Emergency energy storage power station configuration method and system for micro-grid group

Abstract

The invention provides an emergency energy storage power station configuration method and system for a micro-grid group, and relates to the technical field of micro-grid intelligent scheduling, and the method comprises the steps: monitoring the first historical power utilization load data of a first micro-grid in a target micro-grid group, and carrying out the power utilization prediction, and obtaining a first power utilization load prediction sequence; performing power generation capability prediction based on the meteorological prediction data to obtain a first power generation prediction sequence; performing energy storage demand analysis to generate a first energy storage demand sequence; acquiring a plurality of conventional energy storage state parameters of the plurality of conventional energy storage power stations and emergency energy storage state parameters of the emergency energy storage power stations; and carrying out energy storage scheduling balance analysis on the adjacent micro-grid, and carrying out energy storage configuration of the emergency energy storage power station when the analysis result is unbalanced. According to the invention, the technical problem that the reliability of overall power supply is influenced because most of energy storage configurations are scheduled based on the demand of a single micro-grid and a dynamic intelligent scheduling capability across micro-grid groups is lacked in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid intelligent dispatching, and in particular to a method and system for configuring an emergency energy storage power station for a microgrid group. Background Art

[0002] Microgrids are usually composed of distributed energy, energy storage systems and loads. They can operate independently when connected to or disconnected from the main grid. They have high flexibility and reliability, can provide power supply in the local area, and adjust the balance of power supply and demand. However, existing technologies mostly rely on fixed energy storage configuration strategies and lack intelligent scheduling functions for microgrid groups. Specifically, microgrid intelligent scheduling has not been able to fully utilize the advantages of multi-microgrid collaboration, resulting in low scheduling efficiency, especially in emergency situations, unable to quickly respond to the demand for insufficient energy storage, resulting in unstable power supply; moreover, most of the energy storage configurations in existing technologies are based on the needs of a single microgrid for scheduling, lacking dynamic scheduling capabilities across microgrid groups. When a microgrid has an energy storage imbalance, traditional methods often cannot timely deploy emergency energy storage power stations to make up for the energy storage gap. This delayed emergency energy storage configuration easily leads to the microgrid not being able to receive timely support when the load is too high, thereby affecting the reliability of the overall power supply. Summary of the invention

[0003] This application provides an emergency energy storage power station configuration method and system for microgrid groups, aiming to solve the technical problem that most of the energy storage configurations in the prior art are scheduled based on the needs of a single microgrid, lack dynamic intelligent scheduling capabilities across microgrid groups, and thus affect the reliability of the overall power supply.

[0004] In a first aspect disclosed in the present application, a method for configuring an emergency energy storage power station for a microgrid group is provided, the method comprising: monitoring first historical power load data of a first microgrid in a target microgrid group within a preset historical time, and performing power consumption forecasting to obtain a first power load forecast sequence within a preset future time; an interactive meteorological platform, obtaining meteorological forecast data within a preset future time, and predicting the power generation capacity of the first microgrid based on the meteorological forecast data to obtain a first power generation forecast sequence; performing time alignment and energy storage demand analysis on the first power load forecast sequence and the first power generation forecast sequence to generate a first energy storage demand sequence, and so on, obtaining multiple energy storage demand sequences of multiple microgrids in the target microgrid group; obtaining multiple conventional energy storage state parameters of multiple conventional energy storage power stations of the multiple microgrids, and emergency energy storage state parameters of the emergency energy storage power station of the target microgrid group; performing energy storage scheduling balance analysis of adjacent microgrids based on the multiple energy storage demand sequences and the multiple conventional energy storage state parameters, and when the analysis result is unbalanced, performing energy storage configuration of the emergency energy storage power station based on the emergency energy storage state parameters.

[0005] In a second aspect disclosed in the present application, a system for configuring an emergency energy storage power station for a microgrid group is provided, and the system is used for the above-mentioned method for configuring an emergency energy storage power station for a microgrid group, and the system includes: a power consumption prediction module, which is used to monitor the first historical power load data of the first microgrid in the target microgrid group within a preset historical time, and to perform power consumption prediction to obtain a first power load prediction sequence within a preset future time; a power generation capacity prediction module, which is used to interact with the meteorological platform, obtain meteorological forecast data within a preset future time, and perform power generation capacity prediction of the first microgrid based on the meteorological forecast data to obtain a first power generation prediction sequence; an energy storage demand analysis module, which is used to analyze the first power load The load prediction sequence and the first power generation prediction sequence are time aligned and the energy storage demand analysis is performed to generate a first energy storage demand sequence, and so on, to obtain multiple energy storage demand sequences of multiple microgrids in the target microgrid group; an energy storage state parameter acquisition module is used to obtain multiple conventional energy storage state parameters of multiple conventional energy storage power stations in the multiple microgrids, and emergency energy storage state parameters of the emergency energy storage power station in the target microgrid group; an energy storage configuration module is used to perform energy storage scheduling balance analysis of adjacent microgrids according to the multiple energy storage demand sequences and the multiple conventional energy storage state parameters, and when the analysis result is unbalanced, perform energy storage configuration of the emergency energy storage power station based on the emergency energy storage state parameters.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects:

