Method and device for optimizing charge and discharge capacity

Abstract

The embodiments of this specification provide a method and device for optimizing charging and discharging amount, wherein the method for optimizing charging and discharging amount comprises: taking the maximum resource acquisition amount of an energy storage power station within a target period as a goal, combining the charging and discharging amount 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 charging and discharging amount and the total electricity of the energy storage power station at different times within the target period; and optimizing the charging and discharging amount of the energy storage power station at different times according to the objective function and the constraints to generate corresponding processing results.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of new energy technology, and in particular to a method for optimizing charging and discharging capacity. 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, the embodiments of this specification provide a charge and discharge capacity optimization method. One or more embodiments of this specification also involve a charge and discharge capacity optimization device, 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 method for optimizing charge and discharge capacity 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 charge and discharge amount and the total amount of electricity of the energy storage power station at different times within the target cycle;

[0008] According to the objective function and the constraint conditions, the charging and discharging amounts of the energy storage power station at different times are optimized to generate corresponding processing results.

[0009] Optionally, the determining of the constraint conditions of the objective function includes:

[0010] Introducing auxiliary variables, wherein the auxiliary variables are identifiers of the state of charge intervals to which the power of the energy storage power station at different times belongs, and the number of auxiliary variables is equal to the number of state of charge intervals;

[0011] Determine the maximum capacity of the battery in the energy storage power station, and determine the maximum allowable percentage of battery power and the minimum allowable percentage of battery power;

[0012] The first constraint condition of the objective function is constructed based on the auxiliary variable, the charge and discharge amount of the energy storage power station at different times within the target period, the maximum capacity of the battery, the maximum allowable proportion of electricity, and the minimum allowable proportion of electricity.

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

[0014] Introducing auxiliary variables, wherein the auxiliary variables are identifiers of the state of charge intervals to which the power of the energy storage power station at different times belongs, and the number of auxiliary variables is equal to the number of state of charge intervals;

[0015] Determine the charge and discharge powers corresponding to different state of charge intervals, and construct a first sub-constraint condition corresponding to the energy storage power station at the first moment in the target period based on the charge and discharge powers and a first auxiliary variable corresponding to the energy storage power station at the first moment;

[0016] Based on the charging and discharging power and the second auxiliary variable corresponding to the energy storage power station at the i-1th moment in the target period, construct a second sub-constraint condition corresponding to the energy storage power station at the i-th moment, where i is a positive integer greater than 1;

[0017] The first sub-constraint and the second sub-constraint are determined as second constraints of the objective function.

[0018] Optionally, the determining of the constraint conditions of the objective function includes:

[0019] Determine the maximum charging power of the energy storage power station, and determine the interval between the i-th moment and the i-1-th moment in the target cycle, where i is a positive integer greater than 1;

[0020] Determining a single maximum charging amount of the energy storage power station according to the maximum charging power and the interval duration;

[0021] Based on the charge and discharge amount of the energy storage power station at different times within the target period and the single maximum charge amount, a third constraint condition of the objective function is constructed.

[0022] 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:

[0023] 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;

[0024] 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;

[0025] The difference between the resource acquisition value and the resource loss value is determined as the objective function.

[0026] Optionally, the optimizing processing of the charge and discharge amount of the energy storage power station at different times according to the objective function and the constraint condition to generate corresponding processing results includes:

[0027] An integer linear programming solution algorithm is used to solve the maximum value of the objective function based on the constraint conditions to obtain the target charge and discharge amount of the energy storage power station at different times;

[0028] The target charge and discharge amount is determined as a processing result generated by optimizing the charge and discharge amount of the energy storage power station at different times.

[0029] Optionally, the integer linear programming solution algorithm includes: a branch and bound method or a cutting plane method.

