Energy storage scheduling method and device, equipment, storage medium and program product

Abstract

The invention provides an energy storage scheduling method and device, equipment, a storage medium and a program product. The method comprises one or more energy storage resources, and each energy storage resource comprises one or more energy storage devices; acquiring a first equipment parameter of each energy storage resource and a first environment parameter of an environment where the energy storage resource is located; according to the first equipment parameter and the first environment parameter of each energy storage resource, respectively determining a first real-time operation constraint condition, a first energy efficiency loss cost function and a first life attenuation cost function of each energy storage resource; determining a revenue function of one or more energy storage resources according to the first energy efficiency loss cost function, the first life attenuation cost function and the predicted electricity price; and determining a power optimal solution of each energy storage resource when the revenue function is maximized under the first real-time operation constraint condition, and scheduling each energy storage resource according to the power optimal solution of each energy storage resource. According to the intelligent energy storage scheduling method, the economic benefit is maximized.

Description

Technical Field

[0001] This application relates to the technical field of energy storage charge and discharge scheduling, and particularly relates to an energy storage scheduling method, device, equipment, storage medium, and program product. Background Art

[0002] An energy storage power station can store excess electric energy during low power demand periods and release this electric energy during peak demand periods, and is usually used to balance power supply and demand, and improve the stability and efficiency of the power grid.

[0003] The energy storage scheduling of an energy storage power station refers to scheduling the energy storage devices of the energy storage power station to perform charge and discharge operations according to the characteristics of the energy storage devices. How to achieve intelligent scheduling of the energy storage power station is an urgent problem to be solved. Summary of the Invention

[0004] This application provides an energy storage scheduling method, device, equipment, storage medium, and program product to achieve intelligent scheduling of an energy storage power station.

[0005] In a first aspect, this application provides an energy storage scheduling method, including: one or more energy storage resources, each of the energy storage resources including one or more energy storage devices;

[0006] Obtain first device parameters of each of the energy storage resources and first environmental parameters of the environment where the energy storage resources are located;

[0007] According to the first device parameters of each of the energy storage resources and the first environmental parameters, respectively determine first real-time operation constraint conditions, first energy efficiency loss cost functions, and first life attenuation cost functions for each of the energy storage resources; the first real-time operation constraint conditions, the first energy efficiency loss cost functions, and the first life attenuation cost functions are respectively functions with the power of each of the energy storage resources as variables;

[0008] According to the first energy efficiency loss cost functions, the first life attenuation cost functions, and predicted electricity prices, determine a revenue function for the one or more energy storage resources;

[0009] Determine an optimal power solution for each of the energy storage resources when the revenue function is maximized under the first real-time operation constraint conditions, and schedule each of the energy storage resources according to the optimal power solution of each of the energy storage resources.

[0010] Optionally, the obtaining first device parameters of each of the energy storage resources and first environmental parameters of the environment where the energy storage resources are located includes:

[0011] For an energy storage resource including multiple energy storage devices, obtain second device parameters and second environmental parameters of each of the energy storage devices in the energy storage resource;

[0012] Based on the second device parameters and the second environmental parameters of each of the energy storage devices, the first device parameters and the first environmental parameters are calculated.

[0013] Optionally, the method further includes:

[0014] Based on the second device parameters and the second environmental parameters of each of the energy storage devices, the second real-time operation constraint conditions, the second energy efficiency loss cost function, and the second life attenuation cost function of each of the energy storage devices are respectively determined. The second real-time operation constraint function, the second energy efficiency loss cost function, and the second life attenuation cost function are respectively functions with the power of each of the energy storage devices as variables;

[0015] Based on the power optimal solution of the energy storage resources, the scheduling power constraint conditions of each of the energy storage devices are determined;

[0016] Based on the second energy efficiency loss cost function and the second life attenuation cost function, the cost function of the multiple energy storage devices is determined;

[0017] When it is determined that the cost function is minimized under the scheduling power constraint conditions and the second real-time operation constraint conditions, the power optimal solution of each of the energy storage devices is determined, and each of the energy storage devices is scheduled according to the power optimal solution of each of the energy storage devices.

[0018] Optionally, the determining the cost function of the multiple energy storage devices according to the second energy efficiency loss cost function and the second life attenuation cost function includes:

[0019] The sum of the product of the second energy efficiency loss cost function and the first coefficient and the product of the second life attenuation cost function and the second coefficient is determined as the cost function of the multiple energy storage devices; wherein, the first coefficient is the coefficient for converting the energy efficiency loss cost of the energy storage device into an economic cost, and the second coefficient is the conversion coefficient from the life attenuation cost of the energy storage device to an economic cost.

[0020] Optionally, the determining the revenue function of the one or more energy storage resources according to the first energy efficiency loss cost function, the first life attenuation cost function, and the predicted electricity price includes:

[0021] The product of the predicted electricity price and the power of the energy storage resources minus the product of the first energy efficiency loss cost function and the third coefficient, and minus the product of the first life attenuation cost function and the fourth coefficient is determined as the revenue function of the one or more energy storage resources; wherein, the third coefficient is the coefficient for converting the energy efficiency loss cost of the energy storage resources into an economic cost; the fourth coefficient is the conversion coefficient from the life attenuation cost of the energy storage resources to an economic cost.

[0022] Optionally, the method further includes:

[0023] Obtaining power market data, where the power market data includes power market data of multiple power market systems, and the real-time prices of different power market systems are different;

[0024] Determining the predicted electricity price of each power market system according to the power market data of each power market system and the electricity price prediction model corresponding to each power market system;

[0025] The determining the revenue function of the one or more energy storage resources according to the first energy efficiency loss cost function, the first life decay cost function, and the predicted electricity price includes:

[0026] Determining the predicted electricity price corresponding to each energy storage resource according to the power market system corresponding to each energy storage resource;

[0027] Determining the revenue function of the one or more energy storage resources according to the first energy efficiency loss cost function, the first life decay cost function, and the predicted electricity price corresponding to each energy storage resource.

[0028] Optionally, the first device parameters include the rated power and the schedulable capacity of the energy storage resource, and the first environmental parameters include the load upper limit and the standard frequency range of the power consumption area where the energy storage resource belongs; the first real-time operation constraints include:

[0029] The charging and discharging power of the energy storage resource is less than or equal to the rated power; the state of charge of the energy storage resource is less than or equal to the schedulable capacity; the sum of the charging power of the energy storage resource and the user power consumption in the power consumption area is less than or equal to the load upper limit of the power consumption area; the grid frequency of the power consumption area conforms to the standard frequency range.

[0030] Optionally, determining the first energy efficiency loss cost function of each energy storage resource according to the first device parameters and the first environmental parameters of each energy storage resource includes:

[0031] Determining the energy efficiency function of the DC side and the energy efficiency function of the AC side of each energy storage resource according to the first device parameters and the first environmental parameters of each energy storage resource; determining the first energy efficiency loss cost function of each energy storage resource according to the energy efficiency function of the DC side and the energy efficiency function of the AC side.

