Multi-objective optimization method and device for energy storage power station

Abstract

The embodiments of the present specification provide a multi-objective optimization method and device for an energy storage power station, wherein the multi-objective optimization method for the energy storage power station comprises: taking the maximum amount of resource acquisition of the energy storage power station within a target period as the goal, combining the charge and discharge amounts of the energy storage power station at different times within the target period and the clearing electricity price to construct a corresponding objective function; determining the constraints of the objective function, wherein the constraints are used to constrain the number of charge and discharge times of the energy storage power station within the target period and the charge and discharge amounts at different times; determining the charge and discharge amounts of the energy storage power station at different times according to the objective function and the constraints; and optimizing the charge and discharge strategy of the energy storage power station according to the charge and discharge amounts of the energy storage power station at different times.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of new energy technology, and in particular to a multi-objective optimization method for an energy storage power station. Background Art

[0002] Energy storage power stations are equipment systems that store, convert and release cyclical electrical energy through batteries or electromagnetic energy storage media. With the rapid development of battery energy storage technology, a large number of battery energy storage power stations have been put into operation in the power grid and have played a very important role in the operation of the power grid.

[0003] At present, traditional energy storage power stations mainly organize grid operation by means of peak shaving and valley filling. However, with the complexity of grid operation and changes in load patterns, a single charging and discharging control mode is difficult to adapt to the new changes in grid demand. If the output level of the energy storage power station is adjusted only by peak shaving and valley filling, then under extreme operation modes, the voltage of some sites may exceed the limit or the flow level of key interconnection lines may be overloaded, thereby causing large-scale failures in the grid and affecting the safe and stable operation of the grid. Therefore, an effective method is urgently needed to solve such problems. Summary of the invention

[0004] In view of this, an embodiment of this specification provides a multi-objective optimization method for an energy storage power station. One or more embodiments of this specification also involve a multi-objective optimization device for an energy storage power station, a computing device, a computer-readable storage medium, and a computer program to solve the technical defects existing in the prior art.

[0005] According to a first aspect of an embodiment of this specification, a multi-objective optimization method for an energy storage power station is provided, comprising:

[0006] Taking the maximum resource acquisition amount of the energy storage power station within the target period as the goal, a corresponding objective function is constructed by combining the charge and discharge amount of the energy storage power station at different times within the target period and the clearing electricity price;

[0007] Determining the constraint conditions of the objective function, wherein the constraint conditions are used to constrain the number of charge and discharge times of the energy storage power station within a target cycle and the charge and discharge amounts at different times;

[0008] Determining the charge and discharge amount of the energy storage power station at different times according to the objective function and the constraint conditions;

[0009] The charging and discharging strategy of the energy storage power station is optimized according to the charging and discharging amount of the energy storage power station at different times.

[0010] Optionally, the determining of the constraint conditions of the objective function includes:

[0011] Introducing an auxiliary variable, wherein the auxiliary variable is used to represent the absolute value of the charge and discharge amount at different times;

[0012] Based on the auxiliary variables, the charge and discharge amounts of the energy storage power station at different times within a target period, and the single maximum charge amount of the energy storage power station, a first constraint condition of the objective function is constructed.

[0013] Optionally, the determining of the constraint conditions of the objective function includes:

[0014] Determine the maximum capacity and charging efficiency of the batteries in the energy storage power station;

[0015] A second constraint condition of the objective function is constructed based on the charge and discharge amount of the energy storage power station at different times within a target period, the charging efficiency, and the maximum capacity of the battery.

[0016] Optionally, the determining of the constraint conditions of the objective function includes:

[0017] Introducing an auxiliary variable, wherein the auxiliary variable is used to represent the absolute value of the charge and discharge amount at different times;

[0018] Determine the maximum capacity and charging efficiency of the batteries in the energy storage power station, and determine the number of charge and discharge times of the energy storage power station within a target interval;

[0019] A third constraint condition of the objective function is constructed based on the auxiliary variable, the maximum capacity of the battery, the charging efficiency, and the number of charge and discharge times.

