Configuration parameter processing method and device of energy storage equipment

Abstract

The embodiment of the invention provides a configuration parameter processing method and device for energy storage equipment, and the method comprises the steps: determining a to-be-consumed resource of the energy storage equipment as a first index, and constructing a first objective function based on the first index and a configuration parameter, determining a first objective function and a first constraint condition of the energy storage device; processing the configuration parameters through a group optimization algorithm based on a first objective function and a first constraint condition to obtain a corresponding initial combination; determining a to-be-consumed resource of the energy scheduling system as a second index, constructing a second objective function based on the second index and the scheduling parameter, and determining a second constraint condition of the second objective function and the energy scheduling system; performing optimization processing on the scheduling parameters through a group optimization algorithm based on a second objective function and a second constraint condition to obtain a corresponding optimal combination; and adjusting the initial combination according to the optimal combination, and determining an adjustment result as a target configuration parameter of the energy storage equipment.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of new energy technology, and in particular to a method for processing configuration parameters of an energy storage device. Background Art

[0002] In the current energy system transformation process, the widespread application of distributed energy and the increasing proportion of renewable energy have brought unprecedented challenges and opportunities to the power grid. On the one hand, the instability of intermittent energy requires the power grid to have higher flexibility and regulation capabilities to maintain the real-time balance between power supply and power consumption; on the other hand, users have put forward higher requirements for the quality and reliability of energy services, prompting the power service system to adopt more advanced and efficient management measures to ensure the safe and stable operation of the power grid. Based on this, energy storage equipment has been widely used due to its unique peak-shaving and valley-filling functions and rapid response characteristics.

[0003] Energy storage devices can store excess electricity when there is surplus electricity, and release the stored electricity during peak hours, thereby smoothing the power load curve and improving the efficiency of power grid operation. However, the capacity selection of energy storage devices involves the trade-off of multiple complex factors. Although excessive equipment capacity can enhance responsiveness and backup margin, it will greatly increase the cost of initial construction and subsequent operation and maintenance; on the contrary, if the energy storage capacity is insufficient, it may not be able to fully absorb excess electricity or replenish shortages in time, which will affect the performance of the entire system. 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 method for processing configuration parameters of an energy storage device. One or more embodiments of this specification also relate to a configuration parameter processing device of an energy storage 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 processing configuration parameters of an energy storage device is provided, including:

[0006] Determine the to-be-consumed resources of the energy storage device as a first indicator, construct a first objective function based on the first indicator and at least two configuration parameters, and determine first constraint conditions corresponding to the first objective function and the energy storage device respectively;

[0007] Optimizing the at least two configuration parameters by a swarm optimization algorithm based on the first objective function and the first constraint condition to obtain a corresponding initial combination;

[0008] Determine the resources to be consumed of the energy dispatching system as a second indicator, construct a second objective function based on the second indicator and at least two dispatching parameters, and determine second constraints corresponding to the second objective function and the energy dispatching system respectively;

[0009] Optimizing the at least two scheduling parameters by a group optimization algorithm and based on the second objective function and the second constraint condition to obtain a corresponding optimal combination;

[0010] The initial combination is adjusted according to the optimal combination, and the adjustment result is determined as the target configuration parameter of the energy storage device.

[0011] Optionally, the at least two configuration parameters include power parameters of an energy storage device, and the energy dispatching system includes an energy storage device, an energy dispatching device, and an energy consumption device;

[0012] Accordingly, the determining of the second constraint conditions corresponding to the second objective function and the energy dispatching system respectively includes:

[0013] Determining a second constraint condition corresponding to the second objective function based on power parameters corresponding to the energy storage device, the energy scheduling device, and the energy consumption device, respectively, and a power parameter of the energy storage device;

[0014] Based on the energy input power, energy output power and charge state of the energy storage device, a second constraint condition corresponding to the energy scheduling system is determined.

[0015] Optionally, the optimizing the at least two scheduling parameters by using a swarm optimization algorithm and based on the second objective function and the second constraint condition includes:

[0016] Input the power parameter value in the initial combination into the second constraint condition corresponding to the second objective function, and input the power parameter values ​​corresponding to the energy scheduling device and the energy consumption device respectively into the second constraint condition corresponding to the second objective function for processing to generate a corresponding output result;

[0017] The at least two scheduling parameters are optimized by a group optimization algorithm based on the second objective function and the output result.

[0018] Optionally, the at least two configuration parameters include a power parameter, a capacity parameter, a usage time of the energy storage device, and resources to be consumed corresponding to equipment maintenance of the energy storage device;

[0019] Accordingly, the determining of the first constraint conditions corresponding to the first objective function and the energy storage device respectively includes:

[0020] Determining a first constraint condition corresponding to the first objective function based on a power parameter and a capacity parameter of the energy storage device;

[0021] Based on the energy input power, energy output power and charge state of the energy storage device, a first constraint condition corresponding to the energy storage device is determined.

[0022] Optionally, the optimizing the at least two configuration parameters by a swarm optimization algorithm based on the first objective function and the first constraint condition to obtain a corresponding initial combination includes:

[0023] Initializing parameter values ​​of the at least two configuration parameters, and generating a corresponding parameter group based on the initialization result;

[0024] Iteratively optimizing the parameter group using a group optimization algorithm based on the first objective function and the first constraint condition;

[0025] When the iteration is completed, the initial combination of the at least two configuration parameters is screened from the target parameter group obtained after the iteration is completed.