[0032] Optionally, determining the first life decay cost function of each energy storage resource according to the first device parameters and the first environmental parameters of each energy storage resource includes:

[0033] Based on the first device parameters and the first environmental parameters of each energy storage resource, determine the life attenuation rate function of each energy storage resource in the charge and discharge state, and the life attenuation rate function in the static state; based on the life attenuation rate function in the charge and discharge state and the life attenuation rate function in the static state, determine the first life attenuation cost function of each energy storage resource.

[0034] In a second aspect, the present application provides an energy storage scheduling device, including: one or more energy storage resources, and each energy storage resource includes one or more energy storage devices;

[0035] An acquisition module, configured to acquire the first device parameters of each energy storage resource and the first environmental parameters of the environment where the energy storage resource is located;

[0036] A first determination module, configured to respectively determine the first real-time operation constraint conditions, the first energy efficiency loss cost function, and the first life attenuation cost function of each energy storage resource according to the first device parameters and the first environmental parameters of each energy storage resource; the first real-time operation constraint conditions, the first energy efficiency loss cost function, and the first life attenuation cost function are respectively functions with the power of each energy storage resource as a variable;

[0037] A second determination module, configured to determine the revenue function of the one or more energy storage resources according to the first energy efficiency loss cost function, the first life attenuation cost function, and the predicted electricity price;

[0038] A scheduling module, configured to determine the optimal power solution of each energy storage resource when the revenue function is maximized under the first real-time operation constraint conditions, and schedule each energy storage resource according to the optimal power solution of each energy storage resource.

[0039] In a third aspect, the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0040] The memory stores computer-executable instructions;

[0041] The processor executes the computer-executable instructions stored in the memory to implement the method described in the first aspect and various possible designs of the first aspect as above.

[0042] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the processor executes the computer-executable instructions, the method described in the first aspect and various possible designs of the first aspect as above is implemented.

[0043] Fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method described in the first aspect above and various possible designs of the first aspect.

[0044] The energy storage scheduling method, device, equipment, storage medium and program product provided by the present application obtain the first device parameters of each energy storage resource and the first environmental parameters of the environment where the energy storage resource is located, respectively determine the first real-time operation constraint conditions, the first energy efficiency loss cost function and the first life attenuation cost function of each energy storage resource, and determine the revenue function of one or more energy storage resources according to the first energy efficiency loss cost function, the first life attenuation cost function and the predicted electricity price. When determining the maximum of the revenue function under the first real-time operation constraint conditions, the optimal power solution of each energy storage resource is determined, and each energy storage resource is scheduled according to the optimal power solution of each energy storage resource. The predicted electricity price and the energy efficiency loss cost can reflect the impact of energy storage scheduling on economic benefits in the short term, and the life attenuation cost can reflect the impact of energy storage scheduling on economic benefits in the long term. The method of the present application determines the revenue function by predicting the electricity price, the energy efficiency loss cost and the life attenuation cost, comprehensively considers the short-term factors and long-term factors affecting energy storage scheduling, and realizes an intelligent energy storage scheduling method that maximizes economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0046] Figure 1 It is a flowchart of an energy storage scheduling method provided by an embodiment of the present application Figure 1 ;

[0047] Figure 2 It is a flowchart of the first energy storage scheduling method provided by an embodiment of the present application Figure 2 ;

[0048] Figure 3 It is a schematic structural diagram of an energy storage scheduling device provided by an embodiment of the present application;

[0049] Figure 4 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0050] Through the above-mentioned accompanying drawings, the clear embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and text descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0052] At present, the operation of energy storage power stations still faces problems such as high operation costs and uncertain revenue sources, making it difficult to fully exert their application value in the power system. To achieve intelligent scheduling of energy storage power stations, related technologies usually establish a scheduling model for energy storage power stations by setting economic scheduling objectives based on electricity price fluctuations and the state of energy storage devices in the energy storage power station, including the charging state, remaining capacity, etc., of the energy storage devices. The scheduling model of the energy storage power station optimizes the charge and discharge operations of the energy storage power station to maximize economic benefits. Since electricity price fluctuations and the state of energy storage devices can only reflect the impact of energy storage scheduling on economic benefits in the short term, the accuracy of energy storage scheduling methods in related technologies is low.

[0053] In view of this, the present application proposes an energy storage scheduling method. By obtaining the first device parameters of each energy storage resource and the first environmental parameters of the environment where the energy storage resource is located, the first real-time operation constraint conditions, the first energy efficiency loss cost function, and the first life attenuation cost function of each energy storage resource are determined respectively. And based on the first energy efficiency loss cost function, the first life attenuation cost function, and the predicted electricity price, the revenue function of one or more energy storage resources is determined. When the revenue function is maximized under the first real-time operation constraint conditions, the power optimal solution of each energy storage resource is determined, and each energy storage resource is scheduled according to the power optimal solution of each energy storage resource. The predicted electricity price and the energy efficiency loss cost can reflect the impact of energy storage scheduling on economic benefits in the short term, and the life attenuation cost can reflect the impact of energy storage scheduling on economic benefits in the long term. The method of the present application determines the revenue function by predicting the electricity price, the energy efficiency loss cost, and the life attenuation cost, comprehensively considering the short-term and long-term factors affecting energy storage scheduling, and realizes an intelligent energy storage scheduling method that maximizes economic benefits.

[0054] It should be understood that the energy storage scheduling method of the embodiments of the present application can be used for scheduling in any energy storage scheduling scenario. The execution subject of the embodiments of the present application can be an energy storage scheduling analysis tool, or an electronic device or system that implements the energy storage scheduling method. Hereinafter, a system that implements the energy storage scheduling method will be used as an example for description.

[0055] The following uses specific embodiments to elaborate in detail on the technical solution of this application and how the technical solution of this application solves the above technical problems. These several specific embodiments below can be combined with each other, and for the same or similar concepts or processes, they may not be repeated in some embodiments. The embodiments of this application will be described below in conjunction with the accompanying drawings.

[0056] Figure 1 Flow schematic of an energy storage scheduling method provided for an embodiment of this application Figure 1 As Figure 1 shown in, the energy storage scheduling method may, for example, include the following steps:

[0057] S101. Obtain the first device parameters of each energy storage resource and the first environmental parameters of the environment where the energy storage resource is located.

[0058] An energy storage device is a device used to store the electric energy obtained from the power grid. To obtain electric energy from the power grid, it is usually necessary to conduct transactions with the power grid platform, and the power grid platform requires that the total capacity of the energy storage device exceed the capacity required by the power grid transaction. Therefore, the energy storage scheduling method of this application may include one or more energy storage resources, and each energy storage resource includes one or more energy storage devices. It should be understood that in the case where the capacity of a single energy storage device exceeds the capacity required by the power grid platform, the energy storage resource may include a single energy storage device.

