Energy storage scheme planning method and device, computer equipment and medium

Abstract

The embodiment of the invention is suitable for the technical field of energy storage, and provides an energy storage scheme planning method and device, computer equipment and a medium, and the method comprises the steps: determining the power utilization load power in each power utilization time period based on historical power utilization data; according to the equipment information and the power utilization load power of the energy storage equipment, a charging and discharging scheme of the energy storage equipment is determined, the charging and discharging scheme comprises the charging and discharging state and the charging and discharging amount of the energy storage equipment in each power utilization period, and the equipment information comprises the equipment power and the total electric energy capacity; based on the charging and discharging schemes of the energy storage devices, an income curve of the energy storage project is generated, and the income curve is used for representing the corresponding relation between the configuration information of each energy storage device and the income of the energy storage project; and determining target energy storage device configuration information of the energy storage project from the configuration information of the plurality of energy storage devices according to the income curve. Through the method, the energy storage equipment configuration information of the energy storage project can be automatically generated.

Description

Technical Field

[0001] The present application belongs to the field of energy storage technology, and in particular, relates to a planning method, device, computer equipment and medium for an energy storage solution. Background Art

[0002] In order to encourage electricity users to reasonably arrange their electricity usage time and improve the utilization efficiency of electricity resources, the electricity prices at different time periods are generally different. During peak electricity consumption, the electricity price is generally higher, and during low electricity consumption, the electricity price is generally lower.

[0003] If the electricity stored during the low electricity consumption period can be used during the peak electricity consumption period, the electricity cost can be reduced. The above purpose can be achieved by using energy storage equipment. Energy storage equipment refers to a device that can store energy in the form of electricity and release it when needed to supply it to the power system or other equipment. When the electricity price is low, use energy storage equipment to store electricity; when the electricity price is high, use energy storage equipment to release electricity, so that the electricity stored during the low electricity price period can be used during the peak electricity price period, reducing the electricity cost.

[0004] At present, when configuring energy storage equipment, we usually rely on the personal experience of energy storage experts, combined with the user's load and the time period and electricity price of the region, to get a storage allocation plan, that is, to select energy storage equipment with appropriate capacity and power. However, relying on expert experience cannot provide scientific and systematic solutions, and cannot plan energy storage solutions efficiently, accurately and extensively. Summary of the invention

[0005] In view of this, embodiments of the present application provide a method, apparatus, computer device, and medium for planning an energy storage solution, which are used to automatically generate an energy storage solution for an energy storage project and achieve automatic and efficient planning of the energy storage solution.

[0006] A first aspect of an embodiment of the present application provides a method for planning an energy storage solution, including:

[0007] Based on historical electricity consumption data, determine the power load power in each electricity consumption period;

[0008] Determine a charge and discharge scheme of the energy storage device according to the device information of the energy storage device and the power load power, wherein the charge and discharge scheme includes the charge and discharge state and charge and discharge amount of the energy storage device in each power consumption period, and the device information includes the device power and the total power capacity;

[0009] Based on the charging and discharging scheme of the energy storage device, a revenue curve of the energy storage project is generated, wherein the revenue curve is used to characterize the corresponding relationship between the configuration information of each of the energy storage devices and the revenue of the energy storage project;

[0010] According to the yield curve, target energy storage device configuration information of the energy storage project is determined from configuration information of a plurality of energy storage devices.

[0011] A second aspect of an embodiment of the present application provides a planning device for an energy storage solution, characterized by comprising:

[0012] An electricity load power determination module is used to determine the electricity load power in each electricity consumption period based on historical electricity consumption data;

[0013] A charging and discharging scheme determination module, used to determine the charging and discharging scheme of the energy storage device according to the device information of the energy storage device and the power of the power load, wherein the charging and discharging scheme includes the charging and discharging state and the charging and discharging amount of the energy storage device in each power consumption period, and the device information includes the device power and the total power capacity;

[0014] A revenue curve determination module, used to generate a revenue curve of an energy storage project based on a charging and discharging scheme of the energy storage device, wherein the revenue curve is used to characterize a corresponding relationship between configuration information of each of the energy storage devices and the revenue of the energy storage project;

[0015] The energy storage device configuration information determination module is used to determine the target energy storage device configuration information of the energy storage project from the configuration information of the plurality of energy storage devices according to the yield curve.

[0016] A third aspect of an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0017] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect above is implemented.

[0018] A fifth aspect of the embodiments of the present application provides a computer program product. When the computer program product is run on a computer device, the computer device executes the method described in the first aspect.

[0019] Compared with the prior art, the embodiments of the present application have the following advantages:

[0020] By applying the method in the embodiment of the present application, the energy storage equipment configured for the energy storage project can be planned. Based on the historical power load data, the power load power of the energy storage project in each power consumption period can be predicted. According to the equipment power and power load power of the energy storage equipment, the charging and discharging scheme of the energy storage equipment can be determined. The charging and discharging scheme of the energy storage equipment can be the charging and discharging state and charging and discharging amount of each power consumption period when the benefit is maximized. The computer device can determine the charging and discharging scheme of each energy storage device, and then generate the benefit curve of the energy storage device based on the charging and discharging scheme of each energy storage device, so that the energy storage device configuration information of the energy storage project can be generated according to the benefit curve. In the embodiment of the present application, the charging and discharging scheme of the energy storage device with the highest benefit can be automatically generated based on the equipment power of the energy storage device and the predicted power load power. Based on the automatically generated charging and discharging scheme, the energy storage equipment of the energy storage project is configured, realizing scientific and systematic planning and improving the accuracy of the planning. The computer device can perform data processing, optimal charging and discharging scheme calculation, benefit curve determination and energy storage scheme recommendation, thereby avoiding the dependence of energy storage scheme planning on the experience of experts, so that the determination of energy storage device configuration information has a systematic and scientific solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art.