[0026] Optionally, the energy dispatching system includes energy dispatching equipment and energy consuming equipment;

[0027] Accordingly, constructing a second objective function based on the second indicator and at least two scheduling parameters includes:

[0028] The resources to be consumed of the energy scheduling device and the resources to be consumed of the energy consuming device are used as scheduling parameters, and a second objective function is constructed based on the second indicator and the scheduling parameters.

[0029] Optionally, the method for processing configuration parameters of the energy storage device further includes:

[0030] An energy scheduling strategy corresponding to the energy scheduling system is determined based on the optimal combination.

[0031] According to a second aspect of an embodiment of this specification, a configuration parameter processing device for an energy storage device is provided, comprising:

[0032] A first construction module is configured to determine the to-be-consumed resources of the energy storage device as a first indicator, construct a first objective function based on the first indicator and at least two configuration parameters, and determine first constraint conditions corresponding to the first objective function and the energy storage device respectively;

[0033] A first processing module is configured to optimize the at least two configuration parameters through a swarm optimization algorithm based on the first objective function and the first constraint condition to obtain a corresponding initial combination;

[0034] A second construction module is configured to determine the resources to be consumed of the energy scheduling system as a second indicator, construct a second objective function based on the second indicator and at least two scheduling parameters, and determine second constraints corresponding to the second objective function and the energy scheduling system respectively;

[0035] A second processing module is configured to optimize the at least two scheduling parameters through a group optimization algorithm and based on the second objective function and the second constraint condition to obtain a corresponding optimal combination;

[0036] The adjustment module is configured to adjust the initial combination according to the optimal combination, and determine the adjustment result as the target configuration parameter of the energy storage device.

[0037] According to a third aspect of an embodiment of this specification, a computing device is provided, including:

[0038] Memory and processor;

[0039] 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 method for processing configuration parameters of the energy storage device.

[0040] 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 configuration parameter processing methods for the energy storage device are implemented.

[0041] 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 configuration parameter processing method of the above-mentioned energy storage device.

[0042] The configuration parameter processing method of the energy storage device provided in the embodiment of this specification is to determine the resources to be consumed of the energy storage device as the first indicator, construct a first objective function based on the first indicator and at least two configuration parameters, and determine the first constraint conditions corresponding to the first objective function and the energy storage device respectively; optimize at least two configuration parameters through a group optimization algorithm and based on the first objective function and the first constraint conditions to obtain a corresponding initial combination; determine the resources to be consumed of the energy scheduling system as the second indicator, construct a second objective function based on the second indicator and at least two scheduling parameters, and determine the second constraint conditions corresponding to the second objective function and the energy scheduling system respectively; optimize at least two scheduling parameters through a group optimization algorithm and based on the second objective function and the second constraint conditions to obtain a corresponding optimal combination; adjust the initial combination according to the optimal combination, and determine the adjustment result as the target configuration parameter of the energy storage device. In this way, it is beneficial to ensure the accuracy of the energy storage configuration optimization results, thereby improving the stability of the energy scheduling process. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of a method for processing configuration parameters of an energy storage device provided by an embodiment of this specification;

[0044] Figure 2 It is a schematic diagram of a processing process of a configuration parameter processing method of an energy storage device provided by an embodiment of this specification;

[0045] Figure 3 It is a structural schematic diagram of a configuration parameter processing device for energy storage equipment provided by one embodiment of this specification;

[0046] Figure 4 It is a structural block diagram of a computing device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0047] 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.

[0048] 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.

[0049] 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".

[0050] First, the terms involved in one or more embodiments of this specification are explained.

[0051] Energy Storage System (ESS): refers to a device that can store electrical energy in the form of chemical energy, potential energy, kinetic energy, etc., and convert it into electrical energy to supply users when needed.

[0052] 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 to 1. When "SOC = 0", it means that the battery is fully discharged, and when "SOC = 1", it means that the battery is fully charged.

[0053] In view of the unstable impact of the volatility and intermittent characteristics of renewable energy on the power system, the capacity planning of energy storage equipment using the traditional single static planning method can no longer meet the actual needs. This specification embodiment proposes an innovative two-layer optimization model to solve the multiple challenges faced in the design and operation of current energy storage systems in response to the increasingly prominent parameter configuration problem of energy storage equipment in the current energy system.

[0054] The embodiments of this specification introduce the cost analysis of the entire life cycle of energy storage equipment through the construction of the upper optimization model, and comprehensively consider the rated power, capacity and various cost factors of the energy storage equipment during its entire service life, including but not limited to the purchase cost, operation and maintenance cost, scrapping and disposal cost, etc., so as to obtain the most economical and feasible configuration plan. Secondly, taking into account the various uncertainties and random events existing in the actual operation process, the lower optimization model of the embodiments of this specification particularly emphasizes the dynamic coordination and optimization among the source, load and storage. Under the premise of ensuring safe and stable operation, the operating cost of the energy dispatching system is minimized. Through refined dispatching arrangements, the lower optimization model can significantly improve the economic benefits of the virtual power plant, while enhancing the system's ability to respond to emergencies and ensure the stability and reliability of the power grid. Through the organic combination of the upper and lower optimization models, the embodiments of this specification can establish a complete and highly adaptive decision support system to provide power operators with comprehensive information assistance and strategic recommendations.

[0055] In this specification, a configuration parameter processing method of an energy storage device is provided. This specification also relates to a configuration parameter processing device of an energy storage 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.

[0056] Figure 1 A flow chart of a method for processing configuration parameters of an energy storage device provided according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0057] Step 102: Determine the to-be-consumed resources of the energy storage device as a first indicator, construct a first objective function based on the first indicator and at least two configuration parameters, and determine first constraint conditions corresponding to the first objective function and the energy storage device, respectively.