[0020] Optionally, the objective of maximizing the amount of resource acquisition of the energy storage power station within the target period is to construct a corresponding objective function in combination with the charge and discharge amount of the energy storage power station at different times within the target period and the clearing electricity price, including:

[0021] Determine the resource loss value of the energy storage power station within the target period according to the charging amount and clearing electricity price of the energy storage power station at different times within the target period;

[0022] Determine the resource acquisition value of the energy storage power station within the target period according to the discharge amount and clearing electricity price of the energy storage power station at different times within the target period;

[0023] The difference between the resource acquisition value and the resource loss value is determined as the objective function.

[0024] Optionally, determining the charge and discharge amount of the energy storage power station at different times according to the objective function and the constraint condition includes:

[0025] A linear programming solution algorithm is used to solve the maximum value of the objective function based on the constraint conditions to obtain the charge and discharge amount of the energy storage power station at different times.

[0026] Optionally, the linear programming solution algorithm includes: a traditional simplex method, a modified simplex method or an interior point method.

[0027] According to a second aspect of an embodiment of this specification, a multi-objective optimization device for an energy storage power station is provided, comprising:

[0028] A construction module is configured to take the maximum resource acquisition amount of the energy storage power station within the target period as the goal, and to construct a corresponding objective function in combination with the charge and discharge amount of the energy storage power station at different times within the target period and the clearing electricity price;

[0029] A first determination module is configured to determine the constraint conditions of the objective function, wherein the constraint conditions are used to constrain the number of charge and discharge times of the energy storage power station within a target cycle and the charge and discharge amounts at different times;

[0030] A processing module, configured to determine the charge and discharge amount of the energy storage power station at different times according to the objective function and the constraint condition;

[0031] The second determination module is configured to optimize the charging and discharging strategy of the energy storage power station according to the charging and discharging amount of the energy storage power station at different times.

[0032] According to a third aspect of an embodiment of this specification, a computing device is provided, including:

[0033] Memory and processor;

[0034] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement any step of the multi-objective optimization method of the energy storage power station.

[0035] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided, which stores computer-executable instructions, which, when executed by a processor, implement the steps of any one of the multi-objective optimization methods for energy storage power stations.

[0036] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the multi-objective optimization method of the energy storage power station described above.

[0037] An embodiment of the present specification takes the maximum resource acquisition amount of the energy storage power station within the target period as the goal, and constructs a corresponding objective function in combination with the charge and discharge amount of the energy storage power station at different times within the target period and the clearing electricity price; determines the constraint conditions of the objective function, wherein the constraint conditions are used to constrain the number of charge and discharge times of the energy storage power station within the target period and the charge and discharge amount at different times; determines the charge and discharge amount of the energy storage power station at different times according to the objective function and the constraint conditions; optimizes the charge and discharge strategy of the energy storage power station according to the charge and discharge amount of the energy storage power station at different times. In this way, while ensuring that the resource acquisition amount of the energy storage power station is maximized, the charge and discharge operation strategy of the energy storage power station is optimized, which is beneficial to reducing the energy loss of the energy storage power station, improving the operation efficiency of the energy storage power station, and thus helping to improve the stability of the power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of a multi-objective optimization method for an energy storage power station provided by an embodiment of this specification;

[0039] Figure 2 It is a process flow chart of a multi-objective optimization method for an energy storage power station provided by an embodiment of this specification;

[0040] Figure 3 It is a structural schematic diagram of a multi-objective optimization device for an energy storage power station provided by an embodiment of this specification;

[0041] Figure 4 It is a structural block diagram of a computing device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0042] Many specific details are described in the following description to facilitate a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of this specification, so this specification is not limited to the specific implementation disclosed below.

