Energy storage system charging and discharging power calculation method, system, device and storage medium

Abstract

This invention discloses a method, system, device, and storage medium for calculating the charging and discharging power of an energy storage system. The calculation method includes acquiring historical data and daily charging and discharging power curves that maximize revenue from the energy storage system; constructing sample data based on the historical data and daily charging and discharging power curves, and then building a sample dataset; training, testing, and validating the constructed power calculation model using the sample dataset to obtain a target calculation model; and calculating the charging and discharging power of the energy storage system using the target calculation model based on current meteorological data, current photovoltaic power generation data, current energy storage battery data, current power load data, as well as meteorological forecast data, photovoltaic power generation prediction data, energy storage battery prediction data, and power load prediction data. This invention reduces the impact of distributed power sources and random fluctuations in user loads on EMS control strategies, improves the economic benefits of industrial and commercial energy storage systems, and shortens the project cost recovery cycle.

Description

Technical Field

[0001] This invention belongs to the field of energy management technology, and in particular relates to a method, system, device and storage medium for calculating the charging and discharging power of an energy storage system. Background Technology

[0002] Commercial and industrial energy storage is a typical application of energy storage systems on the user side. It can be applied to public facilities such as parks, buildings, and factories. Through the coordinated control of sources, grids, loads, and storage, it can take advantage of peak-valley price differences for arbitrage, smooth out fluctuations in new energy power generation, promote the local consumption of distributed energy, improve power quality, reduce transformer capacity requirements, and provide power support when the external power grid fails, ensuring continuous power supply to important loads.

[0003] In commercial and industrial energy storage systems, the battery management system (BMS), power supply system (PCS), and energy management system (EMS) coordinate with each other to jointly complete the energy dispatching of the energy storage system. For example... Figure 1 As shown, the Battery Management System (BMS) acts as the sensing element, responsible for monitoring, evaluating, protecting, and balancing the energy storage batteries; the Power Conversion System (PCS) acts as the execution element, controlling the charging and discharging process of the energy storage batteries and performing AC / DC conversion; and the Energy Management System (EMS), as the core of the energy storage system, acts as the decision-making element, monitoring and controlling the photovoltaic inverters, PCS, BMS, loads, and other equipment in the industrial and commercial energy storage system. It executes intelligent EMS control strategies, improving the reliability and economy of industrial and commercial energy storage projects by optimizing distributed power output, controlling battery charging and discharging power, and switching loads.

[0004] Intelligent energy management and scheduling algorithms are key to maximizing the benefits of commercial and industrial energy storage systems. Currently, energy management systems (EMS) are implemented in various ways, including: using empirical data combined with time-of-use pricing to formulate day-ahead charging and discharging plans for energy storage systems; prediction methods based on multiple linear regression; probabilistic statistical methods, such as Bayesian networks; and deep learning methods, such as feedforward artificial neural networks.

[0005] The method of formulating day-ahead charging and discharging plans for energy storage systems based on time-of-use pricing relies on experience-based setpoints for energy storage system charging and discharging, which often cannot quickly respond to dynamic changes in photovoltaic power generation and load, leading to reduced utilization of the energy storage system. Multiple linear regression uses least squares estimation to predict the optimal combination of multiple related factors, assuming a linear relationship between independent and dependent variables. However, the time series of distributed power generation output and electricity load have nonlinear characteristics, which the linear model cannot fully express, resulting in poor prediction accuracy. Bayesian networks based on probability statistics are conditional probability graphical models that describe the conditional dependencies between random variables. They are suitable for reasoning with imprecise or uncertain knowledge or information, but lack consideration for the temporal correlation of time-series data. Methods based on traditional artificial neural networks lack memory units, and the models are unidirectional, only using past information for learning and prediction, without considering the impact of future photovoltaic power generation and electricity load on energy management and control strategies. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system, device, and storage medium for calculating the charging and discharging power of an energy storage system, in order to solve the problem that traditional energy management systems (EMS) have difficulty predicting the changing trends of photovoltaic power generation and load power over a period of time, making it difficult to achieve optimal energy dispatch, resulting in reduced economic efficiency for users and extended cost recovery periods for energy storage projects.