[0058] Specifically, the resources to be consumed of the energy storage device may be the resources required for the energy storage device to store energy.

[0059] In the embodiment of this specification, the goal is to minimize the resources to be consumed by the energy storage device, and at least two configuration parameters of the energy storage device are used as parameters to be optimized. The minimum value of the resources to be consumed is obtained by optimizing the at least two configuration parameters.

[0060] Based on this, the embodiment of this specification takes the resources to be consumed of the energy storage device as the first indicator, and constructs a first objective function based on the first indicator and at least two configuration parameters.

[0061] In practical applications, the energy storage device can be used to store power, and the resources to be consumed by the energy storage device can be funds to be consumed, that is, the total operating cost of the energy storage device.

[0062] In an optional implementation, the at least two configuration parameters of the energy storage device include a power parameter, a capacity parameter, a usage time of the energy storage device, and resources to be consumed corresponding to equipment maintenance of the energy storage device.

[0063] Therefore, the first objective function constructed based on the first indicator and at least two configuration parameters in the embodiment of this specification is as follows:

[0064]

[0065] Among them, F is the total operating cost of the energy storage equipment, C inv is the initial purchase cost of the energy storage equipment, It is the operation and maintenance cost of the energy storage equipment.

[0066] The initial purchase cost of the energy storage power station is C inv In order to consider the time value of funds, the embodiment of this specification annualizes it, and the specific expression is as follows:

[0067]

[0068] in, is the power of the energy storage device, is the capacity of the energy storage device, is the total replacement cost of energy storage equipment during the project period, β is the base discount rate, and Y a is the preset service life of the energy storage equipment, Y t is the actual service life of the energy storage equipment, c p is the unit power cost coefficient of energy storage equipment, c E is the unit capacity cost coefficient of energy storage equipment, and β, Y a 、c p 、c E are constants, Y t Greater than 0.

[0069] Operation and maintenance costs of energy storage equipment The specific calculation can be obtained through the following expression:

[0070]

[0071] Among them, RC represents the cycle cost of energy storage equipment, which refers to the cost required for energy storage equipment to complete a charge and discharge cycle. t Represents the depreciation rate of the energy storage equipment at time t, CL t represents the charge and discharge depth of the energy storage device at time t, and RC and d t is a constant.

[0072] It should be noted that T represents the calculation period of the total operating cost of the energy storage equipment, and t is a plurality of time nodes obtained by dividing T according to a preset time length.

[0073] In practical applications, T can be one day, one month, or one year, and the preset duration can be 15 minutes, one hour, or one day, which can be determined according to actual needs and is not limited here.

[0074] When the energy storage device does not change, the discharge depth CL in formula (4) t It can be calculated by the following expression:

[0075]

[0076] Where D(t) is the actual discharge depth of the energy storage device at time t, D R is the rated discharge depth of the energy storage device, β0, β1, and β2 are fitting parameters, which can be obtained by fitting the relationship curve between the discharge depth and the number of failure cycles provided by the manufacturer, D R is a constant, the value of D(t) can be obtained directly.

[0077] After the first objective function is constructed, the first constraint conditions corresponding to the first objective function and the energy storage device can be determined.

[0078] In an optional implementation, determining the first constraint conditions corresponding to the first objective function and the energy storage device respectively includes:

[0079] Determining a first constraint condition corresponding to the first objective function based on a power parameter and a capacity parameter of the energy storage device;

[0080] Based on the energy input power, energy output power and charge state of the energy storage device, a first constraint condition corresponding to the energy storage device is determined.

[0081] Specifically, since the purpose of the embodiments of this specification is to minimize the resources to be consumed by the energy storage device, at least two configuration parameters of the energy storage device are used as parameters to be optimized, and the minimum value of the resources to be consumed is obtained by optimizing the at least two configuration parameters. Therefore, at least two configuration parameters of the energy storage device, such as power parameters and capacity parameters, can be constrained, and the constraint results can be used as the constraint conditions of the first objective function.

[0082] While constraining the power parameters and capacity parameters of the energy storage device to achieve the goal of minimizing the resources to be consumed by the energy storage device, the energy storage device itself must also meet some other conditions, such as the energy input and output power and the state of charge must be kept within a certain range. Therefore, the corresponding constraints of the energy storage device can be determined based on the energy input power, energy output power and state of charge of the energy storage device.

[0083] In the embodiments of this specification, the constraint conditions of the first objective function constructed based on the power parameters and capacity parameters of the energy storage device are shown in formulas (6) and (7).

[0084]

[0085] in, is the maximum capacity of the energy storage device, is the maximum power of the energy storage device, γ is the energy conversion efficiency of the energy storage device, γ is a constant.

[0086]

[0087] in, is the minimum capacity of the energy storage device, is the minimum power of the energy storage device, and and are all constants.

[0088] In addition, the constraints of the energy storage device constructed based on the energy input power, energy output power and charge state of the energy storage device are shown in formulas (8) and (9).

[0089]

[0090] Among them, U ch (t) and U disch (t) are the charging and discharging states of the energy storage device at time t, respectively. A value of 1 indicates yes, and a value of 0 indicates no. and are the upper limit of charging power and the lower limit of discharging power of energy storage equipment, P ch (t) is the charging power of the energy storage device at time t, P disch (t) is the discharge power of the energy storage device at time t.

[0091]

[0092] in, is the lower limit of the state of charge of the energy storage device, is the upper limit of the state of charge of the energy storage device, S SOC is the state of charge of the energy storage device, and is a constant.