[0043] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "a", "said" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0044] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0045] The embodiments of this specification aim at the optimization problem of charging and discharging strategies in the power trading decision algorithm, especially when there is a limit on the number of charging and discharging times, and propose a multi-objective optimization method for energy storage power stations to determine the optimal charging and discharging strategy under the condition of known clearing electricity prices, thereby achieving the maximum resource acquisition.

[0046] In addition, the embodiments of this specification introduce an auxiliary variable of absolute value, which effectively simplifies the expression of the number of charge and discharge times, and also avoids the use of complex nonlinear expressions. This optimization significantly simplifies the complexity of the model when constructing the objective function, simplifies the solution process, and improves the interpretability and practicality of the model.

[0047] In this specification, a multi-objective optimization method for an energy storage power station is provided. This specification also relates to a multi-objective optimization device for an energy storage power station, a computing device, a computer-readable storage medium, and a computer program, which are described in detail one by one in the following embodiments.

[0048] Figure 1 A flowchart of a multi-objective optimization method for an energy storage power station provided according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0049] Step 102: Taking the maximum resource acquisition amount of the energy storage power station within the target period as the goal, a corresponding objective function is constructed in combination with the charge and discharge amount of the energy storage power station at different times within the target period and the clearing electricity price.

[0050] Specifically, the resource acquisition amount of the energy storage power station may be the resource amount of resources acquired by the energy storage power station by providing power to other load devices.

[0051] The embodiment of this specification takes the maximum resource acquisition amount of the energy storage power station as the goal, takes the charge and discharge amount of the energy storage power station at different times as the parameter to be optimized, and optimizes the charge and discharge amount at different times to obtain the maximum value of the resource acquisition amount.

[0052] In addition, since the amount of resource acquisition is related to the clearing electricity price in addition to the charging and discharging amount, the embodiment of this specification takes the maximum amount of resource acquisition of the energy storage power station within the target period as the goal, and constructs a corresponding objective function in combination with the charging and discharging amount of the energy storage power station at different times within the target period and the clearing electricity price.

[0053] In practical applications, the amount of resources acquired by an energy storage power station can be the amount of capital acquired, that is, the total profit or total revenue of the energy storage power station.

[0054] In an optional implementation, the objective is to maximize the amount of resource acquisition of the energy storage power station within the target period, and to construct a corresponding objective function in combination with the charge and discharge amount of the energy storage power station at different times within the target period and the clearing electricity price, including:

[0055] Determine the resource loss value of the energy storage power station within the target period according to the charging amount and clearing electricity price of the energy storage power station at different times within the target period;

[0056] Determine the resource acquisition value of the energy storage power station within the target period according to the discharge amount and clearing electricity price of the energy storage power station at different times within the target period;

[0057] The difference between the resource acquisition value and the resource loss value is determined as the objective function.

[0058] Specifically, the objective function constructed in the embodiment of this specification is as follows:

[0059] Formula (1)

[0060] Among them, f is the resource acquisition of the energy storage power station; i represents the identifier of the moment, and the value range of i is 1 to n, representing each point in an ordered time series. n represents the sample capacity, which represents the number of points in the ordered time series. represents the clearing electricity price at the i-th moment, belongs to real numbers, depending on the situation, Can be positive, negative, or zero. Represents the charge and discharge capacity at the i-th moment. , when discharging . is a constant.

[0061] In the embodiments of this specification, the energy storage power station can obtain resources (income) when discharging to the outside, and the resource acquisition value at the i-th moment is the product of the discharge amount at that moment and the clearing electricity price; while the energy storage power station cannot discharge to the outside during the charging process, so it cannot obtain resources, which is equivalent to a loss for the energy storage power station. The resource loss value of the energy storage power station at the i-th moment is the product of the charging amount at that moment and the clearing electricity price.

[0062] Based on this, the difference between the resource acquisition value and the resource loss value of the energy storage power station within the target period can be determined as the objective function.

