Energy storage control method, device and microgrid

Abstract

This invention discloses an energy storage control method, device, and microgrid. The energy storage control method includes: constructing a load time series based on load data; predicting the load time series using a prediction model to obtain the load power prediction boundary value for the next time step; calculating the energy storage service target power using a power service algorithm on the real-time load data; calculating the boundary value of the energy storage charging and discharging target power based on the load power prediction boundary value, the current load data, and transformer parameters; and performing boundary correction on the energy storage service target power based on the boundary value of the energy storage charging and discharging target power to obtain the energy storage target power. The technical solution of this invention, based on collected load data and using a prediction model to predict the load power prediction boundary value for the next time step, effectively prevents the occurrence of reverse current and overload phenomena.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of grid-connected energy storage technology, and in particular to an energy storage control method, device and microgrid. Background Technology

[0002] With the large-scale development of new energy sources, the combination and complementarity of multiple new energy sources, including energy storage systems, is an inevitable trend. In microgrids, energy storage control is the most critical link. When the energy storage discharge power is too high, the energy output from the energy storage system can easily be fed back into the grid, i.e., reverse current. When the energy storage charging power is too high, transformer overload can easily occur. Current technologies can only mitigate reverse current and overload after they occur, but cannot prevent them from happening in the first place. Summary of the Invention

[0003] This invention provides an energy storage control method, device, and microgrid to solve the problem that microgrid systems cannot prevent backflow and overload phenomena in advance.

[0004] According to one aspect of the present invention, an energy storage control method is provided, comprising:

[0005] Construct a load time series based on load data;

[0006] The load time series is predicted using a prediction model to obtain the load power prediction boundary value for the next time step;

[0007] The target power for energy storage services is calculated from real-time load data using a power service algorithm.

[0008] Based on the predicted load power boundary value, the current load data, and the transformer parameters, calculate the boundary value of the energy storage charging and discharging target power.

[0009] The energy storage target power is obtained by boundary correction based on the boundary value of the energy storage charging and discharging target power.

[0010] Optionally, the method for calculating the boundary value of the energy storage charging and discharging target power includes:

[0011] Based on the current load data and the parameters of the transformer, calculate the boundary value of the current energy storage charging and discharging power;

[0012] Based on the predicted load power boundary value and the transformer parameters, calculate the predicted energy storage charging and discharging power boundary value;

[0013] The boundary value of the target energy storage charging and discharging power is calculated based on the boundary value of the current energy storage charging and discharging power and the boundary value of the predicted energy storage charging and discharging power.

[0014] Optionally, the boundary values ​​of the current energy storage charging and discharging power include: the boundary value of the current energy storage charging power and the boundary value of the current energy storage discharging power; wherein, the boundary value of the current energy storage charging power is calculated in conjunction with the overload protection threshold; and the boundary value of the current energy storage discharging power is calculated in conjunction with the reverse current protection threshold.

[0015] The boundary values ​​for predicting energy storage charging and discharging power include: boundary values ​​for predicting energy storage charging power and boundary values ​​for predicting energy storage discharging power; wherein, the boundary value for predicting energy storage charging power is calculated in conjunction with an overload protection threshold; and the boundary value for predicting energy storage discharging power is calculated in conjunction with an anti-reverse current threshold.

[0016] The boundary values ​​of the target power for energy storage charging and discharging include: the boundary value of the target power for energy storage charging and the boundary value of the target power for energy storage discharging; wherein, the boundary value of the target power for energy storage charging is calculated from the boundary value of the current energy storage charging power and the boundary value of the predicted energy storage charging power; the boundary value of the target power for energy storage discharging is calculated from the boundary value of the current energy storage discharging power and the boundary value of the predicted energy storage discharging power.

[0017] Optionally, the method for calculating the target power of the energy storage includes:

[0018] The energy storage charging and discharging status is judged based on the target power of the energy storage business;

[0019] If energy storage is being charged, the target power of energy storage is calculated based on the boundary values ​​of the target power of energy storage services and the target power of energy storage charging.

[0020] If the energy storage is discharged, the target power of the energy storage is calculated based on the boundary values ​​of the target power of the energy storage business and the target power of the energy storage discharge.

[0021] Optionally, the load data includes load data from systems other than energy storage systems;

[0022] The load data for systems other than energy storage systems include: load data for photovoltaic systems, load data for wind power systems, and load data for electrical equipment.

[0023] Optionally, the method for predicting the load power prediction boundary value includes:

[0024] The predicted load time series is predicted using the predicted model to obtain the predicted load power value at the next time step.