[0030] According to a second aspect of an embodiment of this specification, a charge and discharge capacity optimization device is provided, comprising:

[0031] 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;

[0032] A determination module is configured to determine the constraint conditions of the objective function, wherein the constraint conditions are used to constrain the charge and discharge amount and the total amount of electricity of the energy storage power station at different times within a target period;

[0033] The processing module is configured to optimize the charge and discharge amount of the energy storage power station at different times according to the objective function and the constraint conditions, and generate corresponding processing results.

[0034] According to a third aspect of an embodiment of this specification, a computing device is provided, including:

[0035] Memory and processor;

[0036] 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 charge and discharge capacity optimization method.

[0037] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of any one of the charge and discharge capacity optimization methods are implemented.

[0038] 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 above-mentioned charge and discharge capacity optimization method.

[0039] 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 charge and discharge amount and total electricity of the energy storage power station at different times within the target period; according to the objective function and the constraint conditions, the charge and discharge amount of the energy storage power station at different times is optimized to generate corresponding processing results. In this way, while ensuring that the resource acquisition amount of the energy storage power station is maximized, the charge and discharge amount of the energy storage power station at different times 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of a method for optimizing charge and discharge capacity provided by an embodiment of this specification;

[0041] Figure 2 is a processing flow chart of a charge and discharge capacity optimization method provided by an embodiment of this specification;

[0042] Figure 3 It is a structural schematic diagram of a charge and discharge capacity optimization device provided by an embodiment of this specification;

[0043] Figure 4 It is a structural block diagram of a computing device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0044] 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.

[0045] 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.

[0046] 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".

[0047] First, the terms involved in one or more embodiments of this specification are explained.

[0048] State of Charge (SOC): It is used to reflect the remaining capacity of the battery. Its value is defined as the ratio of the remaining capacity to the battery capacity, usually expressed as a percentage. Its value range is 0~1. When "SOC=0", it means that the battery is fully discharged, and when "SOC=1", it means that the battery is fully charged.

[0049] The embodiments of this specification aim at the problem of optimizing the charging and discharging strategy in the power trading decision algorithm, especially in the case of limiting the SOC range and charging and discharging efficiency, and propose a charging and discharging amount optimization method to determine the optimal charging and discharging strategy under the condition of known clearing electricity price, so as to achieve the maximum resource acquisition.

[0050] In this specification, a charge and discharge capacity optimization method is provided. This specification also relates to a charge and discharge capacity optimization device, a computing device, a computer-readable storage medium, and a computer program, which are described in detail one by one in the following embodiments.

[0051] Figure 1 A flow chart of a charge and discharge capacity optimization method provided according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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:

[0058] 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;

[0059] 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;

[0060] The difference between the resource acquisition value and the resource loss value is determined as the objective function.

[0061] Specifically, the objective function constructed in the embodiment of this specification is as follows:

[0062] Formula (1)

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] Step 104: Determine the constraint conditions of the objective function, wherein the constraint conditions are used to constrain the charge and discharge amount and the total amount of electricity of the energy storage power station at different times within the target cycle.

[0068] 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.

[0069] In an optional implementation, the determining of the constraint conditions of the objective function includes:

[0070] Introducing auxiliary variables, wherein the auxiliary variables are identifiers of the state of charge intervals to which the power of the energy storage power station at different times belongs, and the number of auxiliary variables is equal to the number of state of charge intervals;

[0071] Determine the maximum capacity of the battery in the energy storage power station, and determine the maximum allowable percentage of battery power and the minimum allowable percentage of battery power;

[0072] The first constraint condition of the objective function is constructed based on the auxiliary variable, the charge and discharge amount of the energy storage power station at different times within the target period, the maximum capacity of the battery, the maximum allowable proportion of electricity, and the minimum allowable proportion of electricity.

[0073] Specifically, since the range of the state of charge SOC is [0,1], if it is divided into intervals, three state of charge intervals (SOC intervals) are obtained, and the ranges of the three SOC intervals are: SOC1 interval belongs to , indicating that the SOC power is between 0 and , SOC2 interval belongs to , indicating that the SOC power is to , SOC3 interval belongs to , indicating that the SOC power is to 1. and Represent two thresholds of SOC intervals, which are used to divide different SOC intervals. .