[0022] Figure 1 It is a schematic diagram of the steps of a planning method for an energy storage solution provided in an embodiment of the present application;

[0023] Figure 2 It is a flowchart of another energy storage solution planning method provided in an embodiment of the present application;

[0024] Figure 3 is a schematic diagram of a planning device for an energy storage solution provided in an embodiment of the present application;

[0025] Figure 4 It is a schematic diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In the following description, specific details such as specific system structures, technologies, etc. are proposed for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from hindering the description of the present application.

[0027] In general, different time periods have different electricity prices, namely, peak-valley time-of-use electricity prices. Peak-valley time-of-use electricity prices refer to dividing the 24 hours of a day into multiple time periods such as peak, flat, and valley according to the load changes of the power grid, and setting different electricity price levels for each time period to encourage electricity users to reasonably arrange their electricity use time, reduce peaks and fill valleys, and improve the utilization efficiency of power resources. For example, 0 to 8 o'clock is valley time, 8 to 10 o'clock is peak time, 10 to 12 o'clock is peak time, 12 to 16 o'clock is flat time, and 16 to 24 o'clock is valley time. Different time periods have different electricity prices. Generally, peak time electricity price > peak time electricity price > flat time electricity price > valley time electricity price. The corresponding relationship between electricity price and time period can be determined according to the region.

[0028] Based on the peak-valley time-of-use electricity price, enterprises can use energy storage equipment to achieve peak-valley arbitrage. Peak-valley arbitrage means charging during the flat valley period (a period of low electricity price and low electricity demand) and discharging during the peak period (a period of high electricity price and high electricity demand), thereby earning profits through the difference in electricity prices.

[0029] The energy storage system includes a transformer, which is an important link in voltage conversion and power transmission. The transformer converts the grid voltage into the voltage required by the energy storage device for storage or output, thereby achieving effective energy utilization. In the energy storage system, the transformer often plays the role of regulating voltage and transmitting energy, ensuring the efficient and stable operation of the energy storage system.

[0030] Before implementing an energy storage project, it is necessary to plan the energy storage solution for the project, so as to determine the configured energy storage equipment, the charging and discharging solution of the energy storage equipment, and the transformer used in the project based on the profitability of the energy storage project.

[0031] Based on this, an embodiment of the present application provides a planning method for an energy storage solution.

[0032] The technical solution of the present application is described below through specific embodiments.

[0033] Reference Figure 1 , shows a schematic flow chart of the steps of a method for planning an energy storage solution provided in an embodiment of the present application, which may specifically include the following steps:

[0034] S101, determining the power load power in each power consumption period based on historical power consumption data.

[0035] The execution subject of this embodiment may be a computer device, and the embodiment of this application does not limit the specific type of the computer device. The computer device can obtain relevant information of the energy storage project, and thus plan the energy storage solution based on the relevant information of the energy storage project. The relevant information of the energy storage project may include multiple different types of energy storage devices provided by the user, transformer information used, and decision matrix information of the energy storage solution.

[0036] The above-mentioned historical electricity consumption data may be historical electricity consumption data or historical electricity load data. When planning an energy storage project, the customer's historical electricity load data may be directly obtained, or the customer's historical electricity bill may be obtained to obtain the historical electricity consumption data. If the historical electricity consumption data obtained is historical electricity consumption data, the historical electricity load data may be calculated based on the historical electricity consumption data. Among them, the historical electricity load data may be the quotient of the historical electricity consumption data and time. The computer device may determine the electricity consumption in each electricity consumption period, and the historical electricity load data may be obtained by dividing the electricity consumption by the length of the electricity consumption period.

[0037] When planning an energy storage project, you first need to determine the power consumption information of the energy storage project. The power consumption information of the energy storage project can be determined based on the historical power consumption data of the energy storage project. The users targeted by the energy storage project are already determined, so the historical power consumption data of the users of the energy storage project can be directly obtained. For example, you can directly go to the power supply unit to find the historical power consumption data, or obtain the customer's electricity bill to obtain the historical power consumption data.

[0038] By inputting the historical electricity consumption data of the energy storage project into the trained load data generation model, the power load power of the energy storage project in each electricity consumption period can be predicted. Among them, the load data generation model can be obtained by training the long short-term memory network. When training the long short-term memory network, the computer device can obtain the historical electricity consumption data of multiple energy storage projects in different regions, so as to train the long short-term memory network based on the historical electricity consumption data of multiple energy storage projects in different regions.

[0039] When using historical electricity consumption data of multiple energy storage projects in different regions to train the long short-term memory network, the data needs to be processed to obtain historical electricity load data in a unified format.