[0025] A confidence interval is set, and the load power prediction boundary value is obtained based on the confidence interval and the load power prediction value.

[0026] Optionally, the energy storage control method further includes:

[0027] The prediction model is updated when preset conditions are met.

[0028] Optionally, the prediction model is the ARIMA load prediction model.

[0029] Optionally, the method for making predictions using the ARIMA load forecasting model includes:

[0030] Set limits for the autoregressive term, the number of differences, and the number of moving averages;

[0031] Perform an automatic stationarity test to determine the value of the difference degree;

[0032] With the objective of minimizing the statistical model information criterion, the values ​​of the autoregressive term and the number of moving averages are optimally determined.

[0033] Based on the autoregressive term, the difference frequency, and the moving average frequency, the predicted load power value and its boundary value for the next time step are predicted.

[0034] According to another aspect of the present invention, an energy storage control device is provided, the device comprising:

[0035] The time series construction module is used to construct load time series based on load data.

[0036] The load forecasting module is used to forecast the load time series using a forecasting model to obtain the load power forecast boundary value for the next moment.

[0037] The business power calculation module is used to calculate the target power of energy storage business by using power business algorithms on real-time load data;

[0038] The prediction boundary calculation module is used to calculate the boundary value of the target power for energy storage charging and discharging based on the predicted boundary value of the load power, the load data at the current moment, and the parameters of the transformer.

[0039] The correction module is used to perform boundary correction on the energy storage service target power based on the boundary value of the energy storage charging and discharging target power to obtain the energy storage target power.

[0040] According to another aspect of the present invention, a microgrid is provided, comprising: a load system, an energy storage system, and a control system;

[0041] The control system performs the energy storage control method as described in the first aspect.

[0042] The technical solution of this invention constructs a load time series based on load data; uses a prediction model to predict the load time series and obtains the predicted boundary value of the load power at the next moment; uses a power service algorithm to calculate the target power of energy storage services based on the real-time load data; calculates the boundary value of the target power of energy storage charging and discharging based on the predicted boundary value of the load power, the load data at the current moment, and the transformer parameters; and performs boundary correction on the target power of energy storage services based on the boundary value of the target power of energy storage charging and discharging to obtain the target power of energy storage. The prediction model achieves high accuracy in predicting the load power at the next moment based on load data, thereby controlling the energy storage system to maintain an appropriate energy storage charging and discharging power, effectively preventing reverse current and overload phenomena, and significantly reducing the number of passive recovery attempts in the power grid system. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the electrical structure of a microgrid according to an embodiment of the present invention;

[0044] Figure 2 This is a flowchart of an energy storage control method provided according to an embodiment of the present invention;

[0045] Figure 3 This is a flowchart of another energy storage control method provided according to an embodiment of the present invention;

[0046] Figure 4 This is a flowchart of another energy storage control method provided according to an embodiment of the present invention;

[0047] Figure 5 This is a flowchart of another energy storage control method provided according to an embodiment of the present invention;

[0048] Figure 6 This is a flowchart of a forecasting method using the ARIMA load forecasting model provided by an embodiment of the present invention;

[0049] Figure 7 This is a schematic diagram of the structure of an energy storage control device according to an embodiment of the present invention;

[0050] Figure 8 This is a schematic diagram of the structure of another energy storage control device provided according to an embodiment of the present invention. Detailed Implementation

[0051] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0052] This invention provides an energy storage control method, device, and microgrid. To facilitate understanding of the energy storage control method provided by this invention, the electrical structure of the microgrid using this energy storage control method will first be described.

[0053] Figure 1 This is a schematic diagram of the electrical structure of a microgrid provided in an embodiment of the present invention. Figure 1 As shown, the microgrid includes a load system, an energy storage system 20, and a control system 30. The control system 30 is used to execute the energy storage control method provided in any embodiment of the present invention, which will be explained in subsequent embodiments.

[0054] For example, transformer 41 is connected to bus 50 of the power grid, and a point of common coupling (PCC) 421 is provided on transformer 41 and bus 50. The load system of the microgrid includes electrical equipment ( Figure 1 (Not shown in the image) Photovoltaic system 43 and other distributed energy systems 44. A photovoltaic point 422 is provided between transformer 41 and photovoltaic system 43, and an energy storage point 423 is provided between transformer 41 and energy storage system 20. For example, other distributed energy systems 44 may include wind power systems, etc.