[0093] Step 104: Optimizing the at least two configuration parameters by a swarm optimization algorithm based on the first objective function and the first constraint condition to obtain a corresponding initial combination.

[0094] Specifically, the swarm optimization algorithm includes but is not limited to a genetic algorithm, a particle swarm algorithm, and an artificial fish swarm algorithm.

[0095] After constructing the first objective function and determining the constraints corresponding to the first objective function and the energy storage device, at least two configuration parameters of the energy storage device can be optimized through a group optimization algorithm based on the first objective function and the constraints to obtain an initial combination of the at least two optimized configuration parameters.

[0096] In an optional implementation, the optimizing the at least two configuration parameters by a swarm optimization algorithm based on the first objective function and the first constraint condition to obtain a corresponding initial combination includes:

[0097] Initializing parameter values ​​of the at least two configuration parameters, and generating a corresponding parameter group based on the initialization result;

[0098] Iteratively optimizing the parameter group using a group optimization algorithm based on the first objective function and the first constraint condition;

[0099] When the iteration is completed, the initial combination of the at least two configuration parameters is screened from the target parameter group obtained after the iteration is completed.

[0100] Specifically, the variables included in the first objective function constructed above are and Y t These three configuration parameters, and The optional values ​​of Y can be determined by constraints. t The optional value can be greater than 0.

[0101] In addition, the embodiments of this specification need to ensure that the actual use time of the energy storage device is as long as possible while ensuring that the total operating cost of the energy storage device is as small as possible; therefore, the embodiments of this specification can generate different combinations of optional values ​​of the three configuration parameters, and iteratively optimize each combination through a group optimization algorithm to screen out an initial combination that meets expectations from multiple combinations.

[0102] Taking the particle swarm algorithm as an example, before using the particle swarm algorithm to optimize the configuration parameters, the particle group must be initialized first. That is, if the particle group contains 50 particles, then according to and Y t Corresponding optional values ​​respectively, 50 parameter combinations consisting of parameter values ​​of three different configuration parameters are randomly generated, and the 50 parameter combinations are used as 50 particles to complete the initialization of the particle group (parameter group).

[0103] After the initialization is completed, the parameter group is optimized by iteration and using the particle swarm algorithm. In each iteration, the sample vector corresponding to the combination to be optimized in the parameter group before optimization is input into the first objective function and the first constraint condition, and the optimal group solution of the parameter group before optimization is screened out according to the output result, and the parameter group before optimization is optimized by the particle swarm algorithm to obtain the parameter group of the next iteration; then the iteration information can be obtained and it can be judged whether the iteration information meets the preset iteration termination condition; if it meets, the iteration is completed; if not, the parameter group of the next iteration is used as the parameter group before optimization and the iterative optimization process is continued until the iteration is completed.

[0104] After the iteration is completed, the output group optimal solution is determined as an initial combination of at least two configuration parameters.

[0105] In practical applications, the optimal solution of the group can be determined based on the function value of the first objective function corresponding to the parameter combination. Usually, the parameter combination corresponding to the maximum function value is determined as the optimal solution of the group. When the function values ​​corresponding to the two parameter combinations are both the maximum values, Y t The parameter combination with the larger value is determined as the group optimal solution.

[0106] Step 106: Determine the resources to be consumed of the energy scheduling system as a second indicator, construct a second objective function based on the second indicator and at least two scheduling parameters, and determine second constraints corresponding to the second objective function and the energy scheduling system respectively.

[0107] Specifically, the energy dispatching system includes energy storage equipment, energy dispatching equipment and energy consumption equipment, the energy dispatching equipment includes photovoltaic, wind turbine and virtual power plant, and the energy consumption equipment includes load electrical appliances. The resources to be consumed by the energy dispatching system can be the resources required for the energy dispatching system to perform energy dispatching.

[0108] The embodiment of this specification takes the minimum resources to be consumed of the energy scheduling device as the goal, takes at least two scheduling parameters of the energy scheduling system as parameters to be optimized, and optimizes at least two scheduling parameters to obtain the minimum value of the resources to be consumed.

[0109] Based on this, the embodiment of this specification takes the resources to be consumed of the energy scheduling system as the second indicator, and constructs a second objective function based on the second indicator and at least two scheduling parameters.

[0110] In practical applications, the energy dispatching system can be used for power dispatching, and the resources to be consumed of the energy dispatching system can be funds to be consumed, that is, the total operating cost of the energy dispatching system.

[0111] In an optional implementation, constructing a second objective function based on the second indicator and at least two scheduling parameters includes:

[0112] The resources to be consumed of the energy scheduling device and the resources to be consumed of the energy consuming device are used as scheduling parameters, and a second objective function is constructed based on the second indicator and the scheduling parameters.

[0113] The specific second objective function is as follows:

[0114] minC virtual (t) = C pv (t)+C wt (t)+C DR (t)+C bs (t) (10)

[0115] Among them, C virtual (t) is the total operating cost of the energy dispatch system at time t, C pv (t) is the photovoltaic cost at time t, C wt (t) is the wind turbine cost at time t, C DR (t) is the load cost at time t, C bs (t) is the cost of purchasing and selling electricity of the virtual power plant at time t.

[0116] For photovoltaic cost C pv (t), the specific expression is as follows:

[0117]

[0118] Among them, N pv Indicates the number of photovoltaic units, represents the on-grid electricity price of the i-th photovoltaic unit, represents the control power of photovoltaic unit i at time t, N pv and is a constant, Can be obtained directly.