[0040] After obtaining the historical electricity consumption data of multiple energy storage projects in different regions, the historical electricity consumption data of multiple energy storage projects in different regions can be preprocessed to obtain data with a certain data format, which is convenient for subsequent calculations and data analysis. Data preprocessing can include data cleaning and data formatting, so that the electricity consumption data has the same format, which is convenient for subsequent data processing and analysis. The computer equipment can convert the original data into a load data sequence with a uniform time interval. The time interval can be set by the user. For example, the data can be divided into 15-minute time intervals. After formatting the data, unified electricity load data can be obtained, so that subsequent calculations and analysis can be performed.

[0041] Electricity consumption data is generally cyclical. For example, if the energy storage project is used to provide peak-shaving and valley-filling services for enterprises, the electricity consumption on weekdays is generally higher, and the electricity consumption on weekends and holidays is generally lower. Computer equipment can obtain historical electricity consumption information over multiple cycles. The cycle can be year, month, week, etc. Electricity consumption is generally cyclical, so it is possible to make predictions based on historical electricity consumption data over multiple cycles, making the predicted electricity consumption data more accurate.

[0042] Based on the historical electricity consumption information, generate the historical electricity load data for each electricity consumption period. In addition, the historical electricity consumption data obtained can also include different forms. The collected data may include various forms, mainly the electricity usage of different periods and the load power details of different time intervals. The electricity usage refers to the total power consumption corresponding to each time period type within a certain period, which is generally obtained directly through the meter reading or the power grid electricity bill. Its processing method is relatively direct. The electricity in the current cycle (which may be a single month, multiple months or a whole year) is evenly distributed according to the time period to obtain the average electricity in each time period. Then, these electricity data are converted into load power in the corresponding unit time, that is, the power is calculated by dividing the electricity by the time. In this way, the electricity data can be converted into load power and used for subsequent processing.

[0043] Load power data refers to the actual load power consumption during a period. Due to its complexity and diversity, as well as the common problem of missing data, a series of methods are used to process it. First, periodic pattern recognition technology is used to identify and utilize periodic features in the data. For example, weekday power consumption often has similar patterns, while seasonal power consumption shows similarities in similar periods. Based on these patterns, the missing data can be reasonably estimated and filled.

[0044] The computer equipment can process the historical power consumption information to obtain the historical power load data in each power consumption period, so that the power consumption data can have a fixed format.

[0045] Based on the historical electricity load data, the computer equipment can train the long short-term memory network to obtain the load data generation model. First, a large amount of electricity consumption data from energy storage users in different regions was collected and accumulated, and these data were cleaned and formatted, and randomly shuffled and divided into 70% training set, 15% validation set and 15% test set. Then, the appropriate number of LSTM layers and the number of units in each layer were selected, and the dropout layer was added to prevent overfitting. The mean square error (MSE) was used as the loss function and optimized using the Adam optimizer. By training on the training set and adjusting the parameters on the validation set, a model that achieved excellent performance on the test set was finally obtained, and the load data generation model was finally obtained.

[0046] Based on the above load data generation model, for the historical power consumption data of the new energy storage project to be configured, the historical power consumption data can be formatted first, and then the time period containing missing values ​​and its adjacent data are input into the load data generation model. The load data generation model will combine the trend analysis of historical data and the real-time correction of new data to generate missing power consumption data, thereby supplementing the data.

[0047] The power consumption data of the new energy storage project is input into the load data generation model. The load data generation model can be used to predict the power load data in each power consumption period; thus, the power load power can be determined based on the power load data.

[0048] It should be noted that the periodic pattern recognition method has a wider range of applicability, while the generative model needs to be based on a large amount of data to achieve the best effect. Therefore, in actual engineering applications, it is necessary to analyze the specific situation and select the appropriate method. Through these preprocessing steps, the standardization and consistency of the data can be ensured, laying a solid foundation for subsequent analysis and calculation.

[0049] S102, determining a charge and discharge plan of the energy storage device according to the device information of the energy storage device and the power load power, wherein the charge and discharge plan includes the charge and discharge state and charge and discharge amount of the energy storage device in each power consumption period, and the device information includes the device power and the total power capacity.

[0050] When determining the charging and discharging plan of the energy storage device, the power consumption period can be divided, and then the charging and discharging state and charging and discharging amount of the energy storage device in each power consumption period can be determined. The charging and discharging state can include standby, charging or discharging, and standby means neither charging nor discharging.

[0051] When dividing the power consumption time period, the electricity price in each power consumption time period can be kept the same, so as to facilitate calculation. For example, the above power consumption time period can be divided according to the power price time period, for example, the time period with the same power price can be divided into one power consumption time period. The above power consumption time period can also be planned by hour, and the computer device can divide a day into 24 power consumption time periods. Each power consumption time period can have a corresponding power load power. For a specific day, the computer device divides it into 24 hours (because usually the power price and time period are in hours, the algorithm can be extended to any time unit), recorded as t0 (representing the time period from 0 o'clock to 1 o'clock), t1, t2,..., t23, the power price of each time period is p0, p1, p2,...p23 (unit: yuan / kwh), and the power price reflects the peak and valley state of each time period. Define the actual power load power as l, and the load power of each hour is l1, l2,..., li,..., l23.