[0055] Communication lines 51 connect the control system 30 to the photovoltaic point 422, the photovoltaic system 43, other distributed energy systems 44, the energy storage point 432, and the energy storage system 20. The control system 30 can output control signals to the photovoltaic point 422, the photovoltaic system 43, other distributed energy systems 44, the energy storage point 432, and the energy storage system 20 through the communication lines 51, and the control system 30 can also collect relevant data through the communication lines 51.

[0056] The common connection point 421, photovoltaic point 422 and energy storage point 423 are all equipped with metering devices, such as electricity meters, which are used to measure the corresponding load data of the connected equipment.

[0057] The control system 30 in this microgrid structure is used to execute energy storage control methods. By executing these methods, reverse current and overload phenomena in the grid-connected energy storage system can be effectively prevented. The following embodiments will provide a detailed description of the energy storage control methods executed by the control system 30.

[0058] This invention provides an energy storage control method. Figure 2 This is a flowchart of an energy storage control method provided in an embodiment of the present invention. Figure 2 As shown, the energy storage control method includes:

[0059] S110. Construct a load time series based on the load data.

[0060] Specifically, load data refers to all loads generated by load systems in the microgrid, excluding the energy storage system's power, that require charging and discharging by the energy storage system. For example, load data includes load data from systems other than the energy storage system. This load data includes: load data from photovoltaic (PV) systems, wind power systems, and load data from electrical equipment. PV and wind power systems generate electricity, while load data consumes electricity. The data requiring charging and discharging by the energy storage system is obtained by calculating the difference between the load data and the power generation data of the PV and wind power systems, as well as the bus power supply data.

[0061] For example, before constructing the load time series, the method also includes handling dead values ​​and / or outliers in the load data, filtering out dead values ​​and / or outliers. Dead values ​​are data with a value of zero, or data for which no value was collected; outliers can be values ​​that exhibit abnormal fluctuations during the data collection process. Removing dead values ​​and / or outliers prevents them from affecting the validity of the final results.

[0062] By collecting current and historical load data at regular time intervals, and constructing a load time series based on the acquired load data, the load time series provides historical load data for subsequent forecasting and calculations.

[0063] S120. Use a prediction model to predict the load time series and obtain the load power prediction boundary value for the next time step.

[0064] Specifically, the prediction model can be a method for predicting and calculating a dataset, and this invention does not limit the type of prediction model. For example, for the prediction of load time series, the prediction model is the ARIMA load prediction model. The ARIMA load prediction model can also be called the Autoregressive Integrated Moving Average Model (ARIMA). In this embodiment, the ARIMA load prediction model treats the load time series formed by the load data to be predicted over time as a random sequence and approximates the load time series using a mathematical model. After the model is identified, the load power value at the next moment can be predicted based on the past and current values ​​of the load data, and then the load power prediction boundary values ​​at the next moment can be obtained based on the load power value at the next moment. The load power prediction boundary values ​​include an upper boundary value and a lower boundary value. The upper and lower boundary values ​​of the load power prediction constitute the possible range of the predicted load power value.

[0065] S130. The power service algorithm is used to calculate the real-time load data to obtain the target power for energy storage services.

[0066] Specifically, the power service algorithm is a method for calculating load power after considering the overall economic efficiency of microgrid operation in actual situations, in order to ensure that the cost of normal microgrid operation is as low as possible. For example, taking the peak shaving and valley filling principle in the power grid system as an example, during peak electricity consumption periods, when electricity prices are higher, the power service algorithm calculates real-time load data to reduce the operating power of non-essential electrical equipment or shut down non-essential electrical equipment, while ensuring the normal operation of the microgrid. Conversely, during off-peak electricity consumption periods, when electricity prices are lower, the power service algorithm calculates real-time load data to ensure that all electrical equipment operates normally, thereby reducing overall load costs.

[0067] The target power for energy storage services is calculated using a power service algorithm to minimize load costs. However, this algorithm may lead to reverse current or overload.

[0068] S140. Calculate the boundary value of the target power for energy storage charging and discharging based on the load power prediction boundary value, the current load data, and the transformer parameters.

[0069] Specifically, the target power for energy storage charging and discharging includes the target power for energy storage charging and the target power for energy storage discharging. The target power for energy storage charging, plus the total load power required to satisfy the load system, is the power supplied by the power grid. When charging the energy storage system, the power supplied by the power grid is relatively large. Therefore, it is necessary to set the target power for energy storage charging in conjunction with the transformer parameters to avoid transformer overload.