[0063] In practical applications, the aforementioned ordered time series can be generated by dividing a continuous time length into equal intervals, and this continuous time length can be one day, one month or one year, etc., and the division interval can be 15 minutes, 1 hour or one day, etc., which can be determined according to actual needs and is not limited here.

[0064] Step 104: Determine the constraint conditions of the objective function, wherein the constraint conditions are used to constrain the number of charge and discharge times of the energy storage power station within a target cycle and the charge and discharge amounts at different times.

[0065] Specifically, after the objective function is constructed, the constraint conditions corresponding to the objective function can be determined, and the constraint conditions of the embodiment of this specification are linear constraint conditions.

[0066] In an optional implementation, the determining of the constraint conditions of the objective function includes:

[0067] Introducing an auxiliary variable, wherein the auxiliary variable is used to represent the absolute value of the charge and discharge amount at different times;

[0068] Based on the auxiliary variables, the charge and discharge amounts of the energy storage power station at different times within a target period, and the single maximum charge amount of the energy storage power station, a first constraint condition of the objective function is constructed.

[0069] The auxiliary variables introduced in the embodiments of this specification are , used to indicate The absolute value of . From the definition of absolute value, we know that:

[0070] Formula (2)

[0071] make

[0072] Formula (3)

[0073] This ensures , here we can only ensure The right limit of is From the optimization objective function, we can see that the goal is to maximize , therefore, in When the minimum value is reached, the energy storage station should charge as much as possible; When the maximum value is reached, the energy storage power station should discharge as much as possible, so that the discharge time As large as possible. With this strategy, the function In summary, the auxiliary variable The absolute value of the charge and discharge capacity When the planning problem is solved, ; When the planning problem has no solution, .

[0074] The embodiments of this specification are and The constraints on the variables are as follows:

[0075] Formula (4)

[0076] Formula (5)

[0077] in, Represents the maximum single charge capacity, which is a constant.

[0078] In an optional implementation, the determining of the constraint conditions of the objective function includes:

[0079] Determine the maximum capacity and charging efficiency of the batteries in the energy storage power station;

[0080] A second constraint condition of the objective function is constructed based on the charge and discharge amount of the energy storage power station at different times within a target period, the charging efficiency, and the maximum capacity of the battery.

[0081] Specifically, since the energy storage power station must meet the basic common sense of battery charging and discharging status, that is, it must ensure that there is electricity when discharging and must meet the maximum capacity limit, based on this, the second linear constraint condition constructed is as follows:

[0082] Formula (6)

[0083] in, It is a positive number, representing the maximum capacity of the battery in the energy storage power station, and E represents the charging and discharging efficiency. . and E are constants.

[0084] In an optional implementation, the determining of the constraint conditions of the objective function includes:

[0085] Introducing an auxiliary variable, wherein the auxiliary variable is used to represent the absolute value of the charge and discharge amount at different times;

[0086] Determine the maximum capacity and charging efficiency of the batteries in the energy storage power station, and determine the number of charge and discharge times of the energy storage power station within a target interval;

[0087] A third constraint condition of the objective function is constructed based on the auxiliary variable, the maximum capacity of the battery, the charging efficiency, and the number of charge and discharge times.

[0088] Specifically, as mentioned above, the auxiliary variables introduced in the embodiments of this specification are .

[0089] The third constraint condition constructed based on auxiliary variables, maximum battery capacity, charging efficiency, and charge and discharge times is as follows:

[0090] Formula (7)

[0091] in, Represents the number of charge and discharge times, which is a constant.

[0092] Formula (7) is the constraint expression of the number of charge and discharge times. The equality restriction will not allow Unlimited growth continues until now In the case where the planning problem has a solution, it must be equal to If the planning problem is unsolvable, Will get as close as possible .

[0093] Step 106: Determine the charge and discharge amount of the energy storage power station at different times according to the objective function and the constraint conditions.