[0052] When determining the charging and discharging scheme, each power consumption period may have a maximum charge amount and a maximum discharge amount. In addition, during the charging and discharging process, the maximum discharge amount of the energy storage device for continuous discharge is not higher than the total power capacity of the energy storage device, and the maximum charge amount of the energy storage device for continuous charging is not higher than the total power capacity of the energy storage device.

[0053] Therefore, when determining the charging and discharging plan, it is necessary to determine it based on the above constraints. Therefore, the computer device can determine that each power usage period can have a maximum charging amount and a maximum discharging amount.

[0054] The energy storage device may have device information, wherein the device information may include total electric energy capacity and device power. For example, the total capacity of the energy storage device is Cap, and the power is pW. In addition, the energy storage system also includes a transformer, and the transformer may have a transformer installed capacity, and the transformer installed capacity may be Tc.

[0055] The device power limits the maximum charging power and maximum discharging power of the energy storage device. The computer device can determine the maximum charging power and maximum discharging power of the energy storage device based on the device power and the power load.

[0056] Since the energy storage device discharges to meet the power demand of the power load, the power load also limits the maximum discharge power of the energy storage device. Based on this, the computer device can determine the maximum discharge power of the energy storage device in each power consumption period according to the maximum charging power and the power load power. The computer device can use the smaller value of the device power and the power load power as the maximum discharge power.

[0057] Since the transformer is used to convert the grid voltage into the voltage required by the energy storage device for storage or output, the transformer limits the maximum charging power of the energy storage device. Based on this, the computer device can determine the maximum charging power of the energy storage device in each power consumption period according to the installed power of the transformer and the power of the device. The computer device can determine the power difference between the installed power of the transformer and the power of the power load, and then use the smaller value between the device power and the power difference as the maximum charging power.

[0058] After determining the maximum charging power, the computer device may determine the maximum charging amount of the energy storage device within a unit power usage period according to the maximum charging power. The maximum charging amount may be the product of the maximum charging power and the length of the power usage period.

[0059] After determining the maximum discharge power, the computer device may determine the maximum discharge amount of the energy storage device within a unit power usage period according to the maximum discharge power. The maximum discharge amount may be the product of the maximum discharge power and the length of the power usage period.

[0060] Based on the maximum charge amount and the maximum discharge amount, the computer device can determine a recursive formula for the benefits of the energy storage device after each power usage period. The recursive formula can be used to determine the state of the next power usage period based on the state of the migration power usage period, thereby determining the benefits.

[0061] The above profit recursion formula can be:

[0062]

[0063] charge∈[1, charge_max], charge_max≤j;

[0064] discharge∈[1, discharge_max], discharge_max≤Cap-j

[0065] Wherein, dp[i][j] is the revenue of the energy storage device after the i-th power consumption period, i is the number of the power consumption period, j is the power of the energy storage device after the i-th power consumption period, charge is the actual charge amount of the energy storage device in the i-th power consumption period; discharge is the actual discharge amount of the energy storage device in the i-th power consumption period; pi is the electricity price of the i-th power consumption period; charge_max is the maximum charge amount, discharge_max is the maximum discharge amount; Cap is the total power capacity of the energy storage device; Si is the charge and discharge state of the energy storage device in the i-th power consumption period; Si=0 is used to indicate that the energy storage device is in the standby state in the i-th power consumption period, Si=1 is used to indicate that the energy storage device is in the charging state in the i-th power consumption period, and Si=2 is used to indicate that the energy storage device is in the discharging state in the i-th power consumption period.

[0066] Based on the benefit recursion formula, determine the charging and discharging scheme of the energy storage device. In the embodiment of the present application, the target charging and discharging state and charge and discharge amount of the target power period and each previous power period can be determined according to the benefit recursion formula when the benefit after the target power period is maximized. For example, the first period, the second period and the third period are three consecutive power periods. Assuming that the target power period is the third period, according to the benefit recursion formula, the target charging and discharging state and charge and discharge amount of the target power period and each previous power period are determined when the benefit after the target power period is maximized, which is equivalent to determining the target charging and discharging state and charge and discharge amount of the first period, the second period and the third period when the benefit after the third period is maximized. The first period, the second period and the third period can have 3 different charging and discharging states and multiple different charging and discharging amounts, respectively. Based on the fact that each first period, the second period and the third period can have multiple different charging and discharging states, the computer device can determine 27 different charging and discharging methods. For each charging and discharging method, the computer device can adjust the charging and discharging amount of each power period, thereby calculating the corresponding benefit of each charging and discharging amount. The computer device can determine the charge and discharge state and charge and discharge amount of each time period that maximizes the benefit after the third time period from a plurality of charge and discharge modes and charge and discharge amounts.

[0067] It should be noted that when the energy storage device is currently in an empty state, the charge and discharge state of the next period may only include standby and charging; when the energy storage device is currently in a full state, the charge and discharge state of the next period may only include standby and discharging.