[0007] This invention solves the above-mentioned technical problems through the following technical solution: a method for calculating the charging and discharging power of an energy storage system, comprising:

[0008] Acquire historical data of the energy storage system; wherein, the historical data includes historical meteorological data, historical photovoltaic power generation data, historical energy storage battery data, and historical power load data;

[0009] Obtain the daily charge and discharge power curves that maximize the benefits of the energy storage system;

[0010] Sample data is constructed based on the historical data and the daily charge / discharge power curve, and then a sample dataset is constructed based on the sample data; wherein, the historical data of one day and the corresponding actual charge / discharge power value are used as one sample data.

[0011] Construct a power calculation model;

[0012] The power calculation model is trained, tested, and validated using the sample dataset to obtain the target calculation model;

[0013] Acquire current meteorological data, current photovoltaic power generation data, current energy storage battery data, current power load data, as well as meteorological forecast data, photovoltaic power generation forecast data, energy storage battery forecast data, and power load forecast data;

[0014] Based on current meteorological data, current photovoltaic power generation data, current energy storage battery data, current power load data, as well as meteorological forecast data, photovoltaic power generation prediction data, energy storage battery prediction data, and power load prediction data, the target calculation model is used to calculate the charging and discharging power of the energy storage system.

[0015] Furthermore, the historical meteorological data for each sample includes the daily maximum temperature, daily minimum temperature, temperature at each collection time, humidity at each collection time, and radiation intensity at each collection time;

[0016] The historical photovoltaic power generation data for each sample includes the active power of the photovoltaic inverter at each acquisition time;

[0017] The historical energy storage battery data for each sample includes the state of charge of the energy storage battery at each acquisition time;

[0018] The historical power load data for each sample includes the active power of the load at each acquisition time.

[0019] Furthermore, a power calculation model was built using the PyTorch deep learning framework.

[0020] Furthermore, the power calculation model is a bidirectional gated recurrent neural network or a long short-term memory neural network.

[0021] Furthermore, the bidirectional gated recurrent neural network is trained using the aforementioned sample dataset, including:

[0022] For each sample data, the historical data from the 1st to the tth collection time is input into the forward layer of the bidirectional gated recurrent neural network to obtain the forward output; the historical data from the (t+1)th to the nth collection time is used as the backward layer of the bidirectional gated recurrent neural network to obtain the backward output; where n represents the number of collection times per day.

[0023] The predicted charging and discharging power at the t-th acquisition time is calculated based on the forward and backward outputs.

[0024] Calculate the error loss between the predicted charge / discharge power and the actual charge / discharge power at the t-th acquisition time.

[0025] The parameters of the bidirectional gated recurrent neural network are adjusted based on the error loss to achieve network training.

[0026] Furthermore, the error loss between the predicted charge / discharge power and the actual charge / discharge power at the t-th acquisition time is calculated using the mean square error formula.

[0027] Furthermore, the power load forecast data is determined based on the power consumption plan, which includes production power consumption plan and daily power demand.

[0028] Based on the same concept, the present invention also provides an energy storage system, including an energy management system, a battery management system, an energy storage converter, and an energy storage battery; the energy management system is used to calculate the charging and discharging power according to the energy storage system charging and discharging power calculation method described above, and send the calculated charging and discharging power to the energy storage converter; the energy storage converter is used to receive the calculated charging and discharging power and control the charging and discharging of the energy storage battery according to the charging and discharging power.

[0029] Based on the same concept, the present invention also provides an electronic device, including a memory, a processor, and a computer program / instructions stored in the memory, wherein the processor executes the computer program / instructions to implement the energy storage system charging and discharging power calculation method as described above.

[0030] Based on the same concept, the present invention also provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the energy storage system charging and discharging power calculation method as described above.