[0074] For ease of understanding, it can be understood as =0.2, =0.8.

[0075] In the embodiment of this specification, when the number of state of charge intervals is equal to 3, three auxiliary variables can also be introduced to indicate the state of charge interval to which the power of the energy storage power station belongs at different times.

[0076] Specifically, the first auxiliary variable introduced is , the second auxiliary variable is , the third auxiliary variable is , is the mark that the power of the energy storage station at the i-th moment belongs to the SOC1 interval, is the indicator that the power of the energy storage station at the i-th moment belongs to the SOC2 interval, is the indicator that the power of the energy storage station at the i-th moment belongs to the SOC3 interval, .

[0077] exist When the value of is 1, it means that the power of the energy storage station at the i-th moment belongs to the SOC1 interval. When the value of is 0, it means that the power of the energy storage station at the i-th moment does not belong to the SOC1 interval; When the value of is 1, it means that the power of the energy storage power station at the i-th moment belongs to the SOC2 interval. When the value of is 0, it means that the power of the energy storage station at the i-th moment does not belong to the SOC2 interval; similarly, When the value of is 1, it means that the power of the energy storage power station at the i-th moment belongs to the SOC3 interval. When the value of is 0, it means that the power of the energy storage station at the i-th moment does not belong to the SOC3 interval. Among the three auxiliary variables, if there is an auxiliary variable with a value of 1, there can only be one and only one auxiliary variable with a value of 1, and there is no situation where the values ​​of any two or three auxiliary variables are 1 at the same time.

[0078] Based on this, the first constraint condition constructed is as follows:

[0079] Formula (2)

[0080] Formula (3)

[0081] Formula (4)

[0082] Formula (5)

[0083] Formula (6)

[0084] Formula (7)

[0085] in, Represents the maximum capacity of the battery, and charging is a negative number, so the formula is - . Represents the minimum allowable percentage of battery power; Represents the maximum allowable percentage of battery power. and All are floating point decimals between 0 and 1, that is, . , , , , , , , are all constants.

[0086] The left side of the inequality represents the power of the energy storage power station at the i-th moment, that is, the power of the energy storage power station at the current moment, j represents the sequence from 1 to i, That is, the cumulative sum of the charge and discharge amount from the first moment to the i-th moment, where the charge and discharge amount at different moments is There are positive and negative.

[0087] The right side of the inequality is used to determine the SOC interval to which the power of the energy storage station belongs at the i-th moment. Taking formula (2) and formula (3) as an example, if is equal to 1, which means that the power of the energy storage station at the i-th moment belongs to the SOC1 interval. Therefore, The corresponding interval will be constrained based on the range of the SOC1 interval, thereby limiting it to the constraints of the SOC1 interval; if If it is equal to 0, it means that the power of the energy storage station at the i-th moment does not belong to the SOC1 interval. Therefore, The corresponding interval will relax the SOC range constraint to satisfy the boundary SOC range constraint, that is, the maximum and minimum allowable values ​​of the power that can be obtained. Based on this, the auxiliary variable at each moment can be Correctly display the SOC interval corresponding to the power of the energy storage power station at that moment.

[0088] It should be noted that the embodiments of this specification are described by taking three SOC intervals as an example, and the specific number of divided SOC intervals can be determined according to actual needs and is not limited here.

[0089] In an optional implementation, the determining of the constraint conditions of the objective function includes:

[0090] Introducing auxiliary variables, wherein the auxiliary variables are identifiers of the state of charge intervals to which the power of the energy storage power station at different times belongs, and the number of auxiliary variables is equal to the number of state of charge intervals;

[0091] Determine the charge and discharge powers corresponding to different state of charge intervals, and construct a first sub-constraint condition corresponding to the energy storage power station at the first moment in the target period based on the charge and discharge powers and a first auxiliary variable corresponding to the energy storage power station at the first moment;

[0092] Based on the charging and discharging power and the second auxiliary variable corresponding to the energy storage power station at the i-1th moment in the target period, construct a second sub-constraint condition corresponding to the energy storage power station at the i-th moment, where i is a positive integer greater than 1;

[0093] The first sub-constraint and the second sub-constraint are determined as second constraints of the objective function.