[0059] The first device parameters are indicators and data related to the performance and functions of the energy storage resource. For example, the first device parameters may include the state of charge, battery health status, energy efficiency coefficient, schedulable capacity, etc.

[0060] The first environmental parameters are indicators and data related to the environment where the energy storage resource is located and that affect the energy efficiency and lifespan of the energy storage resource. For example, the first environmental parameters may include environmental temperature, location, altitude, etc.

[0061] To obtain the first device parameters of each energy storage resource, one implementable way is to obtain them based on the management system of the energy storage resource. The management system of the energy storage resource is a system that monitors the operating status of the energy storage resource. This system analyzes and stores the device parameters of the energy storage resource by obtaining the operating status of the energy storage resource. In this way, the first device parameters of the energy storage resource can be obtained through the data interface of the management system of the energy storage resource. One implementable way is to obtain the measurement data of the sensors of the energy storage resource to obtain the first device parameters. In this way, for example, the energy efficiency coefficient of the energy storage resource can be obtained by dividing the measured discharge amount of the power sensor over a period of time by the measured charge amount of the power sensor over a period of time.

[0062] Obtain the first environmental parameters of the environment where each energy storage resource is located. One achievable way is to obtain them based on the management system of the energy storage resource, where the first environmental parameters of the environment where each energy storage resource is located are recorded. In this way, through the data interface of the management system of the energy storage resource, the first environmental parameters of the environment where each energy storage resource is located can be obtained. Another achievable way is to obtain the measurement data of the sensors in the environment where the energy storage resource is located to obtain the first environmental parameters. In this way, for example, the temperature of the environment where the energy storage resource is located can be obtained according to the temperature sensor.

[0063] S102. According to the first device parameters and the first environmental parameters of each energy storage resource, respectively determine the first real-time operation constraint conditions, the first energy efficiency loss cost function, and the first life decay cost function of each energy storage resource.

[0064] Among them, the first real-time operation constraint conditions, the first energy efficiency loss cost function, and the first life decay cost function are respectively functions with the power of each energy storage resource as variables.

[0065] The first real-time operation constraint conditions are the conditions used to constrain the charge and discharge power of the energy storage resource. For example, the charge and discharge power of the energy storage resource should meet the condition of being less than or equal to the rated power.

[0066] The first energy efficiency loss cost function is used to calculate the energy loss caused during the energy conversion and transmission process of the energy storage resource, including the energy conversion loss during the transmission process, and the energy consumed by devices such as transformers in the energy storage resource. The energy efficiency loss is affected by the first device parameters and the first environmental parameters.

[0067] The first life decay cost function is used to calculate the life decay of the hardware of the energy storage resource caused during the operation or stationary state of the energy storage resource. The life decay is affected by the first device parameters and the first environmental parameters.

[0068] According to the first device parameters and the first environmental parameters of each energy storage resource, respectively determine the first real-time operation constraint conditions of each energy storage resource. For example, the power constraint and capacity constraint of each energy storage resource can be determined according to the first device parameters and the first environmental parameters of each energy storage resource. The power constraint, for example, can include that the charge and discharge power of the energy storage resource should meet the condition of being less than or equal to the rated power, and the capacity constraint can include that the capacity calculated according to the charge and discharge power of the energy storage resource should meet the condition of being less than or equal to the schedulable capacity.

[0069] According to the first device parameters and the first environmental parameters of each energy storage resource, determine the first energy efficiency loss cost function of each energy storage resource respectively. One implementable way is to fit a function of the energy efficiency loss cost with the historical device parameters, historical charge and discharge power, and historical environmental parameters through the historical charge and discharge power, historical device parameters, historical environmental parameters, and historical energy efficiency loss cost of the energy storage resource. The historical charge and discharge power, historical device parameters, and historical environmental parameters are the independent variables of the function, and the energy efficiency loss cost is the dependent variable of the function. Further, take the first device parameter of each energy storage resource as the historical device parameter, and the first environmental parameter as the historical environmental parameter to obtain the first energy efficiency loss cost function. Optionally, since the energy efficiency loss of the energy storage resource can be divided into the energy efficiency loss on the DC side in the energy storage resource and the energy efficiency loss on the AC side, the DC side is the device side operating based on direct current, such as a battery pack, and the AC side is the device side operating based on alternating current, such as a transformer, so the first energy efficiency loss cost function of each energy storage resource can also be determined based on the DC side and the AC side respectively.

[0070] According to the first device parameters and the first environmental parameters of each energy storage resource, determine the first life attenuation cost function of each energy storage resource respectively. One implementable way is to fit a function of the life attenuation cost with the historical device parameters, historical charge and discharge power, and historical environmental parameters through the historical charge and discharge power, historical device parameters, historical environmental parameters, and historical life attenuation cost of the energy storage resource. The historical charge and discharge power, historical device parameters, and historical environmental parameters are the independent variables of the function, and the life attenuation cost is the dependent variable of the function. Further, take the first device parameter of each energy storage resource as the historical device parameter, and the first environmental parameter as the historical environmental parameter to obtain the first life attenuation cost function. Optionally, since the life attenuation of the energy storage resource can be divided into the life attenuation cost in the operating condition of the energy storage resource and the life attenuation cost in the static condition, the first life attenuation cost function of each energy storage resource can also be determined based on the operating condition and the static condition of the energy storage resource.

[0071] S103. Determine the revenue function of one or more energy storage resources according to the first energy efficiency loss cost function, the first life attenuation cost function, and the predicted electricity price.

[0072] The predicted electricity price is the electricity price predicted based on the electricity market data. The electricity market data is data related to electricity trading, such as historical electricity price data, publicly disclosed market data, and weather forecast data. For example, input the electricity market data into the electricity price prediction model in the related technology, and the electricity price prediction model can output the predicted electricity price.

[0073] The revenue function of the energy storage resource is a function used to calculate the revenue of one or more energy storage resources. The revenue function of the energy storage resource is a function with the power of each energy storage resource as a variable. For example, the revenue function of the energy storage resource can be the difference between the revenue function of one or more energy storage resources and the cost function. The revenue function can be confirmed as the product of the power of the energy storage resource and the predicted electricity price. The first energy efficiency loss cost function is a function measured by energy efficiency loss and can be converted into a function measured by economic cost, that is, a function measured by price. The first life attenuation cost function can also be converted into a function measured by economic cost. Considering the state of energy efficiency loss and life attenuation of the energy storage resource, the cost function can be confirmed as the sum of the economic cost function converted from the first energy efficiency loss cost function to the first energy efficiency loss cost and the first life attenuation cost function.

[0074] S104. Determine the optimal power solution of each energy storage resource when the revenue function is maximized under the first real-time operation constraint condition, and schedule each energy storage resource according to the optimal power solution of each energy storage resource.

[0075] The revenue function of the energy storage resource represents the revenue of one or more energy storage resources. When the revenue function is maximized under the first real-time operation constraint condition, it is the optimal scheduling of one or more energy storage resources. Determine the optimal power solution of each energy storage resource when the revenue function is maximized under the first real-time operation constraint condition. For example, the optimal power solution of each energy storage resource can be obtained by solving the revenue function based on an optimization solver in related technologies. Further, schedule each energy storage resource according to the optimal power solution of each energy storage resource.