[0031] Beneficial effects

[0032] Compared with the prior art, the advantages of the present invention are as follows:

[0033] This invention utilizes a bidirectional gated recurrent neural network to learn the nonlinear characteristics between photovoltaic power generation capacity, load information, energy storage regulation capacity, and charging / discharging power. It simultaneously predicts from two directions using current and predicted data, and fuses the outputs from both directions to calculate the charging / discharging power of the energy storage system. This effectively captures the temporal relationships and mutual influences between multiple variables in time-series data, achieving accurate calculation of the system's charging / discharging power and thus optimizing the EMS control strategy. This invention reduces the impact of distributed power sources and random fluctuations in user loads on the EMS control strategy, improving the economic efficiency of industrial and commercial energy storage systems and shortening the project payback period. Attached Figure Description

[0034] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only one embodiment of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a structural diagram of an energy storage system in the background technology of this invention;

[0036] Figure 2 This is a flowchart of the method for calculating the charging and discharging power of the energy storage system in an embodiment of the present invention;

[0037] Figure 3 This is a diagram of the bidirectional gated recurrent neural network architecture in an embodiment of the present invention;

[0038] Figure 4 This is a structural diagram of the gated loop unit in an embodiment of the present invention. Detailed Implementation

[0039] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] The technical solutions of this application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0041] Example 1

[0042] like Figure 2 As shown, the energy storage system charging and discharging power calculation method provided in this embodiment of the invention includes the following steps:

[0043] Step 1: Obtain historical data of the energy storage system.

[0044] Historical data includes historical meteorological data, historical photovoltaic (PV) power generation data, historical energy storage battery data, and historical power load data. Historical meteorological data includes daily weather data, including daily maximum and minimum temperatures, temperature at each sampling time, humidity at each sampling time, and radiation intensity at each sampling time. Historical PV power generation data includes the active power of the PV inverter at each sampling time; historical energy storage battery data includes the state of charge (SBC) of the energy storage battery at each sampling time; historical power load data includes the active power of the load at each sampling time. In this embodiment, the sampling interval is 1 minute, meaning that temperature, humidity, radiation intensity, PV inverter active power, energy storage battery SBC, and power load active power are collected every minute. The number of historical data points (n) per day is 24 × 60 = 1440.

[0045] To improve data quality, historical data is also preprocessed. The preprocessing in this invention includes data cleaning, normalization, and other operations.

[0046] Step 2: Obtain the daily charge and discharge power curves that maximize the benefits of the energy storage system.

[0047] In a specific embodiment of the present invention, a daily charge / discharge power curve for maximizing the revenue of the energy storage system is calculated in conjunction with time-of-use (TOU) pricing. To maximize the revenue of the energy storage system, the daily charge / discharge power curve is calculated based on different time periods of TOU pricing, with full-power charging during off-peak hours and the fastest possible discharge during peak hours while considering grid safety (avoiding reverse power transmission to the grid) and battery balance. Based on the daily charge / discharge power curve, the charge / discharge power at each data collection time can be obtained.

[0048] Step 3: Construct sample data based on the historical data obtained in Step 1 and the daily charge and discharge power curves obtained in Step 2, and then construct a sample dataset based on all the sample data.

[0049] In this embodiment, a day's historical data and the corresponding actual charging and discharging power values ​​are used as sample data. Each sample data is a time series data, which includes data from n collection times. The data at the t-th collection time includes the daily maximum temperature, daily minimum temperature, temperature at the t-th collection time, humidity at the t-th collection time, radiation intensity at the t-th collection time, active power of the photovoltaic inverter at the t-th collection time, state of charge of the energy storage battery at the t-th collection time, active power of the load at the t-th collection time, and the actual charging and discharging power value at the t-th collection time. The actual charging and discharging power value at each collection time is determined based on the daily charging and discharging power curve.

[0050] The sample dataset contains multiple sample data sets, and cross-validation is used to divide the sample dataset into training set, test set and validation set.

[0051] Step 4: Construct a power calculation model.