[0007] By accurately monitoring the power load and power generation capacity of the target microgrid group, combining historical data and meteorological forecast data to analyze energy storage demand, the power distribution between microgrids can be optimized and the dispatching efficiency and flexibility of the microgrid group can be improved. By predicting the power load and power generation capacity of multiple microgrids and analyzing the energy storage demand based on the predicted results, the future energy storage demand sequence can be effectively predicted, which provides a reliable basis for the intelligent dispatching of microgrids and ensures that the energy storage system can meet the needs of different microgrids. By obtaining the conventional energy storage state parameters of multiple microgrids and the state parameters of the emergency energy storage power station, the energy storage dispatch balance analysis of adjacent microgrids is carried out to ensure the balance of power between the microgrids and improve the stability and reliability of the microgrid group. When the result of the energy storage dispatch balance analysis is unbalanced, reasonable energy storage configuration is carried out based on the state parameters of the emergency energy storage power station. By timely deploying the emergency energy storage power station, it is ensured that when the energy storage demand cannot be met by the conventional energy storage power station, additional power support is provided, thereby avoiding power outages caused by insufficient energy storage in the microgrid group and improving the emergency response capability of the system.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic flow chart of a method for configuring an emergency energy storage power station for a microgrid group is provided in an embodiment of the present application.

[0010] Figure 2 A schematic diagram of the structure of an emergency energy storage power station configuration system for a microgrid group provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: power consumption prediction module 10 , power generation capacity prediction module 20 , energy storage demand analysis module 30 , energy storage state parameter acquisition module 40 , energy storage configuration module 50 . DETAILED DESCRIPTION

[0012] The embodiments of the present application provide a method and system for configuring an emergency energy storage power station for a microgrid group, thereby solving the technical problem that most energy storage configurations in the prior art are scheduled based on the needs of a single microgrid, lack dynamic intelligent scheduling capabilities across microgrid groups, and thus affect the reliability of the overall power supply.

[0013] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] Embodiment 1, as Figure 1 As shown, an embodiment of the present application provides a method for configuring an emergency energy storage power station for a microgrid group, the method comprising:

[0015] Monitor the first historical power load data of the first microgrid in the target microgrid group within a preset historical time, and perform power consumption forecasting to obtain a first power load forecast sequence within a preset future time.

[0016] Collect the power load data of the first microgrid in the target microgrid group within the preset historical time. These data can be time series data collected from electric meters or other smart devices, usually recorded in units of hours, days or months. The historical data contains the actual power consumption of the first microgrid in the past period of time, including voltage, current, power and other parameters. According to the collected historical power load data, estimate the future power load, for example, perform time series analysis, such as using ARIMA (autoregressive integrated moving average model), LSTM (long short-term memory network), etc. These methods can predict based on the trend of historical data and output the first power load prediction sequence of the future time period, that is, the power load data expected at each moment in a certain period of time in the future.

[0017] The interactive meteorological platform obtains meteorological forecast data within a preset future time, and predicts the power generation capacity of the first microgrid based on the meteorological forecast data to obtain a first power generation forecast sequence.

[0018] The interactive meteorological platform obtains meteorological forecast data for a period of time in the future. The meteorological forecast data includes factors such as temperature, humidity, wind speed, and solar radiation. These data are crucial for the prediction of renewable energy sources such as wind and solar power generation capacity. According to the meteorological forecast data, an appropriate power generation capacity prediction model is used to estimate the power generation capacity of the first microgrid. For example, the power generation of solar panels is predicted using information such as solar radiation and temperature in the meteorological data. Generally, solar power generation is directly related to the intensity of solar radiation and is affected by factors such as weather and time. Based on the wind speed data provided by the meteorological platform, the power generation of wind turbines is estimated. Wind power generation capacity is usually proportional to the cube of the wind speed. The first power generation prediction sequence for the future time period is output by prediction, that is, the power generation capacity data at each moment in a certain period of time in the future.

[0019] The first power load forecast sequence and the first power generation forecast sequence are time aligned and analyzed for energy storage demand to generate a first energy storage demand sequence, and so on to obtain multiple energy storage demand sequences of multiple microgrids in the target microgrid group.

[0020] Choose a suitable time granularity, usually hourly or half-hourly. For microgrids, hourly data is usually reasonable. If the time granularity of power load data and power generation data is inconsistent, interpolation or aggregation methods can be used to convert the data so that the time points of the two are completely consistent. After the data has the same time granularity, time alignment is performed, that is, the values ​​of power load data and power generation forecast data at each time point are matched to form a synchronized data pair.

[0021] According to the difference between the power load and the power generation at each moment, it is determined whether the energy storage system needs to be involved. There are two specific situations. When the power load at a certain moment is greater than the power generation, it means that the microgrid needs to discharge from the energy storage system to meet the power demand. In this case, the energy storage demand is the difference between the power load and the power generation; when the power generation at a certain moment is greater than the power load, it means that the microgrid has surplus power to store. In this case, the energy storage demand is the difference between the power generation and the power load. For each time point, the difference between the power load and the power generation capacity is calculated to generate the corresponding energy storage demand. By calculating the energy storage demand at each time point, a complete first energy storage demand sequence is formed.

[0022] Similar processing is performed on the power load forecast sequences and power generation forecast sequences of all microgrids to generate multiple energy storage demand sequences. These energy storage demand sequences will provide a basis for subsequent energy storage configuration and scheduling, ensuring that the microgrid group can achieve a smooth and reliable power supply in emergency situations.

[0023] Acquire a plurality of conventional energy storage state parameters of a plurality of conventional energy storage power stations of the plurality of microgrids, and an emergency energy storage state parameter of an emergency energy storage power station of the target microgrid group.