[0094] Specifically, after the auxiliary variables are introduced, the objective function and the constraints constitute an integer linear programming problem with auxiliary variables. Therefore, the traditional integer linear programming solution algorithm can be used to efficiently solve the problem.

[0095] Based on this, according to the objective function and the constraints, the charge and discharge amount of the energy storage power station at different times is determined. Specifically, a linear programming solution algorithm can be used, and the maximum value of the objective function is solved based on the constraints to obtain the charge and discharge amount of the energy storage power station at different times.

[0096] In the embodiments of this specification, the linear programming solution algorithm includes: traditional simplex method, modified simplex method or interior point method, etc.

[0097] After introducing auxiliary variables, the embodiment of this specification transforms the charge and discharge optimization problem into a linear programming problem with auxiliary variables. In this way, the solution of this problem can be efficiently solved by using traditional simplex method, modified simplex method, interior point method and other solving methods. Due to the particularity of this design, after the solution is successfully solved, it is necessary to compare whether the solution meets ,if , then the function has a solution; if ,because Will be as close as possible while satisfying the constraints , so in this case we get The value of is the suboptimal solution that is closest to the optimal solution. That is, even if the planning problem has no solution, a suboptimal solution within the constraints can still be obtained, rather than directly outputting no solution. In this way, even if there is no solution, a suboptimal solution can be obtained. This method provides more options and flexibility for solving practical problems.

[0098] Step 108: Optimizing the charging and discharging strategy of the energy storage power station according to the charging and discharging amount of the energy storage power station at different times.

[0099] Specifically, the charging and discharging strategy of the energy storage power station can be represented by the charging and discharging amounts at different times.

[0100] After determining the charge and discharge amounts of the energy storage power station at different times, the charge and discharge strategy is optimized according to the charge and discharge amounts. Specifically, the determined charge and discharge amounts at different times can be directly used as the charge and discharge strategy of the energy storage power station, or the determined charge and discharge amounts at different times can be adjusted accordingly according to emergencies that occur in real time during the charge and discharge process of the energy storage power station, and the adjustment result is determined as the optimized charge and discharge strategy of the energy storage power station.

[0101] This specification introduces auxiliary variables , used to indicate the charge and discharge capacity The absolute value of . Through special constraints, the charge and discharge optimization problem is transformed into a linear programming problem, thereby simplifying the solution process of the entire problem. And because linear programming problems have better numerical stability than nonlinear programming problems, the introduction of auxiliary variables can reduce the numerical error in the calculation process, thereby improving the stability of the solution.

[0102] In addition, the embodiments of this specification use a kind of extreme thinking to make the auxiliary variable Infinitely close to The absolute value of . When the problem has a solution, ; When the problem has no solution, a suboptimal solution can be obtained, thereby improving the intelligence and flexibility of the solution process.

[0103] In addition, the solution method of linear programming problem is more mature and extensive, and the introduction of auxiliary variables After that, nonlinear problems that may be difficult to solve can also be solved by linear programming methods, which expands the scope of application of the problem. In addition, linear programming problems usually have lower solution complexity than nonlinear programming problems. The introduction of auxiliary variables It can reduce the complexity of the problem and reduce the time and resource costs required for solving it.

[0104] An embodiment of the present specification takes the maximum amount of resource acquisition of the energy storage power station within the target period as the goal, and constructs a corresponding objective function in combination with the charge and discharge amount of the energy storage power station at different times within the target period and the clearing electricity price; determines the constraint conditions of the objective function, wherein the constraint conditions are used to constrain the number of charge and discharge times of the energy storage power station within the target period and the charge and discharge amount at different times; determines the charge and discharge amount of the energy storage power station at different times according to the objective function and the constraint conditions; optimizes the charge and discharge strategy of the energy storage power station according to the charge and discharge amount of the energy storage power station at different times. In this way, while ensuring that the benefits of the energy storage power station are maximized, the charge and discharge operation strategy of the energy storage power station is optimized, which is beneficial to reducing the energy loss of the energy storage power station, improving the operating efficiency of the energy storage power station, and thus helping to improve the stability of the power grid operation.