[0070] The target power for energy storage discharge is the power output from the energy storage system when the power supplied by the grid is insufficient to meet the power requirements of the electrical equipment in the load system. During this time, the energy storage system is in a discharging state. Therefore, the set target power for energy storage discharge must avoid exceeding the load power to prevent the system from releasing excessive energy and causing backflow.

[0071] S150. Based on the boundary values ​​of the energy storage charging and discharging target power, the energy storage business target power is corrected to obtain the energy storage target power.

[0072] Specifically, as the aforementioned steps demonstrate, when the target power for energy storage charging and discharging falls within its boundary value, it ensures that the energy storage system connected to the grid does not experience reverse current or overload. The target power for energy storage operations guarantees lower load costs, thereby reducing electricity expenses. Boundary correction based on the boundary value of the target power for energy storage charging and discharging allows the energy storage system to achieve its target power for energy storage operations while also preventing overload and reverse current, thus obtaining the final target power for energy storage.

[0073] The technical solution of this embodiment constructs a load time series based on load data; it uses a prediction model to predict the load time series and obtains the predicted boundary value of the load power at the next moment; it uses a power service algorithm to calculate the target power of energy storage services based on the real-time load data; it calculates the boundary value of the target power of energy storage charging and discharging based on the predicted boundary value of the load power, the current load data, and the transformer parameters; and it performs boundary correction on the target power of energy storage services based on the boundary value of the target power of energy storage charging and discharging to obtain the target power of energy storage. The prediction model achieves high accuracy in predicting the load power at the next moment based on load data, thereby controlling the energy storage system to maintain an appropriate energy storage charging and discharging power, effectively preventing reverse current and overload phenomena, and significantly reducing the number of times the power grid system needs to be passively repaired.

[0074] Optional, Figure 3 This is a flowchart of another energy storage control method provided in an embodiment of the present invention. Based on the above embodiments, as follows... Figure 3 As shown, the calculation method for the boundary values ​​of the target power for energy storage charging and discharging includes:

[0075] S141. Based on the current load data and transformer parameters, calculate the boundary value of the current energy storage charging and discharging power.

[0076] For example, the boundary values ​​of the current energy storage charging and discharging power include: the boundary value of the current energy storage charging power and the boundary value of the current energy storage discharging power. Specifically, the boundary value of the current energy storage charging power is calculated based on the current load data, combined with an overload protection threshold, and the boundary value of the current energy storage discharging power is calculated based on an anti-reverse current threshold.

[0077] Specifically, the boundary values ​​of the current energy storage charging and discharging power can be calculated using the following formula:

[0078] Pcurr-charge-up = Transformer capacity limit value – Pload - Overload protection threshold

[0079] Pcurr-discharge-up = Pload - Anti-backflow threshold

[0080] Wherein, Pcurr-charge-up represents the upper boundary value of the target charging power of the energy storage at the current moment, Pcurr-discharge-up represents the upper boundary value of the target discharging power of the energy storage at the current moment, and Pload represents the actual load value of the energy storage system at the current moment. The overload prevention threshold is the difference between the target charging power of the energy storage and the energy storage power when the transformer is overloaded, preventing transformer overload. The reverse current prevention threshold is the difference between the target discharging power of the energy storage and the power when reverse current occurs in the energy storage system, effectively preventing the energy storage system from releasing excessive electrical energy that flows back to the grid.

[0081] S142. Calculate the boundary values ​​for predicted energy storage charging and discharging power based on the predicted load power boundary values ​​and transformer parameters.

[0082] For example, the boundary values ​​for predicting energy storage charging and discharging power include: boundary values ​​for predicting energy storage charging power and boundary values ​​for predicting energy storage discharging power. Specifically, the boundary values ​​for predicting energy storage charging power are calculated based on the load power prediction boundary values, combined with an overload protection threshold, and the boundary values ​​for predicting energy storage discharging power are calculated based on an anti-reverse current threshold.

[0083] Specifically, the boundary values ​​for predicting energy storage charging and discharging power can be calculated using the following formula:

[0084] Pnext-charge-up = Transformer capacity limit value – Upper boundary value of load power prediction – Overload protection threshold

[0085] Pnext-discharge-up = Lower boundary value of load power prediction - Anti-backflow threshold

[0086] Wherein, Pnext-charge-up represents the upper boundary value of the predicted target power of energy storage charging at the next moment, and Pnext-discharge-up represents the upper boundary value of the predicted target power of energy storage discharging at the next moment.