[0068] Therefore, the computer identification can determine the target charge and discharge state and target charge and discharge amount of the energy storage device in the target power consumption period and each power consumption period before the target power consumption period based on the profit recursion formula. The target charge and discharge state and target charge and discharge amount are the charge and discharge state and charge and discharge amount that maximize the benefit value of the energy storage device after the target power consumption period. Then the computer device can use the target charge and discharge state and target charge and discharge amount in the target power consumption period and each power consumption period before the target power consumption period as the charge and discharge plan of the energy storage device.

[0069] For example, when determining the charging and discharging strategy for each power consumption period, based on the above-mentioned benefit recursion formula, the value of the benefit dp[i][j] can be:

[0070] dp[i][j]=max(dp[i-1][j+discharge]+discharge*pi, dp[i-1][j-charge]

[0071] +charge*-pi,dp[i-1][j])

[0072] Among them, charge∈[1, charge_max], charge_max=min((Tc-li, pW))≤j, discharge∈[1, discharge_max], discharge_max=min(li, pW)≤min(Cap-j, li).

[0073] That is, the computer device can determine the charge and discharge state and charge and discharge amount that maximize the benefit according to the benefit recursion formula under different charge and discharge states. Based on this, the computer device can determine various possible charge and discharge schemes in each power consumption period, each charge and discharge scheme can have a corresponding benefit value, and the computer device can determine the maximum benefit value of the entire system from the maximum benefit value of all time points.

[0074] The above initial value may be 0, that is, initially, the energy storage device is in an empty power state.

[0075] It should be noted that in order to improve computing efficiency, a smaller power can be used for computing, and then the power can be amplified in engineering implementation. Computer equipment uses the minimum power as a unit for recursive computing, which can reduce the computing space and improve the efficiency of the algorithm.

[0076] S103: generating a revenue curve of the energy storage project based on the charging and discharging scheme of the energy storage device, wherein the revenue curve is used to characterize the corresponding relationship between the configuration information of each of the energy storage devices and the revenue of the energy storage project.

[0077] Energy storage equipment has costs when it is built and deployed, for example, the price of energy storage equipment and transformers and the labor costs of building and deploying the energy storage system.

[0078] The computer device can determine the initial cost of the energy storage project corresponding to the configuration information of each energy storage device; determine the annual income corresponding to the configuration information of each energy storage device based on the charging and discharging plan of each energy storage device; determine the internal rate of return corresponding to the configuration information of the energy storage device based on the annual income and the initial cost; and generate a yield curve based on the internal rate of return corresponding to the configuration information of each energy storage device.

[0079] The above energy storage devices may be energy storage devices of different capacities and powers. The computer device may conduct a comprehensive benefit analysis on energy storage devices of different capacities and powers.

[0080] First, users can configure basic parameters such as the rated power of a single energy storage unit, the capacity of a single energy storage unit, the project cycle, and the cost of the energy storage system. The rated power of a single energy storage unit and the capacity of a single energy storage unit refer to the smallest battery unit to be used in an actual energy storage project, and all possible data are analyzed based on this unit. The project cycle represents the estimated execution cycle of the project, which is used to calculate subsequent benefits. The energy storage system cost is used to calculate the basic cost of implementing the energy storage project, and is also used to calculate subsequent benefits.

[0081] According to the parameters configured by the user, the computer equipment can obtain the corresponding optimal charging and discharging strategy and the corresponding maximum income for energy storage devices of different power and capacity. Then, according to the project cycle and cost, the daily income is added up to calculate the estimated annual income amount. According to the project cycle, the total amount of long-term project income can be calculated. According to the annual income of the project and the initial cost investment of the project, the internal rate of return (IRR) is calculated according to the formula:

[0082]

[0083] Where r is the internal rate of return, FV 0 is the initial cost, FV i For subsequent annual income.

[0084] The internal rate of return can be calculated, and the corresponding yield curve can be obtained by putting the income amount under different circumstances together with the internal rate of return.

[0085] S104: Determine target energy storage device configuration information of the energy storage project from configuration information of the plurality of energy storage devices according to the yield curve.

[0086] Each energy storage device has energy storage device configuration information. When planning an energy storage project, the energy storage project may have multiple energy storage device configuration information. The energy storage device configuration information may include the capacity of the energy storage device, the power of the energy storage device, the corresponding income of the energy storage device, the charging and discharging scheme of the energy storage device, and the transformer used.

[0087] When determining the recommended energy storage equipment for the energy storage project, the computer device can recommend energy storage solutions based on multi-criteria decision analysis (MCDA). Multi-criteria decision analysis means that multiple factors need to be considered when recommending energy storage solutions.

[0088] In this embodiment, each energy storage project may have corresponding decision objectives and evaluation criteria. The decision objectives and evaluation criteria may be generated based on the needs of the users of the energy storage project. Based on the decision objectives and evaluation criteria, a decision matrix may be constructed for the energy storage project. The decision matrix may include multiple decision indicators. For example, the decision indicators may include internal rate of return, project service life, brand requirements of energy storage equipment, project cost requirements, project revenue requirements, etc. Each decision indicator may have a corresponding calculation method to determine the corresponding indicator value. For example, the energy storage device may determine the score corresponding to the brand demand item according to the brand ranking in the decision indicator. The higher the ranking, the higher the score.