[0119] For the fan cost C wt (t), the specific expression is as follows:

[0120]

[0121] Among them, N wt Indicates the number of fan units. represents the on-grid electricity price of the i-th wind turbine unit, represents the control power of fan unit i at time t, N wt and is a constant, Can be obtained directly.

[0122] For the load cost C DR (t), the specific expression is as follows:

[0123]

[0124] Among them, N DR is the number of users who respond to demand, x j (t) is the incentive price of user j at time t, P load_DRj (t) is the power corresponding to the user's demand response at time t, [λ] ± is the load ratio interval number of demand response, [λ] ± is a constant.

[0125] In actual applications, during peak hours, due to the large power demand, the virtual power plant can send power demand to users. For example, the power demand can be a request for users to adjust the power consumption period of some load appliances. Users can choose whether to respond to the power demand. The specific response method can be to shut down the appliances. load_DRj (t) can be the sum of the electrical power of the electrical appliances turned off by the user at time t.

[0126] For the virtual power plant, the electricity purchase and sale cost C bs (t), the specific expression is as follows:

[0127]

[0128] Among them, P buy (t) is the power purchased by the virtual power plant at time t, P sell (t) is the power sold by the virtual power plant at time t, C buy (t) and C sell (t) are the prices of electricity purchased and sold by the virtual power plant to the energy storage equipment at time t.

[0129] After the second objective function is constructed, the second constraint conditions corresponding to the second objective function and the energy scheduling system can be determined.

[0130] In an optional implementation, the determining of the second constraint conditions corresponding to the second objective function and the energy scheduling system respectively includes:

[0131] Determining a second constraint condition corresponding to the second objective function based on power parameters corresponding to the energy storage device, the energy scheduling device, and the energy consumption device, respectively, and a power parameter of the energy storage device;

[0132] Based on the energy input power, energy output power and charge state of the energy storage device, a second constraint condition corresponding to the energy scheduling system is determined.

[0133] Specifically, since the purpose of the embodiments of this specification is to minimize the resources to be consumed by the energy scheduling system, at least two scheduling parameters of the energy scheduling system are used as parameters to be optimized, and the minimum value of the resources to be consumed is obtained by optimizing the at least two scheduling parameters. Therefore, at least two scheduling parameters of the energy scheduling system, such as power parameters, can be constrained, and the constraint results can be used as the constraint conditions of the second objective function.

[0134] While constraining the power parameters to achieve the goal of minimizing the resources to be consumed by the energy dispatching system, the energy dispatching system itself must also meet some other conditions, such as the energy input and output power and the state of charge of the energy storage device must be kept within a certain range. Therefore, the corresponding constraints of the energy dispatching system can be determined based on the energy input power, energy output power and state of charge of the energy storage device.

[0135] In the embodiments of this specification, the constraints of the second objective function constructed based on the power parameters corresponding to the energy storage device, the energy scheduling device and the energy consumption device and the power parameters of the energy storage device are as shown in formulas (15) to (20).

[0136] P buy (t)-P sell (t)+P wt (t)+P PV (t) = P load (t)+P ES (t) (15)

[0137] Among them, P wt (t) is the wind turbine power at time t, P PV (t) is the photovoltaic power at time t, P load (t) is the load power at time t, P ES (t) is the power of the energy storage device at time t.

[0138]

[0139] Among them, S i,N is the apparent power of the i-th photovoltaic inverter, is the maximum power angle of the ith photovoltaic group, Dispatching reactive power for photovoltaics.

[0140]

[0141] Among them, S i,N is the apparent power of the ith fan, is the maximum power angle of the i-th group of fans, Dispatching reactive power for wind turbines.

[0142]

[0143] Among them, δ bs,t is a Boolean variable. When set to 1, it means that the virtual power plant purchases electricity at time t. When set to 0, it means that the virtual power plant sells electricity at time t. power is the limit on power purchase and sale of virtual power plant, and it is a constant.

[0144] In addition, the constraints of the energy dispatching system constructed based on the energy input power, energy output power and charge state of the energy storage device are shown in formulas (21) and (22).

[0145]

[0146] Among them, P ch Charging the energy storage, and are the maximum and minimum values ​​of the charging and discharging power of the energy storage, respectively. is the minimum remaining capacity of the energy storage device, is the maximum remaining capacity of the energy storage device.

[0147] Step 108: Optimize the at least two scheduling parameters through a group optimization algorithm and based on the second objective function and the second constraint condition to obtain a corresponding optimal combination.

[0148] Specifically, the swarm optimization algorithm includes but is not limited to a genetic algorithm, a particle swarm algorithm, and an artificial fish swarm algorithm.

[0149] In an optional implementation, the optimizing the at least two scheduling parameters by using a swarm optimization algorithm and based on the second objective function and the second constraint condition includes:

[0150] Input the power parameter value in the initial combination into the second constraint condition corresponding to the second objective function, and input the power parameter values ​​corresponding to the energy scheduling device and the energy consumption device respectively into the second constraint condition corresponding to the second objective function for processing to generate a corresponding output result;

[0151] The at least two scheduling parameters are optimized by a group optimization algorithm based on the second objective function and the output result.

[0152] Specifically, since the initial combination obtained by solving the first objective function and the first constraint condition contains the parameter value of the power parameter of the energy storage device, and the second constraint condition corresponding to the second objective function also involves the power parameter of the energy storage device, when optimizing the scheduling parameters based on the second constraint condition and the second objective function, the power parameter of the energy storage device contained in the initial combination can be obtained. The parameter value can then be expanded. Where m is a variable and a positive integer. satisfy Then you can and The value of is input into the second constraint condition, specifically, formula (15), and the power parameter values ​​corresponding to the energy scheduling equipment and the energy consumption equipment can be input into formula (15) for calculation.