[0076] In the energy storage scheduling method of the present application, by obtaining the first device parameters of each energy storage resource and the first environmental parameters of the environment where the energy storage resource is located, respectively determine the first real-time operation constraint condition, the first energy efficiency loss cost function and the first life attenuation cost function of each energy storage resource, and determine the revenue function of one or more energy storage resources according to the first energy efficiency loss cost function, the first life attenuation cost function and the predicted electricity price. Determine the optimal power solution of each energy storage resource when the revenue function is maximized under the first real-time operation constraint condition, and schedule each energy storage resource according to the optimal power solution of each energy storage resource. The predicted electricity price and the energy efficiency loss cost can reflect the impact of energy storage scheduling on economic benefits in the short term, and the life attenuation cost can reflect the impact of energy storage scheduling on economic benefits in the long term. The method of the present application determines the revenue function by predicting the electricity price, the energy efficiency loss cost and the life attenuation cost, comprehensively considers the short-term factors and long-term factors affecting energy storage scheduling, and realizes an intelligent energy storage scheduling method that maximizes economic benefits.

[0077] The following gives an example illustration of the specific implementation of the energy storage scheduling method proposed in the present application.

[0078] In the situation where the energy storage resources include multiple energy storage devices, the first device parameters of each energy storage resource and the first environmental parameters of the environment where the energy storage resource is located can be obtained based on the device parameters and environmental parameters of the energy storage devices in the energy storage resources, so as to obtain the first device parameters of the energy storage resource and the first environmental parameters of the environment where the energy storage resource is located. After obtaining the power optimal solution of each energy storage resource based on the above steps, further, for the power optimal solution of the energy storage resource, the power optimal solution of each energy storage device in the energy storage resource is obtained to schedule each energy storage device.

[0079] Figure 2 Flow schematic of the first energy storage scheduling method provided by the embodiments of the present application Figure 2 As Figure 2 shown, the following steps may be included:

[0080] S201. For an energy storage resource including multiple energy storage devices, obtain the second device parameters and the second environmental parameters of each energy storage device in the energy storage resource.

[0081] The second device parameters are indicators and data related to the performance and functions of the energy storage device. For example, the second device parameters may include state of charge, battery health state, energy efficiency coefficient, schedulable capacity, etc.

[0082] The second environmental parameters are indicators and data related to the environment where the energy storage device is located and affecting the energy efficiency and lifespan of the energy storage device. For example, the second environmental parameters may include environmental temperature, location, altitude, etc.

[0083] To obtain the second device parameters of each energy storage device, one implementable way is to obtain them based on the management system of the energy storage device. The management system of the energy storage device is a system that monitors the operating state of the energy storage device. This system analyzes and stores the device parameters of the energy storage device by obtaining the operating state of the energy storage device. In this way, through the data interface of the management system of the energy storage device, the second device parameters of the energy storage device can be obtained. One implementable way is to obtain the measurement data of the sensors of the energy storage device to obtain the second device parameters. In this way, for example, the measurement value of the power sensor of the energy storage device can be obtained to obtain the state of charge of the energy storage device. Further, the energy efficiency coefficient of the energy storage device can be obtained by dividing the measured discharge amount of the power sensor over a period of time by the measured charge amount of the power sensor over a period of time.

[0084] Obtain the second environmental parameters of the environment where each energy storage device is located. One possible way is to obtain them based on the management system of the energy storage device. The second environmental parameters of the environment where each energy storage device is located are recorded in the management system of the energy storage device. In this way, through the data interface of the management system of the energy storage device, the second environmental parameters of the environment where each energy storage device is located can be obtained. One possible way is to obtain the measurement data of the sensors in the environment where the energy storage device is located to obtain the second environmental parameters. In this way, for example, the temperature of the environment where the energy storage device is located can be obtained according to the temperature sensor.

[0085] S202. Calculate the first device parameters and the first environmental parameters based on the second device parameters and the second environmental parameters of each energy storage device.

[0086] Based on the second device parameters and the second environmental parameters of each energy storage device, calculate the first device parameters and the first environmental parameters through the calculation methods of the first device parameters and the first environmental parameters.

[0087] Taking the example that the energy storage resource includes multiple energy storage devices and the second device parameters include capacity and state of charge, add the capacities of each energy storage device in the energy storage resource to obtain the capacity of the energy storage resource, and add the products of the capacities and the states of charge of each energy storage device in the energy storage resource to obtain the state of charge of the energy storage resource.

[0088] Taking the example that the energy storage resource includes multiple energy storage devices and the second environmental parameters include temperature and altitude, the average value of the temperatures of the environments where the energy storage devices in the energy storage resource are located can be calculated to obtain the temperature of the energy storage resource. Calculate the average value of the altitudes of the environments where the energy storage devices in the energy storage resource are located to obtain the altitude of the energy storage resource.

[0089] S203. Determine the first real-time operation constraint conditions of each energy storage resource based on the first device parameters and the first environmental parameters of each energy storage resource.

[0090] The first real-time operation constraint condition is a function with the power of each energy storage resource as a variable.

[0091] Taking the example that the first device parameters include the rated power and the schedulable capacity of the energy storage resource, and the first environmental parameters include the load upper limit and the standard frequency range of the power consumption area to which the energy storage resource belongs, the charge and discharge power of the energy storage resource can be determined to be less than or equal to the rated power according to the rated power of each energy storage resource, which is used as one of the conditions in the first real-time operation constraint.

[0092] Optionally, the state of charge of the energy storage resource can be determined to be less than or equal to the schedulable capacity according to the schedulable capacity of each energy storage resource, which is used as one of the conditions in the first real-time operation constraint.

[0093] Optionally, the sum of the charging power of the energy storage resources and the user power consumption within the power consumption area can be determined according to the load upper limit of the power consumption area to which each energy storage resource belongs, and it is less than or equal to the load upper limit of the power consumption area, which is used as one of the conditions in the first real-time operation constraint.

[0094] Optionally, according to the standard frequency range of the power consumption area to which each energy storage resource belongs, it can be determined that the grid frequency of the power consumption area affected by the power of the energy storage resource conforms to the standard frequency range, which is used as one of the conditions in the first real-time operation constraint.

[0095] S204. According to the first device parameter and the first environmental parameter of each energy storage resource, respectively determine the first energy efficiency loss cost function of each energy storage resource.

[0096] The first energy efficiency loss cost function is a function with the power of each energy storage resource as a variable.