[0105] The following combination Figure 2 , the multi-objective optimization method of the energy storage power station is further described. Figure 2 A processing flow chart of a multi-objective optimization method for an energy storage power station provided in an embodiment of this specification is shown, which specifically includes the following steps.

[0106] Step 202: Taking the maximum profit of the energy storage power station in the target period as the goal, a corresponding objective function is constructed in combination with the charge and discharge amount of the energy storage power station at different times in the target period and the clearing electricity price.

[0107] Step 204: introducing auxiliary variables, wherein the auxiliary variables are used to represent the absolute values ​​of the charge and discharge amounts at different times.

[0108] Step 206: construct a first constraint condition of the objective function based on the auxiliary variables, the charge and discharge amount of the energy storage power station at different times within the target period, and the single maximum charge amount of the energy storage power station.

[0109] Step 208: Determine the maximum capacity and charging efficiency of the batteries in the energy storage power station.

[0110] Step 210: Construct a second constraint condition of the objective function based on the charge and discharge amount, charging efficiency, and maximum battery capacity of the energy storage power station at different times within the target cycle.

[0111] Step 212: Introduce auxiliary variables to determine the maximum capacity and charging efficiency of the batteries in the energy storage power station, and determine the number of charge and discharge times of the energy storage power station within the target range.

[0112] Among them, the auxiliary variable is used to represent the absolute value of the charge and discharge amount at different times.

[0113] Step 214: Construct the third constraint of the objective function based on the auxiliary variables, the maximum capacity of the battery, the charging efficiency, and the number of charge and discharge cycles.

[0114] Step 216: adopt a linear programming solution algorithm, and solve the maximum value of the objective function based on the first, second, and third constraints to obtain the charge and discharge amount of the energy storage power station at different times.

[0115] Step 218: Determine the charging and discharging strategy of the energy storage power station according to the charging and discharging amount of the energy storage power station at different times.

[0116] The embodiments of this specification use this processing method to optimize the charging and discharging operation strategy of the energy storage power station while ensuring that the energy storage power station's benefits are maximized, which is beneficial to reducing the energy loss of the energy storage power station and improving the operating efficiency of the energy storage power station, thereby helping to improve the stability of the power grid operation.

[0117] Corresponding to the above method embodiment, this specification also provides a multi-objective optimization device embodiment of an energy storage power station, Figure 3 FIG. 1 is a schematic diagram showing a multi-objective optimization device for an energy storage power station provided by an embodiment of the present specification. Figure 3 As shown, the device comprises:

[0118] A construction module 302 is configured to take the maximum resource acquisition amount of the energy storage power station within the target period as the goal, and to construct a corresponding objective function in combination with the charge and discharge amount of the energy storage power station at different times within the target period and the clearing electricity price;

[0119] A first determination module 304 is configured to determine the constraint conditions of the objective function, wherein the constraint conditions are used to constrain the number of charge and discharge times of the energy storage power station within a target cycle and the charge and discharge amounts at different times;

[0120] The processing module 306 is configured to determine the charge and discharge amount of the energy storage power station at different times according to the objective function and the constraint condition;

[0121] The second determination module 308 is configured to optimize the charging and discharging strategy of the energy storage power station according to the charging and discharging amount of the energy storage power station at different times.

[0122] Optionally, the first determining module 304 is further configured to:

[0123] Introducing an auxiliary variable, wherein the auxiliary variable is used to represent the absolute value of the charge and discharge amount at different times;

[0124] Based on the auxiliary variables, the charge and discharge amounts of the energy storage power station at different times within a target period, and the single maximum charge amount of the energy storage power station, a first constraint condition of the objective function is constructed.