[0024] The microgrid group includes multiple microgrids, each of which has a corresponding conventional energy storage power station for supplying local power storage or power generation to the microgrid. Conventional energy storage status parameters include remaining power and remaining capacity, where the remaining power is the current available power of the energy storage battery, and the remaining capacity is the remaining energy storage capacity of the energy storage battery. The microgrid group also includes an emergency energy storage power station, which is mainly used to supplement energy when the conventional energy storage power station is insufficient. The emergency energy storage status parameters include remaining power and remaining capacity, where the remaining power is the power available for discharge in the emergency energy storage power station, and the remaining capacity is the remaining energy storage capacity of the emergency energy storage power station.

[0025] An energy storage dispatch balance analysis of adjacent microgrids is performed according to the multiple energy storage demand sequences and the multiple conventional energy storage state parameters. When the analysis result is unbalanced, energy storage configuration of the emergency energy storage power station is performed based on the emergency energy storage state parameters.

[0026] Adjacent microgrids refer to microgrids that are adjacent to the target microgrid and interconnected through transmission lines. These microgrids can balance energy storage scheduling through electric energy exchange. By comparing the energy storage demand sequence of the target microgrid, that is, the difference between electricity load and power generation capacity, with the energy storage status of the adjacent microgrid, a scheduling balance analysis is performed. Specifically, the energy storage demand of the current target microgrid is calculated based on the difference in electricity load and power generation, and the energy storage status of the adjacent microgrid is determined, including the remaining power and remaining capacity. The energy storage demand is compared with the energy storage supply capacity of the adjacent microgrid to determine whether it can be scheduled through the adjacent microgrid. When the energy storage demand of the target microgrid exceeds the remaining power of the conventional energy storage power station, energy can be transferred from the adjacent microgrid; conversely, if the energy storage demand of the target microgrid is lower than its conventional energy storage power, the excess power may be stored in the adjacent microgrid.

[0027] When the energy storage dispatch analysis results show that the target microgrid cannot meet the demand through the conventional energy storage power station dispatch of the adjacent microgrid, it means that there is an imbalance in energy storage. Usually, when the energy storage deviation value exceeds the set threshold, it is considered to be a dispatch imbalance. In this case, based on the remaining power and remaining capacity of the emergency energy storage power station, the emergency energy storage power station is started, that is, the electric energy in the emergency energy storage power station is allocated to the target microgrid to make up for the energy storage gap. Through these steps, the microgrid group can realize intelligent and dynamic energy storage dispatch, ensuring that conventional energy storage equipment is used first under normal circumstances, and relying on the emergency energy storage power station to ensure the stability of power supply in emergency situations.

[0028] Furthermore, the method of performing time alignment and energy storage demand analysis on the first power load forecast sequence and the first power generation forecast sequence to generate a first energy storage demand sequence includes:

[0029] The first electricity load forecast sequence and the first power generation forecast sequence are time-aligned. When the first electricity load forecast data at any moment is greater than the first power generation forecast data, a discharge-type energy storage demand is generated, wherein the discharge-type energy storage demand includes a discharge demand amount, and the discharge demand amount is the difference between the first electricity load forecast data and the first power generation forecast data; when the first electricity load forecast data at any moment is less than or equal to the first power generation forecast data, a storage-type energy storage demand is generated, wherein the storage-type energy storage demand includes a storage demand amount, and the storage demand amount is the difference between the first power generation forecast data and the first electricity load forecast data; based on the discharge-type energy storage demand and the storage-type energy storage demand, the first energy storage demand sequence is generated.

[0030] For the power load forecast sequence and power generation forecast sequence of the first microgrid, it is first necessary to ensure that they have the same time granularity and perform time alignment, that is, at each moment, ensure that the power load forecast data and the power generation forecast data can correspond one to one. After time alignment, the power load and power generation at each moment can be compared to determine whether there is a demand for energy storage.

[0031] When the electricity load forecast data at a certain moment is greater than the power generation forecast data, it means that the electricity consumption at that moment exceeds the power generation, so it is necessary to release electricity from the energy storage device to meet the load. In this case, a discharge-type energy storage demand is generated and the discharge demand is calculated. The discharge demand is the difference between the electricity load forecast data and the power generation forecast data, that is, the amount of electricity that the energy storage device needs to release at that moment is the difference between the electricity load and the power generation. This discharge-type energy storage demand indicates that the energy storage battery needs to be mobilized to provide power at that moment.

[0032] When the electricity load forecast data at a certain moment is less than or equal to the power generation forecast data, it means that the power generation at that moment is greater than or equal to the power load, and the remaining electricity can be stored. In this case, a storage-type energy storage demand is generated and the storage demand is calculated. The storage demand is the difference between the power generation forecast data and the power load forecast data, that is, the amount of electricity that the energy storage system needs to store at that moment is the difference between the power generation and the power load. This storage-type energy storage demand indicates that the remaining electricity needs to be stored in the energy storage device.

[0033] The energy storage demand at each moment is classified to generate discharge-type and storage-type energy storage demand. If there is a discharge-type demand at a certain moment, the discharge demand is recorded; if there is a storage-type demand at a certain moment, the storage demand is recorded. The energy storage demand at each moment is arranged in chronological order to generate a complete first energy storage demand sequence. This sequence represents the change in the amount of storage required by the microgrid during the entire forecast period. The energy storage demand sequence will serve as the basis for subsequent energy storage scheduling and configuration, helping to determine whether energy storage equipment needs to be mobilized to ensure a stable energy supply for the microgrid.