[0097] A feasible way is to determine the energy efficiency function on the DC side and the energy efficiency function on the AC side of each energy storage resource according to the first device parameter and the first environmental parameter of each energy storage resource, and determine the first energy efficiency loss cost function of each energy storage resource according to the energy efficiency function on the DC side and the energy efficiency function on the AC side. For example, as shown in formula (1):

[0098] Formula (1)

[0099] Among them, represents the scheduling power of the energy storage resource per unit time, is the energy efficiency loss cost function of charging and discharging at power per unit time; the function is the energy efficiency on the DC side under unit power, and the function is the energy efficiency on the AC side under unit power, is the first device parameter, is the first environmental parameter.

[0100] Determine the function , for example, according to the historical charging and discharging power, historical device parameters, historical environmental parameters and historical energy efficiency on the DC side of the energy storage resource, through the mechanism model in related technologies, fit the function of the energy efficiency on the DC side with historical device parameters, historical charging and discharging power and historical environmental parameters. The historical charging and discharging power, historical device parameters and historical environmental parameters are the variables of the function .

[0101] Determine the function , for example, based on the historical charge-discharge power, historical device parameters, historical environmental parameters, and historical energy efficiency on the AC side of the energy storage resources, a function of the energy efficiency on the AC side with respect to the historical device parameters, historical charge-discharge power, and historical environmental parameters can be fitted through a mechanism model in related technologies. The historical charge-discharge power, historical device parameters, and historical environmental parameters are the variables of the function. of the function.

[0102] S205. Determine the first life attenuation cost function for each energy storage resource according to the first device parameter and the first environmental parameter of each energy storage resource.

[0103] The first life attenuation cost function is a function with the power of each energy storage resource as the variable.

[0104] One possible implementation is to determine the life attenuation rate function of each energy storage resource in the charge-discharge state and the life attenuation rate function in the static state according to the first device parameter and the first environmental parameter of each energy storage resource; and determine the first life attenuation cost function of each energy storage resource according to the life attenuation rate function in the charge-discharge state and the life attenuation rate function in the static state. For example, as shown in Equation (2):

[0105] Equation (2)

[0106] Wherein, represents the scheduling power of the energy storage resource per unit time, is the life attenuation cost function of charging and discharging at power per unit time, the function is the life attenuation rate function of the energy storage resource in the charge-discharge state, is the life attenuation rate function of the energy storage resource in the static state, and are the operating state parameters of the energy storage resource. When the energy storage resource is in the charge-discharge state, is 1, is 0. When the energy storage resource is in the static state, is 1, is 0.

[0107] S206. Determine the revenue function of one or more energy storage resources according to the first energy efficiency loss cost function, the first life attenuation cost function, and the predicted electricity price.

[0108] One possible implementation is to determine the revenue function of one or more energy storage resources by subtracting the product of the predicted electricity price and the power of the energy storage resources from the product of the first energy efficiency loss cost function and the third coefficient, and subtracting the product of the first life attenuation cost function and the fourth coefficient; where the third coefficient is the coefficient for converting the energy efficiency loss cost of the energy storage resources into an economic cost; the fourth coefficient is the conversion coefficient from the life attenuation cost of the energy storage resources to an economic cost. For example, as shown in Equation (3):

[0109] Equation (3)

[0110] where t is the sequence of time periods divided according to the scheduling interval duration, and the maximum sequence order is T, is the duration. For example, if the power of the energy storage resources is adjusted every 15 minutes, then is 15 minutes. If the revenue function of the energy storage resources for one day is determined, then one day is divided into time periods of 15 minutes, and T is 96. N is the number of energy storage resources.

[0111] is the electricity price revenue function of the nth energy storage resource in the tth time period, is the nth energy storage resource, and it is the function for converting the first energy efficiency loss cost into an economic cost in the tth time period, is the nth energy storage resource, and it is the function for converting the first life attenuation cost into an economic cost in the tth time period.

[0112] is the power of the nth energy storage resource in the tth time period, is the predicted electricity price in the tth time period, and the third coefficient is the coefficient for converting the energy efficiency loss cost of the energy storage resources into an economic cost; the fourth coefficient is the conversion coefficient from the life attenuation cost of the energy storage resources to an economic cost.

[0113] The third coefficient For example, it can be the average value of the predicted electricity prices in the time periods from 1 to T. The fourth coefficient can be obtained by calculating the investment cost and life attenuation of the energy storage power station. For example, the total investment cost of the energy storage power station divided by the available operation duration of the energy storage power station gives the annual investment cost of the energy storage power station. If the life attenuation of the energy storage power station is 20% after 3 years of operation, then the investment cost for a 1% life attenuation is calculated based on the investment cost for 3 years of operation to obtain the economic cost of the energy storage power station under unit life attenuation, which is used as the fourth coefficient. In this way, the fourth coefficients of one or more energy storage resources are equal.

[0114] Optionally, the economic cost of the energy storage resource under unit life attenuation can also be calculated based on the economic cost of the energy storage power station under unit life attenuation and the first device parameter of the energy storage resource, and used as the fourth coefficient. For example, if the capacity of the energy storage resource accounts for 0.1 of the total capacity of all energy storage resources, then the economic cost of the energy storage power station under unit life attenuation is multiplied by 0.1 as the fourth coefficient. In this way, the fourth coefficient of each energy storage resource is the same or different, and is determined according to the first device parameter of the energy storage resource.

[0115] The product of the predicted electricity price and the power of the energy storage resource is the expansion of the electricity price revenue function. The product of the first energy efficiency loss cost function and the third coefficient is the expansion of the function for converting the first energy efficiency loss cost into an economic cost. The product of the first life attenuation cost function and the fourth coefficient is the expansion of the function for converting the first life attenuation cost into an economic cost.

[0116] S207. Determine the optimal power solution of each energy storage resource when the revenue function is maximized under the first real-time operation constraint conditions.

[0117] Based on the optimization solver in the related technology, solve the revenue function to determine the optimal power solution of each energy storage resource when the revenue function is maximized under the first real-time operation constraint conditions. Taking the above formula (3) as an example, the optimal solution, that is, obtain the scheduling power of each of the n energy storage resources in each period from 1 to T. Further, according to the optimal power solution of each energy storage resource, obtain the optimal power solution of each energy storage device in each energy storage resource.

[0118] S208. According to the second device parameter and the second environmental parameter of each energy storage device, determine the second real-time operation constraint condition, the second energy efficiency loss cost function, and the second life attenuation cost function of each energy storage device respectively.

[0119] Among them, the second real-time operation constraint function, the second energy efficiency loss cost function, and the second life attenuation cost function are functions with the power of each energy storage device as variables respectively.

[0120] The optimal power solution of each energy storage resource, and further, obtain the optimal power solution of the energy storage devices in each energy storage resource.

[0121] The specific implementation steps can refer to the determination method in steps S203 - S205.

[0122] S209. According to the optimal power solution of the energy storage resource, determine the scheduling power constraint condition of each energy storage device.

[0123] The sum of the powers of the energy storage devices in each energy storage resource is equal to the optimal power solution of the energy storage resource. For the nth energy storage resource, the nth energy storage resource includes Take a storage device as an example. For example, as shown in Equation (4):

[0124] Equation (4)

[0125] Wherein, is the optimal power solution of the nth energy storage resource at time t, is the power of the mth energy storage device in the nth energy storage resource at time t.