[0052] In a specific embodiment of this invention, the power calculation model is constructed using the PyTorch deep learning framework, making the model construction more flexible. In this embodiment, the power calculation model is a bidirectional gated recurrent neural network (i.e., a Bi-GRU model) or a long short-term memory neural network. The basic unit of a long short-term memory neural network consists of an input gate, a forget gate, and an output gate, while the basic unit of a Bi-GRU model consists of an update gate and a reset gate. The Bi-GRU model has a simpler structure, fewer parameters, requires fewer training samples, and has a faster training speed.

[0053] like Figure 3As shown, the bidirectional gated recurrent neural network includes an input layer, a forward layer, a backward layer, and an output layer. The input layer is connected to the forward and backward layers, and the forward and backward layers are connected to the output layer. The input layer receives sample data. Both the forward and backward layers include multiple gated recurrent units (GRUs). The forward layer processes sample data from front to back, and the backward layer processes sample data from back to front. This bidirectional structure can simultaneously capture information from the past and the future, thus more comprehensively modeling the temporal relationships in time series data.

[0054] like Figure 4 As shown, each gated recurrent unit (GRU) is controlled by update and reset gates to manage information transmission. The reset gate determines the impact of data before time t on time t; the update gate determines how to combine the data at time t with the accumulated information before time t. Through these gating mechanisms, the Bi-GRU model can adaptively learn long-term dependencies and multivariate interactions in time series data, effectively handling long-term dependencies while avoiding the vanishing gradient problem. The specific calculation formula for the forward layer is:

[0055] (1)

[0056] (2)

[0057] (3)

[0058] (4)

[0059] (5)

[0060] (6)

[0061] in, This represents the input vector of the feedforward layer. This indicates the hidden state of the parent layer. In the candidate hidden state, This is the output of the forward layer; This indicates that the door is being reset. Indicates an update to the door; and tanh represent the sigmoid and tanh activation functions, respectively. Represents the dot product of matrices; b and b represent the weight matrix and bias of the corresponding gating mechanism and storage unit, respectively.

[0062] Similarly, the output of the backward layer can be calculated. The output layer's output to the feedforward layer. and the output of the backward layer By combining the results, we obtain the final output value. (i.e., the predicted charging and discharging power at the t-th acquisition time), final output value The range is [-1, 1]. The specific calculation formula is:

[0063] (7)

[0064] Step 5: Use the sample dataset to train, test, and validate the power calculation model to obtain the target calculation model.

[0065] Taking a power calculation model as a bidirectional gated recurrent neural network as an example, the bidirectional gated recurrent neural network is trained using each sample data, including:

[0066] Step 5.1: Input the historical data from the 1st to the tth acquisition time into the feedforward layer of the bidirectional gated recurrent neural network to obtain the feedforward output. Using the historical data from the (t+1)th to the nth acquisition time as the backward layer of a bidirectional gated recurrent neural network, the backward output is obtained. Where n represents the number of data collection times per day.

[0067] Historical data for each data collection moment includes the daily maximum temperature, daily minimum temperature, temperature at the corresponding data collection moment, humidity at the corresponding data collection moment, radiation intensity at the corresponding data collection moment, active power of the photovoltaic inverter at the corresponding data collection moment, state of charge of the energy storage battery at the corresponding data collection moment, and active power of the load at the corresponding data collection moment.

[0068] Step 5.2: Based on the forward output and backward output Calculate the predicted charging and discharging power at the t-th data acquisition time. .

[0069] Step 5.3: Calculate the predicted charging and discharging power at the t-th acquisition time. Error loss between the actual charging and discharging power and the actual value.

[0070] In this embodiment, the mean square error formula is used to calculate the predicted charging and discharging power at the t-th acquisition time. The error loss between the predicted and actual charging / discharging power is measured by mean square error, which measures the difference between the predicted and actual values.

[0071] Step 5.4: Adjust the parameters of the bidirectional gated recurrent neural network according to the error loss to achieve network training.

[0072] The backpropagation gradient descent algorithm is used to update the connection weights in the bidirectional gated recurrent neural network (BRN), and the network is trained iteratively to gradually learn the features and patterns of the time series data. To enhance robustness, the sample data in the training set is randomly shuffled. During training, the parameters of the BRN are: 80 training iterations (epochs), 50 batch size, an adaptive estimation Adam optimizer, and other hyperparameters set to their default values.