[0034] Furthermore, the method of performing energy storage dispatch balance analysis of adjacent microgrids according to the multiple energy storage demand sequences and the multiple conventional energy storage state parameters includes:

[0035] Acquire multiple adjacent microgrids of a first microgrid, wherein the multiple adjacent microgrids belong to a target microgrid group; match preset scheduling constraint rules according to a first energy storage demand sequence and a first conventional energy storage state parameter of the first microgrid, and multiple adjacent energy storage demand sequences and multiple adjacent conventional energy storage state parameters of the multiple adjacent microgrids; perform energy storage scheduling based on the preset scheduling constraint rules to obtain a first energy storage scheduling result, wherein the first energy storage scheduling result includes an energy storage deviation value; when the energy storage deviation value is greater than an energy storage deviation threshold, generate an energy storage scheduling balance analysis result as unbalanced.

[0036] A plurality of adjacent microgrids of the first microgrid are obtained. The adjacent microgrids refer to other microgrids that are adjacent to the first microgrid and connected through transmission lines for electric energy exchange. These adjacent microgrids belong to the same microgrid group and can support and serve as backup for each other in power dispatching.

[0037] First, consider the energy storage demand sequence of the first microgrid. These demand sequences represent the energy storage demand of the first microgrid at different time points, including discharge-type energy storage demand and storage-type energy storage demand; obtain the energy storage demand sequence of the adjacent microgrid, and ensure that these sequences are compared with the energy storage demand sequence of the first microgrid to evaluate their mutual influence. Each microgrid, including the first microgrid and the adjacent microgrid, has the state parameters of a conventional energy storage power station, which include the current available power of the energy storage power station and the remaining maximum power that the energy storage power station can store. Match the energy storage demand sequences of the first microgrid and the adjacent microgrid with their conventional energy storage state parameters, and evaluate whether the conditions for energy storage scheduling are met through preset scheduling constraint rules.

[0038] Based on the energy storage demand sequence and conventional energy storage status parameters, combined with preset scheduling constraint rules, energy storage scheduling is performed. Specifically, it is determined whether it is necessary to draw electric energy from adjacent microgrids, or to transmit electric energy from the first microgrid to adjacent microgrids, and the charging and discharging status of the energy storage equipment of each microgrid within a given time period is determined to balance the energy storage demand of the entire microgrid group.

[0039] During the scheduling process, energy storage deviation is generated. This deviation indicates the difference between energy storage supply and demand between the target microgrid and the adjacent microgrid. The energy storage deviation value is the gap between the demand after energy storage scheduling and the actual storage capacity. When the energy storage deviation value is greater than the set energy storage deviation threshold, it indicates that there is an imbalance in energy storage scheduling. In this case, the result of the energy storage scheduling balance analysis is unbalanced, indicating that more energy storage resources (such as emergency energy storage power stations) need to be mobilized for adjustment.

[0040] Furthermore, the preset scheduling constraint rules include:

[0041] Extract the first conventional energy storage remaining power and the first conventional energy storage remaining capacity of the first conventional energy storage state parameter, and extract multiple adjacent conventional energy storage remaining powers and multiple adjacent conventional energy storage remaining capacities of multiple adjacent conventional energy storage state parameters; when the first microgrid has a discharge-type energy storage demand, and the first discharge demand is greater than the first conventional energy storage remaining power, draw electric energy from the multiple adjacent microgrids based on the multiple adjacent conventional energy storage remaining powers; when the first microgrid has a storage-type energy storage demand, and the first storage demand is greater than the first conventional energy storage remaining capacity, store electric energy for the multiple adjacent microgrids based on the multiple adjacent conventional energy storage remaining capacities.

[0042] The first conventional energy storage remaining power refers to the current available power in the first microgrid energy storage power station, usually in kilowatt-hours (kWh). This parameter indicates the amount of power that can be discharged by the current energy storage battery; the first conventional energy storage remaining capacity refers to the maximum remaining storable power of the energy storage power station, that is, the energy storage capacity that has not yet been used by the energy storage system. It indicates how much electrical energy the energy storage equipment can store for future use.

[0043] The adjacent conventional energy storage remaining power is the power currently available to the adjacent microgrid energy storage power station, indicating the electric energy that can be drawn from the adjacent microgrid; the adjacent conventional energy storage remaining capacity refers to the power that can be stored by the adjacent microgrid energy storage power station, indicating the energy storage capacity that has not been used by the adjacent microgrid's energy storage equipment, indicating the electric energy that can be provided to the adjacent microgrid.

[0044] When the power load at a certain moment is greater than the power generation, it means that the first microgrid needs to discharge from the energy storage device to meet the demand. At this time, electric energy needs to be transferred. If the power demand of the first microgrid is greater than the remaining power of its energy storage power station, it needs to transfer electric energy from the adjacent microgrid. In this case, first check whether the energy storage capacity of multiple adjacent microgrids is sufficient. If the remaining power of the adjacent microgrid is greater than or equal to the discharge demand, then transfer electric energy from these adjacent microgrids. Based on the remaining energy storage capacity of the adjacent microgrid, select the appropriate microgrid for power dispatch to meet the energy storage demand of the first microgrid.

[0045] When the power generation at a certain moment is greater than the power load, it means that the first microgrid has excess power available for storage. At this time, the excess power needs to be stored in the energy storage device, and if the power storage demand of the first microgrid is greater than the remaining capacity of its energy storage power station, the excess power needs to be stored in the adjacent microgrid. In this case, when the power storage demand of the first microgrid is greater than its remaining capacity and the adjacent microgrid has sufficient energy storage capacity, the power can be stored in the energy storage power station of the adjacent microgrid. According to the remaining energy storage capacity of the adjacent microgrid, a suitable microgrid is selected for power storage to ensure that the adjacent microgrid has enough space to store excess power.