[0087] S143. Calculate the boundary value of the target power for energy storage charging and discharging based on the current boundary value of the energy storage charging and discharging power and the predicted boundary value of the energy storage charging and discharging power.

[0088] For example, the boundary values ​​of the target power for energy storage charging and discharging include: the boundary value of the target power for energy storage charging and the boundary value of the target power for energy storage discharging. Specifically, the boundary value of the target power for energy storage charging is calculated from the boundary value of the current energy storage charging power calculated in step S411 and the boundary value of the predicted energy storage charging power calculated in step S412; the boundary value of the target power for energy storage discharging is calculated from the boundary value of the current energy storage discharging power calculated in step S411 and the boundary value of the predicted energy storage discharging power calculated in step S412.

[0089] Specifically, the boundary values ​​of the target power for energy storage charging and discharging can be calculated using the following formula:

[0090] Pcharge-up=MIN(Pcurr-charge-up,Pnext-charge-up)

[0091] Pdischarge-up=MIN(Pcurr-discharge-up,Pnext-discharge-up)

[0092] Where Pcharge-up represents the upper boundary value of the target power for energy storage charging, and Pdischarge-up represents the upper boundary value of the target power for energy storage discharging. Choosing the smaller of the upper boundary values ​​for the target power for energy storage charging at the current moment and the next moment, and vice versa, can further reduce the charging and discharging power of the energy storage system, thereby avoiding transformer overload and reverse current in the energy storage system, and protecting the power supply system.

[0093] Optional, Figure 4 This is a flowchart of another energy storage control method provided in an embodiment of the present invention. Based on the above embodiments, as follows... Figure 4 As shown, the calculation method for the target power of energy storage includes:

[0094] S151. Determine the energy storage charging and discharging status based on the energy storage business target power.

[0095] Specifically, the state of the energy storage system—whether it is charging or discharging—can be determined based on the relationship between the target power of the energy storage business and zero. For example, if the target power of the energy storage business is greater than zero, the energy storage system is charging; if the target power of the energy storage business is less than zero, the energy storage system is discharging.

[0096] S152. If energy storage is being charged, the target power of energy storage is calculated based on the boundary values ​​of the target power of energy storage services and the target power of energy storage charging.

[0097] Specifically, when charging an energy storage system, the target energy storage power can be determined through the following quantitative relationship:

[0098] PP = MIN(Pcharge-up, Pt)

[0099] Where PP represents the target power of energy storage, and Pt represents the target power of energy storage operations. Since the target power of energy storage operations is greater than 0 during energy storage system charging, and the boundary value of the target power of energy storage charging is also greater than 0, selecting the smaller of the boundary values ​​of the target power of energy storage operations and the target power of energy storage charging as the target power of energy storage can both reduce electricity load costs and prevent transformer overload due to excessive power demand from the energy storage system, thus protecting the power grid system.

[0100] S153. If the energy storage is discharged, the energy storage target power is calculated based on the boundary values ​​of the energy storage business target power and the energy storage discharge target power.

[0101] Specifically, when an energy storage system discharges, the target power of the energy storage can be determined through the following quantitative relationship:

[0102] PP = MAX(-Pdischarge-up, Pt)

[0103] Where PP represents the target power of energy storage, and Pt represents the target power of energy storage business. Since the target power of energy storage business is less than 0 when the energy storage system discharges, while the boundary value of the target power of energy storage discharge is greater than 0, the boundary value of the target power of energy storage discharge is converted to its negative value and compared with the target power of energy storage business. The larger of the negative value of the boundary value of the target power of energy storage discharge and the target power of energy storage business—that is, the smaller absolute value—is selected as the target power of energy storage. This reduces electricity costs and also prevents backflow during energy storage system discharge.

[0104] Optional, Figure 5 This is a flowchart of another energy storage control method provided in an embodiment of the present invention. Based on the above embodiments, as follows... Figure 5 As shown, the methods for predicting the boundary values ​​of load power prediction include:

[0105] S121. Use a prediction model to predict the load time series and obtain the predicted load power value at the next moment.

[0106] For example, in this embodiment, the ARIMA load forecasting model is used when forecasting the load time series. The forecasting method of the ARIMA load forecasting model is described in detail in the following embodiments.

[0107] S122. Set the confidence interval, and obtain the load power prediction boundary value based on the confidence interval and the load power prediction value.