[0094] Specifically, as mentioned above, the auxiliary variables introduced are , and .

[0095] The second constraint condition based on auxiliary variables and charge and discharge power is as follows:

[0096] Formula (8)

[0097] Formula (9)

[0098] in, Indicates the charge and discharge power corresponding to the SOC1 interval, Indicates the charge and discharge power corresponding to the SOC2 interval, Indicates the charge and discharge power corresponding to the SOC3 interval. , , are all constants.

[0099] Formula (8) represents the range of charge and discharge of the energy storage station at the first moment. Formula (9) represents the range of charge and discharge of the energy storage station at the i-th moment. However, at the i-th moment, the change of charge and discharge needs to refer to the power at the i-1th moment, in order to ensure that the battery power reaches the target range before charging and discharging. If this rule is not followed, it may lead to an unreasonable situation, that is, at the i-th moment, the charge and discharge amount changes in advance without waiting for the battery power at the i-1th moment to fully reach the target range, which is inconsistent with common sense.

[0100] In an optional implementation, the determining of the constraint conditions of the objective function includes:

[0101] Determine the maximum charging power of the energy storage power station, and determine the interval between the i-th moment and the i-1-th moment in the target cycle, where i is a positive integer greater than 1;

[0102] Determining a single maximum charging amount of the energy storage power station according to the maximum charging power and the interval duration;

[0103] Based on the charge and discharge amount of the energy storage power station at different times within the target period and the single maximum charge amount, a third constraint condition of the objective function is constructed.

[0104] Specifically, the third constraint condition constructed is as follows:

[0105] Formula (10)

[0106] Formula (11)

[0107] Among them, p represents the maximum charging and discharging power of the energy storage power station, t represents the charging and discharging time, Indicates the maximum single charge capacity.

[0108] Step 106: According to the objective function and the constraint conditions, the charging and discharging amount of the energy storage power station at different times is optimized to generate corresponding processing results.

[0109] 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.

[0110] Based on this, according to the objective function and the constraints, the charge and discharge amounts of the energy storage power station at different times are optimized to generate corresponding processing results. Specifically, an integer linear programming solution algorithm can be used, and the maximum value of the objective function is solved based on the constraints to obtain the target charge and discharge amounts of the energy storage power station at different times; the target charge and discharge amount is determined as the processing result generated by optimizing the charge and discharge amounts of the energy storage power station at different times.

[0111] In the embodiments of this specification, the integer linear programming solution algorithm can be a branch and bound method or a cutting plane method. In practical applications, other types of integer linear programming solution algorithms can also be used for solution. For example, the integer linear programming problem can be first converted into a linear programming problem by using a constraint relaxation method, and then the linear programming problem can be solved by using an interior point method or a simplex method. The specific algorithm can be determined according to actual needs and is not limited here.

[0112] In addition, after determining the target charge and discharge amounts of the energy storage power station at different times, the charge and discharge strategy of the energy storage power station may also be determined according to the target charge and discharge amounts.

[0113] Specifically, the charging and discharging strategy of the energy storage power station can be represented by the charging and discharging amounts at different times.

[0114] After determining the target charge and discharge amount of the energy storage power station at different times, the charge and discharge strategy is determined according to the target charge and discharge amount. Specifically, the target charge and discharge amount determined at different times can be directly used as the charge and discharge strategy of the energy storage power station, or the target charge and discharge amount determined at different times can be adjusted accordingly according to the emergencies that occur in real time during the charging and discharging process of the energy storage power station, and the adjustment result is determined as the charge and discharge strategy of the energy storage power station.

[0115] The embodiment of this specification simplifies the constraints, specifically introducing auxiliary variables , and , used to indicate the SOC interval to which the power belongs. 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 numerical errors in the calculation process, thereby improving the stability of the solution.