[0046] Furthermore, the preset scheduling constraint rules also include:

[0047] When the first discharge demand is less than or equal to the first conventional energy storage remaining capacity, or the first power storage demand is less than or equal to the first conventional energy storage remaining capacity, the multiple adjacent microgrids are ignored and the first microgrid is self-balanced.

[0048] If the discharge demand of the first microgrid is less than or equal to the remaining power of its conventional energy storage power station, it means that the energy storage power station of the first microgrid has enough power to meet the discharge demand. In this case, there is no need to draw power from the adjacent microgrid; if the storage demand of the first microgrid is less than or equal to the remaining capacity of its conventional energy storage power station, it means that the energy storage power station of the first microgrid has enough remaining capacity to store excess power. In this case, there is no need to store power in the adjacent microgrid. When the above conditions are met, the first microgrid can independently handle its energy storage needs without the support of external resources, which means that the energy storage system of the first microgrid can operate independently without relying on adjacent microgrids for energy storage scheduling. In this case, the energy storage status of the adjacent microgrid or the exchange of power is no longer considered. The first microgrid will self-regulate according to the status of its conventional energy storage system to ensure that sufficient power is provided under discharge-type demand or charging is performed under storage-type demand.

[0049] Furthermore, the method for obtaining a plurality of adjacent microgrids of the first microgrid includes:

[0050] According to the resistance of the transmission line, the power loss coefficient in the power transmission process is calculated; according to the voltage levels of the first microgrid and multiple microgrids in the target microgrid group, multiple voltage loss coefficients are evaluated and obtained; multiple transmission line lengths of the first microgrid and the multiple microgrids are obtained; based on the multiple transmission line lengths, the multiple voltage loss coefficients, and the power loss coefficients, the transmission loss between the first microgrid and the multiple microgrids is evaluated to obtain multiple transmission loss evaluation values; the microgrids with the multiple transmission loss evaluation values ​​less than or equal to the transmission loss evaluation threshold are used as the multiple adjacent microgrids.

[0051] The resistance of a transmission line is one of the key factors that affect the efficiency of power transmission. The greater the resistance, the more power is lost during power transmission. The resistance varies with factors such as the material, length, and cross-sectional area of ​​the transmission line. The resistance of a transmission line is usually provided by the design parameters of the power system, or can be calculated from known line lengths, material types, and cross-sectional areas. Power loss is the energy loss caused by resistance, calculated using Ohm's law and Joule's law. The power loss coefficient is the ratio of power loss to transmitted power, used to indicate the degree of loss during power transmission. This loss coefficient can be calculated using known current and line resistance.

[0052] Voltage loss usually occurs when electricity is transmitted from one end to the other, especially when it is transmitted over long distances. The magnitude of voltage loss is closely related to factors such as current, voltage level, and resistance. Voltage loss may cause the voltage at the receiving end to be lower than that at the sending end, affecting the normal operation of the equipment. The voltage loss coefficient is expressed as the ratio of voltage loss to rated voltage, usually expressed as a percentage. Different voltage levels (such as low voltage, medium voltage, and high voltage) correspond to different voltage loss coefficients, and high-voltage transmission systems usually have lower voltage loss coefficients.

[0053] The length of the transmission line is an important factor affecting power loss and voltage loss. The longer the line, the greater the power loss and voltage loss may be, because electricity will travel a longer distance during transmission and the resistance per unit length will accumulate. The line length can be obtained from the topological structure diagram of the power network or known geographic information system data.

[0054] The longer the transmission line, the greater the loss in the power transmission process. Long-distance transmission will increase resistance and voltage loss, resulting in more energy loss. The voltage loss coefficient is related to the resistance and current of the transmission line. High voltage levels can usually reduce voltage loss. The power loss coefficient reflects the degree of power loss during the transmission process. It is directly related to the resistance of the transmission line, the current transmitted, and the length of the line. Increased power loss means a waste of power. The combined result of all these factors is the evaluation value of transmission loss, which can reflect the power transmission efficiency of each microgrid.

[0055] In order to optimize power dispatch and reduce losses, a transmission loss evaluation threshold is set. Microgrids that exceed this threshold will be excluded from the selection of adjacent microgrids. The threshold setting can be based on the system's energy efficiency requirements and the economy of power transmission. By comparing the loss value with the preset threshold, all microgrids with loss values ​​less than or equal to the threshold are selected as adjacent microgrids. The selected adjacent microgrids have lower power transmission losses, which means that power exchange can be more efficient and has less impact on system stability.

[0056] Furthermore, the method of monitoring the first historical power load data of the first microgrid in the target microgrid group within a preset historical time and performing power consumption forecasting to obtain a first power load forecast sequence within a preset future time includes:

[0057] Based on the first historical electricity load data, extract periodic electricity load data, seasonal electricity load data, and special activity electricity load data; perform time series forecasting analysis based on the periodic electricity load data to obtain periodic electricity load forecast results; extract seasonal characteristics based on the seasonal electricity load data, perform seasonal exponential smoothing analysis based on the seasonal characteristics, and obtain seasonal electricity load forecast results; extract special activity characteristics based on the special activity electricity load data as additional features, combine the seasonal electricity load forecast results and the periodic electricity load forecast results to perform data fusion, and generate the first electricity load forecast sequence.

[0058] Periodic electricity load data refers to fluctuations in electricity load due to periodic factors, such as the difference between weekdays and weekends. This type of data usually shows certain fixed patterns, such as daily peaks and troughs in electricity consumption. Periodic features can be extracted by analyzing the regularity in historical data and using periodic analysis or statistical analysis based on rolling windows.