[0108] Specifically, a confidence interval refers to an estimated interval for a population parameter constructed from sample statistics. A confidence interval represents the probability that the true value of a population parameter falls within the range of the measured result, indicating the degree of confidence in the measured value of the measured parameter. In this embodiment, a confidence interval can represent the probability that the load power forecast value obtained using the ARIMA load forecasting model falls within the load power forecast boundary values. For example, a confidence interval of 95% indicates that the probability that the load power forecast value falls within the load power forecast boundary values ​​is 95%.

[0109] Since excessively high confidence intervals may reduce economic benefits, the benefits, the effectiveness of backflow prevention and overload prevention should be considered comprehensively. The confidence interval should be set according to actual needs, while maintaining a tolerance for the accuracy of the load power prediction value, so as to ensure greater economic benefits while meeting actual needs.

[0110] In the above embodiments, the ARIMA load forecasting model was used for the predicted load power prediction boundary value. The specific prediction method of the ARIMA load forecasting model will be explained below. Figure 6 This is a flowchart of a forecasting method using the ARIMA load forecasting model provided in an embodiment of the present invention. Based on the above embodiment, as follows... Figure 6 As shown, the methods for forecasting using the ARIMA load forecasting model include:

[0111] S1211, Set limits for the autoregressive term, the number of differences, and the number of moving averages.

[0112] Set upper and lower limits for the autoregressive term, the number of differences, and the number of moving averages.

[0113] S1212. Perform an automatic stationarity test to determine the value of the difference degree.

[0114] The system automatically performs stationarity tests on the upper and lower limits of the autoregressive term, the frequency of differencing, and the frequency of moving average. The frequency of differencing is determined when the statistic is less than any statistical value of 1%, 5%, or 10%, and the autoregressive term is close to 0. If the statistic does not meet the requirement of being less than any statistical value of 1%, 5%, or 10%, and the autoregressive term is not close to 0, the frequency of differencing is continuously increased, and the statistic and autoregressive term are tested after each increase, until the test criteria are met.

[0115] S1213. With the goal of minimizing the statistical model information criterion, optimize the values ​​of the autoregressive term and the number of moving averages.

[0116] Statistical model information criteria can include the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). The AIC criterion, also known as the Akaike information criterion, is a standard used to measure the goodness of fit of a statistical model. The BIC criterion, also known as the Bayesian information criterion, is a standard used for model selection.

[0117] Using the minimum values ​​of the AIC and BIC information criteria as the objective, and given a fixed number of differences, we seek the minimum values ​​of the AIC and BIC information criteria corresponding to the number of autoregressive terms and moving averages, thereby determining the number of autoregressive terms and moving averages.

[0118] S1214. Based on the autoregressive term, the difference degree, and the moving average degree, predict the load power and its boundary values ​​for the next time step.

[0119] Based on the final determined autoregressive term, difference degree, and moving average degree, the predicted load power value and the load power prediction boundary value for the next time step are obtained. This automatic optimization prediction method optimizes the application of the ARIMA model, improves prediction accuracy, and thus enhances the grid-connected system's ability to prevent backflow and overload.

[0120] Optionally, based on the above embodiments, the energy storage control method further includes: updating the prediction model when preset conditions are met.

[0121] Specifically, because load data changes in real time, the parameters used in the prediction model are updated to improve the accuracy of the predicted boundary value of load power at the next moment. Data with better parameters is selected to train the prediction model, resulting in an updated prediction model that ensures prediction accuracy and improves the reliability of the prediction results. For example, preset conditions include reaching a preset time interval or the change in load power exceeding a preset threshold.

[0122] This invention also provides an energy storage control device. Figure 7 This is a schematic diagram of the structure of an energy storage control device provided in an embodiment of the present invention. Figure 7 As shown, the energy storage control device includes:

[0123] Time series construction module 100 is used to construct load time series based on load data;

[0124] The load forecasting module 200 is used to forecast the load time series using a forecasting model to obtain the load power forecast boundary value for the next time step.

[0125] The business power calculation module 300 is used to calculate the target power of energy storage business by using power business algorithms on real-time load data.

[0126] The prediction boundary calculation module 400 is used to calculate the boundary value of the target power for energy storage charging and discharging based on the load power prediction boundary value, the load data at the current moment, and the transformer parameters.

[0127] The correction module 500 is used to perform boundary correction on the target power of energy storage business based on the boundary value of the target power of energy storage charging and discharging, so as to obtain the target power of energy storage.

[0128] The energy storage control device provided in the embodiments of the present invention can execute the energy storage control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0129] Optionally, based on the above embodiments, the prediction boundary calculation module 400 includes:

[0130] The current boundary calculation unit is used to calculate the boundary value of the current energy storage charging and discharging power based on the current load data and transformer parameters.