[0116] In addition, the embodiments of this specification carefully balance the characteristics of the specific problem and the numerical characteristics of the solver, so that the constructed auxiliary variables better describe the constraints and linkage relationships between variables. Then, the characteristics of the auxiliary variables are used to easily realize the judgment of the SOC interval.

[0117] In addition, the solution methods for linear programming problems are more mature and extensive. After the introduction of auxiliary variables, nonlinear problems that may not be easy to solve can also be solved by linear programming methods, which expands the scope of application of the problem. Moreover, linear programming problems usually have lower solution complexity than nonlinear programming problems. The introduction of auxiliary variables can reduce the complexity of the problem and reduce the time and resource cost required for solution.

[0118] 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 charge and discharge amount and total electricity of the energy storage power station at different times within the target period; according to the objective function and the constraint conditions, the charge and discharge amount of the energy storage power station at different times is optimized to generate corresponding processing results. In this way, while ensuring that the benefits of the energy storage power station are maximized, the charge and discharge amount of the energy storage power station at different times 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.

[0119] The following combination Figure 2 , the charge and discharge capacity optimization method is further described. Figure 2 A processing flow chart of a charge and discharge capacity optimization method provided in an embodiment of this specification is shown, which specifically includes the following steps.

[0120] Step 202: Taking the maximum profit of the energy storage power station in the target period as the target, 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.

[0121] Step 204: Introduce auxiliary variables.

[0122] Step 206: Determine the maximum capacity of the battery in the energy storage power station and the maximum allowable percentage of battery power and the minimum allowable percentage of battery power.

[0123] Step 208: Construct the first constraint of the objective function based on the auxiliary variables, the charge and discharge amount of the energy storage power station at different times in the target period, the maximum capacity of the battery, the maximum allowable proportion of electricity, and the minimum allowable proportion of electricity.

[0124] Step 210: Introduce auxiliary variables.

[0125] Step 212: Determine the charge and discharge powers corresponding to different state of charge intervals, and construct a first sub-constraint condition corresponding to the energy storage station at the first moment based on the charge and discharge powers and the first auxiliary variable corresponding to the energy storage station at the first moment in the target cycle.

[0126] Step 214: Based on the charging and discharging power and the second auxiliary variable corresponding to the energy storage station at the i-1th moment within the target period, construct a second sub-constraint condition corresponding to the energy storage station at the i-th moment.

[0127] Specifically, the first sub-constraint condition and the second sub-constraint condition together constitute the second constraint condition of the objective function.

[0128] Step 216: Determine the maximum charging power of the energy storage power station, and determine the interval between the i-th moment and the i-1-th moment in the target cycle.

[0129] Step 218: Determine the maximum single charge capacity of the energy storage power station according to the maximum charging power and the interval duration.

[0130] Step 220: Construct a third constraint condition of the objective function based on the charge and discharge amount of the energy storage power station at different times within the target cycle and the single maximum charge amount.

[0131] Step 222: using an integer linear programming solution algorithm, and solving the maximum value of the objective function based on the first, second, and third constraints, to obtain the charge and discharge amounts of the energy storage power station at different times.

[0132] Step 224: 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.

[0133] 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.

[0134] Corresponding to the above method embodiment, this specification also provides a charge and discharge capacity optimization device embodiment, Figure 3 FIG. 1 is a schematic diagram showing the structure of a charge and discharge capacity optimization device provided by an embodiment of the present specification. Figure 3 As shown, the device comprises:

[0135] 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;

[0136] The determination module 304 is configured to determine the constraint conditions of the objective function, wherein the constraint conditions are used to constrain the charge and discharge amount and the total amount of electricity of the energy storage power station at different times within the target cycle;

[0137] The processing module 306 is configured to optimize the charge and discharge amount of the energy storage power station at different times according to the objective function and the constraint conditions, and generate corresponding processing results.