[0059] Seasonal load data reflects fluctuations in electricity consumption due to seasonal changes, such as weather differences between summer and winter. Peak electricity consumption in summer may be due to increased air-conditioning load, while peak electricity consumption in winter may be due to increased heating demand. By analyzing the annual changes in historical electricity load data, seasonal characteristics can be extracted. Seasonal fluctuations are usually manifested as annual cyclical changes.

[0060] Special activity load data reflects the impact of specific events or activities, such as holidays, promotions, meetings, etc. on power load. These activities usually cause sudden fluctuations or abnormal peaks in power load. Analyze emergencies and special activities in historical data, mark the time and power consumption data of these special activities, so as to identify and extract power load patterns during specific activities.

[0061] Use common time series prediction methods, such as ARIMA and LSTM, to build a prediction model based on historical periodic load data. This model can be used to predict periodic load changes in a certain period of time in the future, usually in units of hours, days or months. Based on the periodic time series model, the predicted periodic electricity load sequence will provide periodic load data in the future period.

[0062] Based on the seasonal changes in historical electricity load data, seasonal change patterns are extracted. For example, there are usually significant differences in electricity load in summer and winter, and these changes can be captured through seasonal exponential smoothing methods. Exponential smoothing is a technology commonly used in seasonal time series forecasting. It combines past seasonal fluctuations and trend information and uses smoothing methods to predict future seasonal electricity loads. Based on seasonal exponential smoothing analysis, a forecast sequence of seasonal electricity load is generated, which represents the impact of seasonal factors on electricity load in the future.

[0063] Utilize the special activity time periods marked in historical data, such as holidays and major events, to extract the impact of these specific activities. For example, during certain holidays, there may be special peaks in electricity consumption. Combine the periodic electricity load forecast results and the seasonal electricity load forecast results to obtain a fused basic electricity load forecast sequence. Add the impact of special activity loads as additional features to the fused forecast sequence, and further adjust and optimize the forecast results to ensure accurate prediction of electricity load during special events. By fusing the above-mentioned features, the final electricity load forecast sequence is generated, which can be used as a forecast of electricity demand in future time periods and provide data support for energy storage configuration, energy scheduling, etc.

[0064] In summary, the method for configuring an emergency energy storage power station for a microgrid group provided in the embodiment of the present application has the following technical effects:

[0065] By accurately monitoring the power load and power generation capacity of the target microgrid group, combining historical data and meteorological forecast data to analyze energy storage demand, the power distribution between microgrids can be optimized and the dispatching efficiency and flexibility of the microgrid group can be improved. By predicting the power load and power generation capacity of multiple microgrids and analyzing the energy storage demand based on the predicted results, the future energy storage demand sequence can be effectively predicted, which provides a reliable basis for the intelligent dispatching of microgrids and ensures that the energy storage system can meet the needs of different microgrids. By obtaining the conventional energy storage state parameters of multiple microgrids and the state parameters of the emergency energy storage power station, the energy storage dispatch balance analysis of adjacent microgrids is carried out to ensure the balance of power between the microgrids and improve the stability and reliability of the microgrid group. When the result of the energy storage dispatch balance analysis is unbalanced, reasonable energy storage configuration is carried out based on the state parameters of the emergency energy storage power station. By timely deploying the emergency energy storage power station, it is ensured that when the energy storage demand cannot be met by the conventional energy storage power station, additional power support is provided, thereby avoiding power outages caused by insufficient energy storage in the microgrid group and improving the emergency response capability of the system.

[0066] Embodiment 2 is based on the same inventive concept as the method for configuring an emergency energy storage power station for a microgrid group in the aforementioned embodiment. Figure 2As shown, an embodiment of the present application provides an emergency energy storage power station configuration system for a microgrid group, the system comprising:

[0067] The power consumption prediction module 10 is used to monitor the first historical power load data of the first microgrid in the target microgrid group within a preset historical time, and to perform power consumption prediction to obtain a first power load prediction sequence within a preset future time; the power generation capacity prediction module 20 is used for the interactive meteorological platform to obtain meteorological forecast data within a preset future time, and to perform power generation capacity prediction of the first microgrid based on the meteorological forecast data to obtain a first power generation prediction sequence; the energy storage demand analysis module 30 is used to perform time alignment and energy storage demand analysis on the first power load prediction sequence and the first power generation prediction sequence, and generate a first Energy storage demand sequence, and so on, to obtain multiple energy storage demand sequences of multiple microgrids in the target microgrid group; an energy storage state parameter acquisition module 40, used to obtain multiple conventional energy storage state parameters of multiple conventional energy storage power stations of the multiple microgrids, and emergency energy storage state parameters of the emergency energy storage power station of the target microgrid group; an energy storage configuration module 50, used to perform energy storage scheduling balance analysis of adjacent microgrids according to the multiple energy storage demand sequences and the multiple conventional energy storage state parameters, and when the analysis result is unbalanced, perform energy storage configuration of the emergency energy storage power station based on the emergency energy storage state parameters.

[0068] Furthermore, the energy storage demand analysis module 30 also includes the following operation steps:

[0069] The first electricity load forecast sequence and the first power generation forecast sequence are time-aligned. When the first electricity load forecast data at any moment is greater than the first power generation forecast data, a discharge-type energy storage demand is generated, wherein the discharge-type energy storage demand includes a discharge demand amount, and the discharge demand amount is the difference between the first electricity load forecast data and the first power generation forecast data; when the first electricity load forecast data at any moment is less than or equal to the first power generation forecast data, a storage-type energy storage demand is generated, wherein the storage-type energy storage demand includes a storage demand amount, and the storage demand amount is the difference between the first power generation forecast data and the first electricity load forecast data; based on the discharge-type energy storage demand and the storage-type energy storage demand, the first energy storage demand sequence is generated.