[0126] S210. Determine the cost function of multiple energy storage devices according to the second energy efficiency loss cost function and the second life attenuation cost function.

[0127] A possible implementation method is to determine the sum of the product of the second energy efficiency loss cost function and the first coefficient and the product of the second life attenuation cost function and the second coefficient as the cost function of multiple energy storage devices. Wherein, the first coefficient is the coefficient for converting the energy efficiency loss cost of the energy storage device into an economic cost, and the second coefficient is the conversion coefficient from the life attenuation cost of the energy storage device to the economic cost. For example, taking the nth energy storage resource, the nth energy storage resource includes Take the number of energy storage devices as an example, as shown in Equation (5):

[0128] Equation (5)

[0129] Wherein, is the function for converting the first energy efficiency loss cost of the mth energy storage device in the nth energy storage resource into an economic cost at the tth time period, is the function for converting the first life attenuation cost of the mth energy storage device in the nth energy storage resource into an economic cost at the tth time period.

[0130] is the power of the mth energy storage device in the nth energy storage resource at the tth time period, the first coefficient is the coefficient for converting the energy efficiency loss cost of the energy storage resource into an economic cost, and the first coefficient can be the same as or different from the third coefficient; the second coefficient is the conversion coefficient from the life attenuation cost of the energy storage resource to the economic cost. The product of the second energy efficiency loss cost function and the first coefficient is the expansion of the function for converting the second energy efficiency loss cost into an economic cost, and the product of the second life attenuation cost function and the second coefficient is the expansion of the function for converting the second life attenuation cost into an economic cost.

[0131] The first coefficient For example, it can be the average predicted electricity price within the period from 1 to T. The second coefficient can be the same as the fourth coefficient of the energy storage resource. In this way, the second coefficients of multiple energy storage devices in the same energy storage resource are the same. Optionally, it can also be obtained through the fourth coefficient and the second device parameter of the energy storage device. For example, if the capacity of the energy storage device accounts for 0.1 of the total capacity of the affiliated energy storage resource, then multiply the fourth coefficient by 0.1 as the second coefficient.

[0132] S211. Determine the optimal power solution of each energy storage device when the cost function is minimized under the dispatching power constraint condition and the second real-time operation constraint condition, and dispatch each energy storage device according to the optimal power solution of each energy storage device.

[0133] Based on the optimization solver in the related technology to solve the revenue function, determine the optimal power solution of each energy storage device when the cost function is minimized under the dispatching power constraint condition and the second real-time operation constraint condition, that is, obtain the dispatching power of each energy storage device in each energy storage resource at each time period. Further, dispatch each energy storage resource according to the optimal power solution of each energy storage device.

[0134] The energy storage dispatching method of the present application obtains the first device parameter of each energy storage resource and the first environmental parameter of the environment where the energy storage resource is located, respectively determines the first real-time operation constraint condition, the first energy efficiency loss cost function and the first life attenuation cost function of each energy storage resource, and determines the revenue function of one or more energy storage resources according to the first energy efficiency loss cost function, the first life attenuation cost function and the predicted electricity price. Determine the optimal power solution of each energy storage resource when the revenue function is maximized under the first real-time operation constraint condition, and dispatch each energy storage resource according to the optimal power solution of each energy storage resource. The predicted electricity price and the energy efficiency loss cost can reflect the economic benefits of energy storage dispatching in the short term, and the life attenuation cost can reflect the impact of energy storage dispatching on economic benefits in the long term. The method of the present application determines the revenue function through the predicted electricity price, the energy efficiency loss cost and the life attenuation cost, comprehensively considers the short-term factors and long-term factors affecting energy storage dispatching, and realizes an intelligent energy storage dispatching method that maximizes economic benefits.

[0135] Optionally, the predicted electricity price corresponding to each energy storage resource can be determined according to the power market system participated by the energy storage resource.

[0136] Power market data includes power market data of multiple power market systems, and the real-time prices of different power market systems are different. The power market system may include, for example, a medium- and long-term market, a time-of-use market, a day-ahead market, and / or a real-time market. In this state, power market data can also be obtained, and the predicted electricity price of each power market system can be determined according to the power market data of each power market system and the electricity price prediction model corresponding to each power market system. Among them, the electricity price prediction model can adopt the electricity price prediction model in related technologies, and the predicted electricity price of each power market system can be obtained respectively based on the corresponding electricity price prediction model according to the power market data of each power market system.

[0137] Correspondingly, in the energy storage scheduling method of the present application, according to the first energy efficiency loss cost function, the first life attenuation cost function, and the predicted electricity price, the revenue function of one or more energy storage resources can be determined. The predicted electricity price corresponding to each energy storage resource can be determined according to the power market system corresponding to each energy storage resource, and the revenue function of one or more energy storage resources can be determined according to the first energy efficiency loss cost function, the first life attenuation cost function, and the predicted electricity price corresponding to each energy storage resource.

[0138] According to the power market system corresponding to each energy storage resource, the predicted electricity price of the corresponding power market system is obtained and determined as the predicted electricity price corresponding to each energy storage resource. Then, according to the first energy efficiency loss cost function, the first life attenuation cost function, and the predicted electricity price corresponding to each energy storage resource, the revenue function of one or more energy storage resources is determined. Referring to the revenue function shown in Equation (3), where is the predicted electricity price of the power market system corresponding to each energy storage resource at time t.

[0139] The energy storage scheduling method of the present application determines the predicted electricity price corresponding to each energy storage resource according to the power market system corresponding to each energy storage resource, and determines the revenue function of one or more energy storage resources according to the first energy efficiency loss cost function, the first life attenuation cost function, and the predicted electricity price corresponding to each energy storage resource. It can be applied to the energy storage scheduling of energy storage resources coupled with multiple power market systems. By further determining the revenue function through the predicted electricity price, energy efficiency loss cost, and life attenuation cost, it comprehensively considers the short-term and long-term factors affecting energy storage scheduling, and realizes an intelligent energy storage scheduling method that maximizes economic benefits.

[0140] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0141] Furthermore, it should be noted that although the steps in the flowchart are displayed in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0142] The above is the description of the method embodiments of this application. Next, the device provided by the embodiments of this application will be described.

[0143] Figure 3 The structure diagram of an energy storage scheduling device provided by an embodiment of this application. The energy storage scheduling device includes one or more energy storage resources, and each energy storage resource includes one or more energy storage devices. As Figure 3 shown, the energy storage scheduling device 300 may include, for example: an acquisition module 301, a first determination module 302, a second determination module 303, and a scheduling module 304. Optionally, a prediction module may also be included.