[0131] The prediction boundary calculation unit is used to calculate the boundary values ​​of the predicted energy storage charging and discharging power based on the load power prediction boundary values ​​and the transformer parameters.

[0132] The target boundary calculation unit is used to calculate the boundary value of the target power of energy storage charging and discharging based on the current boundary value of the energy storage charging and discharging power and the predicted boundary value of the energy storage charging and discharging power.

[0133] Optionally, based on the above embodiments, the modified module 500 includes:

[0134] The charging and discharging judgment unit is used to judge the charging and discharging status of energy storage based on the target power of the energy storage business.

[0135] If energy storage is used for charging, the target power of energy storage is calculated based on the boundary values ​​of the target power of energy storage business and the target power of energy storage charging.

[0136] If the energy storage is discharged, the target power of the energy storage is calculated based on the boundary values ​​of the target power of the energy storage business and the target power of the energy storage discharge.

[0137] Optionally, based on the above embodiments, the load forecasting module 200 includes:

[0138] The model prediction unit is used to predict the load time series using a prediction model to obtain the predicted load power value at the next time step.

[0139] The confidence interval setting unit is used to set the confidence interval and obtain the load power prediction boundary value based on the confidence interval and the load power prediction value.

[0140] Optionally, based on the above embodiments, the energy storage control device further includes:

[0141] The model update unit is used to update the prediction model when preset conditions are met.

[0142] For example, Figure 8 This is a schematic diagram of another energy storage control device provided in an embodiment of the present invention. Figure 8 As shown, in this energy storage control device, the time series construction module 100 and the load prediction module 200 can be located in an edge computing device, a cloud platform, and / or a controller. The service power calculation module 300, the prediction boundary calculation module 400, and the correction module 500 are all located in the controller, so that the energy storage control device executes the energy storage control method provided in any embodiment of the present invention. The load prediction module 200 may include a training data update unit and a parameter optimization unit. The parameter optimization unit periodically collects optimal parameters and outputs them to the training data update unit, which trains the data and updates the prediction model. The time series construction module 100 and the load prediction module 200 predict the load power boundary value. The service power calculation module 300, the prediction boundary calculation module 400, and the correction module 500 correct the boundary of the energy storage service target power based on the predicted load power boundary value, thereby obtaining the energy storage target power.

[0143] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method of energy storage control, the method comprising: include: A load time series is constructed based on the load data; the load data includes load data of other systems besides energy storage systems, including load data of photovoltaic systems, load data of wind power systems, and load data of electrical equipment. The load time series is predicted using a prediction model to obtain the load power prediction boundary value for the next time step; The target power for energy storage services is calculated from real-time load data using a power service algorithm. Based on the predicted load power boundary value, the current load data, and the transformer parameters, calculate the boundary value of the energy storage charging and discharging target power. The energy storage target power is obtained by boundary correction based on the boundary value of the energy storage charging and discharging target power; The boundary values ​​of the target power for energy storage charging and discharging include: the boundary value of the target power for energy storage charging and the boundary value of the target power for energy storage discharging. The method for calculating the target power of the energy storage includes: The energy storage charging and discharging status is judged based on the target power of the energy storage business; If energy storage is being charged, the target power of energy storage is calculated based on the boundary values ​​of the target power of energy storage services and the target power of energy storage charging. If the energy storage is discharged, the target power of the energy storage is calculated based on the boundary values ​​of the target power of the energy storage business and the target power of the energy storage discharge. The method for calculating the boundary values ​​of the target power for energy storage charging and discharging includes: Based on the current load data and the parameters of the transformer, calculate the boundary value of the current energy storage charging and discharging power; Based on the predicted load power boundary value and the transformer parameters, calculate the predicted energy storage charging and discharging power boundary value; The boundary value of the target energy storage charging and discharging power is calculated based on the boundary value of the current energy storage charging and discharging power and the boundary value of the predicted energy storage charging and discharging power. The boundary values ​​of the current energy storage charging and discharging power include: the boundary value of the current energy storage charging power and the boundary value of the current energy storage discharging power; wherein, the boundary value of the current energy storage charging power is calculated in conjunction with the overload protection threshold; and the boundary value of the current energy storage discharging power is calculated in conjunction with the reverse current protection threshold. The boundary values ​​for predicting energy storage charging and discharging power include: boundary values ​​for predicting energy storage charging power and boundary values ​​for predicting energy storage discharging power; wherein, the boundary value for predicting energy storage charging power is calculated in conjunction with an overload protection threshold; and the boundary value for predicting energy storage discharging power is calculated in conjunction with an anti-reverse current threshold.