[0070] Furthermore, the energy storage configuration module 50 also includes the following operation steps:

[0071] Acquire multiple adjacent microgrids of a first microgrid, wherein the multiple adjacent microgrids belong to a target microgrid group; match preset scheduling constraint rules according to a first energy storage demand sequence and a first conventional energy storage state parameter of the first microgrid, and multiple adjacent energy storage demand sequences and multiple adjacent conventional energy storage state parameters of the multiple adjacent microgrids; perform energy storage scheduling based on the preset scheduling constraint rules to obtain a first energy storage scheduling result, wherein the first energy storage scheduling result includes an energy storage deviation value; when the energy storage deviation value is greater than an energy storage deviation threshold, generate an energy storage scheduling balance analysis result as unbalanced.

[0072] Furthermore, the preset scheduling constraint rules include:

[0073] Extract the first conventional energy storage remaining power and the first conventional energy storage remaining capacity of the first conventional energy storage state parameter, and extract multiple adjacent conventional energy storage remaining powers and multiple adjacent conventional energy storage remaining capacities of multiple adjacent conventional energy storage state parameters; when the first microgrid has a discharge-type energy storage demand, and the first discharge demand is greater than the first conventional energy storage remaining power, draw electric energy from the multiple adjacent microgrids based on the multiple adjacent conventional energy storage remaining powers; when the first microgrid has a storage-type energy storage demand, and the first storage demand is greater than the first conventional energy storage remaining capacity, store electric energy for the multiple adjacent microgrids based on the multiple adjacent conventional energy storage remaining capacities.

[0074] Furthermore, the preset scheduling constraint rules also include:

[0075] When the first discharge demand is less than or equal to the first conventional energy storage remaining capacity, or the first power storage demand is less than or equal to the first conventional energy storage remaining capacity, the multiple adjacent microgrids are ignored and the first microgrid is self-balanced.

[0076] Furthermore, the energy storage configuration module 50 also includes the following operation steps:

[0077] According to the resistance of the transmission line, the power loss coefficient in the power transmission process is calculated; according to the voltage levels of the first microgrid and multiple microgrids in the target microgrid group, multiple voltage loss coefficients are evaluated and obtained; multiple transmission line lengths of the first microgrid and the multiple microgrids are obtained; based on the multiple transmission line lengths, the multiple voltage loss coefficients, and the power loss coefficients, the transmission loss between the first microgrid and the multiple microgrids is evaluated to obtain multiple transmission loss evaluation values; the microgrids with the multiple transmission loss evaluation values ​​less than or equal to the transmission loss evaluation threshold are used as the multiple adjacent microgrids.

[0078] Furthermore, the power consumption prediction module 10 also includes the following operation steps:

[0079] Based on the first historical electricity load data, extract periodic electricity load data, seasonal electricity load data, and special activity electricity load data; perform time series forecasting analysis based on the periodic electricity load data to obtain periodic electricity load forecast results; extract seasonal characteristics based on the seasonal electricity load data, perform seasonal exponential smoothing analysis based on the seasonal characteristics, and obtain seasonal electricity load forecast results; extract special activity characteristics based on the special activity electricity load data as additional features, combine the seasonal electricity load forecast results and the periodic electricity load forecast results to perform data fusion, and generate the first electricity load forecast sequence.

[0080] Through the above detailed description of a method for configuring an emergency energy storage power station for a microgrid group, those skilled in the art can clearly understand an emergency energy storage power station configuration system for a microgrid group in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0081] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for configuring an emergency energy storage power station for a microgrid group, characterized in that: The method comprises: Monitor the first historical power load data of the first microgrid in the target microgrid group within a preset historical time, and perform power consumption forecasting to obtain a first power load forecast sequence within a preset future time; The interactive meteorological platform obtains meteorological forecast data within a preset future time, and predicts the power generation capacity of the first microgrid based on the meteorological forecast data to obtain a first power generation forecast sequence; Performing time alignment and energy storage demand analysis on the first power load forecast sequence and the first power generation forecast sequence to generate a first energy storage demand sequence, and so on to obtain multiple energy storage demand sequences of multiple microgrids in the target microgrid group; Acquire a plurality of conventional energy storage state parameters of a plurality of conventional energy storage power stations of the plurality of microgrids, and an emergency energy storage state parameter of an emergency energy storage power station of the target microgrid group; An energy storage dispatch balance analysis of adjacent microgrids is performed according to the multiple energy storage demand sequences and the multiple conventional energy storage state parameters. When the analysis result is unbalanced, energy storage configuration of the emergency energy storage power station is performed based on the emergency energy storage state parameters.

2. A method for configuring an emergency energy storage power station for a microgrid group as claimed in claim 1, characterized in that: The method of performing time alignment and energy storage demand analysis on the first power load forecast sequence and the first power generation forecast sequence to generate a first energy storage demand sequence includes: The first power load forecast sequence and the first power generation forecast sequence are time-aligned, and when the first power load forecast data at any time is greater than the first power generation forecast data, a discharge-type energy storage demand is generated, wherein the discharge-type energy storage demand includes a discharge demand amount, and the discharge demand amount is the difference between the first power load forecast data and the first power generation forecast data; When the first power load forecast data at any time is less than or equal to the first power generation forecast data, generating a power storage type energy storage demand, wherein the power storage type energy storage demand includes a power storage demand, and the power storage demand is the difference between the first power generation forecast data and the first power load forecast data; The first energy storage requirement sequence is generated based on the discharge-type energy storage requirement and the electricity storage-type energy storage requirement.