[0125] Optionally, the first determining module 304 is further configured to:

[0126] Determine the maximum capacity and charging efficiency of the batteries in the energy storage power station;

[0127] A second constraint condition of the objective function is constructed based on the charge and discharge amount of the energy storage power station at different times within a target period, the charging efficiency, and the maximum capacity of the battery.

[0128] Optionally, the first determining module 304 is further configured to:

[0129] Introducing an auxiliary variable, wherein the auxiliary variable is used to represent the absolute value of the charge and discharge amount at different times;

[0130] Determine the maximum capacity and charging efficiency of the batteries in the energy storage power station, and determine the number of charge and discharge times of the energy storage power station within a target interval;

[0131] A third constraint condition of the objective function is constructed based on the auxiliary variable, the maximum capacity of the battery, the charging efficiency, and the number of charge and discharge times.

[0132] Optionally, the construction module 302 is further configured to:

[0133] Determine the resource loss value of the energy storage power station within the target period according to the charging amount and clearing electricity price of the energy storage power station at different times within the target period;

[0134] Determine the resource acquisition value of the energy storage power station within the target period according to the discharge amount and clearing electricity price of the energy storage power station at different times within the target period;

[0135] The difference between the resource acquisition value and the resource loss value is determined as the objective function.

[0136] Optionally, the processing module 306 is further configured to:

[0137] A linear programming solution algorithm is used to solve the maximum value of the objective function based on the constraint conditions to obtain the charge and discharge amount of the energy storage power station at different times.

[0138] Optionally, the linear programming solution algorithm includes: a traditional simplex method, a modified simplex method or an interior point method.

[0139] The above is a schematic scheme of a multi-objective optimization device of an energy storage power station of this embodiment. It should be noted that the technical scheme of the multi-objective optimization device of the energy storage power station and the technical scheme of the multi-objective optimization method of the energy storage power station mentioned above belong to the same concept, and the details of the technical scheme of the multi-objective optimization device of the energy storage power station that are not described in detail can be referred to the description of the technical scheme of the multi-objective optimization method of the energy storage power station mentioned above.

[0140] Figure 4 The block diagram of a computing device 400 according to one embodiment of the present specification is shown. The components of the computing device 400 include but are not limited to a memory 410 and a processor 420. The processor 420 is connected to the memory 410 via a bus 430, and the database 450 is used to store data.

[0141] The computing device 400 also includes an access device 440 that enables the computing device 400 to communicate via one or more networks 460. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 440 may include one or more of any type of network interface (e.g., a network interface card (NIC)) that is wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a World Wide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, a near field communication (NFC) interface, and the like.

[0142] In one embodiment of the present specification, the above components of the computing device 400 and Figure 4 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 4 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0143] Computing device 400 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or PC. Computing device 400 may also be a mobile or stationary server.

[0144] The processor 420 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the multi-objective optimization method of the energy storage power station.

[0145] The above is a schematic scheme of a computing device of this embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the multi-objective optimization method of the energy storage power station mentioned above are of the same concept, and the details not described in detail in the technical scheme of the computing device can be found in the description of the technical scheme of the multi-objective optimization method of the energy storage power station mentioned above.

[0146] An embodiment of the present specification further provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the multi-objective optimization method of the energy storage power station.

[0147] The above is a schematic scheme of a computer-readable storage medium of this embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the multi-objective optimization method of the energy storage power station mentioned above belong to the same concept, and the details not described in detail in the technical scheme of the storage medium can be referred to the description of the technical scheme of the multi-objective optimization method of the energy storage power station mentioned above.

[0148] An embodiment of the present specification further provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the multi-objective optimization method of the energy storage power station.

[0149] The above is a schematic scheme of a computer program of this embodiment. It should be noted that the technical scheme of the computer program and the technical scheme of the multi-objective optimization method of the energy storage power station mentioned above are of the same concept, and the details not described in detail in the technical scheme of the computer program can be found in the description of the technical scheme of the multi-objective optimization method of the energy storage power station mentioned above.

