reduced, the overall stability of the system is improved, and the voltage and frequency instability caused by power fluctuations is avoided. By optimizing the synchronization process between energy storage units, the energy loss in power transmission is reduced. Synchronous voltage and frequency make the energy transmission between energy storage units more efficient, reduce the excessive consumption of reactive power, and improve the energy efficiency of the system. By synchronizing voltage and frequency, the energy storage unit can be more coordinated when responding to the load demand of the power grid, reducing the competition and conflict between the units in the power regulation process, and effectively improving the response speed and efficiency. By considering multiple parameters such as frequency difference and phase angle for calculation, the accuracy of frequency and voltage adjustment input is ensured, making the adjustment of energy storage units more flexible and efficient. This method effectively reduces the error caused by single parameter adjustment in traditional methods. Since each energy storage unit makes autonomous adjustments according to the real-time situation of frequency and voltage, the system has stronger robustness. In the face of load fluctuations or energy storage unit failures, adjustments can be made quickly to ensure stable operation of the system. By calculating coupling weights and frequency phase angles, the energy storage units can autonomously coordinate their own frequencies and voltages, avoiding excessive dependence on the central controller and improving the system's distributed control capabilities. By adjusting reactive power and voltage phase angles, voltage consistency between different energy storage units is ensured, avoiding system instability problems caused by voltage fluctuations, especially in the case of high load demand fluctuations, ensuring grid voltage stability. Reasonable adjustment of reactive power can not only ensure voltage stability, but also improve the power quality of the entire system, reduce the impact of voltage spikes and power fluctuations on the load, and ensure the system's efficient response to load changes. The synchronization of reactive power and the adjustment of voltage phase angles enhance the coordination between energy storage units, enabling them to achieve more stable energy management in a complex microgrid environment.

[0079] S3, stores the data generated during the coordination process and performs data backup;

[0080] Specifically, storing the data generated during the coordination process refers to real-time recording of the microgrid coordination control process to generate an operation log, storing the collected energy storage unit data, photovoltaic power generation and wind power generation data, and the coordinated control data generated by the analysis into a database, and sorting the database in chronological order and marking the corresponding tags.

[0081] The control data generated during the coordinated control process reflects the dynamic dispatching of each energy storage unit, photovoltaic and wind power generation unit during the operation of the microgrid. Storing the data in the database helps to preserve the historical dispatching strategy, which is of great reference value for optimizing future dispatching decisions. Through the analysis of historical coordinated control data, the system can discover and predict potential problems in future operation and formulate more efficient dispatching strategies based on past experience. The time-ordered storage method ensures the traceability of the data. The operation of the microgrid is a highly dynamic process. The data in different time periods reflect the operating status of the system under different load conditions. Through time-ordered sorting, data analysts can quickly locate the system behavior within a certain period of time and evaluate the effect of the dispatching strategy. In addition, label storage improves the retrieval efficiency of data. Labels can be flexibly configured according to dimensions such as data category, source, and time, allowing the system to quickly classify and call data.

[0082] Furthermore, data backup means storing the data in the database while using the log file system and memory database to synchronize the data, setting up regular backup strategies, and regularly backing up the historical data generated daily, weekly, and monthly to off-site storage and cloud storage platforms.

[0083] The log file system records the changes of each operation to ensure that when a system failure occurs, it can be restored to a safe state by rolling back the log, reducing the possibility of data loss. At the same time, the introduction of the in-memory database further improves the data processing speed, which is especially suitable for real-time monitoring and high-frequency data writing scenarios. By synchronizing data storage, the system can ensure that data will not be lost even in the event of a crash or network interruption, thereby improving the robustness of the system. By setting a regular backup strategy, the system can back up the data generated daily, weekly, and monthly at different frequencies, ensuring the historical integrity of the data and the security of long-term storage. Regular backup can reduce the loss of historical data due to unexpected situations, and reduce the risk of human operation through automated backup processes. Especially in distributed systems, regular backup strategies can reduce data consistency issues caused by system updates or failures.

[0084] Embodiment 2 is the second embodiment of the present invention. This embodiment is different from the previous embodiment in that:

[0085] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0086] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0087] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0088] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A multi-energy complementary distributed microgrid energy storage coordinated control method, characterized by: include, Collect and pre-process data on renewable energy and energy storage units, monitor and predict the load changes of microgrids and fluctuations in renewable energy generation in real time, and generate short-term charging and discharging plans; The virtual navigator algorithm is used to adjust the power distribution ratio of the energy storage unit in real time and dynamically adjust the charge and discharge rate of the energy storage unit. After the charge and discharge rate is adjusted, the voltage and frequency between different energy storage units are synchronized. Store the data generated during the coordination process and perform data backup; The collecting of renewable energy and energy storage unit data and preprocessing thereof refers to deploying sensors to collect photovoltaic power generation and wind power generation data in the microgrid in real time, and collecting the state of charge, power output, voltage and frequency of the energy storage unit at the same time, preprocessing the collected data, removing data noise and abnormal values, and performing smoothing; The real-time monitoring of the load change of the microgrid and the fluctuation of the renewable energy power generation and prediction refers to using the LSTM model to predict the microgrid load, using feature engineering to extract historical and real-time photovoltaic and wind power generation data features, inputting the extracted historical data features as a training set into the LSTM model for model training, defining a loss function and an Adam optimizer to iteratively optimize the model parameters, stopping the iteration and outputting the model parameters to update the LSTM model when the loss of the LSTM model no longer decreases significantly during the continuous iteration process, and inputting the real-time photovoltaic and wind power generation data features into the LSTM model to obtain future photovoltaic and wind power generation data; Use the ARIMA model to predict short-term load fluctuations of energy storage units. Collect historical load data and input it into the ARIMA model for model training. Use maximum likelihood estimation to determine the model parameters. When the loss of the ARIMA model no longer decreases significantly during continuous iterations, stop iterating and output model parameters to update the ARIMA model. Input the real-time load data of the energy storage unit into the ARIMA model to obtain future load data. Generating a short-term charge and discharge plan refers to calculating the power difference based on the power forecast result and the load forecast result, and determining the charge and discharge status of the energy storage system. If the power difference is greater than or equal to 0, it means that there is excess power generation, and the system will charge the energy storage unit. If the power difference is less than 0, it means that there is insufficient power generation, and the system needs to discharge the energy storage unit. The real-time updating of the power distribution of the energy storage unit and the dynamic adjustment of the charge and discharge rate of the energy storage unit by the virtual navigator algorithm refers to calculating the total power demand that the energy storage unit needs to bear in the current time period according to the microgrid load demand predicted in the short-term discharge plan and the power of renewable energy; Establishing a communication network between the energy storage units, and allocating preliminary power requirements to each energy storage unit according to the calculated total power requirement and the SOC state of each energy storage unit; Define the communication weight between energy storage unit i and energy storage unit j, and select the energy storage unit with the SOC state closest to as the leader, and set the selected leader to B = 1, and set the remaining energy storage units as followers, and set 0 < B ≤ 1. Each energy storage unit shares state information through the communication network between energy storage units; After the communication network is established, the power allocation of each energy storage unit is continuously adjusted according to the guidance of the navigator. The power allocation update formula of each energy storage unit is: ， In the formula, is the power allocation ratio of energy storage unit i at the k+1th moment, is the communication weight between energy storage units i and j, B is the pilot indicator parameter, is the coupling coefficient of the pilot effect, is the power allocation adjustment of energy storage unit i in the kth iteration; Each energy storage unit adjusts its power output according to the information transmitted by the communication network and the guidance of the navigator, and sets the iteration number threshold of power allocation to Q. When the iteration reaches the threshold Q, the final power allocation ratio of each energy storage unit is output; The allocation ratio is converted into a power allocation value for each energy storage unit through a proportional allocation algorithm; Calculate the charge and discharge rate I of each energy storage unit according to the calculated power distribution result: ， Where W is the power output, A is the voltage, is the maximum state of charge of the energy storage unit, is the current state of charge of the energy storage unit; Adjust the actual power output of the energy storage unit according to the calculated charge and discharge rate; The voltage and frequency synchronization between different energy storage units after the charge and discharge rate adjustment refers to collecting the current frequency and voltage of each energy storage unit after completing the power distribution and charge and discharge rate adjustment, and calculating the adjustment input of the frequency synchronization according to the frequency difference between energy storage unit i and energy storage unit j through the consensus algorithm. and voltage adjustment input : ， ， In the formula, is the coupling weight between energy storage units i and j, and is the frequency of energy storage units i and j, is the power of the frequency difference, is the adjustment factor for the phase angle difference, is the hyperbolic tangent function, and is the phase angle of energy storage units i and j, and is the power output of energy storage units i and j, is the attenuation coefficient of power loss, and is the voltage of energy storage units i and j, is the adjustment coefficient of the voltage phase angle, and is the voltage phase angle of energy storage units i and j, is the regulation coefficient of reactive power synchronization, and is the reactive power of energy storage units i and j, is the maximum voltage adjustment range; The frequency and voltage of the energy storage unit are adjusted in real time according to the calculated synchronization input, the frequency and voltage of the energy storage unit are updated, and the frequency and voltage between different energy storage units are gradually synchronized.

2. The multi-energy complementary distributed microgrid energy storage coordinated control method according to claim 1, characterized in that: The data generated during the storage and coordination process refers to real-time recording of the microgrid coordinated control process to generate an operation log, storing the collected energy storage unit data, photovoltaic power generation and wind power generation data and the coordinated control data generated by analysis into a database, which is sorted in chronological order and marked with corresponding tags.

3. The multi-energy complementary distributed microgrid energy storage coordinated control method according to claim 2, characterized in that: The data backup mentioned above refers to storing the data in the database while using the log file system and the memory database to synchronize the data, setting a regular backup strategy, and regularly backing up the historical data generated daily, weekly, and monthly to off-site storage and cloud storage platforms.

4. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the multi-energy complementary distributed microgrid energy storage coordination control method described in any one of claims 1 to 3 are implemented.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-energy complementary distributed microgrid energy storage coordinated control method described in any one of claims 1 to 3 are implemented.

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