3. A method for configuring an emergency energy storage power station for a microgrid group as claimed in claim 2, characterized in that: The method of performing energy storage dispatch balance analysis of adjacent microgrids according to the multiple energy storage demand sequences and the multiple conventional energy storage state parameters includes: Acquire a plurality of adjacent microgrids of the first microgrid, wherein the plurality of adjacent microgrids belong to a target microgrid group; Matching preset scheduling constraint rules according to the first energy storage demand sequence and the first conventional energy storage state parameter of the first microgrid, and the multiple adjacent energy storage demand sequences and the multiple adjacent conventional energy storage state parameters of the multiple adjacent microgrids; Performing energy storage scheduling based on the preset scheduling constraint rule to obtain a first energy storage scheduling result, wherein the first energy storage scheduling result includes an energy storage deviation value; When the energy storage deviation value is greater than the energy storage deviation threshold, the energy storage scheduling balance analysis result generated is unbalanced.

4. A method for configuring an emergency energy storage power station for a microgrid group as claimed in claim 3, characterized in that: The preset scheduling constraint rules include: Extracting a first conventional energy storage remaining power and a first conventional energy storage remaining capacity of a first conventional energy storage state parameter, and extracting a plurality of adjacent conventional energy storage remaining power and a plurality of adjacent conventional energy storage remaining capacities of a plurality of adjacent conventional energy storage state parameters; When the first microgrid is a discharge type energy storage demand, and the first discharge demand is greater than the first conventional energy storage remaining power, electric energy is transferred from the multiple adjacent microgrids based on the multiple adjacent conventional energy storage remaining power; When the first microgrid is a power storage type energy storage demand, and the first power storage demand is greater than the first conventional energy storage remaining capacity, the multiple adjacent microgrids are stored with electric energy based on the multiple adjacent conventional energy storage remaining capacities.

5. A method for configuring an emergency energy storage power station for a microgrid group as claimed in claim 4, characterized in that: The preset scheduling constraint rules also include: When the first discharge demand is less than or equal to the first conventional energy storage remaining capacity, or the first power storage demand is less than or equal to the first conventional energy storage remaining capacity, the multiple adjacent microgrids are ignored and the first microgrid is self-balanced.

6. A method for configuring an emergency energy storage power station for a microgrid group as claimed in claim 3, characterized in that: The method for obtaining a plurality of adjacent microgrids of the first microgrid comprises: Calculate the power loss coefficient during power transmission based on the resistance of the transmission line; Evaluate and obtain multiple voltage loss coefficients according to the voltage levels of the first microgrid and multiple microgrids in the target microgrid group; Obtaining multiple transmission line lengths of the first microgrid and the multiple microgrids; Based on the multiple transmission line lengths, the multiple voltage loss coefficients, and the power loss coefficients, evaluating the transmission loss between the first microgrid and the multiple microgrids to obtain multiple transmission loss evaluation values; The microgrids having the multiple power transmission loss evaluation values ​​less than or equal to the power transmission loss evaluation threshold are taken as the multiple adjacent microgrids.

7. A method for configuring an emergency energy storage power station for a microgrid group as claimed in claim 1, characterized in that: The method of monitoring the first historical power load data of the first microgrid in the target microgrid group within a preset historical time and performing power consumption forecasting to obtain a first power load forecast sequence within a preset future time includes: Based on the first historical power load data, extracting periodic power load data, seasonal power load data, and special activity power load data; Performing time series forecasting analysis based on the periodic power load data to obtain a periodic power load forecasting result; Extracting seasonal characteristics according to the seasonal power load data, performing seasonal exponential smoothing analysis based on the seasonal characteristics, and obtaining seasonal power load forecast results; Special activity features are extracted based on the special activity power load data as additional features, and data fusion is performed with the seasonal power load forecast result and the periodic power load forecast result to generate the first power load forecast sequence.

8. An emergency energy storage power station configuration system for microgrid groups, characterized in that: A method for configuring an emergency energy storage power station for a microgrid group according to any one of claims 1 to 7, the system comprising: The power consumption prediction module is used to monitor the first historical power consumption load data of the first microgrid in the target microgrid group within a preset historical time, and perform power consumption prediction to obtain a first power consumption load prediction sequence within a preset future time; A power generation capacity prediction module is used for the interactive meteorological platform to obtain meteorological forecast data within a preset future time, and to predict the power generation capacity of the first microgrid based on the meteorological forecast data to obtain a first power generation forecast sequence; An energy storage demand analysis module is used to perform time alignment and energy storage demand analysis on the first power load forecast sequence and the first power generation forecast sequence to generate a first energy storage demand sequence, and so on to obtain multiple energy storage demand sequences of multiple microgrids in the target microgrid group; An energy storage state parameter acquisition module, used to acquire a plurality of conventional energy storage state parameters of a plurality of conventional energy storage power stations of the plurality of microgrids, and an emergency energy storage state parameter of an emergency energy storage power station of the target microgrid group; The energy storage configuration module is used to perform energy storage scheduling balance analysis of adjacent microgrids according to the multiple energy storage demand sequences and the multiple conventional energy storage state parameters. When the analysis result is unbalanced, the energy storage configuration of the emergency energy storage power station is performed based on the emergency energy storage state parameters.

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