[0150] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0151] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0152] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0153] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0154] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A multi-objective optimization method for an energy storage power station, comprising: Taking the maximum resource acquisition amount of the energy storage power station within the target period as the goal, a corresponding objective function is constructed by combining the charge and discharge amount of the energy storage power station at different times within the target period and the clearing electricity price; Introducing an auxiliary variable, wherein the auxiliary variable is used to represent the absolute value of the charge and discharge amount at different times; Constructing a first constraint condition based on the auxiliary variable, the maximum capacity of the battery in the energy storage power station, the charging efficiency in the energy storage power station, and the number of charge and discharge times of the energy storage power station in a target interval; Constructing a second constraint condition based on the auxiliary variable, the charge and discharge amount, and the single maximum charge amount of the energy storage power station; Constructing a third constraint condition based on the charge and discharge amount, the charging efficiency, and the maximum capacity of the battery in the energy storage power station; Determining target charge and discharge amounts of the energy storage power station at different target times according to the objective function, the first constraint condition, the second constraint condition, and the third constraint condition; The charging and discharging strategy of the energy storage power station is optimized according to the target charging and discharging amount.

2. The multi-objective optimization method of the energy storage power station according to claim 1, further comprising: Determine the maximum capacity and charging efficiency of the batteries in the energy storage station.

3. The multi-objective optimization method for an energy storage power station according to claim 1, wherein the objective is to maximize the amount of resource acquisition of the energy storage power station within a target period, and a corresponding objective function is constructed in combination with the charge and discharge amount of the energy storage power station at different times within the target period and the clearing electricity price, including: Determine the resource loss value of the energy storage power station within the target period according to the charging amount and clearing electricity price of the energy storage power station at different times within the target period; Determine the resource acquisition value of the energy storage power station within the target period according to the discharge amount and clearing electricity price of the energy storage power station at different times within the target period; The difference between the resource acquisition value and the resource loss value is determined as the objective function.

4. The multi-objective optimization method for an energy storage power station according to claim 1, wherein the target charge and discharge amount of the energy storage power station at different target times is determined according to the objective function, the first constraint condition, the second constraint condition and the third constraint condition, comprising: A linear programming solution algorithm is used to solve the maximum value of the objective function based on the first constraint condition, the second constraint condition and the third constraint condition to obtain the target charge and discharge amount of the energy storage power station at different target times.

5. According to the multi-objective optimization method of the energy storage power station according to claim 4, the linear programming solution algorithm comprises: Traditional simplex method, modified simplex method, or interior point method.

6. A multi-objective optimization device for an energy storage power station, comprising: A construction module is configured to take the maximum resource acquisition amount of the energy storage power station within the target period as the goal, and to construct a corresponding objective function in combination with the charge and discharge amount of the energy storage power station at different times within the target period and the clearing electricity price; A first determination module is configured to introduce an auxiliary variable, wherein the auxiliary variable is used to represent the absolute value of the charge and discharge amount at different times; The first determination module is further configured to construct a first constraint condition based on the auxiliary variable, the maximum capacity of the battery in the energy storage power station, the charging efficiency in the energy storage power station, and the number of charge and discharge times of the energy storage power station in a target interval; The first determination module is further configured to construct a second constraint condition based on the auxiliary variable, the charge and discharge amount, and the single maximum charge amount of the energy storage power station; and to construct a third constraint condition based on the charge and discharge amount, the charging efficiency, and the maximum capacity of the battery in the energy storage power station; A processing module, configured to determine target charge and discharge amounts of the energy storage power station at different target times according to the objective function, the first constraint condition, the second constraint condition, and the third constraint condition; The second determination module is configured to optimize the charging and discharging strategy of the energy storage power station according to the target charging and discharging amount.

7. A computing device comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the multi-objective optimization method of the energy storage power station described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the multi-objective optimization method for an energy storage power station as claimed in any one of claims 1 to 5.

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