[0144] The acquisition module 301 is configured to acquire the first device parameters of each energy storage resource and the first environmental parameters of the environment where the energy storage resource is located;

[0145] The first determination module 302 is configured to respectively determine the first real-time operation constraint conditions, the first energy efficiency loss cost function, and the first life attenuation cost function of each energy storage resource according to the first device parameters and the first environmental parameters of each energy storage resource; the first real-time operation constraint conditions, the first energy efficiency loss cost function, and the first life attenuation cost function are respectively functions with the power of each energy storage resource as a variable;

[0146] The second determination module 303 is configured to determine the revenue function of one or more energy storage resources according to the first energy efficiency loss cost function, the first life attenuation cost function, and the predicted electricity price;

[0147] The scheduling module 304 is configured to determine the optimal power solution for each energy storage resource when the revenue function is maximized under the first real-time operation constraint conditions, and schedule each energy storage resource according to the optimal power solution of each energy storage resource.

[0148] In a possible implementation, the acquisition module 301 is specifically configured to, for an energy storage resource including multiple energy storage devices, acquire the second device parameters and the second environmental parameters of each energy storage device in the energy storage resource; and calculate the first device parameters and the first environmental parameters according to the second device parameters and the second environmental parameters of each energy storage device.

[0149] Correspondingly, in this implementation, the scheduling module 304 is further configured to determine the second real-time operation constraint conditions, the second energy efficiency loss cost function, and the second life attenuation cost function of each energy storage device respectively according to the second device parameters and the second environmental parameters of each energy storage device. The second real-time operation constraint function, the second energy efficiency loss cost function, and the second life attenuation cost function are functions with the power of each energy storage device as variables; determine the scheduling power constraint conditions of each energy storage device according to the optimal power solution of the energy storage resource; and determine the cost function of multiple energy storage devices according to the second energy efficiency loss cost function and the second life attenuation cost function.

[0150] Determine the optimal power solution for each energy storage device when the cost function is minimized under the scheduling power constraint conditions and the second real-time operation constraint conditions, and schedule each energy storage device according to the optimal power solution of each energy storage device.

[0151] Correspondingly, the scheduling module 304 is specifically configured to determine the sum of the product of the second energy efficiency loss cost function and the first coefficient and the product of the second life attenuation cost function and the second coefficient as the cost function of multiple energy storage devices; where the first coefficient is the coefficient for converting the energy efficiency loss cost of the energy storage device into an economic cost, and the second coefficient is the conversion coefficient from the life attenuation cost of the energy storage device to an economic cost.

[0152] In a possible implementation, the second determination module 303 is specifically configured to determine the product of the predicted electricity price and the power of the energy storage resource, subtract the product of the first energy efficiency loss cost function and the third coefficient, and subtract the product of the first life attenuation cost function and the fourth coefficient, as the revenue function of one or more energy storage resources; where the third coefficient is the coefficient for converting the energy efficiency loss cost of the energy storage resource into an economic cost; and the fourth coefficient is the conversion coefficient from the life attenuation cost of the energy storage resource to an economic cost.

[0153] A possible implementation manner is that the prediction module is specifically configured to obtain power market data, where the power market data includes power market data of multiple power market systems, and the real-time prices of different power market systems are different; and determine the predicted electricity prices of each power market system according to the power market data of each power market system and the electricity price prediction model corresponding to each power market system.

[0154] Correspondingly, in this implementation manner, the second determination module 303 is specifically configured to determine the predicted electricity price corresponding to each energy storage resource according to the power market system corresponding to each energy storage resource; and determine the revenue function of one or more energy storage resources according to the first energy efficiency loss cost function, the first life attenuation cost function, and the predicted electricity price corresponding to each energy storage resource.

[0155] A possible implementation manner is that the first device parameters include the rated power and the schedulable capacity of the energy storage resource, and the first environmental parameters include the load upper limit and the standard frequency range of the power consumption area where the energy storage resource is located; the first real-time operation constraints include: the charge and discharge power of the energy storage resource is less than or equal to the rated power; the state of charge of the energy storage resource is less than or equal to the schedulable capacity; the sum of the charging power of the energy storage resource and the power consumption of users in the power consumption area is less than or equal to the load upper limit of the power consumption area; and the grid frequency of the power consumption area conforms to the standard frequency range.

[0156] A possible implementation manner is that the first determination module 302 is specifically configured to determine the energy efficiency function of the DC side and the energy efficiency function of the AC side of each energy storage resource according to the first device parameters and the first environmental parameters of each energy storage resource; and determine the first energy efficiency loss cost function of each energy storage resource according to the energy efficiency function of the DC side and the energy efficiency function of the AC side.

[0157] A possible implementation manner is that the first determination module 302 is specifically configured to determine the life attenuation rate function of each energy storage resource in the charge and discharge state and the life attenuation rate function of each energy storage resource in the static state according to the first device parameters and the first environmental parameters of each energy storage resource; and determine the first life attenuation cost function of each energy storage resource according to the life attenuation rate function in the charge and discharge state and the life attenuation rate function in the static state.

[0158] The device provided in the embodiments of the present application can execute the above method embodiments, and the implementation principles and technical effects are similar, and will not be described in detail here.

[0159] It should be understood that the above device embodiments are merely illustrative, and the devices of the present application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or integrated into another system, or some features can be ignored or not executed.

[0160] Figure 4 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 4 shown, the electronic device may include: at least one processor 401 and a memory 402.

[0161] The memory 402 is used to store a program. Specifically, the program may include program code, and the program code includes computer operation instructions.

[0162] The memory 402 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0163] The processor 401 is configured to execute the computer-executable instructions stored in the memory 402 to implement the method of the foregoing method embodiments. Among them, the processor 401 may be a central processing unit (CPU for short), or a specific integrated circuit (ASIC for short), or one or more integrated circuits configured to implement the embodiments of the present application.

[0164] Optionally, the inspection device may further include a communication interface 403. In a specific implementation, if the communication interface 403, the memory 402, and the processor 401 are independently implemented, the communication interface 403, the memory 402, and the processor 401 may be interconnected through a bus and communicate with each other.

[0165] Optionally, in a specific implementation, if the communication interface 403, the memory 402, and the processor 401 are integrated on a chip, the communication interface 403, the memory 402, and the processor 401 may communicate through an internal interface.

[0166] The present application also provides a computer-readable storage medium, which may include: various media capable of storing program codes, such as USB flash drives, external hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs. Specifically, program instructions are stored in the computer-readable storage medium, and the program instructions are used to implement the actions of the above method embodiments.

[0167] The present application also provides a program product, which includes execution instructions stored in a readable storage medium. At least one processor of the inspection device can read the execution instructions from the readable storage medium, and the execution of the execution instructions by at least one processor causes the inspection device to implement the actions of the above method embodiments.

[0168] In addition, unless otherwise specified, in each embodiment of the present application, each functional unit / module can be integrated into one unit / module, or each unit / module can exist physically alone, or two or more unit / modules can be integrated together. The above integrated unit / module can be implemented in the form of hardware or in the form of a software program module.