[0138] Optionally, the determining module 304 is further configured to:

[0139] Introducing auxiliary variables, wherein the auxiliary variables are identifiers of the state of charge intervals to which the power of the energy storage power station at different times belongs, and the number of auxiliary variables is equal to the number of state of charge intervals;

[0140] Determine the maximum capacity of the battery in the energy storage power station, and determine the maximum allowable percentage of battery power and the minimum allowable percentage of battery power;

[0141] The first constraint condition of the objective function is constructed based on the auxiliary variable, the charge and discharge amount of the energy storage power station at different times within the target period, the maximum capacity of the battery, the maximum allowable proportion of electricity, and the minimum allowable proportion of electricity.

[0142] Optionally, the determining module 304 is further configured to:

[0143] Introducing auxiliary variables, wherein the auxiliary variables are identifiers of the state of charge intervals to which the power of the energy storage power station at different times belongs, and the number of auxiliary variables is equal to the number of state of charge intervals;

[0144] Determine the charge and discharge powers corresponding to different state of charge intervals, and construct a first sub-constraint condition corresponding to the energy storage power station at the first moment in the target period based on the charge and discharge powers and a first auxiliary variable corresponding to the energy storage power station at the first moment;

[0145] Based on the charging and discharging power and the second auxiliary variable corresponding to the energy storage power station at the i-1th moment in the target period, construct a second sub-constraint condition corresponding to the energy storage power station at the i-th moment, where i is a positive integer greater than 1;

[0146] The first sub-constraint and the second sub-constraint are determined as second constraints of the objective function.

[0147] Optionally, the determining module 304 is further configured to:

[0148] Determine the maximum charging power of the energy storage power station, and determine the interval between the i-th moment and the i-1-th moment in the target cycle, where i is a positive integer greater than 1;

[0149] Determining a single maximum charging amount of the energy storage power station according to the maximum charging power and the interval duration;

[0150] Based on the charge and discharge amount of the energy storage power station at different times within the target period and the single maximum charge amount, a third constraint condition of the objective function is constructed.

[0151] Optionally, the construction module 302 is further configured to:

[0152] 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;

[0153] 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;

[0154] The difference between the resource acquisition value and the resource loss value is determined as the objective function.

[0155] Optionally, the processing module 306 is further configured to:

[0156] An integer linear programming solution algorithm is used to solve the maximum value of the objective function based on the constraint conditions to obtain the target charge and discharge amount of the energy storage power station at different times;

[0157] The target charge and discharge amount is determined as a processing result generated by optimizing the charge and discharge amount of the energy storage power station at different times.

[0158] Optionally, the integer linear programming solution algorithm includes: a branch and bound method or a cutting plane method.

[0159] The above is a schematic scheme of a charge and discharge optimization device of this embodiment. It should be noted that the technical scheme of the charge and discharge optimization device and the technical scheme of the above-mentioned charge and discharge optimization method belong to the same concept, and the details not described in detail in the technical scheme of the charge and discharge optimization device can be referred to the description of the technical scheme of the above-mentioned charge and discharge optimization method.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The processor 420 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned charge and discharge capacity optimization method.

[0165] 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 above-mentioned charge and discharge capacity optimization method belong to the same concept, and the details not described in detail in the technical scheme of the computing device can be referred to the description of the technical scheme of the above-mentioned charge and discharge capacity optimization method.

[0166] 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 above-mentioned charge and discharge capacity optimization method.

[0167] 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 above-mentioned charge and discharge capacity optimization method 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 above-mentioned charge and discharge capacity optimization method.

[0168] 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 above-mentioned charge and discharge capacity optimization method.