2. The energy storage control method according to claim 1, characterized in that, The boundary value of the target power for energy storage charging is calculated from the boundary value of the current energy storage charging power and the boundary value of the predicted energy storage charging power; the boundary value of the target power for energy storage discharging is calculated from the boundary value of the current energy storage discharging power and the boundary value of the predicted energy storage discharging power.

3. The energy storage control method of claim 1, wherein, The load data includes load data from systems other than energy storage systems; The load data for systems other than energy storage systems include: load data for photovoltaic systems, load data for wind power systems, and load data for electrical equipment.

4. The method according to claim 1, characterized in that, The method for predicting the boundary values ​​of the load power prediction includes: The predicted load time series is predicted using the predicted model to obtain the predicted load power value at the next time step. A confidence interval is set, and the load power prediction boundary value is obtained based on the confidence interval and the load power prediction value.

5. The energy storage control method according to claim 1, characterized in that, Also includes: The prediction model is updated when preset conditions are met.

6. The energy storage control method according to claim 1, characterized in that, The prediction model is the ARIMA load prediction model.

7. The energy storage control method according to claim 6, characterized in that, The method for forecasting using the ARIMA load forecasting model includes: Set limits for the autoregressive term, the number of differences, and the number of moving averages; Perform an automatic stationarity test to determine the value of the difference degree; With the objective of minimizing the statistical model information criterion, the values ​​of the autoregressive term and the number of moving averages are optimally determined. Based on the autoregressive term, the difference frequency, and the moving average frequency, the predicted load power value and its boundary value for the next time step are predicted.

8. An energy storage control device, characterized in that, include: The time series construction module is used to construct a load time series based on load data; the load data includes load data of other systems besides energy storage systems, including load data of photovoltaic systems, load data of wind power systems, and load data of electrical equipment. The load forecasting module is used to forecast the load time series using a forecasting model to obtain the load power forecast boundary value for the next moment. The business power calculation module is used to calculate the target power of energy storage business by using power business algorithms on real-time load data; The prediction boundary calculation module is used to calculate the boundary value of the target power for energy storage charging and discharging based on the predicted boundary value of the load power, the load data at the current moment, and the parameters of the transformer. The correction module is used to perform boundary correction on the energy storage business target power based on the boundary value of the energy storage charging and discharging target power to obtain the energy storage target power; The boundary values ​​of the target power for energy storage charging and discharging include: the boundary value of the target power for energy storage charging and the boundary value of the target power for energy storage discharging. The correction module includes: A charge / discharge determination unit is used to determine the charge / discharge status of energy storage based on the target power of the energy storage service. If energy storage is being charged, the target power of energy storage is calculated based on the boundary values ​​of the target power of energy storage services and the target power of energy storage charging. If the energy storage is discharged, the target power of the energy storage is calculated based on the boundary values ​​of the target power of the energy storage business and the target power of the energy storage discharge. The prediction boundary calculation module includes: The current boundary calculation unit is used to calculate the boundary value of the current energy storage charging and discharging power based on the current load data and the parameters of the transformer. The prediction boundary calculation unit is used to calculate the boundary value of the predicted energy storage charging and discharging power based on the predicted boundary value of the load power and the parameters of the transformer. The target boundary calculation unit is used to calculate the boundary value of the target energy storage charging and discharging power based on the boundary value of the current energy storage charging and discharging power and the boundary value of the predicted energy storage charging and discharging power. The boundary values ​​of the current energy storage charging and discharging power include: the boundary value of the current energy storage charging power and the boundary value of the current energy storage discharging power; wherein, the boundary value of the current energy storage charging power is calculated in conjunction with the overload protection threshold; and the boundary value of the current energy storage discharging power is calculated in conjunction with the reverse current protection threshold. The boundary values ​​for predicting energy storage charging and discharging power include: boundary values ​​for predicting energy storage charging power and boundary values ​​for predicting energy storage discharging power; wherein, the boundary value for predicting energy storage charging power is calculated in conjunction with an overload protection threshold; and the boundary value for predicting energy storage discharging power is calculated in conjunction with an anti-reverse current threshold.

9. A microgrid, characterized in that, include: Load systems, energy storage systems, and control systems; The control system performs the energy storage control method as described in any one of claims 1-7.

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