[0089] Each decision indicator may also have a weight value, which may be set by the user to indicate the importance of each decision indicator in the energy storage project recommendation process. The weight value of each decision indicator may be determined by expert scoring. For each decision indicator, the expert may input the weight value into the computer device, so that the computer device may obtain the weight value of each decision indicator.

[0090] Specifically, the computer device can determine multiple decision indicators based on the decision matrix of the energy storage project, each decision indicator includes a corresponding weight value, and the internal rate of return is one of the decision indicators. Computer recognition can construct a decision matrix based on the configuration information of multiple energy storage devices. The decision matrix can include multiple energy storage devices and multiple evaluation criteria, and each evaluation criterion corresponds to a decision indicator. Based on the corresponding calculation method, the computer device can determine the decision indicator value corresponding to each energy storage device. After determining the decision indicator value, the decision indicator value of each energy storage device in the decision matrix can be standardized so that the decision indicator values ​​of each energy storage device can be compared on the same dimension. For example, the maximum and minimum standardization and linear standardization methods can be used to standardize the decision matrix to eliminate the differences caused by different dimensions.

[0091] According to the index value of each decision index corresponding to each energy storage device, the weighted index and value corresponding to each energy storage device are calculated, and the weighted index and value is the sum of the products of each index value and the corresponding weight value. Among them, the weight value of each decision index can be determined in advance through expert scoring and hierarchical analysis method.

[0092] The energy storage device corresponding to the largest weighted index and value is used as the recommended energy storage device for the energy storage project.

[0093] In another possible implementation, multiple energy storage devices may be recommended to the user. The computer device may sort the energy storage devices according to the weighted index and value, so that multiple recommended energy storage devices may be determined according to the sorting result.

[0094] The embodiment of the present application can determine the profit recursive formula of the energy storage device based on the device information of the energy storage device, so that the planning of the charging and discharging scheme that maximizes the profit of the energy storage device can be realized based on the profit recursive formula. Based on the planning of the charging and discharging scheme that maximizes the profit of the energy storage device, the computer device can determine the profit curve of each energy storage device, and based on the profit curve and the decision analysis strategy, it can recommend a target energy storage scheme and determine the recommended energy storage device. In this embodiment, the computer device can perform data processing, optimal charging and discharging scheme calculation, profit calculation and energy storage scheme recommendation, thereby comprehensively solving a series of problems in energy storage project planning. It avoids the dependence of energy storage scheme planning on the experience of experts, so that the determination of energy storage device configuration information has a systematic and scientific solution.

[0095] It should be noted that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0096] In order to more clearly explain the scheme in the present application, specific embodiments may be used to explain the scheme in the present application below.

[0097] Figure 2 Schematic diagram of a planning method for an energy storage project provided in an embodiment of the present application. Figure 2As shown, user data can be obtained when planning an energy storage solution. The computer device can determine whether the user data has a specific data format. If the user data does not have a specific data format, the electricity usage data can be allocated according to the cycle based on the electricity usage, so that the user data has a specific data format. Based on the user data with a specific data format and the electricity load details, formatted load data can be obtained. Based on the formatted load data and the relationship between the time period and the electricity price in the current area, the charging and discharging strategy of the energy storage device is determined based on the above-mentioned benefit recursion formula. For different energy storage devices, a benefit curve can be executed. Therefore, based on the benefit curve, an energy storage solution for the energy storage project can be recommended to the user.

[0098] In this embodiment, when planning an energy storage project, the computer device can calculate the optimal charging and discharging strategy for energy storage devices under different capacities and powers based on the power load data provided by the user, as well as the regional electricity price and time period information, and recommend a series of energy storage device selection and charging and discharging plans based on the long-term benefits, internal rate of return and other benefits of each energy storage device.

[0099] Reference Figure 3 , shows a schematic diagram of a planning device for an energy storage solution provided in an embodiment of the present application, which may specifically include an electric load power determination module 31, a charge and discharge scheme determination module 32, a yield curve determination module 33 and an energy storage device configuration information determination module 34, wherein:

[0100] The power load power determination module 31 is used to determine the power load power in each power consumption period based on the historical power consumption data;

[0101] A charging and discharging scheme determination module 32 is used to determine the charging and discharging scheme of the energy storage device according to the device information of the energy storage device and the power of the power load, wherein the charging and discharging scheme includes the charging and discharging state and the charging and discharging amount of the energy storage device in each power consumption period, and the device information includes the device power and the total power capacity;

[0102] A benefit curve determination module 33, used to generate a benefit curve of the energy storage project based on the charging and discharging scheme of the energy storage device, wherein the benefit curve is used to characterize the corresponding relationship between the configuration information of each of the energy storage devices and the benefit of the energy storage project;

[0103] The energy storage device configuration information determination module 34 is used to determine the target energy storage device configuration information of the energy storage project from the configuration information of the plurality of energy storage devices according to the yield curve.