[0153] When the equation is established according to the calculation results, a corresponding output result is generated, which is the power parameter value or extended value of the energy storage device, the power parameter values ​​corresponding to the energy dispatching device and the energy consumption device that make the equation established.

[0154] Then, according to the output of formula (15), combined with formula (16) and formula (18), we can determine and Optional value, P load_DRi (t) and P load_i The optional value of (t) can be greater than or equal to 0.

[0155] After the determination is completed, different combinations can be generated based on the optional values ​​of each scheduling parameter, and each combination can be iteratively optimized through a swarm optimization algorithm to screen out the optimal combination that meets expectations from multiple combinations.

[0156] In the embodiments of this specification, the specific implementation method of optimizing the scheduling parameters by the group optimization algorithm is similar to the specific implementation method of optimizing the configuration parameters by the group optimization algorithm mentioned above, and will not be repeated here.

[0157] Step 110: adjusting the initial combination according to the optimal combination, and determining the adjustment result as the target configuration parameter of the energy storage device.

[0158] Specifically, after obtaining the optimal combination, the power purchase value P corresponding to the power purchase and sale cost value of the virtual power plant included in the optimal combination can be determined. buy (t) and the power value Psell (t), and determine the power value P of the purchased power when the equation (15) is established. buy (t) and the power value P sell (t) The power parameter value P of the corresponding energy storage device ES (t), if the power parameter value P ES (t) If the power parameter value of the energy storage device in the initial combination is not equal, the power parameter value P can be used to calculate the power parameter value P. ES (t), the first objective function and the first constraint condition are used to adjust other configuration parameters of the energy storage device, and then the adjustment results are determined as the target configuration parameters of the energy storage device.

[0159] After determining the target configuration parameters, the capacity and power of the energy storage device can be configured based on the target configuration parameters. The energy storage device configured according to the target configuration parameters can not only make its operating cost as low as possible, but also make its operating time as long as possible, while ensuring that the overall operating cost of the energy dispatching system is as low as possible.

[0160] In addition, the optimal combination obtained by optimizing at least two scheduling parameters through the group optimization algorithm and based on the second objective function and the second constraint condition can also be used to determine the energy scheduling strategy corresponding to the energy scheduling system, that is, to determine the charging and discharging strategy of the energy scheduling system when performing power scheduling.

[0161] The embodiments of this specification calculate the operating cost of the energy storage device over its entire life cycle, select the optimal energy storage device configuration plan from a large number of configuration options, and configure the energy storage device according to the optimal configuration plan. This processing method is conducive to reducing unnecessary equipment redundancy and frequent maintenance to reduce the operating cost of the energy storage equipment, and can recover costs through long-term stable income and even obtain excess profits. In addition, energy storage equipment can store excess green electricity during low-peak electricity consumption periods and release it during peak electricity consumption periods, which not only solves the problem of time misalignment between renewable energy supply and demand, but also helps to reduce the consumption of fossil fuels and promote the evolution of energy structure towards cleaner directions.

[0162] In addition, the fluctuation of wind and solar load output is monitored in real time through the second objective function and the second constraint condition, so as to dynamically adjust the charging and discharging strategy of the energy dispatching system according to the monitoring results, which is conducive to improving the ability of the energy dispatching system to cope with fluctuations in wind and solar load output, such as power grid failures caused by natural disasters, and drastic fluctuations in renewable energy output under extreme weather conditions. Even in the face of such unforeseen situations, the balance can be quickly restored to prevent large-scale power outages and ensure the normal order of public life and industrial production.

[0163] The configuration parameter processing method of the energy storage device provided in the embodiment of this specification is to determine the resources to be consumed of the energy storage device as the first indicator, construct a first objective function based on the first indicator and at least two configuration parameters, and determine the first constraint conditions corresponding to the first objective function and the energy storage device respectively; optimize at least two configuration parameters through a group optimization algorithm and based on the first objective function and the first constraint conditions to obtain a corresponding initial combination; determine the resources to be consumed of the energy dispatching system as the second indicator, construct a second objective function based on the second indicator and at least two dispatching parameters, and determine the second constraint conditions corresponding to the second objective function and the energy dispatching system respectively; optimize at least two dispatching parameters through a group optimization algorithm and based on the second objective function and the second constraint conditions to obtain a corresponding optimal combination; adjust the initial combination according to the optimal combination, and determine the adjustment result as the target configuration parameter of the energy storage device. In this way, it is beneficial to ensure the accuracy of the energy storage configuration optimization results, thereby improving the power supply stability of the power grid.

[0164] The following combination Figure 2 , the configuration parameter processing method of the energy storage device is further described. Figure 2 A schematic diagram of a processing process of a configuration parameter processing method for an energy storage device provided in an embodiment of this specification is shown.

[0165] The embodiments of this specification construct a two-layer optimization model for energy storage capacity configuration. The upper optimization model is a full life cycle cost model that takes into account the energy storage equipment. The goal is to minimize the total operating cost of the energy storage equipment, and the parameters such as the rated power and capacity of the energy storage equipment are used as decision variables to achieve the optimal energy storage configuration plan. The lower optimization model achieves the goal of minimizing the operating cost of the energy scheduling system by coordinating and optimizing the source, load, storage and output.