[0169] When the integrated unit / module is implemented in the form of hardware, the hardware can be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0170] When the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. And the aforementioned memory includes: various media such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0171] In the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0172] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When this program is executed, it executes the steps including the above method embodiments; and the aforementioned storage medium includes: various media such as ROM, RAM, magnetic disks, or optical discs that can store program codes.

Claims

1. A method for energy storage scheduling, characterized in that: The method comprises: one or more energy storage resources, each of the energy storage resources comprising one or more energy storage devices; Acquire a first device parameter of each of the energy storage resources and a first environmental parameter of the environment where the energy storage resource is located; According to the first device parameter and the first environmental parameter of each of the energy storage resources, respectively determine a first real-time operation constraint condition, a first energy efficiency loss cost function, and a first life decay cost function of each of the energy storage resources; the first real-time operation constraint condition, the first energy efficiency loss cost function, and the first life decay cost function are respectively functions with the power of each of the energy storage resources as a variable; Determining a revenue function of the one or more energy storage resources according to the first energy efficiency loss cost function, the first life decay cost function and the predicted electricity price; Determine the optimal power solution of each of the energy storage resources when the benefit function is maximized under the first real-time operation constraint condition, and dispatch each of the energy storage resources according to the optimal power solution of each of the energy storage resources.

2. The method according to claim 1, characterized in that The obtaining of the first device parameter of each of the energy storage resources and the first environment parameter of the environment where the energy storage resource is located includes: For an energy storage resource including a plurality of energy storage devices, obtaining a second device parameter and a second environmental parameter of each energy storage device in the energy storage resource; The first device parameter and the first environmental parameter are calculated according to the second device parameter and the second environmental parameter of each of the energy storage devices.

3. The method according to claim 2, characterized in that The method further comprises: According to the second device parameter and the second environmental parameter of each of the energy storage devices, respectively determine the second real-time operation constraint condition, the second energy efficiency loss cost function and the second life decay cost function of each of the energy storage devices, wherein the second real-time operation constraint function, the second energy efficiency loss cost function and the second life decay cost function are respectively functions with the power of each of the energy storage devices as a variable; Determining a dispatch power constraint condition for each of the energy storage devices according to the optimal power solution of the energy storage resources; Determining cost functions of the plurality of energy storage devices according to the second energy efficiency loss cost function and the second life decay cost function; Determine the optimal power solution for each of the energy storage devices when the cost function is minimized under the scheduling power constraint and the second real-time operation constraint, and schedule each of the energy storage devices according to the optimal power solution for each of the energy storage devices.

4. The method according to claim 3, characterized in that Determining the cost functions of the plurality of energy storage devices according to the second energy efficiency loss cost function and the second life decay cost function includes: The product of the second energy efficiency loss cost function and the first coefficient, and the sum of the product of the second life decay cost function and the second coefficient, are determined as the cost function of the multiple energy storage devices; wherein the first coefficient is a coefficient for converting the energy efficiency loss cost of the energy storage device into economic cost, and the second coefficient is a coefficient for converting the life decay cost of the energy storage device into economic cost.

5. The method according to any one of claims 1 to 4, characterized in that: The determining the benefit function of the one or more energy storage resources according to the first energy efficiency loss cost function, the first life decay cost function and the predicted electricity price includes: The product of the predicted electricity price and the power of the energy storage resource, minus the product of the first energy efficiency loss cost function and the third coefficient, and minus the product of the first life decay cost function and the fourth coefficient, are determined as the profit function of the one or more energy storage resources; wherein the third coefficient is a coefficient for converting the energy efficiency loss cost of the energy storage resource into economic cost; and the fourth coefficient is a conversion coefficient for converting the life decay cost of the energy storage resource into economic cost.

6. The method according to any one of claims 1 to 4, characterized in that: Also includes: Acquiring power market data, wherein the power market data includes power market data of multiple power market systems, and different power market systems have different real-time prices; Determining the predicted electricity price of each of the electricity market systems according to the electricity market data of each of the electricity market systems and the electricity price prediction model corresponding to each of the electricity market systems; The determining the benefit function of the one or more energy storage resources according to the first energy efficiency loss cost function, the first life decay cost function and the predicted electricity price includes: Determining a predicted electricity price corresponding to each of the energy storage resources according to the electricity market system corresponding to each of the energy storage resources; The profit function of the one or more energy storage resources is determined according to the first energy efficiency loss cost function, the first life decay cost function and the predicted electricity price corresponding to each of the energy storage resources.

7. The method according to any one of claims 1 to 4, characterized in that: The first device parameter includes the rated power and dispatchable capacity of the energy storage resource, the first environmental parameter includes the load upper limit and standard frequency range of the power consumption area to which the energy storage resource belongs; the first real-time operation constraint includes: The charging and discharging power of the energy storage resource is less than or equal to the rated power; the charge state of the energy storage resource is less than or equal to the dispatchable capacity; the sum of the charging power of the energy storage resource and the power consumption of users in the power consumption area is less than or equal to the load upper limit of the power consumption area; the power grid frequency of the power consumption area is within the standard frequency range.

8. The method according to any one of claims 1 to 4, characterized in that: Determining a first energy efficiency loss cost function of each of the energy storage resources according to the first device parameter and the first environmental parameter of each of the energy storage resources includes: According to the first device parameters and the first environmental parameters of each of the energy storage resources, determine the energy efficiency function of the DC side and the energy efficiency function of the AC side of each of the energy storage resources; according to the energy efficiency function of the DC side and the energy efficiency function of the AC side, determine the first energy efficiency loss cost function of each of the energy storage resources.

9. The method according to any one of claims 1 to 4, characterized in that: Determining a first life decay cost function of each of the energy storage resources according to the first device parameter and the first environmental parameter of each of the energy storage resources includes: According to the first device parameters and the first environmental parameters of each of the energy storage resources, determine the life decay rate function of each of the energy storage resources in the charging and discharging state, and the life decay rate function in the static state; according to the life decay rate function in the charging and discharging state, and the life decay rate function in the static state, determine the first life decay cost function of each of the energy storage resources.

10. An energy storage scheduling device, characterized in that: include: One or more energy storage resources, each of which includes one or more energy storage devices; An acquisition module, used to acquire a first device parameter of each of the energy storage resources and a first environmental parameter of the environment where the energy storage resource is located; A first determination module is used to determine, according to the first device parameter and the first environmental parameter of each of the energy storage resources, a first real-time operation constraint condition, a first energy efficiency loss cost function, and a first life decay cost function of each of the energy storage resources; the first real-time operation constraint condition, the first energy efficiency loss cost function, and the first life decay cost function are functions with the power of each of the energy storage resources as a variable; A second determination module, configured to determine a benefit function of the one or more energy storage resources according to the first energy efficiency loss cost function, the first life decay cost function and the predicted electricity price; The scheduling module is used to determine the optimal power solution of each of the energy storage resources when the benefit function is maximized under the first real-time operation constraint condition, and schedule each of the energy storage resources according to the optimal power solution of each of the energy storage resources.

11. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 9 when executed by a processor.

13. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 9 when being executed by a processor.

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