[0104] In a possible implementation, the charging and discharging scheme determining module 32 includes:

[0105] A power determination submodule, used to determine the maximum charging power and the maximum discharging power of the energy storage device according to the device power and the power of the power load;

[0106] A maximum charge capacity determination submodule, used to determine the maximum charge capacity of the energy storage device within a unit power consumption period according to the maximum charging power;

[0107] A maximum discharge capacity determination submodule, used to determine the maximum discharge capacity of the energy storage device within a unit power consumption period according to the maximum discharge power;

[0108] A benefit recursion formula determination submodule, used to determine the benefit recursion formula of the energy storage device after each power consumption period based on the maximum charge amount and the maximum discharge amount;

[0109] The charging and discharging scheme determination submodule is used to determine the charging and discharging scheme of the energy storage device based on the benefit recursion formula.

[0110] In a possible implementation, the profit recursion formula is:

[0111]

[0112] charge∈[1, charge_max], charge_max≤j;

[0113] discharge∈[1, discharge_max], discharge_max≤Cap-j

[0114] Wherein, dp[i][j] is the revenue of the energy storage device after the i-th power consumption period, i is the number of the power consumption period, j is the power of the energy storage device after the i-th power consumption period, charge is the actual charge amount of the energy storage device in the i-th power consumption period; discharge is the actual discharge amount of the energy storage device in the i-th power consumption period; pi is the electricity price of the i-th power consumption period; charge_max is the maximum charge amount, discharge_max is the maximum discharge amount; Cap is the total power capacity of the energy storage device; Si is the charge and discharge state of the energy storage device in the i-th power consumption period; Si=0 is used to indicate that the energy storage device is in the standby state in the i-th power consumption period, Si=1 is used to indicate that the energy storage device is in the charging state in the i-th power consumption period, and Si=2 is used to indicate that the energy storage device is in the discharging state in the i-th power consumption period.

[0115] In a possible implementation, the charging and discharging scheme determination submodule includes:

[0116] A power consumption period charging information determination unit, configured to determine a target charge and discharge state and a target charge and discharge amount of the energy storage device in each power consumption period based on the benefit recursion formula, wherein the target charge and discharge state and the target charge and discharge amount are the charge and discharge state and the charge and discharge amount that maximize the benefit value of the energy storage device after the current power consumption period;

[0117] The charging and discharging scheme determining unit is used to determine the charging and discharging scheme of the energy storage device according to the target charging and discharging state and the target charging and discharging amount in each power consumption period.

[0118] In a possible implementation, the power load power determination module 31 includes:

[0119] A historical electricity consumption data acquisition submodule is used to acquire historical electricity consumption data within multiple cycles of the energy storage project, wherein the historical electricity consumption data is historical electricity consumption data or historical electricity load data;

[0120] A data conversion submodule, for converting the historical power consumption data into historical power load data in each power consumption period if the historical power consumption data is historical power consumption data;

[0121] The prediction submodule is used to input the historical power load data into the trained load data generation model to predict the power load power in each power consumption period; the load data generation model is trained based on the long short-term memory network.

[0122] In a possible implementation, the yield curve determination module 32 includes:

[0123] An initial cost determination submodule, used to determine the initial cost of the energy storage project corresponding to each of the energy storage devices;

[0124] An annual revenue determination submodule, used to determine the annual revenue of each of the energy storage devices based on the charging and discharging schemes of each of the energy storage devices;

[0125] An internal rate of return determination submodule, used to determine the internal rate of return corresponding to the energy storage device according to the annual income and the initial cost;

[0126] The yield curve generating submodule is used to generate the yield curve based on the internal rate of return corresponding to each of the energy storage devices.

[0127] In a possible implementation, the energy storage device configuration information determination module 32 includes:

[0128] A decision indicator determination submodule, used to determine a plurality of decision indicators based on the decision matrix of the energy storage project, each decision indicator including a corresponding weight value, and the internal rate of return being one of the decision indicators;

[0129] A weighted summation submodule, used to calculate the weighted index and value corresponding to each energy storage device according to the index value of each decision index corresponding to each energy storage device, wherein the weighted index and value is the sum of the products of each index value and the corresponding weight value;

[0130] The energy storage device configuration information determination submodule is used to use the configuration information of the energy storage device corresponding to the largest weighted index and value as the energy storage device configuration information of the energy storage project.

[0131] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment part.

[0132] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. Figure 4 As shown, the computer device 4 of this embodiment includes: at least one processor 40 ( Figure 4 Only one is shown in the figure), a memory 41, and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, wherein the processor 40 implements the steps of any of the above-mentioned method embodiments when executing the computer program 42.

[0133] The computer device 4 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud computing device. The computer device may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will appreciate that Figure 4 It is only an example of the computer device 4 and does not constitute a limitation on the computer device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0134] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0135] In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the computer device 4. The memory 41 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 41 may also be used to temporarily store data that has been output or is to be output.

[0136] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0137] An embodiment of the present application provides a computer program product. When the computer program product is run on a computer device, the computer device can implement the steps in the above-mentioned method embodiments when executing the computer device.

[0138] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application is described in detail with reference to the above-mentioned embodiments, a person skilled in the art should understand that the technical solutions described in the above-mentioned embodiments can still be modified, or some of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for planning an energy storage solution, characterized in that: include: Based on historical electricity consumption data, determine the power load power in each electricity consumption period; Determine a charge and discharge scheme of the energy storage device according to the device information of the energy storage device and the power load power, wherein the charge and discharge scheme includes the charge and discharge state and charge and discharge amount of the energy storage device in each power consumption period, and the device information includes the device power and the total power capacity; Based on the charging and discharging scheme of the energy storage device, a revenue curve of the energy storage project is generated, wherein the revenue curve is used to characterize the corresponding relationship between the configuration information of each of the energy storage devices and the revenue of the energy storage project; According to the yield curve, target energy storage device configuration information of the energy storage project is determined from configuration information of a plurality of energy storage devices.