[0166] like Figure 2 As shown, the upper-level optimization model is composed of a first objective function and a first constraint condition, and the first objective function is constructed based on the resources to be consumed by the energy storage device and the power parameters and capacity parameters of the energy storage device; the lower-level optimization model is composed of a second objective function and a second constraint condition, and the second objective function is constructed by the resources to be consumed by the energy scheduling system and at least two scheduling parameters of the energy scheduling system.

[0167] The upper optimization model is used to optimize the configuration parameters of the energy storage device. Specifically, at least two configuration parameters can be optimized through a group optimization algorithm based on the first objective function and the first constraint condition to obtain a corresponding initial combination.

[0168] The lower optimization model is used to optimize the dispatching parameters of the energy dispatching system. Specifically, at least two dispatching parameters can be optimized through a group optimization algorithm based on the second objective function and the second constraint to obtain the corresponding optimal combination.

[0169] Since the initial combination obtained by solving the first objective function and the first constraint condition contains the parameter value of the power parameter of the energy storage device, and the second constraint condition corresponding to the second objective function also involves the power parameter of the energy storage device, when optimizing the scheduling parameters based on the second constraint condition and the second objective function, the power parameter value in the initial combination can be combined to achieve it. After the optimization is completed, the initial combination can also be adjusted according to the optimal combination obtained by the optimization to ensure the accuracy of the energy storage configuration optimization result.

[0170] Corresponding to the above method embodiment, this specification also provides an embodiment of a configuration parameter processing device for an energy storage device, Figure 3 FIG. 1 is a schematic diagram showing a configuration parameter processing device for an energy storage device provided by an embodiment of the present specification. Figure 3 As shown, the device comprises:

[0171] A first construction module 302 is configured to determine the to-be-consumed resources of the energy storage device as a first indicator, construct a first objective function based on the first indicator and at least two configuration parameters, and determine first constraint conditions corresponding to the first objective function and the energy storage device respectively;

[0172] A first processing module 304 is configured to optimize the at least two configuration parameters by a swarm optimization algorithm based on the first objective function and the first constraint condition to obtain a corresponding initial combination;

[0173] The second construction module 306 is configured to determine the resources to be consumed of the energy scheduling system as a second indicator, construct a second objective function based on the second indicator and at least two scheduling parameters, and determine the second constraint conditions corresponding to the second objective function and the energy scheduling system respectively;

[0174] The second processing module 308 is configured to optimize the at least two scheduling parameters through a group optimization algorithm and based on the second objective function and the second constraint condition to obtain a corresponding optimal combination;

[0175] The adjustment module 310 is configured to adjust the initial combination according to the optimal combination, and determine the adjustment result as the target configuration parameter of the energy storage device.

[0176] Optionally, the at least two configuration parameters include power parameters of an energy storage device, and the energy dispatching system includes an energy storage device, an energy dispatching device, and an energy consumption device;

[0177] Accordingly, the second building module 306 is further configured to:

[0178] Determining a second constraint condition corresponding to the second objective function based on power parameters corresponding to the energy storage device, the energy scheduling device, and the energy consumption device, respectively, and a power parameter of the energy storage device;

[0179] Based on the energy input power, energy output power and charge state of the energy storage device, a second constraint condition corresponding to the energy scheduling system is determined.

[0180] Optionally, the second processing module 308 is further configured to:

[0181] Input the power parameter value in the initial combination into the second constraint condition corresponding to the second objective function, and input the power parameter values ​​corresponding to the energy scheduling device and the energy consumption device respectively into the second constraint condition corresponding to the second objective function for processing to generate a corresponding output result;

[0182] The at least two scheduling parameters are optimized by a group optimization algorithm based on the second objective function and the output result.

[0183] Optionally, the at least two configuration parameters include a power parameter, a capacity parameter, a usage time of the energy storage device, and resources to be consumed corresponding to equipment maintenance of the energy storage device;

[0184] Accordingly, the first building module 302 is further configured to:

[0185] Determining a first constraint condition corresponding to the first objective function based on a power parameter and a capacity parameter of the energy storage device;

[0186] Based on the energy input power, energy output power and charge state of the energy storage device, a first constraint condition corresponding to the energy storage device is determined.

[0187] Optionally, the first processing module 304 is further configured to:

[0188] Initializing parameter values ​​of the at least two configuration parameters, and generating a corresponding parameter group based on the initialization result;

[0189] Iteratively optimizing the parameter group using a group optimization algorithm based on the first objective function and the first constraint condition;

[0190] When the iteration is completed, the initial combination of the at least two configuration parameters is screened from the target parameter group obtained after the iteration is completed.

[0191] Optionally, the energy dispatching system includes energy dispatching equipment and energy consuming equipment;

[0192] Accordingly, the second building module 306 is further configured to:

[0193] The resources to be consumed of the energy scheduling device and the resources to be consumed of the energy consuming device are used as scheduling parameters, and a second objective function is constructed based on the second indicator and the scheduling parameters.

[0194] Optionally, the configuration parameter processing device of the energy storage device further includes a determination module 312 configured to:

[0195] An energy scheduling strategy corresponding to the energy scheduling system is determined based on the optimal combination.

[0196] The above is a schematic scheme of a configuration parameter processing device for an energy storage device of this embodiment. It should be noted that the technical scheme of the configuration parameter processing device for the energy storage device and the technical scheme of the configuration parameter processing method for the energy storage device described above belong to the same concept, and the details not described in detail in the technical scheme of the configuration parameter processing device for the energy storage device can be referred to the description of the technical scheme of the configuration parameter processing method for the energy storage device described above.