[0169] 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 above-mentioned charge and discharge capacity optimization method belong to the same concept, and the details not described in detail in the technical scheme of the computer program can be referred to the description of the technical scheme of the above-mentioned charge and discharge capacity optimization method.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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 method for optimizing charge and discharge capacity, comprising: Taking the maximum resource acquisition amount of the energy storage power station within the target period as the goal, the 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 auxiliary variables, wherein the auxiliary variables are identifiers of the state of charge intervals to which the power of the energy storage power station at different times belongs, and the number of auxiliary variables is equal to the number of state of charge intervals; Constructing a first constraint condition based on the auxiliary variable, the charge and discharge amount, the maximum capacity of the battery in the energy storage power station, the maximum allowable proportion of battery power, and the minimum allowable proportion of battery power; Based on the charge and discharge powers corresponding to different state of charge intervals and the first auxiliary variable corresponding to the energy storage power station at the first moment in the target period, construct a first sub-constraint condition corresponding to the energy storage power station at the first moment; Based on the charging and discharging power and the second auxiliary variable corresponding to the energy storage power station at the i-1th moment in the target period, construct a second sub-constraint condition corresponding to the energy storage power station at the i-th moment, wherein i is a positive integer greater than 1, and the first sub-constraint condition and the second sub-constraint condition together constitute a second constraint condition; Constructing a third constraint condition based on the charge and discharge amount and the single maximum charge amount of the energy storage power station; According to the objective function, the first constraint condition, the second constraint condition and the third constraint condition, the charging and discharging amount of the energy storage power station at different times is optimized to generate corresponding processing results.

2. The method for optimizing charge and discharge capacity according to claim 1, further comprising: Determine the maximum capacity of the battery in the energy storage power station, and determine the maximum allowable percentage of battery power and the minimum allowable percentage of battery power.

3. The method for optimizing charge and discharge capacity according to claim 1 or 2, further comprising: Determine the maximum charging power of the energy storage power station, and determine the interval between the i-th moment and the i-1-th moment in the target cycle, where i is a positive integer greater than 1; The maximum single charge capacity of the energy storage power station is determined according to the maximum charging power and the interval duration.

4. The method for optimizing the charge and discharge amount according to claim 1, wherein the objective is to maximize the resource acquisition amount of the energy storage power station within the target period, and the 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.

5. The method for optimizing the charge and discharge capacity according to claim 1, wherein the charge and discharge capacity of the energy storage power station at different times is optimized according to the objective function, the first constraint condition, the second constraint condition and the third constraint condition to generate corresponding processing results, including: Using an integer linear programming solution algorithm, and solving 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 times; The target charge and discharge amount is determined as a processing result generated by optimizing the charge and discharge amount of the energy storage power station at different times.

6. The charge and discharge capacity optimization method according to claim 5, wherein the integer linear programming solution algorithm comprises: Branch and bound or cutting plane method.

7. A charge and discharge capacity optimization device, 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 determination module is configured to introduce auxiliary variables, wherein the auxiliary variables are identifiers of the state of charge intervals to which the power of the energy storage power station at different times belongs, and the number of the auxiliary variables is equal to the number of the state of charge intervals; The determination module is further configured to construct a first constraint condition based on the auxiliary variable, the charge and discharge amount, the maximum capacity of the battery in the energy storage power station, the maximum allowable proportion of the battery power, and the minimum allowable proportion of the battery power; based on the charge and discharge power corresponding to different state of charge intervals and the first auxiliary variable corresponding to the energy storage power station at the first moment in the target period, construct a first sub-constraint condition corresponding to the energy storage power station at the first moment; The determination module is further configured to construct a second sub-constraint condition corresponding to the energy storage power station at the i-th moment based on the charge and discharge power and the second auxiliary variable corresponding to the energy storage power station at the i-1th moment within the target cycle, wherein i is a positive integer greater than 1, and the first sub-constraint condition and the second sub-constraint condition together constitute a second constraint condition; construct a third constraint condition based on the charge and discharge amount and the single maximum charge amount of the energy storage power station; The processing module is configured to optimize the charge and discharge amount of the energy storage power station at different times according to the objective function, the first constraint condition, the second constraint condition and the third constraint condition, and generate corresponding processing results.

8. 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 charge and discharge capacity optimization method described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the charge and discharge capacity optimization method according to any one of claims 1 to 6.

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