[0073] The trained bidirectional gated recurrent neural network is evaluated using a validation set, with specific evaluation metrics including root mean square error (RMSE) and mean absolute error (MAE). Based on the evaluation results, the bidirectional gated recurrent neural network is fine-tuned; performance can be improved by adjusting hyperparameters, changing model structure, and other methods. The evaluated and fine-tuned bidirectional gated recurrent neural network becomes the target computational model.

[0074] Step 6: Obtain current meteorological data, current photovoltaic power generation data, current energy storage battery data, current power load data, as well as meteorological forecast data, photovoltaic power generation forecast data, energy storage battery forecast data, and power load forecast data.

[0075] The current meteorological data is real-time data collected by the weather station; the current photovoltaic power generation data is the real-time active power of the photovoltaic inverter; the current energy storage battery data is the real-time state of charge of the energy storage battery; and the current power load data is the real-time active power of the load. The meteorological forecast data comes from the weather station; the photovoltaic power generation forecast data is obtained through existing photovoltaic power generation forecasting methods; the power load forecast data is determined based on the electricity consumption plan, which includes production electricity consumption plans and daily electricity demand; and the energy storage battery forecast data is directly filled with specific values, for example, the predicted state of charge value for the energy storage battery is 0.5.

[0076] Step 7: Based on current meteorological data, current photovoltaic power generation data, current energy storage battery data, current power load data, as well as meteorological forecast data, photovoltaic power generation prediction data, energy storage battery prediction data, and power load prediction data, calculate the charging and discharging power of the energy storage system using the target calculation model.

[0077] The current meteorological data, current photovoltaic power generation data, current energy storage battery data, and current power load data are input into the forward layer of the target calculation model, and the meteorological forecast data, photovoltaic power generation prediction data, energy storage battery prediction data, and power load prediction data are input into the backward layer of the target calculation model. The target calculation model outputs the charging and discharging power of the energy storage system.

[0078] This invention presents a charging and discharging power calculation method based on a bidirectional gated recurrent neural network. This method fully utilizes contextual information, combining current meteorological data, distributed power generation and electricity load trends, and future forecasts to calculate the charging and discharging power of the energy storage system from both past and future perspectives. Experimental results show that this bidirectional gated recurrent neural network-based method overcomes the disadvantages of distributed power generation and electricity load time series, such as nonlinearity, time-varying nature, susceptibility to interference, and limited observation time. It optimizes the performance of energy management and control strategies, improves the economic benefits of commercial and industrial energy storage system projects, and shortens the investment payback period.

[0079] Example 2

[0080] like Figure 1 As shown, the energy storage system provided in this embodiment of the invention includes an energy management system, a battery management system, an energy storage converter, and an energy storage battery. The energy management system is used to calculate the charging and discharging power according to the energy storage system charging and discharging power calculation method in Embodiment 1 of the invention, and sends the calculated charging and discharging power to the energy storage converter. The energy storage converter is used to receive the calculated charging and discharging power and control the charging and discharging of the energy storage battery according to the charging and discharging power, thereby realizing the energy management and control of the energy storage system.

[0081] Example 3

[0082] This invention also provides an electronic device, which includes: a memory, a processor, and a computer program / instructions stored in the memory. The processor executes the computer program / instructions to implement the energy storage system charging and discharging power calculation method in Embodiment 1 of this application.

[0083] Although not shown, the electronic device includes a processor that can perform various appropriate operations and processes based on programs and / or data stored in read-only memory (ROM) or loaded from a storage portion into random access memory (RAM). The processor can be a multi-core processor or may contain multiple processors. In some embodiments, the processor may include a general-purpose main processor and one or more specialized coprocessors, such as a central processing unit, graphics processing unit (GPU), neural network processor (NPU), digital signal processor (DSP), etc. Various programs and data required for device operation are also stored in RAM. The processor, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0084] The processor and memory described above are used together to execute programs / instructions stored in the memory. When the program / instructions are executed by the computer, they can implement the methods, steps, or functions described in the above embodiments.