[0197] 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.

[0198] 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)) 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.

[0199] 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 4The 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.

[0200] 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.

[0201] The processor 420 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the configuration parameter processing method of the energy storage device.

[0202] 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 configuration parameter processing method of the energy storage device described 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 configuration parameter processing method of the energy storage device described above.

[0203] 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 configuration parameter processing method of the energy storage device.

[0204] 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 configuration parameter processing method of the energy storage device described 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 configuration parameter processing method of the energy storage device described above.

[0205] 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 configuration parameter processing method of the energy storage device described above.

[0206] 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 configuration parameter processing method of the energy storage device described 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 configuration parameter processing method of the energy storage device described above.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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 processing configuration parameters of an energy storage device, comprising: Determine the to-be-consumed resources of the energy storage device as a first indicator, construct a first objective function based on the first indicator and at least two configuration parameters, and determine first constraint conditions corresponding to the first objective function and the energy storage device respectively; Optimizing the at least two configuration parameters by a swarm optimization algorithm based on the first objective function and the first constraint condition to obtain a corresponding initial combination; Determine the resources to be consumed of the energy dispatching system as a second indicator, construct a second objective function based on the second indicator and at least two dispatching parameters, and determine second constraints corresponding to the second objective function and the energy dispatching system respectively; Optimizing the at least two scheduling parameters by a group optimization algorithm and based on the second objective function and the second constraint condition to obtain a corresponding optimal combination; The initial combination is adjusted according to the optimal combination, and the adjustment result is determined as the target configuration parameter of the energy storage device.

2. According to the configuration parameter processing method of the energy storage device according to claim 1, the at least two configuration parameters include power parameters of the energy storage device, and the energy dispatching system includes energy storage equipment, energy dispatching equipment and energy consumption equipment; Accordingly, the determining of the second constraint conditions corresponding to the second objective function and the energy dispatching system respectively includes: Determining a second constraint condition corresponding to the second objective function based on power parameters corresponding to the energy storage device, the energy scheduling device, and the energy consumption device, respectively, and a power parameter of the energy storage device; Based on the energy input power, energy output power and charge state of the energy storage device, a second constraint condition corresponding to the energy scheduling system is determined.

3. The method for processing configuration parameters of an energy storage device according to claim 2, wherein the optimizing process of the at least two scheduling parameters by a swarm optimization algorithm and based on the second objective function and the second constraint condition comprises: Input the power parameter value in the initial combination into the second constraint condition corresponding to the second objective function, and input the power parameter values ​​corresponding to the energy scheduling device and the energy consumption device respectively into the second constraint condition corresponding to the second objective function for processing to generate a corresponding output result; The at least two scheduling parameters are optimized by a group optimization algorithm based on the second objective function and the output result.

4. The configuration parameter processing method of the energy storage device according to claim 1, wherein the at least two configuration parameters include a power parameter, a capacity parameter, a usage time, and resources to be consumed corresponding to equipment maintenance of the energy storage device; Accordingly, the determining of the first constraint conditions corresponding to the first objective function and the energy storage device respectively includes: Determining a first constraint condition corresponding to the first objective function based on a power parameter and a capacity parameter of the energy storage device; Based on the energy input power, energy output power and charge state of the energy storage device, a first constraint condition corresponding to the energy storage device is determined.

5. The method for processing configuration parameters of an energy storage device according to claim 1, wherein the at least two configuration parameters are optimized by a swarm optimization algorithm based on the first objective function and the first constraint condition to obtain a corresponding initial combination, comprising: Initializing parameter values ​​of the at least two configuration parameters, and generating a corresponding parameter group based on the initialization result; Iteratively optimizing the parameter group using a group optimization algorithm based on the first objective function and the first constraint condition; When the iteration is completed, the initial combination of the at least two configuration parameters is screened from the target parameter group obtained after the iteration is completed.

6. According to the configuration parameter processing method of the energy storage device according to claim 1, the energy dispatching system comprises an energy dispatching device and an energy consuming device; Accordingly, constructing a second objective function based on the second indicator and at least two scheduling parameters includes: The resources to be consumed of the energy scheduling device and the resources to be consumed of the energy consuming device are used as scheduling parameters, and a second objective function is constructed based on the second indicator and the scheduling parameters.

7. The method for processing configuration parameters of an energy storage device according to claim 1, further comprising: An energy scheduling strategy corresponding to the energy scheduling system is determined based on the optimal combination.

8. A configuration parameter processing device for an energy storage device, comprising: A first construction module is configured to determine the to-be-consumed resources of the energy storage device as a first indicator, construct a first objective function based on the first indicator and at least two configuration parameters, and determine first constraint conditions corresponding to the first objective function and the energy storage device respectively; A first processing module is configured to optimize the at least two configuration parameters through a swarm optimization algorithm based on the first objective function and the first constraint condition to obtain a corresponding initial combination; A second construction module is configured to determine the resources to be consumed of the energy scheduling system as a second indicator, construct a second objective function based on the second indicator and at least two scheduling parameters, and determine second constraints corresponding to the second objective function and the energy scheduling system respectively; A second processing module is configured to optimize the at least two scheduling parameters through a group optimization algorithm and based on the second objective function and the second constraint condition to obtain a corresponding optimal combination; The adjustment module is configured to adjust the initial combination according to the optimal combination, and determine the adjustment result as the target configuration parameter of the energy storage device.

9. 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 configuration parameter processing method of the energy storage device described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for processing configuration parameters of an energy storage device according to any one of claims 1 to 7.

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