2. The method according to claim 1, characterized in that The step of determining the charging and discharging scheme of the energy storage device according to the device information of the energy storage device and the power load power includes: Determining the maximum charging power and the maximum discharging power of the energy storage device according to the device power and the power of the electrical load; Determining the maximum charging capacity of the energy storage device within a unit power consumption period according to the maximum charging power; Determining the maximum discharge capacity of the energy storage device within a unit power consumption period according to the maximum discharge power; Based on the maximum charge amount and the maximum discharge amount, determining a recursive formula for the benefit of the energy storage device after each power consumption period; Based on the benefit recursion formula, a charging and discharging plan of the energy storage device is determined.

3. The method according to claim 2, characterized in that The profit recursion formula is: charge∈[1, charge_max], charge_max≤j; discharge∈[1, discharge_max], discharge_max≤Cap-j Among them, dp[i][j] is the profit of the energy storage device after the i-th power consumption period, i is the number of the power consumption period, j is the power of the energy storage device after the i-th power consumption period, charge is the actual charge amount of the energy storage device in the i-th power consumption period; discharge is the actual discharge amount of the energy storage device in the i-th power consumption period; pi is the electricity price of the i-th power consumption period; charge_max is the maximum charge amount, discharge_max is the maximum discharge amount; Cap is the total power capacity of the energy storage device; Si is the charge and discharge state of the energy storage device in the i-th power consumption period; Si=0 is used to indicate that the energy storage device is in standby state in the i-th power consumption period, Si=1 is used to indicate that the energy storage device is in charging state in the i-th power consumption period, and Si=2 is used to indicate that the energy storage device is in discharging state in the i-th power consumption period.

4. The method according to claim 2 or 3, characterized in that Determining the charging and discharging scheme of the energy storage device based on the benefit recursion formula includes: Based on the benefit recursion formula, determine the target charge and discharge state and target charge and discharge amount of the energy storage device in each power consumption period, wherein the target charge and discharge state and the target charge and discharge amount are the charge and discharge state and charge and discharge amount that maximize the benefit value of the energy storage device after the target power consumption period; The charging and discharging scheme of the energy storage device is determined according to the target charging and discharging state and the target charging and discharging amount in each power consumption period.

5. The method according to claim 1, characterized in that The method of determining the power load power in each power consumption period based on the historical power consumption data includes: Acquire historical electricity consumption data of the energy storage project in multiple cycles, wherein the historical electricity consumption data is historical electricity consumption data or historical electricity load data; If the historical electricity consumption data is historical electricity consumption data, converting the historical electricity consumption data into historical electricity load data in each electricity consumption period; The historical power load data is input into a trained load data generation model to obtain a predicted power load power in each power consumption period; the load data generation model is obtained by training a long short-term memory network.

6. The method according to any one of claims 1 to 3 or 5, characterized in that: The generating a benefit curve of each energy storage device based on the charging and discharging scheme of each energy storage device comprises: Determining the initial cost of the energy storage project corresponding to each of the energy storage devices; Determine the annual revenue of each energy storage device based on the charging and discharging plan of each energy storage device; Determining the internal rate of return corresponding to the energy storage device according to the annual income and the initial cost; The yield curve is generated based on the internal rate of return corresponding to each of the energy storage devices.

7. The method according to claim 6, characterized in that The step of determining target energy storage device configuration information of the energy storage project from configuration information of a plurality of energy storage devices according to the yield curve includes: Based on the decision matrix of the energy storage project, a plurality of decision indicators are determined, each decision indicator includes a corresponding weight value, and the internal rate of return is one of the decision indicators; According to the index value of each decision index corresponding to each energy storage device, calculate the weighted index and value corresponding to each energy storage device, wherein the weighted index and value is the sum of the products of each index value and the corresponding weight value; The configuration information of the energy storage device corresponding to the largest weighted index and value is used as the target energy storage device configuration information of the energy storage project.

8. A planning device for energy storage scheme, characterized in that: include: An electricity load power determination module is used to determine the electricity load power in each electricity consumption period based on historical electricity consumption data; A charging and discharging scheme determination module, used to determine the charging and discharging scheme of the energy storage device according to the device information of the energy storage device and the power of the power load, wherein the charging and discharging scheme includes the charging and discharging state and the charging and discharging amount of the energy storage device in each power consumption period, and the device information includes the device power and the total power capacity; A revenue curve determination module, used to generate a revenue curve of an energy storage project based on a charging and discharging scheme of the energy storage device, wherein the revenue curve is used to characterize a corresponding relationship between configuration information of each of the energy storage devices and the revenue of the energy storage project; The energy storage device configuration information determination module is used to determine the target energy storage device configuration information of the energy storage project from the configuration information of the plurality of energy storage devices according to the yield curve.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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