[0085] Although not shown, embodiments of the present invention also provide a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the energy storage system charging and discharging power calculation method in Embodiment 1 of this application.

[0086] Readable storage media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0087] The above description only discloses specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or modifications that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for calculating the charging and discharging power of an energy storage system, characterized in that, The calculation method includes: Acquire historical data of the energy storage system; wherein, the historical data includes historical meteorological data, historical photovoltaic power generation data, historical energy storage battery data, and historical power load data; Obtain the daily charging and discharging power curves that maximize the benefits of the energy storage system, as the true values ​​of charging and discharging power; Sample data is constructed based on the historical data and the actual charging and discharging power values, and then a sample dataset is constructed based on the sample data; wherein, one day's historical data and the corresponding actual charging and discharging power values ​​are used as one sample data. A power calculation model is constructed, wherein the power calculation model is a bidirectional recurrent neural network; The power calculation model is trained, tested, and validated using the sample dataset to obtain the target calculation model. The training includes: for each sample dataset, inputting historical data from the 1st to the tth acquisition time into the forward layer of the bidirectional recurrent neural network to obtain a forward output; inputting historical data from the (t+1)th to the nth acquisition time into the backward layer of the bidirectional recurrent neural network to obtain a backward output; where n is the number of acquisition times per day; calculating the predicted charging / discharging power value at the tth acquisition time based on the forward and backward outputs; calculating the error loss between the predicted charging / discharging power value at the tth acquisition time and the actual charging / discharging power value; and adjusting the parameters of the bidirectional recurrent neural network based on the error loss to achieve network training. Acquire current meteorological data, current photovoltaic power generation data, current energy storage battery data, current power load data, as well as meteorological forecast data, photovoltaic power generation forecast data, energy storage battery forecast data, and power load forecast data; The current meteorological data, current photovoltaic power generation data, current energy storage battery data, and current power load data are input into the forward layer of the bidirectional recurrent neural network of the target calculation model, and the meteorological forecast data, photovoltaic power generation prediction data, energy storage battery prediction data, and power load prediction data are input into the backward layer of the bidirectional recurrent neural network of the target calculation model; the target calculation model calculates and outputs the charging and discharging power of the energy storage system based on the outputs of the forward and backward layers.

2. The energy storage system charge and discharge power calculation method of claim 1, wherein, Historical meteorological data for each sample includes daily maximum temperature, daily minimum temperature, temperature at each collection time, humidity at each collection time, and radiation intensity at each collection time. The historical photovoltaic power generation data for each sample includes the active power of the photovoltaic inverter at each acquisition time; The historical energy storage battery data for each sample includes the state of charge of the energy storage battery at each acquisition time; The historical power load data for each sample includes the active power of the load at each acquisition time.

3. The method of claim 1, wherein, A power calculation model was built using the PyTorch deep learning framework.

4. The method of claim 1, wherein, The bidirectional recurrent neural network is either a bidirectional gated recurrent neural network or a long short-term memory neural network.

5. The method of claim 1, wherein, The error loss between the predicted charge / discharge power and the actual charge / discharge power at the t-th acquisition time is calculated using the mean square error formula.

6. The method of claim 1-5, wherein, The power load forecast data is determined based on the power consumption plan, which includes production power consumption plan and daily power demand.

7. An energy storage system comprising an energy management system, a battery management system, an energy storage converter and an energy storage battery; characterized in that, The energy management system is used to calculate the charging and discharging power according to the energy storage system charging and discharging power calculation method as described in any one of claims 1 to 6, and send the calculated charging and discharging power to the energy storage converter; the energy storage converter is used to receive the calculated charging and discharging power, and control the charging and discharging of the energy storage battery according to the charging and discharging power.

8. An electronic device comprising a memory, a processor, and a computer program / instructions stored on the memory, wherein, The processor executes the computer program / instructions to implement the energy storage system charging and discharging power calculation method as described in any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon computer programs / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the method for calculating the charging and discharging power of the energy storage system as described in any one of claims 1 to 6.

Images